hypothesis cohort study

Quantitative study designs: Cohort Studies

Quantitative study designs.

Introduction
Cohort Studies
Randomised Controlled Trial
Case Control
Cross-Sectional Studies
Study Designs Home

Cohort Study

Did you know that most people will develop a diagnosable mental illness or disorder, suggesting that only a minority will experience enduring mental health? Or that groups of people at risk of having high blood pressure and other related health issues by the age of 38 can be identified in childhood? Or that a poor credit rating can be indicative of a person’s health status?

These findings (and more) have come out of a large cohort study started in 1972 by researchers at the University of Otago in New Zealand. This study is known as The Dunedin Study and it has followed the lives of 1037 babies born between 1 April 1972 and 31 March 1973 since their birth. The study is now in its fifth decade and has produced over 1200 publications and reports, many of which have helped inform policy makers in New Zealand and overseas.

In Introduction to Study Designs, we learnt that there are many different study design types and that these are divided into two categories: Experimental and Observational. Cohort Studies are a type of observational study.

What is a Cohort Study design?

Cohort studies are longitudinal, observational studies, which investigate predictive risk factors and health outcomes.
They differ from clinical trials, in that no intervention, treatment, or exposure is administered to the participants. The factors of interest to researchers already exist in the study group under investigation.
Study participants are observed over a period of time. The incidence of disease in the exposed group is compared with the incidence of disease in the unexposed group.
Because of the observational nature of cohort studies they can only find correlation between a risk factor and disease rather than the cause.

Cohort studies are useful if:

There is a persuasive hypothesis linking an exposure to an outcome.
The time between exposure and outcome is not too long (adding to the study costs and increasing the risk of participant attrition).
The outcome is not too rare.

The stages of a Cohort Study

A cohort study starts with the selection of a group of participants (known as a ‘cohort’) sourced from the same population, who must be free of the outcome under investigation but have the potential to develop that outcome.
The participants must be identical, having common characteristics except for their exposure status.
The participants are divided into two groups – the first group is the ‘exposure’ group, the second group is free of the exposure.

Types of Cohort Studies

There are two types of cohort studies: Prospective and Retrospective .

How Cohort Studies are carried out

Adapted from: Cohort Studies: A brief overview by Terry Shaneyfelt [video] https://www.youtube.com/watch?v=FRasHsoORj0)

Which clinical questions does this study design best answer?


	What risk factors predict disease?	This looks at dietary and lifestyle risk factors and investigates how they might contribute to hypertension in women.
	What factors cause these outcomes?	This looks at factors in early life that may predict the occurrence of adolescent suicide.
	What happens with this disease over time?	This examines the instances of recovery from a first-time episode of psychosis.
	If the test is positive, what happens to the patient?	This examines recently released adults from prison who have been diagnosed with both a mental illness and substance use disorder and investigates what happens to them following their diagnosis.

What are the advantages and disadvantages to consider when using a Cohort Study?

What does a strong Cohort Study look like?

The aim of the study is clearly stated.
It is clear how the sample population was sourced, including inclusion and exclusion criteria, with justification provided for the sample size. The sample group accurately reflects the population from which it is drawn.
Loss of participants to follow up are stated and explanations provided.
The control group is clearly described, including the selection methodology, whether they were from the same sample population, whether randomised or matched to minimise bias and confounding.
It is clearly stated whether the study was blinded or not, i.e. whether the investigators were aware of how the subject and control groups were allocated.
The methodology was rigorously adhered to.
Involves the use of valid measurements (recognised by peers) as well as appropriate statistical tests.
The conclusions are logically drawn from the results – the study demonstrates what it says it has demonstrated.
Includes a clear description of the data, including accessibility and availability.

What are the pitfalls to look for?

Confounding factors within the sample groups may be difficult to identify and control for, thus influencing the results.
Participants may move between exposure/non-exposure categories or not properly comply with methodology requirements.
Being in the study may influence participants’ behaviour.
Too many participants may drop out, thus rendering the results invalid.

Critical appraisal tools

To assist with the critical appraisal of a cohort study here are some useful tools that can be applied.

Critical appraisal checklist for cohort studies (JBI)

CASP appraisal checklist for cohort studies

Real World Examples

Bell, A.F., Rubin, L.H., Davis, J.M., Golding, J., Adejumo, O.A. & Carter, C.S. (2018). The birth experience and subsequent maternal caregiving attitudes and behavior: A birth cohort study . Archives of Women’s Mental Health .

Dykxhoorn, J., Hatcher, S., Roy-Gagnon, M.H., & Colman, I. (2017). Early life predictors of adolescent suicidal thoughts and adverse outcomes in two population-based cohort studies . PLoS ONE , 12(8).

Feeley, N., Hayton, B., Gold, I. & Zelkowitz, P. (2017). A comparative prospective cohort study of women following childbirth: Mothers of low birthweight infants at risk for elevated PTSD symptoms . Journal of Psychosomatic Research , 101, 24–30.

Forman, J.P., Stampfer, M.J. & Curhan, G.C. (2009). Diet and lifestyle risk factors associated with incident hypertension in women . JAMA: Journal of the American Medical Association , 302(4), 401–411.

Suarez, E. (2002). Prognosis and outcome of first-episode psychoses in Hawai’i: Results of the 15-year follow-up of the Honolulu cohort of the WHO international study of schizophrenia . ProQuest Information & Learning, Dissertation Abstracts International: Section B: The Sciences and Engineering , 63(3-B), 1577.

Young, J.T., Heffernan, E., Borschmann, R., Ogloff, J.R.P., Spittal, M.J., Kouyoumdjian, F.G., Preen, D.B., Butler, A., Brophy, L., Crilly, J. & Kinner, S.A. (2018). Dual diagnosis of mental illness and substance use disorder and injury in adults recently released from prison: a prospective cohort study . The Lancet. Public Health , 3(5), e237–e248.

References and Further Reading

Greenhalgh, T. (2014). How to Read a Paper : The Basics of Evidence-Based Medicine , John Wiley & Sons, Incorporated, Somerset, United Kingdom.

Hoffmann, T. a., Bennett, S. P., & Mar, C. D. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier.

Song, J.W. & Chung, K.C. (2010). Observational studies: cohort and case-control studies . Plastic and Reconstructive Surgery , 126(6), 2234-42.

Mann, C.J. (2003). Observational research methods. Research design II: cohort, cross sectional, and case-control studies . Emergency Medicine Journal , 20(1), 54-60.

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Next: Randomised Controlled Trial >>
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Cohort Studies: Design, Analysis, and Reporting

Affiliations.

1 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH. Electronic address: [email protected].
2 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
PMID: 32658655
DOI: 10.1016/j.chest.2020.03.014

Cohort studies are types of observational studies in which a cohort, or a group of individuals sharing some characteristic, are followed up over time, and outcomes are measured at one or more time points. Cohort studies can be classified as prospective or retrospective studies, and they have several advantages and disadvantages. This article reviews the essential characteristics of cohort studies and includes recommendations on the design, statistical analysis, and reporting of cohort studies in respiratory and critical care medicine. Tools are provided for researchers and reviewers.

Keywords: bias; cohort studies; confounding; prospective; retrospective.

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Published: 13 January 2022

Cohort studies investigating the effects of exposures: key principles that impact the credibility of the results

Anna Miroshnychenko 1 ,
Dena Zeraatkar 1 , 2 ,
Mark R. Phillips ORCID: orcid.org/0000-0003-0923-261X 1 ,
Sophie J. Bakri 3 ,
Lehana Thabane ORCID: orcid.org/0000-0003-0355-9734 1 , 4 ,
Mohit Bhandari ORCID: orcid.org/0000-0001-9608-4808 1 , 5 &
Varun Chaudhary ORCID: orcid.org/0000-0002-9988-4146 1 , 5

for the Retina Evidence Trials InterNational Alliance (R.E.T.I.N.A.) Study Group

Eye volume 36 , pages 905–906 ( 2022 ) Cite this article

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Outcomes research

What are cohort studies?

Cohort studies are observational studies that follow groups of patients with different exposures forward in time and determine outcomes of interest in each exposure group or that investigate the effect of one or more participant characteristics on prognostic outcomes [ 1 ]. The focus of this editorial is on cohort studies that investigate the effects of exposures that may be associated with an increased or a decreased occurrence of the outcome of interest. Cohort studies may be prospective or retrospective in design. In prospective cohort studies, investigators enroll participants, assess exposure status, initiate follow up, and measure the outcome of interest in the future. In retrospective cohort studies, data on both the exposures and outcome of interest have been previously collected.

Purpose of cohort studies

While large well-designed randomized controlled trials (RCTs) represent the optimal design for making inferences about the effects of exposures or interventions on health outcomes, they are often not feasible to conduct—due to costs or challenges of recruiting patients with rare conditions and following patients for sufficient durations. Further, patients included in RCTs may not be representative of patients encountered in practice and the effectiveness of therapies in strict clinical trials may be different than when implemented in routine practice. In such circumstances, well-designed observational studies, which include cohort studies, can play an important role in producing evidence to guide clinical care decisions in ophthalmology. Cohort studies can also be conducted to generate hypotheses and establishing questions for future RCTs.

The differentiating characteristics between observational (e.g., cohort study) and experimental (e.g., RCT) study designs are that in the former the investigator does not intervene and rather “observes” and examines the relationship or association between an exposure and outcome. Examples of cohort studies in ophthalmology include evaluation of a possible association between exposure to ambient air pollution and age-related cataract [ 2 ]; or assessment of the impact of eye preserving therapies for patients with advanced retinoblastoma [ 3 ].

Key determinants of credibility (i.e., internal validity) in cohort studies

Readers considering applying evidence from cohort studies should be mindful of the following factors that affect the credibility or internal validity of cohort studies.

Factors that decrease the credibility of cohort studies

Cohort studies are at serious risk of confounding bias and so adjusting or accounting for confounding factors is a priority in these studies. Confounding occurs when the exposure of interest is associated with another factor that also influences the outcome of interest. Investigators can use various design (e.g., matching) and statistical methods (e.g., adjusted analyses based on regression methods) to deal with known, measured confounders. Readers should assess whether the authors accounted for known confounders of the relationship under investigation in either their design or statistical analysis. Readers should be mindful, however, that possibility of residual confounding caused by unknown or unmeasured confounders always remains.

Inappropriate selection of participants into the cohort study can result in selection bias. Selection bias occurs when selection of participants is related to both the intervention and outcome. Bias in measurement of exposure/outcome, or detection bias, can arise when outcome assessors are aware of intervention status, different methods are used to assess outcomes in the different intervention groups, and/or the exposure status is misclassified differentially or non-differentially (i.e., the probability of individuals being misclassified is different or equal between groups in a study, respectively).

Missing data may also affect the credibility of cohort studies. Bias due to missing data in prospective and retrospective studies arises when follow up data are missing for individuals initially included in the study. Participants with missing outcome data may differ importantly from those with complete data (e.g., they may be healthier or may not have experienced adverse events).

Last, credibility of a cohort study may be affected by the reporting of results. Selective reporting arises when investigators selectively report results in studies in such a way so that the study report highlights or emphasizes evidence supporting a particular hypothesis and does not report or understates evidence supporting an alternative hypothesis. Investigators may selectively report results for timepoints or measures that produced results consistent with their preconceived beliefs or results that were newsworthy and disregard results for timepoints or measures that produced results that were inconsistent with their beliefs or considered not newsworthy. Publication bias refers to the propensity for studies with anomalous, interesting, or statistically significant results to be published at higher rates or to be published more rapidly or to be published in journals with higher visibility.

Factors that increase the credibility of cohort studies

Three uncommon situations can sometimes make us more certain of findings of cohort studies—in some circumstances, these situations can make us as confident of evidence from cohort studies as we would be for evidence from a rigorous RCT. First, when the observed effect is large (typically a relative risk (RR) > 2 or RR < 0.5), biases, such as confounding, are less likely to completely explain the observed effect. Second, we may be more certain of results when we observe a dose-response gradient: biases in non-randomized studies (e.g., confounding and errors in the classification of the exposure) are unlikely to produce spurious dose-response associations., when all suspected biases are believed to act against the observed direction of effect, we can be more certain that the observed effect is not due to the suspected biases. It is, however, difficult to anticipate with sufficient certainty the direction in which effects are likely biased in complex epidemiological studies. Because situations that make us more certain of findings of cohort studies occur infrequently, cohort studies usually provide only low to very low certainty evidence [ 4 ].

Applicability (i.e., external validity) in cohort studies

If the populations, exposures, or outcomes investigated in cohort studies differ from the those of interest in routine or typical settings, the evidence may not be applicable or externally valid. Such judgements depend on whether differences between studies and the question of interest would lead to an appreciable change in the direction or magnitude of effect. Generally, observational studies (e.g., cohort studies) have higher external validity than experimental studies (e.g., RCTs) [ 5 ].

Cohort studies follow a population exposed or not exposed to a potential causal agent forward in time and assess outcomes. Cohort studies are beneficial because these studies allow the investigators to observe a possible association between an exposure and outcome of interest in a population that cannot be randomly subjected to an exposure due to ethical, methodological, or feasibility limitations. Cohort studies, however, have several limitations that should be acknowledged and minimized if possible.

Barrett D, Noble H. What are cohort studies? Evid-Based Nurs. 2019;22:95–6.

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Anna Miroshnychenko, Dena Zeraatkar, Mark R. Phillips, Lehana Thabane, Mohit Bhandari, Varun Chaudhary & Lehana Thabane

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

Dena Zeraatkar

Department of Ophthalmology, Mayo Clinic, Rochester, MN, USA

Sophie J. Bakri

Biostatistics Unit, St. Joseph’s Healthcare-Hamilton, Hamilton, ON, Canada

Lehana Thabane & Lehana Thabane

Department of Surgery, McMaster University, Hamilton, ON, Canada

Mohit Bhandari, Varun Chaudhary, Varun Chaudhary & Mohit Bhandari

Retina Consultants of Texas (Retina Consultants of America), Houston, TX, USA

Charles C. Wykoff

Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA

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Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA

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Retinal Disorders and Ophthalmic Genetics, Stein Eye Institute, University of California, Los Angeles, CA, USA

David Sarraf

The Retina Service at Wills Eye Hospital, Philadelphia, PA, USA

Sunir J. Garg

Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA

Rishi P. Singh

Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA

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Frank G. Holz

Singapore Eye Research Institute, Singapore, Singapore

Tien Y. Wong

Singapore National Eye Centre, Duke-NUD Medical School, Singapore, Singapore

Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia

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Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, VIC, Australia

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Miroshnychenko, A., Zeraatkar, D., Phillips, M.R. et al. Cohort studies investigating the effects of exposures: key principles that impact the credibility of the results. Eye 36 , 905–906 (2022). https://doi.org/10.1038/s41433-021-01897-0

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DOI : https://doi.org/10.1038/s41433-021-01897-0

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Cohort Study: Definition, Benefits & Examples

By Jim Frost Leave a Comment

What is a Cohort Study?

A cohort study is a longitudinal experimental design that follows a group of participants who share a defining characteristic. For example, a cohort study can select subjects who have exposure to a risk factor , are in the same profession, population or generation, or experience a particular event, such as a medical procedure. This design determines whether exposure to a risk factor affects an outcome. Cohort studies are a type of longitudinal study because they track the same set of subjects over time.

Image of a group of people representing a cohort.

Cohort studies are observational designs, meaning that the researchers do not manipulate experimental or environmental conditions. Instead, they collect data over time and try to understand how various factors affect the outcome. These projects can last for periods ranging from weeks to decades, depending on the research questions.

Learn more about Experimental Design: Definition, Types, and Examples .

Examples of Cohort Studies

Researchers frequently use cohort studies to identify disease risk factors and understand how they affect disease incidence rates.

British Doctors

This cohort study ran from 1951 to 2001 and tracked 60,000 participants with various smoking habits. The researchers found a link between smoking, lung cancer, and death rates.

Nurses’ Health

This cohort study started in 1976 and tracks over 120,000 nurses. It assesses risk factors for many major chronic diseases in women.

Framingham Heart

The study tracks 15,000 participants from three generations and started in 1948. It has identified risk factors for high blood pressure and high cholesterol, among others.

Types of Cohort Studies

Cohort designs can be retrospective or prospective.

Retrospective Cohort Study

In a retrospective cohort study, the scientists identify subjects where the outcomes are known when the project starts. For example, they can find patients who already have the condition of interest and compare them to those who do not. They look for patterns in predicting those who developed the disease.

In retrospective designs, the researchers collect their data using existing records. Consequently, they can complete their study more quickly and inexpensively than prospective designs. However, the various factors and other variables might not have been measured consistently or accurately because they weren’t explicitly designed to be part of a cohort study.

Researchers using a retrospective design have to make do with data that other people recorded in the past for other purposes. Those data were not chosen and measured with the project’s needs in mind. Alternatively, some studies might ask the subjects to recall exposure information or use other subjective evaluations, which introduces a variety of biases.

Learn more about Retrospective Studies .

Prospective Cohort Study

In a prospective cohort study, researchers identify subjects based on the cohort, but the outcomes are unknown when the study begins. Typically, the study recruits people with and without exposure to facilitate comparisons. Then, they track the participants over time, record all the necessary data, and watch for patterns in those who develop the outcome of interest.

Prospective designs are more expensive and time consuming than retrospective studies. However, the researchers can measure all the required data at regular intervals.

Generally, conclusions from a prospective cohort study are superior to those from a retrospective design.

Learn more about Prospective Studies .

Benefits of a Cohort Study

Scientists frequently use cohort studies in epidemiological studies, psychology, social sciences, and nursing. This design is great for identifying both protective and risk factors in natural settings and understanding how they affect incident rates.

In other words, this design helps develop an understanding of the variables that increase and decrease the probability of contracting a disease or other condition. Additionally, researchers can track multiple outcomes (e.g., several diseases) in a single cohort. For example, do smokers have an increased incidence of both lung cancer and emphysema?

Typically, researchers recruit a group where some participants have exposure to a risk factor while others do not. Researchers can include multiple subgroups related to various risk and protective factors in the cohort study. In this manner, analysts can track those factors and link them to occurrences of the outcome they’re studying.

When a risk factor is rare, a cohort study can specifically recruit participants with exposure and follow them. In contrast, other methods are unlikely to obtain a sufficient number of subjects exposed to the risk factor, making it difficult to produce meaningful results.

The longitudinal nature of this design allows a cohort study to understand how exposure and timing relate to the outcome. The scientists don’t need to understand those relationships fully to conduct the research. Instead, they can collect data and evaluate relationships as they appear. Additionally, exposure can change over time, providing insight into its relationship with the outcome.

Weaknesses of a Cohort Study

As mentioned earlier, a prospective cohort study can be expensive and time consuming. In some cases, they involve tens of thousands of participants and last for years or decades. The researchers must track these subjects, perform follow-up evaluations regularly, and record all the data. Over this time, participants will drop out, making the results sensitive to attrition bias.

A cohort study is not a true experiment. It’s a type of observational design and, as such, it opens the door to the problem of confounding variables and spurious correlations . The observed relationships between risk factors and the outcome might be only correlational and not causal. Confounders can bias the results. While these studies are an excellent way to identify potential factors, they require follow-up experiments to verify causal relationships. Learn more about Correlation vs. Causation: Understanding the Differences .

Because cohort studies are observational, they do not use random assignment . Researchers must be wary of confounding factors and take appropriate countermeasures.

For more information, read my posts about Observational Studies Explained and Confounding Variables .

Cohort Study vs. Case-Control Study

Cohort and case-control studies are observational designs that medical and epidemiological researchers use to evaluate risk factors. While they are similar, there are crucial differences.

A cohort study evaluates the frequency of a disease/condition by exposure. The researchers assess differences in exposure and see how that relates to differences in the incidence rate.

Does exposure affect the incidence rate?

To answer this question, cohort studies often use regression models to estimate the relationships and control for confounders.

Case-Control

In contrast, a case-control design focuses on the comparative exposure for those who have the condition relative to those without it.

Do people with a condition have greater exposure?

To answer this question, case-control studies typically report an odds ratio .

Case-control designs are always retrospective, whereas cohort research can be retrospective or prospective.

For more information, read my post about Case-Control Studies .

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Cohort studies: prospective and retrospective designs

Posted on 6th March 2019 by Izabel de Oliveira

In epidemiology, the term “cohort” is used to define a set of people followed for a certain period of time. W. H. Frost, a 20th century epidemiologist, was the first to adopt the term in a 1935 publication, when he assessed age-specific and tuberculosis-specific mortality rates. The epidemiological definition of the word currently means:

a group of people with certain characteristics, followed up in order to determine incidence or mortality by any specific disease, all causes of death or some other outcome. [1]

Cohort study design is described as ‘observational’ because, unlike clinical studies, there is no intervention. [2] Because exposure is identified before outcome , cohort studies are considered to provide stronger scientific evidence than other observational studies such as case-control studies. [1] A fundamental characteristic of the study is that at the starting point, subjects are identified and exposure to particular risk factors is assessed. Subsequently, the frequency of the outcome, usually the incidence of disease or death over a period of time, is measured and related to exposure status. [3]

Relative risk (RR) is the measure of association that is applied for the analysis of the results in cohort studies. It compares the incidence of the disease in the exposed group with the incidence in the non-exposed group, hence the name relative risk or risk ratio. If the incidence in the two groups is equal, the value for the RR will be 1, but if the value is greater than 1, this indicates a positive relationship between the risk factor and the outcome. In order to determine if the sample studied reflects a real effect of the risk factor in the population, the sample variability of the findings may be evaluated through tests of significance or confidence intervals. [4]

Advantages of cohort studies include the possibility of examining multiple results from a given exposure, determining disease rates in exposed and unexposed individuals over time, and investigating multiple exposures. In addition, cohort studies are less susceptible to selection bias than case-control studies. The disadvantages are the weaknesses of observational design, the inefficiency to study rare diseases or those with long periods of latency, high costs, time consuming, and the loss of participants throughout the follow-up which may compromise the validity of the results. [5]

Prospective Cohort Studies

Prospective cohort studies are characterised by the selection of the cohort and the measurement of risk factors or exposures before the outcome occurs, thus establishing temporality, an important factor in determining causality. This design provides a different advantage over case-control studies in which exposure and disease are assessed at the same time. [6]

The study is carried out in three fundamental stages: identification of the individuals, observation of each group over time to evaluate the development of the disease in the groups, and comparison of the risk of onset of the disease between exposed and non-exposed groups. [5]

The main disadvantage to prospective cohort studies is the cost. It requires a large number of individuals to be followed up for long periods of time [6] and this can be difficult due to loss to follow-up or withdrawal by the individuals studied. [1] Biases may occur, especially if there is significant loss during follow-up. [6]

It is important to minimise loss to follow-up , a situation in which the researcher loses contact with the individual, resulting in missing data. When loss to follow-up of many individuals occurs, the internal validity of the study is reduced. As a general rule, the loss rate should not exceed 20% of the sample. Any systematic differences related to the outcome or exposure of risk factors for those who drop out and those who remain in the study should be examined, if possible. Strategies to avoid loss to follow-up are to exclude individuals who are likely to be lost, such as those who plan to move, and to obtain information to enable future tracking and to maintain periodic contact. [1]

Prospective design is inefficient and inappropriate for the study of rare diseases, but it becomes more efficient when there is an increase in the frequency of the disease in the population. [6]

The Nurses’ Health Study…

The Nurses’ Health Study (NHS) [7] is among the largest prospective investigation into the risk factors for major chronic diseases in women. Donna Shalala, former Secretary of the U.S. Department of Health and Human Services, called the NHS “one of the most significant studies ever conducted on the health of women.”

The Nurses’ Health Study (NHS) was established by Dr. Frank Speizer in 1976 with continued funding from the National Institutes of Health since then. The primary motivation for the study was to investigate the potential long-term consequences of oral contraceptives which were being prescribed to millions of women.

Nurses were selected as the study population because of their knowledge about health and their ability to provide complete and accurate information regarding various diseases due to their nursing education. They were relatively easy to follow over time and were motivated to participate in a long-term study. The cohort was limited to married women due to the sensitivity of questions about contraceptive use at that time.

The original focus of the study was on contraceptive methods, smoking, cancer, and heart disease, but has expanded over time to include research on many other lifestyle factors, behaviours, personal characteristics, and also other diseases.

