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Effects of weight loss interventions for adults who are obese on mortality, cardiovascular disease, and cancer: systematic review and meta-analysis

• Related content
• Peer review
• Chenhan Ma , foundation year 1 doctor 1 ,
• Alison Avenell , professor 1 ,
• Mark Bolland , associate professor 2 ,
• Jemma Hudson , statistician 1 ,
• Fiona Stewart , research fellow 1 ,
• Clare Robertson , research fellow 1 ,
• Pawana Sharma , research fellow 1 ,
• Cynthia Fraser , information officer 1 ,
• Graeme MacLennan , professor 3
• 1 Health Services Research Unit, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen AB25 2ZD, Scotland, UK
• 2 Bone and Joint Research Group, Department of Medicine, University of Auckland, Private Bag 92 019, Auckland 1142, New Zealand
• 3 Centre for Healthcare Randomised Trials, University of Aberdeen, Health Sciences Building, Foresterhill, Aberdeen AB25 2ZD, Scotland, UK
• Correspondence to: A Avenell a.avenell{at}abdn.ac.uk
• Accepted 4 October 2017

Objective  To assess whether weight loss interventions for adults with obesity affect all cause, cardiovascular, and cancer mortality, cardiovascular disease, cancer, and body weight.

Design  Systematic review and meta-analysis of randomised controlled trials (RCTs) using random effects, estimating risk ratios, and mean differences. Heterogeneity investigated using Cochran’s Q and I 2 statistics. Quality of evidence assessed by GRADE criteria.

Data sources  Medline, Embase, the Cochrane Central Register of Controlled Trials, and full texts in our trials’ registry for data not evident in databases. Authors were contacted for unpublished data.

Eligibility criteria for selecting studies  RCTs of dietary interventions targeting weight loss, with or without exercise advice or programmes, for adults with obesity and follow-up ≥1 year.

Results  54 RCTs with 30 206 participants were identified. All but one trial evaluated low fat, weight reducing diets. For the primary outcome, high quality evidence showed that weight loss interventions decrease all cause mortality (34 trials, 685 events; risk ratio 0.82, 95% confidence interval 0.71 to 0.95), with six fewer deaths per 1000 participants (95% confidence interval two to 10). For other primary outcomes moderate quality evidence showed an effect on cardiovascular mortality (eight trials, 134 events; risk ratio 0.93, 95% confidence interval 0.67 to 1.31), and very low quality evidence showed an effect on cancer mortality (eight trials, 34 events; risk ratio 0.58, 95% confidence interval 0.30 to 1.11). Twenty four trials (15 176 participants) reported high quality evidence on participants developing new cardiovascular events (1043 events; risk ratio 0.93, 95% confidence interval 0.83 to 1.04). Nineteen trials (6330 participants) provided very low quality evidence on participants developing new cancers (103 events; risk ratio 0.92, 95% confidence interval 0.63 to 1.36).

Conclusions  Weight reducing diets, usually low in fat and saturated fat, with or without exercise advice or programmes, may reduce premature all cause mortality in adults with obesity.

Systematic review registration  PROSPERO CRD42016033217.

Introduction

Adults with obesity have an increased risk of premature mortality, cardiovascular disease, some cancers, type 2 diabetes, and many other diseases. 1 2 These associations inform the need for programmes to prevent obesity, but, apart from prevention of type 2 diabetes, 3 4 limited evidence from randomised controlled trials (RCTs) shows that weight loss interventions can prevent serious harm for people with obesity. Evidence from cohort studies has led to debate that deliberate weight loss for people who are overweight or obese, with body mass index (BMI) ≤35 kg/m 2 , might actually be harmful. 5 Studies show that older people, 6 and those with cardiovascular disease 7 who are less markedly obese, might experience adverse consequences from deliberate weight loss. Recent analyses by the Global BMI Mortality Collaboration, however, tried to limit confounding and corrected for reverse causality, finding that the risk of premature mortality was lowest at BMIs of 20-25. 8

Association studies cannot tell us if deliberate weight loss in adults with obesity can reduce their risk of premature mortality, cardiovascular disease, or cancer. Only one systematic review and meta-analysis of RCTs of intentional weight loss in adults with obesity has examined this question. 9 That review included 15 trials, reporting a 15% relative reduction in premature mortality (risk ratio 0.85, 95% confidence interval 0.73 to 1.00), but did not evaluate causes of death or cardiovascular and cancer outcomes. 9 We knew of many other weight loss RCTs with mortality data, as well as cancer and cardiovascular outcomes, from our database of long term RCTs of weight loss interventions for adult obesity, which was developed for health technology assessments 10 11 and is continually updated. We systematically reviewed long term (≥1 year) RCTs of weight loss interventions for adults with obesity to examine the effects of any type of weight loss diet on all cause, cardiovascular, and cancer mortality, cardiovascular disease, cancer, and body weight.

We adhered to the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines for systematic reviews of interventions. 12 We used a prespecified protocol, registered with PROSPERO (CRD42016033217). 13

Search strategy and selection criteria

We included RCTs with adults (mean or median age ≥18 years) and a minimum follow-up of one year. Participants had a mean BMI ≥30 at baseline. Included trials had to be focused clearly on weight loss with a weight reducing diet, with or without advice for increasing physical activity and/or provision of a physical activity programme to attend, compared with a control intervention. We didn’t include trials in pregnant or postpartum women.

We sought summary data for three primary outcomes: all cause mortality, cardiovascular mortality, and cancer mortality. Secondary outcomes were participants with a new cardiovascular event, participants with a new cancer, and weight change. In our main analysis we used cardiovascular mortality and events as defined by the investigators but did not include the development of hypertension. We undertook post hoc analyses of cardiovascular mortality and cardiovascular events as defined in the American College of Cardiology/American Heart Association (ACC/AHA) guidelines. 14

We identified RCTs by searching the full texts of trial reports in our database of all long term (≥1 year) RCTs of weight loss interventions for adults with obesity used in our previous systematic reviews and health technology assessments. Our database is derived from previous search strategies compiled from Medline, Embase, and the Cochrane Central Register of Controlled Trials, from 1966 to December 2015. 10 11 We performed an updated search from August 2015 to December 2016. We didn’t apply any language exclusions. In 2016-17 we contacted the authors of 48 RCTs to clarify data or request unpublished outcome data, where trial reports implied that relevant data might be available; for example, when the trial reported hospital admissions or adverse events without giving further details.

Data analysis

AA and CM independently confirmed study eligibility. CM, FS, CR, and PS extracted data, which were then checked by a second author (AA, CM). Cancer outcome and cardiovascular outcome data (including coding outcomes defined by the ACC/AHA guideline 14 ) were further adjudicated by MB, with differences resolved by Andrew Grey (associate professor in the Department of Medicine, University of Auckland). Two authors (AA, CM, FS, CR, PS) independently assessed quality using the Cochrane risk of bias tool. 15 All differences were resolved by discussion.

We used random effects meta-analysis to analyse pooled outcome data. For binary outcomes, we estimated risk ratios and 95% confidence intervals, using all participants randomised for the denominators. We estimated weighted mean differences and 95% confidence intervals for continuous outcomes, giving preference to intention to treat data and data taking account of dropouts (preferentially baseline observation carried forward) if these were provided. We included outcome data from two cluster RCTs 16 17 using the correction method described in the Cochrane Handbook 18 and the intraclass correlation coefficients reported in the original trial publications. We assessed heterogeneity between studies using Cochran’s Q statistic and the I 2 test. We originally planned meta-regression to investigate heterogeneity in disease outcomes, but I 2 tests for disease outcomes were 0%, so it was not appropriate. We carried out a sensitivity analysis with a random effects bayesian logistic regression model (with non-informative priors) using WinBUGS 1.4.3 19 because some trials reported few events, which may cause sparse data bias. We performed all other analyses using Stata Release 14 20 and used funnel plots to examine small study bias.

For all outcomes we performed prespecified subgroup analyses for sex, age (<60 v ≥60), BMI (<40 v ≥40, later changed to <35 v ≥35 as we found no trial with BMI ≥40), glycaemic control (normal v impaired glucose tolerance or impaired fasting glucose v type 2 diabetes), ethnicity (defined if ≥80% of participants belonged to an ethnic group, otherwise defined as mixed), physical activity interventions (none v advice only v exercise programme provided).

In post hoc additional analyses we added trials in any Asian population group if the mean BMI was ≥25, as diseases associated with obesity are known to occur at lower BMI in Asian populations than other ethnic groups. 21 No single BMI cut-off has been agreed to define obesity in Asian populations. Although the World Health Organization recommends 27.5 as a BMI threshold for a high risk of comorbidities, 21 it also suggests that Asian countries develop their own specific BMI cut-offs for obesity. India and Japan have set ≥25 as the threshold for obesity, 22 23 and in China the risk of comorbidities has been found to increase for BMI over 28. 24

For all outcomes we performed two prespecified sensitivity analyses for allocation concealment (low risk of bias vs other risk of bias) and follow-up (<80% vs ≥80%).

We used GRADE ( grading of recommendations, assessment, development, and evaluations) to judge the quality of the evidence for mortality, cardiovascular, and cancer outcomes. 25

Role of the funding source

The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing this report. CM and AA had full access to all study data and had final responsibility for the decision to submit for publication.

Patient involvement

No patients or members of the public were involved in the development of research questions, the design of the study, or the development of outcome measures. No patients were asked to advise on interpretation or writing up of results. There are plans to disseminate the results of the research to the relevant patient community.

Trial characteristics

We screened 1174 full text trial reports and 5982 titles and abstracts (fig 1 ⇓ ) and identified 54 RCTs for inclusion 3 4 16 17 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 in the final review.

Fig 1  Study selection

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Table 1 ⇓ provides details of the included studies, involving 30 206 adults with obesity. Nine trials (16.7%) included women only, 26 44 45 50 51 52 77 88 94 and two (3.7%) men only. 58 72 Twelve trials (22.2%) recruited participants with no reported existing medical conditions or no reported increased risk of developing comorbidities related to obesity. Other trials recruited participants with increased risk of type 2 diabetes or hypertension or included participants that already had at least one of the following conditions: hypertension, type 2 diabetes, hyperlipidaemia, breast cancer, colorectal adenoma, psychiatric illnesses, cognitive impairment, osteoarthritis of the knee, coronary heart disease, or urinary incontinence.

Characteristics of randomised controlled trials

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Five trials (9.3%) were undertaken in Asian populations, 16 17 59 75 80 but only one with BMI ≥30, 2 16 one trial (1.9%) was in a population of black people in the USA, 50 31 (57.4%) in populations of white people, and 17 (31.5%) in mixed population groups. Thirty one (57.4%) trials took place in North America, 16 (29.6%) in Europe, two (3.7%) in Australia, and one (1.9%) in Brazil. The four trials in Asian populations outside the UK had mean BMIs between 25 and 30. 17 59 75 80 Thirty six (66.7%) trials had participants with a mean or median BMI <35, and 14 (25.9%) had BMIs ≥35 (table 1 ⇑ ).

Most trials recruited predominantly middle aged adults. Fourteen (25.9%) had a mean or median age at baseline of 60 years or more, none had a mean or median age of under 40 years. Thirty one (57.4%) trials followed participants for two years or longer, and seven (13.0%) trials (9,937 participants) followed participants for five years or longer. In 39 trials (72.2%) the drop-out rate was <20% at trial completion.

Detailed descriptions of the weight loss diets were not always clearly provided in the trials. All but one of the trials described at least one of their interventions as being a low fat weight reduction diet (usually ≤30% of energy as fat, although this was not always specified) or had sufficient information to establish that a reduction in fat intake was prescribed. Most trials also described the prescription of a reduction in saturated fat. One trial described using a balanced Mediterranean diet. 79 One trial included the option to undertake a diet with ≤50 g/day of carbohydrate. 96 Two weight loss trials specifically described diets to reduce low glycaemic index as part of their intervention, 26 30 whereas other trials generally described diets that would be compatible with lowering glycaemic indices by increasing intake of complex carbohydrates and dietary fibre. Four trials (7.4%) were based on the DASH (dietary approaches to stop hypertension) diet. 31 39 40 54 Eight (14.8%) trials based their diets on those of the US Diabetes Prevention Program, 4 26 52 60 67 74 93 94 and four trials (7.4%) described basing their content in part on different editions of the Dietary Guidelines for Americans. 64 69 72 76

Only three trials (5.6%) did not report providing exercise advice or an exercise programme. 45 55 68 Twenty two trials (40.7%) provided an exercise programme for participants to attend, and 29 trials (53.7%) described providing advice to increase exercise only, without an exercise programme.

Supplementary figure 1 provides our risk of bias assessments for individual studies. Only 15 trials (27.8%) reported methods of randomisation and allocation concealment judged to be at low risk of bias. Blinding of participants and study personnel was rarely possible, but we judged that lack of blinding of outcome assessment would rarely have been a source of bias except for weight outcomes. Only 10 (18.5%) trials were judged to be at low risk for attrition bias, and 12 (22.2%) at low risk for reporting bias. Seven (13.0%) trials were judged to be at high risk of bias as a result of premature trial termination, 52 65 75 change in the primary outcome, 16 influence of a drug placebo in the control group, 4 or trial investigators reporting that they were sponsored by grants from a commercial weight loss programme 71 or that they were co-owners of a company developing products related to the research. 72

Meta-analyses

Details of our adjudication processes for cardiovascular and cancer outcomes are provided in supplementary tables 1-3. Supplementary table 1 compares all cause mortality, cardiovascular mortality, and cancer mortality across all trials, showing that we were not always able to obtain causes of death from authors.

Based on the GRADE approach for judging quality of the evidence (supplementary table 4) we found high quality evidence from 34 trials (21 699 participants) providing data on all cause mortality (fig 2 ⇓ ), which showed a decrease in premature mortality with weight loss interventions (n=34 trials, 685 events; risk ratio 0.82, 95% confidence interval 0.71 to 0.95; I 2 =0%). The Look AHEAD trial had 54.6% of the weighting in the meta-analysis. 65 66 Without this trial weight loss interventions were still associated with decreased all cause mortality (n=33 trials, 309 events; risk ratio 0.78, 95% confidence interval 0.63 to 0.96; I 2 =0%). The funnel plot showed no evidence of small study bias (Egger’s test P=0.269, supplementary figure 2).

Fig 2  Random effects meta-analysis of the effects of weight loss interventions on all cause mortality. ADAPT=arthritis, diet, and activity promotion trial; CLIP=community level interventions for pre-eclampsia; DPP=diabetes prevention program; DPS=diabetes prevention study; FFIT=football fans in training; Look AHEAD=look action for health in diabetes; PRIDE=program to reduce incontinence by diet and exercise; TAIM=trial of antihypertensive interventions and management; TOHP=trials of hypertension prevention; TONE=trial of nonpharmacologic intervention in the elderly.

