chronic alcoholism research paper

• Open access
• Published: 13 November 2019

Evidence-based models of care for the treatment of alcohol use disorder in primary health care settings: protocol for systematic review

• Susan A. Rombouts 1 ,
• James Conigrave 2 ,
• Eva Louie 1 ,
• Paul Haber 1 , 3 &
• Kirsten C. Morley   ORCID: orcid.org/0000-0002-0868-9928 1  

Systematic Reviews volume  8 , Article number:  275 ( 2019 ) Cite this article

7815 Accesses

3 Citations

Metrics details

Alcohol use disorder (AUD) is highly prevalent and accounts globally for 1.6% of disability-adjusted life years (DALYs) among females and 6.0% of DALYs among males. Effective treatments for AUDs are available but are not commonly practiced in primary health care. Furthermore, referral to specialized care is often not successful and patients that do seek treatment are likely to have developed more severe dependence. A more cost-efficient health care model is to treat less severe AUD in a primary care setting before the onset of greater dependence severity. Few models of care for the management of AUD in primary health care have been developed and with limited implementation. This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings.

We will conduct a systematic review to synthesize studies that evaluate the effectiveness of models of care in the treatment of AUD in primary health care. A comprehensive search approach will be conducted using the following databases; MEDLINE (1946 to present), PsycINFO (1806 to present), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL) (1991 to present), and Embase (1947 to present).

Reference searches of relevant reviews and articles will be conducted. Similarly, a gray literature search will be done with the help of Google and the gray matter tool which is a checklist of health-related sites organized by topic. Two researchers will independently review all titles and abstracts followed by full-text review for inclusion. The planned method of extracting data from articles and the critical appraisal will also be done in duplicate. For the critical appraisal, the Cochrane risk of bias tool 2.0 will be used.

This systematic review and meta-analysis aims to guide improvement of design and implementation of evidence-based models of care for the treatment of alcohol use disorder in primary health care settings. The evidence will define which models are most promising and will guide further research.

Protocol registration number

PROSPERO CRD42019120293.

Peer Review reports

It is well recognized that alcohol use disorders (AUD) have a damaging impact on the health of the population. According to the World Health Organization (WHO), 5.3% of all global deaths were attributable to alcohol consumption in 2016 [ 1 ]. The 2016 Global Burden of Disease Study reported that alcohol use led to 1.6% (95% uncertainty interval [UI] 1.4–2.0) of total DALYs globally among females and 6.0% (5.4–6.7) among males, resulting in alcohol use being the seventh leading risk factor for both premature death and disability-adjusted life years (DALYs) [ 2 ]. Among people aged 15–49 years, alcohol use was the leading risk factor for mortality and disability with 8.9% (95% UI 7.8–9.9) of all attributable DALYs for men and 2.3% (2.0–2.6) for women [ 2 ]. AUD has been linked to many physical and mental health complications, such as coronary heart disease, liver cirrhosis, a variety of cancers, depression, anxiety, and dementia [ 2 , 3 ]. Despite the high morbidity and mortality rate associated with hazardous alcohol use, the global prevalence of alcohol use disorders among persons aged above 15 years in 2016 was stated to be 5.1% (2.5% considered as harmful use and 2.6% as severe AUD), with the highest prevalence in the European and American region (8.8% and 8.2%, respectively) [ 1 ].

Effective and safe treatment for AUD is available through psychosocial and/or pharmacological interventions yet is not often received and is not commonly practiced in primary health care. While a recent European study reported 8.7% prevalence of alcohol dependence in primary health care populations [ 4 ], the vast majority of patients do not receive the professional treatment needed, with only 1 in 5 patients with alcohol dependence receiving any formal treatment [ 4 ]. In Australia, it is estimated that only 3% of individuals with AUD receive approved pharmacotherapy for the disorder [ 5 , 6 ]. Recognition of AUD in general practice uncommonly leads to treatment before severe medical and social disintegration [ 7 ]. Referral to specialized care is often not successful, and those patients that do seek treatment are likely to have more severe dependence with higher levels of alcohol use and concurrent mental and physical comorbidity [ 4 ].

Identifying and treating early stage AUDs in primary care settings can prevent condition worsening. This may reduce the need for more complex and more expensive specialized care. The high prevalence of AUD in primary health care and the chronic relapsing character of AUD make primary care a suitable and important location for implementing evidence-based interventions. Successful implementation of treatment models requires overcoming multiple barriers. Qualitative studies have identified several of those barriers such as limited time, limited organizational capacity, fear of losing patients, and physicians feeling incompetent in treating AUD [ 8 , 9 , 10 ]. Additionally, a recent systematic review revealed that diagnostic sensitivity of primary care physicians in the identification of AUD was 41.7% and that only in 27.3% alcohol problems were recorded correctly in primary care records [ 11 ].

Several models for primary care have been created to increase identification and treatment of patients with AUD. Of those, the model, screening, brief interventions, and referral to specialized treatment for people with severe AUD (SBIRT [ 12 ]) is most well-known. Multiple systematic reviews exist, confirming its effectiveness [ 13 , 14 , 15 ], although implementation in primary care has been inadequate. Moreover, most studies have looked primarily at SBIRT for the treatment of less severe AUD [ 16 ]. In the treatment of severe AUD, efficacy of SBIRT is limited [ 16 ]. Additionally, many patient referred to specialized care often do not attend as they encounter numerous difficulties in health care systems including stigmatization, costs, lack of information about existing treatments, and lack of non-abstinence-treatment goals [ 7 ]. An effective model of care for improved management of AUD that can be efficiently implemented in primary care settings is required.

Review objective

This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings. We aim to evaluate the effectiveness of the models of care in increasing engagement and reducing alcohol consumption.

By providing this overview, we aim to guide improvement of design and implementation of evidence-based models of care for the treatment of alcohol use disorder in primary health care settings.

The systematic review is registered in PROSPERO international prospective register of systematic reviews (CRD42019120293) and the current protocol has been written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) recommended for systematic reviews [ 17 ]. A PRISMA-P checklist is included as Additional file  1 .

Eligibility criteria

Criteria for considering studies for this review are classified by the following:

Study design

Both individualized and cluster randomized trials will be included. Masking of patients and/or physicians is not an inclusion criterion as it is often hard to accomplish in these types of studies.

Patients in primary health care who are identified (using screening tools or by primary health care physician) as suffering from AUD (from mild to severe) or hazardous alcohol drinking habits (e.g., comorbidity, concurrent medication use). Eligible patients need to have had formal assessment of AUD with diagnostic tools such as Diagnostic and Statistical Manual of Mental Disorders (DSM-IV/V) or the International Statistical Classification of Diseases and Related Health Problems (ICD-10) and/or formal assessment of hazardous alcohol use assessed by the Comorbidity Alcohol Risk Evaluation Tool (CARET) or the Alcohol Use Disorders Identification test (AUDIT) and/or alcohol use exceeding guideline recommendations to reduce health risks (e.g., US dietary guideline (2015–2020) specifies excessive drinking for women as ≥ 4 standard drinks (SD) on any day and/or ≥ 8 SD per week and for men ≥ 5 SD on any day and/or ≥ 15 SD per week).

Studies evaluating models of care for additional diseases (e.g., other dependencies/mental health) other than AUD are included when they have conducted data analysis on the alcohol use disorder patient data separately or when 80% or more of the included patients have AUD.

Intervention

The intervention should consist of a model of care; therefore, it should include multiple components and cover different stages of the care pathway (e.g., identification of patients, training of staff, modifying access to resources, and treatment). An example is the Chronic Care Model (CCM) which is a primary health care model designed for chronic (relapsing) conditions and involves six elements: linkage to community resources, redesign of health care organization, self-management support, delivery system redesign (e.g., use of non-physician personnel), decision support, and the use of clinical information systems [ 18 , 19 ].

As numerous articles have already assessed the treatment model SBIRT, this model of care will be excluded from our review unless the particular model adds a specific new aspect. Also, the article has to assess the effectiveness of the model rather than assessing the effectiveness of the particular treatment used. Because identification of patients is vital to including them in the trial, a care model that only evaluates either patient identification or treatment without including both will be excluded from this review.

Model effectiveness may be in comparison with the usual care or a different treatment model.

Included studies need to include at least one of the following outcome measures: alcohol consumption, treatment engagement, uptake of pharmacological agents, and/or quality of life.

Solely quantitative research will be included in this systematic review (e.g., randomized controlled trials (RCTs) and cluster RCTs). We will only include peer-reviewed articles.

Restrictions (language/time period)

Studies published in English after 1 January 1998 will be included in this systematic review.

Studies have to be conducted in primary health care settings as such treatment facilities need to be physically in or attached to the primary care clinic. Examples are co-located clinics, veteran health primary care clinic, hospital-based primary care clinic, and community primary health clinics. Specialized primary health care clinics such as human immunodeficiency virus (HIV) clinics are excluded from this systematic review. All studies were included, irrespective of country of origin.

Search strategy and information sources

A comprehensive search will be conducted. The following databases will be consulted: MEDLINE (1946 to present), PsycINFO (1806 to present), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL) (1991 to present), and Embase (1947 to present). Initially, the search terms will be kept broad including alcohol use disorder (+synonyms), primary health care, and treatment to minimize the risk of missing any potentially relevant articles. Depending on the number of references attained by this preliminary search, we will add search terms referring to models such as models of care, integrated models, and stepped-care models, to limit the number of articles. Additionally, we will conduct reference searches of relevant reviews and articles. Similarly, a gray literature search will be done with the help of Google and the Gray Matters tool which is a checklist of health-related sites organized by topic. The tool is produced by the Canadian Agency for Drugs and Technologies in Health (CADTH) [ 20 ].

See Additional file  2 for a draft of our search strategy in MEDLINE.

Data collection

The selection of relevant articles is based on several consecutive steps. All references will be managed using EndNote (EndNote version X9 Clarivate Analytics). Initially, duplicates will be removed from the database after which all the titles will be screened with the purpose of discarding clearly irrelevant articles. The remaining records will be included in an abstract and full-text screen. All steps will be done independently by two researchers. Disagreement will lead to consultation of a third researcher.

Data extraction and synthesis

Two researchers will extract data from included records. At the conclusion of data extraction, these two researchers will meet with the lead author to resolve any discrepancies.

In order to follow a structured approach, an extraction form will be used. Key elements of the extraction form are information about design of the study (randomized, blinded, control), type of participants (alcohol use, screening tool used, socio-economic status, severity of alcohol use, age, sex, number of participants), study setting (primary health care setting, VA centers, co-located), type of intervention/model of care (separate elements of the models), type of health care worker (primary, secondary (co-located)), duration of follow-up, outcome measures used in the study, and funding sources. We do not anticipate having sufficient studies for a meta-analysis. As such, we plan to perform a narrative synthesis. We will synthesize the findings from the included articles by cohort characteristics, differential aspects of the intervention, controls, and type of outcome measures.

Sensitivity analyses will be conducted when issues suitable for sensitivity analysis are identified during the review process (e.g., major differences in quality of the included articles).

Potential meta-analysis

In the event that sufficient numbers of effect sizes can be extracted, a meta-analytic synthesis will be performed. We will extract effect sizes from each study accordingly. Two effect sizes will be extracted (and transformed where appropriate). Categorical outcomes will be given in log odds ratios and continuous measures will be converted into standardized mean differences. Variation in effect sizes attributable to real differences (heterogeneity) will be estimated using the inconsistency index ( I 2 ) [ 21 , 22 ]. We anticipate high degrees of variation among effect sizes, as a result moderation and subgroup-analyses will be employed as appropriate. In particular, moderation analysis will focus on the degree of heterogeneity attributable to differences in cohort population (pre-intervention drinking severity, age, etc.), type of model/intervention, and study quality. We anticipate that each model of care will require a sub-group analysis, in which case a separate meta-analysis will be performed for each type of model. Small study effect will be assessed with funnel plots and Egger’s symmetry tests [ 23 ]. When we cannot obtain enough effect sizes for synthesis or when the included studies are too diverse, we will aim to illustrate patterns in the data by graphical display (e.g., bubble plot) [ 24 ].

Critical appraisal of studies

All studies will be critically assessed by two researchers independently using the Revised Cochrane risk-of-bias tool (RoB 2) [ 25 ]. This tool facilitates systematic assessment of the quality of the article per outcome according to the five domains: bias due to (1) the randomization process, (2) deviations from intended interventions, (3) missing outcome data, (4) measurement of the outcome, and (5) selection of the reported results. An additional domain 1b must be used when assessing the randomization process for cluster-randomized studies.

Meta-biases such as outcome reporting bias will be evaluated by determining whether the protocol was published before recruitment of patients. Additionally, trial registries will be checked to determine whether the reported outcome measures and statistical methods are similar to the ones described in the registry. The gray literature search will be of assistance when checking for publication bias; however, completely eliminating the presence of publication bias is impossible.

Similar to article selection, any disagreement between the researchers will lead to discussion and consultation of a third researcher. The strength of the evidence will be graded according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach [ 26 ].

The primary outcome measure of this proposed systematic review is the consumption of alcohol at follow-up. Consumption of alcohol is often quantified in drinking quantity (e.g., number of drinks per week), drinking frequency (e.g., percentage of days abstinent), binge frequency (e.g., number of heavy drinking days), and drinking intensity (e.g., number of drinks per drinking day). Additionally, outcomes such as percentage/proportion included patients that are abstinent or considered heavy/risky drinkers at follow-up. We aim to report all these outcomes. The consumption of alcohol is often self-reported by patients. When studies report outcomes at multiple time points, we will consider the longest follow-up of individual studies as a primary outcome measure.

Depending on the included studies, we will also consider secondary outcome measures such as treatment engagement (e.g., number of visits or pharmacotherapy uptake), economic outcome measures, health care utilization, quality of life assessment (physical/mental), alcohol-related problems/harm, and mental health score for depression or anxiety.

This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings.

Given the complexities of researching models of care in primary care and the paucity of a focus on AUD treatment, there are likely to be only a few studies that sufficiently address the research question. Therefore, we will do a preliminary search without the search terms for model of care. Additionally, the search for online non-academic studies presents a challenge. However, the Gray Matters tool will be of guidance and will limit the possibility of missing useful studies. Further, due to diversity of treatment models, outcome measures, and limitations in research design, it is possible that a meta-analysis for comparative effectiveness may not be appropriate. Moreover, in the absence of large, cluster randomized controlled trials, it will be difficult to distinguish between the effectiveness of the treatment given and that of the model of care and/or implementation procedure. Nonetheless, we will synthesize the literature and provide a critical evaluation of the quality of the evidence.

This review will assist the design and implementation of models of care for the management of AUD in primary care settings. This review will thus improve the management of AUD in primary health care and potentially increase the uptake of evidence-based interventions for AUD.

Availability of data and materials

Not applicable.

Abbreviations

Alcohol use disorder

Alcohol Use Disorders Identification test

Canadian Agency for Drugs and Technologies in Health

The Comorbidity Alcohol Risk Evaluation

Cochrane Central Register of Controlled Trials

Diagnostic and Statistical Manual of Mental Disorders

Human immunodeficiency virus

10 - International Statistical Classification of Diseases and Related Health Problems

Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols

Screening, brief intervention, referral to specialized treatment

Standard drinks

World Health Organization

WHO. Global status report on alcohol and health: World health organization; 2018.

The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990–2016. a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry. 2018;5(12):987–1012.

Article   Google Scholar  

WHO. Global strategy to reduce the harmful use of alcohol: World health organization; 2010.

Rehm J, Allamani A, Elekes Z, Jakubczyk A, Manthey J, Probst C, et al. Alcohol dependence and treatment utilization in Europe - a representative cross-sectional study in primary care. BMC Fam Pract. 2015;16:90.

Morley KC, Logge W, Pearson SA, Baillie A, Haber PS. National trends in alcohol pharmacotherapy: findings from an Australian claims database. Drug Alcohol Depend. 2016;166:254–7.

Article   CAS   Google Scholar  

Morley KC, Logge W, Pearson SA, Baillie A, Haber PS. Socioeconomic and geographic disparities in access to pharmacotherapy for alcohol dependence. J Subst Abus Treat. 2017;74:23–5.

Rehm J, Anderson P, Manthey J, Shield KD, Struzzo P, Wojnar M, et al. Alcohol use disorders in primary health care: what do we know and where do we go? Alcohol Alcohol. 2016;51(4):422–7.

Le KB, Johnson JA, Seale JP, Woodall H, Clark DC, Parish DC, et al. Primary care residents lack comfort and experience with alcohol screening and brief intervention: a multi-site survey. J Gen Intern Med. 2015;30(6):790–6.

McLellan AT, Starrels JL, Tai B, Gordon AJ, Brown R, Ghitza U, et al. Can substance use disorders be managed using the chronic care model? review and recommendations from a NIDA consensus group. Public Health Rev. 2014;35(2).

Storholm ED, Ober AJ, Hunter SB, Becker KM, Iyiewuare PO, Pham C, et al. Barriers to integrating the continuum of care for opioid and alcohol use disorders in primary care: a qualitative longitudinal study. J Subst Abus Treat. 2017;83:45–54.

Mitchell AJ, Meader N, Bird V, Rizzo M. Clinical recognition and recording of alcohol disorders by clinicians in primary and secondary care: meta-analysis. Br J Psychiatry. 2012;201:93–100.

Babor TF, Ritson EB, Hodgson RJ. Alcohol-related problems in the primary health care setting: a review of early intervention strategies. Br J Addict. 1986;81(1):23–46.

Kaner EF, Beyer F, Dickinson HO, Pienaar E, Campbell F, Schlesinger C, et al. Effectiveness of brief alcohol interventions in primary care populations. Cochrane Database Syst Rev. 2007;(2):Cd004148.

O'Donnell A, Anderson P, Newbury-Birch D, Schulte B, Schmidt C, Reimer J, et al. The impact of brief alcohol interventions in primary healthcare: a systematic review of reviews. Alcohol Alcohol. 2014;49(1):66–78.

Bertholet N, Daeppen JB, Wietlisbach V, Fleming M, Burnand B. Reduction of alcohol consumption by brief alcohol intervention in primary care: systematic review and meta-analysis. Arch Intern Med. 2005;165(9):986–95.

Saitz R. ‘SBIRT’ is the answer? Probably not. Addiction. 2015;110(9):1416–7.

Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. Bmj. 2015;350:g7647.

Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. Jama. 2002;288(14):1775–9.

Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness: the chronic care model, part 2. Jama. 2002;288(15):1909–14.

CADTH. Grey Matters: a practical tool for searching health-related grey literature Internet. 2018 (cited 2019 Feb 22).

Higgins JPT. Thompson SG. Quantifying heterogeneity in a meta-analysis. 2002;21(11):1539–58.

Google Scholar  

Higgins JPT, Thompson SG, Deeks JJ. Altman DG. Measuring inconsistency in meta-analyses. 2003;327(7414):557–60.

Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ (Clinical research ed). 1997;315(7109):629–34.

Higgins JPT, López-López JA, Becker BJ, Davies SR, Dawson S, Grimshaw JM, et al. Synthesising quantitative evidence in systematic reviews of complex health interventions. BMJ Glob Health. 2019;4(Suppl 1):e000858–e.

Higgins, J.P.T., Sterne, J.A.C., Savović, J., Page, M.J., Hróbjartsson, A., Boutron, I., Reeves, B., Eldridge, S. (2016). A revised tool for assessing risk of bias in randomized trials. In: Chandler, J., McKenzie, J., Boutron, I., Welch, V. (editors). Cochrane methods. Cochrane database of systematic reviews, 10 (Suppl 1). https://doi.org/10.1002/14651858.CD201601 .

Schünemann H, Brożek J, Guyatt G, Oxman A, editor(s). Handbook for grading the quality of evidence and the strength of recommendations using the GRADE approach (updated October 2013). GRADE Working Group, 2013. Available from gdt.guidelinedevelopment.org/app/handbook/handbook.html ).

Download references

Acknowledgements

There is no dedicated funding.

Author information

Authors and affiliations.

Discipline of Addiction Medicine, Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

Susan A. Rombouts, Eva Louie, Paul Haber & Kirsten C. Morley

NHMRC Centre of Research Excellence in Indigenous Health and Alcohol, Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

James Conigrave

Drug Health Services, Royal Prince Alfred Hospital, Camperdown, NSW, Australia

You can also search for this author in PubMed   Google Scholar

Contributions

KM and PH conceived the presented idea of a systematic review and meta-analysis and helped with the scope of the literature. KM is the senior researcher providing overall guidance and the guarantor of this review. SR developed the background, search strategy, and data extraction form. SR and EL will both be working on the data extraction and risk of bias assessment. SR and JC will conduct the data analysis and synthesize the results. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kirsten C. Morley .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1..

PRISMA-P 2015 Checklist.

Additional file 2.

Draft search strategy MEDLINE. Search strategy.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Rombouts, S.A., Conigrave, J., Louie, E. et al. Evidence-based models of care for the treatment of alcohol use disorder in primary health care settings: protocol for systematic review. Syst Rev 8 , 275 (2019). https://doi.org/10.1186/s13643-019-1157-7

Download citation

Received : 25 March 2019

Accepted : 13 September 2019

Published : 13 November 2019

DOI : https://doi.org/10.1186/s13643-019-1157-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Model of care
• Primary health care
• Systematic review

Systematic Reviews

ISSN: 2046-4053

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 08 June 2023

Alcohol consumption and risks of more than 200 diseases in Chinese men

• Pek Kei Im   ORCID: orcid.org/0000-0002-2624-9766 1 ,
• Neil Wright   ORCID: orcid.org/0000-0002-3946-1870 1 ,
• Ling Yang   ORCID: orcid.org/0000-0001-5750-6588 1 , 2 ,
• Ka Hung Chan   ORCID: orcid.org/0000-0002-3700-502X 1 , 3 ,
• Yiping Chen   ORCID: orcid.org/0000-0002-4973-0296 1 , 2 ,
• Huaidong Du   ORCID: orcid.org/0000-0002-9814-0049 1 , 2 ,
• Xiaoming Yang 1 ,
• Daniel Avery   ORCID: orcid.org/0000-0002-9823-9575 1 ,
• Shaojie Wang 5 ,
• Canqing Yu   ORCID: orcid.org/0000-0002-0019-0014 6 , 7 ,
• Jun Lv 6 , 7 ,
• Robert Clarke   ORCID: orcid.org/0000-0002-9802-8241 1 ,
• Junshi Chen 8 ,
• Rory Collins 1 ,
• Robin G. Walters   ORCID: orcid.org/0000-0002-9179-0321 1 , 2 ,
• Richard Peto 1 ,
• Liming Li   ORCID: orcid.org/0000-0001-5873-7089 6 , 7   na1 ,
• Zhengming Chen   ORCID: orcid.org/0000-0001-6423-105X 1 , 2   na1 ,
• Iona Y. Millwood   ORCID: orcid.org/0000-0002-0807-0682 1 , 2   na1 &

China Kadoorie Biobank Collaborative Group

Nature Medicine volume  29 ,  pages 1476–1486 ( 2023 ) Cite this article

31k Accesses

25 Citations

824 Altmetric

Metrics details

• Epidemiology
• Genetics research
• Risk factors

Alcohol consumption accounts for ~3 million annual deaths worldwide, but uncertainty persists about its relationships with many diseases. We investigated the associations of alcohol consumption with 207 diseases in the 12-year China Kadoorie Biobank of >512,000 adults (41% men), including 168,050 genotyped for ALDH2 - rs671 and ADH1B - rs1229984 , with >1.1 million ICD-10 coded hospitalized events. At baseline, 33% of men drank alcohol regularly. Among men, alcohol intake was positively associated with 61 diseases, including 33 not defined by the World Health Organization as alcohol-related, such as cataract ( n  = 2,028; hazard ratio 1.21; 95% confidence interval 1.09–1.33, per 280 g per week) and gout ( n  = 402; 1.57, 1.33–1.86). Genotype-predicted mean alcohol intake was positively associated with established ( n  = 28,564; 1.14, 1.09–1.20) and new alcohol-associated ( n  = 16,138; 1.06, 1.01–1.12) diseases, and with specific diseases such as liver cirrhosis ( n  = 499; 2.30, 1.58–3.35), stroke ( n  = 12,176; 1.38, 1.27–1.49) and gout ( n  = 338; 2.33, 1.49–3.62), but not ischemic heart disease ( n  = 8,408; 1.04, 0.94–1.14). Among women, 2% drank alcohol resulting in low power to assess associations of self-reported alcohol intake with disease risks, but genetic findings in women suggested the excess male risks were not due to pleiotropic genotypic effects. Among Chinese men, alcohol consumption increased multiple disease risks, highlighting the need to strengthen preventive measures to reduce alcohol intake.

Similar content being viewed by others

A burden of proof study on alcohol consumption and ischemic heart disease

Effect of alcohol consumption on kidney function: population-based cohort study

Genomic prediction of alcohol-related morbidity and mortality

Alcohol consumption is a major risk factor for poor physical and mental health, accounting for about 3 million deaths and over 130 million disability-adjusted life years worldwide in 2016 (ref. 1 ). Since the 1990s, alcohol consumption has increased in many low- and middle-income countries, including China, where it almost exclusively involves men 2 , 3 . Among Chinese men, those who reported alcohol consumption in the past 12 months increased from 59% to 85% and yearly per-capita alcohol consumption increased from 7.1 to 11.2 l between 1990 and 2017 and these have been predicted to increase in future years 2 .

Previous epidemiological studies conducted in mainly western populations have provided consistent evidence about the hazards of alcohol drinking for several major diseases, including several types of cancers and cardiovascular diseases (CVDs), liver cirrhosis, infectious diseases (for example tuberculosis and pneumonia) and injuries 4 , 5 , 6 , 7 , 8 , 9 . Large western cohort studies with linkage to hospital records have also investigated the associations of alcohol with risks of several less-common or non-fatal disease outcomes (for example certain site-specific cancers 10 , 11 , 12 , dementia 13 , falls 14 and cataract surgery 15 ). For some (for example stomach cancer), there was suggestive evidence of weak positive associations with heavy drinking 10 , 11 , whereas for others (for example cataract) the limited available evidence has been contradictory 10 , 12 , 13 , 15 ; however, the evidence from western populations, even for diseases known to be associated with alcohol, may not be generalizable to Chinese populations, where the prevalence and types of alcohol drinking (mainly spirits), patterns of diseases (for example high stroke rates) and differences in the ability to metabolize alcohol 8 , 9 , 16 differ markedly from those in western populations 4 , 17 .

For many diseases, including those considered by the World Health Organization (WHO) 4 to be alcohol-related (for example ischemic heart disease (IHD) and diabetes), uncertainty remains about the causal relevance of these associations, which can be assessed in genetic studies using a Mendelian randomization (MR) approach 18 . In such studies, genetic variants can be used as instruments for alcohol consumption to investigate the potential causal relevance of alcohol drinking for diseases, which can limit the biases of confounding and reverse causality common in conventional observational studies 18 . Such studies are particularly informative in East Asian populations where two common genetic variants ( ALDH2 - rs671 and ADH1B - rs1229984 ), which are both rare in western populations, greatly alter alcohol metabolism and strongly affect alcohol intake 19 . Several studies have explored the causal relevance of alcohol consumption with CVD risk factors and morbidity 19 , 20 , 21 , 22 and cancer 16 using these genetic variants, yet findings remain inconclusive for certain diseases (for example IHD) and evidence for other diseases is sparse.

To address these questions, we conducted analyses using observational and genetic approaches to evaluate the associations between alcohol consumption and the risks of a wide range of disease outcomes in the prospective China Kadoorie Biobank (CKB).

Among the 512,724 participants (Supplementary Fig. 1 ), the mean age at baseline was 52 (s.d. 10.7) years, 41% were men and 56% lived in rural areas. Among men, 33% reported drinking alcohol regularly (at least once a week) at baseline (current drinkers), consuming on average 286 g of alcohol per week, mainly from spirits (Supplementary Tables 1 and 2 ). Non-drinkers and ex-drinkers were older and more likely to report poor self-rated health or previous chronic diseases, compared to occasional or current drinkers (Table 1 ). Compared to moderate drinkers (<140 g per week), heavier drinkers were more likely to be rural residents, had received lower education and had more unhealthy lifestyle factors (for example smoking and infrequent fresh fruit intake), higher mean blood pressure and longer duration of drinking (Supplementary Table 3 ). Among male current drinkers, 62% reported drinking daily and 37% engaging in heavy episodic drinking (Supplementary Table 2 ). Among women, only 2% drank alcohol at least weekly (mean intake 116 g per week), but there were similar associations with other baseline characteristics (Table 1 and Supplementary Tables 3 and 4 ) compared to those in men.

During a median of 12.1 (interquartile range 11.1–13.1) years of follow-up, 134,641 men (44,027 drinkers) and 198,430 women (4,420 drinkers) experienced at least one reported hospitalization event or death at age-at-risk 35–84 years, involving a total of 1,111,495 hospitalization episodes. Among men, there were 333,541 (107,857 in current drinkers) recorded events from 207 diseases across 17 International Classification of Diseases Tenth Revision (ICD-10) chapters studied that had at least 80 cases each among current drinkers (Table 2 ), while among women there were 476,986 (11,773) events from 48 diseases across 18 ICD-10 chapters (Supplementary Table 5 ).

Observational associations of alcohol with disease risks

Among men, alcohol drinking was significantly associated with higher risks of 61 disease outcomes from 15 ICD-10 chapters based on two separate analyses, (1) comparing ever-regular versus occasional drinkers and (2) dose–response among current drinkers (Table 2 and Extended Data Fig. 1 ). In each of the analyses in men, there were significant associations of alcohol consumption with 42 diseases (or outcomes), of which 23 were significant in both analyses and the remainder were directionally consistent with one exception (transient cerebral ischemic attacks, ICD-10 code G45) (Fig. 1 ). In further analyses covering all alcohol consumption categories, there were typical U-shaped or J-shaped associations, with excess risks in male ex-drinkers and non-drinkers compared to occasional or moderate drinkers for most of these diseases (Supplementary Table 6 ). Among male ex-drinkers, the overall excess morbidity risks were more considerable for alcohol-associated diseases than for other diseases, but these excess risks were lower with increasing duration after stopping drinking (Extended Data Fig. 2 ).

Cox models ( a ) comparing ever-regular drinkers with occasional drinkers or ( b ) assessing the dose–response per 280 g per week higher usual alcohol intake within current drinkers, were stratified by age at risk and study area and were adjusted for education and smoking. Each solid square represents HR with the area inversely proportional to the variance of the log HR. The horizontal lines indicate 95% CIs. Diseases considered to be alcohol-related by the WHO are indicated with ‘W’ under the ‘WHO’ column. The individual diseases listed included all that showed FDR-adjusted significant associations with alcohol (FDR-adjusted P  < 0.05, indicated with ‘Y’ under the ‘FDR sig.’ column) and WHO alcohol-related diseases that showed nominally significant associations with alcohol ( P  < 0.05). All P values are two-sided. † Included less-common ICD-10 codes within the corresponding ICD-10 chapter that were not individually investigated in the present study. ‘Less-common psychiatric and behavioral conditions’ consisted of ICD-10 codes F00–F99, excluding F32, F33 and F99. ‘Less-common circulatory diseases’ consisted of ICD-10 codes I00–I99, excluding I10, I11, I20, I21, I24, I25, I27, I42, I46, I48–I51, I60–I67, I69, I70, I80 and I83. ‘Less-common injury, poisoning and other external causes’ consisted of ICD-10 codes S00–T98, excluding S06, S09, S22, S32, S42, S52, S62, S72, S82, S92 and T14.

Of the 61 diseases positively associated with alcohol intake in male participants, 28 were considered by the WHO to be alcohol-related diseases, including tuberculosis (A15–A19 and B90), six site-specific cancers including cancers in the larynx (C32), esophagus (C15), liver (C22), colon (C18), rectum (C19 and C20) and lips, oral cavity and pharynx (C00–C14), diabetes (E10–E14), epilepsy (G40 and G41), several hypertensive diseases (I10 and I11) and cerebrovascular diseases (I61, I63, I65, I66, I67, I69 and G45), chronic IHD (I25), cardiomyopathy (I42), pneumonia (J12–J18), alcoholic liver disease (K70) and liver cirrhosis (K74), pancreatitis (K85 and K86) and external causes including self-harm (X60–X84), falls (W00–W19), transport accidents (V01–V99) and other external causes (rest of V–Y) (Fig. 1 and Extended Data Fig. 3 ). Of these 28 diseases, 22 showed significant dose–response associations with alcohol intake. The hazard ratios (HRs) per 280 g per week higher intake for the aggregated WHO alcohol-related diseases were 1.22 (95% confidence interval (CI) 1.19–1.25) (Supplementary Table 7 for detailed outcome classification), ranging from 1.12 (1.05–1.20) for pneumonia to 1.97 (1.80–2.15) for esophageal cancer.