Retrospective Cohort Studies

Cohort studies can also be retrospective. Retrospective cohorts are also called historical cohorts. [1,8] A retrospective cohort study considers events that have already occurred. Health records of a certain group of patients would already have been collected and stored in a database, so it is possible to identify a group of patients – the cohort – and reconstruct their experience as if it had been prospectively followed up. [2]

Although patient information was probably collected prospectively, the cohort would not have initially identified the goal of following individuals and investigating the association between risk factor and outcome. In a retrospective study, it is likely that not all relevant risk factors have been recorded. This may affect the validity of a reported association between risk factor and outcome when adjusted for confounding . In addition, it is possible that the measurement of risk factors and outcomes would not have been as accurate as in a prospective cohort study. [2]

Many of the advantages and disadvantages of retrospective cohort studies are similar to those of prospective studies. As previously described, retrospective cohort studies are typically constructed from previously collected records, in contrast to prospective design, which involves identification of a prospectively followed group, with the objective of investigating the association between one or more risk factors and outcome. However, an advantage to both study designs is that exposure to risk factors can be recorded before the outcome occurs. This is important because it allows the sequence of risk and outcome factors to be evaluated. [8]

Use of previously collected and stored records in a database indicates that the retrospective cohort study is relatively inexpensive and quick and easy to perform. However, with retrospective cohorts, it is possible that not all relevant risk factors have been identified and recorded. Another disadvantage is that many health professionals will have become involved in patient care, making the measurement of risk factors and outcomes less consistent than that achieved with a prospective study design. [8]

Dying to be famous…

Rock and pop fame is associated with risk taking, substance use and premature mortality. This retrospective cohort study [9] examined the relationships between fame and premature mortality and tested how these relationships vary with the type of performer (solo or band member) and nationality and whether the cause of death was linked to adverse childhood experiences.

The cohort included 1,489 rock and pop stars that reached fame between 1956 and 2006. The study examined the risk and protective factors for star mortality, relative contributions of adverse childhood experiences and other performance characteristics to cause premature death between rock and pop stars.

Although artists are generally not accessible through search techniques, considerable information is available through biographical publications, news and other media coverage. The accuracy and completeness of the data collected from the media and biographical sources cannot be quantified. However, such limitations are unlikely to have generated the patterns identified in this study.

The study concluded that the association between fame and mortality is mainly conditioned to performers’ characteristics. Adverse experiences in their lives predisposed them to adopt health-damaging behaviours, and fame and wealth provide greater opportunities to engage in risk-taking. Young people wish to emulate their idols, so it is important they recognise that drug abuse and risk-taking may be rooted in negative experiences rather than seeing them related to success.

Take home points:

Cohort studies are appropriate studies to evaluate associations between multiple exposures and multiple outcomes.
An advantage of prospective and retrospective cohort designs is that they are able to examine the temporal relationship between the exposure and the outcome.

Izabel de Oliveira

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nice page, i reccomend to heve ppt of the updates on the site with references.

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Cluster Randomized Trials: Concepts

This blog summarizes the concepts of cluster randomization, and the logistical and statistical considerations while designing a cluster randomized controlled trial.

Expertise-based Randomized Controlled Trials

This blog summarizes the concepts of Expertise-based randomized controlled trials with a focus on the advantages and challenges associated with this type of study.

An introduction to different types of study design

Conducting successful research requires choosing the appropriate study design. This article describes the most common types of designs conducted by researchers.

Module Index
Epiville Chamber of Commerce
About this site
Requirements

Cohort Study

Introduction
Learning Objectives
Student Role

Study Design

Data Collection
Data Analysis
Discussion Questions
Print Module

From your studies, you know that the first step in any research plan is to generate a solid hypothesis to guide the investigation.So, while the WEPI1 newscast and Ms. Doll provided you with interesting information, you decide to visit a local hospital to inquire about individuals with Susser Syndrome.

Upon initial review of the cases, it does appear that a number of affected individuals did, in fact, work at the Glop Industries manufacturing plant. However, other individuals not associated with the factory have been affected as well (albeit in smaller numbers). You learn that Glop industries keeps meticulous information about its employees' work histories, which you decide to use in your study. With this exposure information from the employee records, you want to conduct a cohort study. Since both the exposure and outcome have already occurred, and since you have access to the exposure data collected prior to the disease outcome, you decide to design a retrospective cohort study (Please see Aschengrau & Seage pp. 147, and 206-208 for more information).

1. Based on the facts as presented, which do you think is the best hypothesis to investigate in this retrospective cohort study?

Those who develop Susser Syndrome are more likely to have participated in the manufacturing of SUPERCLEAN than those who did not develop Susser Syndrome.
Those who are exposed to chemicals involved in the production of SUPERCLEAN (via direct exposure at the factory) have a higher risk of developing Susser Syndrome than those who are not exposed.
Residents of Epiville have a higher risk of developing Susser Syndrome compared with the residents of a neighboring community.

2. Based on your hypothesis, what would be the best way to define exposure?

Provide all workers at the Glop Industries manufacturing plant with individual air quality instruments to take daily readings in order to compile weekly doses of exposure to SUPERCLEAN.
Ask workers about their professional activities at the factory and estimate their exposure to SUPERCLEAN.
Look for sources of information at the factory which record individual worker exposures throughout their employment.

Your supervisor assembles a team to begin the investigation. After a little groundwork, you find that the employee health clinic at Glop Industries keeps records of annual medical examinations for all employees beginning with their hiring date. You also learn that the factory's human resources department has records of each worker's employment history which you can use to determine exposure to chemicals involved in the SUPERCLEAN production. Among the 40 job positions at the factory, only 5 are directly involved with the production of SUPERCLEAN.

After talking with some environmental experts and epidemiologists, you believe that an individual needs to have been exposed to SUPERCLEAN for at least 6 months before a sufficient dose of the chemical accumulates and physiological changes start taking place. Thus, exposure to SUPERCLEAN production chemicals for less than 6 months will not lead to Susser Syndrome.

You are presented with the job descriptions that are exposed to SUPERCLEAN and Glop Industries' air monitoring records.

Job Category	Maximum allowable level of exposure to SUPERCLEAN	Number of workers
A	120-150 ppm	800
B	150-175 ppm	200
C	175-200 ppm	500
D	200-225 ppm	150
E	?225 ppm	250
a. ppm = parts per million b. Exposed to SUPERCLEAN for at least 6 months, started working at Glop Industries on or before September 2002

After some deliberation, you define the exposed groups as low, medium or high exposure (depending on the maximum allowable level of exposure to SUPERCLEAN for their job category) and the unexposed group as employees either not involved with SUPERCLEAN production or those working less than 6 months in SUPERCLEAN production.

You now have the basic framework of your retrospective cohort study. You have redefined your hypothesis to incorporate your assumptions about the induction period and you have clearly defined your exposure variable. You are obviously excited to get out there and begin collecting data but you must first determine who is eligible for the study.

3. How would you define eligibility criteria for study participants? [Aschengrau & Seage pp. 203-205]

Everyone working at the factory is eligible
Only those who have worked at the factory as of September 1, two years ago, AND had been on the job for at least 6 months AND who were shown to be healthy at their initial or annual health check-ups as indicated by employee medical records
Exclude workers who in the last three months exhibited symptoms of the disease

4. On what would you base your definition of Susser Syndrome?[Aschengrau & Seage pp. 217-219]

Neurological symptoms alone
Self-diagnosis of the participants
Combination of neurological symptoms and laboratory tests

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Designing and Conducting Analytic Studies in the Field

Brendan R. Jackson And Patricia M. Griffin

Analytic studies can be a key component of field investigations, but beware of an impulse to begin one too quickly. Studies can be time- and resource-intensive, and a hastily constructed study might not answer the correct questions. For example, in a foodborne disease outbreak investigation, if the culprit food is not on your study’s questionnaire, you probably will not be able to implicate it. Analytic studies typically should be used to test hypotheses, not generate them. However, in certain situations, collecting data quickly about patients and a comparison group can be a way to explore multiple hypotheses. In almost all situations, generating hypotheses before designing a study will help you clarify your study objectives and ask better questions.

Generating Hypotheses
Study Designs for Testing Hypotheses
Types of Observational Studies for Testing Hypotheses
Selection of Controls in Case–Control Studies
Matching in Case–Control Studies
Example: Using an Analytic Study to Solve an Outbreak at a Church Potluck Dinner (But Not That Church Potluck)
Outbreaks with Universal Exposure

The initial steps of an investigation, described in previous chapters, are some of your best sources of hypotheses. Key activities include the following:

By examining the sex distribution among persons in outbreaks, US enteric disease investigators have learned to suspect a vegetable as the source when most patients are women. (Of course, generalizations do not always hold true!)
In an outbreak of bloodstream infections caused by Serratia marcescens among patients receiving parenteral nutrition (food administered through an intravenous catheter), investigators had a difficult time finding the source until they noted that none of the 19 cases were among children. Further investigation of the parenteral nutrition administered to adults but not children in that hospital identified contaminated amino acid solution as the source ( 1 ).
Focus on outliers. Give extra attention to the earliest and latest cases on an epidemic curve and to persons who recently visited the neighborhood where the outbreak is occurring. Interviews with these patients can yield important clues (e.g., by identifying the index case, secondary case, or a narrowed list of common exposures).
Determine sources of similar outbreaks. Consult health department records, review the literature, and consult experts to learn about previous sources. Be mindful that new sources frequently occur, given ever-changing social, behavioral, and commercial trends.
Conduct a small number of in-depth, open-ended interviews. When a likely source is not quickly evident, conducting in-depth (often >1 hour), open-ended interviews with a subset of patients (usually 5 to 10) or their caregivers can be the best way to identify possible sources. It helps to begin with a semistructured list of questions designed to help the patient recall the events and exposures of every day during the incubation period. The interview can end with a “shotgun” questionnaire (see activity 6) ( Box 7.1 ). A key component of this technique is that one investigator ideally conducts, or at least participates in, as many interviews as possible (five or more) because reading notes from others’ interviews is no substitute for soliciting and hearing the information first-hand. For example, in a 2009 Escherichia coli O157 outbreak, investigators were initially unable to find the source through general and targeted questionnaires. During open-ended interviews with five patients, the interviewer noted that most reported having eaten strawberries, a particular type of candy, and uncooked prepackaged cookie dough. An analytic study was then conducted that included questions about these exposures; it confirmed cookie dough as the source ( 3 ).
Ask patients what they think. Patients can have helpful thoughts about the source of their illness. However, be aware that patients often associate their most recent food exposure (e.g., a meal) with illness, whereas the inciting exposure might have been long before.
Consider administering a shotgun questionnaire. Such questionnaires, which typically ask about hundreds of possible exposures, are best used on a limited number of patients as part of hypothesis-generating interviews. After generating hypotheses, investigators can create a questionnaire targeted to that investigation. Although not an ideal method, shotgun questionnaires can be used by multiple interviewers to obtain data about large numbers of patients ( Box 7.1 ).

In November 2014, a US surveillance system for foodborne diseases (PulseNet) detected a cluster (i.e., a possible outbreak) of listeriosis cases based on similar-appearing Listeria monocytogenes isolates by pulsed-field gel electrophoresis of the isolates. No suspected foods were identified through routine patient interviews by using a Listeria -specific questionnaire with approximately 40 common food sources of listeriosis (e.g., soft cheese and deli meat). The outbreak’s descriptive epidemiology offered no clear leads: the sex distribution was nearly even, the age spectrum was wide, and the case-fatality rate of approximately 20% was typical. Notably, however, 3 of the 35 cases occurred among previously healthy school-aged children, which is highly unusual for listeriosis. Most cases occurred during late October and early November.

Investigators began reinterviewing patients by using a hypothesis-generating shotgun questionnaire with more than 500 foods, but it did not include caramel apples. By comparing the first nine patient responses with data from a published survey of food consumption, strawberries and ice cream emerged as hypotheses. However, several interviewed patients denied having eaten these foods during the month before illness. An investigator then conducted lengthy, open-ended interviews with patients and their family members. During one interview, he asked about special foods eaten during recent holidays, and the patient’s wife replied that her husband had eaten prepackaged caramel apples around Halloween. Although produce items had been implicated in past listeriosis outbreaks, caramel apples seemed an unlikely source. However, the interviewer took note of this connection because he had previously interviewed another patient who reported having eaten caramel apples. This event underscores the importance of one person conducting multiple interviews because that person might make subtle mental connections that may be missed when reviewing other interviewers’ notes. In fact, several other investigators listening to the interview noted this exposure—among hundreds of others—but thought little of it.

In this investigation, the finding of high strawberry and ice cream consumption among patients, coupled with the timing of the outbreak during a holiday period, helped make a sweet food (i.e., caramel apples) seem more plausible as the possible source.

To explore the caramel apple hypothesis, investigators asked five other patients about this exposure, and four reported having eaten them. On the basis of these initial results, investigators designed and administered a targeted questionnaire to patients involved in the outbreak, as well as to patients infected with unrelated strains of L. monocytogenes (i.e., a case–case study). This study, combined with testing of apples and the apple packing facility, confirmed that caramel apples were the source (2). Had a single interviewer performed multiple open-ended interviews to generate hypotheses before the shotgun questionnaire, the outbreak might have been solved sooner.

As evident in public health and clinical guidelines, randomized controlled trials (e.g., trials of drugs, vaccines, and community-level interventions) are the reference standard for epidemiology, providing the highest level of evidence. However, such studies are not possible in certain situations, including outbreak investigations. Instead, investigators must rely on observational studies, which can provide sufficient evidence for public health action. In observational studies, the epidemiologist documents rather than determines the exposures, quantifying the statistical association between exposure and disease. Here again, the key when designing such studies is to obtain a relevant comparison group for the patients ( Box 7.2 ).

Because field analytic studies are used to quantify the association between exposure and disease, defining what is meant by exposure and disease is essential. Exposure is used broadly, meaning demographic characteristics, genetic or immunologic makeup, behaviors, environmental exposures, and other factors that might influence a person’s risk for disease. Because precise information can help accurately estimate an exposure’s effect on disease, exposure measures should be as objective and standard as possible. Developing a measure of exposure can be conceptually straightforward for an exposure that is a relatively discrete event or characteristic—for example, whether a person received a spinal injection with steroid medication compounded at a specific pharmacy or whether a person received a typhoid vaccination during the year before international travel. Although these exposures might be straightforward in theory, they can be subject to interpretation in practice. Should a patient injected with a medication from an unknown pharmacy be considered exposed? Whatever decision is made should be documented and applied consistently.

Additionally, exposures often are subject to the whims of memory. Memory aids (e.g., restaurant menus, vaccination cards, credit card receipts, and shopper cards) can be helpful. More than just a binary yes or no, the dose of an exposure can also be enlightening. For example, in an outbreak of fungal bloodstream infections linked to contaminated intravenous saline flushes administered at an oncology clinic, affected patients had received a greater number of flushes than unaffected patients ( 4 ). Similarly, in an outbreak of Listeria monocytogenes infections, the association with deli meat became apparent only when the exposure evaluated was consumption of deli meat more than twice a week ( 5 ).

Defining disease (e.g., does a person have botulism?) might sound simple, but often it is not; read more about making and applying disease case definitions in Chapter 3 .

Three types of observational studies are commonly used in the field. All are best performed by using a standard questionnaire specific for that investigation, developed on the basis of hypothesis-generating interviews.

Observational Study Type 1: Cohort

In concept, a cohort study, like an experimental study, begins with a group of persons without the disease under study, but with different exposure experiences, and follows them over time to find out whether they experience the disease or health condition of interest. However, in a cohort study, each person’s exposure is merely recorded rather than assigned randomly by the investigator. Then the occurrence of disease among persons with different exposures is compared to assess whether the exposures are associated with increased risk for disease. Cohort studies can be prospective or retrospective.

Prospective Cohort Studies

A prospective cohort study enrolls participants before they experience the disease or condition of interest. The enrollees are then followed over time for occurrence of the disease or condition. The unexposed or lowest exposure group serves as the comparison group, providing an estimate of the baseline or expected amount of disease. An example of a prospective cohort study is the Framingham Heart Study. By assessing the exposures of an original cohort of more than 5,000 adults without cardiovascular disease (CVD), beginning in 1948 and following them over time, the study was the first to identify common CVD risk factors ( 6 ). Each case of CVD identified after enrollment was counted as an incident case. Incidence was then quantified as the number of cases divided by the sum of time that each person was followed (incidence rate) or as the number of cases divided by the number of participants being followed (attack rate or risk or i ncidence proportion). In field epidemiology, prospective cohort studies also often involve a group of persons who have had a known exposure (e.g., survived the World Trade Center attack on September 11, 2001 [ 7 ]) and who are then followed to examine the risk for subsequent illnesses with long incubation or latency periods.

Retrospective Cohort Studies

A retrospective cohort study enrolls a defined participant group after the disease or condition of interest has occurred. In field epidemiology, these studies are more common than prospective studies. The population affected is often well-defined (e.g., banquet attendees, a particular school’s students, or workers in a certain industry). Investigators elicit exposure histories and compare disease incidence among persons with different exposures or exposure levels.

Observational Study Type 2: Case–Control

In a case–control study, the investigator must identify a comparison group of control persons who have had similar opportunities for exposure as the case-patients. Case–control studies are commonly performed in field epidemiology when a cohort study is impractical (e.g., no defined cohort or too many non-ill persons in the group to interview). Whereas a cohort study proceeds conceptually from exposure to disease or condition, a case–control study begins conceptually with the disease or condition and looks backward at exposures. Excluding controls by symptoms alone might not guarantee that they do not have mild cases of the illness under investigation. Table 7.1 presents selected key differences between a case–control and retrospective cohort study.

Benefits and drawbacks of three observational study types commonly used in field investigations
Feature	Retrospective cohort study	Case–control study	Case–case study
Sample size	Larger	Smaller	Smaller
Costs	More (because of size)	Less	Less
Study time	Short	Short	Short
If disease is rare	Inefficient	Efficient	Efficient (if comparison cases already identified)
If exposure is rare	Efficient	Inefficient	Inefficient
If multiple exposures are relevant	Often can examine	Can examine	Can examine
If patients have multiple outcomes	Can examine	Cannot examine	Cannot examine
Natural history	Can ascertain	Cannot ascertain	Cannot ascertain
Disease risk	Can measure	Cannot measure	Cannot measure
Recall bias	Potential problem	Potential problem	Generally less of a problem
Selection bias	Potential problem	Potential problem	Potential problem
If population is not well-defined	Difficult	Advantageous	Advantageous

Observational Study Type 3: Case–Case

In case–case studies, a group of patients with the same or similar disease serve as a comparison group (8). This method might require molecular subtyping of the suspected pathogen to distinguish outbreak-associated cases from other cases and is especially useful when relevant controls are difficult to identify. For example, controls for an investigation of Listeria illnesses typically are patients with immunocompromising conditions (e.g., cancer or corticosteroid use) who might be difficult to identify among the general population. Patients with Listeria isolates of a different subtype than the outbreak strain can serve as comparisons to help reduce bias when comparing food exposures. However, patients with similar illnesses can have similar exposures, which can introduce a bias, making identifying the source more difficult. Moreover, other considerations should influence the choice of a comparison group. If most outbreak-associated case-patients are from a single neighborhood or are of a certain race/ethnicity, other patients with listeriosis from across the country will serve as an inadequate comparison group.

Considerations for Selecting Controls

Selecting relevant controls is one of the most important considerations when designing a case–control study. Several key considerations are presented here; consult other resources for in-depth discussion ( 9,10 ). Ideally, controls should

Thoroughly reflect the source population from which case-patients arose, and
Provide a good estimate of the level of exposure one would expect from that population. Sometimes the source population is not so obvious, and a case–control study using controls from the general population might be needed to implicate a general exposure (e.g., visiting a specific clinic, restaurant, or fair). The investigation can then focus on specific exposures among persons with the general exposure (see also next section).

Controls should be chosen independently of any specific exposure under evaluation. If you select controls on the basis of lack of exposure, you are likely to find an association between illness and that exposure regardless of whether one exists. Also important is selecting controls from a source population in a way that minimizes confounding (see Chapter 8 ), which is the existence of a factor (e.g., annual income) that, by being associated with both exposure and disease, can affect the associations you are trying to examine.

When trying to enroll controls who reflect the source population, try to avoid overmatching (i.e., enrolling controls who are too similar to case-patients, resulting in fewer differences among case-patients and controls than ought to exist and decreased ability to identify exposure–disease associations). When conducting case–control studies in hospitals and other healthcare settings, ensure that controls do not have other diseases linked to the exposure under study.

Commonly Used Control Selection Methods

When an outbreak does not affect a defined population (e.g., potluck dinner attendees) but rather the community at large, a range of options can be used to determine how to select controls from a large group of persons.

Random-digit dialing . This method, which involves selecting controls by using a system that randomly selects telephone numbers from a directory, has been a staple of US outbreak investigations. In recent years, however, declining response rates because of increasing use of caller identification and cellular phones and lack of readily available directory listings of cellular phone numbers by geographic area have made this method increasingly difficult. Even when this method was most useful, often 50 or more numbers needed to be dialed to reach one household or person who both answered and provided a usable match for the case-patient. Commercial databases that include cellular phone numbers have been used successfully to partially address this problem, but the method remains time-consuming ( 11 ).
Random or systematic sampling from a list . For investigations in settings where a roster is available (e.g., attendees at a resort on certain dates), controls can be selected by either random or systematic sampling. Government records (e.g., motor vehicle, voter, or tax records) can provide lists of possible controls, but they might not be representative of the population being studied ( 11 ). For random sampling, a table or computer-generated list of random numbers can be used to select every n th persons to contact (e.g., every 12th or 13th).
Neighborhood . Recruiting controls from the same neighborhood as case-patients (i.e., neighborhood matching) has commonly been used during case–control studies, particularly in low-and middle-income countries. For example, during an outbreak of typhoid fever in Tajikistan ( 12 ), investigators recruited controls by going door-to-door down a street, starting at a case-patient’s house; a study of cholera in Haiti used a similar method ( 13 ). Typically, the immediately neighboring households are skipped to prevent overmatching.
Patients’ friends or relatives . Using friends and relatives as controls can be an effective technique when the characteristics of case-patients (e.g., very young children) make finding controls by a random method difficult. Typically, the investigator interviews a patient or his or her parent, then asks for the names and contact information for more friends or relatives who are needed as controls. One advantage is that the friends of an ill person are usually willing to participate, knowing their cooperation can help solve the puzzle. However, because they can have similar personal habits and preferences as patients, their exposures might be similar. Such overmatching can decrease the likelihood of finding the source of the illness or condition.
Databases of persons with exposure information . Sources of data on persons with exposure information include survey data (e.g., FoodNet Population Survey [ 14 ]), public health databases of patients with other illnesses or a different subtype of the same illness, and previous studies. ( Chapter 4 describes additional sources.)

When considering outside data sources, investigators must determine whether those data provide an appropriate comparison group. For example, persons in surveys might differ from case-patients in ways that are impossible to determine. Other patients might be so similar to case-patients that risky exposures are unidentifiable, or they might be so different that exposures identified as risks are not true risks.

To help control for confounding, controls can be matched to case-patients on characteristics specified by investigators, including age group, sex, race/ethnicity, and neighborhood. Such matching does not itself reduce confounding, but it enables greater efficiency when matched analyses are performed that do ( 15 ). When deciding to match, however, be judicious. Matching on too many characteristics can make controls difficult to find (making a tough process even harder). Imagine calling hundreds of random telephone numbers trying to find a man of a particular ethnicity aged 50–54 years who is then willing to answer your questions. Also, remember not to match on the exposure of interest or on any other characteristic you wish to examine. Matched case–control study data typically necessitate a matched analysis (e.g., conditional logistic regression) ( 15 ).

Matching Types

The two main types of matching are pair matching and frequency matching.

Pair Matching

In pair matching, each control is matched to a specific case-patient. This method can be helpful logistically because it allows matching by friends or relatives, neighborhood, or telephone exchange, but finding controls who meet specific criteria can be burdensome.

Frequency Matching

In frequency matching, also called category matching , controls are matched to case-patients in proportion to the distribution of a characteristic among case-patients. For example, if 20% of case-patients are children aged 5–18 years, 50% are adults aged 19–49 years, and 30% are adults 50 years or older, controls should be enrolled in similar proportions. This method works best when most case-patients have been identified before control selection begins. It is more efficient than pair matching because a person identified as a possible control who might not meet the criteria for matching a particular case-patient might meet criteria for one of the case-patient groups.

Number of Controls

Most field case–control studies use control-to-case-patient ratios of 1:1, 2:1, or 3:1. Enrolling more than one control per case-patient can increase study power, which might be needed to detect a statistically significant difference in exposure between case-patients and controls, particularly when an outbreak involves a limited number of cases. The incremental gain of adding more controls beyond three or four is small because study power begins to plateau. Note that not all case-patients need to have the same number of controls. Sample size calculations can help in estimating a target number of controls to enroll, although sample sizes in certain field investigations are limited more by time and resource constraints. Still, estimating study power under a range of scenarios is wise because an analytic study might not be worth doing if you have little chance of detecting a statistically significant association. Sample size calculators for unmatched case–control studies are available at http://www.openepi.com and in the StatCalc function of Epi Info ( https://www.cdc.gov/epiinfo ).