Fewer trials reported data for cardiovascular mortality and cancer mortality, resulting in considerable uncertainty in the estimates of effects of weight loss interventions on these outcomes. We found moderate quality evidence for an effect on cardiovascular mortality (n=8 trials, 134 events; risk ratio 0.93, 95% confidence interval 0.67 to 1.31; I 2 =0%) and very low quality evidence for an effect on cancer mortality (n=8 trials, 34 events; risk ratio 0.58, 95% confidence interval 0.30 to 1.11; I 2 =0%) (figs 3 and 4 ⇓ ). Limiting cardiovascular mortality to ACC/AHA defined events did not influence this result, as the data were identical (n=8 trials, 134 events; risk ratio 0.93, 95% confidence interval 0.67 to 1.31; I 2 =0%).

Fig 3  Random effects meta-analysis of the effects of weight loss interventions on cardiovascular mortality. DPP=diabetes prevention program; DPS=diabetes prevention study.

Fig 4  Random effects meta-analysis of the effects of weight loss interventions on cancer mortality. DPS=diabetes prevention study.

Twenty four trials (15 176 participants) reported high quality evidence on participants developing new cardiovascular events (n=24, 1043 events; risk ratio 0.93, 95% confidence interval 0.83 to 1.04; I 2 =0%). Using events classified according to ACC/AHA definitions, results were very similar (fig 5 ⇓ , supplementary figure 3). Nineteen trials (6330 participants) provided very low quality evidence on participants developing new cancers (n=19, 103 events; risk ratio 0.92, 95% confidence interval 0.63 to 1.36; I 2 =0%) (fig 6 ⇓ ). Bayesian meta-analyses for all of the above outcomes provided similar results (supplementary table 5).

Fig 5  Random effects meta-analysis of the effects of weight loss interventions on participants with a cardiovascular event. CLIP=community level interventions for pre-eclampsia; DPP=diabetes prevention program; FFIT=football fans in training.

Fig 6  Random effects meta-analysis of the effects of weight loss interventions on participants developing cancer. DPS=diabetes prevention study.

Interventions had a beneficial effect on weight change after one year (n=44, mean difference −3.42 kg; 95% confidence interval −4.09 to −2.75 kg; I 2 =92%), after two years (n=20, mean difference −2.51 kg; 95% confidence interval −3.42 to −1.60 kg; I 2 =89%) and after three or more years (n=8, mean difference −2.56 kg; 95% confidence interval −3.50 to −1.62 kg; I 2 =87%) (supplementary figures 4 to 6). Heterogeneity for each of these meta-analyses was very high (I 2 =87% to 92%), reflecting the wide diversity of weight loss interventions and their effects on weight.

Sensitivity analyses

Sensitivity analyses for allocation concealment (low risk of bias versus other risk of bias) and completion of follow-up (<80% v ≥80% of participants completed) did not show any statistically significant heterogeneity for mortality, cardiovascular outcomes, or cancer outcomes (supplementary table 6).

Weight change at final follow-up was lower in trials with low risk of bias for allocation concealment (n=17, mean difference −2.33 kg; 95% confidence interval −2.87 to −1.79 kg) than for trials with high or unclear risk of bias for allocation concealment (n=31, mean difference −3.24 kg; 95% confidence interval −4.00 to −2.49 kg).

Weight change at final follow-up was lower in trials with completed follow-up of less than 80% (n=15, MD −2.09 kg; 95% CI: −2.80 to −1.37 kg) than for trials with follow-up of 80% or more (n=33, MD −3.13 kg; 95% CI: −3.71 to −2.55 kg).

Subgroup analyses

We undertook many subgroup analyses, including post hoc analyses with the addition of trials in Asian populations with BMI ≥25 (supplementary table 6, supplementary figures 7-9). Tests for subgroup differences for mortality, cardiovascular outcomes, and cancer outcomes provided weak evidence that participants without type 2 diabetes might be at lower risk of a new cardiovascular event than participants with type 2 diabetes or those with impaired glucose tolerance or impaired fasting glycaemia. Similarly, we found weak evidence that groups of white participants may be at lower risk of a new cardiovascular event than black, mixed, or Asian population groups when following weight loss interventions.

Subgroup analyses for weight change at final follow-up provided weak evidence that participants aged 60 or over lost more weight than younger participants and that participants in trials in Asian populations lost less weight than those in trials with other population groups. Similarly, we found weak evidence of better long term weight loss with trials that provided a physical activity programme, compared with trials that gave only physical activity advice or did not report providing physical activity advice.

We found high quality evidence that weight reducing diets for adults with obesity, usually low in fat and low in saturated fat, were associated with a 18% relative reduction in premature mortality over a median trial duration of two years, corresponding to six fewer deaths per 1000 participants (95% confidence interval two to 10). This evidence provides a further reason for weight reducing diets to be offered alongside their already proven benefits, such as type 2 diabetes prevention. We were unable to show effects on cardiovascular and cancer mortality, or participants developing cardiovascular events or new cancers, although fewer trials reported events for these outcomes, resulting in much uncertainty around their effect estimates.

We identified 34 trials reporting mortality data compared with 15 in the previous systematic review by Kritchevsky and colleagues, 9 which included weight loss interventions irrespective of baseline BMI, and we made very considerable efforts to clarify data and retrieve unpublished data from 48 trialists. We used a comprehensive search strategy with full text searching of trials in our obesity database. The trials we included were not necessarily designed to collect data on mortality, cardiovascular, and cancer outcomes, although larger trials generally were. 65 66 81 82 83 84 85 86 87 We might have failed to identify all trials with outcome data, if trialists did not present these outcomes or presented them as unspecified adverse events. This may have biased results, although we could not see obvious funnel plot asymmetry for all cause mortality. Trials generally excluded participants with a recent diagnosis of cancer, but this was not always clear, so some participants may have had a recurrence of cancer, rather than a new event. Many of the trials had quite intensive control group interventions, and the unblinded nature of the interventions could have led to more medical treatment in control groups, tending to reduce differences between groups. 65 Using GRADE to assess the quality of the evidence aids interpretation of the limitations of the evidence. We undertook sensitivity and subgroup analyses, including post hoc analyses, which should be regarded with caution. Individual patient data meta-analyses are required for further exploration of these subgroup findings.

In systematic reviews of controlled cohort studies, bariatric surgery has been associated with significant reductions in mortality, cardiovascular events, myocardial infarction, stroke, and risk of cancer. 97 98 A systematic review and meta-analysis of population prospective cohort studies by Flegal and colleagues found that BMIs of 30 to <35 were not associated with higher mortality, compared with BMIs of 18.5 to <25. 5 By contrast, the Global BMI Mortality Collaboration found that obesity (BMI 30 to <35) was associated with higher mortality; the investigators reduced reverse causality by examining data in non-smokers and excluding the first five years of follow-up. 8 Their findings were consistent for men and women, up to 89 years, and in the four continents examined. Similar findings were seen for deaths due to coronary heart disease, stroke, cancer, and respiratory disease. Our findings for BMI from RCT evidence are consistent with data from the Global BMI Mortality Collaboration. 8 Epidemiological studies can demonstrate the risks of higher BMIs and, therefore, the necessity for preventing obesity, but epidemiological associations between changes in body weight and changes in disease and mortality are often limited by the lack of information on the intentionality of that weight loss. Furthermore, treatment effects found in RCTs might differ from those expected in epidemiological studies, whereby epidemiological studies might overestimate benefits. 99

Evidence from systematic reviews indicates that physical activity as an adjunct to weight reducing diets might be more effective than diets alone, in terms of weight loss and improvements in blood lipids and blood pressure. 100 We were unable to show differences for mortality, cardiovascular disease, and cancer between weight reducing diets alone, diets plus advice on exercise, and diets plus an exercise programme for people to attend, for which we had limited statistical power. The majority of RCTs of weight loss interventions for obesity in adults have used low fat, weight reducing diets. But a recent systematic review by Tobias and colleagues 101 found that low carbohydrate weight reducing diets were more effective for weight loss than low fat, weight reducing diets, but found no difference between low fat, weight reducing diets (defined as <30% fat) and higher fat, weight reducing diets on weight loss. Recent US guidelines 102 have been criticised for the lack of evidence from RCTs to support guidance. 103 Thus, we must consider whether the type of weight loss diet, particularly low fat, weight reducing diets, usually with <10% of energy as saturated fat, affects important health outcomes beyond cardiovascular risk factors or weight. 100 That all except one of the interventions included here used a low fat, weight reducing diet provides important evidence on all cause mortality for weight reduction with this type of diet. We do not have the evidence to establish whether other forms of weight reducing diets have this effect, and we cannot dissociate the effects of weight loss from the use of low fat diets in our results.

We encourage investigators studying weight reducing diets to adhere to CONSORT guidance on reporting harms by always reporting clinically important outcomes and adverse events, irrespective of whether they think these events are related to the interventions. 104 Collecting and reporting major disease outcomes in weight reducing trials for obesity is important, particularly cardiovascular disease and cancer. We did not have sufficient data to examine whether other types of diet or physical activity influence outcomes or whether certain groups in the population are more or less likely to benefit.

In conclusion, weight reducing diets, usually low in fat and low in saturated fat, with or without an exercise component, may reduce premature all cause mortality in adults who are obese. By implication, our data support public health measures to prevent weight gain and facilitate weight loss using these types of diet.

What is already known on this subject

Whether recommendations to follow weight reducing diets can reduce premature mortality, cardiovascular disease, and cancer for adults who are obese is unclear

What this study adds

Weight reducing diets, usually low in fat and saturated fat, with or without exercise advice or programmes, may reduce premature all cause mortality in adults who are obese

Our data provide supporting evidence for public health measures to prevent weight gain and facilitate weight loss using diets low in fat and saturated fat

We thank Andrew Grey for helping to resolve discrepancies in data extraction and interpretation for cardiovascular events and cancer events. We thank trialists from 16 studies for clarifying or providing additional information for this review (Andrews 2011, Aveyard 2016, Bennett 2012, de Vos 2014, Finnish Diabetes Prevention Study 2009, Goodwin 2014, Green 2015, Horie 2016, Hunt (FFIT) 2014, Katula 2013, Li (Da Qing) 2014, Logue 2005, Ma 2013, O’Neil 2016, Rejeski (CLIP) 2011, Uusitupa 1993) and others who provided information, but their trials were later found not to fulfil our inclusion criteria.

Contributors and sources: AA, CM, MJB, CF, and GM designed this study. CM, AA, and CF searched the literature. CM, AA, FS, CR, PS, and MJB extracted data. CM, AA, JH, MJB, and GM analysed data. CM and AA wrote the first draft of the manuscript. All authors contributed to revisions of the manuscript. AA is the guarantor.

Funding: The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorate.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorate. No author has financial relationships with any organisations that might have an interest in the submitted work in the previous three years.

Data sharing: All data are included in the paper or supplementary appendix. No additional data are available.

Transparency: AA and CM affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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Obesity research: Moving from bench to bedside to population

* E-mail: [email protected]

Affiliation Diabetes Research Program, Department of Medicine, New York University Grossman School of Medicine, New York, New York, United States of America

• Ann Marie Schmidt

Published: December 4, 2023

• https://doi.org/10.1371/journal.pbio.3002448
• Reader Comments

Globally, obesity is on the rise. Research over the past 20 years has highlighted the far-reaching multisystem complications of obesity, but a better understanding of its complex pathogenesis is needed to identify safe and lasting solutions.

Citation: Schmidt AM (2023) Obesity research: Moving from bench to bedside to population. PLoS Biol 21(12): e3002448. https://doi.org/10.1371/journal.pbio.3002448

Copyright: © 2023 Ann Marie Schmidt. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: AMS received funding from U.S. Public Health Service (grants 2P01HL131481 and P01HL146367). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

Abbreviations: EDC, endocrine disruptor chemical; GIP, gastric inhibitory polypeptide; GLP1, glucagon-like peptide 1; HFCS, high-fructose corn syrup

This article is part of the PLOS Biology 20th anniversary collection.

Obesity is a multifaceted disorder, affecting individuals across their life span, with increased prevalence in persons from underrepresented groups. The complexity of obesity is underscored by the multiple hypotheses proposed to pinpoint its seminal mechanisms, such as the “energy balance” hypothesis and the “carbohydrate–insulin” model. It is generally accepted that host (including genetic factors)–environment interactions have critical roles in this disease. The recently framed “fructose survival hypothesis” proposes that high-fructose corn syrup (HFCS), through reduction in the cellular content of ATP, stimulates glycolysis and reduces mitochondrial oxidative phosphorylation, processes that stimulate hunger, foraging, weight gain, and fat accumulation [ 1 ]. The marked upswing in the use of HFCS in beverages and foods, beginning in the 1980s, has coincided with the rising prevalence of obesity.

The past few decades of scientific progress have dramatically transformed our understanding of pathogenic mechanisms of obesity ( Fig 1 ). Fundamental roles for inflammation were unveiled by the discovery that tumor necrosis factor-α contributed to insulin resistance and the risk for type 2 diabetes in obesity [ 2 ]. Recent work has ascribed contributory roles for multiple immune cell types, such as monocytes/macrophages, neutrophils, T cells, B cells, dendritic cells, and mast cells, in disturbances in glucose and insulin homeostasis in obesity. In the central nervous system, microglia and their interactions with hypothalamic neurons affect food intake, energy expenditure, and insulin sensitivity. In addition to cell-specific contributions of central and peripheral immune cells in obesity, roles for interorgan communication have been described. Extracellular vesicles emitted from immune cells and from adipocytes, as examples, are potent transmitters of obesogenic species that transfer diverse cargo, including microRNAs, proteins, metabolites, lipids, and organelles (such as mitochondria) to distant organs, affecting functions such as insulin sensitivity and, strikingly, cognition, through connections to the brain [ 3 ].

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Basic, clinical/translational, and epidemiological research has made great strides in the past few decades in uncovering novel components of cell-intrinsic, intercellular, and interorgan communications that contribute to the pathogenesis of obesity. Both endogenous and exogenous (environmental) stressors contribute to the myriad of metabolic perturbations that impact energy intake and expenditure; mediate innate disturbances in the multiple cell types affected in obesity in metabolic organelles and organs, including in immune cells; and impair beneficial interkingdom interactions of the mammalian host with the gut microbiome. The past few decades have also witnessed remarkable efforts to successfully treat obesity, such as the use of the incretin agonists and bariatric surgery. Yet, these and other strategies may be accompanied by resistance to weight loss, weight regain, adverse effects of interventions, and the challenges of lifelong implementation. Hence, through leveraging novel discoveries from the bench to the bedside to the population, additional strategies to prevent obesity and weight regain post-weight loss, such as the use of “wearables,” with potential for implementation of immediate and personalized behavior modifications, may hold great promise as complementary strategies to prevent and identify lasting treatments for obesity. Figure created with BioRender.

https://doi.org/10.1371/journal.pbio.3002448.g001

Beyond intercellular communication mediated by extracellular vesicles, the discovery of interactions between the host and the gut microbiome has suggested important roles for this interkingdom axis in obesity. Although disturbances in commensal gut microbiota species and their causal links to obesity are still debated, transplantation studies have demonstrated relationships between Firmicutes/Bacteroidetes ratios and obesity [ 4 ]. Evidence supports the concept that modulation of gut microbiota phyla modulates fundamental activities, such as thermogenesis and bile acid and lipid metabolism. Furthermore, compelling discoveries during the past few decades have illustrated specific mechanisms within adipocytes that exert profound effects on organismal homeostasis, such as adipose creatine metabolism, transforming growth factor/SMAD signaling, fibrosis [ 5 ], hypoxia and angiogenesis, mitochondrial dysfunction, cellular senescence, impairments in autophagy, and modulation of the circadian rhythm. Collectively, these recent discoveries set the stage for the identification of potential new therapeutic approaches in obesity.