The 33 other diseases showing false discovery rate (FDR)-adjusted significant positive associations with alcohol drinking in men included lung (C34) and stomach (C16) cancers, cataract (H25 and H26), six digestive diseases such as gastroesophageal reflux disease (K21) and gastric ulcer (K25), three musculoskeletal conditions, including gout (M10), three fracture types (S22, S42 and S72), and the aggregates of less-common psychiatric and behavioral conditions and circulatory diseases (Fig. 1 and Extended Data Fig. 4 ). Of these 33 diseases, 22 showed significant dose–response associations, with HRs per 280 g per week higher intake ranging from 1.16 (95% CI 1.04–1.30) for lung cancer to 1.94 (1.43–2.63) for purpura and other hemorrhagic conditions (D69) and 1.20 (1.16–1.24) for the aggregated CKB new alcohol-associated diseases. In contrast, three diseases showed FDR-adjusted significant inverse associations with alcohol drinking (other nontoxic goiter (E04), hyperplasia of prostate (N40) and inguinal hernia (K40)). Overall, for all-cause morbidity, the HR per 280 g per week higher intake was 1.12 (1.10–1.14) in male current drinkers.

Supplementary Figs. 2 – 4 show the dose–response associations for all disease outcomes investigated in male current drinkers. For alcohol-associated diseases and for total morbidity, the dose–response associations were unaltered after additional covariate adjustments or excluding participants with poor baseline health conditions (Supplementary Fig. 5 and Supplementary Table 8 ). Moreover, the associations were similar across various male population subgroups, but seemed to be stronger in younger men, urban residents and higher socioeconomic groups for new alcohol-associated diseases (Supplementary Fig. 6 ).

Among male current drinkers, drinking daily, heavy episodic drinking and drinking spirits were each associated with higher risks for alcohol-related diseases, but most of these associations were attenuated to the null after adjusting for total alcohol intake (Extended Data Fig. 5 ); however, for a given total alcohol intake among male current drinkers, drinking daily was associated with 30–40% higher risks of alcohol-related cancers (1.30, 1.17–1.45) and liver cirrhosis (1.39, 1.13–1.72), compared to non-daily drinking. Similarly, heavy episodic drinking was associated with higher risks of diabetes (1.23, 1.12–1.34) and IHD (1.11, 1.03–1.19), whereas drinking outside of meals was associated with 49% (1.49, 1.19–1.86) higher risk of liver cirrhosis than drinking with meals. The risks of all major alcohol-associated diseases were higher with longer duration of alcohol consumption in men (Extended Data Fig. 6 ).

Among women, due to few reported current drinkers there was a lack of statistical power to detect any associations of self-reported alcohol intake with disease risks (Supplementary Table 5 , Extended Data Fig. 7 and Supplementary Fig. 7 ).

Genetic associations of alcohol with disease risks

A genetic instrument for alcohol intake was derived using ALDH2 - rs671 (G > A) and ADH1B - rs1229984 (G > A) genotypes. The overall A-allele frequency was 0.21 for ALDH2 - rs671 and 0.69 for ADH1B - rs1229984 , with both A-alleles being more common in southern than northern study areas (Supplementary Table 9 ). Both ALDH2 - rs671 and, to a lesser extent, ADH1B - rs1229984 were strongly associated with alcohol drinking in men, but much less so in women (Supplementary Table 10 ). In men, the derived genetic instrument predicted a >60-fold difference (range 4–255 g per week, C1 to C6) in mean alcohol intake, whereas in women mean alcohol intake remained low (<10 g per week) across genetic categories (Supplementary Table 11 ). Both variants and the derived instrument were not associated with smoking or other major self-reported baseline characteristics, except for a small difference in fresh fruit intake by ALDH2 - rs671 genotype in men.

Among men, genotype-predicted mean alcohol intake was positively associated with higher risks of CKB WHO alcohol-related (HR per 280 g per week higher genotype-predicted mean male alcohol intake: 1.14, 95% CI 1.09–1.20) and CKB new alcohol-associated (1.06, 1.01–1.12) diseases (Fig. 2 ), both of which were slightly weaker than the conventional associations. For certain diseases, however, the genetic associations were stronger, with HRs of 1.38 (1.27–1.49) for stroke, 2.30 (1.58–3.35) for liver cirrhosis and 2.33 (1.49–3.62) for gout, in men (Fig. 3 and Extended Data Fig. 8 ). For individual genetic variants, the associations were directionally consistent (Extended Data Figs. 9 and 10 ). Conversely, there were no significant dose–response genotypic associations with IHD, inguinal hernia or hyperplasia of prostate in men. For other alcohol-associated diseases, higher genotype-predicted mean male alcohol intake was significantly associated with higher risks of esophageal cancer, cataract, occlusion and stenosis of cerebral arteries, sequelae of cerebrovascular disease, essential primary hypertension and fractures of ribs, sternum or thoracic spine. There were also suggestive positive genotypic associations with several digestive tract cancer types (liver, colon and stomach) and circulatory and digestive diseases, and significant inverse associations with lung cancer and other chronic obstructive pulmonary disease (J44) in men (Extended Data Figs. 8 – 10 ). Sensitivity analyses using different analytical methods to adjust for confounding by study area, or a two-stage least-squares MR approach, did not alter the main genetic findings in men (Supplementary Table 12 ). In contrast, genotypes that increased alcohol intake in men were not adversely associated with most alcohol-related disease risks among women (for example HR 1.00 (0.97–1.04) for all morbidity among female non-drinkers; Supplementary Fig. 7 and Extended Data Figs. 8 – 10 ).

Each box represents HR with the area inversely proportional to the variance of the group-specific log hazard within subplot. The vertical lines indicate group-specific 95% CIs. Conventional epidemiological analyses relate self-reported drinking patterns to risks of diseases (reference group is occasional drinkers), using Cox models stratified by age at risk and study area and adjusted for education and smoking. Within current drinkers, HRs were plotted against usual alcohol intake and were calculated per 280 g per week higher usual alcohol intake. Genetic epidemiological analyses relate genetic categories to risks of diseases (reference group is the genotype group with lowest genotype-predicted mean male alcohol intake), using Cox models stratified by age at risk and study area and adjusted for genomic principal components. The HR per 280 g per week higher genotype-predicted mean male alcohol intake was calculated from the inverse-variance-weighted mean of the slopes of the fitted lines in each study area. The corresponding slopes in women were summarized in text and the slopes of the fitted line by sex were compared and assessed for heterogeneity using chi-squared tests (indicated by P for heterogeneity by sex). All P values are two-sided. Analyses of these aggregated outcomes were based on first recorded event of the aggregate during follow-up and participants may have had multiple events of different types of diseases. ‘All alcohol-related diseases’ includes the first recorded event from ‘CKB WHO alcohol-related diseases’ or ‘CKB new alcohol-associated diseases’ during follow-up.

Each box represents HR with the area inversely proportional to the variance of the group-specific log hazard within subplot. The vertical lines indicate group-specific 95% CIs. Conventional epidemiological analyses relate self-reported drinking patterns to risks of diseases (reference group is occasional drinkers), using Cox models stratified by age at risk and study area and adjusted for education and smoking. Within current drinkers, HRs were plotted against usual alcohol intake and were calculated per 280 g per week higher usual alcohol intake. Genetic epidemiological analyses relate genetic categories to risks of diseases (reference group is the genotype group with lowest genotype-predicted mean male alcohol intake), using Cox models stratified by age at risk and study area and adjusted for genomic principal components. The HR per 280 g per week higher genotype-predicted mean male alcohol intake was calculated from the inverse-variance-weighted mean of the slopes of the fitted lines in each study area. The corresponding slopes in women were summarized in text and the slopes of the fitted line by sex were compared and assessed for heterogeneity using chi-squared tests (indicated by P for heterogeneity by sex). All P values are two-sided. Corresponding ICD-10 codes, IHD (I20–I25); stroke (I60, I61, I63 and I64); liver cirrhosis (K70 and K74); gout (M10); inguinal hernia (K40); hyperplasia of prostate (N40).

Hospitalizations associated with alcohol drinking

Among men, ever-regular drinkers had higher numbers of hospitalizations for any causes than occasional drinkers, particularly for cancer hospitalizations, and these differences increased with increasing age at risk, except for CVD hospitalizations (Supplementary Fig. 8 ).

This prospective study provides a comprehensive assessment of the impact of alcohol consumption on a very wide range of disease outcomes in Chinese adults. Among men, alcohol consumption was associated with significantly higher risks of 61 diseases, including 33 not previously reported as alcohol-related diseases by the WHO, and higher risks of hospitalizations for any causes. For a given total amount, drinking daily, heavy episodic drinking and drinking outside of meals exacerbated the risks of four major diseases in Chinese men. Moreover, most of these associations in Chinese men were confirmed in genetic analyses, at least when assessed collectively, and are likely to reflect the effects alcohol consumption itself rather than any pleiotropic effects of the genetic instruments.

Based primarily on observational findings in western populations, alcohol consumption has been considered by the WHO 4 and the Global Burden of Disease (GBD) study 23 to be related to about 20 distinct disease categories, involving chronic diseases and cancers largely in the gastrointestinal system, several CVD types, infectious diseases and injuries. The observational analyses largely confirmed these known associations (Supplementary Table 13 ), but also provided insights into additional hazards of certain drinking patterns suggested by previous studies 8 , 9 , 24 , 25 . Moreover, this study discovered 33 additional alcohol-associated diseases across various body systems in Chinese men that had not been previously reported by the WHO. For these 33 disease outcomes, their associations with alcohol intake were confirmed in genetic analyses, at least collectively as well as for certain specific diseases (for example gout), as was the case for a similar number of WHO alcohol-related diseases. The somewhat smaller relative (but not absolute) risks of alcohol drinking with major diseases at older than younger age in men from observational analyses were consistent with previous studies of other risk factors (for example blood pressure 26 and smoking 27 ), which could be driven by a number of factors such as selection bias 27 and comorbidities.

For certain major WHO alcohol-related diseases, particularly IHD and ischemic stroke, observational studies, including this study, have consistently reported J-shaped associations, with those who drank moderately (for example 1–2 units a day) having the lowest risks 6 , 28 ; however, these apparent protective effects of moderate drinking probably largely reflect residual confounding (for example non-drinkers having worse health and socioeconomic profiles than occasional drinkers) and uncontrolled reverse causation (for example sick-quitter effect where pre-existing poor health or changes in health conditions lead to alcohol cessation), including the difficulty in defining abstainers (for example ex-drinkers may be reported as non-drinkers) as the reference group in many previous studies 3 , 29 . In this study, we used occasional drinkers rather than non-drinkers as the reference group, which, together with separate dose–response analyses among current drinkers, helped to reduce but not eliminate any such biases, which could largely be mitigated in genetic analyses using an MR approach.

To date the existing MR studies for alcohol have focused mainly on CVD types 30 , 31 , 32 and cancers 33 , 34 , 35 , with limited data for other diseases. Moreover, previous studies mainly involved European-ancestry populations and hence were constrained by availability of relatively weak genetic instruments. Using genetic instruments specific to East Asian populations that predicted >60-fold difference in alcohol consumption, we previously reported evidence for the causal relevance and apparent dose–response effects of alcohol consumption on upper-aerodigestive tract cancers 16 and stroke 19 . These findings were further corroborated by subsequent European ancestry-based MR studies 30 , 32 , 36 and the analyses presented in this study with additional follow-up data. In contrast to stroke, we found no reliable genetic evidence for a cardioprotective, nor harmful, effect of moderate drinking on risk of IHD in men, consistent with findings in other MR studies 30 , 32 . The present study also demonstrated a log-linear genetic association of alcohol with liver cirrhosis and suggestive positive associations for several WHO alcohol-related digestive tract cancers in men. Moreover, separate genetic analyses among women suggests that the excess risks observed among men were due chiefly to alcohol per se rather than to potential pleiotropic effects of the alcohol-related genotypes. Further larger genetic studies are required to confirm and elucidate the potential causal relevance for each of the other WHO alcohol-related diseases individually.

For the new alcohol-associated diseases identified in this study, the available prospective epidemiological evidence has been sparse and mostly confined to western populations. For gout, previous western prospective studies have reported positive associations 37 , 38 and an MR study of 8,000 Korean men has also reported positive associations of alcohol consumption with hyperuricemia, a risk factor for gout 39 . The present study provides genetic evidence that alcohol drinking increases the risk of gout. Consistent with the present study, previous European-ancestry-based observational studies 40 , 41 and one MR study 42 also reported positive associations of alcohol intake with risks of several fracture types. The available prospective evidence on associations between alcohol drinking and risk of cataract has been conflicting 15 , 43 and one European-ancestry-based MR study reported no genetic associations 44 . We found a significant dose–response association between alcohol and risk of cataract among Chinese men in observational analyses, which was supported by the present genetic analyses.

For several other diseases (for example gastroesophageal reflux disease and gastric ulcer), the observational findings provide additional evidence to the existing literature 5 , 45 , 46 , 47 , but the supporting genetic evidence is still constrained by limited statistical power. Similarly, our observational findings for lung and stomach cancers were generally consistent with evidence provided by previous prospective studies 7 , 11 , 48 , 49 ; however, the causal relevance of these associations remains to be elucidated in future larger MR studies with appropriate consideration of the potential gene–environment interactions between ALDH2 - rs671 and alcohol intake (the effect of alcohol intake on cancer risks being modified by ALDH2 - rs671 genotype due to excessive acetaldehyde) 16 and other aldehyde exposures 50 in cancer risks, which might similarly affect the genetic associations for respiratory diseases and other potential acetaldehyde-related diseases. In observational analyses, we found significant inverse associations for inguinal hernia, prostate hyperplasia and other nontoxic goiter, but not for several other diseases previously inversely associated with alcohol drinking, including non-Hodgkin lymphoma 48 , kidney cancer 48 , thyroid cancer 48 and gallstones 51 . The genetic analyses, albeit with limited power, did not provide reliable evidence supporting the inverse associations with these outcomes. Future well-powered genetic investigations are warranted for less-common diseases in different populations.

The strengths of this study include the prospective design, large sample size, detailed information on alcohol consumption and drinking patterns, completeness of follow-up and a wide range of morbidity outcomes analyzed. We were also able to assess the potential causal relevance of the associations using two powerful East Asian genetic variants. Moreover, the extremely low drinking prevalence in women (regardless of their genotypes) enabled assessment for potential pleiotropy, further supporting the genetic findings among men.

Nevertheless, the study also has limitations. First, it is still possible that heavy drinking was under-reported, which could have underestimated the hazards of heavy episodic drinking. Second, as in many population-based cohort studies, extreme problematic drinkers and certain alcohol-related disease events may be under-represented, but this should not affect the assessment of the associations of alcohol with most disease outcomes. Third, while the repeated measures of alcohol consumption available in the re-survey subsets allowed us to estimate long-term usual mean alcohol intake at the group level to account for regression dilution bias, we were unable to study the effects of longitudinal alcohol drinking trajectories on health. Fourth, we were unable or underpowered to study diseases that do not normally require hospitalization (for example dementia and depression), nor alcohol-related diseases only affecting women, given the low proportion of female drinkers (for example <70 cases of breast cancer in female drinkers). While the low female drinking prevalence in CKB was consistent with findings in a nationwide survey 52 , it is possible that women may be more likely to under-report drinking than men for cultural and social reasons. Hence our null findings in women should be interpreted with caution and not be taken as a lack of alcohol-related harms in women in general, especially in the context of rising alcohol consumption among Asian women 2 . Fifth, as spirits were the main beverage type and our genetic instrument did not distinguish between beverage types, we were unable to assess beverage-specific effects on disease risks, including wine consumption, which is uncommon in China 17 and has been proposed as potentially cardioprotective due to other non-alcoholic components in red wine 53 . Sixth, although our genetic analyses allowed comparison of the overall genetic effects of negligible, moderate and high mean alcohol intake levels for major and overall morbidities, we had limited power to confidently clarify any small threshold effects in the low consumption end, especially for individual diseases. Finally, the genetic analyses lacked statistical power to assess the associations with several individual alcohol-associated diseases so these findings should still be viewed as hypothesis-generating.

In recent decades, several studies have estimated the alcohol-attributable disease burden, involving predominantly WHO alcohol-related diseases. These estimates were based mainly on observational evidence and included the potentially biased U- or J-shaped associations with IHD and ischemic stroke 1 , 23 , 54 . We have demonstrated in both conventional and genetic analyses that alcohol drinking is associated with hazards in a dose–response manner with a much wider range of disease outcomes than previously considered by the WHO 4 and the GBD study 23 and do not find any evidence for protective effects for IHD or stroke, suggesting that the actual alcohol-attributable disease burden is likely to be much greater than widely believed.

Overall, the present study demonstrated substantial hazards of alcohol consumption with a wide range of disease outcomes among Chinese men. The findings reinforce the need to lower population mean levels of alcohol consumption as a public health priority in China. Future estimation of the alcohol-attributable disease burden worldwide and in specific regions should incorporate new genetic evidence from the present and any future studies about the likely causal relevance of alcohol consumption for a broad range of disease outcomes.

Study population

Details of the CKB study design and methods have been previously reported 55 . Briefly, 512,724 adults aged 30–79 years were recruited from ten geographically diverse (five rural and five urban) areas across China during 2004–2008. At local study assessment clinics, trained health workers administered a laptop-based questionnaire recording sociodemographic factors, lifestyle (for example alcohol drinking, smoking, diet and physical activity) and medical history; undertook physical measurements (for example blood pressure and anthropometry); and collected a blood sample for long-term storage. Two resurveys of ~5% randomly selected surviving participants were subsequently conducted in 2008 and 2013–2014 using similar procedures.

Ethics approval

Ethical approval was obtained from the Ethical Review Committee of the Chinese Centre for Disease Control and Prevention (Beijing, China, 005/2004) and the Oxford Tropical Research Ethics Committee, University of Oxford (UK, 025-04). All participants provided written informed consent.

Assessment of alcohol consumption

Detailed questionnaire assessment of alcohol consumption has been described previously 3 , 17 , 56 . In the baseline questionnaire, participants were asked how often they had drunk alcohol during the past 12 months (never or almost never, occasionally, only at certain seasons, every month but less than weekly or usually at least once a week). Those who had not drunk alcohol at least weekly in the past 12 months were asked whether there was a period of at least a year before that when they had drunk some alcohol at least once a week. Based on their past and current drinking history, participants were classified into: non-drinkers (had never drunk alcohol in the past year and had not drunk in most weeks in the past); ex-drinkers (had not drunk alcohol in most weeks in the past year but had done so in the past); occasional drinkers (had drunk alcohol but less than weekly in the past year and had not drunk alcohol in most weeks in the past); and current drinkers (had drunk alcohol on a weekly basis (regularly) in the past year).

Current drinkers were asked further questions about their drinking patterns, including frequency, beverage type (beer, grape wine, rice wine, weak spirits with <40% alcohol content and strong spirits with ≥40% alcohol content) and amount consumed on a typical drinking day, mealtime drinking habits, age started drinking in most week and their experience of flushing or dizziness after drinking.

Alcohol intake level was estimated based on the reported frequency (taken as the median of the reported frequency intervals; 1.5 for 1–2 d per week, 4 for 3–5 d per week, 6.5 for 6–7 d per week), beverage type and amount consumed, assuming the following alcohol content by volume (v/v) typically seen in China: beer 4%, grape wine 12%, rice wine 15%, weak spirits 38% and strong spirits 53% 57 . Among current drinkers, men were grouped into four consumption categories (<140, 140–279, 280–419 and 420+ g per week) and women into three categories (<70, 70–139 and 140+ g per week), broadly based on the recommended cutoffs for alcohol categories by the WHO 58 and national drinking guidelines. Heavy episodic drinking was defined as consuming >60 g of alcohol on a typical drinking occasion for men and >40 g per occasion for women 58 . Drinking outside of meals was defined as usually drinking between or after meals or having no regular patterns (versus usually drinking with meals). Duration of drinking was derived by the difference in years between age at baseline and age started drinking.

Ex-drinkers were asked how long (in years) ago they had stopped drinking in most weeks. Ex-drinkers were grouped with current drinkers as ‘ever-regular drinkers’.

Follow-up for mortality and morbidity

The vital status of participants was obtained periodically from local death registries, supplemented by annual active confirmation through local residential, health insurance and administrative records. Additional information on morbidity was collected through linkage with disease registries (for cancer, stroke, IHD and diabetes) and the national health insurance system, which record any episodes of hospitalization and almost has universal coverage. All events were coded with ICD-10 codes, blinded to the baseline information. By 1 January 2019, 56,550 (11%) participants had died, 311,338 (61%) were ever hospitalized, but only 4,028 (<1%) were lost to follow-up.

Outcome measures

To enable a ‘phenome-wide’ investigation, all recorded diseases and injuries (referred to as ‘diseases’ for simplicity) coded by three-character ICD-10 codes were reviewed. ICD-10 codes were combined (where appropriate) based on disease characteristics and their potential relationships with alcohol consumption 4 , 8 , 10 , 59 . Disease end points were curated based on diseases considered to be causally impacted by alcohol by the WHO 4 , 59 and major diseases previously shown to be related to alcohol in CKB and other large prospective cohort studies 8 , 10 , while retaining maximal granularity. Diseases with at least 80 cases recorded during follow-up among current drinkers, separately by sex, were analyzed individually to capture a wide range of specific conditions while ensuring reasonable statistical power (around 60–80% power to detect a HR of 2.00 per 280 g per week higher usual alcohol intake at P  < 0.01 and P  < 0.05, respectively). Within each ICD-10 chapter, diseases with <80 events were grouped into a ‘less-common’ category. Several ICD-10 chapters considered not directly relevant in this population (for example perinatal-origin diseases (chapter XVI) and congenital conditions (XVII); pregnancy-related diseases (XV) in men) were excluded.

Major diseases defined by the WHO as likely to be causally related with alcohol consumption 4 , including several cancers (mouth and throat, esophagus, colon-rectum, liver and female breast), diabetes mellitus, IHD, stroke, liver cirrhosis and external causes, were also selected a priori for detailed analyses of associations with drinking patterns (daily drinking, heavy episodic drinking, mealtime habit, spirit drinking and drinking duration). Similarly, diseases that were significantly and adversely associated with alcohol in the ‘phenome-wide’ investigations (either with ever-regular versus occasional drinking or in dose–response associations with amounts consumed) were further categorized as ‘CKB WHO alcohol-related diseases’ and ‘CKB new alcohol-associated diseases’ respectively for genetic investigation of causality. Detailed outcome classifications are reported in Supplementary Table 7 .

Genotyping and alcohol genetic instruments

The two East Asian genetic variants ( ALDH2 - rs671 and ADH1B - rs1229984 ) were genotyped in 168,050 participants (151,347 randomly selected, 16,703 selected as part of nested case–control studies of CVD and chronic obstructive pulmonary disease, which were only included in analyses of relevant outcomes; Supplementary Fig. 1 ) using Affymetrix Axiom ( n  = 100,396) or custom Illumina GoldenGate ( n  = 93,125) arrays at BGI (Shenzhen, China), with some overlap between them. Among 25,471 participants genotyped with both arrays, the concordance was >99.9% for both variants. Where discordant, genotypes obtained from the Affymetrix Axiom array were used.

The genetic instrument for alcohol was derived from ALDH2 - rs671 and ADH1B - rs1229984 and ten study areas from the random genotyped subset of male participants to avoid potential selection bias, using a previously developed method in CKB 19 . Briefly, nine genotype combinations were defined based on the genotypes for each of the two variants (each AA, AG or GG). As alcohol use varies greatly by study area, among men, mean alcohol intake was calculated for each of these nine genotype across ten study areas (that is a total of 90 genotype-area combinations) to reflect a wide range of alcohol consumption, assigning an intake of 5 g per week to occasional drinkers and excluding ex-drinkers from the calculation. Ex-drinkers were excluded from the calculation of mean alcohol intake as their baseline intake did not reflect their long-term intake; nevertheless, they were included in subsequent genetic analyses once they had been assigned a genetic group. These 90 combinations were then grouped into six categories (C1–C6) according to their corresponding mean intake values, at cutoff points of 10, 25, 50, 100 and 150 g per week, selected to facilitate investigation of the causal effects of alcohol across a wide range of mean alcohol intakes while allowing adequate sample size in each category for reliable comparisons. In this way participants (including ex-drinkers) were classified only based on their genotypes and study area, but not on individual self-reported drinking patterns. Comparisons of these six genetic categories can, where analyses are stratified by area, be used to estimate the genotypic effects on disease risks.

To facilitate the comparison of genotypic effects between sexes (pleiotropic effects), women were classified into the same six categories as men based on their genotypes and study area, regardless of female alcohol intake. This allowed comparison of genotypic effects between men (where genotype were strongly associated with alcohol intake) and women (where alcohol intake was low in all genotypic categories) (Supplementary Tables 10 and 11 ).

Statistical analysis

Given the extremely low alcohol use among women 3 , 17 , the analyses were conducted separately by sex but focused chiefly on men. All CKB participants and the genotyped subset with genomic principal components (PCs; derived from genome-wide genotyping array data and were informative for CKB population structure) 60 were included in conventional and genetic analyses, respectively (Supplementary Fig. 1 ). Means and percentages of baseline characteristics were calculated by self-reported alcohol consumption patterns and by genotype categories, adjusted for age (in 10-year intervals), ten study areas and (for genetic analysis) genomic PCs 60 to control for differences in genetic distribution due to population stratification, as appropriate.

For conventional observational analyses, Cox proportional hazard models were used to estimate HRs for individual diseases associated with different alcohol consumption categories (in three broad categories: occasional drinkers, ever-regular drinkers, non-drinkers; and in 6–7 detailed categories: occasional drinkers, ex-drinkers, non-drinkers, 3–4 further current drinker groups defined by alcohol intake level) and among current drinkers with continuous levels of alcohol intake (per 280 g per week in men, per 100 g per week in women) or with categories of alcohol intake (<140, 140–279, 280–419 and 420+ g per week in men; <70, 70–139 and 140+ g per week in women). The Cox models were stratified by age at risk (5-year groups between 35–84 years) and ten areas and adjusted for education (four groups: no formal school, primary school, middle or high school and technical school/college or above) and smoking status (six groups in men: never, occasional, ex-regular, current <15, current 15–24, current ≥25 cigarettes equivalent per day; four groups in women: never, occasional, ex-regular and current). Smoking data have been previously validated against exhaled carbon monoxide 61 . Competing risks from all-cause mortality for disease events were handled by censoring participants at death from any cause to estimate cause-specific HRs comparing event rates in participants who were alive and free of the disease of interest 62 . To reduce biases from residual confounding and uncontrolled reverse causation related to the choice of using non-drinkers (for example sick-quitter effect, pre-existing poor health or social disadvantages leading to alcohol cessation or abstinence) as the reference group 3 , 29 , we used occasional drinkers as the reference group, together with separate dose–response analyses among current drinkers. To account for within-person variation of alcohol intake over the follow-up period, repeat alcohol measures for participants who attended the two resurveys were used to estimate usual alcohol intake (Supplementary Table 1 ) and correct for regression dilution bias 9 , 63 . The shapes of dose–response associations between alcohol and disease risks were assessed among current drinkers by plotting the HRs of predefined baseline consumption categories against the corresponding mean usual alcohol intake. Log HR estimates and the corresponding standard errors for baseline alcohol intake, modeled as a continuous variable, were divided by the regression dilution ratio (0.53 for both men and women; calculated using the McMahon–Peto method 64 ) to obtain estimated HRs per 280 g per week higher usual alcohol intake among male current drinkers and HRs per 100 g per week among female current drinkers. For analyses involving drinking patterns, additional adjustments were conducted for total alcohol intake (continuous) and baseline age (continuous; for drinking duration analysis) where appropriate.

Sensitivity analyses were performed by (1) additional adjustments for further covariates (household income (<10,000, 10,000–19,999, 20,000–34,999 and ≥35,000 yuan per year), fresh fruit intake (4–7 d per week and ≤3 d per week), physical activity (continuous, in metabolic equivalent of task per hour per day), body mass index (<22, 22–24.9, 25–26.9 and ≥27 kg m 2 ); and (2) excluding individuals with poor self-reported health or previous major chronic diseases (including self-reported coronary heart diseases, stroke, transient ischemic attack, tuberculosis, emphysema or bronchitis, liver cirrhosis or chronic hepatitis, peptic ulcer, gallstone or gallbladder disease, kidney disease, rheumatoid arthritis, cancer and diabetes) at baseline. For all aggregated end points (for example CKB WHO alcohol-related, CKB new alcohol-associated and all morbidity), subgroup analyses were conducted by baseline age (<55, 55–64 and ≥65 years), area (urban and rural), education (primary school or below, middle school, high school or above), household income (<10,000, 10,000–19,999 and 20,000+ yuan per year) and smoking status (ever-regular and never-regular), with heterogeneity or trend assessed by chi-squared tests 65 . HRs for diseases associated with years of stopping among ex-drinkers compared to occasional drinkers were also estimated.

In genetic analyses, Cox regression, stratified by age at risk and study area and adjusted for 11 genomic PCs 60 , were used to estimate HRs for major alcohol-related diseases associated with the six genetic categories (C1–C6). Log HRs were plotted against the genotype-predicted mean male alcohol intake in the six categories. To control for potential confounding by population structure, similar analyses were repeated within each study area using age-at-risk-stratified and genomic PC-adjusted Cox models. A line of best fit was fitted through the log HRs against genotype-predicted mean male alcohol intake in the genetic categories present in the corresponding study area, using meta-regression. These within-area slopes (each reflecting purely genotypic effects) were combined by inverse-variance-weighted meta-analysis to yield the overall area-stratified genotypic associations, which controlled for any potential bias resulted from variations due to population structure, summarized as HR per 280 g per week higher genotype-predicted mean male alcohol intake. For total morbidity and aggregated alcohol-associated outcomes, sensitivity analyses were performed by (1) using age-at-risk- and area-stratified and genomic PC-adjusted Cox models to estimate HR per 280 g per week (area-adjusted genotypic associations); and (2) using a two-stage least-squares approach 66 .

Genotypic analyses in women were conducted not to assess the health effects of alcohol in women, but to investigate the extent to which the genotypes studied in men had pleiotropic effects (genotypic effects not mediated by drinking patterns). As few women consumed alcohol, any genotypic effects of the six genetic categories that are mediated by drinking alcohol should be much smaller in women than in men, but any other pleiotropic genotypic effects should be similar in both sexes. Hence, among women, we used the same genetic categories as in men and related the genotypic effects in women to the mean male alcohol intake in these six categories, which allows comparisons of genetic findings by sex and assessment of potential pleiotropy. To further remove the small genotypic effects on alcohol use in women (Supplementary Tables 10 and 11 ), we restricted the genetic analyses to female non-drinkers in sensitivity analyses.

The genotypic associations of individual genetic variants ( rs671 , rs1229984 ; GG versus AG genotype) with alcohol-related disease risks were also assessed using a similar area-stratified approach.

The proportional hazards assumption was tested using scaled Schoenfeld residuals for the pre-specified major diseases (no clear evidence of violation was found). For analyses involving more than two exposure categories, the floating absolute risks were used to estimate group-specific 95% CIs for all categories including the reference group 9 , 19 , 67 . All P values were two-sided. Statistical significance (at the 5% level) was evaluated using both FDR-adjusted P values applied within ICD-10 chapters to correct for multiple testing in the ‘phenome-wide’ investigation 68 , 69 , 70 and conventional P values for hypothesis testing for observational analyses of WHO alcohol-related diseases, analyses of drinking patterns and genetic analyses.

To assess the cumulative burden of alcohol consumption, the total number of hospitalizations were estimated for ever-regular versus occasional drinkers using the mean cumulative count, which does not assume independence between hospitalizations and all-cause mortality 71 , 72 , 73 . All analyses used R software (v.4.0.5).

Ethics and inclusion statement

In accordance with the Nature Portfolio journals’ editorial policies, the research has included local researchers from China throughout the research process, including study design, study implementation, data ownership and authorship. The roles and responsibilities were agreed among collaborators ahead of the research and capacity-building plans, including data collection and study implementation skills for local researchers, were discussed and delivered. This research is locally relevant to the studied country and included local collaborative partners in all aspects of the study, thus, will provide local and regional organizations with epidemiological evidence on the health impacts of alcohol consumption to inform public health policies.

This research was not restricted nor prohibited in the setting of the researchers. The study was approved by local ethics review committee. The research raised no risks related to stigmatization, incrimination, discrimination, animal welfare, the environment, health, safety, security or other personal or biorisks. No biological materials, cultural artifacts or associated traditional knowledge has been transferred out of the country. In preparing the manuscript, the authors have reviewed and cited local and regional relevant studies.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The CKB is a global resource for the investigation of lifestyle, environmental, blood biochemical and genetic factors as determinants of common diseases. The CKB study group is committed to making the cohort data available to the scientific community in China, the United Kingdom and worldwide to advance knowledge about the causes, prevention and treatment of disease. For detailed information on what data are currently available to open access users, how to apply for them and the timeline for data access (12–16 weeks), please visit the CKB website: https://www.ckbiobank.org/data-access . Researchers who are interested in obtaining the raw data from the CKB study that underlines this paper should contact [email protected]. A research proposal will be requested to ensure that any analysis is performed by bona fide researchers and, where data are not currently available to open access researchers, is restricted to the topic covered in this paper. Further information is available from the corresponding authors upon request.