More than One Control Group

Sometimes the choice of a control group is so vexing that investigators decide to use more than one type of control group (e.g., a hospital-based group and a community group). If the two control groups provide similar results and conclusions about risk factors for disease, the credibility of the findings is increased. In contrast, if the two control groups yield conflicting results, interpretation becomes more difficult.

Since the 1940s, field epidemiology students have studied a classic outbreak of gastrointestinal illness at a church potluck dinner in Oswego, New York ( 16 ). However, the case study presented here, used to illustrate study designs, is a different potluck dinner.

In April 2015, an astute neurologist in Lancaster, Ohio, contacted the local health department about a patient in the emergency department with a suspected case of botulism. Within 2 hours, four more patients arrived with similar symptoms, including blurred vision and shortness of breath. Health officials immediately recognized this as a botulism outbreak.

If the source is a widely distributed commercial product, then the population to study is persons across the United States and possibly abroad.
If the source is airborne, then the population to study is residents of a single city or area.
If the source is food from a restaurant, then the population to study is predominantly local residents and some travelers.
If the source is a meal at a workplace or social setting, then the population to study is meal attendees.
If the source is a meal at home, then the population to study is household members and any guests.

Descriptive epidemiology and questioning of the case-patients revealed that all had eaten at the same church potluck dinner and had no other common exposures, making the potluck the likely exposure site and attendees the likely source population. Thus, an analytic study would be targeted at potluck attendees, although investigators must remain alert to case-patients among nonattendees. As initial interviews were conducted, more cases of botulism were being diagnosed, quickly increasing to more than 25. The source of the outbreak needed to be identified rapidly to halt further exposure and illness.

List of foods served at the potluck.
Approximate number of attendees.
A case definition.
Information from 5–10 hypothesis-generating interviews with a few case-patients or their family members.
A cohort study would be a reasonable option because a defined group exists (i.e., a cohort) of exposed persons who could be interviewed in a reasonable amount of time. The study would be retrospective because the outcome (i.e., botulism) has already occurred, and investigators could assess exposures retrospectively (i.e., foods eaten at the potluck) by interviewing attendees.
In a cohort study, investigators can calculate the attack rate for botulism among potluck attendees who reported having eaten each food and for those who had not. For example, if 20 of the 30 attendees who had eaten a particular food (e.g., potato salad) had botulism, you would calculate the attack rate by dividing 20 (corresponding to cell a in Handout 7.1 ) by 30 (total exposed, or a + b), yielding approximately 67%. If 5 of the 45 attendees who had not eaten potato salad had botulism, the attack rate among the unexposed—5 / 45, corresponding to c/ (c + d)—would be approximately 11%. The risk ratio would be 6, which is calculated by dividing the attack rate among the exposed (67%) by the attack rate among the unexposed (11%).
A case–control study would be the most feasible option because the entire cohort could not be identified and because the large number of attendees could make interviewing them all difficult. Rather than interview all non-ill persons, a subset could be interviewed as control subjects.
The method of control subject selection should be considered carefully. If all attendees are not interviewed, determining the risk for botulism among the exposed and unexposed is impossible because investigators would not know the exposures for all non-ill attendees. Instead of risk, investigators calculate the odds of exposure, which can approximate risk. For example, if 20 (80%) of 25 case-patients had eaten potato salad, the odds of potato salad exposure among case-patients would be 20/ 5 = 4 (exposed/ unexposed, or a/ c in Handout 7.2 ). If 10 (20%) of 50 selected controls had eaten potato salad, the odds of exposure among control subjects would be 10/ 40 = 0.25 (or b/ d in Handout 7.2). Dividing the odds of exposure among the case-patients (a/ c) by the odds of exposure among control subjects (b / d) yields an odds ratio of 16 (4/ 0.25). The odds ratio is not a true measure of risk, but it can be used to implicate a food. An odds ratio can approximate a risk ratio when the outcome or disease is rare (e.g., roughly <5% of a population). In such cases, a/ b is similar to a/ (a + b). The odds ratio is typically higher than the risk ratio when >5% of exposed persons in the analysis have the illness.

In the actual outbreak, 29 (38%) of 77 potluck attendees had botulism. The investigators performed a cohort study, interviewing 75 of the 77 attendees about 52 foods served ( 17 ). The attack rate among persons who had eaten potato salad was significantly and substantially higher than the attack rate among those who had not, with a risk ratio of 14 (95% confidence interval 5–42). One of the potato salads served was made with incorrectly home-canned potatoes (a known source of botulinum toxin), and samples of discarded potato salad tested positive for botulinum toxin, supporting the findings of the analytic study. (Of note, persons often blame potato salad for causing illness when, in fact, it rarely is a source. This outbreak was a notable exception.)

In field epidemiology, the link between exposure and illness is often so strong that it is evident despite such inherent study limitations as small sample size and exposure misclassification. In this outbreak, a few of the patients with botulism reported not having eaten potato salad, and some of the attendees without botulism reported having eaten it. In epidemiologic studies, you rarely find 100% concordance between exposure and outcome for various reasons, including incomplete or erroneous recall because remembering everything eaten is difficult. Here, cross-contamination of potato salad with other foods might have helped explain cases among patients who had not eaten potato salad because only a small amount of botulinum toxin is needed to produce illness.

Two-by-Two Table to Calculate the Relative Risk, or Risk Ratio, in Cohort Studies

Two- by- two tables are covered in more detail in Chapter 8 .

Cohort Study Approach
	Ill	Not Ill
Exposed	a	b
Unexposed	c	d

Risk Ratio = Incidence in exposed over Incidence in unexposed = a over a+b over c over c+d

Two-by-Two Table to Calculate the Odds Ratio in Case–Control Studies

A risk ratio cannot be calculated from a case–control study because true attack rates cannot be calculated.

Case-Control Study Approach
	III (Cases)	Not III (Controls)
Exposed	a	b
Unexposed	c	d

Odds ratio = Odds of exposure in cases over Odds of exposure in controls = a/c over b/d = ad over bc

What kind of study would you design if your hypothesis-generating interviews lead you to believe that everyone, or nearly everyone, was exposed to the same suspected infection source? How would you test hypotheses if all barbecue attendees, ill and non-ill, had eaten the chicken or if all town residents had drunk municipal tap water, and no unexposed group exists for comparison? A few factors that might be of help are the exposure timing (e.g., a particularly undercooked batch of barbeque), the exposure place (e.g., a section of the water system more contaminated than others), and the exposure dose (e.g., number of chicken pieces eaten or glasses of water drunk). Including questions about the time, place, and frequency of highly suspected exposures in a questionnaire can improve the chances of detecting a difference ( 18 ).

Cohort, case–control, and case–case studies are the types of analytic studies that field epidemiologists use most often. They are best used as mechanisms for evaluating—quantifying and testing—hypotheses identified in earlier phases of the investigation. Cohort studies, which are oriented conceptually from exposure to disease, are appropriate in settings in which an entire population is well-defined and available for enrollment (e.g., guests at a wedding reception). Cohort studies are also appropriate when well-defined groups can be enrolled by exposure status (e.g., employees working in different parts of a manufacturing plant). Case–control studies, in contrast, are useful when the population is less clearly defined. Case–control studies, oriented from disease to exposure, identify persons with disease and a comparable group of persons without disease (controls). Then the exposure experiences of the two groups are compared. Case–case studies are similar to case–control studies, except that controls have an illness not linked to the outbreak. Case–control studies are probably the type most often appropriate for field investigations. Although conceptually straightforward, the design of an effective epidemiologic study requires many careful decisions. Taking the time needed to develop good hypotheses can result in a questionnaire that is useful for identifying risk factors. The choice of an appropriate comparison group, how many controls per case-patient to enroll, whether to match, and how best to avoid potential biases are all crucial decisions for a successful study.

This chapter relies heavily on the work of Richard C. Dicker, who authored this chapter in the previous edition.

Gupta N, Hocevar SN, Moulton-Meissner HA, et al. Outbreak of Serratia marcescens bloodstream infections in patients receiving parenteral nutrition prepared by a compounding pharmacy. Clin Infect Dis. 2014;59:1–8.
Angelo K, Conrad A, Saupe A, et al. Multistate outbreak of Listeria monocytogenes infections linked to whole apples used in commercially produced, prepackaged caramel apples: United States, 2014–2015. Epidemiol Infect. 2017;145:848–56.
Neil KP, Biggerstaff G, MacDonald JK, et al. A novel vehicle for transmission of Escherichia coli O157: H7 to humans: multistate outbreak of E. coli O157: H7 infections associated with consumption of ready-to-bake commercial prepackaged cookie dough—United States, 2009. Clin Infect Dis. 2012;54:511–8.
Vasquez AM, Lake J, Ngai S, et al. Notes from the field: fungal bloodstream infections associated with a compounded intravenous medication at an outpatient oncology clinic—New York City, 2016. MMWR. 2016;65:1274–5.
Gottlieb SL, Newbern EC, Griffin PM, et al. Multistate outbreak of listeriosis linked to turkey deli meat and subsequent changes in US regulatory policy. Clin Infect Dis. 2006;42:29–36.
Framingham Heart Study: A Project of the National Heart, Lung, and Blood Institute and Boston University. Framingham, MA: Framingham Heart Study; 2017. https://www.framinghamheartstudy.org/
Jordan HT, Brackbill RM, Cone JE, et al. Mortality among survivors of the Sept 11, 2001, World Trade Center disaster: results from the World Trade Center Health Registry cohort. Lancet. 2011;378:879–87.
McCarthy N, Giesecke J. Case– case comparisons to study causation of common infectious diseases. Int J Epidemiol. 1999;28:764–8.
Rothman KJ, Greenland S. Modern epidemiology . 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.
Wacholder S, McLaughlin JK, Silverman DT, Mandel JS. Selection of controls in case–control studies. I. Principles. Am J Epidemiol. 1992;135:1019–28.
Chintapalli S, Goodman M, Allen M, et al. Assessment of a commercial searchable population directory as a means of selecting controls for case–control studies. Public Health Rep. 2009;124:378–83.
Centers for Disease Control and Prevention. Epidemiologic case studies: typhoid in Tajikistan. http://www.cdc.gov/epicasestudies/classroom_typhoid.html
Dunkle SE, Mba-Jonas A, Loharikar A, Fouche B, Peck M, Ayers T. Epidemic cholera in a crowded urban environment, Port-au-Prince, Haiti. Emerg Infect Dis. 2011;17:2143–6.
Centers for Disease Control and Prevention. Foodborne Diseases Active Surveillance Network (FoodNet): population survey. http://www.cdc.gov/foodnet/surveys/population.html
Pearce N. Analysis of matched case–control studies. BMJ. 2016;352:1969.
Centers for Disease Control and Prevention. Case studies in applied epidemiology: Oswego: an outbreak of gastrointestinal illness following a church supper. http://www.cdc.gov/eis/casestudies.html
McCarty CL, Angelo K, Beer KD, et al. Notes from the field.: large outbreak of botulism associated with a church potluck meal—Ohio, 2015. MMWR. 2015;64:802–3.
Tostmann A, Bousema JT, Oliver I. Investigation of outbreaks complicated by universal exposure. Emerg Infect Dis. 2012;18:1717–22.

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What Is a Retrospective Cohort Study? | Definition & Examples

What Is a Retrospective Cohort Study? | Definition & Examples

Published on February 10, 2023 by Tegan George . Revised on June 22, 2023.

A retrospective cohort study is a type of observational study that focuses on individuals who have an exposure to a disease or risk factor in common. Retrospective cohort studies analyze the health outcomes over a period of time to form connections and assess the risk of a given outcome associated with a given exposure.

It is crucial to note that in order to be considered a retrospective cohort study, your participants must already possess the disease or health outcome being studied.

When to use a retrospective cohort study, examples of retrospective cohort studies, advantages and disadvantages of retrospective cohort studies, other interesting articles, frequently asked questions.

Retrospective cohort studies are a type of observational study . They are often used in fields related to medicine to study the effect of exposures on health outcomes. While most observational studies are qualitative in nature, retrospective cohort studies are often quantitative , as they use preexisting secondary research data. They can be used to conduct both exploratory research and explanatory research .

Retrospective cohort studies are often used as an intermediate step between a weaker preliminary study and a prospective cohort study , as the results gleaned from a retrospective cohort study strengthen assumptions behind a future prospective cohort study.

A retrospective cohort study could be a good fit for your research if:

A prospective cohort study is not (yet) feasible for the variables you are investigating.
You need to quickly examine the effect of an exposure, outbreak, or treatment on an outcome.
You are seeking to investigate an early-stage or potential association between your variables of interest.

Retrospective cohort studies use secondary research data, such as existing medical records or databases, to identify a group of people with an exposure or risk factor in common. They then look back in time to observe how the health outcomes developed. Case-control studies rely on primary research , comparing a group of participants with a condition of interest to a group lacking that condition in real time.

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Retrospective cohort studies are common in fields like medicine, epidemiology, and healthcare.

You collect data from participants’ exposure to organophosphates, focusing on variables like the timing and duration of exposure, and analyze the health effects of the exposure. Example: Healthcare retrospective cohort study You are examining the relationship between tanning bed use and the incidence of skin cancer diagnoses.

Retrospective cohort studies can be a good fit for many research projects, but they have their share of advantages and disadvantages.

Advantages of retrospective cohort studies

Retrospective cohort studies are a great choice if you have any ethical considerations or concerns about your participants that prevent you from pursuing a traditional experimental design .
Retrospective cohort studies are quite efficient in terms of time and budget. They require fewer subjects than other research methods and use preexisting secondary research data to analyze them.
Retrospective cohort studies are particularly useful when studying rare or unusual exposures, as well as diseases with a long latency or incubation period where prospective cohort studies cannot yet form conclusions.

Disadvantages of retrospective cohort studies

Like many observational studies, retrospective cohort studies are at high risk for many research biases . They are particularly at risk for recall bias and observer bias due to their reliance on memory and self-reported data.
Retrospective cohort studies are not a particularly strong standalone method, as they can never establish causality . This leads to low internal validity and external validity .
As most patients will have had a range of healthcare professionals involved in their care over their lifetime, there is significant variability in the measurement of risk factors and outcomes. This leads to issues with reliability and credibility of data collected.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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The primary difference between a retrospective cohort study and a prospective cohort study is the timing of the data collection and the direction of the study.

A retrospective cohort study looks back in time. It uses preexisting secondary research data to examine the relationship between an exposure and an outcome. Data is collected after the outcome you’re studying has already occurred.

Alternatively, a prospective cohort study follows a group of individuals over time. It collects data on both the exposure and the outcome of interest as they are occurring. Data is collected before the outcome of interest has occurred.

Retrospective cohort studies are at high risk for research biases like recall bias . Whenever individuals are asked to recall past events or exposures, recall bias can occur. This is because individuals with a certain disease or health outcome of interest are more likely to remember and/or report past exposures differently to individuals without that outcome. This can result in an overestimation or underestimation of the true relationship between variables and affect your research.

No, retrospective cohort studies cannot establish causality on their own.

Like other types of observational studies , retrospective cohort studies can suggest associations between an exposure and a health outcome. They cannot prove without a doubt, however, that the exposure studied causes the health outcome.

In particular, retrospective cohort studies suffer from challenges arising from the timing of data collection , research biases like recall bias , and how variables are selected. These lead to low internal validity and the inability to determine causality.

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Step-by-step guide to hypothesis testing in statistics

Hypothesis testing in statistics helps us use data to make informed decisions. It starts with an assumption or guess about a group or population—something we believe might be true. We then collect sample data to check if there is enough evidence to support or reject that guess. This method is useful in many fields, like science, business, and healthcare, where decisions need to be based on facts.

Learning how to do hypothesis testing in statistics step-by-step can help you better understand data and make smarter choices, even when things are uncertain. This guide will take you through each step, from creating your hypothesis to making sense of the results, so you can see how it works in practical situations.

What is Hypothesis Testing?

Table of Contents

Hypothesis testing is a method for determining whether data supports a certain idea or assumption about a larger group. It starts by making a guess, like an average or a proportion, and then uses a small sample of data to see if that guess seems true or not.

For example, if a company wants to know if its new product is more popular than its old one, it can use hypothesis testing. They start with a statement like “The new product is not more popular than the old one” (this is the null hypothesis) and compare it with “The new product is more popular” (this is the alternative hypothesis). Then, they look at customer feedback to see if there’s enough evidence to reject the first statement and support the second one.

Simply put, hypothesis testing is a way to use data to help make decisions and understand what the data is really telling us, even when we don’t have all the answers.

Importance Of Hypothesis Testing In Decision-Making And Data Analysis

Hypothesis testing is important because it helps us make smart choices and understand data better. Here’s why it’s useful:

Reduces Guesswork : It helps us see if our guesses or ideas are likely correct, even when we don’t have all the details.
Uses Real Data : Instead of just guessing, it checks if our ideas match up with real data, which makes our decisions more reliable.
Avoids Errors : It helps us avoid mistakes by carefully checking if our ideas are right so we don’t make costly errors.
Shows What to Do Next : It tells us if our ideas work or not, helping us decide whether to keep, change, or drop something. For example, a company might test a new ad and decide what to do based on the results.
Confirms Research Findings : It makes sure that research results are accurate and not just random chance so that we can trust the findings.

Here’s a simple guide to understanding hypothesis testing, with an example:

1. Set Up Your Hypotheses

Explanation: Start by defining two statements:

Null Hypothesis (H0): This is the idea that there is no change or effect. It’s what you assume is true.
Alternative Hypothesis (H1): This is what you want to test. It suggests there is a change or effect.

Example: Suppose a company says their new batteries last an average of 500 hours. To check this:

Null Hypothesis (H0): The average battery life is 500 hours.
Alternative Hypothesis (H1): The average battery life is not 500 hours.

2. Choose the Test

Explanation: Pick a statistical test that fits your data and your hypotheses. Different tests are used for various kinds of data.

Example: Since you’re comparing the average battery life, you use a one-sample t-test .

3. Set the Significance Level

Explanation: Decide how much risk you’re willing to take if you make a wrong decision. This is called the significance level, often set at 0.05 or 5%.

Example: You choose a significance level of 0.05, meaning you’re okay with a 5% chance of being wrong.

4. Gather and Analyze Data

Explanation: Collect your data and perform the test. Calculate the test statistic to see how far your sample result is from what you assumed.

Example: You test 30 batteries and find they last an average of 485 hours. You then calculate how this average compares to the claimed 500 hours using the t-test.

5. Find the p-Value

Explanation: The p-value tells you the probability of getting a result as extreme as yours if the null hypothesis is true.

Example: You find a p-value of 0.0001. This means there’s a very small chance (0.01%) of getting an average battery life of 485 hours or less if the true average is 500 hours.

6. Make Your Decision

Explanation: Compare the p-value to your significance level. If the p-value is smaller, you reject the null hypothesis. If it’s larger, you do not reject it.

Example: Since 0.0001 is much less than 0.05, you reject the null hypothesis. This means the data suggests the average battery life is different from 500 hours.

7. Report Your Findings

Explanation: Summarize what the results mean. State whether you rejected the null hypothesis and what that implies.

Example: You conclude that the average battery life is likely different from 500 hours. This suggests the company’s claim might not be accurate.

Hypothesis testing is a way to use data to check if your guesses or assumptions are likely true. By following these steps—setting up your hypotheses, choosing the right test, deciding on a significance level, analyzing your data, finding the p-value, making a decision, and reporting results—you can determine if your data supports or challenges your initial idea.

Understanding Hypothesis Testing: A Simple Explanation

Hypothesis testing is a way to use data to make decisions. Here’s a straightforward guide:

1. What is the Null and Alternative Hypotheses?

Null Hypothesis (H0): This is your starting assumption. It says that nothing has changed or that there is no effect. It’s what you assume to be true until your data shows otherwise. Example: If a company says their batteries last 500 hours, the null hypothesis is: “The average battery life is 500 hours.” This means you think the claim is correct unless you find evidence to prove otherwise.
Alternative Hypothesis (H1): This is what you want to find out. It suggests that there is an effect or a difference. It’s what you are testing to see if it might be true. Example: To test the company’s claim, you might say: “The average battery life is not 500 hours.” This means you think the average battery life might be different from what the company says.

2. One-Tailed vs. Two-Tailed Tests

One-Tailed Test: This test checks for an effect in only one direction. You use it when you’re only interested in finding out if something is either more or less than a specific value. Example: If you think the battery lasts longer than 500 hours, you would use a one-tailed test to see if the battery life is significantly more than 500 hours.
Two-Tailed Test: This test checks for an effect in both directions. Use this when you want to see if something is different from a specific value, whether it’s more or less. Example: If you want to see if the battery life is different from 500 hours, whether it’s more or less, you would use a two-tailed test. This checks for any significant difference, regardless of the direction.

3. Common Misunderstandings

Clarification: Hypothesis testing doesn’t prove that the null hypothesis is true. It just helps you decide if you should reject it. If there isn’t enough evidence against it, you don’t reject it, but that doesn’t mean it’s definitely true.
Clarification: A small p-value shows that your data is unlikely if the null hypothesis is true. It suggests that the alternative hypothesis might be right, but it doesn’t prove the null hypothesis is false.
Clarification: The significance level (alpha) is a set threshold, like 0.05, that helps you decide how much risk you’re willing to take for making a wrong decision. It should be chosen carefully, not randomly.
Clarification: Hypothesis testing helps you make decisions based on data, but it doesn’t guarantee your results are correct. The quality of your data and the right choice of test affect how reliable your results are.

Benefits and Limitations of Hypothesis Testing

Clear Decisions: Hypothesis testing helps you make clear decisions based on data. It shows whether the evidence supports or goes against your initial idea.
Objective Analysis: It relies on data rather than personal opinions, so your decisions are based on facts rather than feelings.
Concrete Numbers: You get specific numbers, like p-values, to understand how strong the evidence is against your idea.
Control Risk: You can set a risk level (alpha level) to manage the chance of making an error, which helps avoid incorrect conclusions.
Widely Used: It can be used in many areas, from science and business to social studies and engineering, making it a versatile tool.

Limitations

Sample Size Matters: The results can be affected by the size of the sample. Small samples might give unreliable results, while large samples might find differences that aren’t meaningful in real life.
Risk of Misinterpretation: A small p-value means the results are unlikely if the null hypothesis is true, but it doesn’t show how important the effect is.
Needs Assumptions: Hypothesis testing requires certain conditions, like data being normally distributed . If these aren’t met, the results might not be accurate.
Simple Decisions: It often results in a basic yes or no decision without giving detailed information about the size or impact of the effect.
Can Be Misused: Sometimes, people misuse hypothesis testing, tweaking data to get a desired result or focusing only on whether the result is statistically significant.
No Absolute Proof: Hypothesis testing doesn’t prove that your hypothesis is true. It only helps you decide if there’s enough evidence to reject the null hypothesis, so the conclusions are based on likelihood, not certainty.

Final Thoughts

Hypothesis testing helps you make decisions based on data. It involves setting up your initial idea, picking a significance level, doing the test, and looking at the results. By following these steps, you can make sure your conclusions are based on solid information, not just guesses.

This approach lets you see if the evidence supports or contradicts your initial idea, helping you make better decisions. But remember that hypothesis testing isn’t perfect. Things like sample size and assumptions can affect the results, so it’s important to be aware of these limitations.

In simple terms, using a step-by-step guide for hypothesis testing is a great way to better understand your data. Follow the steps carefully and keep in mind the method’s limits.

What is the difference between one-tailed and two-tailed tests?

A one-tailed test assesses the probability of the observed data in one direction (either greater than or less than a certain value). In contrast, a two-tailed test looks at both directions (greater than and less than) to detect any significant deviation from the null hypothesis.

How do you choose the appropriate test for hypothesis testing?

The choice of test depends on the type of data you have and the hypotheses you are testing. Common tests include t-tests, chi-square tests, and ANOVA. You get more details about ANOVA, you may read Complete Details on What is ANOVA in Statistics ? It’s important to match the test to the data characteristics and the research question.

What is the role of sample size in hypothesis testing?

Sample size affects the reliability of hypothesis testing. Larger samples provide more reliable estimates and can detect smaller effects, while smaller samples may lead to less accurate results and reduced power.

Can hypothesis testing prove that a hypothesis is true?

Hypothesis testing cannot prove that a hypothesis is true. It can only provide evidence to support or reject the null hypothesis. A result can indicate whether the data is consistent with the null hypothesis or not, but it does not prove the alternative hypothesis with certainty.