Although the above discoveries focus largely on perturbations in energy metabolism (energy intake and expenditure) as drivers of obesity, a recently published study suggests that revisiting the timeline of obesogenic forces in 20th and 21st century society may be required. The authors tracked 320,962 Danish schoolchildren (born during 1930 to 1976) and 205,153 Danish male military conscripts (born during 1939 to 1959). Although the overall trend of the percentiles of the distributions of body mass index were linear across the years of birth, with percentiles below the 75th being nearly stable, those above the 75th percentile demonstrated a steadily steeper rise the more extreme the percentile; this was noted in the schoolchildren and the military conscripts [ 6 ]. The authors concluded that the emergence of the obesity epidemic might have preceded the appearance of the factors typically ascribed to mediating the obesogenic transformation of society by several decades. What are these underlying factors and their yet-to-be-discovered mechanisms?

First, in terms of endogenous factors relevant to individuals, stressors such as insufficient sleep and psychosocial stress may impact substrate metabolism, circulating appetite hormones, hunger, satiety, and weight gain [ 7 ]. Reduced access to healthy foods rich in vegetables and fruits but easy access to ultraprocessed ingredients in “food deserts” and “food swamps” caused excessive caloric intake and weight gain in clinical studies [ 8 ]. Second, exogenous environmental stresses have been associated with obesity. For example, air pollution has been directly linked to adipose tissue dysfunction [ 9 ], and ubiquitous endocrine disruptor chemicals (EDCs) such as bisphenols and phthalates (found in many items of daily life including plastics, food, clothing, cosmetics, and paper) are linked to metabolic dysfunction and the development of obesity [ 10 ]. Hence, factors specific to individuals and their environment may exacerbate their predisposition to obesity.

In addition to the effects of exposure to endogenous and exogenous stressors on the risk of obesity, transgenerational (passed through generations without direct exposure of stimulant) and intergenerational (direct exposure across generations) transmission of these stressors has also been demonstrated. A leading proposed mechanism is through epigenetic modulation of the genome, which then predisposes affected offspring to exacerbated responses to obesogenic conditions such as diet. A recent study suggested that transmission of disease risk might be mediated through transfer of maternal oocyte-derived dysfunctional mitochondria from mothers with obesity [ 11 ]. Additional mechanisms imparting obesogenic “memory” may be evoked through “trained immunity.”

Strikingly, the work of the past few decades has resulted in profound triumphs in the treatment of obesity. Multiple approved glucagon-like peptide 1 (GLP1) and gastric inhibitory polypeptide (GIP) agonists [ 12 ] (alone or in combinations) induce highly significant weight loss in persons with obesity [ 13 ]. However, adverse effects of these agents, such as pancreatitis and biliary disorders, have been reported [ 14 ]. Therefore, the long-term safety and tolerability of these drugs is yet to be determined. In addition to pharmacological agents, bariatric surgery has led to significant weight loss as well. However, efforts to induce weight loss through reduction in caloric intake and increased physical activity, pharmacological approaches, and bariatric surgery may not mediate long-term cures in obesity on account of resistance to weight loss, weight regain, adverse effects of interventions, and the challenges of lifelong implementation of these measures.

Where might efforts in combating obesity lie in the next decades? At the level of basic and translational science, the heterogeneity of metabolic organs could be uncovered through state-of-the-art spatial “omics” and single-cell RNA sequencing approaches. For example, analogous to the deepening understanding of the great diversity in immune cell subsets in homeostasis and disease, adipocyte heterogeneity has also been suggested, which may reflect nuances in pathogenesis and treatment approaches. Further, approaches to bolster brown fat and thermogenesis may offer promise to combat evolutionary forces to hoard and store fat. A better understanding of which interorgan communications may drive obesity will require intensive profiling of extracellular vesicles shed from multiple metabolic organs to identify their cargo and, critically, their destinations. In the three-dimensional space, the generation of organs-on-a-chip may facilitate the discovery of intermetabolic organ communications and their perturbations in the pathogenesis of obesity and the screening of new therapies.

Looking to prevention, recent epidemiological studies suggest that efforts to tackle obesity require intervention at multiple levels. The institution of public health policies to reduce air pollution and the vast employment of EDCs in common household products could impact the obesity epidemic. Where possible, the availability of fresh, healthy foods in lieu of highly processed foods may be of benefit. At the individual level, focused attention on day-to-day behaviors may yield long-term benefit in stemming the tide of obesity. “Wearable” devices that continuously monitor the quantity, timing, and patterns of food intake, physical activity, sleep duration and quality, and glycemic variability might stimulate on-the-spot and personalized behavior modulation to contribute to the prevention of obesity or of maintenance of the weight-reduced state.

Given the involvement of experts with wide-ranging expertise in the science of obesity, from basic science, through clinical/translational research to epidemiology and public health, it is reasonable to anticipate that the work of the next 2 decades will integrate burgeoning multidisciplinary discoveries to drive improved efforts to treat and prevent obesity.

Acknowledgments

The author is grateful to Ms. Latoya Woods of the Diabetes Research Program for assistance with the preparation of the manuscript and to Ms. Kristen Dancel-Manning for preparation of the Figure accompanying the manuscript.

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Home > Books > Role of Obesity in Human Health and Disease

Top 100 Most Cited Studies in Obesity Research: A Bibliometric Analysis

Submitted: 07 June 2021 Reviewed: 14 June 2021 Published: 22 December 2021

DOI: 10.5772/intechopen.98877

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Obesity represents a major global public health problem. In the past few decades the prevalence of obesity has increased worldwide. In 2016, an estimated 1.9 billion adults were overweight; of these more than 650 million were obese. There is an urgent need for potential solutions and deeper understanding of the risk factors responsible for obesity. A bibliometric analysis study was designed to provide a comprehensive overview of top 100 most cited studies on obesity indexed in Web of Science database. The online search was conducted on June 6, 2021 using the keywords “Obesity” OR “Obese” OR “Overweight” in title filed with no limitations on document types or languages. The top 100 cited studies were selected in descending order based on number of citations. The obtained data were imported in to Microsoft Excel 2019 to extract the basic information such as title, authors name, journal name, year of publication and total citations. In addition, the data were also imported in to HistCite™ for further citation analysis, and VOSviewer software for windows to plot the data for network visualization mapping. The initial search retrieved a total of 167,553 documents on obesity. Of the total retrieved documents, only top 100 most cited studies on obesity were included for further analysis. These studies were published from 1982 to 2017 in English language. Most of the studies were published as an article (n = 84). The highly cited study on obesity was “Establishing a standard definition for child overweight and obesity worldwide: international survey” published in BMJ-British Medical Journal (Impact Factor 39.890, Incites Journal Citation Reports, 2021) in 2000 cited 10,543 times. The average number of citations per study was 2,947.22 (ranging from 1,566 to 10,543 citations). Two studies had more than 10,000 citations. A total of 2,272 authors from 111 countries were involved. The most prolific author was Flegal KM authored 14 studies with 53,558 citations. The highly active country in obesity research was United States of America. The included studies were published in 33 journals. The most attractive journal was JAMA-Journal of the American Medical Association (Impact Factor 56.272) published 17 studies and cited globally 51,853 times. The most frequently used keywords were obesity (n = 87) and overweight (n = 22). The countries with highest total link strength was United States of America (n = 155), followed by England (n = 140), and Scotland (n = 130). Our results show that most number of highly cited studies were published in developed countries. The findings of this study can serve as a standard benchmark for researchers to provide the quality bibliographic references and insights into the future research trends and scientific cooperation in obesity research.

• bibliometric analysis

Author Information

Tauseef ahmad *.

• Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China

*Address all correspondence to: [email protected];, [email protected]

1. Introduction

Obesity represents a major public health challenge, in the past few decades the prevalence of obesity has increased worldwide and associated with serious adverse health outcomes [ 1 , 2 ]. According to the statistics of World Health Organization, in 2016, an estimated 1.9 billion adults (18 years and older) were overweight, of these more than 650 million were obese. In 2019, 38 million children (under age of 5 years) were overweight or obese [ 3 ].

Obesity associated comorbidities including certain cancer, depression, fatty liver disease, hepatic steatosis, hyperlipidemia, hypertension, obstructive sleep apnea, orthopedic conditions, type 2 diabetes mellitus and social isolation [ 1 , 4 , 5 ]. There is an urgent need for potential solutions and deeper understanding of the risk factors responsible for obesity.

Bibliometric type studies are of great interest, conducted not only to present an overall overview of the published scientific literature but also critical and subjective summarization of the most influential scientific studies [ 6 , 7 , 8 ].

This study aimed to provide a comprehensive overview of top 100 most cited studies on obesity. The finding can serve as a standard benchmark for researchers and to provide the quality bibliographic references.

3.1 Study design

Bibliometric citation analysis study.

3.2 Searching strategy and database

On June 6, 2021 the online search was conducted on Web of Science, Core Collection database (Philadelphia, Pennsylvania, United State of America). The search keywords used were “Obesity” OR “Obese” OR “Overweight” in title filed with no limitations on documents types or languages. The top 100 cited studies were selected in descending order based on number of citations.

3.3 Data extraction

The obtained studies were imported in to Microsoft Excel 2019 to extract the basic information such as title, authors name, journal name, year of publication and total citations. In addition, the downloaded dataset were imported in to HistCite™ for further citation analysis.

3.4 Visualization network

Visualization network co-authorship countries and co-occurrence all keywords were plotted by using VOSviewer software version 1.6.15 ( https://www.vosviewer.com/ ) for windows.

4. Ethical approval

This study did not involve any human or animal subjects, thus, ethical approval was not required.

The initial search retrieved a total of 167,553 documents on obesity indexed in Web of Science database. Of the total retrieved documents, only top 100 most studies on obesity were included in this study. The included studies were published in English language. Most of the studies were published as an article (n = 84) followed by review (n = 14) and letter (n = 1). The average number of citations per study was 2,947.22, ranging from 1,566 to 10,543 citations.

The most cited study on obesity was “Establishing a standard definition for child overweight and obesity worldwide: international survey” published in BMJ-British Medical Journal in 2000 cited 10,543 times. Another study “Positional cloning of the mouse obese gene and its human homolog” published in Nature in 1994 was cited 10,214 times. A total of 10 studies were cited more than 5,000 times. Furthermore, 52 studies were cited at least 2,000 times, while the remaining studies were cited more than 1,500 times. The top 100 studies on obesity is presented in Table 1 .

5.1 Most prolific authors

A total of 2,272 authors contributed to top 100 most cited studies. The most prolific author was Flegal KM authored 14 studies with 53,558 citations, followed by followed by Carroll MD (n = 10, citations = 36,950), and Ogden CL (n = 9, citations = 34,784). Only nine authors authored at least five studies as shown in Table 2 . In addition, only 22 authors contributed in at least three studies.

Top 100 most cited studies on obesity.

Note: LCS: Local citation score; LCS/t: Local citation score per year; GCS: Global citation score; GCS/t: Global citation score per year.

Authors with at least 4 studies.

5.2 Most active countries

A total 111 countries were involved in top 100 most cited studies on obesity. The most active country was United States of America (studies contributed: 75, citations: 217,788), followed by United Kingdom (studies contributed: 18, citations: 57,015), Canada (studies contributed: 9, citations: 17,920), Japan (studies contributed: 9, citations: 26,695), France (studies contributed: 8, citations: 21,228), Sweden (studies contributed: 8, citations: 20,632), and Netherlands (studies contributed: 7, citations: 13,018) as shown in Table 3 . Only 21 countries were involved at least in four studies.

Country with at least 3 studies.

Note: LCS: Local citation score; GCS: Global citation score.

5.3 Journals

The top 100 most cited studies were published in 33 journals. The most attractive journal was JAMA-Journal of the American Medical Association published 17 studies and cited globally 51,853 times as shown in Table 4 . Only seven journals published at least 4 studies, six journals published two studies each, while the remaining journals published a single study each.

Journals published at least 4 studies.

Note: IF: Impact Factor, Incites Journal Citation Reports, 2021; Q: Quartile; LCS: Local citation score; LCS/t: Local citation score per year; GCS: Global citation score; GCS/t: Global citation score per year.

5.4 Commonly used keywords

A total of 366 keywords were used in the top 100 most cited studies. The most widely used keywords were obesity (n = 87) and overweight (n = 22) as shown in Table 5 .

The keywords used at least ten times.

5.5 Year of publication

The top 100 most cited on obesity were published from 1982 to 2017 as shown in Figure 1 . The highest number of studies were published in 2006 (n = 9, citations = 29,552) and 2007 (n = 7, citations = 19,035) as presented in Figures 1 and 2 .

Publication years of top 100 most cited studies in obesity research.

Total global citation score per year of top 100 most cited studies in obesity research.

5.6 Co-authorship countries network visualization

The minimum number of studies for a country was fixed at 3. Of the total countries, only 38 countries were plotted based on total link strength (TLS) as shown in Figure 3 . The countries with highest TLS were United States of America (155), England (140), and Scotland (130).

Co-authorship countries network visualization. Two clusters are formed; red color represents cluster 1 (24 items), and green color represents cluster 2 (14 items).

5.7 Co-occurrence all keywords network visualization

Of the total keywords, only 69 were plotted as shown in Figure 4 . The keyword body-mass index has the highest TLS 117, followed by overweight (65), adipose-tissue (56), prevalence (53), weight (52), and obesity (49).

Co-occurrence all keywords network visualization. Three clusters are formed; red color represents cluster 1 (29 items), green color represents cluster 2 (26 items), and blue color represents cluster 3 (14 items).

6. Discussion

In recent years, bibliometric type studies have been increased significantly, these studies not only recognize the most influential studies in certain area but also determine the research shift and other important insights into the bibliometric parameters. Globally, obesity is a major public health problem and the prevalence has increased in the past few decades. Therefore, this study was undertaken to recognize the most influential studies in obesity research and provide essential bibliographic information. To the best of our knowledge this is the first bibliometric analysis on top 100 most cited studies on obesity indexed in Web of Science database. The highly cited study in obesity research received a total of 10,543 citations. The study published in a highly rated journal in medicine had an impact factor of 39.890 and placed in quartile 1 (Q1) category. The study entitled “Establishing a standard definition for child overweight and obesity worldwide: international survey” provides cut off points for body mass index in childhood of six large nationally representative cross sectional growth studies [ 9 ].

Another study received a total of 10,218 citations. The study titled “Positional cloning of the mouse obese gene and its human homologue” discusses the potential role of obese gene and these genes may function as part of a signaling pathway from adipose tissue that acts to regulate the size of the body fat depot [ 10 ].