Code availability

The codes used for the data analyses in this study can be made available by contacting the corresponding authors. Access to codes will be granted for requests for academic use within 4 weeks of application.

Shield, K. et al. National, regional, and global burdens of disease from 2000 to 2016 attributable to alcohol use: a comparative risk assessment study. Lancet Public Health 5 , e51–e61 (2020).

PubMed   Google Scholar  

Manthey, J., Shield, K. D., Rylett, M., Hasan, O. S. M., Probst, C. & Rehm, J. Global alcohol exposure between 1990 and 2017 and forecasts until 2030: a modelling study. Lancet 393 , 2493–2502 (2019).

Im, P. K. et al. Patterns and trends of alcohol consumption in rural and urban areas of China: findings from the China Kadoorie Biobank. BMC Public Health 19 , 217 (2019).

PubMed   PubMed Central   Google Scholar  

World Health Organization. Global Status Report on Alcohol and Health 2018 (World Health Organization, 2018).

Corrao, G., Bagnardi, V., Zambon, A., La & Vecchia, C. A meta-analysis of alcohol consumption and the risk of 15 diseases. Prev. Med. 38 , 613–619 (2004).

Ferrari, P. et al. Lifetime alcohol use and overall and cause-specific mortality in the European Prospective Investigation into Cancer and nutrition (EPIC) study. BMJ Open 4 , e005245 (2014).

Yang, L. et al. Alcohol drinking and overall and cause-specific mortality in China: nationally representative prospective study of 220,000 men with 15 years of follow-up. Int. J. Epidemiol. 41 , 1101–1113 (2012).

Im, P. K. et al. Alcohol drinking and risks of total and site-specific cancers in China: a 10-year prospective study of 0.5 million adults. Int J. Cancer 149 , 522–534 (2021).

CAS   PubMed   PubMed Central   Google Scholar  

Im, P. K. et al. Alcohol drinking and risks of liver cancer and non-neoplastic chronic liver diseases in China: a 10-year prospective study of 0.5 million adults. BMC Med. 19 , 216 (2021).

Allen, N. E. et al. Moderate alcohol intake and cancer incidence in women. J. Natl Cancer Inst. 101 , 296–305 (2009).

Jayasekara, H. et al. Lifetime alcohol intake, drinking patterns over time and risk of stomach cancer: a pooled analysis of data from two prospective cohort studies. Int J. Cancer 148 , 2759–2773 (2021).

Botteri, E. et al. Alcohol consumption and risk of urothelial cell bladder cancer in the European prospective investigation into cancer and nutrition cohort. Int J. Cancer 141 , 1963–1970 (2017).

CAS   PubMed   Google Scholar  

Sabia, S. et al. Alcohol consumption and risk of dementia: 23 year follow-up of Whitehall II cohort study. BMJ 362 , k2927 (2018).

Tan, G. J. al. The relationship between alcohol intake and falls hospitalization: results from the EPIC-Norfolk. Geriatr. Gerontol. Int. https://doi.org/10.1111/ggi.14219 (2021).

Chua, S. Y. L. et al. Alcohol consumption and incident cataract surgery in two large UK cohorts. Ophthalmology https://doi.org/10.1016/j.ophtha.2021.02.007 (2021).

Im, P. K. et al. Alcohol metabolism genes and risks of site-specific cancers in Chinese adults: an 11-year prospective study. Int. J. Cancer 150 , 1627–1639 (2022).

Millwood, I. Y. et al. Alcohol consumption in 0.5 million people from 10 diverse regions of China: prevalence, patterns and socio-demographic and health-related correlates. Int. J. Epidemiol. 42 , 816–827 (2013).

Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23 , R89–R98 (2014).

Millwood, I. Y. et al. Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China. Lancet 393 , 1831–1842 (2019).

Au Yeung, S. L. et al. Moderate alcohol use and cardiovascular disease from Mendelian randomization. PLoS ONE 8 , e68054 (2013).

Shin, M. J., Cho, Y., Davey & Smith, G. Alcohol consumption, aldehyde dehydrogenase 2 gene polymorphisms, and cardiovascular health in Korea. Yonsei Med J. 58 , 689–696 (2017).

Taylor, A. E. et al. Exploring causal associations of alcohol with cardiovascular and metabolic risk factors in a Chinese population using Mendelian randomization analysis. Sci. Rep. 5 , 14005 (2015).

Griswold, M. G. et al. Alcohol use and burden for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 392 , 1015–1035 (2018).

Google Scholar  

Simpson, R. F. et al. Alcohol drinking patterns and liver cirrhosis risk: analysis of the prospective UK Million Women Study. Lancet Public Health 4 , e41–e48 (2019).

Roerecke, M. & Rehm, J. Alcohol consumption, drinking patterns, and ischemic heart disease: a narrative review of meta-analyses and a systematic review and meta-analysis of the impact of heavy drinking occasions on risk for moderate drinkers. BMC Med. 12 , 182 (2014).

Lacey, B. et al. Age-specific association between blood pressure and vascular and non-vascular chronic diseases in 0.5 million adults in China: a prospective cohort study. Lancet Glob. Health 6 , e641–e649 (2018).

Hernán, M. A., Alonso, A. & Logroscino, G. Cigarette smoking and dementia: potential selection bias in the elderly. Epidemiology 19 , 448–450 (2008).

Rehm, J., Rovira, P., Llamosas-Falcón, L. & Shield, K. D. Dose-response relationships between levels of alcohol use and risks of mortality or disease, for all people, by age, sex, and specific risk factors. Nutrients 13 , 2652 (2021).

Rehm, J., Irving, H., Ye, Y., Kerr, W. C., Bond, J. & Greenfield, T. K. Are lifetime abstainers the best control group in alcohol epidemiology? On the stability and validity of reported lifetime abstention. Am. J. Epidemiol. 168 , 866–871 (2008).

Lankester, J., Zanetti, D., Ingelsson, E. & Assimes, T. L. Alcohol use and cardiometabolic risk in the UK Biobank: a Mendelian randomization study. PLoS ONE 16 , e0255801 (2021).

Rosoff, D. B., Davey Smith, G., Mehta, N., Clarke, T.-K. & Lohoff, F. W. Evaluating the relationship between alcohol consumption, tobacco use, and cardiovascular disease: a multivariable Mendelian randomization study. PLoS Med. 17 , e1003410 (2020).

Larsson, S. C., Burgess, S., Mason, A. M. & Michaelsson, K. Alcohol consumption and cardiovascular disease: a Mendelian randomization study. Circ. Genom. Precis. Med. 13 , e002814 (2020).

Larsson, S. C. et al. Smoking, alcohol consumption, and cancer: a mendelian randomisation study in UK Biobank and international genetic consortia participants. PLoS Med. 17 , e1003178 (2020).

Zhou, X. et al. Alcohol consumption, DNA methylation and colorectal cancer risk: results from pooled cohort studies and Mendelian randomization analysis. Int. J. Cancer https://doi.org/10.1002/ijc.33945 (2022).

Zhou, X. et al. Alcohol consumption, blood DNA methylation and breast cancer: a Mendelian randomisation study. Eur. J. Epidemiol. 37 , 701–712 (2022).

Biddinger, K. J. et al. Association of habitual alcohol intake with risk of cardiovascular disease. JAMA Netw. Open 5 , e223849 (2022).

Wang, M., Jiang, X., Wu, W. & Zhang, D. A meta-analysis of alcohol consumption and the risk of gout. Clin. Rheumatol. 32 , 1641–1648 (2013).

Neogi, T., Chen, C., Niu, J., Chaisson, C., Hunter, D. J. & Zhang, Y. Alcohol quantity and type on risk of recurrent gout attacks: an internet-based case-crossover study. Am. J. Med 127 , 311–318 (2014).

Jee, Y. H., Jung, K. J., Park, Y. B., Spiller, W. & Jee, S. H. Causal effect of alcohol consumption on hyperuricemia using a Mendelian randomization design. Int J. Rheum. Dis. 22 , 1912–1919 (2019).

Berg, K. M. et al. Association between alcohol consumption and both osteoporotic fracture and bone density. Am. J. Med. 121 , 406–418 (2008).

Søgaard, A. J. et al. The association between alcohol consumption and risk of hip fracture differs by age and gender in Cohort of Norway: a NOREPOS study. Osteoporos. Int. 29 , 2457–2467 (2018).

Yuan, S., Michaëlsson, K., Wan, Z. & Larsson, S. C. Associations of smoking and alcohol and coffee intake with fracture and bone mineral density: a Mendelian randomization study. Calcif. Tissue Int. 105 , 582–588 (2019).

Gong, Y., Feng, K., Yan, N., Xu, Y. & Pan, C.-W. Different amounts of alcohol consumption and cataract: a meta-analysis. Optom. Vis. Sci. 92 , 471–479 (2015).

Yuan, S., Wolk, A. & Larsson, S. C. Metabolic and lifestyle factors in relation to senile cataract: a Mendelian randomization study. Sci. Rep. 12 , 409 (2022).

Pan, J., Cen, L., Chen, W., Yu, C., Li, Y. & Shen, Z. Alcohol consumption and the risk of gastroesophageal reflux disease: a systematic review and meta-analysis. Alcohol Alcohol. 54 , 62–69 (2019).

Strate, L. L., Singh, P., Boylan, M. R., Piawah, S., Cao, Y. & Chan, A. T. A prospective study of alcohol consumption and smoking and the risk of major gastrointestinal bleeding in men. PLoS ONE 11 , e0165278 (2016).

Yuan, S. & Larsson, S. C. Adiposity, diabetes, lifestyle factors and risk of gastroesophageal reflux disease: a Mendelian randomization study. Eur. J. Epidemiol. 37 , 747–754 (2022).

Bagnardi, V. et al. Alcohol consumption and site-specific cancer risk: a comprehensive dose–response meta-analysis. Br. J. Cancer 112 , 580–593 (2015).

Shen, C., Schooling, C. M., Chan, W. M., Xu, L., Lee, S. Y. & Lam, T. H. Alcohol intake and death from cancer in a prospective Chinese elderly cohort study in Hong Kong. J. Epidemiol. Community Health 67 , 813–820 (2013).

Kuroda, A. et al. Effects of the common polymorphism in the human aldehyde dehydrogenase 2 (ALDH2) gene on the lung. Respir. Res. 18 , 69 (2017).

Cha, B. H., Jang, M. J. & Lee, S. H. Alcohol consumption can reduce the risk of gallstone disease: a systematic review with a dose–response meta-analysis of case–control and cohort studies. Gut Liver 13 , 114–131 (2019).

Ma, G. S., Xhu, D. H., Hu, X. Q., Luan, D. C., Kong, L. Z. & Yang, X. Q. The drinking practice of people in China. Acta Nutrimenta Sin. 27 , 362–365 (2005).

Arranz, S., Chiva-Blanch, G., Valderas-Martínez, P., Medina-Remón, A., Lamuela-Raventós, R. M. & Estruch, R. Wine, beer, alcohol and polyphenols on cardiovascular disease and cancer. Nutrients 4 , 759–781 (2012).

Bryazka, D. et al. Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020. Lancet 400 , 185–235 (2022).

Chen, Z. et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int. J. Epidemiol. 40 , 1652–1666 (2011).

Im, P. K. et al. Problem drinking, wellbeing and mortality risk in Chinese men: findings from the China Kadoorie Biobank. Addiction 115 , 850–862 (2020).

Cochrane, J., Chen, H., Conigrave, K. M. & Hao, W. Alcohol use in China. Alcohol Alcohol. 38 , 537–542 (2003).

World Health Organization. International Guide for Monitoring Alcohol Consumption and Related Harm (World Health Organization, 2000).

Rehm, J. et al. The relationship between different dimensions of alcohol use and the burden of disease-an update. Addiction 112 , 968–1001 (2017).

Walters, R. G. et al. Genotyping and population structure of the China Kadoorie Biobank. Preprint at medRxiv https://doi.org/10.1101/2022.05.02.22274487 (2022).

Zhang, Q. et al. Exhaled carbon monoxide and its associations with smoking, indoor household air pollution and chronic respiratory diseases among 512,000 Chinese adults. Int. J. Epidemiol. 42 , 1464–1475 (2013).

Lau, B., Cole, S. R. & Gange, S. J. Competing risk regression models for epidemiologic data. Am. J. Epidemiol. 170 , 244–256 (2009).

Clarke, R., Shipley, M. & Lewington, S. et al. Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. Am. J. Epidemiol. 150 , 341–353 (1999).

MacMahon, S., Peto, R. & Cutler, J. et al. Blood pressure, stroke, and coronary heart disease. Part 1, prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Lancet 335 , 765–774 (1990).

Early Breast Cancer Trialists’ Collaborative Group. Section 5: Statistical Methods. Treatment of Early Breast Cancer. Worldwide Evidence, 1985–1990 (Oxford University Press, 1990).

Burgess, S. et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 4 , 186 (2019).

Easton, D. F., Peto, J. & Babiker, A. G. Floating absolute risk: an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group. Stat. Med. 10 , 1025–1035 (1991).

Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57 , 289–300 (1995).

Braun, J. M., Kalloo, G., Kingsley, S. L. & Li, N. Using phenome-wide association studies to examine the effect of environmental exposures on human health. Environ. Int. 130 , 104877 (2019).

Glickman, M. E., Rao, S. R. & Schultz, M. R. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J. Clin. Epidemiol. 67 , 850–857 (2014).

Cook, R. J. & Lawless, J. F. Marginal analysis of recurrent events and a terminating event. Stat. Med. 16 , 911–924 (1997).

Dong, H., Robison, L. L., Leisenring, W. M., Martin, L. J., Armstrong, G. T. & Yasui, Y. Estimating the burden of recurrent events in the presence of competing risks: the method of mean cumulative count. Am. J. Epidemiol. 181 , 532–540 (2015).

Ghosh, D. & Lin, D. Y. Nonparametric analysis of recurrent events and death. Biometrics 56 , 554–562 (2000).

Download references

Acknowledgements

The chief acknowledgment is to the participants, the project staff and the China National Centre for Disease Control and Prevention (CDC) and its regional offices for assisting with the fieldwork. We thank J. Mackay in Hong Kong; Y. Wang, G. Yang, Z. Qiang, L. Feng, M. Zhou, W. Zhao. and Y. Zhang in China CDC; L. Kong, X. Yu and K. Li in the Chinese Ministry of Health; and S. Clark, M. Radley and M. Hill in the CTSU, Oxford, for assisting with the design, planning, organization and conduct of the study. A complete list of members of the China Kadoorie Collaborative Group is provided in the Supplementary Information. The CKB baseline survey and the first re-survey were supported by the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up of the CKB study has been supported by Wellcome grants to Z.C. at Oxford University (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z) and grants to L.L. from the National Natural Science Foundation of China (82192901, 82192904 and 82192900) and from the National Key Research and Development Program of China (2016YFC0900500). DNA extraction and genotyping was supported by grants to Z.C. from GlaxoSmithKline and the UK Medical Research Council (MC-PC-13049, MC-PC-14135). The UK Medical Research Council (MC_UU_00017/1, MC_UU_12026/2, MC_U137686851), Cancer Research UK (C16077/A29186; C500/A16896) and the British Heart Foundation (CH/1996001/9454) provide core funding to the CTSU and Epidemiological Studies Unit at Oxford University for the project. P.K.I. is supported by an Early Career Research Fellowship from the Nuffield Department of Population Health, University of Oxford. K.H.C. acknowledges support from the British Heart Foundation Centre of Research Excellence, University of Oxford (RE/18/3/34214). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any author accepted manuscript version arising from this submission.

Author information

These authors jointly supervised this work: Liming Li, Zhengming Chen, Iona Y. Millwood.

Authors and Affiliations

Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK

Pek Kei Im, Neil Wright, Ling Yang, Ka Hung Chan, Yiping Chen, Huaidong Du, Xiaoming Yang, Daniel Avery, Robert Clarke, Rory Collins, Robin G. Walters, Richard Peto, Zhengming Chen, Iona Y. Millwood, Maxim Barnard, Derrick Bennett, Ruth Boxall, Johnathan Clarke, Ahmed Edris Mohamed, Hannah Fry, Simon Gilbert, Andri Iona, Maria Kakkoura, Christiana Kartsonaki, Hubert Lam, Kuang Lin, James Liu, Mohsen Mazidi, Sam Morris, Qunhua Nie, Alfred Pozarickij, Paul Ryder, Saredo Said, Dan Schmidt, Becky Stevens, Iain Turnbull, Baihan Wang, Lin Wang & Pang Yao

Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK

Ling Yang, Yiping Chen, Huaidong Du, Robin G. Walters, Zhengming Chen, Iona Y. Millwood, Derrick Bennett, Ruth Boxall & Christiana Kartsonaki

Oxford British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK

Ka Hung Chan

Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China

NCD Prevention and Control Department, Qingdao CDC, Qingdao, China

Shaojie Wang, Liang Cheng, Ranran Du, Ruqin Gao, Feifei Li, Shanpeng Li, Yongmei Liu, Feng Ning, Zengchang Pang, Xiaohui Sun, Xiaocao Tian, Yaoming Zhai & Hua Zhang

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China

Canqing Yu, Jun Lv, Liming Li & Dianjianyi Sun

Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China

Canqing Yu, Jun Lv, Liming Li, Xiao Han, Can Hou, Qingmei Xia, Chao Liu, Pei Pei & Dianjianyi Sun

China National Center for Food Safety Risk Assessment, Beijing, China

• Junshi Chen

WHO Collaborating Center for Tobacco Cessation and Respiratory Diseases Prevention, China-Japan Friendship Hospital, Beijing, China

Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Beijing, China

NCD Prevention and Control Department, Guangxi Provincial CDC, Nanning, China

Naying Chen, Duo Liu & Zhenzhu Tang

NCD Prevention and Control Department, Liuzhou CDC, Liubei, Liuzhou, China

Ningyu Chen, Qilian Jiang, Jian Lan, Mingqiang Li, Yun Liu, Fanwen Meng, Jinhuai Meng, Rong Pan, Yulu Qin, Ping Wang, Sisi Wang, Liuping Wei & Liyuan Zhou

NCD Prevention and Control Department, Gansu Provincial CDC, Lanzhou, China

Caixia Dong, Pengfei Ge & Xiaolan Ren

NCD Prevention and Control Department, Maijixiang CDC, Maijixiang, Tianshui, China

Zhongxiao Li, Enke Mao, Tao Wang, Hui Zhang & Xi Zhang

NCD Prevention and Control Department, Hainan Provincial CDC, Haikou, China

Jinyan Chen, Ximin Hu & Xiaohuan Wang

NCD Prevention and Control Department, Meilan CDC, Meilan, Haikou, China

Zhendong Guo, Huimei Li, Yilei Li, Min Weng & Shukuan Wu

NCD Prevention and Control Department, Heilongjiang CDC, Harbin, China

Shichun Yan, Mingyuan Zou & Xue Zhou

NCD Prevention and Control Department, Nangang CDC, Harbin, China

Ziyan Guo, Quan Kang, Yanjie Li, Bo Yu & Qinai Xu

NCD Prevention and Control Department, Henan Provincial CDC, Zhengzhou, China

Liang Chang, Lei Fan, Shixian Feng, Ding Zhang & Gang Zhou

NCD Prevention and Control Department, Huixian CDC, Huixian, China

Yulian Gao, Tianyou He, Pan He, Chen Hu, Huarong Sun & Xukui Zhang

NCD Prevention and Control Department, Hunan Provincial CDC, Changsha, China

Biyun Chen, Zhongxi Fu, Yuelong Huang, Huilin Liu, Qiaohua Xu & Li Yin

NCD Prevention and Control Department, Liuyang CDC, Liuyang, China

Huajun Long, Xin Xu, Hao Zhang & Libo Zhang

NCD Prevention and Control Department, Jiangsu Provincial CDC, Nanjing, China

Jian Su, Ran Tao, Ming Wu, Jie Yang, Jinyi Zhou & Yonglin Zhou

NCD Prevention and Control Department, Wuzhong CDC, Wuzhong, Suzhou, China

Yihe Hu, Yujie Hua, Jianrong Jin, Fang Liu, Jingchao Liu, Yan Lu, Liangcai Ma, Aiyu Tang & Jun Zhang

NCD Prevention and Control Department, Licang CDC, Qingdao, China

Wei Hou, Silu Lv & Junzheng Wang

NCD Prevention and Control Department, Sichuan Provincial CDC, Chengdu, China

Xiaofang Chen, Xianping Wu, Ningmei Zhang & Xiaoyu Chang

NCD Prevention and Control Department, Pengzhou CDC, Pengzhou, Chengdu, China

Xiaofang Chen, Jianguo Li, Jiaqiu Liu, Guojin Luo, Qiang Sun & Xunfu Zhong

NCD Prevention and Control Department, Zhejiang Provincial CDC, Hangzhou, China

Weiwei Gong, Ruying Hu, Hao Wang, Meng Wang & Min Yu

NCD Prevention and Control Department, Tongxiang CDC, Tongxiang, China

Lingli Chen, Qijun Gu, Dongxia Pan, Chunmei Wang, Kaixu Xie & Xiaoyi Zhang

You can also search for this author in PubMed   Google Scholar

• , Zhengming Chen
• , Robert Clarke
• , Rory Collins
• , Liming Li
• , Chen Wang
• , Richard Peto
• , Robin G. Walters
• , Daniel Avery
• , Maxim Barnard
• , Derrick Bennett
• , Ruth Boxall
• , Ka Hung Chan
• , Yiping Chen
• , Johnathan Clarke
• , Huaidong Du
• , Ahmed Edris Mohamed
• , Hannah Fry
• , Simon Gilbert
• , Pek Kei Im
• , Andri Iona
• , Maria Kakkoura
• , Christiana Kartsonaki
• , Hubert Lam
• , Kuang Lin
• , James Liu
• , Mohsen Mazidi
• , Iona Y. Millwood
• , Sam Morris
• , Qunhua Nie
• , Alfred Pozarickij
• , Paul Ryder
• , Saredo Said
• , Dan Schmidt
• , Becky Stevens
• , Iain Turnbull
• , Baihan Wang
• , Neil Wright
• , Ling Yang
• , Xiaoming Yang
• , Qingmei Xia
• , Dianjianyi Sun
• , Canqing Yu
• , Naying Chen
• , Zhenzhu Tang
• , Ningyu Chen
• , Qilian Jiang
• , Mingqiang Li
• , Fanwen Meng
• , Jinhuai Meng
• , Ping Wang
• , Sisi Wang
• , Liuping Wei
• , Liyuan Zhou
• , Caixia Dong
• , Pengfei Ge
• , Xiaolan Ren
• , Zhongxiao Li
• , Hui Zhang
• , Jinyan Chen
• , Xiaohuan Wang
• , Zhendong Guo
• , Huimei Li
• , Shukuan Wu
• , Shichun Yan
• , Mingyuan Zou
• , Ziyan Guo
• , Quan Kang
• , Yanjie Li
• , Liang Chang
• , Shixian Feng
• , Ding Zhang
• , Gang Zhou
• , Yulian Gao
• , Tianyou He
• , Huarong Sun
• , Xukui Zhang
• , Biyun Chen
• , Zhongxi Fu
• , Yuelong Huang
• , Huilin Liu
• , Qiaohua Xu
• , Huajun Long
• , Hao Zhang
• , Libo Zhang
• , Jinyi Zhou
• , Yonglin Zhou
• , Yujie Hua
• , Jianrong Jin
• , Jingchao Liu
• , Liangcai Ma
• , Aiyu Tang
• , Jun Zhang
• , Liang Cheng
• , Ranran Du
• , Ruqin Gao
• , Feifei Li
• , Shanpeng Li
• , Yongmei Liu
• , Feng Ning
• , Zengchang Pang
• , Xiaohui Sun
• , Xiaocao Tian
• , Shaojie Wang
• , Yaoming Zhai
• , Hua Zhang
• , Junzheng Wang
• , Xiaofang Chen
• , Xianping Wu
• , Ningmei Zhang
• , Xiaoyu Chang
• , Jianguo Li
• , Jiaqiu Liu
• , Guojin Luo
• , Qiang Sun
• , Xunfu Zhong
• , Weiwei Gong
• , Ruying Hu
• , Meng Wang
• , Lingli Chen
• , Dongxia Pan
• , Chunmei Wang
• , Kaixu Xie
•  & Xiaoyi Zhang

Contributions

P.K.I., I.Y.M., L.Y. and Z.C. contributed to the conception of this paper. P.K.I., N.W., K.H.C., I.Y.M. and Z.C. planned the statistical analysis. P.K.I. analyzed the data and drafted the manuscript. P.K.I., I.Y.M. and Z.C. contributed to the interpretation of the results and the revision of manuscript. R. Collins, R.P., J.C., L.L. and Z.C. designed the study. L.L., Z.C., I.Y.M., L.Y., Y.C., Y.G., H.D., S.W., C.Y., J.L., J.C., R. Collins, R. Clarke and R.G.W. contributed to data acquisition and general study management. X.Y. and D.A. provided administrative and technical support. All authors critically reviewed the manuscript and approved the final submission.

Corresponding authors

Correspondence to Zhengming Chen or Iona Y. Millwood .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Medicine thanks Shiu Lun Au Yeung, Yan-Bo Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ming Yang, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended data fig. 1 adjusted hrs for icd−10 chapter−specific morbidities associated with ever-regular drinking and with usual alcohol intake, in men..

Cox models comparing ever-regular drinkers with occasional drinkers, or assessing the dose–response per 280 g/week higher usual alcohol intake within current drinkers, were stratified by age-at-risk and study area and adjusted for education and smoking. Each solid square or diamond represents HR with the area inversely proportional to the variance of the log HR. The horizontal lines indicate 95% CIs. CI, confidence interval; HR hazard ratio; ICD-10, International Classification of Diseases, 10th Revision.

Extended Data Fig. 2 Adjusted HRs for different aggregated and all-cause morbidities associated with years after stopping drinking, in men.

Cox models comparing ex-drinker groups with occasional drinkers were stratified by age-at-risk and study area and were adjusted for education and smoking. Each box represents HR with the area inversely proportional to the variance of the group-specific log hazard within subplot. The vertical lines indicate group-specific 95% CIs for various ex-drinker groups. The shaded strip indicate the group-specific 95% CIs for occasional drinkers. The numbers above the error bars are point estimates for HRs. CI, confidence interval; HR hazard ratio; CKB, China Kadoorie Biobank; WHO, World Health Organization.

Extended Data Fig. 3 Associations of alcohol consumption with risks of 28 diseases previously defined as alcohol-related by the WHO, in male current drinkers.

Cox models were stratified by age-at-risk and study area and were adjusted for education and smoking. HRs were plotted against usual alcohol intake and were calculated per 280 g/week higher usual alcohol intake. All specific diseases displayed were significantly associated with alcohol intake (ever-regular drinking or per 280 g/week higher usual alcohol intake) after multiple testing correction (FDR-adjusted p<0.05), except transient cerebral ischemic attacks and related syndromes (ICD-10 code: G45), occlusion and stenosis of precerebral arteries (I65) and pancreatitis (K85-K86) which showed statistical significance at nominal level (p<0.05). Each box represents HR with the area inversely proportional to the variance of the group-specific log hazard within subplot. The vertical lines indicate group-specific 95% CIs. The numbers above the error bars are point estimates for HRs and the numbers below are number of events. All P values are two-sided. CI, confidence interval; HR hazard ratio; CKB, China Kadoorie Biobank; FDR, false discovery rate; ICD-10, International Classification of Diseases, 10th Revision; WHO, World Health Organization.

Extended Data Fig. 4 Associations of alcohol consumption with risks of 36 diseases not previously defined as alcohol-related, in male current drinkers.

Cox models were stratified by age-at-risk and study area and were adjusted for education and smoking. HRs were plotted against usual alcohol intake and were calculated per 280 g/week higher usual alcohol intake. All specific diseases displayed were significantly associated with alcohol intake (ever-regular drinking or per 280 g/week higher usual alcohol intake) after multiple testing correction (FDR-adjusted p<0.05). Each box represents HR with the area inversely proportional to the variance of the group-specific log hazard within subplot. The vertical lines indicate group-specific 95% CIs. The numbers above the error bars are point estimates for HRs and the numbers below are number of events. All P values are two-sided. CI, confidence interval; HR hazard ratio; CKB, China Kadoorie Biobank; FDR, false discovery rate.

Extended Data Fig. 5 Adjusted HRs for major diseases associated with drinking patterns, in male current drinkers.

Cox models were stratified by age-at-risk and study area and were adjusted for education and smoking and for total alcohol intake where indicated. Each solid square or diamond represents HR with the area inversely proportional to the variance of the log HR. The horizontal lines indicate 95% CIs. CI, confidence interval; HR hazard ratio; HED, heavy episodic drinking; CKB, China Kadoorie Biobank; WHO, World Health Organization.

Extended Data Fig. 6 Adjusted HRs for major diseases associated with duration of drinking, in male current drinkers.

Cox models were stratified by age-at-risk and study area and were adjusted for education, smoking, total alcohol intake and baseline age in (A). (B) had the same model specifications as (A) plus further adjustments for income, physical activity, fruit intake and body mass index. (C) had the same model specifications as (A) and excluded participants with poor self-reported health or prior chronic disease at baseline. Each box represents HR with the area inversely proportional to the variance of the group-specific log hazard. The horizontal lines indicate group-specific 95% CIs. All P values are two-sided. CI, confidence interval; HR hazard ratio; CKB, China Kadoorie Biobank; WHO, World Health Organization.

Extended Data Fig. 7 Adjusted HRs for ICD−10 chapter−specific morbidities associated with ever-regular drinking and with usual alcohol intake, in women.

Cox models comparing ever-regular drinkers with occasional drinkers, or assessing the dose–response per 100 g/week higher usual alcohol intake within current drinkers, were stratified by age-at-risk and study area and adjusted for education and smoking. Each solid square or diamond represents HR with the area inversely proportional to the variance of the log HR. The horizontal lines indicate 95% CIs. CI, confidence interval; HR hazard ratio; ICD-10, International Classification of Diseases, 10th Revision.

Extended Data Fig. 8 Adjusted HRs per 280 g/week higher genotype-predicted mean male alcohol intake for specific alcohol-associated diseases by ICD-10 chapters, in men and women.

Cox modes, stratified by age-at-risk and adjusted for genomic principal components, were used to relate genetic categories to risks of diseases within each study area. The HR per 280 g/week higher genotype-predicted mean male alcohol intake was calculated from the inverse-variance-weighted mean of the slopes of the fitted lines in each study area. Each solid square or diamond represents HR per 280 g/week higher genetically-predicted mean male alcohol intake, with the area inversely proportional to the variance of the log HR. The horizontal lines indicate 95% CIs. Diseases considered to be alcohol-related by the WHO are indicated with ‘Y’ under the ‘WHO’ column. The ‘RC’ column indicates the number of study areas that contributed to the overall area-stratified genotypic associations, as for certain less common diseases some study areas may not have enough number of cases to contribute to the inverse-variance-weighted meta-analysis. The ‘P het’ column indicates the p-value from a \(\chi\) 2 test for heterogeneity between sexes. All P values are two-sided. † Included less common ICD-10 codes within the corresponding ICD-10 chapter which were not individually investigated in the present study. CI, confidence interval; HR hazard ratio; ICD-10, International Classification of Diseases, 10th Revision; WHO, World Health Organization.

Extended Data Fig. 9 Adjusted HRs associated with GG versus AG genotype of ALDH2 - rs671 for specific alcohol-associated diseases by ICD-10 chapters, in men and women.

Area-specific genotypic effects (GG vs. AG genotype) were estimated within each study area (thus each reflecting the purely genotypic effects) using age-at-risk-stratified and genomic principal components-adjusted Cox models and were combined by inverse-variance-weighted meta-analysis to yield the overall area-stratified genotypic associations. Each solid square represents HR for GG vs. AG genotype, with the area inversely proportional to the variance of the log HR. The horizontal lines indicate 95% CIs. Diseases considered to be alcohol-related by the WHO are indicated with ‘Y’ under the ‘WHO’ column. The ‘RC’ column indicates the number of study areas that contributed to the overall area-stratified genotypic associations, as for certain less common diseases some study areas may not have enough number of cases to contribute to the inverse-variance-weighted meta-analysis. The ‘P het’ column indicates the P value from a \(\chi\) 2 test for heterogeneity between sexes. All P values are two-sided. † Included less common ICD-10 codes within the corresponding ICD-10 chapter which were not individually investigated in the present study. CI, confidence interval; HR hazard ratio; ICD-10, International Classification of Diseases, 10th Revision; WHO, World Health Organization.