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Ueda P , Söderling J , Wintzell V, et al. GLP-1 Receptor Agonist Use and Risk of Suicide Death. JAMA Intern Med. Published online September 03, 2024. doi:10.1001/jamainternmed.2024.4369

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GLP-1 Receptor Agonist Use and Risk of Suicide Death

1 Division of Clinical Epidemiology, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
2 Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
3 The Swedish National Diabetes Register, Västra Götalandsregionen, Gothenburg, Sweden
4 Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
5 Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
6 HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Science, Norwegian University of Science and Technology, Trondheim, Norway
7 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
8 Department of Pediatrics, Stanford University School of Medicine, Stanford, California
9 Danish Cancer Institute, Copenhagen, Denmark
Editor's Note Glucagon-Like Peptide-1 Receptor Agonists and Suicidality Timothy S. Anderson, MD, MAS; Deborah Grady, MD, MPH JAMA Internal Medicine
Original Investigation Psychiatric Safety of Semaglutide for Weight Management in People Without Known Major Psychopathology Thomas A. Wadden, PhD; Gregory K. Brown, PhD; Christina Egebjerg, PhD; Ofir Frenkel, MD; Bryan Goldman, MS; Robert F. Kushner, MD; Barbara McGowan, PhD; Maria Overvad, MD; Anders Fink-Jensen, MD JAMA Internal Medicine

Question What is the association between use of glucagon-like peptide-1 (GLP-1) receptor agonists and risk of suicide death in patients treated in routine clinical practice?

Findings In this cohort study of 298 553 adults initiating a GLP-1 receptor agonist or a sodium-glucose cotransporter-2 inhibitor nationwide in Sweden and Denmark, the incidence rate of suicide death was low, and those initiating GLP-1 receptor agonist use did not experience increased risk.

Meaning This study provides reassuring data showing that those initiating GLP-1 receptor agonist use were not at increased risk of suicide death, although the study could not assess small risk increases.

Importance Concerns have been raised regarding a link between use of glucagon-like peptide-1 (GLP-1) receptor agonists and increased risk of suicidality and self-harm.

Objective To assess the association between use of GLP-1 receptor agonists and the risk of suicide death in routine clinical practice.

Design, Setting, and Participants This active-comparator new-user cohort study used nationwide register data from Sweden and Denmark from 2013 to 2021. Adults 18 to 84 years old who initiated treatment with GLP-1 receptor agonists or the comparator sodium-glucose cotransporter-2 (SGLT2) inhibitors were included. Data were analyzed from March to June 2024.

Exposure Initiation of treatment with a GLP-1 receptor agonist or SGLT2 inhibitor.

Main Outcomes and Measures The primary outcome was suicide death recorded in the cause of death registers. Secondary outcomes were the composite of suicide death and nonfatal self-harm and the composite of incident depression and anxiety-related disorders. Using propensity score weighting, hazard ratios (HRs) with 95% CIs were calculated separately in the 2 countries and pooled in a meta-analysis.

Results In total, 124 517 adults initiated a GLP-1 receptor agonist and 174 036 initiated an SGLT2 inhibitor; among GLP-1 receptor agonist users, the mean (SD) age was 60 (13) years, and 45% were women. During a mean (SD) follow-up of 2.5 (1.7) years, 77 suicide deaths occurred among users of GLP-1 receptor agonists and 71 suicide deaths occurred among users of SGLT2 inhibitors: weighted incidences were 0.23 vs 0.18 events per 1000 person-years (HR, 1.25; 95% CI, 0.83-1.88), with an absolute difference of 0.05 (95% CI, −0.03 to 0.16) events per 1000 person-years. The HR was 0.83 (95% CI, 0.70-0.97) for suicide death and nonfatal self-harm, and the HR was 1.01 (95% CI, 0.97-1.06) for incident depression and anxiety-related disorders.

Conclusions and Relevance This cohort study, including mostly patients with type 2 diabetes, does not show an association between use of GLP-1 receptor agonists and an increased risk of suicide death, self-harm, or incident depression and anxiety-related disorders. Suicide death among GLP-1 receptor agonist users was rare, and the upper limit of the confidence interval was compatible with an absolute risk increase of no more than 0.16 events per 1000 person-years.

Glucagon-like peptide-1 (GLP-1) receptor agonists are increasingly used in the treatment of type 2 diabetes and obesity. Concerns have been raised regarding suicidality and self-harm linked to use of GLP-1 receptor agonists. 1 In July 2023, the European Medicines Agency launched an investigation into the safety signal following around 150 spontaneous reports received by the agency regarding suicidal thoughts and thoughts of self-harm potentially associated with the drug. An effect of GLP-1 receptor agonists on suicidality is plausible, as GLP-1 receptors are present in the central nervous system and GLP-1 receptor agonists have been shown to cross the blood-brain barrier. 2 , 3 Previous studies have linked bariatric surgery and weight-reduction drugs to a potentially increased risk of suicide and self-harm. 4 , 5 Conversely, it has also been suggested that GLP-1 receptor agonists may protect against depression. This hypothesis is based on studies indicating that depression and type 2 diabetes may have partly overlapping causes, including neuroinflammation, and that GLP-1 receptor agonists show neuroprotective properties. 6

We conducted a cohort study using nationwide data from Sweden and Denmark to examine the association of GLP-1 receptor agonist use with suicide death. In secondary analyses, we assessed the association of GLP-1 receptor agonist use with the composite of suicide and nonfatal self-harm, as well as the composite of incident depression and anxiety-related disorders.

We used health and administrative registers in Sweden and Denmark, including the population registers, 7 , 8 prescription drug registers, 9 , 10 national patient registers, 11 , 12 and cause of death registers, 13 , 14 all with nationwide coverage in each country. The registers provide data on demographic variables and vital status, all filled prescriptions from all pharmacies in each country, diagnoses and procedures registered during all outpatient specialist care visits and hospitalizations, and causes of death. Sweden and Denmark have similar universal health care systems and register infrastructure. 15 Using the same study protocol, we conducted the analyses in Sweden and Denmark separately and performed a meta-analysis of the 2 country-specific estimates.

The study was approved by the Swedish Ethical Review Authority. Informed consent was not needed. Ethical approval is not required for register-based research in Denmark. The study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines for cohort studies.

Using an active-comparator new-user design, 16 we included all new users of either GLP-1 receptor agonists or sodium-glucose cotransporter-2 (SGLT2) inhibitors (comparator) who were 18 to 84 years old during 2013 to 2021 (eTable 1 in Supplement 1 ). Patients entered the cohort at the time of their first filled prescription for either a GLP-1 receptor agonist or an SGLT2 inhibitor during the study period. SGLT2 inhibitors were used as the comparator because this drug has no known association with suicide death and was used in similar clinical situations (predominantly as second-line or third-line glucose-lowering drugs for treatment of type 2 diabetes) during the study period, with both GLP-1 receptor agonists and SGLT2 inhibitors being recommended for patients with type 2 diabetes and high cardiovascular risk. Both drugs were primarily used for type 2 diabetes during the study period.

New use was defined as initiation of either drug among patients with no previous use of either drug at any time prior to cohort entry. Exclusion criteria were end-stage illness (severe malnutrition, cachexia, dementia, coma), history of dialysis, kidney transplant, major pancreatic disease, use of preparation of liraglutide with obesity indication before cohort entry (the comparator, SGLT2 inhibitors, are not used for this indication; liraglutide with obesity indication was defined using specific product codes), and no health care contact in the past year (to ensure a minimum level of health care contact and registration of health data), defined as neither outpatient care contact, hospital admission, nor use of any prescription drug (eTable 2 in Supplement 1 ).

The primary outcome was suicide death (primary or contributing cause of death), including both confirmed suicides and self-inflicted deaths of undetermined intent 17 , 18 (eTable 3 in Supplement 1 ). Secondary outcomes were a composite of suicide death and nonfatal self-harm recorded as a physician-assigned diagnosis during hospitalizations or outpatient hospital visits. 19 Self-harm was also analyzed as a separate outcome. Although nonfatal self-harm is known to be underreported in Denmark, 19 we used this outcome also in Denmark because underreporting was unlikely to differ between the study drugs, thus allowing for calculation of relative risk estimates. For the analyses of outcomes including self-harm, we excluded patients with self-harm within 3 months prior to cohort entry (eTable 2 in Supplement 1 ). This exclusion was done to avoid outcome misclassification: potential follow-up health care visits after a self-harm event may be registered with self-harm as the diagnosis and could be erroneously regarded as a new event in the analysis. Another secondary outcome was a composite of incident depression and anxiety-related disorders, recorded as diagnoses during hospitalizations or outpatient visits, or filled prescriptions for antidepressants (eTable 3 in Supplement 1 ). For this analysis, we further excluded patients with previous psychiatric disorders, defined as any psychiatric disease diagnosis or use of psychiatric medications at any time prior to cohort entry (eTable 4 in Supplement 1 ).

Patients were considered as exposed to the drug that they initiated at cohort entry until end of follow-up and were followed to outcome event, emigration, 5 years of follow-up, or end of study period (December 31, 2021). In each country separately, we estimated a propensity score for the probability of GLP-1 receptor agonist treatment conditional on variables at cohort entry, covering sociodemographic characteristics, calendar year of cohort entry, and medical history, including psychiatric diagnoses, recent health care contacts for psychiatric conditions, and prescription drug use (eTable 5 in Supplement 1 ). We controlled for confounding using the propensity score and standardized mortality ratio weighting to estimate the average treatment effect among the treated, as this can directly inform clinical decision-making. 20 After exclusion of patients outside of the common range of the propensity score for the 2 groups, and those at the 1% tails of the distribution of the common range (trimming), 21 , 22 the propensity score was reestimated, and those outside of the common range of the new propensity score were excluded. Reestimation of the propensity score after trimming is important because the model derived from the untrimmed population is mis-specified in the population that remains after trimming. 21 Standardized differences below 10% after weighting were considered as good balance between exposure groups. We estimated hazard ratios (HRs) using Cox regression, with days since cohort entry as the time scale. We performed a meta-analysis of the country-specific estimates, with a fixed-effect model using the method of Mantel and Haenszel. The absolute risk difference for the primary outcome was calculated as HR − 1 multiplied by the rate in the comparator group.

We performed a subgroup analysis for the primary outcome by history of psychiatric disorders (eTable 4 in Supplement 1 ), as the investigated safety signal is of most importance for treatment decisions among those at high risk. 23 We also performed subgroup analyses for those who initiated liraglutide and semaglutide, respectively. Propensity scores were reestimated for each secondary outcome and subgroup analysis.

We performed additional analyses for the primary and secondary outcomes. We applied an as-treated exposure definition, in which patients were censored at switch-to or add-on treatment with the other study drug or treatment discontinuation. Treatment duration was estimated based on the number of days covered by the filled prescriptions plus a 90-day period between prescriptions and after the last prescription. Furthermore, we restricted the follow-up to 1 year to assess whether a possible risk increase emerges shortly after treatment initiation.

Due to remaining imbalance in 1 baseline variable after weighting in Denmark (no use of other glucose-lowering drugs in the past 6 months), we adjusted for this variable in Denmark in post hoc sensitivity analysis of the primary and secondary outcomes. As the secondary outcome of incident depression and anxiety-related disorders included use of antidepressants, which can be prescribed also for other conditions, we performed a sensitivity analysis defining the outcome using only diagnoses registered during health care visits (eTable 3 in Supplement 1 ).

As the primary outcome analysis showed no statistically significant association, we calculated the E-value representing the minimum strength of association that an unmeasured confounder would need to have with both GLP-1 receptor agonist treatment and suicide death (conditional on the covariates included in the propensity score) to shift the confidence interval toward an increased risk such that it excludes the null. 24 Confidence intervals not including 1.0 were considered as showing a statistically significant risk difference. Data were analyzed from March to June 2024, and analyses were conducted using SAS, version 9.4 (SAS Institute).

In total, 124 517 users of GLP-1 receptor agonists and 174 036 users of SGLT2 inhibitors were included ( Figure 1 ). Among GLP-1 receptor agonist users, the mean (SD) age was 60 (13) years and 45% were women. The most commonly used GLP-1 receptor agonists were liraglutide (50%) and semaglutide (41%). Patient characteristics before and after weighting for Sweden and Denmark separately are summarized in Table 1 . The propensity score distributions are shown in eFigures 1 and 2 in Supplement 1 . Patient characteristics were well balanced after weighting, except for the proportion of patients in Denmark with no use of other glucose-lowering drugs in the past 6 months ( Table 1 ). The median (IQR) follow-up time in the primary outcome analysis was 2.8 (1.2-4.8) years for GLP-1 receptor agonist users and 2.1 (0.8-3.6) years for SGLT2 inhibitor users in Sweden, and 2.1 (0.8-4.7) years for GLP-1 receptor agonist users and 2.2 (0.8-3.9) years for SGLT2 inhibitor users in Denmark. Combining the 2 countries, mean (SD) follow-up was 2.5 (1.7) years: 2.7 (2.3) years for GLP-1 receptor agonists and 2.3 (1.6) years for SGLT2 inhibitors.

During follow-up, 77 GLP-1 receptor agonist users and 71 SGLT2 inhibitor users died by suicide. The weighted incidence rate was 0.23 vs 0.18 events per 1000 person-years (HR, 1.25; 95% CI, 0.83-1.88), with an absolute difference of 0.05 (95% CI, −0.03 to 0.16) events per 1000 person-years ( Table 2 and Figure 2 ). The country-specific HR was 1.44 (95% CI, 0.87-2.37) for Sweden and 0.94 (95% CI, 0.46-1.91) for Denmark. In the subgroup analysis, the HR was 1.25 (95% CI, 0.77-2.02) for those with a history of psychiatric disorders and 1.44 (95% CI, 0.71-2.92) for those without such a history. The HR was 1.35 (95% CI, 0.85-2.15) for those initiating liraglutide and 0.74 (95% CI, 0.33-1.67) for those initiating semaglutide ( Table 2 ).

After exclusion of those with nonfatal self-harm within 3 months prior to cohort entry, 124 459 users of GLP-1 receptor agonists and 173 985 users of SGLT2 inhibitors were included in the analysis of self-harm (patient characteristics are summarized in eTable 6 in Supplement 1 ). The HR was 0.83 (95% CI, 0.70-0.97) for the composite outcome of suicide death and nonfatal self-harm and 0.77 (95% CI, 0.65-0.91) for self-harm ( Table 3 and eFigures 3 and 4 in Supplement 1 ).

After exclusion of those with previous psychiatric disorders, 72 420 users of GLP-1 receptor agonists and 111 083 users of SGLT2 inhibitors were included in the analysis of incident depression and anxiety-related disorders (patient characteristics are summarized in eTable 7 in Supplement 1 ). The HR was 1.01 (95% CI, 0.97-1.06) ( Table 3 and eFigure 5 in Supplement 1 ). Results for Sweden and Denmark separately are summarized in eTable 8 (primary outcome) and eTable 9 (secondary outcomes) in Supplement 1 .

In the analyses of suicide death, the HR was 1.27 (95% CI, 0.76-2.15) when using the as-treated exposure definition (mean [SD] follow-up time was 1.7 [1.5] years for GLP-1 receptor agonists and 1.5 [1.3] years for SGLT2 inhibitors) and 1.11 (95% CI, 0.54-2.28) when restricting the follow-up to the first year ( Table 2 ). The post hoc sensitivity analysis, in which the variable no use of other glucose-lowering drug in the past 6 months was adjusted for, yielded an HR consistent with that of the main analysis in Denmark (eTable 8 in Supplement 1 ). The additional analyses for the secondary outcomes and the sensitivity analysis in which the secondary outcome of incident depression and anxiety-related disorders was restricted to diagnoses registered during health care visits are summarized in Table 3 . The E-value for the primary analysis showed that an unmeasured confounder would need to be associated with both GLP-1 receptor agonist use and suicide death by a risk ratio of at least 1.7 to shift the confidence interval to exclude the null.

In this cohort study of nationwide data from 2 countries, we found no statistically significant increased risk of suicide death for GLP-1 receptor agonists vs SGLT2 inhibitors used predominantly for type 2 diabetes. The upper limit of the confidence interval was compatible with up to an 88% relative-risk increase of suicide death, which corresponded to an absolute risk increase of no more than 0.16 per 1000 person-years. The findings indicate that the absolute risks of suicide death in broad groups of patients using GLP-1 receptor agonists are low and any potential risk increase would be small. We also found a slightly lower risk of self-harm and no statistically significant association with incident depression and anxiety-related disorders.

The investigated safety concern for GLP-1 receptor agonist use was based on spontaneous reports of suicidal thoughts and thoughts of self-harm. 1 To cover a wider range of conditions and outcomes associated with thoughts of self-harm and suicide, 25 , 26 we assessed suicide death, suicide death and nonfatal self-harm, and self-harm and incident depression and anxiety-related disorders. In randomized clinical trials of GLP-1 receptor agonists, increases in suicidality, depression, anxiety, and other adverse mental health outcomes have not been observed. 27 , 28 However, many trials excluded those at highest risk of suicidality through exclusion criteria or based on investigators’ judgment such that uncertainty remained regarding the generalizability of the findings to broader patient groups, and the statistical power was low due to few events. For example, a meta-analysis of clinical trials of GLP-1 receptor agonists included only 18 events of suicidal behavior among exposed patients. 28

A previous study 29 using electronic health records from the TriNetX Analytics Network assessed diagnoses of suicidal ideation during 6 months after treatment initiation with the GLP-1 receptor agonist semaglutide. In separate analyses, the study included 52 783 patients with overweight or obesity and 27 282 patients with type 2 diabetes who were prescribed semaglutide, then propensity score matched them with users of non–GLP-1 receptor agonist antiobesity or glucose-lowering medications. Semaglutide use was associated with very large relative reductions in suicidal ideation diagnosis of around 70% in both sets of analyses, indicating that the study design likely introduced important biases. For example, immortal time bias 30 may have been introduced as a new-user design was not used: patients with semaglutide prescriptions were first included regardless of their comparator drug use history, and the comparator group was selected from the remaining patients prescribed the comparator drug (ie, the exposure status for the comparator group was conditioned on not being prescribed semaglutide after cohort entry). Moreover, in the analyses of patients with type 2 diabetes, any other glucose-lowering drugs, including those used as first-line therapies, were included as the comparator, although this may have led to time-lag bias through misalignment in disease progression between the groups. 31 Furthermore, suicide death during follow-up could not be accounted for as data on vital status and causes of death were not available.

This study expands the knowledge regarding the safety of GLP-1 receptor agonists by providing data about the risk of suicide death associated with use of the drugs using an active-comparator new-user design, 16 which avoids time-related biases 31 and aligns patients in the exposed vs control group with respect to disease stage. Moreover, confounders were adjusted for using a propensity score, including a wide range of patient characteristics, and the nationwide registers enabled analysis of national study populations with virtually complete data on cause of death. 13 , 14 This study supports the conclusions of the European Medicines Agency’s investigation and the US Food and Drug Administration’s preliminary evaluation 32 that the available evidence does not support a causal association between use of GLP-1 receptor agonists and suicidal and self-injurious thoughts and actions. 33

This study has limitations. We adjusted for potential confounders, including psychiatric disorders and socioeconomic status recorded in the registers, but unmeasured confounding may have affected the results. During the study period, GLP-1 receptor agonists were predominantly used for treatment of type 2 diabetes. The drugs are increasingly used also in patients with obesity who do not have diabetes, and the study findings might not be generalizable to this group of patients. Although it is possible that some patients included in the study used GLP-1 receptor agonists off label for weight reduction, this proportion is likely to be low; a Danish study showed that the proportion of new users of semaglutide with no evidence of a type 2 diabetes diagnosis in the register data (a glycated hemoglobin value in the diabetic range, previous use of glucose-lowering medications, or diagnosis during hospital contacts) was 1% to 5% in 2018 to 2020 and 15% in 2021. 34 Moreover, in the present analyses, liraglutide (50%) and semaglutide (41%) were the most used GLP-1 receptor agonists; the subgroup analyses for each of the 2 GLP-1 receptor agonists had low numbers of events, and associations with suicidality could differ between individual drugs. Furthermore, mean follow-up time for GLP-1 receptor agonist users was 2.7 years, and although 25% of the GLP-1 receptor agonist users were followed for at least 4.7 years, risks may emerge with longer-term use. Although subgroup analyses with low numbers of events should be interpreted with much caution, especially when the subgroup hypothesis was not formulated a priori based on a suggested mechanism, 35 the HR for suicide death in Sweden was 1.44 (95% CI, 0.87-2.37), with this nominal risk difference emerging around 3 years after cohort entry.

Some suicide deaths could also be misclassified. 36 In an assessment of the reliability of the cause of death register from 2008, the proportion of suicide deaths with intent confirmed by experts was 81% in Sweden and 90% in Denmark, with few accidents and natural deaths being reclassified as suicides. 36 We also included self-inflicted deaths of undetermined intent in the primary outcome definition, as an investigation by the National Center for Suicide Research and Prevention in Sweden showed that 20% of suicide cases in the country were coded as of undetermined intent and 70% to 75% of self-inflicted deaths with unknown intent were reclassified as suicide after further investigation. 18 The overall rates of suicide death in the study population were largely in line with national estimates in both Denmark 37 and Sweden. 38 Nonfatal self-harm is known to be underreported in Denmark, 19 and the absolute risks of the secondary outcome analysis, including this outcome, are likely underestimated, as is demonstrated by the substantially lower rates observed in Denmark compared to Sweden. We could also not assess suicidal thoughts and self-harm that did not result in suicide death or a registered diagnosis during contact with the health care system. The low risk of suicide death meant that the study had limited power to assess smaller risk increases.

In this binational cohort study including predominantly patients with type 2 diabetes, use of GLP-1 receptor agonists compared with SGLT2 inhibitors was not associated with an increased risk of suicide death, self-harm, or incident depression and anxiety-related disorders. While reassuring, the study could not rule out smaller absolute risk differences for suicide death, and future studies with more outcome events should be performed as data accumulate.

Accepted for Publication: July 3, 2024.

Published Online: September 3, 2024. doi:10.1001/jamainternmed.2024.4369

Corresponding Author: Peter Ueda, MD, PhD, Division of Clinical Epidemiology, Department of Medicine, Solna, Karolinska Institutet, 17176 Stockholm, Sweden ( [email protected] ).

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

Concept and design: Ueda, Wintzell, Pasternak.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Ueda.

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

Statistical analysis: Söderling, Wintzell.

Obtained funding: Ueda, Pasternak.

Administrative, technical, or material support: Wintzell, Melbye, Pasternak.

Supervision: Pasternak.

Conflict of Interest Disclosures: Dr Ueda reported grants from the Karolinska Institutet and the Strategic Research Area Epidemiology program during the conduct of the study. Dr Svanström reported being a former employee of IQVIA. Dr Pazzagli was supported by a grant from the Swedish Research Council for Health, Working Life, and Welfare. Dr Eliasson reported support from Konung Gustaf V:s and Drottning Victorias Frimurarstiftelse and personal fees from Novo Nordisk, Sanofi, Eli Lilly, Amgen, AstraZeneca, Boehringer Ingelheim, and Abbott outside the submitted work. Dr Melbye was supported by a grant from the Danish Cancer Society. Dr Hviid reported grants from the Novo Nordisk Foundation, the Lundbeck Foundation, and the Denmark Independent Research Fund outside the submitted work. Dr Pasternak reported grants from the Karolinska Institutet during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was supported by the Strategic Research Area Epidemiology program at Karolinska Institutet.

Role of the Funder/Sponsor: Karolinska Institutet had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Open access
Published: 02 September 2024

10-year trajectory of Life’s Essential 8 and incident hypertension: a community-based cohort study

Jiwen Zhong 1 na1 ,
Jinguo Jiang 2 na1 ,
Liang Guo 3 , 4 ,
Yang Liu 5 ,
Shouling Wu 7 ,
Xinyi Peng 8 ,
Shuohua Chen 7 ,
Xueying Qin 5 , 6 ,
Shaohong Dong 9 ,
Ruijun Huang 1 &
Wei Zheng 1

Lipids in Health and Disease volume 23 , Article number: 278 ( 2024 ) Cite this article

Metrics details

The health effects of Life’s Essential 8 (LE8) on chronic diseases have been disclosed, but its association with hypertension remains unknown. The current study aimed to explore the potential link between 10-year LE8 trajectory and the incidence of hypertension.

LE8 was constructed from four behaviors and four metabolic factors, ranging from 0 to 100. Latent mixture models were used to identify trajectories of LE8 scores during 2006 to 2016. Incident hypertension was diagnosed based on self-reported clinical diagnoses and physical examinations from 2016 to 2020. Cox models were employed to assess the association of LE8 trajectories with hypertension. In addition to incorporating the mean hs-CRP levels from 2006 to 2016, age, sex, monthly income, educational level, and occupation at recruitment were adjusted for as confounding factors.