The top 100 most cited were published in 33 journals. The most attractive and core journals in obesity research were JAMA-Journal of the American Medical Association (n = 17), and Nature (n = 14) had an impact factor of 56.272, and 49.962 respectively. A total of 31 studies were published in these two journals with a total citations of 100,377, thus representing the quality of work and aiming of the authors for high impact factor journals. Influential studies on obesity were published in higher impact factor journals. Furthermore, studies published in higher impact factor journals are more likely to be cited by the scientific community. The impact factor shows importance and quality of a journal [ 109 ]. The top three authors based on number of studies in obesity research were Flegal KM (n = 14, citations = 53,558), followed by Carroll MD (n = 10, citations = 36,950), and Ogden CL (n = 9, citations = 34,784). In our study, the leading country was United States of America contributed in a total of 75 studies with a total citations of 217,788. The finding is in line with studies in other research areas [ 110 , 111 , 112 , 113 ].

7. Conclusion

This study provides a comprehensive information of the most cited studies in obesity research. Majority of the most cited studies were published by developed countries in higher impact factor journals. The current study might be helpful to researchers for insights into the future research trends and scientific cooperation.

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Identifying factors associated with obesity traits in undergraduate students: a scoping review

• Published: 05 September 2020
• Volume 65 , pages 1193–1204, ( 2020 )

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• Rita E. Morassut 1 ,
• Chenchen Tian 1 &
• David Meyre 1 , 2  

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This scoping review identifies factors associated with obesity traits including body mass index, weight, and body fat percentage in undergraduate students.

We searched CINAHL, EMBASE, MEDLINE, and PsycINFO for original studies of undergraduate students where an obesity trait was associated with a risk factor.

Two-hundred sixty-eight articles were included comprising of 251 studies: 186 cross-sectional, 50 cohort, 11 interventional, and 4 qualitative. We extracted data on risk/protective factors, obesity traits, and the direction of effect between them. We identified a variety of factors including age, sex, ethnicity, socioeconomic status, religion, diet, eating habits, physical activity, sedentary activity, sleep, stress, university campus life, alcohol use, smoking, psychiatric disorders, body image, eating attitude, eating regulation, personality, sociocultural influences, and genetics. The majority of associations were cross-sectional. For longitudinal findings, usually only one study investigated each trait.

Conclusions

This review identifies a need for higher quality evidence to support results from cross-sectional studies and replication of findings of longitudinal studies. This review identifies gaps in the literature, generates hypotheses, guides researchers to plan future studies, and helps decision-makers design obesity-prevention programs in universities.

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Acknowledgements

We would like to thank Laura E. Banfield for her assistance in designing the literature search strategy and expertise on database selection.

DM holds a Canada Research Chair in Genetics of Obesity. REM has been supported by the Canadian Institutes of Health Research Canada Graduate Scholarship.

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Rita E. Morassut, Chenchen Tian & David Meyre

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Morassut, R.E., Tian, C. & Meyre, D. Identifying factors associated with obesity traits in undergraduate students: a scoping review. Int J Public Health 65 , 1193–1204 (2020). https://doi.org/10.1007/s00038-020-01458-4

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A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

Affiliations.

• 1 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• 2 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia. Electronic address: [email protected].
• 3 RIADI Laboratory, University of Manouba, Manouba, Tunisia; College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
• 4 Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• PMID: 34426171
• DOI: 10.1016/j.compbiomed.2021.104754

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Keywords: Diseases; Machine learning; Obesity; Overweight; Risk factors.

Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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• Published: 27 January 2020

Epidemiology and Population Health

Evidence from big data in obesity research: international case studies

• Emma Wilkins 1 ,
• Ariadni Aravani 1 ,
• Amy Downing 1 ,
• Adam Drewnowski 2 ,
• Claire Griffiths 3 ,
• Stephen Zwolinsky 3 ,
• Mark Birkin 4 ,
• Seraphim Alvanides 5 , 6 &
• Michelle A. Morris   ORCID: orcid.org/0000-0002-9325-619X 1  

International Journal of Obesity volume  44 ,  pages 1028–1040 ( 2020 ) Cite this article

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Background/objective

Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of ‘big data’ presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital , has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). ‘Additional computing power’ introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered.

Methods and results

Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle.

Conclusions

The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.

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Acknowledgements

The ESRC Strategic Network for Obesity was funded via ESRC grant number ES/N00941X/1. The authors would like to thank all of the network investigators ( https://www.cdrc.ac.uk/research/obesity/investigators/ ) and members ( https://www.cdrc.ac.uk/research/obesity/network-members/ ) for their participation in network meetings and discussion which contributed to the development of this paper.

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Emma Wilkins, Ariadni Aravani, Amy Downing & Michelle A. Morris

Center for Public Health Nutrition, University of Washington, Seattle, WA, USA

Adam Drewnowski

School of Sport, Leeds Beckett University, Leeds, UK

Claire Griffiths & Stephen Zwolinsky

Leeds Institute for Data Analytics and School of Geography, University of Leeds, Leeds, UK

• Mark Birkin

Engineering and Environment, Northumbria University, Newcastle, UK

Seraphim Alvanides

GESIS—Leibniz Institute for the Social Sciences, Cologne, Germany

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Wilkins, E., Aravani, A., Downing, A. et al. Evidence from big data in obesity research: international case studies. Int J Obes 44 , 1028–1040 (2020). https://doi.org/10.1038/s41366-020-0532-8

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Psychiatric disorders and obesity: A review of association studies

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Address for correspondence: Dr. Vikas Menon, E-mail: [email protected]

Received 2016 Nov 27; Revised 2017 Feb 22; Accepted 2017 Mar 18.

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

Background:

Inconsistent evidence exists regarding the strength, direction, and moderators in the relationship between obesity and psychiatric disorders.

This study aims to summarize the evidence on the association between psychiatric illness and obesity with particular attention to the strength and direction of association and also the possible moderators in each postulated link.

Materials and Methods:

Systematic electronic searches of MEDLINE through PubMed, ScienceDirect, PsycINFO, and Google Scholar were carried out from inception till October 2016. Generated abstracts were screened for eligibility to be included in the review. Study designs that evaluated the strength of relationship between obesity and psychiatric disorders were included in the study. Quality assessment of included studies was done using the Newcastle–Ottawa checklist tool.

From a total of 2424 search results, 21 eligible articles were identified and reviewed. These included studies on obesity and depression ( n = 15), obesity and anxiety (four) and one each on obesity and personality disorders, eating disorder (ED), attention deficit hyperactivity disorder, and alcohol use. Maximal evidence existed for the association between depression and obesity with longitudinal studies demonstrating a bidirectional link between the two conditions. The odds ratios (ORs) were similar for developing depression in obesity (OR: 1.21–5.8) and vice versa (OR: 1.18–3.76) with a stronger association observed in women. For anxiety disorders, evidence was mostly cross-sectional, and associations were of modest magnitude (OR: 1.27–1.40). Among other disorders, obesity, and EDs appear to have a close link (OR: 4.5). Alcohol use appears to be a risk factor for obesity and not vice versa but only among women (OR: 3.84).

Conclusion:

Obesity and depression have a significant and bidirectional association. Evidence is modest for anxiety disorders and inadequate for other psychiatric conditions. Gender appears to be an important mediator in these relationships.

KEY WORDS: Anxiety, depression, metabolic syndrome, obesity, psychiatry, review

Introduction

As per the World Health Organization (WHO) global estimates in 2014, almost 40% of adults are overweight (body mass index [BMI] ≥25 kg/m 2 ) with nearly a third of them obese (BMI ≥30 kg/m 2 ).[ 1 ] India, with its rapid urbanization and changing socioeconomic landscape, is experiencing an increase in obesity rates among its population.[ 2 , 3 , 4 ] In a recent nationally representative study, an estimated 135 million people were found to be suffering from generalized obesity and high prevalence rates were noted in both rural and urban areas.[ 5 ] This rising trend has also been reflected in childhood obesity with several Indian and international studies showing alarmingly increasing trends over the last decade.[ 6 , 7 , 8 , 9 ] These figures make it clear that obesity is assuming epidemic proportions cutting across age, sociocultural and ethnic boundaries, thus becoming a significant public health issue.

Although the physical comorbidity burden in obesity is well established,[ 10 , 11 ] its relation to mental health is relatively less explored. In the last couple of decades, however, evidence is gradually accumulating on the association between various psychiatric disorders and obesity, particularly among those seeking treatment for the same.[ 12 , 13 , 14 ] Despite this, knowledge gaps exist with regard to the strength and the direction of the association between obesity and various psychiatric conditions. Further, given the methodological differences between the studies, there is a need to synthesize the available evidence in this area so that clinicians and researchers have a better understanding of the links between obesity and psychiatric disorders. This has the potential to inform clinical evaluation and identify further research targets in this area such as the possible neurobiological links between obesity and psychiatric disorders. From a management perspective, it has been shown that early identification and management of common psychiatric problems can optimize outcomes among obesity patients presenting for surgical treatment. Hence, practicing clinicians need to be well informed about the same.

With this background, we carried out the present systematic review with the objective of summarizing the available evidence on the association between psychiatric illness and obesity with particular attention to the strength and direction of association and also the possible moderators in each postulated link. In this context, moderators refer to those variables that may influence the strength of relationship between two other variables (here, referring to psychiatry and obesity).

Materials and Methods

Inclusion and exclusion criteria.

Using the patient, intervention, comparison, outcomes, and study design criteria, all studies that assessed the association between obesity and psychiatric disorders or vice versa were included provided they met the following criteria:

The study provided a quantitative measure of association between obesity (explanatory variable) and specific psychiatric illness (outcome variable) or vice versa. Psychiatric outcomes should, necessarily, have been mentioned as “risk” to be included as it provides a quantitative estimate of the strength of the association

Studies done in populations with no prior medical comorbidities such as diabetes. This was done as chronic medical illness is a major confounder in the relationship between obesity and psychiatric illness[ 15 , 16 ]

Studies that did not use alternate definitions of obesity such as abdominal obesity/sarcopenic obesity

Studies published in English language peer-reviewed journals.

Search strategy and study selection

Electronic searches of MEDLINE through PubMed, ScienceDirect, PsycINFO, and Google Scholar were carried out from inception till October 2016. Our aim as stated above was to identify studies which directly looked into the association between psychiatric illness and obesity. “Psychiatric illness” was defined as any International Classification of Diseases, Tenth Edition coded category between F00 and F99. Obesity was defined as per the WHO definition based on BMI ≥30 kg/m 2 .[ 1 ] For this review, literature search was carried out using random combinations of the following keywords – “obesity”/”overweight”/”weight gain”/”weight changes” and “psychiatry”/”psychiatric illness”/”depression”/”anxiety”/”eating disorders (EDs)”/”binge eating”/”bulimia”/”personality disorders (PDs)”/”substance use”/”alcohol”/”nicotine”/”cannabis.” The initial search was carried out in PubMed, after which a similar search was done in other search engines to identify relevant articles. A supplemental Google Search using random combinations of the above terms was also done to further comb the extant literature. There was no restriction on the date of publication.

The titles and abstracts of the studies that met broad inclusion criteria were examined independently by the two authors (VM and TMR). In case of inadequate information in the abstract, both the authors independently scrutinized the full texts of potentially relevant articles to select those that met the inclusion criteria for the present review. Any disagreements at this stage (e.g., which assessment point to choose in cohorts that were analyzed repeatedly) were sorted out through mutual discussion until consensus. Following this, consolidated list of abstracts was drawn up after removing duplicates. In addition, reference lists of included studies were manually examined to check for potential articles by both the authors independently. Citation indexing services and conference proceedings were not included in the present review, the latter primarily due to concerns about incomplete reporting of data and uncertainty about the study quality.

Data extraction and quality assessment

Quality assessment of included studies was performed concurrently with data extraction by the two authors independently. The data extracted from the articles include the author and year of study, place of conduct of study, characteristics of the study population, sample size, study design, primary objective, fully adjusted measure of association such as risk (either odds ratio [OR]/relative risk [RR]), and any significant moderators/mediators. We used an adapted version of the Newcastle–Ottawa Quality Assessment Scale to critically appraise the selected articles which fell into the category of observational studies. This tool has good psychometric properties such as content validity and inter-rater reliability.[ 17 ] It includes items such as representativeness of sampling procedures, validity of assessment measures employed, response rate, and whether the study controlled for at least three essential confounders. Reporting on methodological aspects of a study than numerical scores has been suggested to be more appropriate for nonrandomized observational studies included in systematic reviews and hence this tool was preferred for quality assessment.[ 18 ] A response rate of 60% was considered adequate, based on prior systematic reviews of observational studies.[ 19 ] We rated the individual components of the quality assessment tool (criteria met, not met, not reported) and derived an overall rating for the quality of every study (high, moderate, poor) included in the review. Quality assessment was not separately done for systematic reviews and meta-analysis included but we relied on the authors’ quality analysis reported in the paper. For effect estimates, we relied on values reported by the authors and did not calculate summary measures or perform additional analysis. We did not attempt a meta-analysis as none of the identified studies were randomized controlled trials.

The flowchart for literature search is shown in Figure 1 . After applying the inclusion and exclusion criteria, 21 studies were identified for synthesis from an initial search yielding a combined total of 2424 articles. Majority of included studies were on depression ( n = 15) while four were on anxiety and one each on PDs, EDs, attention-deficit hyperactivity disorder (ADHD) and alcohol use. Of these, two studies evaluated a range of psychiatric outcomes in obesity and have been described in appropriate sections. Thus, a total of 21 papers were included in the review of which eight were cross-sectional studies, six longitudinal, two systematic reviews, and five were meta-analyses papers. Quality assessment of observational studies showed that four studies were rated high on the quality assessment checklist and others were moderate ( n = 10). Data extracted from the selected studies are presented in Table 1 .

Flowchart for literature search

Characteristics of included studies

Obesity and depression

From the 15 studies identified in this area, three cross-sectional studies done in general adult population had an average OR of 1.33 for depression in obesity.[ 20 , 21 , 22 ] This finding was replicated in a meta-analysis of longitudinal studies in adolescents with roughly similar odds of developing depression in obesity (OR: 1.4) and vice versa (OR: 1.7).[ 23 ] Thus, the study provided evidence for a bidirectional link between obesity and depression with depressed adolescents having about 70% higher risk for being obese. Broadly, similar findings were also echoed in a 10-year prospective study on older women with obese people having 38% higher risk for developing depression and depressed people having 10% elevated risk for obesity compared to controls.[ 24 ] These effect sizes were consistently higher among females (pooled OR: 1.32) than males (pooled OR: 1.00) in community-based studies suggesting a significant moderating role for gender in this relationship.[ 25 ] In another exclusive meta-analysis of longitudinal studies, the odds of being depressed in obesity (OR: 1.55) and conversely, odds of obesity in depression (OR: 1.58) was similar.[ 26 ] Findings from three longitudinal studies on adolescents/young adults show that the prospective risk of developing depression in obese individuals were significantly higher in females.[ 27 , 28 , 29 ] One study, interestingly, also noted that obesity had a protective effect against depression in males (OR: 0.31) while it predicted depression among young women (OR: 2.14).[ 30 ] Gender as a significant moderator of the obesity-depression association was also observed in the meta-analysis by Blaine, with a significantly higher risk (150%) among females (OR: 2.5).[ 31 ] Other studies have noted the role of ethnicity as a possible moderator, wherein only in white women, obesity was associated with significantly greater likelihood of depression.[ 32 ] Some evidence for severity of obesity as a possible moderator in the association between depression and obesity has also been observed with one study noting modest correlations between obesity indices and severity of depression ( r = 0.49).[ 33 ] Interestingly, Atlantis and Baker, in their systematic review of epidemiological studies aimed at determining whether obesity causes depression, have found weak evidence for obesity increasing incidence of depression and point out the need for methodologically rigorous prospective cohort studies in this regard.[ 34 ]

Obesity and anxiety

Among the four studies that were reviewed in this area, one found poor correlation between BMI and anxiety across both the genders ( r = 0.024 in males and 0.083 in females),[ 35 ] while another cross-sectional study also reported a similar lack of association between BMI and both anxiety and depression ( r = 0.15 and 0.1, respectively).[ 36 ] In a large nationally representative survey, where relationship of different psychiatric disorders in obesity was studied, the authors found that obese people had 27% increased lifetime risks of being diagnosed with panic disorder (OR: 1.27).[ 20 ] In a meta-analysis that evaluated both prospective and cross-sectional studies separately,[ 37 ] mixed results were observed in the two prospective studies with one study showing significant association between obesity and anxiety disorders only in men (OR for men 1.50 vs. women 0.99) while the other study, carried out only among women, showed an extremely high association (OR: 6.27). Hence, the moderating role of gender was inconclusive here. In the same meta-analysis, the 14 cross-sectional studies showed a positive but varying association (OR: 1.10–2.73). An inconsistency index of 84.3% was noted by the authors pointing toward significant heterogeneity in the results.