Extended Data Fig. 10 Adjusted HRs associated with GG versus AG genotype of ADH1B - rs1229984 for specific alcohol-associated diseases by ICD-10 chapters, in men and women.

Supplementary information, supplementary information.

Supplementary Figs. 1–8 and Supplementary Tables 1–13.

Reporting Summary

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Im, P.K., Wright, N., Yang, L. et al. Alcohol consumption and risks of more than 200 diseases in Chinese men. Nat Med 29 , 1476–1486 (2023). https://doi.org/10.1038/s41591-023-02383-8

Download citation

Received : 16 December 2022

Accepted : 02 May 2023

Published : 08 June 2023

Issue Date : June 2023

DOI : https://doi.org/10.1038/s41591-023-02383-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Uncovering newly identified aldehyde dehydrogenase 2 genetic variants that lead to acetaldehyde accumulation after an alcohol challenge.

• Freeborn Rwere
• Joseph R. White
• Eric R. Gross

Journal of Translational Medicine (2024)

Alcohol consumption may be a risk factor for cerebrovascular stenosis in acute ischemic stroke and transient ischemic attack

• Xiaoyan Guo

BMC Neurology (2024)

Western diets and chronic diseases

• Timon E. Adolph
• Herbert Tilg

Nature Medicine (2024)

Mapping multimorbidity progression among 190 diseases

Communications Medicine (2024)

The aldehyde dehydrogenase 2 rs671 variant enhances amyloid β pathology

Nature Communications (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Substance Use Disorders and Addiction: Mechanisms, Trends, and Treatment Implications

Information & authors, metrics & citations, view options, insights into mechanisms related to cocaine addiction using a novel imaging method for dopamine neurons, treatment implications of understanding brain function during early abstinence in patients with alcohol use disorder, relatively low amounts of alcohol intake during pregnancy are associated with subtle neurodevelopmental effects in preadolescent offspring, increased comorbidity between substance use and psychiatric disorders in sexual identity minorities, trends in nicotine use and dependence from 2001–2002 to 2012–2013, conclusions, information, published in.

• Substance-Related and Addictive Disorders
• Addiction Psychiatry
• Transgender (LGBT) Issues

Competing Interests

Export citations.

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu .

To download the citation to this article, select your reference manager software.

There are no citations for this item

View options

Login options.

Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.

Not a subscriber?

Subscribe Now / Learn More

PsychiatryOnline subscription options offer access to the DSM-5-TR ® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.

Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).

Share article link

Copying failed.

NEXT ARTICLE

Request username.

Can't sign in? Forgot your username? Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

Create a new account

Change password, password changed successfully.

Your password has been changed

Reset password

Can't sign in? Forgot your password?

Enter your email address below and we will send you the reset instructions

If the address matches an existing account you will receive an email with instructions to reset your password.

Your Phone has been verified

As described within the American Psychiatric Association (APA)'s Privacy Policy and Terms of Use , this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences. Please read the entire Privacy Policy and Terms of Use. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy and Terms of Use, including the utilization of cookies.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Effect of chronic alcohol consumption on brain structure in males with alcohol use disorder without a familiar history of alcoholism

Affiliations.

• 1 Biomedical Research Institute Hospital 12 de Octubre, Madrid, Spain; Psychology Department, Faculty of Education & Health, Camilo José Cela University, Madrid, Spain. Electronic address: [email protected].
• 2 The Brain, Mind and Behavior Research Center at the University of Granada, Granada, Spain; School of Psychology, Department of Personality, Assessment and Psychological Treatment, The University of Granada, Granada, Spain.
• 3 Biomedical Research Institute Hospital 12 de Octubre, Madrid, Spain.
• 4 Biomedical Research Institute Hospital 12 de Octubre, Madrid, Spain; Faculty of Medicine, The Complutense University of Madrid, Madrid, Spain; Addictive Diseases Network, C' arlos III Health Institute, Madrid, Spain.
• 5 Biomedical Research Institute Hospital 12 de Octubre, Madrid, Spain; Psychology Department, Faculty of Education & Health, Camilo José Cela University, Madrid, Spain.
• PMID: 35287051
• DOI: 10.1016/j.jpsychires.2022.03.005

Structural brain damages caused by chronic alcohol consumption have been extensively reported. However, the neuroimaging findings in people with alcohol use disorder (AUD) are relatively inconsistent. This inconsistency may be due to the influence of different variables that are not always considered, such as the presence of a family history of alcoholism (FHA). The main aim of this research is to study the gray (GM) and white matter (WM) volumes in male participants with AUD without FHA compared to healthy control males (HC) without FHA. For this study, we included 19 participants with AUD without FHA and 18 HC males without FHA. T1-weighted images were acquired with a General Electric Signa Exite 1.5 T scanner. GM and WM tissues were calculated using Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL). All analyses were controlled for age and total brain volume. The statistical threshold was calculated with AlphaSim and further adjusted to account for the non-isotropic smoothness of structural images, according to Hayasaka et al. (2004). The obtained main results showed that, relative to the HC group, the participants with AUD without FHA had significantly lower GM in several brain structures, reflecting relatively purely the effects of chronic alcohol intake on brain volume. GM structure integrity is relevant for the efficient functioning of low and high-order cognitive processes used in everyday life, and its damage seems to be related to the severity/intensity/chronicity of the AUD. As such, it becomes relevant to assess and follow brain structural changes through the dependence course.

Keywords: Alcohol use disorder; Brain structure; Family history of alcoholism; Gray matter; Magnetic resonance imaging.

Copyright © 2022. Published by Elsevier Ltd.

PubMed Disclaimer

Similar articles

• Brain volumes in alcohol use disorder: Do females and males differ? A whole-brain magnetic resonance imaging mega-analysis. Maggioni E, Rossetti MG, Allen NB, Batalla A, Bellani M, Chye Y, Cousijn J, Goudriaan AE, Hester R, Hutchison K, Li CR, Martin-Santos R, Momenan R, Sinha R, Schmaal L, Solowij N, Suo C, van Holst RJ, Veltman DJ, Yücel M, Thompson PM, Conrod P, Mackey S, Garavan H, Brambilla P, Lorenzetti V. Maggioni E, et al. Hum Brain Mapp. 2023 Sep;44(13):4652-4666. doi: 10.1002/hbm.26404. Epub 2023 Jul 12. Hum Brain Mapp. 2023. PMID: 37436103 Free PMC article.
• Patterns of regional gray matter loss at different stages of schizophrenia: A multisite, cross-sectional VBM study in first-episode and chronic illness. Torres US, Duran FL, Schaufelberger MS, Crippa JA, Louzã MR, Sallet PC, Kanegusuku CY, Elkis H, Gattaz WF, Bassitt DP, Zuardi AW, Hallak JE, Leite CC, Castro CC, Santos AC, Murray RM, Busatto GF. Torres US, et al. Neuroimage Clin. 2016 Jun 3;12:1-15. doi: 10.1016/j.nicl.2016.06.002. eCollection 2016. Neuroimage Clin. 2016. PMID: 27354958 Free PMC article.
• A combination of support vector machine and voxel-based morphometry in adult male alcohol use disorder patients with cognitive deficits. Dai X, Gao L, Zhang H, Wei X, Liu Z. Dai X, et al. Brain Res. 2021 Nov 15;1771:147644. doi: 10.1016/j.brainres.2021.147644. Epub 2021 Aug 31. Brain Res. 2021. PMID: 34478708
• Cortical and subcortical gray matter shrinkage in alcohol-use disorders: a voxel-based meta-analysis. Yang X, Tian F, Zhang H, Zeng J, Chen T, Wang S, Jia Z, Gong Q. Yang X, et al. Neurosci Biobehav Rev. 2016 Jul;66:92-103. doi: 10.1016/j.neubiorev.2016.03.034. Epub 2016 Apr 21. Neurosci Biobehav Rev. 2016. PMID: 27108216 Review.
• Neuroimaging in alcohol use disorder: From mouse to man. Fritz M, Klawonn AM, Zahr NM. Fritz M, et al. J Neurosci Res. 2022 May;100(5):1140-1158. doi: 10.1002/jnr.24423. Epub 2019 Apr 22. J Neurosci Res. 2022. PMID: 31006907 Free PMC article. Review.
• The relationship between alcohol consumption and amygdala volume in a community-based sample. Pho C, Yu FF, Palka JM, Brown ES. Pho C, et al. Brain Imaging Behav. 2024 Apr 3. doi: 10.1007/s11682-024-00879-6. Online ahead of print. Brain Imaging Behav. 2024. PMID: 38568283
• Effects of white matter lesion grading on the cognitive function of patients with chronic alcohol dependence. Ren Y, Meng K, Sun Y, Wu M, Li S, Zhao W, Sun Y, Zhu X, Yin C. Ren Y, et al. Am J Transl Res. 2023 Feb 15;15(2):1129-1139. eCollection 2023. Am J Transl Res. 2023. PMID: 36915744 Free PMC article.

Publication types

• Search in MeSH

Related information

Linkout - more resources, full text sources.

• Elsevier Science
• MedlinePlus Health Information

Research Materials

• NCI CPTC Antibody Characterization Program
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

• Introduction
• Conclusions
• Article Information

BMI indicates body mass index; SES, socioeconomic status.

a Variables smoking status, SES, drinking pattern, former drinker bias only, occasional drinker bias, median age, and gender were removed.

b Variables race, diet, exercise, BMI, country, follow-up year, publication year, and unhealthy people exclusion were removed.

eAppendix. Methodology of Meta-analysis on All-Cause Mortality and Alcohol Consumption

eReferences

eFigure 1. Flowchart of Systematic Search Process for Studies of Alcohol Consumption and Risk of All-Cause Mortality

eTable 1. Newly Included 20 Studies (194 Risk Estimates) of All-Cause Mortality and Consumption in 2015 to 2022

eFigure 2. Funnel Plot of Log-Relative Risk (In(RR)) of All-Cause Mortality Due to Alcohol Consumption Against Inverse of Standard Error of In(RR)

eFigure 3. Relative Risk (95% CI) of All-Cause Mortality Due to Any Alcohol Consumption Without Any Adjustment for Characteristics of New Studies Published between 2015 and 2022

eFigure 4. Unadjusted, Partially Adjusted, and Fully Adjusted Relative Risk (RR) of All-Cause Mortality for Drinkers (vs Nondrinkers), 1980 to 2022

eTable 2. Statistical Analysis of Unadjusted Mean Relative Risk (RR) of All-Cause Mortality for Different Categories of Drinkers for Testing Publication Bias and Heterogeneity of RR Estimates From Included Studies

eTable 3. Mean Relative Risk (RR) Estimates of All-Cause Mortality Due to Alcohol Consumption up to 2022 for Subgroups (Cohorts Recruited 50 Years of Age or Younger and Followed up to 60 Years of Age)

Data Sharing Statement

• Errors in Figure and Supplement JAMA Network Open Correction May 9, 2023

See More About

Sign up for emails based on your interests, select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Get the latest research based on your areas of interest.

Others also liked.

• Download PDF
• X Facebook More LinkedIn

Zhao J , Stockwell T , Naimi T , Churchill S , Clay J , Sherk A. Association Between Daily Alcohol Intake and Risk of All-Cause Mortality : A Systematic Review and Meta-analyses . JAMA Netw Open. 2023;6(3):e236185. doi:10.1001/jamanetworkopen.2023.6185

Manage citations:

© 2024

• Permissions

Association Between Daily Alcohol Intake and Risk of All-Cause Mortality : A Systematic Review and Meta-analyses

• 1 Canadian Institute for Substance Use Research, University of Victoria, Victoria, British Columbia, Canada
• 2 Department of Psychology, University of Portsmouth, Portsmouth, Hampshire, United Kingdom
• Correction Errors in Figure and Supplement JAMA Network Open

Question   What is the association between mean daily alcohol intake and all-cause mortality?

Findings   This systematic review and meta-analysis of 107 cohort studies involving more than 4.8 million participants found no significant reductions in risk of all-cause mortality for drinkers who drank less than 25 g of ethanol per day (about 2 Canadian standard drinks compared with lifetime nondrinkers) after adjustment for key study characteristics such as median age and sex of study cohorts. There was a significantly increased risk of all-cause mortality among female drinkers who drank 25 or more grams per day and among male drinkers who drank 45 or more grams per day.

Meaning   Low-volume alcohol drinking was not associated with protection against death from all causes.

Importance   A previous meta-analysis of the association between alcohol use and all-cause mortality found no statistically significant reductions in mortality risk at low levels of consumption compared with lifetime nondrinkers. However, the risk estimates may have been affected by the number and quality of studies then available, especially those for women and younger cohorts.

Objective   To investigate the association between alcohol use and all-cause mortality, and how sources of bias may change results.

Data Sources   A systematic search of PubMed and Web of Science was performed to identify studies published between January 1980 and July 2021.

Study Selection   Cohort studies were identified by systematic review to facilitate comparisons of studies with and without some degree of controls for biases affecting distinctions between abstainers and drinkers. The review identified 107 studies of alcohol use and all-cause mortality published from 1980 to July 2021.

Data Extraction and Synthesis   Mixed linear regression models were used to model relative risks, first pooled for all studies and then stratified by cohort median age (<56 vs ≥56 years) and sex (male vs female). Data were analyzed from September 2021 to August 2022.

Main Outcomes and Measures   Relative risk estimates for the association between mean daily alcohol intake and all-cause mortality.

Results   There were 724 risk estimates of all-cause mortality due to alcohol intake from the 107 cohort studies (4 838 825 participants and 425 564 deaths available) for the analysis. In models adjusting for potential confounding effects of sampling variation, former drinker bias, and other prespecified study-level quality criteria, the meta-analysis of all 107 included studies found no significantly reduced risk of all-cause mortality among occasional (>0 to <1.3 g of ethanol per day; relative risk [RR], 0.96; 95% CI, 0.86-1.06; P  = .41) or low-volume drinkers (1.3-24.0 g per day; RR, 0.93; P  = .07) compared with lifetime nondrinkers. In the fully adjusted model, there was a nonsignificantly increased risk of all-cause mortality among drinkers who drank 25 to 44 g per day (RR, 1.05; P  = .28) and significantly increased risk for drinkers who drank 45 to 64 and 65 or more grams per day (RR, 1.19 and 1.35; P  < .001). There were significantly larger risks of mortality among female drinkers compared with female lifetime nondrinkers (RR, 1.22; P  = .03).

Conclusions and Relevance   In this updated systematic review and meta-analysis, daily low or moderate alcohol intake was not significantly associated with all-cause mortality risk, while increased risk was evident at higher consumption levels, starting at lower levels for women than men.

The proposition that low-dose alcohol use protects against all-cause mortality in general populations continues to be controversial. 1 Observational studies tend to show that people classified as “moderate drinkers” have longer life expectancy and are less likely to die from heart disease than those classified as abstainers. 2 Systematic reviews and meta-analyses of this literature 3 confirm J-shaped risk curves (protective associations at low doses with increasing risk at higher doses). However, mounting evidence suggests these associations might be due to systematic biases that affect many studies. For example, light and moderate drinkers are systematically healthier than current abstainers on a range of health indicators unlikely to be associated with alcohol use eg, dental hygiene, exercise routines, diet, weight, income 4 ; lifetime abstainers may be systematically biased toward poorer health 5 ; studies fail to control for biases in the abstainer reference group, in particular failing to remove “sick quitters” or former drinkers, many of whom cut down or stop for health reasons 2 ; and most studies have nonrepresentative samples leading to an overrepresentation of older White men. Adjustment of cohort samples to make them more representative has been shown to eliminate apparent protective associations. 6 Mendelian randomization studies that control for the confounding effects of sociodemographic and environmental factors find no evidence of cardioprotection. 7

We published 2 previous systematic reviews and meta-analyses that investigated these hypotheses. The first of these focused on all-cause mortality, 8 finding negligible reductions in mortality risk with low-volume alcohol use when study-level controls were introduced for potential bias and confounding, such as the widespread practice of misclassifying former drinkers and/or current occasional drinkers as abstainers (ie, not restricting reference groups to lifetime abstainers). 8 Our alcohol and coronary heart disease (CHD) mortality meta-analysis of 45 cohort studies 9 found that CHD mortality risk differed widely by age ranges and sex of study populations. In particular, young cohorts followed up to old age did not show significant cardio-protection for low-volume use. Cardio-protection was only apparent among older cohorts that are more exposed to lifetime selection biases (ie, increasing numbers of “sick-quitters” in the abstainer reference groups and the disproportionate elimination of drinkers from the study sample who had died or were unwell).

The present study updates our earlier systematic review and meta-analysis for all-cause mortality and alcohol use, 8 including studies published up to July 2021 (ie, 6.5 years of additional publications). The study also investigated the risk of all-cause mortality for alcohol consumption according to (1) median ages of the study populations (younger than 56 years or 56 years and older), replicating the methods of Zhao et al 9 ; (2) the sex distribution of the study populations, and (3) studies of cohorts recruited before a median age of 51 years of age and followed up in health records until a median age of at least 60 years (ie, with stricter rules to further minimize lifetime selection biases). Because younger cohorts followed up to an age at which they may experience heart disease are less likely to be affected by lifetime selection biases, 9 we hypothesized that such studies would be less likely to show reduced mortality risks for low-volume drinkers. Finally, we reran the analyses using occasional drinkers (<1 drink per week) as the reference, for whom physiological health benefits are unlikely. Occasional drinkers are a more appropriate reference group, given evidence demonstrating that lifetime abstainers may be biased toward ill health. 10

The present study updates the systematic reviews and meta-analyses described above 8 by including studies published up to July 2021 to investigate whether the risk differed for subgroups. The study protocol was preregistered on the Open Science Framework. 11 Inclusion criteria, search strategy, study selection, data extraction, and statistical analytical methods of the study are summarized in later sections (see eAppendix in Supplement 1 for more details).

The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) reporting guideline. 12 The review sought cohort studies of all-cause mortality and alcohol consumption. We identified all potentially relevant articles published up to July 31, 2021, regardless of language, by searching PubMed and Web of Science, through reference list cross-checking of previous meta-analyses (eFigure 1 in Supplement 1 ). There were 87 studies identified by Stockwell et al. 8 After inclusion of 20 new studies meeting inclusion criteria, there were a total of 107 cohort studies (eTable 1 in Supplement 1 ). 13 - 32

Three coders (J. Z., F. A., and J. C.) reviewed all eligible studies to extract and code data independently from all studies fulfilling the inclusion criteria. Data extracted included (1) outcome, all-cause mortality; (2) measures of alcohol consumption; (3) study characteristics, including cohort ages at recruitment and follow-up; (4) types of misclassification error of alcohol consumers and abstainers; (5) controlled variables in individual studies. Alcoholic drinks were converted into grams per day according to country-specific definitions if not otherwise defined. 33 , 34

We also assessed publication bias, heterogeneity, and confounding of covariates that might potentially affect the association of interest using several statistical approaches. 35 - 41 Relative risk (RR), including hazard ratios or rate ratios, were converted to natural log-transformed formats to deal with skewness. Publication bias was assessed through visual inspection of the funnel plot of log-RR of all-cause mortality due to alcohol consumption against the inverse standard error of log-RR 42 and Egger’s linear regression method. 36 We also plotted forest graphs of log-RR of all-cause mortality for any level of drinking to assess heterogeneity among studies. 42 The between-study heterogeneity of RRs were assessed using Cochran Q 37 and the I 2 statistic. 38 If heterogeneity was detected, mixed-effects models were used to obtain the summarized RR estimates. Mixed-effects regression analyses were performed in which drinking groups and control variables were treated as fixed-effects with a random study effect because of significant heterogeneity. 43

All analyses were weighted by the inverse of the estimated variance of the natural log relative risk. Variance was estimated from reported standard errors, confidence intervals, or number of deaths. The weights for each individual study were created using the inverse variance weight scheme and used in mixed regression analysis to get maximum precision for the main results of the meta-analysis. 42 In comparison with lifetime abstainers, the study estimated the mean RR of all-cause mortality for former drinkers (ie, now completely abstaining), current occasional (<9.1 g per week), low-volume (1.3-24.0 g per day), medium-volume (25.0-44.0 g per day), high-volume (45.0-64.0 g) and highest-volume drinkers (≥65.0 grams per day). The analyses adjusted for the potential confounding effects of study characteristics including the median age and sex distribution of study samples, drinker biases, country where a study was conducted, follow-up years and presence or absence of confounders. Analyses were also repeated using occasional drinkers as the reference group. We used t tests to calculate P values, and significance was set at .05. All statistical analyses were performed using SAS version 9.4 (SAS Institute) and the SAS MIXED procedure was used to model the log-transformed RR. 44 Data were analyzed from September 2021 to August 2022.

There were 724 estimates of the risk relationship between level of alcohol consumption and all-cause mortality from 107 unique studies 13 - 32 , 45 - 131 , including 4 838 825 participants and 425 564 deaths available for the analysis. Table 1 describes the sample characteristics of the metadata. Of 39 studies 13 , 15 , 18 , 21 , 23 - 26 , 29 , 31 , 45 - 47 , 49 , 50 , 52 - 54 , 57 - 59 , 62 , 64 , 70 , 80 , 81 , 85 , 87 , 91 , 94 , 96 , 100 , 104 , 107 , 118 , 124 , 125 , 127 , 130 reporting RR estimates for men and women separately, 33 14 , 17 , 48 , 51 , 61 , 63 , 66 , 68 , 69 , 72 , 76 , 79 , 83 , 84 , 86 , 88 , 90 , 92 , 93 , 97 , 98 , 101 , 103 , 105 , 109 - 111 , 113 - 115 , 119 , 120 , 128 were for males only, 8 16 , 65 , 73 , 99 , 102 , 108 , 112 , 123 for females only, and 30 13 , 19 - 22 , 26 - 30 , 32 , 55 , 56 , 67 , 71 , 74 , 75 , 77 , 78 , 82 , 84 , 89 , 95 , 106 , 116 , 117 , 121 , 122 , 126 , 129 for both sexes. Twenty-one studies 13 , 17 , 19 , 21 , 22 , 26 , 27 , 45 - 58 (220 risk estimates) were free from abstainer bias (ie, had a reference group of strictly defined lifetime abstainers). There were 50 studies 14 - 16 , 18 , 20 , 23 - 25 , 29 , 59 - 99 (265 risk estimates) with both former and occasional drinker bias; 28 studies 28 , 30 - 32 , 100 - 122 , 130 (177 risk estimates) with only former drinker bias; and 8 studies 123 - 129 , 131 (62 risk estimates) with only occasional drinker bias.

Unadjusted mean RR estimates for most study subgroups categorized by methods/sample characteristics showed markedly or significantly higher RRs for alcohol consumers as a group vs abstainers. Exceptions were for studies with less than 10 years of follow-up and those with some form of abstainer bias ( Table 1 ). Bivariable analyses showed that mortality risks for alcohol consumers varied considerably according to other study characteristics, such as quality of the alcohol consumption measure, whether unhealthy individuals were excluded at baseline, and whether socioeconomic status was controlled for ( Table 1 ).

No evidence of publication bias was detected either by inspection of symmetry in the funnel plot of log-RR estimates and their inverse standard errors (eFigure 2 in Supplement 1 ) or by Egger linear regression analysis (eTable 2 in Supplement 1 , all P > .05 for each study group). Significant heterogeneity was observed across studies for all drinking categories confirmed by both the Q statistic ( Q 723  = 5314.80; P  < .001) and I 2 estimates (all >85.87%). (See eFigure 3 in Supplement 1 for forest plot of unadjusted risk estimates of mortality risks for the 20 newly identified studies).

Pooled unadjusted estimates (724 observations) showed significantly higher risk for former drinkers (RR, 1.22; 95% CI, 1.11-1.33; P  = .001) and significantly lower risk for low-volume drinkers (RR, 0.85; 95% CI, 0.81-0.88; P  = .001) compared with abstainers as defined in the included studies ( Table 2 ; eFigure 4 in Supplement 1 ). In the fully adjusted model, mortality RR estimates increased for all drinking categories, becoming nonsignificant for low-volume drinkers (RR, 0.93; 95% CI, 0.85-1.01; P  = .07), occasional drinkers (>0 to <1.3 g of ethanol per day; RR, 0.96; 95% CI, 0.86-1.06; P  = .41), and drinkers who drank 25 to 44 g per day (RR, 1.05; 95% CI, 0.96-1.14; P  = .28). There was a significantly increased risk among drinkers who drank 45 to 64 g per day (RR, 1.19; 95% CI, 1.07-1.32; P  < .001) and 65 or more grams (RR, 1.35; 95% CI, 1.23-1.47; P  < .001). The Figure shows the changes in RR estimates for low-volume drinkers when removing each covariate from the fully adjusted model. In most cases, removing study-level covariates tended to yield lower risk estimates from alcohol use.

Table 2 presents the RR estimates when occasional drinkers were the reference group. In fully adjusted models, higher though nonsignificant mortality risks were observed for both abstainers and medium-volume drinkers (RR, 1.04; 95% CI, 0.94-1.16; P  = .44 and RR, 1.09; 95% CI, 0.96-1.25; P  = .19, respectively). There were significantly elevated risks for both high and higher volume drinkers (RR, 1.24; 95% CI, 1.07-1.44; P  = .004 and RR, 1.41; 95% CI, 1.23-1.61; . P  = 001, respectively).

As hypothesized, there was a significant interaction between cohort age and mortality risk ( P  = .02; F 601  = 2.93) and so RR estimates for drinkers were estimated in analyses stratified by median age of the study populations at enrollment ( Table 3 ). In unadjusted and partially adjusted analyses, older cohorts displayed larger reductions in mortality risk associated with low-volume consumption than younger cohorts. However, in fully adjusted analyses with multiple covariates included for study characteristics, these differences disappeared. Younger cohorts also displayed greater mortality risks than older cohorts at higher consumption levels. Among studies in which participants were recruited at age 50 years or younger and followed up to age 60 years (ie, there was likely reduced risk of lifetime selection bias) higher RR estimates were observed for all drinking groups vs lifetime abstainers. These differences were significant in all drinking groups except low-volume drinkers (eTable 3 in Supplement 1 ).

Across all levels of alcohol consumption, female drinkers had a higher RR of all-cause mortality than males ( P for interaction  = .001). As can be seen in Table 4 , all female drinkers had a significantly increased mortality risk compared with female lifetime nondrinkers (RR, 1.22; 95% CI, 1.02-1.46; P  = .03). Compared with lifetime abstainers, there was significantly increased risk of all-cause mortality among male drinkers who drank 45 to 64 g per day (RR, 1.15; 95% CI, 1.03-1.28; P  = .01) and drank 65 or more (RR, 1.34; 95% CI, 1.23-1.47; P  < .001), and among female drinkers who drank 25 to 44 g per day (RR, 1.21; 95% CI, 1.08-1.36; P  < .01), 45 to 64 g (RR, 1.34; 95% CI, 1.11-1.63; P  < .01) and 65 or more grams (RR, 1.61; 95% CI, 1.44-1.80; P  = .001).

In fully adjusted, prespecified models that accounted for effects of sampling, between-study variation, and potential confounding from former drinker bias and other study-level covariates, our meta-analysis of 107 studies found (1) no significant protective associations of occasional or low-volume drinking (moderate drinking) with all-cause mortality; and (2) an increased risk of all-cause mortality for drinkers who drank 25 g or more and a significantly increased risk when drinking 45 g or more per day.

Several meta-analytic strategies were used to explore the role of abstainer reference group biases caused by drinker misclassification errors and also the potential confounding effects of other study-level quality covariates in studies. 2 Drinker misclassification errors were common. Of 107 studies identified, 86 included former drinkers and/or occasional drinkers in the abstainer reference group, and only 21 were free of both these abstainer biases. The importance of controlling for former drinker bias/misclassification is highlighted once more in our results which are consistent with prior studies showing that former drinkers have significantly elevated mortality risks compared with lifetime abstainers.

In addition to presenting our fully adjusted models, a strength of the study was the examination of the differences in relative risks according to unadjusted and partially adjusted models, including the effect of removing individual covariates from the fully adjusted model. We found evidence that abstainer biases and other study characteristics changed the shape of the risk relationship between mortality and rising alcohol consumption, and that most study-level controls increased the observed risks from alcohol, or attenuated protective associations at low levels of consumption such that they were no longer significant. The reduced RR estimates for occasional or moderate drinkers observed without adjustment may be due to the misclassification of former and occasional drinkers into the reference group, a possibility which is more likely to have occurred in studies of older cohorts which use current abstainers as the reference group. This study also demonstrates the degree to which observed associations between consumption and mortality are highly dependent on the modeling strategy used and the degree to which efforts are made to minimize confounding and other threats to validity.

It also examined risk estimates when using occasional drinkers rather than lifetime abstainers as the reference group. The occasional drinker reference group avoids the issue of former drinker misclassification that can affect the abstainer reference group, and may reduce confounding to the extent that occasional drinkers are more like low-volume drinkers than are lifetime abstainers. 2 , 8 , 132 In the unadjusted and partially adjusted analyses, using occasional drinkers as the reference group resulted in nonsignificant protective associations and lower point estimates for low-volume drinkers compared with significant protective associations and higher point estimates when using lifetime nondrinkers as the reference group. In the fully adjusted models, there were nonsignificant protective associations for low-volume drinkers whether using lifetime abstainers or occasional drinkers as the reference group, though this was only a RR of 0.97 for the latter.

Across all studies, there were few differences in risk for studies when stratified by median age of enrollment above or below age 56 years in the fully adjusted analyses. However, in the subset of studies who enrolled participants aged 50 years or younger who were followed for at least 10 years, occasional drinkers and medium-volume drinkers had significantly increased risk of mortality and substantially higher risk estimates for high- and higher-volume consumption compared with results from all studies. This is consistent with our previous meta-analysis for CHD, 9 in which younger cohorts followed up to older age did not show a significantly beneficial association of low-volume consumption, while older cohorts, with more opportunity for lifetime selection bias, showed marked, significant protective associations.

Our study also found sex differences in the risk of all-cause mortality. A larger risk of all-cause mortality for women than men was observed when drinking 25 or more grams per day, including a significant increase in risk for medium-level consumption for women that was not observed for men. However, mortality risk for mean consumption up to 25 g per day were very similar for both sexes.

A number of limitations need to be acknowledged. A major limitation involves imperfect measurement of alcohol consumption in most included studies, and the fact that consumption in many studies was assessed at only 1 point in time. Self-reported alcohol consumption is underreported in most epidemiological studies 133 , 134 and even the classification of drinkers as lifetime abstainers can be unreliable, with several studies in developed countries finding that the majority of self-reported lifetime abstainers are in fact former drinkers. 135 , 136 If this is the case, the risks of various levels of alcohol consumption relative to presumed lifetime abstainers are underestimates. Merely removing former drinkers from analyses may bias studies in favor of drinkers, since former drinkers may be unhealthy, and should rightly be reallocated to drinking groups according to their history. However, this has only been explored in very few studies. Our study found that mortality risk differed significantly by cohort age and sex. It might be that the risk is also higher for other subgroups, such as people living with HIV, 137 a possibility future research should investigate.

The number of available studies in some stratified analyses was small, so there may be limited power to control for potential study level confounders. However, the required number of estimates per variable for linear regression can be much smaller than in logistic regression, and a minimum of at least 2 estimates per variable is recommended for linear regression analysis, 138 suggesting the sample sizes were adequate in all models presented. It has been demonstrated that a pattern of binge (ie, heavy episodic) drinking removes the appearance of reduced health risks even when mean daily volume is low. 139 Too few studies adequately controlled for this variable to investigate its association with different outcomes across studies. Additionally, our findings only apply to the net effect of alcohol at different doses on all-cause mortality, and different risk associations likely apply for specific disease categories. The biases identified here likely apply to estimates of risk for alcohol and all diseases. It is likely that correcting for these biases will raise risk estimates for many types of outcome compared with most existing estimates.

This updated meta-analysis did not find significantly reduced risk of all-cause mortality associated with low-volume alcohol consumption after adjusting for potential confounding effects of influential study characteristics. Future longitudinal studies in this field should attempt to minimize lifetime selection biases by not including former and occasional drinkers in the reference group, and by using younger cohorts (ie, age distributions that are more representative of drinkers in the general population) at baseline.

Accepted for Publication: February 17, 2023.

Published: March 31, 2023. doi:10.1001/jamanetworkopen.2023.6185

Correction: This article was corrected on May 9, 2023, to fix errors in the Figure and Supplement.

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Zhao J et al. JAMA Network Open .

Corresponding Author: Jinhui Zhao, PhD, Canadian Institute for Substance Use Research, University of Victoria, PO Box 1700 STN CSC, Victoria, BC V8Y 2E4, Canada ( [email protected] ).

Author Contributions: Drs Zhao and Stockwell had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Zhao, Stockwell, Naimi, Churchill, Sherk.

Acquisition, analysis, or interpretation of data: Zhao, Stockwell, Naimi, Clay.