7500 participants aged 40.28 ± 10.35 years were included in the study, of whom 2907 (38.76%) were women. Five LE8 trajectory patterns were identified. After around four-year follow-up, 667 hypertension events were observed. Compared to the Low-Stable trajectory, the hazard ratios and 95% confidence intervals for the Moderate-Increasing, Moderate-Decreasing, Moderate-Stable, and High-Stable trajectories were 0.51 (0.40, 0.65), 0.81 (0.64, 1.02), 0.45 (0.36, 0.58), 0.23 (0.16, 0.33), respectively. The risk of incident hypertension decreased as participants improved their LE8 status. The robustness of the primary results was confirmed through several sensitivity analyses.

Conclusions

LE8 trajectories were associated with the incident hypertension. People who improved their LE8 scores over time experienced a decreased risk of hypertension, even if they started with lower LE8 scores initially.

Introduction

Hypertension accounted for 10.8 million deaths worldwide and 2.6 million deaths in China, according to the Global Burden of Disease Study 2019 [ 1 ]. Blood pressure (BP) is the most common contributing factor for cardiovascular diseases (CVD) [ 2 , 3 ]. Hypertension is a leading risk factor for all-cause mortality both globally and in China [ 4 ]. Therefore, it is imperative to explore novel strategies to reduce the health burden and promote longevity.

The American Heart Association (AHA) initially proposed Life’s Simple 7 (LS7), which includes diet, body mass index (BMI), physical activity (PA), smoking, BP, blood lipids (BL), and fasting blood glucose (FBG), as a tool to assess cardiovascular health (CVH) and to encourage its measurement, thereby advancing the promotion of CVH [ 5 ]. Recognizing the importance of sleep quality as a crucial behavior for CVH and the need for more precise CVH measurement, the AHA developed Life’s Essential 8 (LE8), which incorporated sleep quality for a more comprehensive assessment of CVH. Each factor of LE8 ranges from 0 to 100 [ 6 ]. These distinctions allow the public to better understand and quantify their CVH levels using LE8 and suggest that LE8 may serve as a more reliable and precise indicator of CVH compared to LS7. Furthermore, the potential value of LE8 in evaluating hypertension risk among the normotensive population is barely explored.

Besides its association with CVD, LE8 had also been linked to various other diseases [ 7 , 8 , 9 , 10 , 11 ] and incident hypertension [ 12 ]. However, none of these studies described the 10-year trajectory of LE8 or explored the potential benefits of improving LE8 among normotensive individuals using repeated measurements. Therefore, the longitudinal association of LE8 with incident hypertension was evaluated using data from the biennially followed Kailuan cohort study.

Study population

The Kailuan study began in 2006–2007 in the Kailuan community in Tangshan, China. It was strategically designed to elucidate potential health determinants [ 13 , 14 , 15 ]. A total of 101,510 adults were recruited between June 2006 and October 2007. Each participant underwent thorough assessments, which primarily encompassed three sections: a standardized questionnaire survey, clinical examinations, and laboratory testing. These assessments were conducted biennially.

The present study estimated the effect of the 10-year trajectory of LE8 on incident hypertension. Initially, 48,521 participants were excluded: 44,655 due to existing hypertension and 3866 due to incomplete LE8 at baseline. Additionally, 45,489 participants with new-onset hypertension or those who were lost to follow-up during the trajectory construction from 2006 to 2016 were excluded, as the goal of this study was to observe hypertension cases from 2016 to 2020, and hypertension was the outcome event that should be excluded during trajectory construction according to previous studies [ 13 ]. Finally, 7500 participants who were free of hypertension after the trajectory construction period were included (Fig. 1 ).

Flow diagram for participants included in the study

Data collection

Medical workers conducted face-to-face interviews to gather demographic factors (such as sex, birthdate, level of education, monthly income, and occupation), health conditions (including self-reported clinical diagnoses of chronic diseases, and medication history), and lifestyle information (such as smoking habits, PA levels, duration of sleep, daily diet, and alcohol consumption) using a standardized questionnaire. Educational levels were determined according to the number of years of schooling, with categories including primary school or below (≤ 6 years), middle school (7–9 years), and high school or above (> 9 years). Monthly income was self-reported and categorized as < 800 CNY or ≥ 800 CNY. Occupation types were classified based on responses to questions about employment in mining and engagement in manual or mental labor. Participants were categorized as coal miners if they worked in the mine and mainly performed manual labor. Those working in the mine but engaged in mental labor were classified as other blue-collar workers. Participants not working in the mine but mainly involved in manual labor were also categorized as other blue-collar workers, while those primarily engaged in mental labor and not working in the mine were classified as white-collar workers. Participants were asked about clinically diagnosed conditions, including hypertension and other chronic diseases, with follow-up questions about medication usage if conditions were affirmed. Smoking habits were assessed by inquiring whether participants currently smoked or had quit smoking, along with detailed questions about smoking frequency. PA levels were assessed based on weekly frequency and duration of sessions lasting at least 20 min. Participants reported their average nightly sleep duration in hours. Dietary habits were evaluated through questions about flavor preferences, frequency of consuming fatty foods, and tea consumption. Alcohol consumption was categorized into three groups: current, past, and never, with detailed information gathered from participants who reported current or past drinking, including drinking patterns and withdrawal behaviors. Interviewers recorded participants’ responses to each question.

The weight and height of each participant were measured by trained medical workers, and weight (kg) divided by height squared (m 2 ) was used for BMI (kg/m 2 ) calculation. The procedures for the measurement of biochemical indicators were as follows: venous blood samples were extracted from the antecubital vein by a professional nurse following an overnight fast of about eight to twelve hours. All the samples were stored at -80 °C. Subsequently, an auto-analyzer machine (Hitachi 747; Hitachi, Tokyo, Japan) was utilized to measure biochemical indices, including FBG, total cholesterol (TC), HDL-C, and high-sensitivity C-reactive protein (hs-CRP) in these blood samples at the central laboratory of Kailuan Hospital [ 14 , 16 ]. Non-HDL-C levels were calculated as TC subtracting HDL-C. BP metrics were assessed three times with a calibrated mercury sphygmomanometer while interviewees were seated after a 5-minute rest period. BP was re-assessed if the difference between adjacent two measurements was more than five mmHg. The mean of the three BP measurements was used for subsequent analysis [ 17 , 18 ].

Assessment of life’s essential 8

The LE8 primarily included four health-related indicators (BMI, FBG, BL, and BP) and four lifestyle factors (tobacco smoking, diet, sleep duration, and PA). Each component of LE8 was assessed biennially. Detailed definitions and scoring methodologies for the components of LE8 were outlined in Table S1 and elsewhere [ 19 , 20 , 21 ]. The LE8 score was counted by summing the scores of the each factor and then dividing the total score by eight. This resulted in a LE8 score ranging from 0 to 100. The LE8 trajectories in the current study depicted different change models of LE8 from 2006 to 2016 utilizing a latent mixture model.

Assessment of hypertension

In each follow-up survey, the interviewees were asked whether they had been clinically diagnosed with hypertension. If the answer was affirmative, further inquiries to obtain detailed information on antihypertensive medication intake. The procedure for BP measurement was explained in a previous section. Hypertension was determined based on one of the following criteria: self-reported clinical diagnosis, intake of antihypertensive drugs, or increased systolic/diastolic BP (≥ 140/90 mm Hg) [ 22 ].

Assessment of covariates

The potential confounders were selected based on previous studies [ 23 , 24 ]. Demographic information, including age, sex, monthly income, educational status, and occupation, was included. Alcohol consumption was categorized as never, past, or current based on information acquired during the interview. Given the significant impact of inflammation on the cardiovascular system [ 25 ], the mean hs-CRP level during the period from 2006 to 2016 was also included as an important covariate. Apart from hs-CRP, the other covariates were collected in 2006 and incorporated into subsequent analyses according to a previous study [ 13 ].

Statistical analysis

Continuous variables were described using mean ± standard deviation and median with interquartile range (25–75%), while categorical variables were presented as numbers and percentages. The person-time was calculated from the 2016 survey until the onset of hypertension, death, loss to follow-up, or the endpoint (December 31, 2020), whichever came first.

A latent mixture model within the PROC TRAJ procedure was employed to classify the trajectory patterns of LE8 from 2006 to 2016, following established methodology [ 26 , 27 ]. Various parameters and fit statistics were used to determine the optimal number of trajectories, including the proportion in each trajectory, Bayesian Information Criteria, average posterior probability in each trajectory, and visual inspection of the trajectories. The model with five trajectories provided the best fit (Table S2 ). Models with different functional forms (e.g., 3-cubic, 2-quadratic, and 1-linear) were compared and evaluated based on their significance levels, utilizing the highest polynomial term. Ultimately, we identified two pattern with quadratic order terms and three patterns with up to cubic order terms, which met the statistical conditions necessary for constructing trajectory patterns. The Low-Stable trajectory was set as the reference for subsequent analysis.

The cumulative incidence of hypertension by LE8 trajectories was described using the Kaplan-Meier method, followed by a Log-rank test. The association between these LE8 trajectory patterns and the risk of hypertension was evaluated using the Cox model to calculate hazard ratios (HR) and 95% confidence intervals (CI). Before constructing the model, the proportional hazards assumption was satisfied using Schoenfeld residual methods [ 28 ]. The crude model was adjusted for none, while Model 1 was adjusted for age (years), sex (women, men), education level (elementary school or below, middle school, high school and above), monthly income (< 800, ≥ 800 CNY), occupation (coal miner, other blue-collar, white collar), alcohol consumption (never, abstainer, current), and mean hs-CRP concentration during 2006–2016. Additionally, LE8 scores in 2006, 2008, 2010, 2012, 2014, and 2016 were included as additional adjustments based on Model 1 to examine whether the association of LE8 trajectories with incident hypertension could be explained by a single LE8 measurement in the follow-up.

The association between the slope of LE8 during 2006–2016 and hypertension risk was examined. The magnitude of the annual change rate indicates the extent of the annual change, with a larger magnitude indicating a greater rate of change and a steeper trend for LE8. The positive or negative symbol associated with the slope indicates the overall direction of change: a positive slope denotes an increase, while a negative slope represents a decrease. The slope calculation methods were in accordance with a previous study [ 29 ]. A restricted cubic spline model with three (25th, 50th, 75th percentiles) knots was performed to evaluate the underlying relationship between LE8 slope and incident hypertension after adjusting for the impact of confounders and the 2006 LE8. This approach helps capture potential non-linear associations between the annual change rate of LE8 and hypertension risk.

Note: x: Timepoint of Measurement y: Life’s Essential 8 Slope: Annual Change Rate

Several sensitivity analyses were conducted. First, the primary results were re-analyzed after excluding women, considering the distribution of sex and the particularity of occupation. Second, participants were divided into five groups according to the quintile of 2016 LE8, and numeric values were assigned to the LE8 groups to examine the trend in the adjusted model. Third, stratified and interaction analyses were performed for age (< 45, ≥ 45) and sex (women, men). Fourth, to account for differences in gender and age among the five trajectory groups, the strata option was used for sex and age modeled as a time scale, allowing for better control of the impact of these factors. Fifth, BP was removed from LE8, and then the LE8 score was recalculated using the remaining seven factors to exclude the impact derived from BP. Sixth, to address the issue of multiple comparisons and potential inflation of type-I error rates, the Bonferroni correction was applied by adjusting alpha and the corresponding CIs. The alpha level was set as 0.05/4, as there were four hypothesis tests conducted. Seventh, the ‘evalue hr’ code in Stata was used to calculate E-values to consider the potential impact derived from unmeasured residual factors [ 30 ]. The comparison between the inclusion and exclusion groups was conducted because the non-participant population was significantly larger than the participant population. Individuals who self-reported clinically diagnosed hypertension were further excluded to address potential errors in self-reported hypertension.

All analyses were conducted using Stata/MP2 V17 (Stata Corp LLC, Texas, TX, United States) and SAS 9.4 (SAS Institute, Cary, NC), and a 2-sided P value < 0.05 was deemed significant.

A total of 7500 individuals were included in the analyses, all of whom met the inclusion criteria and were free of hypertension from 2006 to 2016. The mean (SD) age at baseline in 2016 was 40.28 (10.35) years, with 2,907 (38.76%) of the participants being women. Five LE8 trajectories from 2006 to 2016 were determined: Low-Stable ( n = 762), Moderate-Increasing ( n = 1275), Moderate-Decreasing ( n = 1172), Moderate-Stable ( n = 2690), and High-Stable ( n = 1601) (Fig. 2 ). Individuals in the High-Stable trajectory tended to be women, younger, engaged in other blue-collar occupations, abstainers from alcohol, and had higher educational statuses (Table 1 ).

Mean Life’s Essential 8 score from 2006 to 2016 according to five Life’s Essential 8 trajectories. High-Stable (brown line) means participants maintained a high LE8 score. Moderate-Stable (magenta line) means participants maintained a moderate LE8 score. Moderate-Decreasing (orange line) means participants started with a moderate LE8 score and then decreased. Moderate-Increasing (blue line) means participants started with a moderate LE8 score and then increased. Low-Stable (purple line) means participants maintained a low LE8 score

The incidence of hypertension significantly varied across trajectories ( P for log-rank test < 0.001) (Figure S1 ). In the trajectory analysis, a total of 667 incident hypertension events were diagnosed during the period from 2016 to 2020. LE8 trajectories were significantly associated with new-onset hypertension (Table 2 ). Compared to the Low-Stable trajectory, the Moderate-Increasing (adjusted HR: 0.51; 95% CI, 0.40, 0.65), Moderate-Deceasing (adjusted HR: 0.81; 95% CI, 0.64, 1.02), Moderate-Stable (adjusted HR: 0.45; 95% CI, 0.36, 0.58), and High-Stable (adjusted HR: 0.23; 95% CI, 0.16, 0.33) trajectories were all related to a lower risk of hypertension. These results remained stable after additional adjustments for LE8 scores in 2006, 2008, 2010, 2012, 2014, and 2016 as continuous variables. Improvement in LE8 was related to a decreased incident hypertension risk, and the faster the growth, the more the risk falls (Fig. 3 ).

Association between annual change rate of Life’s Essential 8 and incident hypertension. The y-axis is hazard ratio with the area within dotted line representing 95% CIs. Data were fitted by a restricted cubic spline Cox proportional hazards regression model adjusted for age (years), sex (women, men), educational level (elementary school or below, middle school, high school and above), income per month (< 800, ≥ 800 CNY), occupation (coal miner, other blue-collar, white collar), alcohol consumption (never, abstainer, current), mean serum concentration of hs-CRP during 2006–2016, and 2006 Life’s Essential 8 measurement

After excluding female participants, the adjusted HR comparing the High-Stable trajectory with the Low-Stable one was 0.28 (95% CI, 0.17, 0.46) (Table S3 ). Consistently, a higher LE8 level was related to a lower risk of incident hypertension ( P -trend < 0.001) (Table S4 ). Moreover, the results from stratified analysis further supported the primary findings, and no effect-modifying factors were found in the association between LE8 trajectories and incident hypertension ( P -interaction > 0.05 for all) (Table S5 ). The results remained virtually unchanged when age was modeled as a time scale and sex was used as the strata option (Table S6 ). LE7, removing BP from LE8, was strongly associated with the risk of incident hypertension, and the results were consistent with the primary findings (Table S7 ). Slight changes were observed after Bonferroni correction (Table S8 ). The E-value tested the sensitivity to unmeasured confounding, and the primary findings remained stable, suggesting that they may not be invalidated with the current evidence (Table S9 ). Excluded participants were older, had relatively lower LE8 levels, higher systemic inflammation, were predominantly men, and had lower educational attainment compared to those included (Table S10 ). The results remained consistent with the primary findings after accounting for potential errors in self-reported hypertension (Table S11 ).

Five distinct trajectories were identified, which revealed varying associations with the risk of hypertension. Individuals who consistently maintained the highest LE8 level over the 10-year period experienced a remarkable 78% reduction in the risk of developing incident hypertension compared to those who consistently remained at the lowest LE8 level. The findings suggested that improvement in LE8 status was beneficial for hypertension prevention, regardless of baseline LE8 status, and indicate that the faster the improvement, the better.

The present study included 7500 participants who were free of hypertension from 2006 to 2010, representing a subset of the original Kailuan cohort. This selection was made due to the high incidence of hypertension and the restrictive inclusion criteria necessary for the current research objectives. The included participants were not fully representative, as they were generally healthier than those excluded. Specifically, they were younger, had a favorable sex distribution, and possessed higher levels of education. These factors naturally positioned them at an advantageous status for hypertension prevention, making them less likely to develop hypertension. Although the current study may have underestimated the actual association between LE8 trajectories and incident hypertension, a significant association between LE8 trajectories and incident hypertension persisted even after adjusting for each LE8 measurement from 2006 to 2010. Research unveiled the health effects of LE8 on various adverse health conditions [ 13 , 14 , 15 , 16 ]. However, prior studies primarily focused on individual health behaviors within LE8 and their associations with incident hypertension [ 31 ]. Another study involving 52,990 participants demonstrated that a higher LE8 score is linked to a reduced risk of hypertension [ 12 ]. Yet, the health effects of the 10-year trajectory of LE8 and its improvement on hypertension remained unexplored. Recently, a study indicated that a higher LE8 was linked to a reduced risk of hypertension. [ 32 ]. Another study delved into the association of four health behaviors within LE8 with the incidence of hypertension, revealing that each health behavior was independently associated with incident hypertension, and improvement in any of these behaviors was related to a reduced risk of developing hypertension [ 31 ]. The Jackson Heart Study also revealed that unhealthy lifestyles, characterized by less PA, smoking, and an absence of an ideal diet, were linked to a heightened risk of hypertension [ 33 ]. Additionally, sleep duration, as a new factor added to LE8, has been reported to be related to the risk of hypertension, indicating that poor sleep quality is associated with new-onset hypertension [ 34 ]. However, these studies failed to comprehensively investigate the association of complete LE8 with hypertension and discuss the long-term pattern of LE8 and its association with hypertension.

The current findings contributed to the body of evidence suggesting an inverse association between LE8 and the risk of hypertension. Importantly, despite the relatively low LE8 status at baseline, the improvement in LE8 significantly attenuated the risk of hypertension significantly (HR: 0.61, 95% CI: 0.48, 0.78 for Moderate-Increasing trajectory; HR: 0.79, 95% CI: 0.62, 0.99 for Moderate-Decreasing trajectory). These results underscored a critical concern, emphasizing the effectiveness of enhancing healthy behaviors and metabolic status within the context of LE8 as a preventive measure against the development of hypertension. The findings from a previous large-scale study revealed that arterial stiffness status partially mediated the association between LE8 and stroke, suggesting that the stiffness of blood vessels may serve as a potential mechanism [ 35 ]. These results underscored the importance of robust public health efforts aimed at improving CVH, which may counterbalance arterial stiffness. In essence, despite an overall decreasing trend in LE8 over time, individuals still have the potential to enhance their LE8 status. Regardless of whether they had lower LE8 levels at the beginning or not, they can benefit from improving their LE8 status. This highlighted the significance of ongoing efforts to promote healthy behaviors and metabolic health, as they may contribute to maintaining or improving CVH and potentially mitigate the risk of hypertension.

In contemporary times, individuals with ideal LE8 scores were relatively scarce [ 36 , 37 , 38 ]. However, the benefits of achieving a high LE8 extended beyond CVH, impacting a wide range of organs and systems [ 13 , 14 , 15 , 16 ]. The rate of improvement in LE8 over time emerges as a critical factor in reducing the risk of hypertension, with lower hypertension risk observed in individuals experiencing faster LE8 increases. These findings underscored the importance of continuous improvement in LE8 for reducing hypertension risk and enhancing CVH. LE8 may serve as an effective and feasible tool for evaluating the future risk of hypertension and identifying populations at high risk. This, in turn, could guide individuals towards addressing their health deficiencies, potentially preventing hypertension and its associated complications. Moreover, the utilization of LE8 could inform health managers in crafting timely and real-time health policies for residents, enhancing public health outcomes. Given the costs associated with LE8 measurement, alternative strategies such as implementing cost-effective campaigns such as health education and health knowledge contests could encourage the public to optimize their CVH status. Overall, striving to attain higher LE8 scores could prove to be a valuable strategy, offering a substantial reduction in hypertension risk and improving overall CVH in the population.

Some studies derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of individuals aged 45 and older in China, reported that approximately 25% of participants developed hypertension from 2011 to 2018 [ 39 , 40 , 41 ]. Although this rate was higher than the 8.89% incidence observed in the current study, it was difficult to draw a definitive conclusion. Because the lower incidence in this study may be attributed to the fact that participants were free of hypertension for the previous ten years, which could introduce a healthy population effect and attenuate the association between LE8 and incident hypertension. However, the significantly inverse association between LE8 and the risk of hypertension in this study remained, which further emphasized the health benefits of enhancing LE8.

Research conducted within the Atherosclerosis Risk in Communities study revealed a significant association between improvement in LS7 from midlife to late life and enhanced systolic and diastolic function, along with improvements in cardiovascular structure and function [ 42 , 43 ]. Moreover, a Chinese cohort study involving more than 40,000 individuals demonstrated a potential role of LE8 in mitigating atherosclerosis progression [ 35 ]. Additionally, a study reported a noteworthy association between a higher cardiovascular score and decreased concentrations of pro-atherosclerotic and cardiac stress factors, as well as neurohormonal biomarkers, while simultaneously exhibiting higher levels of cardioprotective biomarkers [ 44 ]. Overall, these studies provided valuable insights into the biological pathways through which improvements in LE8 and CVH may influence cardiovascular structure, function, and disease progression, further highlighting the importance of optimizing CVH to reduce the risk of hypertension and improve overall cardiovascular outcomes.

Strengths and limitations

LE8 data were collected repeatedly from 2006 to 2016, ensuring consistency and minimizing random error. Moreover, this study was the first to discuss the impact of 10-year trajectories of LE8 and incident hypertension. However, several limitations should be acknowledged. First, the Kailuan cohort was predominantly composed of men, primarily representing people from the Kailuan community, potentially limiting the generalizability of current findings. Second, while efforts were made to account for potential confounders, some variables remain unmeasured. This limitation was mitigated by calculating an E-value to assess their potential impact and verify the stability of current findings. Third, the 10-year duration of LE8 trajectories examined in the current study may not entirely reflect an individual’s CVH throughout their lifespan. Fourth, the current results relied heavily on questionnaires, which could introduce biases and reduce reliability. To address this issue, future studies should consider quantifying LE8 components where feasible. For example, biomarkers such as urinary cotinine could be utilized to quantify smoking status, thereby enhancing the accuracy of LE8 assessments. Fifth, the complex interplay of factors such as alcohol consumption with obesity, diabetes, and smoking suggests careful consideration. Despite removing BP from LE8 calculation and conducting sensitivity analysis to examine the stability of the current findings, caution was advised in interpreting the observed associations. Finally, while the reliability of current findings through various analyses was verified, such as E-value calculation, using age as time scale and sex as strata option, and Bonferroni correction, further studies are needed to comprehensively quantify these factors and their associations with hypertension.

The findings of this study underscored a significant link between the 10-year trajectories of LE8 and the risk of incident hypertension. Maintaining a high level of LE8 or improving LE8 status was linked to a decreased risk of hypertension. Notably, individuals with initially low LE8 levels could also experience a reduced risk of hypertension by enhancing their LE8 status, potentially even reversing their risk of hypertension. These results highlighted the importance of encouraging efforts aimed at optimizing and maintaining high CVH status among the public. Furthermore, they suggested that the LE8 could serve as a powerful tool for evaluating the risk of hypertension. By enhancing LE8 status, individuals and healthcare providers alike may effectively mitigate the risk of hypertension and promote CVH.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

American Heart Association

Body mass index

Blood pressure

Blood lipids

Cardiovascular health

Confidence interval

China Health and Retirement Longitudinal Study

Fasting blood glucose

Hazard ratio

High-density lipoprotein cholesterol

high-sensitivity C-reactive protein

Life’s Simple 7

Life’s Essential 8

Physical activity

Standard deviation

Total cholesterol

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Acknowledgements

This study appreciates the participants and study staff/researchers involved in the Kailuan study.

Author information

Jiwen Zhong and Jinguo Jiang contributed equally to this work.