Obesity and personality disorders

In the systematic review on 68 studies,[ 38 ] the odds of having any PD was greater among obese people (OR: 1.2–1.95) and this relationship was directly proportional to the severity of obesity (24% risk in Class III obesity). Further, the association was more significant among females where the authors noted higher rates of avoidant and antisocial PD in females with severe obesity (38%) when compared to females without obesity (30%). This difference was not significant in men.

Obesity and eating disorders

Darby et al . conducted a study to assess time trends in the prevalence of comorbid ED in obesity over a 10-year period.[ 39 ] Their findings showed that comorbid ED and obesity had increased from 1% to 3.5% over the study period. This rise in prevalence was significantly higher than increase in rates of obesity or ED alone.

Obesity and attention deficit hyperactivity disorder

In a large epidemiological study, the authors explored the association between adult obesity and lifetime/remitted/persisting ADHD. Adult persistent ADHD was found to be significantly associated with obesity (OR: 1.44).[ 40 ] In women, the association was significant for all the three categories (remitted/persisting/lifetime ADHD). Notably, when corrected for possible confounders, association between obesity and lifetime ADHD continued to be significant only among females (adjusted OR: 1.09) but not in males (adjusted OR: 0.98).

Obesity and alcohol use

One longitudinal study, among young adults, explored prospective three-way association between obesity, depression, and alcohol use. In this study, alcohol use disorders prospectively predicted obesity only among women (OR: 3.84). Obesity, however, did not seem to be a clear risk factor for alcohol use.[ 30 ]

Although a significant amount of literature is available on obesity and psychiatric illness, the current review has specifically looked into the strength of association of each psychiatric illness with obesity. Clearly, the evidence was more voluminous and strongest for depression, with most of the studies reporting significant association for the presence of depression in obesity.[ 21 , 22 , 23 , 24 , 26 , 28 , 31 , 32 ] However, most of these studies were cross-sectional in nature which cannot conclusively establish the cause-and-effect relationship between the two conditions due to design limitations. This can only be established through longitudinal research designs. Only few such longitudinal studies were available which gave evidence for a reciprocal link between depression and obesity and this was replicated in a meta-analysis paper also which provides stronger evidence of the association.[ 23 , 24 , 26 , 34 ] Variability in effect sizes across studies may stem from methodological differences such as the cutoffs used for measuring BMI, methods used to measure psychiatric outcomes such as depression (clinical vs. rating scales), varying lengths of follow-up in longitudinal designs and the nature of effect estimate used (RR vs. absolute risk estimates). Subgroup analysis based on parameters such as age, gender, and differences in measurement methods were reported in a few studies and this may also have contributed to some of the differences observed.

The role of gender as a moderator in this relationship was evident in a handful of studies that showed greater effect sizes in females,[ 22 , 25 , 28 ] and specifically, in adolescent females.[ 27 , 31 ] The earlier onset of puberty in females and the complex hormonal and biological changes that accompany this phenomenon may result in earlier onset of obesity among females and this may persist from adolescence to adulthood.[ 41 , 42 ] The inherently higher dissatisfaction with their bodies among females combined with societal pressures to remain thin may affect self-esteem and enhance stress, which may further increase the risk of both obesity and depression preferentially among females across life span.[ 43 , 44 ] One study provided preliminary evidence of the role of ethnicity as a possible moderator.[ 32 ] Other moderators identified included severity of obesity.[ 33 ] A few studies have shown that both overweight and obesity were risk factors for depression,[ 26 ] whereas few studies have pointed out that overweight is actually a protective factor against depression.[ 22 ] Clearly, this disparity needs to be addressed in future studies with appropriate designs. The mechanisms postulated for depression in obesity include the social dimensions of weight such as negative self-perceptions and stigma as well as the health consequences of being overweight.[ 45 , 46 , 47 ] Conversely, the mechanisms leading to obesity in depression mostly focus on ethnic and lifestyle factors.[ 48 ] Recently, the role of serotonin and particularly, the 5-hydroxytryptamine type 3 receptor has come under scrutiny as a therapeutic target to reduce the burden of comorbid depression and obesity.[ 49 ]

For anxiety disorders, the number of studies was comparatively fewer. From the reviewed articles, the correlation between obesity and anxiety appears less robust in comparison to depression.[ 35 , 36 ] One study has shown that obese individuals had a higher odds of lifetime panic disorder.[ 20 ] In the meta-analysis that was reviewed, though the pooled OR pointed toward a significant association, the inconsistency index was high.[ 37 ] With the exception of specific phobia and social anxiety, evidence is largely mixed in studies which performed subgroup analysis by subtypes of anxiety disorders.[ 50 , 51 , 52 , 53 ] The relative lack of longitudinal studies assessing the relationship between anxiety disorders and obesity precludes clear conclusions regarding the direction of association.

The relationship between obesity and PDs appears complex. Few studies have explored specific personality traits in obese individuals and among these, neuroticism and impulsivity has been consistently replicated.[ 54 , 55 , 56 ] Moreover, among obese people attending a bariatric surgery clinic, roughly a quarter had clinical evidence of borderline PD.[ 57 ] Our review shows that the odds of having any PD in obese individuals are high and cluster C traits (avoidant/dependent) are often predominant.[ 38 ]

EDs-obesity-other psychiatric illness could form a vicious cycle and many studies have looked into psychiatric illness in the context of comorbid ED and obesity. However, the direct association between ED and obesity has been rarely studied. In the only such study included in this review, the authors have shown that though ED and obesity are increasing in general population, the odds of having comorbid ED and obesity have increased around 4.5 times, indirectly pointing toward an underlying association between these disorders.[ 39 ]

One prospective study has reported increased the prevalence of obesity in adults with a childhood history of ADHD.[ 58 ] The study included in the present review looked into the strength of association of this relationship and pointed out that persistent ADHD in childhood was associated with obesity later and more so in females.[ 40 ] However, as there are very few studies addressing this question, one needs to wait for more evidence before clinical recommendations can be made.

Several neuroimaging studies have implicated a common neurobiology in feeding and substance use such as reinforcement of the reward pathway.[ 59 , 60 ] As such, there has been a trend to consider obesity as a part of “addiction.”[ 61 , 62 ] However, studies that explored the strength of this association are scarce. Here, we have reviewed an article which studied the interaction between obesity-substance use and depression and show that obesity rates were more in those with substance use disorders and this association was stronger in females.[ 30 ]

Our findings have important clinical and research implications. With steadily increasing rates of obesity globally, an understanding of the impact of obesity on prevalence rates of mental disorders assumes significance. This may provide inputs from a mental health prevention and promotion standpoint. It may inform the development of prediction tools and better interventions. It may also spur research on the causal pathways and mechanisms mediating the relationship. Figure 2 represents an evidence based model for understanding the association between obesity and psychiatric disorders with key moderators in this relationship and is adapted from earlier works in this field.[ 63 ]

Evidence-based model for association between psychiatric disorders and obesity (moderators shown in ellipse and disorders in rectangle boxes - arrows indicate the direction of association observed)

A growing body of evidence suggests that there may be pathophysiological links between psychiatric disorders such as schizophrenia and metabolic conditions such as obesity and diabetes.[ 64 , 65 , 66 , 67 ] Further, obesity (or high BMI) has been identified consistently as a risk factor for metabolic syndrome among psychiatric populations receiving treatment with agents such as clozapine.[ 68 , 69 ] This three-way link between obesity, metabolic syndrome, and psychiatric disorders presents significant opportunities to improve our understanding of pathogenesis of psychiatric disorders and develop newer therapeutic targets.

Psychiatric evaluation may be an important component of comprehensive obesity care and merits further evaluation. This can be expected to optimize therapeutic and functional outcomes. Furthermore, as the bidirectional link becomes apparent, the treatment of the psychiatric illness may bring down the obesity burden and vice versa. Future research opportunities in this area include clarifying the relation between obesity severity and clinical subtypes of depression and anxiety given the heterogeneous nature of these conditions. Researchers should also consider important methodological issues such as measuring obesity through objective rather than self-reported measures and possible confounders such as physical comorbidities to unravel the complex relationship between obesity and psychiatric illness.

Limitations of the present review include the confounding effects of unmeasured medical comorbidities and differences in the way outcomes were assessed. The included studies were quite heterogeneous in characteristics of the study population. As none of the included studies were randomized controlled trials, we did not perform a meta-analysis. We were unable to peruse certain databases due to limitations of institutional access. Furthermore, it is possible that some studies may have been missed as it did not fit our inclusion criteria or remain unpublished or unavailable on academic databases. We tried to keep the studies homogeneous with regard to their design and domains of outcome studied so as to render an interpretation of findings easier and that allied with the quality assessment done are advantages of the present review.

Obesity and psychiatric illness are closely linked, and the evidence is strong and reciprocal for depression, modest, and inconsistent for anxiety disorders and inadequate for other psychiatric conditions. Apart from depression, the causal relationships between obesity and other psychiatric disorders could not be established from available data. Although both genders appeared to be at risk of psychiatric disorders in obesity and vice versa, many of these associations were stronger in females indicating a possible moderating role for gender in this relationship. Based on current evidence, there is a need to carry out a cost-effectiveness analysis of a multidisciplinary approach to the management of obesity.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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Research priority setting in obesity: a systematic review

Halima iqbal, rosemary r c mceachan, melanie haith-cooper.

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Received 2021 Jul 23; Accepted 2021 Nov 11.

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/ .

Obesity research priority setting, if conducted to a high standard, can help promote policy-relevant and efficient research. Therefore, there is a need to identify existing research priority setting studies conducted in the topic area of obesity and to determine the extent to which they followed good practice principles for research priority setting.

Studies examining research priority setting in obesity were identified through searching the MEDLINE, PBSC, CINAHL, PsycINFO databases and the grey literature. The nine common themes of good practice in research priority setting were used as a methodological framework to evaluate the processes of the included studies. These were context, use of a comprehensive approach, inclusiveness, information gathering, planning for implementation, criteria, methods for deciding on priorities, evaluation and transparency.

Thirteen articles reporting research prioritisation exercises conducted in different areas of obesity research were included. All studies reported engaging with various stakeholders such as policy makers, researchers and healthcare professionals. Public involvement was included in six studies. Methods of research prioritisation commonly included both Delphi and nominal group techniques and surveys. None of the 13 studies fulfilled all nine of the good practice criteria for research priority setting, with the most common limitations including not using a comprehensive approach and lack of inclusivity and evaluating on their processes.

There is a need for research priority setting studies in obesity to involve the public and to evaluate their exercises to ensure they are of high quality.

Keywords: obesity, research priority setting, obesity research agenda

Introduction

Setting priorities for research helps to direct the most effective use of resources, such as research capacity, time and funds, to ensure an optimal health impact (Terry et al. 2018 ). Research priority setting in health, informed by stakeholders, can assist in the identification of topical and relevant issues, and unresolved questions regarding prevention, diagnosis and treatment of health conditions using a process that is explicit, iterative and inclusive (Rudan et al. 2010 ). There is currently no consensus on the definition of research priority setting, but there is agreement on a range of activities that centre on identifying, prioritising and reaching agreement on the research areas or questions deemed important to stakeholders (Tong et al. 2019 ). In the past, research-funding organisations and researchers developed their own research agendas without consulting key stakeholders (Graham et al. 2020 ). In recent times, however, there has been a focus on research needing to address questions that have relevance to those very people it intends to help (Dawson et al. 2017 ). It has been advocated that priority setting processes must also be fair, informed by credible evidence, of high quality and involve a broad range of stakeholders (Nasser et al. 2013 ; Sibbald et al. 2009 ; Viergever et al. 2010 ). Adopting a systematic and transparent approach to the identification of health research priorities can help to ensure that funded research has a public health benefit and make efficient and equitable use of limited resources (Bryant et al. 2014 ). Developing research agendas with target populations increases the potential for success and is more likely to be well received and relevant to their needs.

Nine common themes of good practice in research priority setting

There are currently no published guidelines for reporting priority setting for health research (Tong et al. 2019 ). In the absence of a gold standard approach, a checklist of nine common themes for good practice in health research prioritisation was developed by Viergever et al. ( 2010 ). In developing the checklist, expert consultation was initiated, and a literature review identified several methodological approaches which were combined to draw together a comprehensive outline of common views on what constituted good practice in health research priority setting (Viergever and Roderik 2010 ). The aim was to facilitate a transparent and comprehensive priority setting via this checklist and accommodate the flexibility required by different contexts.

The nine themes contained within the checklist broadly fall into three different categories: preparatory work, deciding on priorities and after priorities have been set. Each category contains corresponding practices that further identify the goals in each step. There are five related practices within preparatory work , namely context, use of a comprehensive approach (established frameworks providing structured guidance for research prioritisation), inclusiveness, information gathering and planning for implementation. There are two related practices within deciding on priorities, namely criteria and methods for deciding on priorities, and two within after priorities have been set, namely evaluation and transparency. See Table 1 for a detailed description of each theme.

Checklist for health research priority setting adapted from Viergever et al. ( 2010 )

The worldwide prevalence of obesity has significantly increased over the past few decades, leading the trend to be termed a ‘global epidemic’ by the World Health Organization and a serious threat to public health (World Health Organization 2017 ). Moreover, obesity is a global issue because it concerns both developed and developing countries (Cassi et al. 2017 ). The most recent available statistics from 2018/19 show that in England, a significant proportion of adults were overweight or obese, namely 67% of men and 60% of women (NHS Digital 2020 ). Of these, 26% of men and 29% of women were obese, and morbid obesity has also increased, from under 1% in 1993, to 3% in 2018 (NHS Digital 2020 ). Excess levels of fat in the body increase the risk of disease (Pollack et al. 2020 ) and obesity is a major risk factor for developing a range of conditions including cardiovascular disease, type 2 diabetes, muscular disorders, respiratory conditions and a host of psychological problems (Fruh 2017 ). A recent report by Public Health England highlights that the COVID-19 pandemic has brought to the fore the health crisis caused by overweight and obesity (Public Health England 2020 ). Both international and national research has consistently identified obesity as one of the key factors linked with severe outcomes from COVID-19 (Dietz and Santos-Burgoa 2020 ; Halvatsiotis et al. 2020 ). The direct annual costs resulting from obesity to the UK National Health Service (NHS) are reportedly estimated to reach £9.7 billion ($13.2 billion) by 2050, with wider costs to society predicted to reach just under £50 billion ($67.8 billion) per year by 2050 (Bradford Metropolitan District Council 2019 ).