Drafting of the manuscript: Zhao, Stockwell, Clay.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Zhao, Churchill.

Obtained funding: Zhao, Stockwell, Sherk.

Administrative, technical, or material support: Zhao, Stockwell, Naimi.

Supervision: Zhao, Stockwell, Naimi.

Conflict of Interest Disclosures: Dr Stockwell reported receiving personal fees from Ontario Public Servants Employees Union for expert witness testimony and personal fees from Alko outside the submitted work. Dr Sherk reported receiving grants from Canadian Centre on Substance Use and Addiction (CCSA) during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was partly funded by the CCSA as a subcontract for a Health Canada grant to develop guidance for Canadians on alcohol and health.

Role of the Funder/Sponsor: Health Canada had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. CCSA staff conducted a preliminary search to identify potentially relevant articles but did not participate in decisions about inclusion/exclusion of studies, coding, analysis, interpretation of results or approving the final manuscript.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We gratefully acknowledge contributions by Christine Levesque, PhD (CCSA), and Nitika Sanger, PhD (CCSA), who conducted a preliminary literature search for potentially relevant articles. We also acknowledge the leadership of Drs Catherine Paradis, PhD (CCSA), and Peter Butt, MD (University of Saskatchewan), who cochaired the process of developing Canada’s new guidance on alcohol and health, a larger project which contributed some funds for the work undertaken for this study. We are grateful to Fariha Alam, MPH (Canadian Institute for Substance Use and Research), for her help coding the studies used in this study. None of them received any compensation beyond their normal salaries for this work.

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

Advertisement

Alcohol-Induced Neuropathy in Chronic Alcoholism: Causes, Pathophysiology, Diagnosis, and Treatment Options

• The Pathobiology of Alcohol Consumption (P Molina and M Ronis, Section Editors)
• Open access
• Published: 23 October 2020
• Volume 8 , pages 87–97, ( 2020 )

Cite this article

You have full access to this open access article

• Iga Dudek 1 ,
• Danuta Hajduga 1 ,
• Cezary Sieńko 1 ,
• Amr Maani 2 ,
• Elżbieta Sitarz 3 ,
• Monika Sitarz 4 &
• Alicja Forma   ORCID: orcid.org/0000-0001-8714-7627 1  

17k Accesses

7 Citations

Explore all metrics

Purpose of the Review

Alcohol abuse causes a wide range of disorders that affect the nervous system. These include confusion, cerebellar ataxia, peripheral neuropathy, and cognitive impairment. Chronic and excessive alcohol consumption is the primary cause of peripheral neuropathy. It is worth noting that peripheral neuropathy has no reliable treatment due to the poor understanding of its pathology.

Recent Findings

Coasting is a major feature of alcoholic neuropathy, largely due to chronic alcohol abuse. Its major features are hyperalgesia, allodynia, and burning pain. Even though much research was done in this area, still we do not have a full understanding of the mechanism of alcoholic neuropathy. However, some theories have been proposed. These include direct or indirect effects of alcohol metabolites, impaired axonal transport, suppressed excitatory nerve pathway activity, or imbalance in neurotransmitters. Activation of spinal cord microglia, mGlu5 spinal cord receptors, and hypothalamic-pituitary-adrenal axis also seem to be implicated in the pathophysiology of this alcoholic neuropathy. The goal of treatment is to impede further damage to the peripheral nerves while also restoring their normal physiology. Alcohol abstinence, intake of balanced diets, and treatment with medications are suggested including benfotiamine, alpha-lipoic acid, acetyl- l -carnitine, vitamin E, methylcobalamin, myo-inositol, N -acetylcysteine, capsaicin, tricyclic antidepressants, or antiepileptic drugs.

This review focuses on the many pathways that play a role in the onset and development of alcohol-induced neuropathy, as well as present the possible treatment strategies of this disorder, providing insights into a further search of new treatment modalities.

Similar content being viewed by others

Alcoholic Neuropathy

Alcohol-related peripheral neuropathy: a systematic review and meta-analysis

Alcohol Use Disorders and Neurological Illnesses

Avoid common mistakes on your manuscript.

Introduction

Alcohol-related diseases are caused by excessive consumption of alcohol. Alcohol-related ailments occur either with or without coexisting nutritional or vitamin deficiencies [ 1 ]. Excessive alcohol abuse seems to be the motivational drive for further lifetime drinking in excessive amounts that might ultimately lead to alcohol-induced neuropathy. Alcohol abuse and chronic pain associated with alcohol-related neuropathy present high comorbidity [ 2 ••]. A recent report by the World Health Organization says that approximately 3 million people die each year due to alcohol abuse [ 3 ]. Total alcohol per capita of the world population over the age of 15 has increased from 5.5 l of pure alcohol in 2005 to 6.4 l in 2016. Excessive alcohol consumption is one of the major factors that increase the mortality rate primarily in males aged 15–59 particularly in Eastern Europe [ 4 , 5 ]. Alcohol abuse is believed to contribute to the onset of over 60 types of diseases and injuries and a cause of at least 200 others at the same time [ 6 ]. Excessive alcohol consumption leads to the severe failures of digestive, cardiovascular, and nervous (central and peripheral) systems [ 6 , 7 , 8 , 9 , 10 ]. The most frequent disorders related to alcohol abuse include alcoholic liver disease (including steatosis, steatohepatitis, cirrhosis, or hepatocellular carcinoma), pancreatitis, gastritis, hypertension, arrhythmia, or cardiomyopathy [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. The prevalence of the alcoholic liver disease among heavy drinkers is estimated at 15–30% [ 22 ]. Among disorders of the nervous system, Korsakoff’s syndrome, anterograde amnesia, cerebellar ataxia, sleep disorders, cognitive deficits, and alcohol neuropathy are of the highest prevalence [ 23 , 24 , 25 , 26 , 27 ••, 28 , 29 , 30 ]. Besides, alcohol consumption significantly contributes to the onset of cancers such as a cancer of the oral cavity, pharynx, larynx, esophagus, liver, or colon. Toxic and metabolic effects of alcohol vary depending on age, dose, and duration of exposure [ 31 ]. Neuropathies, regardless of etiology, are fairly common especially among the elderly, attaining an 8% prevalence rate [ 32 , 33 ]. Besides the alcohol, neuropathies may be induced by various factors including toxins (chemotherapeutic agents, heavy metals, industrial chemicals), infections (borreliosis, HIV), the course of metabolic diseases (diabetes, uremia, acromegaly), or they may be related to systemic diseases such as sarcoidosis, polyarteritis nodosa, or rheumatoid arthritis [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. Idiopathic neuropathy, designated as chronic idiopathic axonal polyneuropathy (CIAP), constitutes the most frequent neuropathy so far [ 45 , 46 , 47 ]. Besides, within the peripheral nervous system, neuropathies may be classified into mono-, poly-, and multifocal neuropathies [ 48 ].

Toxic effects of alcohol are associated with several mechanisms that include the direct or indirect activity of alcohol metabolites, oxidative stress induction, and the translocation of the intestinal endotoxins into the bloodstream. DNA, proteins, and lipids might be damaged due to acetaldehyde and oxidants produced during alcohol metabolism. Alcohol-related tissue hypoxia and impaired mitochondrial functions and singling pathways are highly prevalent molecular consequences of alcohol abuse. Alcohol is metabolized by three pathways—by the alcohol dehydrogenase in the cytosol, the Microsomal Ethanol Oxidizing System (MEOS), and the catalase which is contained in peroxisomes (Fig. 1 ).

Three major pathways of alcohol metabolism

Alcohol-induced neuropathy, also known as alcohol-related peripheral neuropathy (ALN), is a toxic polyneuropathy that leads to the damage of sensory, motor, and autonomic nerve fibers leading to the thinning of the myelin sheaths and further impairments of neural functions [ 14 , 49 ]. ALN is characterized by spontaneous burning pain, hyperalgesia, and allodynia. Regarding pain pathways, several pain-related receptors, such as δ-opioid receptors, κ-opioid receptors, and nociception opioid receptors, are highly expressed within different regions including the peripheral nervous system, spinal cord, and the supraspinal regions, significantly contributing to the induction and further intensification of pain. Besides, the key mechanism of chronic pain includes the long-term potentiation of glutamatergic transmission. The percentage of alcohol-dependent patients affected by ALN is estimated to be 66% [ 50 , 51 ]. The pathophysiology of ALN involves underlying mechanisms that include direct or indirect effects of alcohol metabolites, impaired axonal transport, suppressed excitatory nerve pathway activity, or imbalance in neurotransmitters [ 52 , 53 , 54 ]. An essential risk factor regarding the etiology of ALN is the amount of alcohol consumed throughout the years since alcohol displays direct toxicity on nerve fibers [ 55 ]. It is estimated that consumption of more than 100 ml of ethyl alcohol per day significantly increases the risk of ALN [ 56 ]. Recent studies show contradictory information about the role of malnutrition and micronutrients (thiamine) deficiency in the pathogenesis of ALN; however, it is assumed that these might induce the progression of ataxia or movement disorders [ 55 , 57 ]. Nevertheless, heavy alcohol drinkers are usually significantly malnourished because of the improperly balanced diet and impaired absorption of the essential nutrients and elements [ 58 , 59 ].

Alcohol Abuse Diagnostic Criteria and Biomarkers

Generally, alcohol abuse might be defined as the difficulties in the control of alcohol consumption or repetitive and continuous alcohol consumption. Diagnostic criteria for alcohol use disorder are highly differentiated; however, the most common criteria currently used are found in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Except for DSM-5, diagnostic criteria of alcohol abuse were proposed by the International Classification of Diseases (11th revision) that was updated in 2015. According to DSM-5, alcohol abuse might be diagnosed when at least two of the following symptoms appear in the 12-month period, and these are (1) greater alcohol intake or prolonged alcohol intake that was not initially intended, (2) unsuccessful efforts to control the amounts of alcohol consumed, (3) activities that aim to obtain alcohol, (4) a strong desire to use alcohol, (5) repetitive alcohol consumption and coexisting failures in the everyday life activities, (6) continuous alcohol consumption despite the appearing of the disturbed interpersonal relationships, (7) given up of crucial activities only to obtain alcohol, (8) alcohol abuse in physically hazardous situations, (9) the continuation of alcohol consumption being aware of its toxic side effects, (10) the appearance of alcohol tolerance, and (11) the appearance of a withdrawal syndrome. ICD-11 criteria proposed the following criteria: criterion A—difficulties in controlling alcohol consumption, criterion B—alcohol consumption becomes a priority of an addicted individual, and criterion C refers to the presence of physiological symptoms. Symptoms that are typical for alcohol abusers include poor coordination, inappropriate behavior and unstable moods, or impaired attention and/or memory. Besides, chronic and continuous intake of alcohol might induce the appearance of an alcohol withdrawal syndrome that includes such symptoms as nausea, vomiting, hallucinations, sweating, rapid heartbeat, anxiety, or seizures. Alcohol abusers are usually motivated for further drinking due to the induction of the neuroadaptive changes, primarily within the brain neurotransmissions systems, such excessive drinking further leads to the induction of physiological symptoms and other disorders such as ALN. Moreover, alcohol abusers with ALN tend to drink greater amounts of alcohol in order to alleviate neuropathic pain. Besides, alcohol is an effective analgesic that might contribute to the motivation for further drinking.

Biomarkers of alcohol abuse include carbohydrate-deficient transferrin (CDT) and phosphatidylethanol (PEth). CDT is an indirect metabolite of ethanol and constitutes either a marker of prolonged, heavy alcohol consumption or a marker of relapse. Peth on the other hand is a direct alcohol metabolite that can be measured to monitor alcohol consumption as well as for the identification of early signs of alcohol-related clinical manifestations. Other non-specific biomarkers useful in the diagnosis of alcohol use disorder are gamma-glutamyl transferase (GGT), mean corpuscular volume (MCV) of the red blood cells, and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels.

Diagnosis of Alcohol-Related Peripheral Neuropathy

In order to diagnose ALN, usually, several tests are needed to be performed to provide a complete and reliable diagnosis. Besides blood chemistry test and complete blood count (CBC), esophagogastroduodenoscopy is needed when a patient vomits and has nausea for an unknown reason; X-rays of the gastrointestinal tract can also be performed. Electromyography and nerve conduction tests are performed in order to reveal signs of ALN. In some cases, a nerve biopsy is needed. Sensory functions and reflexes can be tested during a neurological examination.

Alcohol-Related Peripheral Neuropathy – History of Discovery and Exact Definition

The first reports about the possible role of excessive alcohol consumption and induction of ALN were introduced in 1787 [ 60 ]. The concept was supported by another study from 1822 [ 61 ]. Lettsom has observed that paralysis and hypoesthesia related to ALN presented a higher prevalence rate in lower limbs compared to upper limbs [ 60 ].

Toxic neuropathies may be classified into three distinct groups depending on the etiology: neuropathies with damaged perikaryon, myelopathies where alcohol affects Schwann cell proliferation and myelin formation, and distal axonopathy [ 62 , 63 ]. In 1977, Behse and Buchtal examined the differences in the microscopic findings and nerve conduction of the sural nerves of 37 patients with ALN and 6 patients with neuropathy induced by malnutrition after gastrectomy [ 56 ]. The results have shown that distal axonopathy in patients with ALN affected both myelinated and unmyelinated fibers. The underlying manifestation of ALN is axonal degeneration of the neurons of both the sensory and motor systems, following damage to the neuronal cell bodies [ 64 , 65 , 66 , 67 , 68 ]. Small fiber neuropathy is a typical manifestation of early stages of ALN, while the prolonged course of the disease leads to the degeneration of larger fibers, axonal sprouting, and subsequent regeneration of fibers [ 65 , 69 ]. The majority of patients with ALN initially present with symmetrical polyneuropathy mainly in the lower distal extremities since primarily the initial manifestations occur in the most distal part of the axons [ 70 ]. Thus, longer axons are more prone to be initially affected [ 71 ]. During ALN progression, axonal transport is less effective and synaptic transmission is disrupted [ 72 ]. Further, retrograde degeneration or so-called dying back phenomenon occurs, reaching back the perikaryon in advanced stages of ALN [ 73 , 74 ]. Burning and throbbing pain along with autonomic dysfunctions are induced mainly via the degeneration of unmyelinated C and Aδ fibers [ 68 , 75 ]. The dysfunctions within fibers of greater diameter manifest as a disturbed sense of vibration, proprioception, and impaired tendon reflexes [ 76 ].

Intake of Alcohol

Pathogenesis of ALN is related to either direct or indirect (or both) toxic effects of alcohol on nerve fibers. The probability of ALN manifestation increases with the duration of alcohol abuse [ 77 , 78 ]. ALN develops in a dose-dependent manner; thus, what is crucial is the total lifetime dose of ethanol (TLDE) [ 55 , 71 ]. According to Ammendola et al. (2001), TLDE and duration of alcohol dependence make up the two most significant risk factors of ALN [ 77 ]. Vittadini et al. (2001) showed that the largest percentage of ALN was in patients who consumed wine [ 71 ]. It could be probably due to the need for larger amounts of wine to induce alcohol intoxication or because of the possible contamination such as lead, which also causes toxic effects [ 79 ]. However, Ferdinandis et al. (2008) did not show the relationship between the higher incidences of ALN among patients who consume illicit (contaminated) alcohol compared to legal spirit drinkers [ 80 ]. ALN is believed to be related to risk factors associated with the excessive intake of alcohol such as malnutrition, deficiency of nutrients and elements (thiamine in particular), or direct effects of alcohol metabolites on the cells and tissues; however, the exact mechanism has not been deciphered yet. ALN is more prevalent among heavy and chronic alcohol drinkers compared to those who drink alcohol only episodically. Several studies proved the higher incidence rate of ALN among females compared to males; besides, females tend to present greater severity of ALN symptoms [ 81 ].

ALN Pathophysiology

The exact pathophysiology of ALN has not been yet determined. Primarily, it was assumed that the progression of ALN symptoms is due to malnutrition and micronutrient deficiency (mainly B1 hypovitaminosis) [ 82 , 83 ]. Indeed, these factors contribute to the progression of ALN symptoms; however, they do not constitute direct factors that manifest in ALN development [ 84 ]. Current postulation holds that dysfunctions within the central and peripheral nervous system are due to both direct and indirect toxic effects of alcohol [ 31 , 85 , 86 , 87 ]. Indirect effects are mainly induced by vitamin deficiencies (B1, B2, B3, B5, B6, B7, B9, and B12) [ 84 , 88 ].

Chronic alcohol consumption leads to malnutrition with dysfunctions in protein and lipid metabolism which affect the metabolic pathways and progression of ALN symptoms within the central and peripheral nervous systems [ 89 ]. The direct toxic effects of alcohol and its metabolites (mainly acetaldehyde) are crucial in ALN etiology [ 64 ]. Acetaldehyde is involved in neuronal damage [ 90 , 91 ]. It has been demonstrated that incubation of neural cells with advanced glycation end products of acetaldehyde (AA-AGE) induced dose-dependent degradation of neuronal cells while the addition of AA-AGE antibodies reduced neurotoxicity [ 51 , 90 ]. Other findings showed that decreased activity of aldehyde dehydrogenase leads to peripheral neuropathy [ 76 , 91 ].

The mechanism of direct damage of nerve fibers due to alcohol intoxication remains unclear. Activation of spinal cord microglia, mGlu5 spinal cord receptors, and hypothalamic-pituitary-adrenal axis appear to be implicated in this process [ 92 , 93 , 94 , 95 , 96 , 97 ]. Oxidative stress also leads to the indirect damage of nerve fibers via the release of free radicals and proinflammatory cytokines with protein kinase C and ERK kinase phosphorylation [ 98 , 99 , 100 , 101 ]. Besides, ALN is characterized by insulin and insulin-like growth factor (IGF) resistance, which results in impaired trophic factor signaling [ 102 , 103 ].

Ethanol and its toxic metabolites affect neural metabolism including metabolic activities in the nucleus, lysosomes, peroxisomes, endoplasmic reticulum, and cytoplasm [ 104 ]. The morphological basis of post-alcoholic damage of neural tissue includes primary axonopathy and secondary demyelination of motor and sensory (especially small) fibers [ 105 ]. Demyelination is probably the effect of axoplasmic transmission slowdown; such degeneration so-called dying back bears semblance to Wallerian degeneration [ 64 , 84 ]. An animal study on axonal transport in vitro using dorsal roots of the sciatic nerve showed decreased axonal transmission after long-term ethanol consumption [ 106 ]. In vivo study on rats showed impaired retrograde axonal transport [ 107 , 108 ]. Thus, ALN might be induced by the combination of the effects of the direct activity of alcohol metabolites on the nerve fibers along with nutritional deficiencies primarily in a form of thiamine deficiency.

ALN develops slowly over months or years [ 109 , 110 ]. ALN can manifest differently, and patients might experience one, two, or even more clinical manifestations of ALN. Patients who have ALN might present such symptoms as cramps, impaired movement of the limbs, muscle atrophy, muscle weakness, spasms, or contractions, loss of sensation, or feeling of tingling. Besides, the gastrointestinal and urinary systems are also affected and include the presence of diarrhea, constipation, nausea, swallowing difficulties, abdominal bloating, and urinary retention.

During the initial stages of ALN, the disease may appear asymptomatic and demonstrable only on electroneurographic investigation [ 71 , 111 , 112 ]. Because ALN is a length-dependent axonopathy, it manifests mainly in a “stocking-glove” form, affecting the lower extremities at the beginning [ 28 , 113 ]. The main symptoms of ALN include dysesthesia, paresthesia, numbness, and pain in the lower extremities which progressively reach higher parts of the body [ 114 , 115 , 116 , 117 ]. The pain is described as burning, cramp-like, or itching; also, a common symptom is a subjective feeling of cold in both feet [ 118 , 119 , 120 , 121 , 122 , 123 ]. The symptoms deteriorate through touch and pressure which intensify pain while standing or walking [ 124 ]. Further progression of ALN leads to the weakening of tendon reflexes or total areflexia and disturbed proprioception, which additionally impair the ability to walk [ 28 , 113 ]. ALN further manifests as weakness and atrophy of muscles due to the damage of greater motor fibers and impaired neuromuscular transmission.

Genetic Background

Genetic factors and family history of heavy drinking is significantly involved in the pathogenesis of ALN. There is no evidence proving the influence of the human leukocyte antigen (HLA) interindividual variations on the induction of ALN [ 125 ]. Masaki et al. (2004) performed a study about the association between Glu-487 → Lys mutation (single-nucleotide polymorphism) in aldehyde dehydrogenase-2 (ALDH2) and alcoholic polyneuropathy [ 126 ]. Patients with ALDH2*2 (Lys) mutation present poorer ability to metabolize acetaldehyde, an intermediate product of ethanol. Comparisons were made between clinical symptoms and nerve conduction between heterozygotes with mutated allele ALDH2*2/ALDH2*1 and homozygotes ALDH2*1/ALDH2*1 (Glu-487) without a mutation. Among alcoholics with a mutated allele, a decrease in sensory action potential amplitudes (SNAP) within the median and sural nerves was shown. The study suggests that acetaldehyde cumulation is significant in ALN etiology. Damages to the median nerve are not very specific for ALN which initially manifests within lower extremities; further, the abovementioned mutation is primarily prevalent in South Asian countries [ 126 ].

ALN and Gender

The prevalence of ALN among the genders is unclear. Females, generally tend to drink less alcohol, are better abstainers, and present the smaller probability of the development of alcohol-related diseases [ 127 , 128 ]. However, compared to males, the symptoms of excessive alcohol consumption manifest earlier in females [ 129 , 130 ]. Alcohol-related liver cirrhosis may occur even a few years earlier in females compared to males [ 131 ]. The prevalence of alcoholic cardiomyopathy appears to be similar among males and females; however, males present a higher disease burden [ 132 , 133 ]. Furthermore, females tend to be more vulnerable to the brain damage and neurotoxic effects of alcohol [ 134 ]. Computed tomography (CT) scans showed that among alcohol-dependent patients, the brain volumes were reduced to increase the volume of cerebrospinal fluid; these changes were induced in females in less time [ 135 , 136 ]. Ammendola et al. (2000) showed an inverse correlation of the sensory-evoked potential (SEP) amplitude of the sural nerve which informs about sensory dysfunctions and is altered even in asymptomatic patients throughout the course alcohol dependence [ 137 ]. The correlation was more significant in females. The mouse model of the injection of β-estradiol in males resulted in higher activity of cytosolic alcohol dehydrogenase (ADH), microsomal aniline hydroxylase (ANH), and aldehyde dehydrogenase (ALDH) which are crucial in ethanol metabolism [ 138 ]. Female mouse with injected testosterone showed the decreased activity of cytosolic isoform of ALDH which implies that those enzymes are sensitive to estrogen and testosterone and alcohol metabolism is greater in females.

Malnutrition and Micronutrients Deficiency

The onset of ALN is intensified by several risk factors such as malnutrition, thiamine deficiency, direct and indirect toxic effects of alcohol and its metabolites on nerve fibers, and genetic predispositions of patients [ 55 , 139 , 140 , 141 , 142 , 143 ]. It is still unclear what is the major determinant in the pathogenesis of ALN. Primarily, thiamine deficiency is the crucial risk factor of ALN since it induces the progression of Korsakoff’s syndrome and beriberi [ 144 , 145 ]. Due to similar histologic and electrophysiological symptoms, it was believed that ALN may make up a subtype of beriberi [ 146 ]. Further research has confirmed the role of thiamine in the pathogenesis of ALN—the well-balanced diet and vitamin B1 supplementation significantly decreased the severity of ALN symptoms [ 147 , 148 ]. However, the limitations of those studies include the lack of the possibility to measure the amount of vitamin B1 in the serum; further, patients who were involved in the study have received an unrefined form of the supplement. Later, the results have been supported by Victor and Adams (1961)—among 12 patients with ALN, neuropathic symptoms were alleviated just after thiamine supplementation, even though the alcohol consumption was previously completely reduced [ 149 ]. Koike et al. (2003) compared clinical and histological differences between ALN with and without thiamine deficiency [ 65 ]. Also, the results of the group of 32 patients with non-alcoholic thiamine deficiency neuropathy were considered. Thiamine deficiency resulted in the progression of sensory dysfunctions; further, histological examination of the sural nerves revealed the loss of small nerve fibers and segmental demyelination. Patients with non-alcoholic thiamine deficiency neuropathy showed more abrupt onset of symptoms, mainly in a form of motor dysfunctions; biopsy showed damage to greater fibers with subperineurial edema. ALN with thiamine deficiency was manifested as a variable mixture of these symptoms. It was proposed that ALN pathogenesis, besides thiamine deficiency itself, could be due to its inappropriate use in the organism or transketolase deficiency [ 150 ]. Further, alcohol impairs vitamin B1 absorption and its storage in the liver [ 151 , 152 , 153 ].

Autonomic Neuropathy

Alcohol abuse contributes to peripheral neuropathy development involving both somatic and autonomic nerves [ 154 , 155 ]. However, impairments of autonomic functions are scarcer and less intensified, and, usually, clinical symptoms are delayed [ 156 ]. According to many studies, alcohol-induced autonomic neuropathy (AAN) not only leads to potential damage to internal organs but also increases the mortality rate of patients [ 157 , 158 ]. It was observed that abstinence may lead to the regression of several symptoms of AAN [ 159 ].

The prevalence of impairments in ANS in alcohol-dependent patients varies from 20 to 99% [ 160 ]. Symptoms of AAN are due to impairments in both sympathetic and parasympathetic autonomic fibers of the cardiovascular, digestive, and urogenital systems. Appenzeller and Ogin (1974) showed that alcohol-dependent and diabetic patients had a reduced number of large fibers (greater than 5 μm) and greater density of autonomic fibers (possibly because of the degeneration followed by a partial regeneration) [ 161 ]. The reduction of internodal length contributes to the decreased speed of nerve conduction which may be implemented in impairments in perspiration, baroreceptor reflexes, and functions of internal organs. To determine the functions of the sympathetic division of the autonomic nervous system (ANS), sympathetic skin response (SSR) is used; the abnormal results of this test suggest subclinical transmission impairments [ 162 ]. Navarro et al. (1993) showed that nearly half of the alcohol-dependent patients without AAN symptoms and any aberrations in electrophysiologic studies presented abnormal SSR results [ 163 ]. In a similar study, SSR was used to assess the number of reactive sweat glands (SGN), which turned out to be decreased in alcohol-dependent patients [ 164 ].

Regarding the parasympathetic division of ANS, most of the studies are focused on the assessment of nerve conduction mainly in oculomotor and vagus nerves; these include pupil cycle time (PCT) and cardiovascular reflex tests correspondingly [ 160 ]. Further, ECG changes and functions of the digestive tract (dyspeptic symptoms, stomach and gallbladder motility, orocecal transit time) can also be assessed [ 162 , 165 ]. PCT seems to be valuable due to the correlation between prolongation of pupil oscillation and exacerbations of cardiovascular symptoms which presents the colinear involvement of parasympathetic division of ANS.

Symptoms of AAN are non-specific; in the sympathetic division, these include impairments in perspiration, orthostatic hypotension, whereas in parasympathetic hoarseness, swallowing difficulties, or cardiac arrhythmias [ 111 , 166 ]. Gastrointestinal symptoms include delayed stomach emptying and intestinal transit, dyspepsia, and faster emptying of the gallbladder [ 165 ]. Besides, approximately 55% of men with AAN develop erectile dysfunctions [ 167 ]. Cardiac arrhythmias in patients with AAN might increase the probability of sudden cardiac death, which is probably due to toxic effects of alcohol on a cardiac muscle that is also observed in alcoholic cardiomyopathy [ 168 , 169 ].

Other coexisting, alcohol-related diseases may induce exacerbation of AAN symptoms. It was shown that patients with liver cirrhosis (regardless of its etiology) present dysfunctions in ANS, primarily within the vagus nerve [ 170 ]. However, the pathogenesis is yet unclear. Proposed mechanisms include circulatory disturbances in liver cirrhosis, metabolic and neurohormonal (renin-angiotensin-aldosterone system) dysfunctions, excessive nitric oxide production, oxidative stress, and inflammatory mediators [ 11 , 171 ]. There is a strong correlation between AAN and Child-Pugh scale which suggests that liver cirrhosis progression is related to impairments in ANS [ 172 ]. Alcohol-abusing patients with liver cirrhosis and vagus nerve neuropathy are at higher risk of a sudden death compared to patients without impairments within the nervous system [ 173 , 174 ].

Treatment of ALN aims to reduce further damage to the peripheral nerves and restore their normal functioning. What is crucial during ALN treatment is the alleviation of the major causation of ALN which is alcohol abuse. Alcohol abuse treatment might lead to a resolution of neuropathic pain and alleviation of its symptoms. This can be achieved by complete alcohol abstinence and a balanced diet primarily supplemented by B6, B12, and E vitamins, as well as folate, thiamine, and niacin. Prevention and treatment of ALN may be also achieved via other alternative treatment strategies including benfotiamine, alpha-lipoic acid, acetyl- l -carnitine, vitamin E, methylcobalamin, myo-inositol, N -acetylcysteine, capsaicin, tricyclic antidepressants, or antiepileptic drugs [ 51 ]. Benzodiazepines are commonly used to reduce the symptoms of alcohol withdrawal syndrome; acamprosate and naltrexone are effective to treat alcohol dependence; however, the latter usually induces withdrawal symptoms [ 175 ]. Cognitive-behavior therapies are suggested to avoid relapses [ 30 ]. Further, serotonin-norepinephrine reuptake inhibitors are prescribed to treat alcohol-induced neuropathic pain via exerting antinociceptive properties by increasing serotonergic and noradrenergic neurotransmissions [ 71 ]. In an animal model, Kaur et al. (2017) showed that curcumin and sildenafil administrated alone or in combination represent a therapeutic advantage in alcohol-induced neuropathic pain [ 176 ].

Conclusions

Alcohol abuse affects the peripheral and the central nervous system adversely. A common adverse effect of chronic alcohol consumption is alcohol neuropathy. Common symptoms include paresthesias, pain, and ataxia. We do not know precisely how many people are affected by alcohol neuropathy, but research has shown that at least 66% of chronic alcohol abusers may have some form of neuropathy. Neuropathy has multifactorial causes, ranging from nutritional deficiencies to the toxic effects that alcohol has on neurons. Because of the many effects that alcohol has on the organism, it is important that patients with alcoholic neuropathy be managed by a team of inter-professionals in the health industry. The way alcohol neuropathy is being managed presently is not satisfactory. Treatment is dependent on nutrition and abstinence from alcohol. However, there is poor compliance on the part of patients, resulting in the progression of the condition and ultimately, poor quality of life. Residual neuropathy occurs even in patients who have practiced abstinence. While one may find relief from conventional treatment, the addictive nature or side effects of some medications makes it undesirable to use it for the long term. These treatments, in some cases, only suppress the symptoms but do not treat the underlying pathology. However, alternative therapies do not have side effect and tackle nutritional deficiencies and oxidative stress. Intensive research has been done on medications like alpha-lipoic acid, benfotiamine, acetyl- l -carnitine, and methylcobalamin. Other botanical or nutrient therapies include myo-inositol, vitamin E, topical capsaicin, and N -acetylcysteine. Morbidity can be decreased by the use of modern treatment and nutrients.

Papers of particular interest, published recently, have been highlighted as: •• Of major importance

Grochowski C, Blicharska E, Baj J, Mierzwinska A, Brzozowska K, Forma A, et al. Serum iron, magnesium, copper, and manganese levels in alcoholism: a systematic review. Molecules. 2019;24(7):1361.

PubMed Central   Google Scholar  

•• Edwards S, Vendruscolo LF, Gilpin NW, Wojnar M, Witkiewitz K. Alcohol and pain: a translational review of preclinical and clinical findings to inform future treatment sstrategies. Alcohol Clin Exp Res. 2020;44(2):368–83. This study provides a comprehensive review of both preclinical and clinical findings that are related to the alcohol abuse and the induction of chronic pain.

World Health Organization. Global status report on alcohol and health 2018. World Health Organization 2018; https://wwwwhoint/publications/i/item/global-status-report-on-alcohol-and-health-2018 Accessed 24 Jul 2020.

Zaridze D, Brennan P, Boreham J, Boroda A, Karpov R, Lazarev A, et al. Alcohol and cause-specific mortality in Russia: a retrospective case-control study of 48,557 adult deaths. Lancet. 2009;373:2201–14.

CAS   PubMed   PubMed Central   Google Scholar  

World Health Organization. Global status report on alcohol and health 2011. World Health Organization. 2011; https://wwwwhoint/substance_abuse/publications/alcohol_2011/en/ Accessed 34 Jul 2020.

Rocco A, Compare D, Angrisani D, Sanduzzi Zamparelli M, Nardone G. Alcoholic disease: liver and beyond. World J Gastroenterol. 2014;20(40):14652–9.

Ferraguti G, Pascale E, Lucarelli M. Alcohol addiction: a molecular biology perspective. Curr Med Chem. 2015;22(6):670–84.