Authors and Affiliations

Department of Critical Care Medicine, Zhuhai People’s Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, 51900, Guangdong, China

Jiwen Zhong, Ruijun Huang & Wei Zheng

Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China

Jinguo Jiang

Department of Cardiology, Renmin Hospital of Wuhan University, 430060, Wuchang, Wuhan, China

Hubei Key Laboratory of Cardiology, 430060, Wuchang, Wuhan, China

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38# Xueyuan Road, Haidian District, 100191, Beijing, China

Yang Liu & Xueying Qin

Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China

Xueying Qin

Department of Cardiology, Kailuan General Hospital, No.57 Xinhua East Road, 063000, Tangshan, Hebei Province, China

Shouling Wu & Shuohua Chen

Hypertension Center, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases of China, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037, Beijing, China

Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, The Second Clinical Medical College, The First Affiliated Hospital, Shenzhen People’s Hospital, Jinan University, Southern University of Science and Technology), 518020, Shenzhen, Guangdong, China

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J.Z. and J.J.: Conceptualization, Software, Formal analysis, Writing - original draft, Writing - review & editing. L.G., Y.L., S.W., X.P., S.C., X.Q., and S.D.: Writing - review & editing., R.H., and W.Z.: Conceptualization, Review & editing, Supervision, Resources. J.Z. and J.J.: contributed equally as co-first authors. R.H. and W.Z.: contributed equally as co-corresponding authors. All authors read and approved the final manuscript.

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Correspondence to Ruijun Huang or Wei Zheng .

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Zhong, J., Jiang, J., Guo, L. et al. 10-year trajectory of Life’s Essential 8 and incident hypertension: a community-based cohort study. Lipids Health Dis 23 , 278 (2024). https://doi.org/10.1186/s12944-024-02257-z

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Received : 07 April 2024

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DOI : https://doi.org/10.1186/s12944-024-02257-z

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Life’s essential 8
Hypertension
Trajectory analysis

Lipids in Health and Disease

ISSN: 1476-511X

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Open access
Published: 02 September 2024

Change in body temperature, not acute-phase reaction, predict anti-Osteoporosis efficacy after the first administration of Zoledronic acid: a prospective observational cohort study

Yanping Du 1 , 2 na1 ,
Weijia Yu 1 , 2 na1 ,
Haixin Gou 3 ,
Yiming Lei 3 ,
Tongkai Zhang 4 ,
Wenjing Tang 1 , 2 ,
Minmin Chen 1 , 2 ,
Huilin Li 1 , 2 &
Qun Cheng 1 , 2

BMC Musculoskeletal Disorders volume 25 , Article number: 694 ( 2024 ) Cite this article

Metrics details

Acute-phase reactions (APRs) are common among people treated for the first time with zoledronate (ZOL). The current view is that both the APRs caused by ZOL and its efficacy are related to the mevalonic acid pathway. However, the relationship between APRs and ZOL efficacy remains unclear.

This was a prospective observational cohort study involving postmenopausal women with osteoporosis in Shanghai, China, for 1 year. A total of 108 patients with an average age of 67.4 ± 5.8 years were treated with 5 mg intravenous ZOL for the first time. Data on demographic characteristics, APRs, blood counts, bone turnover markers, including C-telopeptide collagen crosslinks (CTX) and N-terminal propeptide of type 1 collagen (PINP), and bone mineral density (BMD) were collected.

(1) The results did not reveal a relationship between APRs and changes in bone turnover markers and BMD but showed that changes in body temperature (T) within 3 days after administration were positively correlated with changes in the BMD of the LS at Month 12 (β = 0.279 P = 0.034). (2) This effect was mediated mainly by changes in serum CTX (b = 0.046, 95% CI [0.0010–0.0091]). (3) The ROC curve revealed that when T increased by 1.95 °C, the sensitivity and specificity of identifying clinically important changes in LS BMD after 1 year were optimized.

Conclusions

In this study, we tested the hypothesis that people with elevated body T after initial ZOL treatment had greater improvements in BMD and better outcomes.

Trial registration

NCT, NCT03158246. Registered 18/05/2017.

Peer Review reports

Introduction

Osteoporosis has become a major public health problem with impacts on both quality of life and quantity of life. In China, the prevalence of osteoporosis among adults aged 40 years or older is 5.0% among men and 20.6% among women [ 1 ]. Bisphosphonates (BPSs) are the most commonly used therapy for osteoporosis [ 2 ].

Zoledronic acid (ZA), a third-generation nitrogen-containing BP, represents the most potent inhibitor of bone resorption and is long-acting in bone marrow, clearly improving the bone mineral density (BMD) and reducing the risk of vertebral, nonvertebral and hip fractures [ 3 , 4 ]. ZA has been recommended in guidelines as a first-line treatment for extremely high fracture risk [ 5 , 6 ]. Currently, ZOL is widely used in the clinic because of its significant efficacy, convenient annual administration, good patient compliance and few gastrointestinal reactions [ 7 ].

As a unique reaction after the administration of nitrogenous bisphosphonate (NBPS), acute-phase reactions (APRs) after the infusion of 5 mg of zoledronic acid for the first time are common. Fever and musculoskeletal pain occurred 24 to 36 h after NBP administration. Fever was noted to be associated with a decline in circulating lymphocyte number and increases in circulating γδ-T cells, IL-6 and tumour necrosis factor-alpha (TNF-a). These reactions are collectively referred as the acute phase response.

The main mechanism of ZOL is to inhibit the activity of farnesyl pyrophosphate (FPP) synthetase, the key enzyme of the mevalonate pathway, which can selectively suppress osteoclastic bone resorption. This mechanism may also be the basis for the occurrence of APRs [ 8 ]. ZOL inhibits FPP synthetase, resulting in the accumulation of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), the upstream intermediates of FPP synthetase of this metabolic pathway in monocytes, and then activates γδ- T cells via the release of proinflammatory cytokines, such as interleukin-6, interferon-γ and tumour necrosis factor β [ 9 , 10 , 11 ]. Therefore, APRs are more significant after ZOL treatment in those who are more sensitive to the mevalonate pathway. Consequently, both APRs caused by ZOL and its efficacy are likely related to the mevalonic acid pathway.

Approximately 30-54.9% of patients treated with a once yearly intravenous infusion of ZOL experienced an ARP, characterized by a transient flu-like syndrome with fever, chills, flushes, fatigue, malaise, and musculoskeletal and gastrointestinal symptoms following the first infusion [ 12 , 13 ]. Therefore, we designed the present study, which lasted for 1 year after the first ZOL treatment infusion. In this study, we explored the relationships between APR and changes in bone turnover markers and bone mineral density after the first application of ZOL in postmenopausal women with osteoporosis to assess whether the therapeutic effect could be predicted by the occurrence of APR after the first ZOL administration.

Study design

This was a prospective observational cohort study involving postmenopausal women with osteoporosis in Shanghai, China, for 1 year. The study was registered at ClinicalTrials.gov via the Protocol Registration and Results System (PRS) on 18/05/2017 (ClinicalTrials. gov ID: NCT03158246, First posted date 18/05/2017) by Cttq. The Formula n = 2[(Z α +Z β )σ/δ] 2 was used to calculate the sample size. We calculated the sample size that was necessary to achieve a statistical power of 80% with a significance level of 0.05. Thus, α was set at a level of 0.05, whereas β was set at a level of 0.2. Therefore, Z α was 1.96 (2-sided testing), whereas Z β was 0.84. According to our previous research, σ, which was the standard deviation of the change in BMD between the two groups, was 0.022; δ, the difference in the mean BMD between the two groups, was 0.009. Therefore, the calculated sample size (n) was determined to be 95. Considering the loss to follow-up, the sample size was increased by 15%, resulting in a final sample size of 110. The flowchart of this study is shown in Fig. 1 . A total of 110 patients were ultimately enrolled. A total of 108 patients with an average age of 67.4 ± 5.8 years and a mean body mass index of 22.3 ± 3.2 kg/m 2 who were treated with 5 mg intravenous ZOL for the first time between September 2017 and March 2018 completed all the visits and were included in the analysis. Two subjects were excluded from the study. The first subject withdrew her informed consent because she moved to another city 2 months after enrolling in the study. The second subject passed away in a car accident 7 days after enrolment. In our final analysis, we chose not to incorporate the data from these two patients because of an acceptable loss to follow-up rate of 1.8% and because their withdrawals were unrelated to the examination of drug side effects and therapeutic effects. A large amount of missing data can severely undermine the validity of inference and conclusions of a study. It is generally believed that if the proportion of missing values is less than 5%, samples containing missing values can be deleted, which will not have a significant effect on the overall analysis of the dataset. Otherwise, multiple imputation, weighting and maximum likelihood-based methods can be used to deal with incomplete data. Intravenous ZOL was administered at a dose of 5 mg over 15 min, and calcium (600 mg/d) and vitamin D (400 IU/d) were given as basic supplements following the infusion. To ensure compliance with treatment, calcium and vitamin D supplements were uniformly given by the investigators. A diary card was provided to request that the subjects record medication usage and adverse effects. Telephone interviews were conducted on Days 1 − 3 and at Months 3 and 9. Adverse events and calcium and vitamin D supplement consumption of the subjects were recorded. Clinical visits were conducted on Day 14 and at Months 6 and 12. The subjects were required to return the remaining calcium and vitamin D supplements and empty medicine bottles. Compliance with treatment was judged on the basis of the remaining amount of supplements. New calcium and vitamin D supplements were subsequently distributed for the next clinical visit. Oral T (T) at baseline and every morning for 3 days and on Day 14 after the infusion was recorded. Baseline and Day 14 blood samples were used to measure complete blood counts, liver and kidney function, and bone turnover marker (BTM) levels. Moreover, BTMs were also measured at Months 6 and 12. The BMDs of the lumbar spine (L2–4) and left total hip were measured at baseline and at 6 and 12 months after infusion. The investigators were blinded to the treatment allocation. The technicians involved in the measurement of BMD and BTM were blinded to any discernible clinical information regarding the participants, employing a rigorous blinding process. The study protocol and procedures were approved by the ethics committee of the hospital (No: 20170068).

Flowchart of this study

Study populations

Postmenopausal women aged from 46 to 80 years with osteoporosis eligible for intravenous ZOL were eligible for inclusion if they had a bone mineral density T score of -2.5 or less at either the lumbar spine, hip, or femoral neck; a T score between − 1.0 and − 2.5; fragility fracture of the proximal humerus, pelvis, or distal forearm; or low-trauma spine or hip fracture regardless of BMD. Moreover, they had to be capable of monitoring and recording basic data on their body T and clinical symptoms.

The exclusion criteria were as follows: (1) patients who had previously used bisphosphonates; (2) patients who had used parathyroid hormone; (3) patients who had undergone surgical treatment in the past three months; (4) patients with a total serum calcium level of less than 2.1 mmol/L (8.4 mg/dL) or a serum-free calcium level of less than 0.95 mmol/L (3.8 mg/dL) or untreated hypocalcaemia; (5) patients with a Cockcroft calculated creatinine clearance of less than 35 mL/min; and (6) patients who refused to take ZOL, those with causes of osteoporosis other than postmenopausal osteoporosis, and patients with other diseases with an unstable status. Informed consent was obtained from all individual participants included in the study.

Study methods

Basic information collection and anthropometric measurements.

Medical history, including fracture history and intake of antiosteoporosis drugs, was assessed, and a physical examination was performed. Height was measured using a stadiometer to the nearest 0.01 m. Body weight was measured by applying a standard balance beam scale to the nearest 0.1 kg. Body mass index (BMI) was calculated as the body weight divided by the square of the height (kg/m 2 ).

Measurement of bone mineral density and the definition of osteoporosis

The BMDs of the lumbar spine (L2–4) and left total hip were measured with a dual-energy X-ray absorptiometry densitometer (Hologic Delphi A; Hologic Inc., Methuen, MA, USA). The precision error in our laboratory was 0.8% for the lumbar spine, 1.05% for the femur neck and 0.97% for the total hip. The densitometer was standardized with a standard phantom before each measurement.

Laboratory measurements

All blood samples were obtained in the morning after a 10 h overnight fast and were stored immediately at − 80 °C for subsequent assays. N-terminal propeptides of type 1 collagen (P1NP), C-telopeptide collagen crosslinks (CTXs) and 25OHD were measured via electrochemical luminescence (Roche Diagnostics, Boston, MA, USA), with intra- and interassay coefficients of variance (CVs) below 3.5% and 8.4% for CTX, below 2.6% and 4.1% for P1NP, and below 7.8% and 10.7% for 25OHD. Blood counts included white blood cell (WBC), neutrophil (N), lymphocyte (L), monocyte (M), eosinophil (E), basophil (B), red blood cell (RBC), and platelet (PLT) counts.

Definition of APR

Previous analyses of APRs have been performed in several studies, and we used a similar definition for our study [ 14 ]. The definition utilized adverse events, which were reported as occurring within 3 days of the first administration of ZOL. The adverse events were categorized according to the Medical Dictionary for Regulatory Activities (MedDRA) version 9 [ 15 ]. Preferred terms meeting the definition of APR were grouped into five symptom clusters: fever; musculoskeletal events (e.g., pain and joint swelling); gastrointestinal events (e.g., abdominal pain, vomiting, and diarrhoea); eye inflammation; and other events (including fatigue, nasopharyngitis, and oedema). Oral T was checked in each patient at 06:00 at baseline and on Days 1 to 3 and 14 after ZOL administration. If a patient’s body temperature exceeded 37.3 °C, it was remeasured every 4 h. Standardized digital thermometers were utilized for all temperature measurements. Body temperature data were centralized and stored in a single location rather than being dispersed across multiple sites or investigators. Thresholds were based on prior study recommendations [ 16 ].

Statistical analysis

SPSS v23 (SPSS Inc., Chicago, IL, USA) was used to analyse the data. The Shapiro‒Wilk test was performed to determine whether variables were normally distributed. Continuous variables are expressed as means with standard deviations. Skewed data are presented as the median with the interquartile range (25–75%). Classification variables are expressed as percentages. Data that were not normally distributed (all blood counts) were log transformed to normality before being used in the statistical analyses. After normality, independence, and equal variance (homoscedasticity) of the variables were checked, two-sample t tests or Mann‒Whitney U tests were used to analyse differences between groups. After the independence of variables (noncollinearity), normality of the residuals, independence between observations, and equal variances were checked, multivariate linear regression models were constructed to analyse the correlation between dependent variables, such as BMD, and independent continuous variables, such as the change in temperature, PINP, CTX, and blood count indices. The SPSS PROCESS macro programme was used to test the mediating effect of CTX on the relationship between T and BMD. The analysis facilitated estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtain confidence intervals [ 17 ]. In PROCESS, Model 4 with 10,000 interactions was established to determine the mediation of the regression models, CTX with T and BMD. The regression coefficient of T to CTX was denoted as path a, as shown in Fig. 2 . Second, the regression coefficient of T to BMD as the total effect was denoted path c. Third, the regression coefficients for both T and CTX to BMD as the direct effect were denoted paths b and c. Finally, the indirect effect was examined by 95% bootstrapped confidence intervals (CIs) using 10,000 bootstrapped samples. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to estimate the utility, and the cut-off value, sensitivity and specificity were calculated simultaneously. Statistical significance was set at P < 0.05.

Mediating effect of change in CTX between change in T and change in BMD. CTX: cross-linked C-telopeptide of type I collagen; T: temperature; BMD: bone mineral density AUC: area under the curve; BMD: bone mineral density; LS: lumbar spine

Baseline and follow-up characteristics of the studied population

The baseline and follow-up characteristics of the subjects are shown in Table 1 . The body T increased significantly within 3 days after the administration of ZOL and returned to the baseline level on Day 14. The BMD of the lumbar spine (LS) and femur neck (FN) increased significantly at 6 and 12 months after treatment. Compared with the baseline level, the BMD of the LS increased by 2.2% (Month 6) and 2.5% (Month 12), and the BMD of the FN increased by 1.7% (Month 6) and 2.1% (Month 12). In terms of bone metabolism turnover markers, the level of serum CTX decreased significantly after 2 weeks of treatment, with a decrease of 92.0%. The level of CTX increased slightly at 6 and 12 months after treatment, but it was still significantly lower than the baseline level, with decreases of 80.0% and 71.5%, respectively. P1NP did not change significantly 2 weeks after treatment but decreased at 6 and 12 months after treatment, with decrease rates of 62.9% (Month 6) and 56.0% (Month 12), respectively. The WBC count increased 2 weeks after treatment, the proportion of neutrophils increased substantially, and the percentages of lymphocytes and monocytes decreased.

Adverse events occurring within 3 days of infusion and treatment-related changes in BMD caused by APRs

As shown in Table S1 , 75.9% of the subjects had APRs within 3 days after treatment, of which fever and chills were the most common, accounting for 64.8% of the subjects, followed by musculoskeletal pain, accounting for 38.9% of the subjects, and gastrointestinal, ophthalmic and other symptoms accounted for 13.0%, 3.7% and 29.6% of the subjects, respectively. The subjects were categorized into two groups by APR occurrence: APR+ (i.e., with APR) and APR- (i.e., without APR) (Table 2 ). The results revealed no significant difference between the two groups in BMD changes in the LS, FN or total hip (TH) at Months 6 and 12 (Table 2 ).

Relationship between factors and change in BMD adjusted for age and BMI

Multivariate linear regression models were constructed to analyse the correlations between changes in body T within 3 days and 25OHD at baseline, and changes in BTM (baseline vs. 12 months) and changes in BMD (baseline vs. 12 months) are shown in Table 3 . The results showed that after adjustment for age and BMI, changes in T (baseline vs. 3 days) were positively correlated with changes in LS BMD (baseline vs. 12 months) (β = 0.270 P = 0.034), and changes in CTX (baseline vs. 12 months) were negatively correlated with LS BMD (baseline vs. 12 months) (β=-0.486 P = 0.000). The change in PINP (baseline vs. 12 months) was negatively correlated with the change in LS BMD (baseline vs. 12 months) (β=-0.409 P = 0.001). The 25OHD at baseline was not correlated with the change in BMD. None of the factors were correlated with changes in FN BMD or TH BMD (baseline vs. 12 months).

Mediating effect of change in CTX (baseline vs. 12 months) between changes in T (baseline vs. 3 days) and changes in LS BMD (baseline vs. 12 months)

Table 4 ; Fig. 2 illustrate the results of the bootstrapped mediation analysis to determine whether CTX mediated the relationship between the change in T BMD and the change in LS BMD. With respect to the total effect, the change in T was positively linked to the change in LS BMD (b = 0.123, P = 0.00371). Furthermore, the change in T was negatively correlated with the change in the serum CTX concentration. The change in serum CTX was negatively correlated with the change in BMD. Moreover, CTX had a significant mediating effect on the relationship between the change in T and the change in the BMD of LS patients (b = 0.046, 95% CI [0.0010–0.0091]). However, there was no direct effect between the change in T and the change in BMD. The results suggested that the influence of the change in T on the change in the BMD of the LS was mainly mediated by the change in CTX.

Change in T (baseline vs. 3 days) to predict the increase in LS BMD (baseline vs. 12 months)

According to the clinical consensus, a change in BMD by more than 2.77 times the precision error is defined as the least significant change (LSC) [ 18 ]. In this study, the precision errors were 0.80% for the spine, 1.05% for the femur neck and 0.97% for the total hip; thus, the LSCs were 2.2% for the spine, 2.90% for the femur neck and 2.67% for the total hip. Therefore, a change in LS BMD of more than 2.2% at 12 months was defined as a clinically significant change. The ROC curve was used to observe the sensitivity and specificity of the change in T to judge the clinically significant change in LS BMD. The results showed that when T increased by 1.95 °C, the sensitivity and specificity of judging the clinical significance change in LS BMD were optimized. The sensitivity and specificity were 68.2% and 78.1%, respectively, and the area under the curve was 0.724 (Fig. 3 ).

Change in T to predict the increase in BMD in LS

In this study, we investigated the hypothesis that the treatment effect in patients with APRs after the first administration of ZOL was better than that in patients without APRs. However, the results did not reveal a relationship between the APRs and changes in bone turnover markers and BMD but revealed that changes in T within 3 days after administration were positively correlated with changes in the BMD of the LS at month 12 (β = 0.270 P = 0.034). This effect was mediated mainly by changes in the serum CTX concentration (b = 0.046, 95% CI [0.0010–0.0091]). Moreover, the ROC curve revealed that when T increased by 1.95 °C, the sensitivity and specificity of judging the clinical importance of the change in LS BMD after 1 year were optimized.

APRs are common among people who are treated for the first time with ZOL. The incidence varies from 30–54.9% [12–13,19−20] . APRs generally appear within 3 days following the first infusion, and common symptoms include fever, musculoskeletal pain, and gastrointestinal symptoms [ 19 ]. APRs are more common among younger subjects, those using nonsteroidal anti-inflammatory drugs (NSAIDs), and non-Japanese Asians, whereas they are less common in smokers, patients with diabetes, previous oral bisphosphonate users and Latin Americans [ 20 ]. Currently, research on APRs has focused mostly on drug safety. However, according to previous studies, the mevalonate pathway that causes APRs is also related to the efficacy of the medication. Therefore, we hypothesized that people with APRs after the first application of ZOL would experience better efficacy after treatment. At present, only 3 studies have been conducted to examine the relationship between APRs and treatment efficacy, and the results were inconsistent [ 21 , 22 , 19 ]. Subanalyses of the phase III ZONE study conducted in Japan demonstrated that patients with APRs presented significantly greater increases in total hip BMD at 6 and 12 months and greater suppression of BTMs compared with patients without APRs [ 22 ]. Another study conducted by Lu suggested a potential association between APRs occurrence and decreased refracture risk in patients with osteoporotic fractures and in patients undergoing orthopaedic surgery [ 19 ]. The HORIZON Pivotal Fracture Trial showed no significant difference in treatment-related changes in BMD with or without APRs; however, subjects starting ZOL who experienced APRs had a greater reduction in vertebral fracture risk [ 21 ]. In addition to racial differences, an important reason for this inconsistency lies in the choice of research methods. APRs are composed of a group of symptoms, including fever, musculoskeletal pain, gastrointestinal symptoms, and eye symptoms. In fact, most of these symptoms are self-reported by patients and are difficult to quantify and compare objectively, resulting in deviations in the results. Fever is the most common symptom in APRs. In studies in Japan and the United States, 75.7% and 47.5% of people with APRs, respectively, had fever symptoms [ 21 , 22 ]. Therefore, in this study, body T, the most common and easily quantifiable indicator of APRs, was selected as the observation indicator to analyse its possible correlation with the efficacy of ZOL after treatment, including changes in BTM and BMD.

Our results revealed that when grouped by APRs, there were no significant differences in changes in BMD in the LS, FN, or TH between the two groups at Months 6 and 12, which is consistent with the results reported in the HORIZON-PFT study. However, when we used the change in body T within 3 days after the first infusion of ZOL as an observation indicator, the results showed that there wasrevealed a significant positive correlation between the changes in T (baseline vs. 3 days) and LS BMD (baseline vs. 12 months) (β = 0.270 P = 0.034). Moreover, further mediating effect analysis revealed that the influence of the change in T on the change in the BMD of LS patients was mediated mainly by the change in the serum CTX level (b = 0.046, 95% CI [0.0010–0.0091]).

Bone continuously undergoes modelling and remodelling to maintain bone metabolism and structural completion. This process of self-renewal, in which osteoclasts constantly absorb old bone and osteoblasts constantly form new bone, is known as bone turnover. Bone turnover markers (BTMs) are metabolic products or enzymes produced during bone turnover processes and are classified as bone formation or bone resorption markers. Serum CTX is a sensitive marker recommended by the IOF to reflect bone resorption [ 23 ]. Our results revealed a negative correlation between the changes in serum CTX levels at Week 2, Month 6, and Month 12 after infusion of ZOL and the changes in BMD in the LS and TH at Month 12. Although our study did not evaluate the occurrence of fractures because it was a one-year clinical trial, according to the reported studies, compared with those with the lowest quartile level of serum CTX, the risk of vertebral fracture increased by 1.4–2.2 times, and the risk of nonvertebral fracture increased by 1.8–2.5 times in the subjects with the highest quartile of serum CTX independent of BMD [ 24 ]. The greater the decrease in serum level of CTX, the greater is the decrease in fracture risk [ 25 ].

Notably, our results suggested that the impact of changes in body T on serum CTX after infusion of ZOL may be related to monocytes. The changes in T within 3 days after treatment were negatively correlated with the changes in serum CTX levels at Month 6 and Month 12. Moreover, the changes in T were also negatively correlated with the changes in monocytes in the peripheral blood at Week 2 (Table S2 ). Next, we subclassified the subjects into four groups according to the degree of monocyte reduction after treatment, and the changes in serum CTX were compared between the lowest quartile and highest quartile groups. The results revealed that the decrease in serum CTX was greater in subjects in the highest quartile group compared with those in the lowest quartile group, and the decrease in CTX at Month 12 was significantly different (− 78.2% vs. − 57.8%, P < 0.05) (Table S3 ).