Research is critical to inform prevention and treatment strategies to tackle obesity. Although there is a plethora of research examining the multitude of factors influencing obesity, research budgets are finite. Research priority setting can assist in making the most effective use of budgets by identifying the most relevant research areas according to different stakeholders. There is an emphasis on the need for research priority setting exercises to be explicit in their processes (Tong et al. 2019 ). Research priority setting guidelines and/or frameworks can help improve future research prioritisation in obesity, thus increasing the value and contribution of research aimed at reducing the obesity levels of populations.

The aim of this systematic review was to identify research priority setting exercises that have been conducted in obesity and to examine whether they had applied good practice principles in health research priority setting.

The systematic review followed the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Shamseer et al. 2015 ).

Search strategy and process of study selection

The search was undertaken between 14–15 November 2020, using four electronic health databases, namely MEDLINE, PBSC, CINAHL and PsychINFO. The following Boolean search term combinations were used:

‘research priority setting’ [all fields] OR ‘research prioritization’ [all fields] OR ‘research prioritisation’ [all fields] OR ‘research priorities’ [all fields] OR ‘research agenda’ [all fields]

‘obesity’ OR ‘child obesity’ [all fields] OR ‘childhood obesity’ [all fields] OR ‘pediatric obesity’ [all fields] OR ‘obesity prevention’ [all fields] OR ‘obesity treatment’ [all fields]

We searched databases from their inception to November 2020. Only titles and abstracts published in English were included. The principal researcher (HI) independently conducted the article search. Searches in the grey literature included Google Scholar, Cochrane methods priority setting, the James Lind Alliance (a well-established priority-setting partnership method) and reference lists of selected articles to identify eligible papers. The search string ‘research priority setting and obesity’ was applied to Google Scholar. The first ten pages of Google Scholar were examined for additional articles. All authors contributed and refined the review’s search strategy.

Inclusion and exclusion criteria

The review included any study describing a process of conducting a research prioritisation exercise in obesity. To be included in the review, studies must have outlined participants’ characteristics, stated the methods used to obtain research and identified well-established outcomes. International studies were included provided they were written in the English language. Studies were excluded if they did not mention health research, had not described the research prioritisation process or had assessed priorities for practice and policy rather than research (quality indicators). Also excluded were studies that did not focus on obesity research prioritisation.

Across all databases, the search yielded 249 citations, of which 203 remained after duplicates were removed. After the titles and abstracts had been screened, 26 articles underwent full-text screening. Of these publications, 13 studies met our inclusion criteria and were finally included in the analysis. Of the 13 excluded studies, four did not focus mainly on research prioritisation, one was a study protocol, two did not focus on obesity, four were non-research articles and two failed to include the methods and processes. All authors discussed and agreed on the selected papers. References were managed with EndNote X9 for ease. The PRISMA flowchart is displayed in Fig. 1 .

PRISMA 2009 flow diagram

Quality assessment tool

In the absence of a gold standard approach to research priority setting, the checklist of nine common themes for good practice in health research priority setting by Viergever et al. ( 2010 ) was used to ascertain whether the research prioritisation exercises in each included study complied with good practice principles in their processes. This checklist has been previously used to evaluate or guide research prioritisation exercises (Doolan-Noble et al. 2019 ; Iqbal et al. 2021 ; Mador et al. 2016 ; Reveiz et al. 2013 ; Tong et al. 2015 ;) and has identified weaknesses prevalent in their processes. The checklist was specifically designed for health research priority setting and, as such, can identify issues that may have been otherwise overlooked by traditional quality appraisal tools.

Data synthesis and extraction

A descriptive synthesis was conducted to outline study characteristics and outcomes, and to determine how many good practice principles each study followed. Studies could score between 0 (demonstrated none of the good practice principles) to 20 (demonstrated all of the good practice principles). One researcher (HI) independently extracted study characteristics, methods and outcomes. The relevant data were inserted into comprehensive data extraction checklist forms developed specifically for the quality synthesis. The quality appraisal criteria were applied by two researchers and resolved through discussion (HI and MC).

Studies were conducted in research priority setting in the area of obesity for childhood obesity (Botchwey et al. 2018 ; Byrne et al. 2008 ; Curtin et al. 2017 ; Gallagher et al. 2010 ; Hennessy et al. 2018 ; McPherson et al. 2016 ; Ramirez et al. 2011 ; Taylor et al. 2013 ; Ward et al. 2013 ), adult obesity (Hill et al. 2019 ; Hill et al. 2020 ; Mama et al. 2014 ), and obesity more generally (McKinnon et al. 2009 ). Studies were conducted in the areas of childhood obesity prevention or treatment (Byrne et al. 2008 ; Gallagher et al. 2010 ; Hennessy et al. 2018 ; Taylor et al. 2013 ), youth physical activity and healthy weight (Botchwey et al. 2018 ), healthy weight among youth with autism spectrum disorder and other developmental disabilities (Curtin et al. 2017 ), preconception priorities for maternal obesity prevention (Hill et al. 2019 ), pregnancy priorities for maternal obesity prevention (Hill et al. 2020 ), obesity reduction (Mama et al. 2014 ), obesity in children with physical disabilities (McPherson et al. 2016 ), obesity in Latino children (Ramirez et al. 2011 ), obesity policy (McKinnon et al. 2009 ) and obesity prevention in early care and education settings (Ward et al. 2013 ). The prioritisation exercises were all conducted in high income countries, namely Australia (4), the UK (1) and the US (8).

Seven studies did not include any patient or public involvement in their establishment of research priorities, yet involved a wide range of other stakeholders such as researchers, policy makers/leaders and healthcare professionals (Botchwey et al. 2018 ; Byrne et al. 2008 ; Gallagher et al. 2010 ; Hennessy et al. 2018 ; McKinnon et al. 2009 ; Taylor et al. 2013 ; Ward et al. 2013 ). One study solely involved the public in identifying priorities (Mama et al. 2014 ) and the remaining five studies involved the public alongside other stakeholders (Curtin et al. 2017 ; Hill et al. 2019 ; Hill et al. 2020 ; McPherson et al. 2016 ; Ramirez et al. 2011 ). Frequently cited methods used to identify priorities were surveys, Delphi techniques and the nominal group technique.

The main outcome of the studies was the generation of research priorities relevant to the topic and scope of each study. The priorities were described as prioritised research ideas/gaps/areas, prioritised lists, research priorities and prioritised themes. All 13 studies are displayed in Table 2 below.

Study characteristics for the included empirical studies with quality scores

When matched against the checklist of good practice principles in research priority setting as defined by Viergever et al. ( 2010 ), none of the studies adhered to all the principles outlined in the checklist (see Table 3 ).

Appraisal of comprehensiveness of reporting

Summary of the comprehensiveness of studies in reporting good practice principles

Theme 1: context.

The focus of the exercise was made clear in all studies, as were the underlying values and principles of each study. These included the need to engage the community in identifying obesity research priorities (Mama et al. 2014 ), or to foster collaboration amongst interdisciplinary research experts in the field of healthy weight, prevention of weight gain and maintenance of healthy weight (Gallagher et al. 2010 ; Hennessy et al. 2018 ; Taylor et al. 2013 ), or to develop a research agenda leveraging the collective expertise of a range of stakeholders (McPherson et al. 2016 ). However, the resources used for the exercises were made explicit in very few studies. Where information was provided, these included the use of materials used during the exercise such as cards to write knowledge gaps on (McPherson et al. 2016 ), flipcharts and numbered stickers for ranking (Hennessy et al. 2018 ), the use of audio-recorders (Mama et al. 2014 ) and the use of facilitators (Gallagher et al. 2010 ; Hennessy et al. 2018 ; Hill et al. 2020 ; Hill et al. 2019 ; McKinnon et al. 2009 ; McPherson et al. 2016 ) and project staff members to take notes and capture details around the issues raised (Ward et al. 2013 ), as well as the use of a statistician, data analyst and administrative support staff (Curtin et al. 2017 ). In one study, the use of a transcription service was disclosed (Mama et al. 2014 ). The economic/financial and political environment in which the prioritisation exercise took place was not disclosed in any of the studies.

Theme 2: Use of a comprehensive approach

None of the studies reported the use of established, structured, step-by-step frameworks specifically designed for research priority setting to guide their prioritisation processes, such as the James Lind Alliance (JLA) methodology (JLA 2020 ), the Essential National Health Research (ENHR) strategy (COHRED 2009 ), the Combined Approach Matrix (CAM) (Ghaffar 2009 ) and the Child Health and Nutrition Research Initiative (CHNRI) (Rudan 2016 ). None of the studies developed their own frameworks to guide their exercises.

Theme 3: Inclusiveness

Across prioritisation exercises, participants comprised a diverse range of stakeholders. Samples were inclusive of health service managers, medical practitioners, healthcare practitioners, academics, interdisciplinary researchers, dietitians, scientists, government agencies, policy leaders and experts in the field of child obesity more generally. Two studies solely involved researchers in the process (Gallagher et al. 2010 ; Taylor et al. 2013 ). Public involvement in the exercise was made explicit in six studies only (Curtin et al. 2017 ; Hill et al. 2020 ; Hill et al. 2019 ; Mama et al. 2014 ; McPherson et al. 2016 ; Ramirez et al. 2011 ). Although all studies discussed participant characteristics, some were more detailed in their descriptions by disclosing the sex of participants (Hennessy et al. 2018 ; Mama et al. 2014 ; Ramirez et al. 2011 ), with women overwhelmingly outnumbering men in two studies (Hennessy et al. 2018 ; Ramirez et al. 2011 ). An appropriate representation of regional participation was included in most studies that did not involve the public, as well as the incorporation of relevant sectors.

Theme 4: Information gathering

In some studies, a core planning group or committee suggested initial priorities to direct the process (Gallagher et al. 2010 ; Ramirez et al. 2011 ; Ward et al. 2013 ), or researchers identified the initial areas and other stakeholders prioritised the selected areas (Botchwey et al. 2018 ; Byrne et al. 2008 ). The use of technical data was reported in most studies. These included reviews of guidelines and recommendations (Hill et al. 2020 ; Hill et al. 2019 ), as well as literature searches, reports and systematic reviews (Botchwey et al. 2018 ; Hill et al. 2020 ; Ramirez et al. 2011 ). Surveys were conducted to obtain broad input on the selected topic areas (Botchwey et al. 2018 ; Byrne et al. 2008 ; Curtin et al. 2017 ), as were questionnaires (Ramirez et al. 2011 ; Taylor et al. 2013 ). Workshops (Gallagher et al. 2010 ; Hennessy et al. 2018 ; Hill et al. 2019 ; Hill et al. 2020 ; McPherson et al. 2016 ), group meetings (Curtin et al. 2017 ; McPherson et al. 2016 ; Ward et al. 2013 ) and brainstorming sessions were also reported as a means of generating information (Curtin et al. 2017 ), as well as presentations (McPherson et al. 2016 ; Ward et al. 2013 ).

Theme 5: Planning for implementation

Most of the studies did not report their plans for implementing identified priorities. Several community projects were established from two research priority setting studies (Gallagher et al. 2010 ; Ramirez et al. 2011 ). Plans for implementing pilot studies were established from a research agenda (Ramirez et al. 2011 ). Ongoing activities influenced by the identified priorities were reported in two studies (Hill et al. 2019 ; Hill et al. 2020 ). The research agenda shaped four initial projects in another study (Botchwey et al. 2018 ) and finally, one study secured a large team grant to address some items on their research agenda (McPherson et al. 2016 ).

Theme 6: Criteria

Criteria to focus discussion on research priorities were mentioned in six studies (Botchwey et al. 2018 ; Hill et al. 2020 ; Hill et al. 2019 ; McKinnon et al. 2009 ; McPherson et al. 2016 ; Ramirez et al. 2011 ). Cited criterion included research priorities that had the greatest long-term impact, and what would have the most immediate impact (Botchwey et al. 2018 ), prevalence or burden attributable to the proposed problem (Hill et al. 2019 ), provision, potential and proposed transformation attributable to the problem (Hill et al. 2020 ), preventative effect with respect to obesity development, and implementation feasibility (Hill et al. 2020 ), and the most appropriate and feasible methods for initiating research efforts (McPherson et al. 2016 ).

Theme 7: Methods for deciding on priorities

Studies either adopted a metrics approach (Botchwey et al. 2018 ; Byrne et al. 2008 ; Curtin et al. 2017 ; Gallagher et al. 2010 ; Taylor et al. 2013 ; Ward et al. 2013 ), a consensus approach (McPherson et al. 2016 ; Ramirez et al. 2011 ) or a combination of both (Hennessy et al. 2018 ; Hill et al. 2019 ; Hill et al. 2020 ). Likert scales were utilised in one study for ranking priorities (Ramirez et al. 2011 ), as were numbered stickers (Hennessy et al. 2018 ). The Delphi method was the most used method for deciding on priorities, both in its original form (Byrne et al. 2008 ; Ramirez et al. 2011 ; Taylor et al. 2013 ) and adapted versions, followed by the nominal group technique (Hennessy et al. 2018 ). In two studies, the Delphi technique was combined with the nominal group technique (Hill et al. 2019 ; Hill et al. 2020 ). One study used a modified nominal group technique to determine priorities (McKinnon et al. 2009 ). Another study did not use ranking and/or consensus to determine priorities, and instead searched for themes in the data and described these as the priorities (Mama et al. 2014 ).

Theme 8: Evaluation

There were no reported plans to update the priorities. One study mentioned that the research agenda would be reviewed, re-evaluated and refined (Curtin et al. 2017 ).

Theme 9: Transparency

Most of the studies were explicit in their priority setting processes, despite not using a well-established framework, although some were more transparent than others (Gallagher et al. 2010 ; Hennessy et al. 2018 ; Hill et al. 2020 ; Hill et al. 2019 ; Ramirez et al. 2011 ). The majority of studies outlined how the priorities were set. In most cases, it was clear which stakeholders identified initial topics, which stakeholders added generated additional input and who exactly prioritised.

Some studies also highlighted the limitations of their prioritisation exercise, such as acknowledging the lack of public involvement altogether (Hennessy et al. 2018 ), the possibility of unequal representation of disciplines (Hill et al. 2019 ; Hill et al. 2020 ), the lack of participation in person by children or youth (McPherson et al. 2016 ) and the lack of men that participated (Hennessy et al. 2018 ). Further highlighted limitations were around the issue of generalisability. This included the small sample size (Taylor et al. 2013 ), method of sample recruiting (Mama et al. 2014 ) and the possibility of selection bias due to the participants not being randomly selected (Ramirez et al. 2011 ). Other challenges were also highlighted, such as issues encountered in achieving consensus during the prioritisation phases (Hennessy et al. 2018 ), and the steps taken to reduce potential limitations when using the nominal group technique (Hennessy et al. 2018 ; Hill et al. 2019 ). One study reported pilot testing the questionnaire used to elicit priorities utilising a survey instrument, and subsequently revising it for improvement (Ramirez et al. 2011 ).