CAS   PubMed   Google Scholar  

Haas SL, Ye W, Löhr JM. Alcohol consumption and digestive tract cancer. Curr Opin Clin Nutri Metabol Care. 2012;15(5):457–67.

CAS   Google Scholar  

Braillon A. Treatment of alcohol use disorders in patients with liver disease. J Hepatol. 2016;65(6):1270.

PubMed   Google Scholar  

Harper KM, Knapp DJ, Criswell HE, Breese GR. Vasopressin and alcohol: a multifaceted relationship. Psychopharmacology. 2018;235(12):3363–79.

Rehm J, Taylor B, Mohapatra S, Irving H, Baliunas D, Patra J, et al. Alcohol as a risk factor for liver cirrhosis: a systematic review and meta-analysis. Drug Alcohol Rev. 2010;29(4):437–45.

Davis BC, Bajaj JS. Effects of alcohol on the brain in cirrhosis: beyond hepatic encephalopathy. Alcohol Clin Exp Res. 2018;42(4):660–7.

Singh VK, Yadav D, Garg PK. Diagnosis and management of chronic pancreatitis. Jama. 2019;322(24):2422.

Li G, Zhu L, Cao Z, Wang J, Zhou F, Wang X, et al. A new participant in the pathogenesis of alcoholic gastritis: Pyroptosis. Cell Physiol Biochem. 2018;49(1):406–18.

Gardner JD, Mouton AJ. Alcohol effects on cardiac function. Comprehen Physiol. 2015;5(2):791–802.

Gallagher C, Hendriks JM, Elliott AD, Wong CX, Rangnekar G, Middeldorp ME, et al. Alcohol and incident atrial fibrillation - a systematic review and meta-analysis. Int J Cardiol. 2017;246:46–52.

Maisch B. Alcoholic cardiomyopathy. Herz. 2016;41(6):484–93.

Fernández-Sola J. The effects of ethanol on the heart: alcoholic cardiomyopathy. Nutrients. 2020;12(2):572.

Petroni ML, Brodosi L, Marchignoli F, Musio A, Marchesini G. Moderate alcohol intake in non-alcoholic fatty liver disease: to drink or not to drink? Nutrients. 2019;11(12):3048.

Arranz S, Chiva-Blanch G, Valderas-Martínez P, Medina-Remón A, Lamuela-Raventós RM, Estruch R. Wine, beer, alcohol and polyphenols on cardiovascular disease and cancer. Nutrients. 2012;4(7):759–81.

Cahill PA, Redmond EM. Alcohol and cardiovascular disease-modulation of vascular cell function. Nutrients. 2012;4(4):297–318.

PubMed   PubMed Central   Google Scholar  

Kang YJ, Zhou Z. Zinc prevention and treatment of alcoholic liver disease. Mol Asp Med. 2005;26(4–5):391–404.

Zahr NM, Pfefferbaum A. Alcohol's effects on the brain: neuroimaging results in humans and animal models. Alcohol Res. 2017;38(2):183–06.

Arts N, Walvoort S, Kessels R. Korsakoff's syndrome: a critical review. Neuropsychiatr Dis Treat. 2017;13:2875–90.

Chakravorty S, Chaudhary NS, Brower KJ. Alcohol dependence and its relationship with insomnia and other sleep disorders. Alcohol Clin Exp Res. 2016;40(11):2271–82.

Angarita GA, Emadi N, Hodges S, Morgan PT. Sleep abnormalities associated with alcohol, cannabis, cocaine, and opiate use: a comprehensive review. Addict Sci Clin Pract. 2016;11(1):9.

•• Fama R. Introduction to the special section on alcohol: Review of cognitive, emotional, and neural deficits and recovery with sustained abstinence and treatment. Neuropsychology. 2019;33(6):757–9. This article is of high importance since it provides a comprehensive review regarding the alterations within various systems that are induced by chronic alcohol consumption as well as possible treatment strategies.

Hammoud N, Jimenez-Shahed J. Chronic neurologic effects of alcohol. Clin Liver Dis. 2019;23(1):141–55.

Grochowski C, Blicharska E, Bogucki J, Proch J, Mierzwinska A, Baj J, et al. Increased aluminum content in certain brain structures is correlated with higher silicon concentration in alcoholic use disorder. Molecules. 2019;24(9):1721.

CAS   PubMed Central   Google Scholar  

Schuckit MA. Alcohol-use disorders, Lancet. 2009;7;373(9662):492–501.

Monte SMDL, Kril JJ. Human alcohol-related neuropathology. Acta Neuropathol. 2013;127(1):71–90.

Martyn CN, Hughes RA. Epidemiology of peripheral neuropathy. J Neurol Neurosurg Psychiatry. 1997;62:310–8.

Hoffman E, Staff N, Kabat B, Robb J, St. Sauver J, Klein C. A population based assessment of polyneuropathy and associated disability. American Academy of Neurology, San Diego, CA. Neurology. 2015;84(16):1644–1651.

Mygland A, Monstad P. Chronic polyneuropathies in Vest-Agder, Norway. Eur J Neurol. 2001;8:157–65.

Staff NP, Windebank AJ. Peripheral neuropathy due to vitamin deficiency, toxins, and medications. CONTINUUM: Lifelong Learn Neurol. 2014;20:1293–306.

Google Scholar  

Katona I, Weis J. Diseases of the peripheral nerves. Handbook of Clinical Neurology Neuropathology. 2018;453–74.

Cherry CL, Wadley AL, Kamerman PR. Diagnosing and treating HIV-associated sensory neuropathy: a global perspective. Pain Manag. 2016;6(2):191–9.

Straube R, Müller G, Voit-Bak K, Tselmin S, Julius U, Schatz U, et al. Metabolic and non-metabolic peripheral neuropathy: is there a place for therapeutic apheresis? Horm Metab Res. 2019;51(12):779–84.

Newton SC, Marshall S. Effects of anodyne treatment on local blood flow and cutaneous sensation of the foot in a patient with peripheral neuropathy secondary to acromegaly. Physiother Theory Pract. 2019;:1–8.

Ungprasert P, Matteson EL. Neurosarcoidosis. Rheum Dis Clin N Am. 2017;43(4):593–606.

Boysson HD, Guillevin L. Polyarteritis Nodosa neurologic manifestations. Neurol Clin. 2019;37(2):345–57.

Aggarwal D. Prevalence of autonomic neuropathy in patients of rheumatoid arthritis and its correlation with disease severity. J Clin Diagn Res. 2017;11(4):OC09–13.

Suliga E, Kozieł D, Ciesla E, Rebak D, Głuszek-Osuch M, Głuszek S. Consumption of alcoholic beverages and the prevalence of metabolic syndrome and its components. Nutrients. 2019;11(11):2764.

Skórzyńska-Dziduszko KE, Kimber-Trojnar Ż, Patro-Małysza J, Olszewska A, Zaborowski T, Małecka-Massalska T. An interplay between obesity and inflammation in gestational diabetes mellitus. Curr Pharm Biotechnol. 2016;17:603–13.

Rudolph T, Farbu E. Hospital-referred polyneuropathies-causes, prevalences, clinical- and neurophysiological findings. Eur J Neurol. 2007;14:603–8.

Ho KWD, Rempe T, Jerath N, Antony A. Dorsal root ganglion stimulation as a potentially effective treatment for painful hereditary and idiopathic axonal polyneuropathy: a retrospective case series. Neuromodulation: Technol Neur Interf. 2019. https://doi.org/10.1111/ner.12924 .

Bechakra M, Nieuwenhoff MD, Rosmalen JV, Groeneveld GJ, Huygen FJ, Zeeuw CID, et al. Pain-related changes in cutaneous innervation of patients suffering from bortezomib-induced, diabetic or chronic idiopathic axonal polyneuropathy. Brain Res. 1730;2020:146621.

Hanewinckel R, Ikram MA, Van Doorn PA. Peripheral neuropathies. Handb Clin Neurol. 2016;138:263–82.

Munukutla S, Pan G, Deshpande M, Thandavarayan RA, Krishnamurthy P, Palaniyandi SS. Alcohol toxicity in diabetes and its complications: a double trouble? Alcohol Clin Exp Res. 2016;40(4):686–97.

Sadowski A, Houck RC. Alcoholic neuropathy. [Updated 2020 Apr 17]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2020 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK499856/ .

Chopra K, Tiwari V. Alcoholic neuropathy: possible mechanisms and future treatment possibilities. Br J Clin Pharmacol. 2012;73(3):348–62.

Noble JM, Weimer LH. Neurologic complications of alcoholism. Continuum (Minneap Minn). 2014;20:624–41.

Mukherjee S. Alcoholism and its effects on the central nervous system. Curr Neurovasc Res. 2013;10(3):256–62.

Mcintosh C, Chick J. Alcohol and the nervous system. J Neurol Neurosurg Psychiatry. 2004;75(3):16–21.

Monforte R, Estruch R, Valls-Solé J, Nicolás J, Villalta J, Urbano-Marquez A. Autonomic and peripheral neuropathies in patients with chronic alcoholism: a dose-related toxic effect of alcohol. Arch Neurol. 1995;52(1):45–51.

Behse F, Buchthal F. Alcoholic neuropathy: clinical, electrophysiological and biopsy findings. Ann Neurol. 1977;2:95–110.

Mellion M, Gilchrist JM, de la Monte S. Alcohol-related peripheral neuropathy: nutritional, toxic, or both? Muscle Nerve. 2011;43(3):309–16.

Baj J, Flieger W, Teresiński G, Buszewicz G, Sitarz E, Forma A, et al. Magnesium, calcium, potassium, sodium, phosphorus, selenium, zinc, and chromium levels in alcohol use disorder: a review. J Clin Med. 2020;9(6):1901.

Dolar-Szczasny J, Święch A, Flieger J, Tatarczak-Michalewska M, Niedzielski P, Proch J, et al. Levels of trace elements in the aqueous humor of cataract patients measured by the inductively coupled plasma optical emission spectrometry. Molecules. 2019;24:4127.

Lettsom JC. Some remarks on the effects of lignum quassiae amarae. Mem Med Soc Lond. 1787;1:128.

Jackson J. On a peculiar disease resulting from the use of ardent spirits. N Engl J Med Surg. 1822;11:351.

Manji H. Toxic neuropathy. Curr Opin Neurol. 2011;24(5):484–90.

Little AA, Albers JW. Clinical description of toxic neuropathies. In: Handbook of Clinical Neurology Occupational Neurology; 2015. p. 253–96.

Koike H, Mori K, Misu K, Hattori N, Ito H, Hirayama M, et al. Painful alcoholic polyneuropathy with predominant small- fiber loss and normal thiamine status. Neurology. 2001;56:1727–32.

Koike H, Iijima M, Sugiura M, Mori K, Hattori N, Ito H, et al. Alcoholic neuropathy is clinicopathologically distinct from thiamine-deficiency neuropathy. Ann Neurol. 2003;54:19–29.

Tackmann W, Minkenberg R, Strenge H. Correlation of electrophysiological and quantitative histological findings in the sural nerve of man. Studies on alcoholic neuropathy. J Neurol. 1977;216:289–99.

Tredici G, Minazzi M. Alcoholic neuropathy. An electronmicroscopic study. J Neurol Sci. 1975;25:333–46.

Laporte A, Richard H, Bonnaud E, Henry P, Vital A, Georges-cauld D. A spin label study of myelin fluidity with normal and pathological peripheral nerves. J Neurol Sci. 1979;43:345–6.

Zambelis T, Karandreas N, Tzavellas E, Kokotis P, Liappas J. Large and small fiber neuropathy in chronic alcohol-dependent subjects. J Peripher Nerv Syst. 2005;10:375–81.

Koike H, Sobue G. Alcoholic neuropathy. Curr Opin Neurol. 2006;19(5):481–6.

Vittadini G, Buonocore M, Colli G, Terzi M, Fonte R, Biscaldi G. Alcoholic polyneuropathy: a clinical and epidemiological study. Alcohol Alcohol. 2001;36:393–400.

Samantaray S, Knaryan VH, Patel KS, Mulholland PJ, Becker HC, Banik NL. Chronic intermittent ethanol induced axon and myelin degeneration is attenuated by calpain inhibition. Brain Res. 1622;2015:7–21.

Savoldi F. Polineuropatie. In: Ceroni M, Camana C, Franciotta DM, Giardini G, Farilla C, Soffiantini F, editors. Lezioni di Neurologia, Vol. 1. Pavia: C.U.S.L; 1995. p. 119–39.

Manzo L, Costa LG. Manifestations of neurotoxicity in occupational diseases. In: Costa LG, Manzo L, editors. Occupational Neurotoxicology, vol. 2. Boca Raton: CRC Press; 1998. p. 1–20.

Mellion ML, Silbermann E, Gilchrist JM, Machan JT, Leggio L, de la Monte S. Small-fiber degeneration in alcohol-related peripheral neuropathy. Alcohol Clin Exp Res. 2014;38(7):1965–72.

Zeng L, Alongkronrusmee D, van Rijn RM. An integrated perspective on diabetic, alcoholic, and drug-induced neuropathy, etiology, and treatment in the US. J Pain Res. 2017;20(10):219–28.

Ammendola A, Tata MR, Aurilio C, Ciccone G, Gemini D, Ammendola E, et al. Peripheral neuropathy in chronic alcoholism: a retrospective cross-sectional study in 76 subjects. Alcohol Alcohol. 2001;36(3):271–5.

Estruch R, Nicolás JM, Villegas E, Junqué A, Urbano-Márquez A. Relationship between ethanol-related diseases and nutritional status in chronically alcoholic men. Alcohol Alcohol. 1993;28:543–50.

Kaufmann A. Lead in wine. Food Addit Contam. 1998;15(4):437–45.

Ferdinandis TGHC, De Silva HJ. Illicit alcohol consumption and neuropathy-a preliminary study in Sri Lanka. Alcohol Alcohol. 2008;43:171–3.

Dina OA, Gear RW, Messing RO, Levine JD. Severity of alcohol-induced painful peripheral neuropathy in female rats: role of estrogen and protein kinase (A and C epsilon) Neuroscience. 2007;145:350–6.

Frank LL. Thiamin in clinical practice. J Parenter Enter Nutr. 2015;39(5):503–20.

Callaghan BC, Price RS, Chen KS, Feldman EL. The importance of rare subtypes in diagnosis and treatment of peripheral neuropathy. JAMA Neurol. 2015;72(12):1510.

Kucera P, Balaz M, Varsik P, Kurca E. Pathogenesis of alcoholic neuropathy. Bratisl Lek Listy. 2002;103(1):26–9.

Costardi JVV, Nampo RAT, Silva GL, Ribeiro MAF, Stella HJ, Stella MB, et al. A review on alcohol: from the central action mechanism to chemical dependency. Revista da Associaćčo Médica Brasileira. 2015;61(4):381–7.

Abrahao KP, Salinas AG, Lovinger DM. Alcohol and the brain: neuronal molecular targets, synapses, and circuits. Neuron. 2017;96(6):1223–38.

Montesinos J, Alfonso-Loeches S, Guerri C. Impact of the innate immune response in the actions of ethanol on the central nervous system. Alcohol Clin Exp Res. 2016;40(11):2260–70.

Thomson AD. Mechanisms of vitamin deficiency in chronic alcohol misusers and the development of the Wernicke-Korsakoff syndrome. Alcohol Alcohol. 2000;35(Supplement_1):2–1.

Daré BL, Lagente V, Gicquel T. Ethanol and its metabolites: update on toxicity, benefits, and focus on immunomodulatory effects. Drug Metab Rev. 2019;51(4):545–61.

Takeuchi M, Saito T. Cytotoxicity of acetaldehyde-derived advanced glycation end-products (AA-AGE) in alcoholic-induced neuronal degeneration. Alcohol Clin Exp Res. 2005;29(Suppl.):220S–4S.

Chen CH, Ferreira JC, Gross ER, Mochly-Rosen D. Targeting aldehyde dehydrogenase 2: new therapeutic opportunities. Physiol Rev. 2014;94(1):1–34.

Henriques JF, Portugal CC, Canedo T, Relvas JB, Summavielle T, Socodato R. Microglia and alcohol meet at the crossroads: microglia as critical modulators of alcohol neurotoxicity. Toxicol Lett. 2018;283:21–31.

Walter TJ, Vetreno RP, Crews FT. Alcohol and stress activation of microglia and neurons: brain regional effects. Alcohol Clin Exp Res. 2017;41(12):2066–81.

Crews FT, Lawrimore CJ, Walter TJ, Coleman LG. The role of neuroimmune signaling in alcoholism. Neuropharmacology. 2017;122:56–73.

Kumar J, Ismail Z, Hatta NH, Baharuddin N, Hapidin H, Bee Y-TG, et al. Alcohol addiction- metabotropic glutamate receptor subtype 5 and its ligands: how they all come together? Curr Drug Targets. 2018;19(8):907–15.

Rachdaoui N, Sarkar DK. Effects of alcohol on the endocrine system. Endocrinol Metab Clin N Am. 2013;42(3):593–615.

Ferrari L, Levine E, Levine J. Independent contributions of alcohol and stress axis hormones to painful peripheral neuropathy. Neuroscience. 2013;228:409–17.

Reddy VD, Padmavathi P, Bulle S, Hebbani AV, Marthadu SB, Venugopalacharyulu N, et al. Association between alcohol-induced oxidative stress and membrane properties in synaptosomes: a protective role of vitamin E. Neurotoxicol Teratol. 2017;63:60–5.

Hu Q, Wei J, Liu Y, Fei X, Hao Y, Pei D, et al. Discovery and identification of potential biomarkers for alcohol-induced oxidative stress based on cellular metabolomics. Biomedical Chromatography. 2017;31(7).

Roberto M, Patel RR, Bajo M. Ethanol and cytokines in the central nervous system. The Neuropharmacology of Alcohol Handbook of Experimental Pharmacology. 2017;397–431.

Leclercq S, Timary PD, Delzenne NM, Stärkel P. The link between inflammation, bugs, the intestine and the brain in alcohol dependence. Translat Psychiatry. 2017;7(2).

Nguyen VA, Le T, Tong M, Mellion M, Gilchrist J, Monte SMDL. Experimental alcohol-related peripheral neuropathy: role of insulin/IGF resistance. Nutrients. 2012;4(8):1042–57.

Nguyen VA, Le T, Tong M, Silbermann E, Gundogan F, Monte SMDL. Impaired insulin/IGF signaling in experimental alcohol-related myopathy. Nutrients. 2012;4(8):1058–75.

Manzo-Avalos S, Saavedra-Molina A. Cellular and mitochondrial effects of alcohol consumption. Int J Environ Res Public Health. 2010;7(12):4281–304.

Ludin HP, Tackman W. Polyneuropathien. Stuttgart: Georg Thieme Verlag; 1984. p. 250–5.

McLane JA. Decreased axonal transport in rat nerve following acute and chronic ethanol exposure. Alcohol. 1987;4:385–9.

Hellweg R, Baethge C, Hartung HD, Brückner MK, Arendt T. NGF level in the rat sciatic nerve is decreased after long-term consumption of ethanol. Neuroreport. 1996;7:777–80.

Malatova Z, Cizkova D. Effect of ethanol on axonal transport of cholinergic enzymes in rat sciatic nerve. Alcohol. 2002;26:115–20.

Mawdsley C, Mayer RF. Nerve conduction in alcoholic polyneuropathy. Brain. 1965;88:335–56.

Thiele B, StĆlberg E. Single fibre EMG findings in polyneuropathies of different aetiology. J Neurol Neurosurg Psychiatry. 1975;38:881–7.

Johnson RH, Eisenhofer G, Lambie DG. The effects of acute and chronic ingestion of ethanol on the autonomic nervous system. Drug Alcohol Depend. 1986;18(4):319–28.

Bednarik J, Vlckova-Moravcova E, Bursova S, Belobradkova J, Dusek L, Sommer C. Etiology of small-fiber neuropathy. J Peripher Nerv Syst. 2009;14(3):177–83.

Schuchardt V. Alcohol and the peripheral nervous system. Ther Umsch. 2000;57(4):196–9.

Watson JC, Dyck PJB. Peripheral neuropathy: a practical approach to diagnosis and symptom management. Mayo Clin Proc. 2015;90(7):940–51.

Hughes RAC. Peripheral neuropathy. BMJ. 2002;324(7335):466–469.

Terkelsen AJ, Karlsson P, Lauria G, Freeman R, Finnerup NB, Jensen TS. The diagnostic challenge of small fibre neuropathy: clinical presentations, evaluations, and causes. Lancet Neurol. 2017;16(11):934–44.

Barrell K, Smith AG. Peripheral neuropathy. Med Clin N Am. 2019;103(2):383–97.

Victor M. The effects of alcohol on the nervous system. In: Mendelson JH, Mello NK, editors. Medical diagnosis and treatment of alcoholism. New York: McGraw-Hill, Inc.; 1992. p. 201–62.

Watson JC. Central neuropathic pain: syndromes, pathophysiology, and treatments. Clin Pain Manag. 2008;374–87.

Cohen SP, Mao J. Neuropathic pain: mechanisms and their clinical implications. Bmj. 2014;348(feb05 6).

Zilliox LA. Neuropathic pain. CONTINUUM: Lifelong Learn Neurol. 2017;23(2):512–32.

Smith ESJ. Advances in understanding nociception and neuropathic pain. J Neurol. 2017;265(2):231–8.

Otis J, Macone A. Neuropathic pain. Semin Neurol. 2018;38(06):644–53.

Rajani P. Maiya, Robert O. Messing, Peripheral systems: neuropathy, Handbook of Clinical Neurology Volume 125, 2014, pp. 513–525.

Rösler M, Bellaire W, Hengesch G, Giannitsis D, Jarovici A. Genetic markers in alcoholism: no association with HLA. Arch Psychiatr Nervenkr. 1983;233:327–31.

Masaki T, Mochizuki H, Matsushita S, Yokoyama A, Kamakura K, Higuchi S. Association of aldehyde dehydrogenase-2 polymorphism with alcoholic polyneuropathy in humans. Neurosci Lett. 2004;363:288–90.

Erol A, Karpyak VM. Sex and gender-related differences in alcohol use and its consequences: contemporary knowledge and future research considerations. Drug Alcohol Depend. 2015;1(156):1–13.

Langenau B, Bergmann N, Oluwagbemigun. The association between alcohol consumption and serum metabolites and the modifying effect of smoking. Nutrients. 2019;11(10):2331.

Ashley MJ, Olin JS, le Riche WH, Kornaczewski A, Schmidt W, Rankin JG. Morbidity in alcoholics. Evidence for accelerated development of physical disease in women. Arch Intern Med. 1977;137(7):883–7.

Loft S, Olesen KL, Dossing M. Increased susceptibility to liver disease in relation to alcohol consumption in women. Scand J Gastroenterol. 1987;22:1251–6.

Saunders JB, Davis M, Williams R. Do women develop alcoholic liver disease more readily than men? Bmj. 1981;282(6270):1140–3.

Piano MR, Phillips SA. Alcoholic cardiomyopathy: pathophysiologic insights. Cardiovasc Toxicol. 2014;14(4):291–308.

Piano MR. Alcoholic cardiomyopathy. J Am Coll Cardiol. 2018;71(20):2303–5.

Prendergast MA. Do women possess a unique susceptibility to the neurotoxic effects of alcohol? J Am Med Wom Assoc. 2004;59:225–7.

Jacobson R. The contributions of sex and drinking history to the CT brain scan changes in alcoholics. Psychol Med. 1986;16:547–59.

Mann K, Batra A, Gunther A, Schroth G. Do women develop alcoholic brain damage more readily than men? Alcohol Clin Exp Res. 1992;16:1052–6.

Ammendola A, Gemini D, Iannaccone S, Argenzio F, Ciccone G, Ammendola E, et al. Gender and peripheral neuropathy in chronic alcoholism: a clinical-electroneurographic study. Alcohol Alcohol. July 2000;35(4):368–71.

Kishimoto R, Ogishi Y, Ueda M, Matsusaki M, Amako K, Goda K, et al. Gender-related differences in mouse hepatic ethanol metabolism. J Nutr Sci Vitaminol (Tokyo). 2002;48:216–24.

Victor M. Polyneuropathy due to nutritional deficiency and alcoholism. In: Dyck PJ, Thomas PK, Lambert EH, editors. Peripheral neuropathy. Philadelphia: W. B. Saunders; 1975. p. 1030–66.

Bosch EP, Pelham RW, Rasool CG, Chattorjee A, Lasch RW, Brown L. Animal models of alcoholic neuropathy: morphological, electrophysiological and biochemical findings. Muscle Nerve. 1979;2:133–44.

Pessione F, Gerchstein JL, Rueff B. Parental history of alcoholism: a risk factor for alcohol-related peripheral neuropathies. Alcohol Alcohol. 1995;30:749–54.

Huas D, Allemand H, Loiseau D, Pessione F, Rueff B. Prévalence du risque et des maladies liés ą l'alcool dans la clientŹle adulte du généraliste. Revue du Praticien: Med Gen. 1993;7:39–44.

Hamel J, Logigian EL. Acute nutritional axonal neuropathy. Muscle Nerve. 2017;57(1):33–9.

Chandrakumar A, Bhardwaj A, Jong GW’T. Review of thiamine deficiency disorders: Wernicke encephalopathy and Korsakoff psychosis. J Basic Clin Physiol Pharmacol. 2018;30(2):153–62.

Whitfield KC, Bourassa MW, Adamolekun B, Bergeron G, Bettendorff L, Brown KH, et al. Thiamine deficiency disorders: diagnosis, prevalence, and a roadmap for global control programs. Ann N Y Acad Sci. 2018;1430(1):3–43.

Shattuck GC. The relation of beri-beri to polyneuritis from other causes. Am J Trop Med. 1928;8:539–43.

Minot GR, Strauss MB, Cobb S. Alcoholic polyneuritis: dietary deficiency as a factor in its production. N Engl J Med. 1933;208:1244–9.

Strauss MB. The etiology of alcoholic polyneuritis. Am J Med Sci. 1935;189:378–82.

Victor M, Adams RD. On the etiology of alcoholic neurologic diaseases with special reference to the role of nutrition. Am J Clin Nutr. 1961;9:379–97.

Paladin F, Russo PG. The haematic thiamine level in the course of alcoholic neuropathy. Eur Neurol. 1987;26:129–33.

Hoyumpa AM. Mechanism of thiamine deficiency in chronic alcoholism. Am J Clin Nutr. 1980;3:2759–61.

Tomasulo PA, Katner RM, Iber FL. Impairment of thiamine absorption in alcoholism. Am J Clin Nutr. 1968;21:1340–4.

Leevy CM, Baker H, Ten Hove W, Frank O, Cherrick GR. B-complex vitamins in liver disease of the alcoholic. Am J Clin Nutr. 1965;16:339–46.

Agelink MW, Malessa R, Weisser U, Lemmer W, Zeit T, Majewski T, et al. Alcoholism, peripheral neuropathy (PNP) and cardiovascular autonomic neuropathy (CAN). J Neurol Sci. 1998;161:135–42.

Ciaula AD, Grattagliano I, Portincasa P. Chronic alcoholics retain dyspeptic symptoms, pan-enteric dysmotility, and autonomic neuropathy before and after abstinence. J Dig Dis. 2016;17(11):735–46.

Low PA, Walsh JC, Huang CY, McLeod JG. The sympathetic nervous system in alcoholic neuropathy. A clinical and pathological study. Brain. 1975;98(3):357–64.

Milovanovic B, Milinic N, Trifunovic D, Krotin M, Filipovic B, Bisenic V, et al. Autonomic dysfunction in alcoholic cirrhosis and its relation to sudden cardiac death risk predictors. Gen Physiol Biophys. 2009;28:251–61.

Johnson RH, Robinson BJ. Mortality in alcoholic with autonomic neuropathy. J Neurol Neurosurg Psychiatry. 1988;51:476–80.

Tan ET, Johnson RH, Lambie DG, Whiteside EA. Alcoholic vagal neuropathy: recovery following prolonged abstinence. J Neurol Neurosurg Psychiatry. 1984;47(12):1335–7.

Miralles R, Espadaler JM, Navarro X, Rubiés-Prat J. Autonomic neuropathy in chronic alcoholism: evaluation of cardiovascular, pupillary and sympathetic skin responses. Eur Neurol. 1995;35(5):287–92.

Appenzeller O, Ogin G. Myelinated fibres in human paravertebral sympathetic chain: white rami communicantes in alcoholic and diabetic patients. J Neurol Neurosurg Psychiatry. 1974;37(10):1155–61.

Nazliel B, Arikan Z, Irkeć C, Karakilić H. SSR abnormalities in chronic alcoholics. Addict Behav. 2007;32(6):1290–4.

Navarro X, Miralles R, Espadaler JM, Rubiés-Prat J. Comparison of sympathetic sudomotor and skin responses in alcoholic neuropathy. Muscle Nerve. 1993;16(4):404–7.

Kennedy WR, Navarro X. Sympathetic sudomotor func- tion in diabetic neuropathy. Arch Neurol. 1989;46:1182–6.

Di Ciaula A, Grattagliano I, Portincasa P. Chronic alcoholics retain dyspeptic symptoms, pan-enteric dysmotility, and autonomic neuropathy before and after abstinence. J Dig Dis. 2016;17(11):735–46.

Melgaard B, Somnier F. Cardiac neuropathy in chronic alcoholics. Clin Neurol Neurosurg. 1981;83(4):219–24.

Ravaglia S, Marchioni E, Costa A, Maurelli M, Moglia A. Erectile dysfunction as a sentinel symptom of cardiovascular autonomic neuropathy in heavy drinkers. J Peripher Nerv Syst. 2004;9(4):209–14.

Kupari M., Koskinen P. Alcohol, cardiac arrhythmias and sudden death. Novartis Found Symp 1998;216:68–79; discussion 79-85.

Yokoyama A, Ishii H, Takagi T, Hori S, Matsushita S, Onishi S, et al. Prolonged QT interval in alcoholic auto- nomic nervous dysfunction. Alcohol Clin Exp Res. 1992;16:1090–2.

Dillon JF, Plevris JN, Nolan J, Ewing DJ, Neilson JM, Bouchier IA, et al. Autonomic function in cirrhosis assessed by cardiovascular reflex tests and 24-hour heart rate variability. Am J Gastroenterol. 1994;89:1544–15447.

Muller S, Henriksen JH. Cardiovascular complications of cirrhosis. Gut. 2008;57:268–78.

Henriksen JH, MŅller S, Ring-Larsen H, Christensen NJ. The sympathetic nervous system in liver disease. J Hepatol. 1998;29:328–41.

Hendrickse MT, Triger DR. Peripheral and cardiovas- cular autonomic impairment in chronic liver disease: prevalence and relation to hepatic function. J Hepatol. 1992;16:177–83.

Fleckenstein JF, Frank S, Thuluvath PJ. Presence of autonomic neuropathy is a poor prognostic indicator in patients with advanced liver disease. Hepatology. 1996;23:471–5.

Liang J, Olsen RW. Alcohol use disorders and current pharmacological therapies: the role of GABAA receptors. Acta Pharmacol Sin. 2014;35(8):981–93.

Kaur M, Singh A, Kumar B, Singh SK, Bhatia A, Gulati M, et al. Protective effect of co-administration of curcumin and sildenafil in alcohol induced neuropathy in rats. Eur J Pharmacol. 2017;805:58–66.

Download references

Author information

Authors and affiliations.

Chair and Department of Forensic Medicine, Medical University of Lublin, 20-090, Lublin, Poland

Iga Dudek, Danuta Hajduga, Cezary Sieńko & Alicja Forma

Chair and Department of Human Anatomy, Medical University of Lublin, 20-090, Lublin, Poland

Chair and 1st Department of Psychiatry, Psychotherapy and Early Intervention, Medical University of Lublin, 20-439, Lublin, Poland

Elżbieta Sitarz

Department of Conservative Dentistry with Endodontics, Medical University of Lublin, 20-090, Lublin, Poland

Monika Sitarz

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Alicja Forma .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interests.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on The Pathobiology of Alcohol Consumption

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Dudek, I., Hajduga, D., Sieńko, C. et al. Alcohol-Induced Neuropathy in Chronic Alcoholism: Causes, Pathophysiology, Diagnosis, and Treatment Options. Curr Pathobiol Rep 8 , 87–97 (2020). https://doi.org/10.1007/s40139-020-00214-w

Download citation

Accepted : 12 October 2020

Published : 23 October 2020

Issue Date : December 2020

DOI : https://doi.org/10.1007/s40139-020-00214-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Peripheral nervous system
• Autonomic nervous system
• Alcohol metabolites
• Find a journal
• Publish with us
• Track your research

• Search Menu
• Sign in through your institution
• Author Guidelines
• Submission Site
• Open Access
• About Alcohol and Alcoholism
• About the Medical Council on Alcohol
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Contact the MCA
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Introduction, the heritage of the 19th century — a concept of addiction, temperance and degeneration, trying to eradicate alcoholism — different approaches, after prohibition — the creation of a modern disease concept, one or many types of alcoholism — genetic findings and potential subtypes, the core of alcohol dependence — tolerance and withdrawal or sensitization and reward craving, the disease concept revisited, new treatment options and future directions.