Currently, both peripheral monocytes and γδ T cells are believed to be rapidly activated after treatment with ZOL, which ultimately affects the clinical severity of APR [ 20 , 26 , 27 ]. ZOL is taken up by monocytes, which thereby acquire the ability to ‘‘present’’ isoprenoid metabolites or related structures to γδ T cells [ 28 ]. Through mutual crosstalk, both monocytes and γδ T cells undergo a series of activation and differentiation steps that ultimately determine the severity of the APRs and replicate similar events to those that occur in acute infection [ 9 ]. Notably, the effects on circulating monocytes appear to be transient, lasting only 3 days; subsequently, rapid inactivation and extravasation occur [ 20 ]. Therefore, in our study, we observed a significant decrease in the number of monocytes at week 2 after treatment. On the one hand, the number of decreased monocytes reflects the severity of APRs in the body. On the other hand, it also reflects the sensitivity of osteoclasts to ZOL. Osteoclasts differentiate from monocyte-derived macrophage precursors and are responsible for bone absorption. Therefore, changes in the number of serum mononuclear cells after treatment can partially indicate the sensitivity of osteoclasts to ZOL. As shown in our study, the subjects with greater reductions in monocyte counts also presented greater decreases in serum CTX levels. The level of CTX reflects the activity of osteoclasts and the level of bone resorption.

Special attention should be given to a recent study conducted by Lu, which focused on elderly osteoporotic fracture patients undergoing orthopaedic surgery [ 19 ]. In that study, the long-term outcomes of APRs after initial ZOL administration were investigated. The results demonstrated that patients who experienced APR had a 73% lower rate of refracture than did those who did not, which indicated that patients with APRs experienced better drug efficacy. Our research yielded similar findings, showing that the increase in body T within 3 days after initial ZOL was related to changes in BTM and an increase in BMD after one year. However, surprisingly, Lu’s research also revealed that patients who had an APR had a 97% higher risk of mortality than patients who did not. In Lu’s study, all the subjects presented newly identified hip/morphological vertebral osteoporotic fractures and had undergone orthopaedic surgery in the hospital. Most of the subjects had received ZOL treatment for 7 − 14 days after the orthopaedic operation. In fact, acute fracture events and surgery comprise a special period for patients with osteoporosis. Research has shown that trauma itself can exacerbate the occurrence of ARPs [ 29 ]. Compared with those after nonsurgical interventions, the odds ratios of experiencing APRs after minimally invasive or open surgery were 3.54 and 5.71, respectively. Therefore, the results of Lu’s study can only represent the impact of ARPs on mortality and refracture after ZOL treatment of hospitalized osteoporotic fracture patients. However, the subjects in our study were recruited from the outpatient service, and none of them had undergone surgical treatment in the past three months. Therefore, our results are more universally applicable to patients with osteoporosis.

Both the APRs and efficacy of ZOL were related to the mevalonate pathway. Our results suggested that those patients who are sensitive to ZOL were more likely to have an increase in body T, a greater decrease in serum CTX and a greater increase in BMD after the first dose. A three-year HORIZEN-PFT study has shown that people with APRs are less likely to suffer vertebral fractures [ 21 ]. In our study, the degree of increase in body T after initial medication might indicate the sensitivity of patients to ZOL and potentially predict the treatment efficacy to some extent. Our study further revealed that an increase in body T of 1.95 °C or more within 3 days after the first administration of ZOL predicted a clinically significant increase in lumbar bone density at 1 year after the first administration of ZOL.

There are several limitations to our study. (1) While this study was conducted as a cohort study, it is worth noting that the follow-up period was limited to one year. The current view is that there is a significant change in BTM after 3 months of treatment with ZOL. A significant change in BMD was observed after 6 months of treatment with ZOL. Moreover, the fastest increase in BMD can be observed after the first year of treatment with ZOL [ 30 , 31 ]. Therefore, osteoporosis efficacy, including changes in BTM and BMD, can be evaluated over one year of follow-up. However, the relatively short duration of the follow-up period might account for the observation limited to changes in BTM and BMD, with no occurrences of fractures recorded within our study cohort. Three existing studies have investigated the long-term effects of zoledronic acid in patients with and without APR. One of them is the HORIZON PFT study, which lasted for 3 years and focused on postmenopausal women. BMD and fracture occurrence were observed [ 21 ]. The results suggest that women initiating ZOL who experience an APR will have a greater reduction in vertebral fracture risk with ZOL. However, there was no significant difference in treatment-related changes in BMD. Another study conducted in Japanese patients treated with a once yearly intravenous infusion of ZOL 5 mg for 2 years investigated the relationship between APR and efficacy [ 22 ]. Patients with APRs presented significantly greater increases in total hip BMD at 6 and 12 months and greater reductions in BTMs than patients without APRs. However, fracture risk was not assessed in this study. The other is a retrospective study involving elderly osteoporotic fracture patients who underwent orthopaedic surgery, which has been discussed above [ 19 ]. (2) Despite meeting the requirements established by our power analysis, the sample size was still relatively small. (3) Given the higher prevalence of osteoporosis among postmenopausal women, the study participants in this exploratory study were limited to postmenopausal women residing in Shanghai. The results of this study present evidence that individuals exhibiting elevated body T levels following initial ZOL treatment may experience greater improvements in BMD. (4) In this study, certain external variables, such as lifestyle and nutritional status, were not collected, which might compromise the robustness of the findings. To gain a more comprehensive understanding of the long-term efficacy, safety, and sustained effects of ZOL, further studies with longer follow-up durations are needed. In future studies with longer follow-up durations (e.g., longer than 2 years), the change in occurrence of fractures can be better evaluated. In addition, in further research, external variables such as lifestyle, exercise and dairy intake will be collected to increase the validity of the results.

In summary, the results of this study indicated for the first time that (1) there was no significant correlation between the occurrence of ARPs within 3 days of the first administration of ZOL and the change in BMD after 1 year, whereas the increase in body T positively correlated with the increase in BMD, suggesting that the increase in body T predicted better treatment. (2) The correlation between the increase in body T and increase in BMD was mediated by the change in serum CTX. (3) An increase in body T of 1.95 °C or above predicted that the BMD in the LS would increase significantly 1 year after the first administration of ZOL.

In this study, we tested the hypothesis that people with elevated body T after initial ZOL treatment had greater improvements in BMD and better outcomes. The establishment of this hypothesis will be conducive to the prediction of the clinical curative effect and guide doctors in timely intensive treatment for people with poor curative effects. It is widely recommended in guidelines that the measurement of main serum bone turnover biomarkers, namely, CTX and PINP, should be performed over a time period ranging from 3 − 6 months posttreatment for assessment of drug efficacy. However, the use of a body temperature monitor allows for a timelier, cost-effective, and convenient assessment in comparison, particularly in regions where access to these laboratory tests may be limited. More importantly, since APRs are very common in patients with initial ZOL treatment, the establishment of this hypothesis will help alleviate patients’ fear and anxiety surrounding APRs, improve patients’ confidence and compliance with antiosteoporosis therapy, and increase humanistic care for patients.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Area under the curve

Acute-phase reactions

Bone turnover marker

Bone mineral density

Body mass index

Bisphosphonates

Confidence intervals

C-telopeptide collagen crosslinks

Coefficients of variance

Dimethylallyl diphosphate

Farnesyl pyrophosphate

Isopentenyl diphosphate

Lumbar spine

Least significant change

Medical Dictionary for Regulatory Activities

Mevalonate kinase

Nitrogenous bisphosphonate

Nonsteroidal anti-inflammatory drugs

N-terminal propeptide of type 1 collagen

Phosphomevalonate kinase

Red blood cell

Operating characteristic curve

Temperature

White blood cell

Zoledronate

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This study was supported by National Key R&D Program of China (2018YFC2000205), Shanghai Municipal Health Commission (20214Y0519), Bethune Foundation Project (G-X-2019-1107-2), Bethune Foundation Project (GX2021C04), Shanghai Science and Technology Commission Science and Technology Innovation Action Plan Industry University Research Medical Cooperation Project (17DZ1920206). The funding body had no role in the design of the study and collection, analysis, interpretation of data or in writing the manuscript.

Author information

Yanping Du and Weijia Yu contributed equally to this work and should be considered co-first author.

Authors and Affiliations

Department of Osteoporosis and Bone Disease, Research Section of Geriatric Metabolic Bone Disease, Huadong Hospital Affiliated to Fudan University, Shanghai Geriatric Institute, Shanghai, China

Yanping Du, Weijia Yu, Wenjing Tang, Minmin Chen, Huilin Li & Qun Cheng

Research Section of Geriatric Metabolic Bone Disease, Shanghai Geriatric Institute, Shanghai, China

Department of Traditional Chinese Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China

Haixin Gou & Yiming Lei

Department of Massage, Huadong Hospital Affiliated to Fudan University, Shanghai, China

Tongkai Zhang

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Contributions

QC has contributed to the conception and design of this study. YD, WY performed statistical analysis and wrote the first draft of the manuscript. QC, YD, WY, WT, MC, HL revised the manuscript and provided the meritorious support. YD, WY, HG, YL, TZ, WT, MC, HL participated in recruitment and examinations of patients, they also contributed to the acquisition and analysis of data; HG, YL, TZ performed laboratory analysis. All authors interpreted data. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Qun Cheng .

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee (The Ethics Committee of The Huadong Hospital, No: 20170068) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants prior to enrollment. Ethics approval was obtained from Ethics committee of The Huadong Hospital.

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Du, Y., Yu, W., Gou, H. et al. Change in body temperature, not acute-phase reaction, predict anti-Osteoporosis efficacy after the first administration of Zoledronic acid: a prospective observational cohort study. BMC Musculoskelet Disord 25 , 694 (2024). https://doi.org/10.1186/s12891-024-07781-8

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DOI : https://doi.org/10.1186/s12891-024-07781-8

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Part 1. Overview Information

National Institutes of Health ( NIH )

R01 Research Project Grant

April 4, 2024 - Overview of Grant Application and Review Changes for Due Dates on or after January 25, 2025. See Notice NOT-OD-24-084 .
August 31, 2022 - Implementation Changes for Genomic Data Sharing Plans Included with Applications Due on or after January 25, 2023. See Notice NOT-OD-22-198 .
August 5, 2022 - Implementation Details for the NIH Data Management and Sharing Policy. See Notice NOT-OD-22-189 .

See Section III. 3. Additional Information on Eligibility .

This Notice of Funding Opportunity (NOFO) encourages grant applications from investigators interested in conducting basic, mechanistic research into the biological/genetic causes of cancer health disparities. These research project grants (R01) will support innovative studies designed to investigate biological/genetic bases of cancer health disparities, such as (1) mechanistic studies of biological factors associated with cancer health disparities, including those related to basic research in cancer biology or cancer prevention strategies, (2) the development and testing of new methodologies and models, and (3) secondary data analyses. This NOFO is also designed to aid and facilitate the growth of a nationwide cohort of scientists with a high level of basic research expertise in cancer health disparities research who can expand available resources and tools, such as biospecimens, patient derived models, and methods that are necessary to conduct basic research in cancer health disparities.

Not Applicable

Application Due Dates			Review and Award Cycles
New	Renewal / Resubmission / Revision (as allowed)	AIDS - New/Renewal/Resubmission/Revision, as allowed	Scientific Merit Review	Advisory Council Review	Earliest Start Date
October 05, 2024 *	November 05, 2024 *	Not Applicable	March 2025	May 2025	July 2025

All applications are due by 5:00 PM local time of applicant organization.

Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date.

It is critical that applicants follow the instructions in the Research (R) Instructions in the How to Apply - Application Guide , except where instructed to do otherwise (in this NOFO or in a Notice from NIH Guide for Grants and Contracts ).

Conformance to all requirements (both in the How to Apply - Application Guide and the NOFO) is required and strictly enforced. Applicants must read and follow all application instructions in the How to Apply - Application Guide as well as any program-specific instructions noted in Section IV. When the program-specific instructions deviate from those in the How to Apply - Application Guide , follow the program-specific instructions.

Applications that do not comply with these instructions may be delayed or not accepted for review.

There are several options available to submit your application through Grants.gov to NIH and Department of Health and Human Services partners. You must use one of these submission options to access the application forms for this opportunity.

Use the NIH ASSIST system to prepare, submit and track your application online.
Use an institutional system-to-system (S2S) solution to prepare and submit your application to Grants.gov and eRA Commons to track your application. Check with your institutional officials regarding availability.
Use Grants.gov Workspace to prepare and submit your application and eRA Commons to track your application.

Part 2. Full Text of Announcement

Section i. notice of funding opportunity description.

This notice of funding opportunity (NOFO) encourages grant applications from investigators interested in conducting basic, mechanistic research into the biological/genetic causes of cancer health disparities. These research project grants (R01) will support innovative studies designed to investigate biological/genetic bases of cancer disparities, such as (1) mechanistic studies of biological factors associated with cancer disparities, including those related to basic research in cancer biology or cancer prevention strategies, (2) the development and testing of new methodologies and models, and (3) secondary data analyses. This NOFO is also designed to aid and facilitate the growth of a nationwide cohort of scientists with a high level of basic research expertise in cancer health disparities research who can expand available resources and tools, such as biospecimens, patient-derived models, and methods that are necessary to conduct basic research in cancer health disparities.

Please note that this NOFO will be reissued on the simplified review framework (SRF) template in 2025 to allow for resubmissions and renewals. See Simplified Review Framework for NIH Research Project Grant Applications.

In the United States, several racial/ethnic populations demonstrate increased incidence and/or more aggressive disease for specific cancer types. For example, African American males have higher rates of prostate and lung cancer, compared to their Caucasian-American counterparts, and both males and females exhibit a higher incidence of colorectal cancer and multiple myeloma. Hispanic/Latino individuals have the highest rates of cervical cancer and pediatric acute lymphoblastic leukemia (ALL) and one of the poorest survival rates for ALL. Similarly, Asians and Pacific Islanders have the highest incidence rates for liver and stomach cancer while American Indians and Alaska Natives have both the highest incidence and the highest mortality rates in kidney and renal pelvis cancer.

The causes of these cancer health disparities are multifactorial, including barriers in access to healthcare, cultural barriers, environmental disadvantage, differences in diet and lifestyle, ancestry-related risk factors, persistent co-morbidities, and chronic stress exposure due to discrimination and social isolation. An increasing number of studies demonstrate that even when socioeconomic and access to care factors are accounted for, incidence and mortality gaps persist between racial/ethnic populations for some cancer types, which suggests a role for biological contributors. Such studies have included identification of ancestry-related differences in DNA, RNA, and/or protein expression that are associated with cancer risk and/or progression. Other studies have shown the presence of differential tumor microenvironment components among diverse racial/ethnic populations indicating a potential role for immunity and inflammation in contributing to cancer health disparities.

These complex biological factors may enhance understanding of the differences observed in cancer incidence, prevalence, morbidity, and mortality rates among underrepresented populations. The NCI encourages investigations of such biological factors to increase our understanding of the mechanisms that play a role in cancer health disparities.

Specific Research Objectives

The goal of this NOFO is to stimulate interest in the characterization and functional analysis of biological factors associated with cancer health disparities and to provide funding opportunities in this area. Applications should focus on basic cancer research, consistent with the research interests of the NCI's Division of Cancer Biology (DCB) , Division of Cancer Prevention (DCP) , and Center for Cancer Health Equity (CCHE):

The DCB supports research on the discovery and characterization of basic pathways and mechanisms that regulate the development of a pre-malignant state, initiation of cellular transformation and cancer cell progression, formation of the tumor microenvironment, metastasis, and host responses to cancer, including immunologic or metabolic responses.

The CCHE supports cancer health disparity research focused on basic, hypothesis-driven studies that explicitly address the unequal burden of cancer amongst racial/ethnic minorities or other underserved populations across the cancer continuum (prevention, early detection, diagnosis, treatment, and survivorship).

The DCP supports research that will generate new information about molecular processes that are susceptible to intervention throughout the cancer continuum until invasive cancer and underlying mechanisms of cancer and its sequelae (i.e., mechanistic studies on the prevention or treatment of acute and chronic symptoms and morbidities related to cancer and its treatment), developing effective cancer screening and prevention strategies, discovering early detection biomarkers, and pinpointing mechanistically targeted nutrients in cancer prevention.

This NOFO encourages basic research projects that will develop and test new methodologies and new research technologies focused on specific topics in cancer health disparities. The availability of annotated clinical samples as well as enabling technologies (genomics/epigenomics, proteomics, metabolomics, single-cell analysis, imaging) make it feasible to study biological factors that contribute to cancer health disparities among different racial/ethnic populations.

Research projects must propose to investigate the interplay of race/ethnicity and/or other social determinants with cancer biology to mechanistically explain an unequal burden of cancer among populations. As such, proposed studies are encouraged to use biospecimens, patient-derived models, and/or data sets derived from different racial/ethnic and/or underserved groups. Studies investigating age and/or gender disparities, in the absence of race/ethnicity variables, are not solicited. Research projects using a comparative research design between at least two populations are encouraged, in which one or more is underserved.

Projects that are strictly hypothesis-generating, exploratory, and correlative studies are discouraged. As this NOFO is focused upon basic research, immediate clinically translational potential of the proposed project is NOT a requirement for the proposed projects.

Research topics of interest include but are not limited to :

Causal drivers of early onset of certain cancer types in specific populations
Genetic/epigenetic mechanisms of cancer susceptibility differences among racial/ethnic populations, such as epigenetic drivers and or suppressors
Understanding how race/ethnicity impacts disease penetrance in individuals who inherit a cancer susceptibility gene
Understanding if race/ethnicity has a role in regression of precancerous lesions
Understanding if risk factors, including environmental exposures, differ across race/ethnicity to influence the development of precancerous lesions
Identifying cancer risk and early detection biomarkers among underrepresented populations
Examining how stress impacts the progression of symptoms across different population groups
Identifying underlying mechanisms of symptoms that are responsible for altering treatment regimens that increase the risk of mortality for racial/ethnic minority patients with cancer
Understanding the process through which precision therapies improve symptom management to reduce health disparities
Detecting similarities and differences in cancer metabolism (e.g. alterations in metabolic fuel sources, fatty acid synthesis, lipid metabolism, glycolysis, nutrient uptake) among racial/ethnic populations
Utilizing New 3D cellular models, organoids, xenografts, patient-derived models, and microfluidic systems designed to recapitulate and investigate cancer health disparities
Identifying epithelial and mesenchymal markers in circulating tumor cells in cancer patients of distinct racial/ethnic groups
Investigating how social health disparities may cause adverse gene expression that confers increased cancer risk and/or aggressiveness
Understanding the role of microbiota in cancer health disparities during tumorigenesis and cancer progression
Examining the role of oncogenic pathogens in the development of cancer health disparities during tumorigenesis and cancer progression in different populations groups
Using computational analysis and modeling for predicting aggressive tumors in distinct racial/ethnic populations
Understanding the biological mechanisms behind differences in toxicity and symptoms in different population groups
Understanding the biological mechanisms of how stress impacts the progression of symptoms in racial/ethnic minority groups
Deciphering the mechanisms of accumulated exposure to environmental toxins across populations
Understanding the biological processes through which precision interventions improve symptom management to reduce cancer health disparities
Investigating the biological bases of differences among racial/ethnic populations in response to cancer immunotherapies and/or development of immune-related adverse events induced by cancer immunotherapies.

Non-responsive Applications

The following types of studies are not responsive to this NOFO- applications proposing such studies will be considered non-responsive and will not be reviewed or considered for funding:

Genome-Wide Association Studies (GWAS);
Behavioral, social, environmental, or community/population-based studies that are not incorporating biological mechanisms in the specific aims; or
Studies that do not propose cancer health disparity research.

See Section VIII. Other Information for award authorities and regulations.

Section II. Award Information

Grant: A financial assistance mechanism providing money, property, or both to an eligible entity to carry out an approved project or activity.

The OER Glossary and the How to Apply - Application Guide provide details on these application types. Only those application types listed here are allowed for this NOFO.

Not Allowed: Only accepting applications that do not propose clinical trials.

Need help determining whether you are doing a clinical trial?

The number of awards is contingent upon NIH appropriations and the submission of a sufficient number of meritorious applications.

The scope of the proposed project should determine the project period. The maximum project period is 5 years.

NIH grants policies as described in the NIH Grants Policy Statement will apply to the applications submitted and awards made from this NOFO.

Section III. Eligibility Information

1. eligible applicants eligible organizations higher education institutions public/state controlled institutions of higher education private institutions of higher education the following types of higher education institutions are always encouraged to apply for nih support as public or private institutions of higher education: hispanic-serving institutions historically black colleges and universities (hbcus) tribally controlled colleges and universities (tccus) alaska native and native hawaiian serving institutions asian american native american pacific islander serving institutions (aanapisis) nonprofits other than institutions of higher education nonprofits with 501(c)(3) irs status (other than institutions of higher education) nonprofits without 501(c)(3) irs status (other than institutions of higher education) for-profit organizations small businesses for-profit organizations (other than small businesses) local governments state governments county governments city or township governments special district governments indian/native american tribal governments (federally recognized) indian/native american tribal governments (other than federally recognized) federal governments eligible agencies of the federal government u.s. territory or possession other independent school districts public housing authorities/indian housing authorities native american tribal organizations (other than federally recognized tribal governments) faith-based or community-based organizations regional organizations non-domestic (non-u.s.) entities (foreign organizations) foreign organizations non-domestic (non-u.s.) entities (foreign organizations) are eligible to apply. non-domestic (non-u.s.) components of u.s. organizations are eligible to apply. foreign components, as defined in the nih grants policy statement , are allowed. required registrations applicant organizations applicant organizations must complete and maintain the following registrations as described in the how to apply - application guide to be eligible to apply for or receive an award. all registrations must be completed prior to the application being submitted. registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. failure to complete registrations in advance of a due date is not a valid reason for a late submission, please reference nih grants policy statement section 2.3.9.2 electronically submitted applications for additional information system for award management (sam) – applicants must complete and maintain an active registration, which requires renewal at least annually . the renewal process may require as much time as the initial registration. sam registration includes the assignment of a commercial and government entity (cage) code for domestic organizations which have not already been assigned a cage code. nato commercial and government entity (ncage) code – foreign organizations must obtain an ncage code (in lieu of a cage code) in order to register in sam. unique entity identifier (uei) - a uei is issued as part of the sam.gov registration process. the same uei must be used for all registrations, as well as on the grant application. era commons - once the unique organization identifier is established, organizations can register with era commons in tandem with completing their grants.gov registrations; all registrations must be in place by time of submission. era commons requires organizations to identify at least one signing official (so) and at least one program director/principal investigator (pd/pi) account in order to submit an application. grants.gov – applicants must have an active sam registration in order to complete the grants.gov registration. program directors/principal investigators (pd(s)/pi(s)) all pd(s)/pi(s) must have an era commons account. pd(s)/pi(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in era commons. if the pd/pi is also the organizational signing official, they must have two distinct era commons accounts, one for each role. obtaining an era commons account can take up to 2 weeks. eligible individuals (program director/principal investigator) any individual(s) with the skills, knowledge, and resources necessary to carry out the proposed research as the program director(s)/principal investigator(s) (pd(s)/pi(s)) is invited to work with their organization to develop an application for support. individuals from diverse backgrounds, including underrepresented racial and ethnic groups, individuals with disabilities, and women are always encouraged to apply for nih support. see, reminder: notice of nih's encouragement of applications supporting individuals from underrepresented ethnic and racial groups as well as individuals with disabilities , not-od-22-019 , and notice of nih's interest in diversity, not-od-20-031 . for institutions/organizations proposing multiple pds/pis, visit the multiple program director/principal investigator policy and submission details in the senior/key person profile (expanded) component of the how to apply - application guide . 2. cost sharing.

This NOFO does not require cost sharing as defined in the NIH Grants Policy Statement NIH Grants Policy Statement Section 1.2 Definition of Terms.

3. Additional Information on Eligibility

Number of Applications

Applicant organizations may submit more than one application, provided that each application is scientifically distinct.

The NIH will not accept duplicate or highly overlapping applications under review at the same time, per NIH Grants Policy Statement Section 2.3.7.4 Submission of Resubmission Application . This means that the NIH will not accept:

A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.
A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.
An application that has substantial overlap with another application pending appeal of initial peer review (see NIH Grants Policy Statement 2.3.9.4 Similar, Essentially Identical, or Identical Applications ).

Section IV. Application and Submission Information

1. requesting an application package.

The application forms package specific to this opportunity must be accessed through ASSIST, Grants.gov Workspace or an institutional system-to-system solution. Links to apply using ASSIST or Grants.gov Workspace are available in Part 1 of this NOFO. See your administrative office for instructions if you plan to use an institutional system-to-system solution.