This review provides an assessment of research priority setting initiatives in the area of obesity. Most of the prioritisation exercises focussed on obesity topics including causes, prevention and management. Of the 13 identified studies, ten concentrated on child obesity, three on adult obesity and one focussed on obesity more generally. The application of a checklist of good practice principles in research priority setting identified the strengths and weaknesses inherent in each study. None of the studies fulfilled all the good practice principles as outlined by the checklist. It is clear that more effort needs to be made in studies examining obesity research priority setting to ensure that their processes are of a high quality. It is important to note however, that two studies (Byrne et al. 2008 ; McKinnon et al. 2009 ) were conducted before the checklist of nine common themes of good practice was published in 2010. In addition, literature advocating the need for research priority setting to be fair, legitimate, informed by credible evidence, include a wide range of stakeholders and be transparent, has only more recently been strongly advocated (Bhaumik et al. 2015 ; Nasser et al. 2013 ; Tong et al. 2019 ; Viergever et al. 2010 ) which may be as a result of the increase in research prioritisation exercises in the past two decades. Our findings suggest that the greatest limitations of studies when applied to the checklist of good practice concerned the criteria use of comprehensive approach, inclusiveness and evaluation.

None of the studies used comprehensive well-established research priority setting frameworks such as the JLA methodology, the ENHR strategy, the CAM and the CHNRI initiative. These established schemata were all developed before the studies were undertaken and provide step-by-step guidance for the entire process, while covering many of the points on the checklist (Viergever et al. 2010 ). It is argued by Viergever et al. ( 2010 ) that the use of these structurally well-defined tools and methods should at least be considered, and that they will gradually replace commonly used methods such as the Delphi method (Yoshida 2016 ), which was a frequently used method used to establish obesity priorities in the identified studies.

It is concerning that only six of the 13 studies in this review involved the public as stakeholders and even then, the public were significantly underrepresented in the sample (Hill et al. 2020 ; Hill et al. 2019 ; McPherson et al. 2016 ; Ramirez et al. 2011 ), with another study not making clear how many public stakeholders were involved in the process (Curtin et al. 2017 ). Interestingly, of the seven studies that scored the highest in this review, six of them involved the public in the generation of priorities. It is well established in the literature that community engagement in research priority setting is crucial for establishing research questions that are relevant to them. Previous studies have demonstrated that the research priorities of other stakeholders do not align with those of the public (Brady et al. 2020 ; Manikam et al. 2017 ; Owens et al. 2008 ; Tallon et al. 2000 ; Voigt et al. 2010 ). A 2014 report systematically reviewed research priority setting studies from the period 1966 to 2014 and found that in the 91 studies, researcher and government involvement was strong, yet involvement of other key stakeholders was limited (McGregor et al. 2014 ). To ensure the incorporation of public and patients in the process, guidelines are available such as the Guidance for Reporting Involvement of Patients and the Public (GRIPP) checklist (Staniszewska et al. 2017 ), which was developed to aid in improving the quality, consistency and transparency of reporting the inclusion of patients and the public in research. The checklist offers a comprehensive list of issues that require consideration when reporting activities in relation to public and patient involvement. It must be noted, however, that it fails to offer information on how the public and patient contributors are to be recruited (Dawson et al. 2017 ). Additionally, it does not offer explicit consideration for representing the diversity of the population relevant to the topic area (Dawson et al. 2017 ). It is unclear in the current review whether public stakeholders were representative of the community at large, i.e. whether there was inclusion of Black and minority ethnic stakeholders in the samples. In addition to ensuring the inclusion of the public in research priority setting exercises, it is recommended that key characteristics of the sample are recorded and reported so that issues in relation to inclusion and diversity can be understood.

With regard to evaluation, a small number of studies in this review described strategies for the implementation of identified priorities, yet none measured the impact of the prioritisation. This can be done, for example, by performing an impact assessment reviewing the research performed (Viergever and Roderik 2010 ). The authors of a 2014 report (McGregor et al. 2014 ) argued that many of the exercises failed to translate the result of the prioritisation process into implementation of projects. It was further highlighted that the exercises were rarely repeated due to the lack of follow-up. The authors of the current review would strongly endorse the use of good practice guidelines, such as the one used to critically appraise the studies in this review, or the Reporting Guideline for Priority Setting of Health Research (REPRISE) by Tong et al. ( 2019 ).

In summary, one can say that while research priority setting studies in the topic area of obesity do exist, they vary in scope and in quality. Although a wide range of stakeholders were involved in the prioritisation processes, public involvement was either non-existent or limited. The use of a comprehensive approach in research priority setting and/or adherence to good practice guidelines could enrich obesity priority setting processes to ensure the identified obesity priorities are relevant, transparent and can assist in implementation efforts. It is imperative that the public be involved in the obesity research priority setting process, resulting in research agendas that have incorporated their unmet needs. This can improve the relevance and legitimacy of research and ultimately achieve better health outcomes in obesity.

Authors’ contributions

Conceptualisation: Halima Iqbal, Melanie Haith-Cooper, Rosie McEachan, Jane West; Methodology: Halima Iqbal; Formal analysis and investigation: Halima Iqbal, Melanie Haith-Cooper; Writing – original draft preparation: Halima Iqbal; Writing – review and editing: Halima Iqbal, Melanie Haith-Cooper, Rosie McEachan, Jane West; Resources: Halima Iqbal; Supervision: Halima Iqbal

This work was supported by the National Institute for Health Research (NIHR) under its Applied Research Collaboration (ARC) Yorkshire and Humber in the form of Ph.D. funding to HI [NIHR200166], the UK Prevention Research Partnership (UKPRP) in the form of funding to JW and RM [MR/S037527/1], the NIHR Clinical Research Network in the form of funding to JW, and the NIHR ARC Yorkshire and Humber in the form of funding to RM

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The authors declare they have no financial or non-financial interests to disclose.

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This is a systematic review. The University of Bradford Ethics Committee has confirmed that no ethical approval is required

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Relationship between emotional eating and nutritional intake in adult women with overweight and obesity: a cross-sectional study

• Hadis Zare 1 ,
• Habibollah Rahimi 2 ,
• Abdollah Omidi 3 ,
• Faezeh Nematolahi 1 , 4 &
• Nasrin Sharifi 1  

Nutrition Journal volume  23 , Article number:  129 ( 2024 ) Cite this article

104 Accesses

Metrics details

Emotional eating (EE) is particularly prevalent in overweight or obese women, who may turn to food as a way to cope with stress, sadness, or anxiety. Limited research has been conducted on the association between EE and nutritional intake. Therefore, present study was designed to explore this association in adult women with overweight and obesity.

In this cross-sectional study, the relationship between EE and nutritional intake in 303 overweight and obese women (aged 18–50 years) was examined. The researchers used the validated semi-quantitative Food Frequency Questionnaire (FFQ) to assess participants’ nutritional intake and the Dutch Eating Behavior Questionnaire (DEBQ) to evaluate their eating behavior. To determine the association between EE and nutritional intake, we employed the multiple linear regression analysis.

The frequency of high intensity EE was 64.4% among the study participants and the mean total score of EE subscale of DBEQ was 2.32 ± 0.81. The total score of EE was positively associated with the energy intake (β = 0.396, P  = 0.007), even after adjusting for age and BMI. In addition, a significant inverse association was found between the score of EE and the daily intake of calcium (β= -0.219, P  = 0.026), riboflavin (β= -0.166, P  = 0.043), and vitamin B12 (β= -0.271, P  = 0.035), independent from energy and age. Also the results showed a significant positive association between the score of EE and the frequency of daily intake of cracker, muffin, cake, cream cake, pastry, candy, ice cream, pickles, melon, hydrogenated vegetable oil, peanut, salted and roasted seeds, and corn-cheese puff snack.

This study found that overweight or obese women with higher intensity of EE might have a higher intake of energy and a lower intake of dietary calcium, riboflavin and vitamin B12. Integrating a balanced diet with psychotherapy is suggested to help individuals with EE reducing the urge to eat in response to emotions.

Peer Review reports

Introduction

The prevalence of obesity has increased dramatically over the last decades, making it a major public health concern worldwide [ 1 , 2 ]. This increase in the prevalence of obesity is more concerning among women [ 3 ]. According to the latest report by the World Health Organization (WHO), approximately 15% of women around the world are suffering from obesity [ 4 ]. Obesity often coexists with eating disorders [ 5 ]. Eating disorders have different types, such as anorexia nervosa, bulimia nervosa, binge eating and stress-induced restrained appetite [ 5 ]. Of note, other eating behaviors might be co-occurred with overweight and obesity such as emotional eating (EE) [ 6 ]. EE is defined as overeating after experiencing negative emotions and feelings [ 7 ], which is the tendency to eat unhealthy foods to cope with negative emotions such as depression [ 8 ] and anxiety [ 9 ]. Also, EE occurs due to the inability to express emotions and lack of emotion regulation [ 10 ]. Other factors include genetics [ 11 ] and stress [ 12 ]. Stress and negative emotions can make a person lose control and cause an increase in appetite, leading to overeating [ 13 ]. Appetite regulation is done through two mechanisms. First, in the hedonic phase, by stimulating the reward system, the production of dopamine and serotonin after eating delicious foods is increased. Second, through homeostatic regulation, which is regulated by biological needs and via regulating hormones , such as leptin, it affects appetite and the eating process [ 14 ]. Overeating with negative emotions is associated with an increase in the number of dopamine D2 receptors in lean and obese individuals [ 15 ]. Response to pleasure stimulates opioid receptors and dopamine is released by stimulating opioid receptors [ 16 ].

It is noteworthy that the prevalence of EE in women is reported to be higher than in men [ 17 ]and that the response of men to stress is more in the form of decreased appetite [ 18 , 19 ]. EE has been suggested to be one of the major barriers to weight-loss diet adherence [ 20 ]. Consequently, the increased desire for high-calorie foods such as fatty foods and sweet carbohydrates [ 21 ] leads to excessive energy consumption and weight gain during the negative emotional conditions [ 22 ]. This increase in weight can raise the risk of obesity, diabetes [ 23 ], elevated levels of low-density lipoprotein-cholesterol (LDL-C) and total serum cholesterol [ 24 ], hypertension [ 25 ], the occurrence and persistence of metabolic syndrome [ 26 ], and failure of the diet [ 27 ]. Additionally, it can lead to decreased self-esteem [ 28 ].The treatment methods that have been considered for this disorder include psychotherapy [ 29 ], nutrition therapy, and exercise [ 30 ]. EE with increasing intake of high-calorie foods causes poor quality of the diet received [ 21 ]. The quality of the diet depends on the intake of fiber, protein, fruits, and vegetables [ 31 ]. Generally, it depends on enhancing the intake of micronutrients and protein [ 32 ]. Dietary quality is considered a modifiable risk factor for mental disorders via many biological mechanisms, such as inflammation, oxidative stress, and the intestinal microbiome. Additionally, previous studies have confirmed that fatty acids and micronutrients such as vitamin D, vitamin C, and zinc play a positive role in improving mental disorders and diseases [ 33 , 34 , 35 , 36 , 37 , 38 ].

Nutrients are known to play a role in the development of EE. On the other hand, the dietary pattern of individuals with this disorder includes high-calorie intake and poor intake of nutrients. This leads to a vicious cycle between nutrient deficiencies and the occurrence and persistence of EE. Hence, this study was carried out to investigate the associations between EE and nutritional intake in women with overweight and obesity.

Study design

The present study is a cross-sectional observation performed using the Dutch Eating Behavior Questionnaire (DEBQ) and the validated semi-quantitative Food Frequency Questionnaire (FFQ) [ 39 ] to explore the relationship between emotional eating and nutritional intake in adult women with overweight and obesity. This study was conducted between February 10, 2020, and April 21, 2022.

Participants

The study population included women who had been referred to the comprehensive health service centers of Kashan City affiliated with Kashan University of Medical Sciences, which had the following criteria: (1) Iranian women aged 18–50 years, (2) Women with a body mass index (BMI) above 25 kg/m 2 .

The exclusion criteria for this study were as follows: (1) Individuals with chronic diseases such as diabetes and hypothyroidism, (2) Individuals with certain and diagnosed syndromic, monogenic, and polygenic causes of obesity [ 40 ], (3) Individuals with any type of cancer, (4) Individuals with major mental disorders such as bulimia, bipolar disorder, schizophrenia, and major depressive disorder, (5) Pregnant women, (6) Breastfeeding mothers, (7) Women in menopause, (8) Professional athletes, (9) Individuals who used alcohol, (10) Individuals who smoked cigarettes, 11) Individuals who used antidepressants and sedatives, and 12) Individuals who used herbal supplements to reduce or increase appetite/body metabolism.

Since no previous study had examined the prevalence of emotional eating in Iran, 30 women completed the DEBQ as a pilot study. Based on the results obtained, 30% of the women reported EE. Consequently, the sample size was calculated according to the following formula: n = Z 2 pq/d 2 = (1.96) 2  × 0.3 × 0.7/ (0.06) 2 =225 . Therefore, it was determined that 225 samples were necessary for this study. Taking into account the possibility of 20% dropouts and non-responses, the number of samples increased to 270 women.

This study was performed in line with the principles of the Declaration of Helsinki, and all the participants signed the written informed consent form. Approval was granted by the Ethics Committee of Kashan University of Medical Sciences, Kashan, Iran (IR.KAUMS.MEDNT.REC.1399.209). Due to the pandemic conditions of COVID-19, it was impossible to complete the questionnaire face-to-face. For this reason, the questionnaires were made available to the participants online (the address of the website: www.porsall.com ). The exception was in the case of illiterate people who were a limited number in the present study. All the questionnaires in the present study were completed in the form of face-to-face online interviews with these participants. A trained person was responsible for conducting interviews with illiterate participants. This study had three questionnaires: The general questionnaire, the DEBQ, and the validated semi-quantitative FFQ. Their details have been explained as follows.

Questionnaires

The initial questionnaire used in this study was a general one that aimed to gather demographic information such as age, marital status, level of education, and employment status. It also included questions on current medications, physical activity levels, and anthropometric variables such as weight, height, and BMI. This questionnaire was designed specifically for the present study.