• < Previous

ONE HUNDRED YEARS OF ALCOHOLISM: THE TWENTIETH CENTURY

• Article contents
• Figures & tables
• Supplementary Data

Karl Mann, Derik Hermann, Andreas Heinz, ONE HUNDRED YEARS OF ALCOHOLISM: THE TWENTIETH CENTURY, Alcohol and Alcoholism , Volume 35, Issue 1, January 2000, Pages 10–15, https://doi.org/10.1093/alcalc/35.1.10

• Permissions Icon Permissions

The past 100 years witnessed the formation of a disease concept of alcoholism and a rapid increase in the knowledge of its aetiopathology and treatment options. In the first half of the century, public sanctions aimed at the abolition of alcoholism. In the United States, alcohol prohibition was revoked in the economic turmoil of the Great Depression. In Germany, proposed medical procedures to reduce the fertility of alcoholics had catastrophic consequences during the fascist dictatorship. A revived focus on alcoholics as patients with a right to medical treatment came out of self-organized groups, such as Alcoholics Anonymous. The current disease concept includes the psychosocial and neurobiological foundations and consequences of alcoholism. Neurobiological research points to the dispositional factor of monoaminergic dysfunction and indicates that neuroadaptation and sensitization may play a role in the maintenance of addictive behaviour. New treatment options include pharmacological approaches and indicate that behaviour and motivational therapy and the attendance of patient groups may equally reduce the relapse risk. The task of the future will be to apply scientific discoveries in the best interest of the patients and to support their efforts to be respected like subjects suffering from other diseases.

Alcoholism research and treatment underwent significant changes in the 20th century. Within the last 100 years, a disease concept was formed, which is now widely accepted, the psychosocial and neurobiological consequences of alcoholism have been characterized and treatment programmes have been established and continuously refined. First attempts were made to formulate models of the disposition and development of alcohol dependence that integrate both neurobiological and psychosocial findings. In this essay, we will highlight some of the cornerstones of our present understanding of alcoholism and reflect on some of the organizations and research traditions whose activities were crucial in the development of current concepts. Given the scope of the subject, this review will be both incomplete and subjective, and we will be unable to mention many subjects and institutions whose contributions to current alcoholism concepts were as important and fundamental as the ones we are able to discuss.

An uncontrollable, overwhelming and irresistible desire to consume alcohol was described by Benjamin Rush in 1784, and delirium tremens was independently described by both Pearson and Sutton in 1813 ( Kielhorn, 1988 ). Alcohol craving and withdrawal symptoms were integral parts of the concept of addiction and of the destructive effects of alcohol consumption promoted by the temperance movement in the 19th century ( Levine, 1984 ). In several European countries and in the United States, temperance movements were stimulated by the excessive consumption of liquor and other highly distilled alcoholic beverages, which was uninhibited by cultural traditions and appeared especially problematic among poor working class families during industrialization ( Levine, 1984 ; Henkel, 1998 ). There was, however, a fundamental difference to current concepts of alcoholism: the temperance movement suggested that anyone who consumed excessive amounts of alcohol would suffer from alcohol-related problems and did not suggest that alcoholism could affect certain specifically vulnerable individuals primarily ( Levine, 1984 ; Heather, 1992 ).

A focus on the individual was promoted by degenerationism, the theory that biological factors, toxic environmental influences or moral vices may trigger a cascade of social, moral and medical problems, which increase in each generation and will finally lead to the extinction of that family ( Bynum, 1984 ). The theory of degeneration was based on the pre-Darwinian concept that acquired character traits were passed on to the offspring and assumed that an array of different symptoms and diseases, such as impulsivity, alcoholism, strokes, dementia, microcephaly and epilepsy, were all expressions of one underlying pathology — degeneration ( Hermle, 1986 ).

Degenerationism thus offered a medical explanation for the social problems which were so visible at the end of the 19th century, and excessive alcohol consumption played a crucial role in the concept, as it was seen as a vice which also affects the next generation. In the early 20th century, the degeneration theory suffered from an increasing knowledge about modes of transmission of heritable traits, which pointed to the separate inheritance of different mental and physical diseases, and distinguished between heritable traits and toxic effects on the germ plasm or embryo, thus fundamentally questioning the postulate of the inheritance of acquired traits ( Hermle, 1986 ). However, degenerationism substantially contributed to the concerns about the specific alcohol-related problems of certain individuals.

In the first 30 years of the 20th century, degenerationism and the successors of the temperance movement sparked widespread political activities in the field of alcohol addiction. In the United States, the Anti-Saloon League followed the approach of the temperance movement and focused on the general problems of alcohol consumption. It succeeded in the implementation of alcohol prohibition, which was legally enforced from 1919 to 1933. Prohibition was initially successful in reducing alcohol intake; however, illegal alcohol consumption slowly increased in the late 1920s ( Tyrrell, 1997 ). Prohibition was finally abolished not so much because it failed to abolish alcohol intake, but because of shifting priorities in the Great Depression, when it was argued that liquor production would create jobs and that alcohol taxes might help to reduce income taxes ( Levine, 1984 ).

In Germany, the focus on the individual and their heritable vulnerability to alcohol addiction was imbued with alarmist concerns about the proliferation of the mentally ill, which was supposed to threaten the survival of the nation or ‘race.' Consequently, compulsory sterilization of ‘severe alcoholics' was already advocated by some medical doctors before it was legalized during the Nazi dictatorship. The number of alcohol-dependent patients murdered during the Nazi regime is unknown ( Henkel, 1998 ).

It was in the wake of the failure of prohibition that the current concept of alcoholism was formed, and the worldwide shock about the cruelty and inhumanity of Nazi politics may have promoted the modern disease concept with its focus on individual therapy and its emphasis that alcohol addiction is a disease just like any other physical or mental malady ( Levine, 1984 ; Henkel, 1998 ). A decisive point was the foundation of Alcoholics Anonymous (AA) in the late 1930s. Similar to previous temperance movements, Alcoholics Anonymous displayed a sympathetic and supporting attitude towards the addicted person, but unlike previous groups, AA was only for alcoholics and was not concerned with the general level of alcohol consumption in the population. In fact, the view that all it would take to create an alcohol addict would be his excessive alcohol consumption was no longer persuasive after the end of prohibition ( Levine, 1984 ). Likewise, the existence of alcohol tolerance and withdrawal was widely neglected in the 1930s and early 1940s, although delirium tremens due to alcohol withdrawal had clearly been described by Hare 1910 in the British Journal of Inebriety ( Edwards, 1990 ). Jellinek (1942) and the Yale Summer School on Alcohol Studies agreed with AA that alcoholism would be a disease with a progressive character and not a moral failing. The 1954 report of the World Health Organization (WHO) reflected this new focus on the individual and stated that ‘the personal make-up is the determining factor, but the pharmacological action (of alcohol) plays a significant role’ ( Edwards, 1990 ). However, it was not until the mid-1950s that convulsions and delirium tremens regained public attention as symptoms of alcohol withdrawal, largely due to the detailed reports of Victor and Adams (1953) and Isbell et al . (1955). In 1955, the WHO acknowledged that ‘very serious withdrawal symptoms’, such as convulsions or delirium, may follow the discontinuation of a prolonged period of very heavy alcohol intake ( Edwards, 1990 ). In his famous book on the disease concept of alcoholism, Jellinek (1960) referred repeatedly to the WHO reports and placed the adaptation of cell metabolism, tolerance and the withdrawal symptoms at the heart of his alcoholism concept, because they would ‘bring about ‘craving’ and a loss of control or inability to abstain.’; In his review of the perception of alcohol withdrawal symptoms in the scientific literature, Edwards (1990) noted that Jellinek's new focus on withdrawal symptoms was ‘in very sharp contrast to the earlier stance of the Yale school.’ It is possible that it was easier to rediscover the physical complications of alcohol withdrawal, because the new disease concept allowed attribution of these complications to an individual disposition rather than to some general effect that prolonged alcohol intake would have on every consumer.

In Germany, the modern disease concept of alcoholism was promoted by Feuerlein (1967, 1996) and others who emphasized that alcohol-dependent patients should have the same entitlement to medical treatment as other patients. It was not until 1968 that a German federal court formally confirmed full insurance coverage of alcoholism-related medical treatment costs, although alcoholism had already been considered a disease since 1915 ( Jellinek, 1960 ).

While it had long been observed that the familial risk for alcoholism is increased, it was only because of twin and adoption studies that a genetic contribution to alcoholism was confirmed ( Kaji, 1960 ; Cadoret and Gath, 1978 ). The observation that family members who share half of their genes are not more likely to develop alcoholism compared with family members who share only a quarter of their genes was incompatible with the simple genetic mechanism of inheritance ( Bleuler, 1955 ; Schuckit et al ., 1972 ).

Based on adoption studies, Cloninger et al . (1981) suggested the existence of two types of alcoholism, a mostly environmentally triggered, late-onset type 1 and a male-limited type 2 with a high genetic loading, legal problems and moderate alcohol consumption. The attempt to distinguish between two subtypes of alcoholism stimulated considerable research efforts. Many authors, however, questioned the dichotomy and argued that once patients suffering from comorbid antisocial personality disorder were excluded, the distinction between type 1 and type 2 alcoholics no longer offered clinical subtypes with distinct severity ( Irwin et al ., 1990 ). Instead, subgrouping was suggested to be based on age of onset, the presence of childhood risk factors such as hyperactivity, and severity of alcoholism ( Schuckit et al ., 1995 ; Johnson et al ., 1996 ). Alcoholism types may thus vary on a continuum of severity, rather than represent distinctly different disease entities ( Bucholz et al ., 1996 ). The genetic disposition to alcoholism may manifest in such unsuspicious forms as a low level of response to alcohol intake in subjects not yet accustomed to chronic alcohol intoxication ( Schuckit and Smith, 1996 ). A low level of alcohol response has recently been associated with an increased availability of raphe serotonin transporters and a low central serotonin turnover rate ( Heinz et al ., 1998 ; Schuckit et al ., 1999 ). A low serotonin turnover rate is a potential marker of early-onset alcoholism ( Fils-Aime et al ., 1996 ) and may be caused or aggravated by early social stress experiences ( Higley et al ., 1996 a , b ). These findings may help to link the clinical disposition to alcoholism with the growing literature on neurobiological alterations that precede and follow the manifestation of alcohol dependence.

The last three decades of the twentieth century witnessed a rapidly increasing knowledge of the neurobiological correlates of alcohol dependence. Edwards focused on the development of alcohol tolerance and the manifestation of withdrawal when chronic alcohol intake is terminated ( Edwards et al ., 1977 ). His groundbreaking work was used by the WHO in the International Classification of Diseases (ICD-9) and operationalized in the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R) as criteria of dependence ( Jurd, 1992 ).

Neurobiological research pointed to alcohol-induced stimulation of inhibitory GABAergic, and the inhibition of excitatory glutamatergic, neurotransmission ( Koob, 1992 ; Tsai et al ., 1995 ). To ensure homeostatic regulation, GABA A receptors may be down-regulated and, indeed, brain imaging studies observed reduced cortical GABA A receptors among alcoholics ( Abi-Dargham et al ., 1998 ). When the sedative effects of alcohol are suddenly withdrawn during early abstinence, reduced GABAergic inhibition and increased glutamatergic excitatory neurotransmission may manifest as anxiety, seizures and autonomic dysregulation ( Tsai et al ., 1995 ). Alcohol consumption may then be reinstated to reduce withdrawal, thus acting as a negative reinforcer ( Edwards, 1990 ). Associative learning may transform neutral emotional or environmental stimuli into alcohol-associated cues that induce a conditioned compensatory response to alcohol, ‘conditioned withdrawal’, and craving ( Ludwig et al ., 1974 ; McCusker and Brown, 1990 ). Acamprosate, a drug used to reduce craving in abstinent alcoholics, blocks glutamatergic N -methyl-d-aspartate receptors and may exert its therapeutic effects by decreasing conditioned withdrawal ( Verheul et al ., 1999 ).

However, cue-induced craving is only moderately associated with the severity of physical reactions such as changes in heart rate and skin conductance to cue presentation ( Niaura et al ., 1988 ). A secondary, potentially independent pathway has been suggested that may induce alcohol craving due to the mood-enhancing, positive-reinforcing effects of alcohol consumption ( Wise, 1988 ; Koob and Le Moal, 1997 ). This pathway seems to involve the so-called dopaminergic reward system and its opioidergic stimulation via μ-opiate receptors ( Spanagel et al ., 1992 ; Di Chiara, 1995 ). The role of the dopaminergic system may lie in the direction of attention towards reward-indicating stimuli, rather than in the induction of euphoria or positive mood states ( Schultz et al ., 1995 ; Berridge and Robinson, 1998), which are associated with alcohol consumption and may be mediated by opioidergic neurotransmission ( Volpicelli et al ., 1995 ). Stimulus-dependent dopamine release may be specifically vulnerable to sensitization, thus mediating a stronger behavioural response upon re-exposure to the drug-associated cue ( Robinson and Berridge, 1993 ). These observations may have important implications for our understanding of the ‘addiction memory’ and for therapeutic strategies: systematic cue exposure and response prevention might help to extinguish conditioned craving, although therapeutic study results so far are ambiguous ( O'Brien et al ., 1998 ), and naltrexone medication may prevent cue-induced reinstatement of alcohol craving (Katner, 1999).

The focus on cue-induced craving and the underlying learning mechanisms ( Glautier et al ., 1994 ; Carter and Tiffany, 1999 ) has revived the discussion on whether the disease concept of alcoholism should be replaced by a social learning perspective ( Heather, 1992 ). What was not being denied are the organic consequences of chronic alcohol intake, such as brain atrophy ( Mann et al ., 1995 ) or neuroadaptive processes such as a reduction of central dopamine D2 receptors ( Volkow et al ., 1996 ). Rather, it is argued that cigarette smoking similarly causes physical dependence or neuroadaptation without therefore being considered a disease. The disease concept may label patients and promote apathy associated with the ‘sick role’ ( Heather, 1992 ).

A response to these concerns rests on several arguments. Firstly, it is argued that the sick role per se does not stigmatize patients and that the stigma associated with specific diseases such as ‘consumption’ never promoted similar attempts to deny its disease status, and instead promoted relabelling as tuberculosis ( Keller, 1976 ). Secondly, it is argued that a state may be called a disease even in the absence of abnormalities of anatomic structure. A case in point may be essential hypertension, which is commonly understood as a disease, although the aetiology and pathogenesis are currently unknown. Keller (1976) suggested calling alcoholism a disease, because its behavioural manifestations represent a disablement. This argument resembles the concept of a mental disorder given by the American Psychiatric Association (1987), which argued that a mental disorder is characterized by present distress, disability, or a significantly increased risk of suffering death, pain, disability, or an important loss of freedom. Culver and Gert (1982) added that the state must exist ‘in the absence of a distinct (external) sustaining cause’, so that distress due to political oppression may be distinguished from a mental malady. Applying this definition to cigarette smoking indicates that smoking should be considered a mental disorder, as it is associated with the increased risk of suffering death, and it would thus be considered a malady or disease by Culver and Gert (1982). This brings up the question of whether fast driving then must be called a disease, as it increases the risk of dying in a traffic accident. It could be answered that the association between fast driving and traffic accidents is rather low and that the habit of driving fast might be terminated without experiencing the distress associated with drug withdrawal symptoms.

As aloof as these discussions sometimes appear, they have important implications for the treatment of alcoholism. In 1956, a Board of the American Medical Association (AMA) passed a resolution that urged hospitals to admit patients with alcoholism equally with patients treated for other diseases. This act is usually seen as the moment when alcoholism was formally recognized as a disease in the United States; however, alcoholism was already listed as a disease in 1933 in the Standard Classified Nomenclature of Diseases, which was approved by the AMA and the American Psychiatric Association ( Keller, 1976 ). Yet the 1956 resolution highlights the important legal issues that are associated with the disease status of alcoholism, not least being the question of whether treatment costs should be covered by health insurances ( Jurd, 1992 ). Research in the field of costs and benefits of alcoholism therapy supported the demand to treat alcoholism within the medical system ( Holder, 1998 ).

The last decade of the 20th century witnessed substantial progress in treatment options and strategies. Of special importance is the general practitioner, who sees the vast majority of patients with alcohol problems, while fewer than 10% actually enter specialized treatment programmes ( Wienberg, 1992 ). Brief interventions in primary health care institutions are very often effective in reducing alcohol consumption ( Bien et al ., 1993 ). For those patients who need more extensive treatment, primary health care services have a gatekeeper function. Motivational enhancement in primary health care ( Miller and Rollnick, 1991 ) can effectively increase the participation in treatment programmes and was associated with reduced subsequent relapse rates ( Bien et al ., 1993 ). Specialized treatment programmes were evaluated in project MATCH. Project MATCH examined three treatment options, cognitive behaviour therapy, twelve-step facilitation according to the AA programme and motivational enhancement therapy, and found them similarly effective ( Project Match Research Group, 1998 ). As disappointing as this result may be for the discovery of prospective indicators of treatment response, it shows that the major treatment options available to alcoholics worldwide work successfully and that the eclectic combination of behaviour therapy and the attendance of self-help groups may indeed combine two powerful treatment strategies. With naltrexone and acamprosate, two pharmaceuticals are available that successfully reduce the relapse risk during early abstinence ( O'Malley et al ., 1996 ; Sass et al ., 1996 ). However, even with an accompanying medical treatment, most alcoholics relapse. The goal of the future will therefore be to describe subgroups of patients that may respond positively to specific medications. As acamprosate and naltrexone affect different neurotransmitter systems, neurobiological screening of alcoholics may help to discover predictors of treatment response. Preliminary results indicate that sleep disorders, EEG activity and delayed recovery of dopamine receptor sensitivity during early abstinence are associated with the relapse risk and may help to identify patients who require specific treatment strategies ( Bauer, 1994 ; Heinz et al ., 1996 ; Brower et al ., 1998 ; Winterer et al ., 1998 ).

Basic research has profoundly helped to understand alcohol effects at the level of signal transduction. We now know that drugs affect neurotransmitter release, receptor sensitivity, post-synaptic second-messenger mechanisms and, perhaps most importantly, gene expression ( Koob, 1992 ; Nestler, 1994 ). These observations indicate that human fate is not passively determined by the genetic constitution, but rather that biological and ultimately environmental stimuli regulate gene expression. Increasing knowledge of the molecular mechanisms of dependence may enable us to target these pathological conditions more specifically than we are able today.

Finally, the history of the last 100 years warns us that ‘ethics are not an option,’ as Edwards stated in a 1999 conference at the Central Institute of Mental Health, Mannheim. That alcoholism had been considered a disease in Germany since 1915 ( Jellinek, 1960 ) did not prevent the dehumanizing treatment of patients with alcohol dependence during the Nazi era. It is an integral part of the professional mission to assist patients in their effort to be treated equally inside and outside of medical therapy. Our increasing knowledge about the disposition towards alcohol dependence and a high relapse risk can help to identify patients with demands for special therapeutic efforts, it should never be used to stigmatize these subjects. To monitor the consequences of our research is part of the professional duty.

Author to whom correspondence should be addressed.

Abi-Dargham, A., Krystal, J. H., Anjivel, S., Scanley, B. E., Zoghbi, S., Baldwin, R. M., Rajeevan, N., Ellis, S., Petrakis, I. L., Seibyl, J. P., Charney, D. S., Laruelle, M. and Innis, R. B. ( 1998 ) Alterations of benzodiazepine receptors in type II alcoholic subjects measured with SPECT and [123]Iomezanil. American Journal of Psychiatry 155 , 1550 –1555.

American Psychiatric Association (1987) Diagnostic and Statistical Manual of Mental Disorders , 3rd edn, revised. American Psychiatric Association, Washington, DC.

Bauer, L. O. ( 1994 ) Electroencephalographic and autonomic predictors of relapse in alcohol-dependent patients. Alcoholism: Clinical and Experimental Research 18 , 755 –760.

Berridge, K. C. and Robinson, T. E. ( 1988 ) What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Research Reviews 28 , 309 –369.

Bien, T. H., Miller, W. R. and Tonigan, S. J. ( 1993 ) Brief interventions for alcohol problems: a review. Addiction 88 , 315 –336.

Bleuler, M. (1955) Familial and personal background of chronic alcoholics. In Etiology of Chronic Alcoholism , Dietholm, O. ed., pp. 110–166. Charles C. Thomas, Springfield, IL.

Brower, K. J., Aldrich, M. S. and Hall, J. M. ( 1998 ) Polysomnographic and subjective sleep predictors of alcoholic relapse. Alcoholism: Clinical and Experimental Research 22 , 1864 –1871.

Bucholz, K. K., Heath, A. C., Reich, T., Hesselbrock, V. M., Kramer, J. R., Nurnberger, J. I., Jr. and Schuckit, M. A. ( 1996 ) Can we subtype alcoholism? A latent class analysis of data from relatives of alcoholics in a multicenter family study of alcoholism. Alcoholism: Clinical and Experimental Research 20 , 1462 –1471.

Bynum, W. F. ( 1984 ) Alcoholism and degeneration in 19th century European medicine and psychiatry. British Journal of Addiction 79 , 59 –70.

Cadoret, R. J. and Gath, A. ( 1978 ) Inheritance of alcoholism in adoptees. British Journal of Psychiatry 132 , 252 –258.

Carter, B. L. and Tiffany, S. T. ( 1999 ) Meta-analysis of cue-reactivity in addiction research. Addiction 94 , 327 –340.

Cloninger, C. R., Bohman, M. and Sigvardsson, S. ( 1981 ) Inheritance of alcohol abuse. Cross-fostering analysis of adopted men. Archives of General Psychiatry 38 , 861 –868.

Culver, C. and Gert, B. (1982) Philosophy in Medicine . Oxford University Press, Oxford.

Di Chiara, G. ( 1995 ) The role of dopamine in drug abuse viewed from the perspective of its role in motivation. Drug and Alcohol Dependence 38 , 95 –137.

Edwards, G. ( 1990 ) Withdrawal symptoms and alcohol dependence: fruitful mysteries. British Journal of Addiction 85 , 447 –461.

Edwards, G., Gross, M. M., Keller, M. et al . (1977) Alcohol-related Disabilities . WHO Offset Publication No. 32. World Health Organization, Geneva.

Feuerlein, W. ( 1967 ) Der Alkoholismus in sozialpsychiatrischer Sicht. Medizinische Klinik 62 , 922 –926.

Feuerlein, W. ( 1996 ) Alkoholismus als Krankheit. Herz 21 , 213 –216.

Fils-Aime, M. L., Eckhardt, M. J., George, D. T., Brown, G. L., Mefford, I. and Linnoila, M. ( 1996 ) Early-onset alcoholics have lower cerebrospinal fluid 5-hydroxyindolacetic acid levels than late-onset alcoholics. Archives of General Psychiatry 53 , 211 –216.

Glautier, S., Drummond, C. and Remington, B. ( 1994 ) Alcohol as an unconditioned stimulus in human classical conditioning. Psychopharmacology (Berlin) 116 , 360 –368.

Heather, N. ( 1992 ) Why alcoholism is not a disease. The Medical Journal of Australia 156 , 212 –215.

Heinz, A., Dufeu, P., Kuhn, S., Dettling, M., Gräf, K. J., Kürten, I., Rommelspacher, H. and Schmidt, L. G. ( 1996 ) Psychopathological and behavioral correlates of dopaminergic sensitivity in alcohol-dependent patients. Archives of General Psychiatry 53 , 1123 –1128.

Heinz, A., Higley, J. D., Gorey, J. G., Saunders, R. C., Jones, D. W., Hommer, D., Zajicek, K., Suomi, S. J., Lesch, K. P., Weinberger, D. R. and Linnoila, M. ( 1998 ) In vivo association between alcohol intoxication, aggression and serotonin transporter availability in non-human primates. American Journal of Psychiatry 155 , 1023 –1028.

Henkel, D. (1998) ‘Die Trunksucht ist die Mutter der Armut.’ In Sucht und Armut, Henkel, D. and Vogt, L. eds, pp. 13–79. Leske and Budrich, Opladen.

Hermle, L. ( 1986 ) Die Degenerationslehre in der Psychiatrie. Fortschritte der Neurologie und Psychiatrie 54 , 69 –79.

Higley, J. D., Suomi, S. J. and Linnoila, M. ( 1996 ) A non-human primate model of type II excessive alcohol consumption? Part 1. Low cerebrospinal fluid 5-hydroxyindoleacetic acid concentrations and diminished social competence correlate with excessive alcohol consumption. Alcoholism: Clinical and Experimental Research 20 , 629 –642.

Higley, J. D., Suomi, S. J. and Linnoila, M. ( 1996 ) A non-human primate model of type II alcoholism? Part 2. Diminished social competence and excessive aggression correlates with low cerebrospinal fluid 5-hydroxyindoleacetic acid concentrations. Alcoholism: Clinical and Experimental Research 20 , 643 –650.

Holder, H. D. ( 1998 ) The cost offsets of alcoholism treatment. Recent Developments in Alcoholism 14 , 361 –374.

Irwin, M., Schuckit, M. and Smith, T. L. ( 1990 ) Clinical importance of age of onset in type 1 and type 2 primary alcoholics. Archives of General Psychiatry 47 , 320 –324.

Isbell, H., Fraser, H. F., Wikler, A., Belleville, R. E. and Eiserman, A. J. ( 1955 ) An experimental study of the etiology of ‘rum fits' and delirium tremens. Quarterly Journal of Studies on Alcohol 16 , 1 –33.

Jellinek, E. M. (1942) Alcohol Addiction and Chronic Alcoholism . Yale University Press, New Haven.

Jellinek, E. M. (1960) The Disease Concept of Alcoholism . Hillhouse , New Brunswick.

Johnson, E. O., van den Bree, M. B. M. and Pickens, R. W. ( 1996 ) Subtypes of alcohol-dependent men: a typology based on relative genetic and environmental loading. Alcoholism: Clinical and Experimental Research 20 , 1472 –1480.

Jurd, S. M. ( 1992 ) Why alcoholism is a disease. The Medical Journal of Australia 156 , 215 –217.

Kaji, L. (1960) Alcoholism in Twins: Studies on the Etiology and Sequels of Abuse of Alcohol . Almqvist and Wiksell, Stockholm.

Katner, S. N., Magalong, J. G. and Weiss, F. ( 1999 ) Reinstatement of alcohol-seeking behavior by drug-associated discriminative stimuli after prolonged extinction in the rat. Neuropsychopharmacology 20 , 471 –479.

Keller, M. ( 1976 ) The disease concept of alcoholism revisited. Journal of Studies on Alcohol 37 , 1694 –1717.

Kielhorn, F. W. ( 1988 ) Zur Geschichte des Alkoholismus: Pearson, Sutton und das Delirium tremens. Suchtgefahren 34 , 111 –114.

Koob, G. F. ( 1992 ) Drugs of abuse: anatomy, pharmacology and function of reward pathways. Trends in Pharmacological Sciences 13 , 177 –184.

Koob, G. F. and Le Moal, M. ( 1997 ) Drug abuse: hedonic homeostatic dysregulation. Science 278 , 52 –58.

Levine, H. G. ( 1984 ) The alcohol problem in America: from temperance to alcoholism. British Journal of Addiction 79 , 109 –119.

Ludwig, A. M., Wikler, A. and Stark, L. H. ( 1974 ) The first drink: psychobiological aspects of craving. Archives of General Psychiatry 30 , 539 –547.

Mann, K., Mundle, G., Strayle, M. and Wakat, P. ( 1995 ) Neuroimaging in alcoholism: CT and MRI results and clinical correlates. Journal of Neural Transmission 99 , 145 –155.

McCusker, C. G. and Brown, K. ( 1990 ) Alcohol-predictive cues enhance tolerance and precipitate ‘craving’ for alcohol in social drinkers. Journal of Studies on Alcoholism 51 , 494 –499.

Miller, W. R. and Rollnick, S. (1991) Motivational Interviewing: Preparing People to Change Addictive Behavior . Guilford Press, New York.

Nestler, E. J. ( 1994 ) Molecular neurobiology of drug addiction. Neuropsychopharmacology 11 , 77 –87.

Niaura, R. S., Rohsenow, D. J., Binkoff, J. A., Monti, P. M., Pedrazza, M. and Abrams, D. B. ( 1988 ) Relevance of cue reactivity to understanding alcohol and smoking relapse. Journal of Abnormal Psychology 97 , 133 –152.

O'Brien, C., Childress, A. R., Ehrman, R. and Robbins, S. J. ( 1998 ) Conditioning factors in drug abuse: can they explain compulsion? Journal of Psychopharmacology 12 , 15 –22.

O'Malley, S. S., Jaffe, A. J., Chang, G., Rode, S., Schottenfeld, R., Meyer, R. E. and Rounsaville, B. ( 1996 ) Six-month follow-up of naltrexone and psychotherapy for alcohol dependence. Archives of General Psychiatry 53 , 217 –224.

Project MATCH Research Group ( 1998 ) Matching alcoholism treatment to client heterogeneity: project MATCH three-year drinking outcomes. Alcoholism: Clinical and Experimental Research 22 , 1300 –1311.

Robinson, T. E. and Berridge, K. C. ( 1993 ) The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Research Reviews 18 , 247 –291.

Sass, H., Soyka, M., Mann, K. and Zieglgänsberger, W. ( 1996 ) Relapse prevention by acamprosate: results from a placebo-controlled study on alcohol dependence. Archives of General Psychiatry 53 , 673 –680.

Schuckit, M. A. and Smith, T. L. ( 1996 ) An 8-year follow-up of 450 sons of alcoholics and control subjects. Archives of General Psychiatry 45 , 211 –216.

Schuckit, M. A., Goodwin, D. A. and Winokur, G. ( 1972 ) A study of alcoholism in half-siblings. American Journal of Psychiatry 129 , 1132 –1136.

Schuckit, M. A., Tipp, J. E., Smith, T. L., Shapiro, E., Hesselbrock, V. M., Buchholz, K. K., Reich, T. and Nurnberger, J. I. ( 1995 ) An evaluation of type A and B alcoholics. Addiction 90 , 1189 –1203.

Schuckit, M. A., Mazzanti, C., Smith, T. L., Ahmed, U., Radel, M., Iwata, N. and Goldman, D. ( 1999 ) Selective genotyping for the role of 5-HT 2A , 5-HT 2C , and GABAα 6 receptors and the serotonin transporter in the level of response to alcohol: a pilot study. Biological Psychiatry 45 , 647 –651.

Schultz, W., Dayan, P. and Montague, P. R. ( 1995 ) A neural substrate of prediction and reward. Science 275 , 1593 –1599.

Spanagel, R., Herz, A. and Shippenberg, T. S. ( 1992 ) Opposing tonically active endogeneous opioid systems modulate the mesolimbic dopaminergic pathway. Proceedings of the National Academy of Sciences of the USA 89 , 2046 –2050.

Tsai, G., Gastfriend, D. R. and Coyle, J. T. ( 1995 ) The glutamatergic basis of human alcoholism. American Journal of Psychiatry 152 , 332 –340.

Tyrrell, I. ( 1997 ) The US prohibition experiment: myths, history and implications. Addiction 92 , 1405 –1409.

Verheul, R., Van den Brink, W. and Geerlings, P. ( 1999 ) A three-pathway psychobiological model of craving for alcohol. Alcohol and Alcoholism 34 , 197 –222.

Victor, M. and Adams, R. E. ( 1953 ) The effect of alcohol on the nervous system. Research Publications of the Association for Research on Nervous and Mental Disease 32 , 526 –573.

Volkow, N. D., Wang, G. J., Fowler, J. S., Logan, J., Hitzemann, R., Ding, Y. S., Pappas, N., Shea, C. and Piscani, K. ( 1996 ) Decreases in dopamine receptors but not in dopamine transporters in alcoholics. Alcoholism: Clinical and Experimental Research 20 , 1594 –1598.

Volpicelli, J. R., Watson, N. T., King, A. C., Sherman, C. E. and O'Brien, C. P. ( 1995 ) Effect of naltrexone on alcohol ‘high’ in alcoholics. American Journal of Psychiatry 152 , 613 –615.

Wienberg, G. (1992) Die vergessene Mehrheit . Zur Realität der Versorgung alkohol- und medikamentenabhängiger Patienten . Psychiatrie Verlag, Berlin.

Winterer, G., Klöppel, B., Heinz, A., Schmidt, L. G., Frick, K. and Herrmann, W. M. ( 1998 ) Quantitative EEG (QEEG) analysed with artificial neural networks predicts relapse in patients with chronic alcoholism and points to a frontally pronounced disturbance. Psychiatry Research 78 , 101 –113.