The second questionnaire used in this study was the DEBQ. The diagnosis of emotional eating in the present study was based on DEBQ, which consists of 33 items divided into three scales: restrained eating (10 items), emotional eating (13 items), and external eating (10 items) [ 41 ]. This questionnaire utilized a five-point Likert scale ranging from ‘never’ (1) to ‘most of the time’ (5). Summing the scores of the items in each subscale and dividing it by the number of items gives the total score of each subscale. Higher scores indicate a higher rate of eating behavior related to that subscale. In present study, we used the obtained total score of “emotional eating” subscale as a surrogate of EE status in our participants. In addition, to find the frequency of EE among the study sample we applied the cut-off-points for total score of “emotional eating” subscale that was used by van Strien et al. [ 42 ]. According to this cut-off-point, participants whose total score for emotional eating subscale was equal to and above 2.6 had high intensity of EE, and subjects whose mean total score in this subscale was less than 1.8 do not have this disorder. The DEBQ has the validity of retesting, internal consistency (Cronbach’s alpha coefficients between 0.8 and 0.95), and appropriate factor validity. The validity of this questionnaire in Iran has been reported as favorable, and its validity has been announced using the Kuder-Richardson method, which was equal to 0.74 [ 41 ]. The Cronbach’s alpha was 0.91 for restrained eating and 0.95 for emotional eating across the three subscales.

The validated semi-quantitative FFQ was the third questionnaire used in this study to inquire about the intake of 168 food items during the last year. The validity and reliability of this FFQ have been confirmed by Mirmiran et al. [ 39 ]. The questionnaire also provides a standard portion size for each food item, which is more commonly understood by the general population.

The daily intake of each food item was calculated by multiplying the portion of each food item consumed into grams. Next, it was analyzed using Nutritionist IV software to calculate the average daily food intake, including energy intake, macronutrients, and micronutrients for each individual. In addition, the frequency of daily consumption of each food item in the FFQ was calculated in order to evaluate the relationship between EE and the consumption of each food item.

Statistical analysis

Descriptive statistics such as mean, standard deviation, frequency, etc., were used to show the basic characteristics of participants. Data distribution was examined for normality by the Kolmogorov–Smirnov test. Multiple linear regression analysis was used to determine the association between the score of EE and daily intake of dietary nutrients, while adjusting for confounding variables such as energy intake, age and BMI. In addition, participants in whom their nutrient intake was estimated to be outlier were excluded from the analysis. Also, the relationship between the score of EE and the daily frequency intake of each food items in FFQ was evaluated by the multiple linear regression. The score of EE, age, BMI, and the energy intake were selected as independent variables while the value of daily dietary nutrients as well as the frequency intake of each food item were entered in the model as dependent variables. Regression coefficients are given as standardized β values, referring to the number of standard deviations (SDs) the dependent variable changes, per SD increase of independent variables. Data was analyzed using SPSS statistical software, version 16 (SPSS Inc, Chicago, Ill). Two-sided P values < 0.05 were considered statistically significant.

General characteristics of the participants

In this study, 4000 women were contacted, of which 496 overweight or obese women agreed to participate. Of the 496 women, 193 were excluded from the study due to exclusion criteria, and 303 were examined. The flow diagram for participant selection is illustrated in Fig.  1 .

Flow diagram for the selection of participants

The age range of the participants was 18–50 years, and their mean age was 35.56 years with a standard deviation of 8.44. The participants had an average weight of 80.36 ± 12.5 kg, an average height of 160.49 ± 15.5 cm, and an average BMI of 30.66 ± 4.5 kg/m 2 .

The demographic characteristics of the participants of this study are summarized in Table  1 . Most of the participants were married (92.4%). In total, 66% of participants were housewives. Regarding education level, 50% had undergraduate and postgraduate education, and 25.4% had university education.

The frequency of high intensity EE was 64.4% among the study participants (the mean score of EE ≥ 2.6). In addition, the mean score of EE subscale of DBEQ was 2.32 ± 0.81.

The relationship between EE and nutrient intake

Table  2 presents the results of the multiple linear regression analysis. In this analysis, the independent association between the score of EE and the daily intake of energy was assessed, while adjusting for confounding variables such as age and BMI. Based on the obtained results, there was a significant positive association between EE and the daily intake of energy. Also, the relationship between the score of EE and the daily intake of macro- and micro- nutrients was evaluated while adjusting for the energy intake and age. The results showed a significant inverse association between the score of EE and the daily intake of calcium, riboflavin (vitamin B2) and vitamin B12 (cobalamin)(Table  2 ).

The relationship between EE and the frequency of Daily Consumption of Food items

The association between the score of EE and the frequency of daily consumption of food items was assessed by the multiple linear regression analysis while adjusting for the age of the participants as a confounding variable. Because the number of food items in the used FFQ was large (168 items), in order to clarify and better understand the relationship, only the results that were statistically significant are presented in Table  3 . The analysis revealed a significant positive association between the score of EE and the frequency of daily intake of cracker, muffin, cake, cream cake, pastry, candy, ice cream, pickles, melon, hydrogenated vegetable oil, peanut, salted and roasted seeds and corn-cheese puff snack.

The findings of the present study showed a high rate of EE in the study participants, which was equal to 64.4%. This rate was near to the prevalence of EE that was reported among overweight or obese participants in previous studies [ 43 , 44 ]. Madali et al. evaluated EE predisposition of Turkish individuals [ 44 ]. They found that 74.1% of obese participants were emotional eaters [ 44 ]. Similar to our study, they conducted their research during the COVID-19 pandemic [ 44 ]. However, unlike present study, their participants were both men and women, and the method used for assessing EE was Emotional Eating Scale [ 44 ]. They concluded that sudden lifestyle changes and the increase in stress levels during COVID-19 pandemic may affect emotional eating behavior [ 44 ].

According to the surveys conducted, there are very few studies about the role of emotional eating in nutritional intake and vice versa [ 21 , 45 , 46 ]. The present study is one of the first studies that examined the relationship between EE and nutritional intake in Iranian overweight and obese women. We found a positive association between EE and daily intake of energy, independent from age and BMI. This higher energy intake in individuals with EE is consistent with the results of the previous studies [ 47 , 48 ]. Consuming high amounts of hyper-tasty energy-dense foods that contain high fat and sugar levels can be a reason for higher energy intake in emotional eaters [ 49 ]. The results of the study by Alejandra Betancourt-Núñez et al. showed that individuals with EE who had abdominal obesity followed a dietary pattern with many snacking and fast food [ 46 ]. However, in a study conducted by Madali et al. in Turkey during the COVID-19 pandemic, a rise in the intake of fresh fruits and vegetables, eggs, red meat and milk was reported by emotional eaters while they reported a decrease in the intake of junk foods such as chips, biscuits, chocolates, bread, syrupy desserts, and pastries [ 44 ]. To explain these unexpected findings, they stated that during the quarantine period of the COVID-19 pandemic, Turkish people had more access to vegetables and fruits, and since they had more time at home to cook, they decreased the intake of ready-to-eat foods. As a result, it seems that some factors and conditions such as availability of certain foods, changes in mood such as stress, depression and anxiety, as well as culture might affect the types of foods consumed by emotional eaters. In the present study the positive association was found between total score of EE and the consumption of energy-dense, high-sugary, high-fat and salty foods such as cracker, muffin, cake, cream cake, pastry, candy, ice cream, pickles, melon, peanut, salted and roasted seeds and corn-cheese puff snack. In fact, emotional eaters increase the consumption of high-sugary, high fat foods to cope with the negative emotions such as depression, stress and anxiety. Food is a powerful natural reward that triggers the release of dopamine and consequently activates pleasure and reward centers in the brain. A person repeatedly eats certain foods to experience this positive feeling of satisfaction. Thus, overeating and morbid obesity result from satisfying habits [ 50 ]. Some foods, especially those high in sugar and fat, are substantial rewards that reinforce eating (even without an energy requirement) and cause a learned association between stimulus and reward [ 51 ].

In current study, the increase in the intensity of EE was associated with the intake of more hydrogenated vegetable oil that is a source of saturated fatty acids and trans fatty acids (TFA). One of the interesting findings in the present study was the increase in the intake of some salty food items, such as pickled cucumber, salty roasted seeds, and corn-cheese puff snack following the increase in the intensity of EE. Most of Iranians consume pickles with meals specially with fast foods. They also use salty nuts and seeds as a snack between meals. It seems that negative emotions increase the craving for salty foods. Researchers suggested that dopaminergic and opioidergic neurotransmission within ventral striatal brain regions associated with reward may be important for salt craving [ 52 , 53 ]. Of note, the increased intake of sugar, saturated fatty acids, TFA and salt can predispose the emotional eaters to insulin resistance, cardiovascular diseases and hypertension, as well as nutrient deficiency, especially in those who suffered from overweight and obesity.

From one point of view, the relationship between EE and micronutrient deficiencies can be considered as a vicious cycle. As it has been discussed, negative emotional conditions such as depression, anxiety and stress trigger some individuals to eat unhealthy energy-dense foods to alleviate the unwanted emotions. This can lead to an unbalanced diet with some nutrient deficiency. Then, a nutrient deficiency may alter the neurotransmitters synthesis or function that consequently affects mental health and increase the risk of depression and mood disorders; and the cycle continues. In the present study, the increased total score of EE was associated with the lower intake of calcium, riboflavin and vitamin B12. This result may be due to a lower intake of dairy products (the good sources of calcium and riboflavin) by emotional eaters in the present study. In fact, we found an inverse relationship between the EE score and each of the food items in the dairy group, although this relationship was not statistically significant for each dairy items (data not shown). A low intake of dietary calcium not only increases the risk of osteoporosis in emotional eaters, but also can impair mood and mental states, which are part of the triggers of EE. The results of the study on 14,971 participants from the US National Health and Nutrition Examination Survey (NHANES) showed a significant negative association between calcium intake and the risk of depressive symptoms [ 54 ]. Evidence from research has shown that extracellular calcium influx has an important role in a variety of neural functions [ 55 ]. The modulation of extracellular calcium concentration may have a role in regulating emotions, maybe through the direct impact of calcium on stabilizing the plasma membrane [ 56 ]. In addition, calcium stimulates tryptophan hydroxylase in the biosynthetic pathways that lead to the manufacture of serotonin [ 57 ]. Therefore, any disruptions in the control of calcium levels can have a significant impact on cellular function, potentially affecting mood. Similar to dietary calcium, B vitamins, including riboflavin and vitamin B12, which had an inverse relationship with EE in the present study, also have an effect on mood and mental states [ 58 ]. As a result, reducing their intake can intensify EE in whom suffered from this eating behavior disorder. Some previous studies have reported an inverse association between riboflavin and psychological disorders [ 59 , 60 ]. The involvement of riboflavin coenzymes in the processes of re-methylation of homocysteine may provide an explanation for the possible effects of riboflavin on mental health [ 61 ]. Also, It has been reported that vitamin B12 deficiency causes mental disorders in many persons [ 62 ]. Vitamin B12 is a vital substance that plays a crucial role in the synthesis of monoamine neurotransmitters in the brain [ 63 ]. Specifically, the methylcobalamin form of this vitamin helps convert homocysteine to methionine, which is essential for the formation of S-adenosyl methionine (SAMe). SAMe acts as a methyl donor for the production of monoamine neurotransmitters. Monoamine neurotransmitters, including norepinephrine, dopamine, and serotonin, play an important role in maintaining a normal mood [ 64 , 65 ].

Based on the findings of the present study, it seems that it is very important to pay attention to the dietary pattern of overweight and obese women who suffer from EE. By modifying the food pattern and following a balanced diet that prevents nutritional deficiencies, it is possible to break the vicious cycle of emotional eating-nutrient deficiency and prevent weight gain and chronic diseases associated with obesity such as glucose intolerance, cardiovascular diseases and some cancers.

For individuals struggling with EE, integrating dietary recommendations with psychotherapy can be profoundly beneficial. A balanced diet rich in whole foods, including fruits, vegetables, lean proteins, low fat dairy and whole grains, can help stabilize blood sugar levels and mood, potentially reducing the urge to eat in response to emotions. Pairing these dietary changes with psychotherapy allows for a holistic approach: while a nutritionist can provide strategies for healthier eating patterns, a therapist can help address the underlying emotional triggers and develop healthier coping mechanisms. Together, these approaches support both the physiological and psychological aspects of EE, fostering sustainable changes in behavior and improving overall well-being.

There were some strong points in our study. To the best of our knowledge, it is the first study to examine the association between EE and nutritional intake in Iranian women who were overweight or obese. Additionally, FFQ was utilized to assess food intake in our study. This method offers a comprehensive overview of an individual’s dietary habits, taking into account a more extended period of typically one year, which gives it an advantage over other methods. However, the present study has some limitations. Firstly, studies investigating food intake are often affected by selection and recall bias, which may also influence this research. Furthermore, the method used in this study to evaluate nutrient intake may have some limitations in terms of underestimating or overestimating the actual intake. However, we controlled for this by excluding those who were outlier regarding the energy intake. Lastly, due to the COVID-19 pandemic, it was not feasible to conduct face-to-face interviews to collect questionnaire data and the participants’ body weight and height information were acquired based on their statements.

Designing a future study that includes a larger and diverse population of Iranian women will provide findings that can be generalized to a wider community. Additionally, it is suggested to conduct clinical trials to determine the beneficial effects of following a special dietary pattern or taking the necessary supplements for women suffering from EE.

The present study aimed to evaluate the relationship between EE and nutritional intake in women with overweight and obesity. The findings of this study indicated that the increased intensity of EE was associated to the increased intake of energy in overweight or obese women, even after adjusting for age and BMI. This increase in daily energy intake among emotional eaters in the current study might be due to their increased intake of cracker, muffin, cake, cream cake, pastry, candy, ice cream, pickles, melon, peanut, salted and roasted seeds and corn-cheese puff snack. In addition, the higher intensity of EE was significantly related to a lower intake of calcium, riboflavin and vitamin B12 after adjusting for energy and age. It seems that for individuals struggling with EE, integrating a balanced diet with psychotherapy can help stabilize blood sugar levels and mood and potentially reducing the urge to eat in response to emotions.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to the rules and regulations of the Research Center for Biochemistry and Nutrition in Metabolic Diseases at the Kashan University of Medical Science, but are available from the corresponding author on reasonable request.

Abbreviations

Emotional Eating

Food Frequency Questionnaire

Dutch Eating Behavior Questionnaire

World Health Organization

Low-Density Lipoprotein-Cholesterol

High-Density Lipoprotein Cholesterol

Body Mass Index

S-Adenosyl Methionine

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Acknowledgements

We thank the Vice-Chancellor for Research and Technology of Kashan University of Medical Sciences and the Research Center for Biochemistry and Nutrition in Metabolic Diseases, Kashan University of Medical Sciences, Kashan, Iran.

This work was supported by the Vice-Chancellor for Research and Technology of Kashan University of Medical Sciences, Kashan, Iran (grant number: 99210).

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Hadis Zare, Faezeh Nematolahi & Nasrin Sharifi

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Habibollah Rahimi

Department of Clinical Psychology, School of Medicine, Kashan University of Medical Sciences, Kashan, Iran

Abdollah Omidi

Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran

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N.S. and H.Z. designed, supervised and drafted the research. H.Z. and A.O. involved in study design, data collection and drafting the manuscript. H.R. contributed to the study design and the statistical analysis of the data. F.N. contributed in conception, study design and data collection. All authors reviewed the final manuscript and approved it.

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Zare, H., Rahimi, H., Omidi, A. et al. Relationship between emotional eating and nutritional intake in adult women with overweight and obesity: a cross-sectional study. Nutr J 23 , 129 (2024). https://doi.org/10.1186/s12937-024-01030-3

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DOI : https://doi.org/10.1186/s12937-024-01030-3

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