Wise, R. A. ( 1988 ) The neurobiology of craving: implications for the understanding and treatment of addiction. Journal of Abnormal Psychology 97 , 118 –132.

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1464-3502
• Copyright © 2024 Medical Council on Alcohol and Oxford University Press
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Alcohol Res
• v.40(1); 2019

Alcohol Use Disorder and Depressive Disorders

Alcohol use disorder (AUD) and depressive disorders are among the most prevalent psychiatric disorders and co-occur more often than expected by chance. The aim of this review is to characterize the prevalence, course, and treatment of co-occurring AUD and depressive disorders. Studies have indicated that the co-occurrence of AUD and depressive disorders is associated with greater severity and worse prognosis for both disorders. Both pharmacologic and behavioral treatments have demonstrated efficacy for this population. However, treatment response is somewhat modest, particularly for drinking outcomes, highlighting the importance of further research on the etiology and treatment of co-occurring AUD and depressive disorders. Key future directions include studies to understand the heterogeneity of both AUD and depressive disorders, research on novel treatment approaches to enhance outcomes, and better understanding of sex and gender differences.

Introduction

Psychiatric disorders, such as anxiety and mood disorders, commonly co-occur with alcohol use disorder (AUD). Depressive disorders are the most common psychiatric disorders among people with AUD. 1 The co-occurrence of these disorders is associated with greater severity and worse prognosis than either disorder alone, 2 , 3 including a heightened risk for suicidal behavior. 4 This review provides an overview of the literature on the co-occurrence of AUD and depressive disorders and includes data on prevalence, course, and treatment outcomes. High-priority future research directions are suggested to better understand the co-occurrence of these conditions and to improve treatments.

Much of the published literature on the co-occurrence of AUD and depressive disorders uses the classifications from the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). 5 Where possible, this review specifies if the cited literature used the DSM-IV classifications for diagnosis (alcohol abuse or alcohol dependence) or the fifth edition (DSM-5) classification for diagnosis (AUD). 6 If a study reported results based on the combined DSM-IV diagnoses (i.e., included participants with alcohol abuse and participants with alcohol dependence), this review refers to the diagnosis as “DSM-IV AUD.” Although DSM-IV and DSM-5 AUD share many symptoms, the diagnoses are defined differently. In the DSM-5, AUD requires at least two symptoms, whereas DSM-IV alcohol abuse required only one symptom. Also, from DSM-IV to DSM-5, modifications were made to the symptoms that were included as diagnostic criteria. For example, the criterion of legal problems related to alcohol was removed, and the criterion of alcohol craving was added. Thus, where possible, this review identifies which version of the DSM was used in a study.

Overview of Depressive Disorders

Depressive disorders are complex and heterogeneous syndromes. These disorders are characterized by disrupted mood (e.g., low, numb, or irritable), along with an array of cognitive (e.g., feelings of worthlessness and difficulty concentrating) and physical (e.g., fatigue and lack of energy) symptoms. The DSM-5 includes seven distinct disorders under the category of depressive disorders, including major depressive disorder, persistent depressive disorder (dysthymia), premenstrual dysphoric disorder, substance/medication-induced depressive disorder, disruptive mood dysregulation disorder, other specified depressive disorder, and unspecified depressive disorder. 6 This review focuses on major depressive disorder, dysthymia, and substance-induced depressive disorder, which are the depressive disorders that have been studied most often in both the general population and among people with AUD.

Major depressive disorder is characterized by the presence of five or more symptoms that are present for at least 2 weeks. One of these symptoms must include depressed mood or anhedonia (significant loss of interest or pleasure in activities). Other symptoms are disturbances in appetite, sleep, psychomotor behaviors, energy, concentration, and decision-making; beliefs about worthlessness or guilt; and thoughts of suicide or suicide attempt. Dysthymia is more chronic than major depressive disorder, yet it is typically a milder disorder, characterized by at least 2 years of depressed mood and at least two additional symptoms, including dysfunction in appetite, sleep, energy, self-esteem, concentration, or decision-making, and feelings of hopelessness. Alcohol-induced depressive disorder refers to a depressive-like syndrome (characterized by depressed mood or anhedonia) that occurs only during and shortly after alcohol intoxication or withdrawal, remits after 3 to 4 weeks of alcohol abstinence, and is associated with significant distress and impairment.

Prevalence of depressive disorders and AUD

Major depressive disorder is the most common psychiatric disorder, affecting an estimated 10% to 15% of people in their lifetime, according to U.S. and international population-based surveys. 7 , 8 Dysthymia is less common than major depressive disorder, affecting less than 2% of people in their lifetime. 9

Likewise, major depressive disorder is the most common co-occurring psychiatric disorder among people with DSM-IV AUD. 1 Considering the prevalence of major depressive disorder and AUD in the general population, co-occurrence of these disorders is more frequent than can be expected based on chance, with odds ratios indicating a small effect size. Specifically, people with DSM-IV AUD, relative to those with no AUD, are 2.3 times more likely to also have major depressive disorder in the previous year, and they are 1.7 times more likely to have dysthymia in the previous year. 1 The prevalence of depressive disorders is greater among those with alcohol dependence, as compared to those diagnosed with alcohol abuse, with high prevalence of depression reported among treatment-seekers. People with DSM-IV alcohol dependence are 3.7 times more likely to also have major depressive disorder, and 2.8 times more likely to have dysthymia, in the previous year. Among people in treatment for DSM-IV AUD, almost 33% met criteria for major depressive disorder in the past year, and 11% met criteria for dysthymia. However, major depressive disorder is the most common co-occurring disorder among people who have AUD, partly because it is among the most common disorders in the general population.

Data from large population-based surveys suggest that the prevalence of alcohol-induced depression is small. For example, among people who also had a substance use disorder, less than 1% of their depressive disorders were classified as substance induced. 1 Studies have found a much higher prevalence of substance-induced depressive disorder among patients with AUD who were in treatment settings, when compared with studies of general population samples. One study reported that more than 25% of patients experienced a substance-induced depressive episode in their lifetime. 10 Nonetheless, studies have found that many cases initially diagnosed as substance-induced depression were later reclassified as independent depression (i.e., not substance induced) because the condition persisted after a period of abstinence. 11

Disproportionately affected populations

Several groups are disproportionately affected by co-occurring AUD and depressive disorders. For example, women are 1.5 to 2 times more likely in their lifetime to experience major depressive disorder than men. 12 Likewise, women with DSM-IV AUD are more likely than men with DSM-IV AUD to meet the criteria for major depressive disorder or dysthymia. 13 , 14 Sex differences are not limited to prevalence; they also are observed in the course of depressive disorders. A longitudinal study of young adults found that depression predicted alcohol problems in women but not in men. 15 This finding is consistent with reports from retrospective studies that examined relative age of onset for AUD and depressive disorders, in which women were more likely to experience depression before AUD, whereas men were more likely to develop AUD before depression. 16 , 17

Although race and ethnicity are clearly factors in the risk for developing AUD or depressive disorders, studies examining racial and ethnic differences in the prevalence of co-occurring AUD and depressive disorders have been hampered by small sample sizes, which make group comparisons difficult. 18 Nonetheless, data strongly support significant disparities in health care for co-occurring AUD and depressive disorders among racial and ethnic minority groups. The likelihood of receiving AUD care is similar across racial and ethnic groups, but people who identify as Black or Latino are significantly less likely than people who identify as White to receive services for mood and anxiety disorders or to receive integrated mental health and substance use disorder care. 19 , 20

Pathways to Co-Occurrence

Several potential developmental pathways have been proposed to explain the high rate of co-occurring AUD and depressive disorders, including: (1) depressive disorders increase risk for AUD, (2) AUD increases risk for depressive disorders, and (3) both conditions share pathophysiology or have common risk factors. Although evidence supports all three of these pathways, much research is still needed to understand the development of co-occurrence.

Much of the research on the development of co-occurring AUD and depressive disorders has relied on retrospective and longitudinal studies that examine the age of onset of the disorders. These studies have yielded mixed evidence. Some studies indicate that depressive disorders typically precede the onset of AUD, 21 others suggest that AUD generally precedes depressive disorders, 22 and still others report that the order of onset varies by gender (with women more likely to have earlier onset of depression than men). 17

Literature on the onset of substance use among youth and young adults has indicated that internalizing symptoms (e.g., depression and anxiety) generally protect against the onset of alcohol misuse in adolescents. 23 However, the association between internalizing symptoms and risk for alcohol use and misuse is influenced by key moderating factors, such as the presence of both internalizing and externalizing symptoms (e.g., impulsivity and aggression), 23 motives for substance use, 24 and gender. 25 For example, research has indicated that internalizing symptoms are a risk factor for the development of AUD in women but not in men. 25

AUD has been associated with risk for the onset of depressive symptoms and disorders. In one review, regular or heavy drinking in adolescents was shown to be associated with the risk for developing depressive symptoms and disorders. 26 In studies of adults, DSM-IV AUD was associated with risk for the onset of major depressive disorder and with dysthymia. 22 , 27

Research on the possibility of a common pathophysiology of co-occurring AUD and depressive disorders is limited, yet it is a growing area of inquiry. Studies of genetic liability have identified some evidence that AUD and depressive disorders share susceptibility. 28 – 30 Although much remains to be understood about the possible shared pathophysiology for these conditions, a number of candidate systems and processes have been identified, such as dysfunction in the reward and stress systems. 31

Data from studies of depressive disorders suggest that specific symptom profiles may reflect distinct pathophysiology. For example, different symptom types have been associated with electrical activity (measured by electroencephalogram) in the brain while patients are at rest. 32 A diagnosis of major depressive disorder can involve 227 unique symptom combinations; 6 thus, the combination of symptoms from AUD and depressive disorders can take many forms. Consideration of disorder heterogeneity is essential to better understand the development of the co-occurring disorders.

Course and prognosis

The prognosis of co-occurring AUD and depression is highly variable and depends on several factors, such as age of onset and the severity of the disorders. For example, DSM-IV alcohol dependence (particularly severe dependence) has been associated with persistence of depressive disorders, whereas alcohol abuse has not. 33 Furthermore, the association between depressive disorders and AUD outcomes depends on how depression was measured. A diagnosis of major depressive disorder typically has been associated with worse AUD treatment outcomes, 2 , 3 whereas more severe depressive symptoms alone have not been associated with worse AUD treatment outcomes, when compared to less severe depressive symptoms. 2 Depressive symptoms have been shown to significantly improve after a period of abstinence from alcohol (typically 3 to 4 weeks), 34 which may explain the lack of association between symptoms and drinking outcomes outside of the context of a depressive disorder.

Evidence from longitudinal data on whether AUD worsens depression outcomes is somewhat mixed, with some studies finding evidence for worse outcomes and others finding no difference. 35 However, large studies have suggested that recovery from both conditions is linked, with remission from one condition strongly related to remission from the other. 36 For example, results from a large ( N = 2,876) multisite trial of treatment for depressive disorders found that patients who had co-occurring substance use disorder had a lower likelihood of depressive disorder remission and had a longer time to remission, when compared to patients with no substance use disorder. 37

Although alcohol-induced depressive disorder is defined by remission of the depression after discontinuation of alcohol, the disorder has been associated with risk for onset of later major depressive disorder. 11 Another study reported that patients with alcohol-induced depressive disorders experienced worse alcohol-related outcomes than patients with alcohol dependence who had other types of depressive disorders. 38

Treatment of Co-Occurring AUD and Depressive Disorders

Many randomized trials have investigated treatments for co-occurring AUD and depressive disorders. In this section, trials that used medication and psychotherapy treatments are discussed, as are the effects of those treatments on depressive symptoms and AUD symptoms.

Medication trials

Medication trials for co-occurring AUD and depressive disorders have focused mostly on antidepressant medications. Several meta-analyses have integrated these findings. 39 – 42 In general, the research shows that for people with co-occurring AUD and depressive disorders, antidepressants are more effective than placebo at reducing symptoms of depression. The magnitude of the benefit of medication over placebo is similar to the benefit reported in studies of people diagnosed with depression alone. 40 , 41 Few medication trials have compared treatments directly; most trials compare a single medication with a placebo. Thus, little is known about the comparative effectiveness of active treatments. 39 However, meta-analyses have suggested that older antidepressant medications, such as tricyclic antidepressants, are more effective at reducing depressive symptoms than newer agents, such as selective serotonin reuptake inhibitors (SSRIs). 40 , 42 These results may be attributable—at least in part—to a large placebo response reported in studies of SSRIs. 41

The effects of antidepressants on drinking outcomes are modest. 40 , 42 However, the effect of antidepressant medications on drinking outcomes may be dependent on how those medications affect depression. Some evidence indicates that depression mediates the effect of antidepressants on drinking outcomes. 43 Consistent with these findings, a meta-analysis of trials of antidepressant treatment for people with AUD only (i.e., without co-occurring depression) did not demonstrate a significant effect on drinking outcomes when compared to treatment with placebo. 42

Studies of patients with co-occurring AUD and depressive disorders have demonstrated that treatments using medications (e.g., naltrexone) for AUD are safe and effective for reducing drinking and depression symptoms. 44 , 45 A meta-analysis of studies that used acamprosate to treat AUD found similar effects among people with and without depression, but these researchers also found a strong effect of alcohol abstinence on remission of depression. 46 Combinations of antidepressants and AUD medications (e.g., sertraline with naltrexone and acamprosate with escitalopram) 47 , 48 have also shown some promise for the treatment of these co-occurring disorders, with positive outcomes for both AUD and depressive symptoms.

Psychosocial treatments and mutual help

Researchers have examined the effects of behavioral and psychosocial therapies on co-occurring AUD and depressive disorders, although many of these studies have had small sample sizes. A meta-analysis of 12 studies that examined combined motivational interviewing and cognitive behavioral therapy for AUD and depression found significant, but modest, improvements in both depression and drinking outcomes. 49 These results are consistent with an earlier meta-analysis of several psychotherapies (e.g., interpersonal psychotherapy and cognitive behavioral therapy) that also indicated relatively modest, but positive, effects for depression and drinking outcomes. 50

Several studies have examined a transdiagnostic behavioral approach to treatment, which integrates the treatments for AUD and depressive symptoms. Behavioral activation is a behavioral therapy that specifically targets reward dysfunction to improve mood through better engagement with natural reinforcers. Treatment with behavioral activation therapy has demonstrated efficacy for depressive disorders 51 and for AUD; 52 thus, it may be particularly promising for treating the co-occurring disorders. A therapy called “life enhancement treatment for substance use,” or “LETS ACT,” is a modification of behavioral activation therapy for people with substance use disorders. This therapy has been shown to reduce substance-related consequences and improve likelihood of abstinence in samples of adults with substance dependence (including alcohol dependence). 52 In another study, an integrated cognitive behavioral therapy treatment for depressive disorders and substance use disorders was associated with greater reduction in alcohol use, but similar reductions in depression, when compared with the control condition, which was a 12-step facilitation therapy. 53

Some researchers have suggested that the effects of psychotherapy may account for some of the pill placebo response observed in medication studies. Specifically, for medication trials in which all participants also received some form of psychotherapy, pill placebo response rates were higher than they were for studies that did not include psychotherapy in the pill placebo condition. 41 Likewise, in a study of sertraline and naltrexone in which all participants received weekly psychotherapy, sertraline had no additive benefit. 54 These findings suggest that the psychotherapies used in these trials may have provided some antidepressant effect, either directly or through their effects on drinking.

Mutual-help groups also can be effective elements of treatment for co-occurring AUD and depressive disorders. Attendance at Alcoholics Anonymous (AA) meetings has been shown to decrease symptoms of depression. 55 In one study, researchers found that a reduction in depression mediated the effect that AA meeting attendance had on drinking outcomes, 56 indicating that a change in depression symptoms may be a mechanism through which attendance at AA meetings improves drinking outcomes.

Future Research Directions

Research has substantially improved understanding of the etiology, course, and treatment of co-occurring AUD and depressive disorders. However, significant gaps remain in our understanding of these two disorders, and these gaps present important opportunities for future research.

More knowledge about optimal treatments for co-occurring AUD and depressive disorders is needed. Although medication and behavioral therapy have both shown promise, response rates have been somewhat modest. Efforts to enhance treatment outcomes would benefit from investigation into the characteristics of people who do not respond to existing treatments. A better understanding of the heterogeneity within this population will inform more personalized treatment approaches and might ultimately improve treatment response.

The substantial variability in the course of co-occurring AUD and depressive disorders may reflect discrete underlying mechanisms, requiring distinct treatment approaches. For example, AUD that develops after the onset of a depressive disorder and is characterized by coping motives for alcohol use may differ critically from a depressive disorder that develops following chronic alcohol administration. Data from studies of depression indicate that the substantial variability in the symptoms presented reflects a heterogeneous pathophysiology, 32 yet research on heterogeneity in co-occurring AUD and depressive disorders remains limited. Although little is known about the possible shared pathophysiology of AUD and depressive disorders, preclinical research has identified common disruptions in reward and stress processing that are important candidates for further research. 31 Efforts to better characterize the mechanistic processes that may underlie observed clinical presentations will help identify more precise and personalized interventions.

Future research that leverages novel technologies, such as ecological momentary assessment and multimodal neuroimaging, will enhance our understanding of the interactions between mood and alcohol use and how those interactions may influence the nature, course, and treatment of co-occurring AUD and depressive disorders. Assessment of co-occurring AUD and depressive disorders using dimensional measures rather than discrete, categorical measures will be critical to understanding the full spectrum of severity of these conditions, including subclinical presentations.

Finally, the etiology, course, and treatment of both AUD and depression differ substantially by gender. Women have been underrepresented in much of the research on co-occurring AUD and depressive disorders, particularly in the early research on this topic. The research needs more representation of women to increase understanding of the sex differences and to better characterize the mechanisms underlying women’s heightened vulnerability for depressive disorders. For example, an important area for future research could be women who have co-occurring AUD and premenstrual dysphoric disorder, which is a depressive disorder characterized by a fluctuation of mood symptoms across the menstrual cycle. 6 Likewise, research is urgently needed to better understand co-occurring AUD and depressive disorders among racial and ethnic minorities. These populations experience disparities in access to care for AUD and depressive disorders but are underrepresented in studies of these disorders.

People with AUD have a heightened risk for depressive disorders, which are the most common co-occurring psychiatric disorders for this population. AUD and depressive disorders appear to share some behavioral, genetic, and environmental risk factors, yet these shared risks remain poorly understood.

Diagnosis and treatment of the commonly co-occurring AUD and depressive disorders have many challenges. Diagnosis is particularly challenging because of overlapping symptoms, such as the depressant effects of alcohol, and because of features that are common to both alcohol withdrawal and depressive disorders, such as insomnia and psychomotor agitation. The DSM-5 distinguishes a substance-induced disorder from a primary depressive disorder based on whether “the substance is judged to be etiologically related to the symptoms.” 6 (p180) Accordingly, any diagnosis of depression during active periods of drinking or during acute alcohol withdrawal should be made provisionally. Attempts to diagnose depression should focus on identifying periods of depression outside periods of drinking or withdrawal and should use collateral information (e.g., reports from family members or significant others) when possible. If depressive symptoms persist after a period of abstinence—4 weeks is the typical recommendation—a diagnosis of an independent (i.e., not substance-induced) depressive disorder can be made with more confidence. 6

Nonetheless, substance-induced depression is also associated with the risk for independent depressive disorders. Thus, treatment of depression should be considered, along with close monitoring of mood, for people who have substance-induced depression. 11 Treatment studies have supported the effects of both AUD medications (e.g., naltrexone) 44 and antidepressants 47 for the treatment of co-occurring AUD and depressive disorders. However, because of a lack of comparative trials on effectiveness (i.e., studies comparing more than one active treatment), the most effective approach is unknown. Behavioral therapy is understudied in this population despite evidence supporting the therapy as treatment for depressive disorders 51 and AUD 57 separately. Indeed, in placebo-controlled studies of medications for co-occurring AUD and depression, the inclusion of behavioral therapy as part of the standard treatment may explain the small effect sizes often observed. Behavioral activation therapy—a treatment that targets disruption in reward functioning, which is a common dysfunction in both AUD and depressive disorders—may have particular promise for treating the co-occurring disorders. 52

Despite the availability of several evidence-based medications and behavioral therapy approaches for treating co-occurring AUD and depressive disorders, improvements in treatment for this population are clearly needed. Consideration of disorder heterogeneity and key subgroup differences may help develop more targeted and personalized treatments to improve outcomes for this population.

Acknowledgments

This article was supported by the Charles Engelhard Foundation and National Institute on Drug Abuse grants K23DA035297 and K24DA022288.

Financial Disclosure

Dr. McHugh declares no competing financial interests. Dr. Weiss has been a consultant to Alkermes, Braeburn, Daiichi-Sankyo, GW Pharmaceuticals, Indivior, Janssen, and US WorldMeds.

• Grant BF, Stinson FS, Dawson DA, et al. Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004; 61 (8):807–816. [ PubMed ] [ Google Scholar ]
• Greenfield SF, Weiss RD, Muenz LR, et al. The effect of depression on return to drinking: A prospective study. Arch Gen Psychiatry. 1998; 55 (3):259–265. [ PubMed ] [ Google Scholar ]
• Hasin D, Liu X, Nunes E, et al. Effects of major depression on remission and relapse of substance dependence. Arch Gen Psychiatry. 2002; 59 (4):375–380. [ PubMed ] [ Google Scholar ]
• Conner KR, Gamble SA, Bagge CL, et al. Substance-induced depression and independent depression in proximal risk for suicidal behavior. J Stud Alcohol Drugs. 2014; 75 (4):567–572. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994. [ Google Scholar ]
• American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Washington, DC: American Psychiatric Association; 2013. [ Google Scholar ]
• Hasin DS, Goodwin RD, Stinson FS, et al. Epidemiology of major depressive disorder: Results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry. 2005; 62 (10):1097–1106. [ PubMed ] [ Google Scholar ]
• Kessler RC, Ormel J, Petukhova M, et al. Development of lifetime comorbidity in the World Health Organization world mental health surveys. Arch Gen Psychiatry. 2011; 68 (1):90–100. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kessler RC, Chiu WT, Demler O, et al. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005; 62 (6):617–627. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schuckit MA, Tipp JE, Bergman M, et al. Comparison of induced and independent major depressive disorders in 2,945 alcoholics. Am J Psychiatry. 1997; 154 (7):948–957. [ PubMed ] [ Google Scholar ]
• Nunes EV, Liu X, Samet S, et al. Independent versus substance-induced major depressive disorder in substance-dependent patients: Observational study of course during follow-up. J Clin Psychiatry. 2006; 67 (10):1561–1567. [ PubMed ] [ Google Scholar ]
• Kessler RC. Epidemiology of women and depression. J Affect Disord. 2003; 74 (1):5–13. [ PubMed ] [ Google Scholar ]
• Kessler RC, Crum RM, Warner LA, et al. Lifetime co-occurrence of DSM-III-R alcohol abuse and dependence with other psychiatric disorders in the National Comorbidity Survey. Arch Gen Psychiatry. 1997; 54 (4):313–321. [ PubMed ] [ Google Scholar ]
• Khan S, Okuda M, Hasin DS, et al. Gender differences in lifetime alcohol dependence: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Alcohol Clin Exp Res. 2013; 37 (10):1696–1705. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Moscato BS, Russell M, Zielezny M, et al. Gender differences in the relation between depressive symptoms and alcohol problems: A longitudinal perspective. Am J Epidemiol. 1997; 146 (11):966–974. [ PubMed ] [ Google Scholar ]
• Hanna EZ, Grant BF. Gender differences in DSM-IV alcohol use disorders and major depression as distributed in the general population: Clinical implications. Compr Psychiatry. 1997; 38 (4):202–212. [ PubMed ] [ Google Scholar ]
• Prescott CA, Aggen SH, Kendler KS. Sex-specific genetic influences on the comorbidity of alcoholism and major depression in a population-based sample of U.S. twins. Arch Gen Psychiatry. 2000; 57 (8):803–811. [ PubMed ] [ Google Scholar ]
• Smith SM, Stinson FS, Dawson DA, et al. Race/ethnic differences in the prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychol Med. 2006; 36 (7):987–998. [ PubMed ] [ Google Scholar ]
• Hatzenbuehler ML, Keyes KM, Narrow WE, et al. Racial/ethnic disparities in service utilization for individuals with co-occurring mental health and substance use disorders in the general population: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry. 2008; 69 (7):1112–1121. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Nam E, Matejkowski J, Lee S. Racial/ethnic differences in contemporaneous use of mental health and substance use treatment among individuals experiencing both mental illness and substance use disorders. Psychiatr Q. 2017; 88 (1):185–198. [ PubMed ] [ Google Scholar ]
• Abraham HD, Fava M. Order of onset of substance abuse and depression in a sample of depressed outpatients. Compr Psychiatry. 1999; 40 (1):44–50. [ PubMed ] [ Google Scholar ]
• Fergusson DM, Boden JM, Horwood LJ. Tests of causal links between alcohol abuse or dependence and major depression. Arch Gen Psychiatry. 2009; 66 (3):260–266. [ PubMed ] [ Google Scholar ]
• Colder CR, Frndak S, Lengua LJ, et al. Internalizing and externalizing problem behavior: A test of a latent variable interaction predicting a two-part growth model of adolescent substance use. J Abnorm Child Psychol. 2018; 46 (2):319–330. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Crum RM, Mojtabai R, Lazareck S, et al. A prospective assessment of reports of drinking to self-medicate mood symptoms with the incidence and persistence of alcohol dependence. JAMA Psychiatry. 2013; 70 (7):718–726. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Foster KT, Hicks BM, Iacono WG, et al. Gender differences in the structure of risk for alcohol use disorder in adolescence and young adulthood. Psychol Med. 2015; 45 (14):3047–3058. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pedrelli P, Shapero B, Archibald A, et al. Alcohol use and depression during adolescence and young adulthood: A summary and interpretation of mixed findings. Curr Addict Rep. 2016; 3 (1):91–97. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Falk DE, Yi HY, Hilton ME. Age of onset and temporal sequencing of lifetime DSM-IV alcohol use disorders relative to comorbid mood and anxiety disorders. Drug Alcohol Depend. 2008; 94 (1–3):234–245. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Andersen AM, Pietrzak RH, Kranzler HR, et al. Polygenic scores for major depressive disorder and risk of alcohol dependence. JAMA Psychiatry. 2017; 74 (11):1153–1160. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kuo PH, Gardner CO, Kendler KS, et al. The temporal relationship of the onsets of alcohol dependence and major depression: Using a genetically informative study design. Psychol Med. 2006; 36 (8):1153–1162. [ PubMed ] [ Google Scholar ]
• Zhou H, Polimanti R, Yang BZ, et al. Genetic risk variants associated with comorbid alcohol dependence and major depression. JAMA Psychiatry. 2017; 74 (12):1234–1241. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Renoir T, Pang TY, Lanfumey L. Drug withdrawal-induced depression: Serotonergic and plasticity changes in animal models. Neurosci Biobehav Rev. 2012; 36 (1):696–726. [ PubMed ] [ Google Scholar ]
• Webb CA, Dillon DG, Pechtel P, et al. Neural correlates of three promising endophenotypes of depression: Evidence from the EMBARC study. Neuropsychopharmacology. 2016; 41 (2):454–463. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Boschloo L, Vogelzangs N, van den Brink W, et al. Alcohol use disorders and the course of depressive and anxiety disorders. Br J Psychiatry. 2012; 200 (6):476–484. [ PubMed ] [ Google Scholar ]
• Liappas J, Paparrigopoulos T, Tzavellas E, et al. Impact of alcohol detoxification on anxiety and depressive symptoms. Drug Alcohol Depend. 2002; 68 (2):215–220. [ PubMed ] [ Google Scholar ]
• Sullivan LE, Fiellin DA, O’Connor PG. The prevalence and impact of alcohol problems in major depression: A systematic review. Am J Med. 2005; 118 (4):330–341. [ PubMed ] [ Google Scholar ]
• Hasin DS, Tsai WY, Endicott J, et al. Five-year course of major depression: Effects of comorbid alcoholism. J Affect Disord. 1996; 41 (1):63–70. [ PubMed ] [ Google Scholar ]
• Davis LL, Wisniewski SR, Howland RH, et al. Does comorbid substance use disorder impair recovery from major depression with SSRI treatment? An analysis of the STAR*D level one treatment outcomes. Drug Alcohol Depend. 2010; 107 (2–3):161–170. [ PubMed ] [ Google Scholar ]
• Samet S, Fenton MC, Nunes E, et al. Effects of independent and substance-induced major depressive disorder on remission and relapse of alcohol, cocaine and heroin dependence. Addiction. 2013; 108 (1):115–123. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Agabio R, Trogu E, Pani PP. Antidepressants for the treatment of people with co-occurring depression and alcohol dependence. Cochrane Database Syst Rev. 2018; 4 :CD008581. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Iovieno N, Tedeschini E, Bentley KH, et al. Antidepressants for major depressive disorder and dysthymic disorder in patients with comorbid alcohol use disorders: A meta-analysis of placebo-controlled randomized trials. J Clin Psychiatry. 2011; 72 (8):1144–1151. [ PubMed ] [ Google Scholar ]
• Nunes EV, Levin FR. Treatment of depression in patients with alcohol or other drug dependence: A meta-analysis. JAMA. 2004; 291 (15):1887–1896. [ PubMed ] [ Google Scholar ]
• Torrens M, Fonseca F, Mateu G, et al. Efficacy of antidepressants in substance use disorders with and without comorbid depression. A systematic review and meta-analysis. Drug Alcohol Depend. 2005; 78 (1):1–22. [ PubMed ] [ Google Scholar ]
• Nunes EV, Quitkin FM, Donovan SJ, et al. Imipramine treatment of opiate-dependent patients with depressive disorders. A placebo-controlled trial. Arch Gen Psychiatry. 1998; 55 (2):153–160. [ PubMed ] [ Google Scholar ]
• Petrakis I, Ralevski E, Nich C, et al. Naltrexone and disulfiram in patients with alcohol dependence and current depression. J Clin Psychopharmacol. 2007; 27 (2):160–165. [ PubMed ] [ Google Scholar ]
• Salloum IM, Cornelius JR, Thase ME, et al. Naltrexone utility in depressed alcoholics. Psychopharmacol Bull. 1998; 34 (1):111–115. [ PubMed ] [ Google Scholar ]
• Lejoyeux M, Lehert P. Alcohol-use disorders and depression: Results from individual patient data meta-analysis of the acamprosate-controlled studies. Alcohol Alcohol. 2011; 46 (1):61–67. [ PubMed ] [ Google Scholar ]
• Pettinati HM, Oslin DW, Kampman KM, et al. A double-blind, placebo-controlled trial combining sertraline and naltrexone for treating co-occurring depression and alcohol dependence. Am J Psychiatry. 2010; 167 (6):668–675. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Witte J, Bentley K, Evins AE, et al. A randomized, controlled, pilot study of acamprosate added to escitalopram in adults with major depressive disorder and alcohol use disorder. J Clin Psychopharmacol. 2012; 32 (6):787–796. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Riper H, Andersson G, Hunter SB, et al. Treatment of comorbid alcohol use disorders and depression with cognitive-behavioural therapy and motivational interviewing: A meta-analysis. Addiction. 2014; 109 (3):394–406. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hesse M. Integrated psychological treatment for substance use and co-morbid anxiety or depression vs. treatment for substance use alone. A systematic review of the published literature. BMC Psychiatry. 2009; 9 :6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dimidjian S, Hollon SD, Dobson KS, et al. Randomized trial of behavioral activation, cognitive therapy, and antidepressant medication in the acute treatment of adults with major depression. J Consult Clin Psychol. 2006; 74 (4):658–670. [ PubMed ] [ Google Scholar ]
• Daughters SB, Magidson JF, Anand D, et al. The effect of a behavioral activation treatment for substance use on post-treatment abstinence: A randomized controlled trial. Addiction. 2018; 113 (3):535–544. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lydecker KP, Tate SR, Cummins KM, et al. Clinical outcomes of an integrated treatment for depression and substance use disorders. Psychol Addict Behav. 2010; 24 (3):453–465. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Farren CK, Scimeca M, Wu R, et al. A double-blind, placebo-controlled study of sertraline with naltrexone for alcohol dependence. Drug Alcohol Depend. 2009; 99 (1–3):317–321. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kelly TM, Daley DC, Douaihy AB. Treatment of substance abusing patients with comorbid psychiatric disorders. Addict Behav. 2012; 37 (1):11–24. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wilcox CE, Tonigan JS. Changes in depression mediate the effects of AA attendance on alcohol use outcomes. Am J Drug Alcohol Abuse. 2018; 44 (1):103–112. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• National Institute on Alcohol Abuse and Alcoholism. Matching Alcoholism Treatments to Client Heterogeneity: Project MATCH posttreatment drinking outcomes. J Stud Alcohol. 1997; 58 (1):7–29. [ PubMed ] [ Google Scholar ]