2. Content and Form of Application Submission

It is critical that applicants follow the instructions in the Research (R) Instructions in the How to Apply - Application Guide except where instructed in this notice of funding opportunity to do otherwise. Conformance to the requirements in the How to Apply - Application Guide is required and strictly enforced. Applications that are out of compliance with these instructions may be delayed or not accepted for review.

Page Limitations

All page limitations described in the How to Apply – Application Guide and the Table of Page Limits must be followed.

The following section supplements the instructions found in the How to Apply – Application Guide and should be used for preparing an application to this NOFO.

SF424(R&R) Cover

All instructions in the How to Apply - Application Guide must be followed.

SF424(R&R) Project/Performance Site Locations

Sf424(r&r) other project information, sf424(r&r) senior/key person profile, r&r or modular budget, r&r subaward budget, phs 398 cover page supplement, phs 398 research plan.

All instructions in the How to Apply - Application Guide must be followed, with the following additional instructions:

Resource Sharing Plan : Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the How to Apply - Application Guide .

Other Plan(s):

All instructions in the How to Apply - Application Guide must be followed, with the following additional instructions:

All applicants planning research (funded or conducted in whole or in part by NIH) that results in the generation of scientific data are required to comply with the instructions for the Data Management and Sharing Plan. All applications, regardless of the amount of direct costs requested for any one year, must address a Data Management and Sharing Plan.

Appendix: Only limited Appendix materials are allowed. Follow all instructions for the Appendix as described in the How to Apply - Application Guide .

No publications or other material, with the exception of blank questionnaires or blank surveys, may be included in the Appendix.

PHS Human Subjects and Clinical Trials Information

When involving human subjects research, clinical research, and/or NIH-defined clinical trials (and when applicable, clinical trials research experience) follow all instructions for the PHS Human Subjects and Clinical Trials Information form in the How to Apply - Application Guide , with the following additional instructions:

If you answered “Yes” to the question “Are Human Subjects Involved?” on the R&R Other Project Information form, you must include at least one human subjects study record using the Study Record: PHS Human Subjects and Clinical Trials Information form or Delayed Onset Study record.

Study Record: PHS Human Subjects and Clinical Trials Information

Delayed Onset Study

Note: Delayed onset does NOT apply to a study that can be described but will not start immediately (i.e., delayed start). All instructions in the How to Apply - Application Guide must be followed.

PHS Assignment Request Form

Foreign organizations.

Foreign (non-U.S.) organizations must follow policies described in the NIH Grants Policy Statement , and procedures for foreign organizations described throughout the How to Apply Application Guide.

3. Unique Entity Identifier and System for Award Management (SAM)

See Part 2. Section III.1 for information regarding the requirement for obtaining a unique entity identifier and for completing and maintaining active registrations in System for Award Management (SAM), NATO Commercial and Government Entity (NCAGE) Code (if applicable), eRA Commons, and Grants.gov

4. Submission Dates and Times

Part I. contains information about Key Dates and times. Applicants are encouraged to submit applications before the due date to ensure they have time to make any application corrections that might be necessary for successful submission. When a submission date falls on a weekend or Federal holiday , the application deadline is automatically extended to the next business day.

Organizations must submit applications to Grants.gov (the online portal to find and apply for grants across all Federal agencies). Applicants must then complete the submission process by tracking the status of the application in the eRA Commons , NIH’s electronic system for grants administration. NIH and Grants.gov systems check the application against many of the application instructions upon submission. Errors must be corrected and a changed/corrected application must be submitted to Grants.gov on or before the application due date and time. If a Changed/Corrected application is submitted after the deadline, the application will be considered late. Applications that miss the due date and time are subjected to the NIH Grants Policy Statement Section 2.3.9.2 Electronically Submitted Applications .

Applicants are responsible for viewing their application before the due date in the eRA Commons to ensure accurate and successful submission.

Information on the submission process and a definition of on-time submission are provided in the How to Apply – Application Guide .

5. Intergovernmental Review (E.O. 12372)

This initiative is not subject to intergovernmental review.

6. Funding Restrictions

All NIH awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement .

Pre-award costs are allowable only as described in the NIH Grants Policy Statement Section 7.9.1 Selected Items of Cost .

7. Other Submission Requirements and Information

Applications must be submitted electronically following the instructions described in the How to Apply - Application Guide . Paper applications will not be accepted.

Applicants must complete all required registrations before the application due date. Section III. Eligibility Information contains information about registration.

For assistance with your electronic application or for more information on the electronic submission process, visit How to Apply – Application Guide . If you encounter a system issue beyond your control that threatens your ability to complete the submission process on-time, you must follow the Dealing with System Issues guidance. For assistance with application submission, contact the Application Submission Contacts in Section VII.

Important reminders:

All PD(s)/PI(s) must include their eRA Commons ID in the Credential field of the Senior/Key Person Profile form . Failure to register in the Commons and to include a valid PD/PI Commons ID in the credential field will prevent the successful submission of an electronic application to NIH. See Section III of this NOFO for information on registration requirements.

The applicant organization must ensure that the unique entity identifier provided on the application is the same identifier used in the organization’s profile in the eRA Commons and for the System for Award Management. Additional information may be found in the How to Apply - Application Guide .

See more tips for avoiding common errors.

Upon receipt, applications will be evaluated for completeness and compliance with application instructions by the Center for Scientific Review and responsiveness by components of participating organizations , NIH. Applications that are incomplete, non-compliant and/or nonresponsive will not be reviewed.

Requests of $500,000 or more for direct costs in any year

Applicants requesting $500,000 or more in direct costs in any year (excluding consortium F&A) must contact a Scientific/ Research Contact at least 6 weeks before submitting the application and follow the Policy on the Acceptance for Review of Unsolicited Applications that Request $500,000 or More in Direct Costs as described in the SF424 (R&R) Application Guide.

Recipients or subrecipients must submit any information related to violations of federal criminal law involving fraud, bribery, or gratuity violations potentially affecting the federal award. See Mandatory Disclosures, 2 CFR 200.113 and NIH Grants Policy Statement Section 4.1.35 .

Send written disclosures to the NIH Chief Grants Management Officer listed on the Notice of Award for the IC that funded the award and to the HHS Office of Inspector Grant Self Disclosure Program at [email protected] .

Post Submission Materials

Applicants are required to follow the instructions for post-submission materials, as described in the policy

Section V. Application Review Information

1. criteria.

Only the review criteria described below will be considered in the review process. Applications submitted to the NIH in support of the NIH mission are evaluated for scientific and technical merit through the NIH peer review system.

For this particular announcement, note the following:

Immediate clinically translational potential of the proposed project is NOT a requirement for this FOA.

Reviewers will provide an overall impact score to reflect their assessment of the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved, in consideration of the following review criteria and additional review criteria (as applicable for the project proposed).

Reviewers will consider each of the review criteria below in the determination of scientific merit and give a separate score for each. An application does not need to be strong in all categories to be judged likely to have major scientific impact. For example, a project that by its nature is not innovative may be essential to advance a field.

Does the project address an important problem or a critical barrier to progress in the field? Is the prior research that serves as the key support for the proposed project rigorous? If the aims of the project are achieved, how will scientific knowledge, technical capability, and/or clinical practice be improved? How will successful completion of the aims change the concepts, methods, technologies, treatments, services, or preventative interventions that drive this field?

Specific to this NOFO : Does the proposed research project have the potential to advance the understanding of biological mechanisms contributing to cancer health disparities in underrepresented populations?

Are the PD(s)/PI(s), collaborators, and other researchers well suited to the project? If Early Stage Investigators or those in the early stages of independent careers, do they have appropriate experience and training? If established, have they demonstrated an ongoing record of accomplishments that have advanced their field(s)? If the project is collaborative or multi-PD/PI, do the investigators have complementary and integrated expertise; are their leadership approach, governance, and organizational structure appropriate for the project?

Does the application challenge and seek to shift current research or clinical practice paradigms by utilizing novel theoretical concepts, approaches or methodologies, instrumentation, or interventions? Are the concepts, approaches or methodologies, instrumentation, or interventions novel to one field of research or novel in a broad sense? Is a refinement, improvement, or new application of theoretical concepts, approaches or methodologies, instrumentation, or interventions proposed?

Are the overall strategy, methodology, and analyses well-reasoned and appropriate to accomplish the specific aims of the project? Have the investigators included plans to address weaknesses in the rigor of prior research that serves as the key support for the proposed project? Have the investigators presented strategies to ensure a robust and unbiased approach, as appropriate for the work proposed? Are potential problems, alternative strategies, and benchmarks for success presented? If the project is in the early stages of development, will the strategy establish feasibility and will particularly risky aspects be managed? Have the investigators presented adequate plans to address relevant biological variables, such as sex, for studies in vertebrate animals or human subjects?

If the project involves human subjects and/or NIH-defined clinical research, are the plans to address 1) the protection of human subjects from research risks, and 2) inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion or exclusion of individuals of all ages (including children and older adults), justified in terms of the scientific goals and research strategy proposed?

Specific to this NOFO : Is the scientific approach proposed adequate to effectively study cancer health disparities between at least two populations, in which one or more are underserved?

Will the scientific environment in which the work will be done contribute to the probability of success? Are the institutional support, equipment, and other physical resources available to the investigators adequate for the project proposed? Will the project benefit from unique features of the scientific environment, subject populations, or collaborative arrangements?

As applicable for the project proposed, reviewers will evaluate the following additional items while determining scientific and technical merit, and in providing an overall impact score, but will not give separate scores for these items.

For research that involves human subjects but does not involve one of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate the justification for involvement of human subjects and the proposed protections from research risk relating to their participation according to the following five review criteria: 1) risk to subjects, 2) adequacy of protection against risks, 3) potential benefits to the subjects and others, 4) importance of the knowledge to be gained, and 5) data and safety monitoring for clinical trials.

For research that involves human subjects and meets the criteria for one or more of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate: 1) the justification for the exemption, 2) human subjects involvement and characteristics, and 3) sources of materials. For additional information on review of the Human Subjects section, please refer to the Guidelines for the Review of Human Subjects .

When the proposed project involves human subjects and/or NIH-defined clinical research, the committee will evaluate the proposed plans for the inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion (or exclusion) of individuals of all ages (including children and older adults) to determine if it is justified in terms of the scientific goals and research strategy proposed. For additional information on review of the Inclusion section, please refer to the Guidelines for the Review of Inclusion in Clinical Research .

The committee will evaluate the involvement of live vertebrate animals as part of the scientific assessment according to the following three points: (1) a complete description of all proposed procedures including the species, strains, ages, sex, and total numbers of animals to be used; (2) justifications that the species is appropriate for the proposed research and why the research goals cannot be accomplished using an alternative non-animal model; and (3) interventions including analgesia, anesthesia, sedation, palliative care, and humane endpoints that will be used to limit any unavoidable discomfort, distress, pain and injury in the conduct of scientifically valuable research. Methods of euthanasia and justification for selected methods, if NOT consistent with the AVMA Guidelines for the Euthanasia of Animals, is also required but is found in a separate section of the application. For additional information on review of the Vertebrate Animals Section, please refer to the Worksheet for Review of the Vertebrate Animals Section.

Reviewers will assess whether materials or procedures proposed are potentially hazardous to research personnel and/or the environment, and if needed, determine whether adequate protection is proposed.

For Resubmissions, the committee will evaluate the application as now presented, taking into consideration the responses to comments from the previous scientific review group and changes made to the project.

For Renewals, the committee will consider the progress made in the last funding period.

As applicable for the project proposed, reviewers will consider each of the following items, but will not give scores for these items, and should not consider them in providing an overall impact score.

Reviewers will assess whether the project presents special opportunities for furthering research programs through the use of unusual talent, resources, populations, or environmental conditions that exist in other countries and either are not readily available in the United States or augment existing U.S. resources.

Reviewers will assess the information provided in this section of the application, including 1) the Select Agent(s) to be used in the proposed research, 2) the registration status of all entities where Select Agent(s) will be used, 3) the procedures that will be used to monitor possession use and transfer of Select Agent(s), and 4) plans for appropriate biosafety, biocontainment, and security of the Select Agent(s).

Reviewers will comment on whether the Resource Sharing Plan(s) (e.g., Sharing Model Organisms ) or the rationale for not sharing the resources, is reasonable.

For projects involving key biological and/or chemical resources, reviewers will comment on the brief plans proposed for identifying and ensuring the validity of those resources.

Reviewers will consider whether the budget and the requested period of support are fully justified and reasonable in relation to the proposed research.

2. Review and Selection Process Applications will be evaluated for scientific and technical merit by (an) appropriate Scientific Review Group(s) convened by the Center for Scientific Review, in accordance with NIH peer review policy and procedures , using the stated review criteria . Assignment to a Scientific Review Group will be shown in the eRA Commons. As part of the scientific peer review, all applications will receive a written critique. Applications may undergo a selection process in which only those applications deemed to have the highest scientific and technical merit (generally the top half of applications under review) will be discussed and assigned an overall impact score. Applications will be assigned on the basis of established PHS referral guidelines to the appropriate NIH Institute or Center. Applications will compete for available funds with all other recommended applications submitted in response to this NOFO. Following initial peer review, recommended applications will receive a second level of review by the appropriate national Advisory Council or Board. The following will be considered in making funding decisions: Scientific and technical merit of the proposed project as determined by scientific peer review. Availability of funds. Relevance of the proposed project to program priorities. If the application is under consideration for funding, NIH will request "just-in-time" information from the applicant as described in the NIH Grants Policy Statement Section 2.5.1. Just-in-Time Procedures . This request is not a Notice of Award nor should it be construed to be an indicator of possible funding. Prior to making an award, NIH reviews an applicant’s federal award history in SAM.gov to ensure sound business practices. An applicant can review and comment on any information in the Responsibility/Qualification records available in SAM.gov. NIH will consider any comments by the applicant in the Responsibility/Qualification records in SAM.gov to ascertain the applicant’s integrity, business ethics, and performance record of managing Federal awards per 2 CFR Part 200.206 “Federal awarding agency review of risk posed by applicants.” This provision will apply to all NIH grants and cooperative agreements except fellowships. 3. Anticipated Announcement and Award Dates

After the peer review of the application is completed, the PD/PI will be able to access his or her Summary Statement (written critique) via the eRA Commons . Refer to Part 1 for dates for peer review, advisory council review, and earliest start date.

Information regarding the disposition of applications is available in the NIH Grants Policy Statement Section 2.4.4 Disposition of Applications .

Section VI. Award Administration Information

1. award notices.

A Notice of Award (NoA) is the official authorizing document notifying the applicant that an award has been made and that funds may be requested from the designated HHS payment system or office. The NoA is signed by the Grants Management Officer and emailed to the recipient’s business official.

In accepting the award, the recipient agrees that any activities under the award are subject to all provisions currently in effect or implemented during the period of the award, other Department regulations and policies in effect at the time of the award, and applicable statutory provisions.

Recipients must comply with any funding restrictions described in Section IV.6. Funding Restrictions . Any pre-award costs incurred before receipt of the NoA are at the applicant's own risk. For more information on the Notice of Award, please refer to the NIH Grants Policy Statement Section 5. The Notice of Award and NIH Grants & Funding website, see Award Process.

Institutional Review Board or Independent Ethics Committee Approval: Recipient institutions must ensure that protocols are reviewed by their IRB or IEC. To help ensure the safety of participants enrolled in NIH-funded studies, the recipient must provide NIH copies of documents related to all major changes in the status of ongoing protocols.

2. Administrative and National Policy Requirements

The following Federal wide and HHS-specific policy requirements apply to awards funded through NIH:

The rules listed at 2 CFR Part 200 , Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards.
All NIH grant and cooperative agreement awards include the NIH Grants Policy Statement as part of the terms and conditions in the Notice of Award (NoA). The NoA includes the requirements of this NOFO. For these terms of award, see the NIH Grants Policy Statement Part II: Terms and Conditions of NIH Grant Awards, Subpart A: General and Part II: Terms and Conditions of NIH Grant Awards, Subpart B: Terms and Conditions for Specific Types of Grants, Recipients, and Activities .
HHS recognizes that NIH research projects are often limited in scope for many reasons that are nondiscriminatory, such as the principal investigator’s scientific interest, funding limitations, recruitment requirements, and other considerations. Thus, criteria in research protocols that target or exclude certain populations are warranted where nondiscriminatory justifications establish that such criteria are appropriate with respect to the health or safety of the subjects, the scientific study design, or the purpose of the research. For additional guidance regarding how the provisions apply to NIH grant programs, please contact the Scientific/Research Contact that is identified in Section VII under Agency Contacts of this NOFO.

All federal statutes and regulations relevant to federal financial assistance, including those highlighted in NIH Grants Policy Statement Section 4 Public Policy Requirements, Objectives and Other Appropriation Mandates.

Recipients are responsible for ensuring that their activities comply with all applicable federal regulations. NIH may terminate awards under certain circumstances. See 2 CFR Part 200.340 Termination and NIH Grants Policy Statement Section 8.5.2 Remedies for Noncompliance or Enforcement Actions: Suspension, Termination, and Withholding of Support .

3. Data Management and Sharing

Consistent with the 2023 NIH Policy for Data Management and Sharing, when data management and sharing is applicable to the award, recipients will be required to adhere to the Data Management and Sharing requirements as outlined in the NIH Grants Policy Statement . Upon the approval of a Data Management and Sharing Plan, it is required for recipients to implement the plan as described.

4. Reporting

When multiple years are involved, recipients will be required to submit the Research Performance Progress Report (RPPR) annually and financial statements as required in the NIH Grants Policy Statement Section 8.4.1 Reporting. To learn more about post-award monitoring and reporting, see the NIH Grants & Funding website, see Post-Award Monitoring and Reporting .

A final RPPR, invention statement, and the expenditure data portion of the Federal Financial Report are required for closeout of an award, as described in the NIH Grants Policy Statement Section 8.6 Closeout . NIH NOFOs outline intended research goals and objectives. Post award, NIH will review and measure performance based on the details and outcomes that are shared within the RPPR, as described at 2 CFR Part 200.301.

Section VII. Agency Contacts

We encourage inquiries concerning this funding opportunity and welcome the opportunity to answer questions from potential applicants.

eRA Service Desk (Questions regarding ASSIST, eRA Commons, application errors and warnings, documenting system problems that threaten submission by the due date, and post-submission issues)

Finding Help Online: https://www.era.nih.gov/need-help (preferred method of contact) Telephone: 301-402-7469 or 866-504-9552 (Toll Free)

General Grants Information (Questions regarding application instructions, application processes, and NIH grant resources) Email: [email protected] (preferred method of contact) Telephone: 301-480-7075

Grants.gov Customer Support (Questions regarding Grants.gov registration and Workspace) Contact Center Telephone: 800-518-4726 Email: [email protected]

Anu Sharman, Ph.D. National Cancer Institute (NCI) Telephone: 240-276-6250 Email: [email protected]

Tiffany Wallace, Ph.D. National Cancer Institute (NCI) Telephone: 240-276-5114 Email: [email protected]

Asad Umar, D.V.M., Ph.D. National Cancer Institute (NCI) Telephone: 240-276-7070 Email: [email protected]

Amy Rubinstein, Ph.D. Center for Scientific Review (CSR) Telephone: 301-408-9754 Email: [email protected]

Shane Woodward National Cancer Institute (NCI) Telephone: 240-276-6303 Email: [email protected]

Section VIII. Other Information

Recently issued trans-NIH policy notices may affect your application submission. A full list of policy notices published by NIH is provided in the NIH Guide for Grants and Contracts . All awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement .

Awards are made under the authorization of Sections 301 and 405 of the Public Health Service Act as amended (42 USC 241 and 284) and under Federal Regulations 42 CFR Part 52 and 2 CFR Part 200.

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Comparison of cranial cruciate ligament rupture incidence among parous and nulliparous rottweiler bitches: evidence from a lifetime cohort study supporting a paradigm of pregnancy-associated protection against subsequent non-reproductive disease outcomes.

Simple Summary

1. introduction, 2. materials and methods, 2.1. study population, 2.2. ascertainment of ccl status, 2.3. reproductive history, reason for nulliparity, 2.4. other exposure variables, 2.4.1. lifetime ovary exposure, 2.4.2. body condition, 2.4.3. dietary pattern, 2.4.4. habitual physical activity, 2.4.5. participation in work/sport activities, 2.4.6. adult height, 2.5. statistical analysis, 3.1. reproductive history and ccl rupture incidence in the overall study sample, 3.2. comparison of characteristics of bitches in the nulliparous and parous groups, 3.3. parity, the production of live offspring, is associated with significant ccl rupture risk reduction, 3.4. the main finding, the association between parity and ccl rupture risk reduction, is not explained by owner decision on why nulliparous females did not become parous, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Click here to enlarge figure

Variable	Females from the Exceptional Aging in Rottweilers Study
Variable	Total (n = 65)	Nulliparous (n = 33)	Parous (n = 32)	p-Value

# of U.S. states, n	30 states and Canada	17 states and Canada	20 states and Canada	--
# of households, n	61	33	31	--

1985–1997, n (%)	23 (35.4)	10 (30.3)	13 (40.6)	0.38
1998–2010, n (%)	42 (64.6)	23 (69.7)	19 (59.4)	0.38
(years), median (IQR)	14.0 (13.6, 14.5)	14.0 (13.7, 14.4)	13.9 (13.5, 14.7)	0.79
(years), median (IQR)	5.8 (3.9, 7.1)	4.3 (3.2, 5.9)	6.8 (5.6, 8.0)	<0.001
(cm), median (IQR)	58.4 (57.2, 61.0)	58.4 (57.2, 60.8)	61.0 (58.4, 62.2)	0.07
, n (%)	22 (33.8)	10 (30.3)	12 (37.5)	0.54

Never, n (%)	35 (53.8)	21 (63.6)	14 (43.8)	0.11
Ever, n (%)	30 (46.2)	12 (36.4)	18 (56.3)	0.11

Low risk, n (%)	44 (67.7)	20 (60.6)	24 (75.0)	0.22
High risk, n (%)	21 (32.3)	13 (39.4)	8 (25.0)	0.22

No work/sport activity, n (%)	37 (56.9)	18 (54.5)	19 (59.4)	0.69
Work/sport activity, n (%)	28 (43.1)	15 (45.5)	13 (40.6)	0.69

Any CCL rupture, n (%)	17 (26.2)	14 (42.4)	3 (9.4)	0.004
Bilateral CCL rupture, n (% of CCL ruptures)	10 (58.8)	9 (64.3)	1 (33.3)	0.005
Age at first rupture (years), median (IQR)	6.5 (4.9, 8.2)	5.8 (4.4, 8.4)	7.9 (7.7, n/a)	0.19
CCL rupture incidence rate (95% CI) expressed as cases per 10,000 DYAR	237 (138–380)	427 (233–716)	77 (16–226)	0.003

Variable	Unadjusted OR (95% CI)	p-Value	Adjusted OR (95% CI)	p-Value

Non-parous	1.0 (ref)	--	--	--
Parous	0.14 (0.04–0.56)	0.005	0.06 (0.01–0.46)	0.006

≥5.8 years	1.0 (ref)	--	--	--
<5.8 years	2.36 (0.75–7.42)	0.14	0.97 (0.21–4.60)	0.97

Precedent Overweight	1.0 (ref)	--	--	--
Not overweight	1.34 (0.37–4.82)	0.66	0.52 (0.11–2.47)	0.41

Never	1.0 (ref)	--	--	--
Ever	1.45 (0.48–4.39)	0.52	2.38 (0.60–9.47)	0.22

Low risk	1.0 (ref)	--	--	--
High risk	0.83 (0.25–2.78)	0.77	0.34 (0.07–1.58)	0.17

No work/sport activity	1.0 (ref)	--	--	--
Work/sport activity	3.34 (1.05–10.64)	0.04	5.43 (1.27–23.51)	0.02

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Share and Cite

Carrillo, A.E.; Maras, A.H.; Suckow, C.L.; Chiang, E.C.; Waters, D.J. Comparison of Cranial Cruciate Ligament Rupture Incidence among Parous and Nulliparous Rottweiler Bitches: Evidence from a Lifetime Cohort Study Supporting a Paradigm of Pregnancy-Associated Protection against Subsequent Non-Reproductive Disease Outcomes. Animals 2024 , 14 , 2608. https://doi.org/10.3390/ani14172608

Carrillo AE, Maras AH, Suckow CL, Chiang EC, Waters DJ. Comparison of Cranial Cruciate Ligament Rupture Incidence among Parous and Nulliparous Rottweiler Bitches: Evidence from a Lifetime Cohort Study Supporting a Paradigm of Pregnancy-Associated Protection against Subsequent Non-Reproductive Disease Outcomes. Animals . 2024; 14(17):2608. https://doi.org/10.3390/ani14172608

Carrillo, Andres E., Aimee H. Maras, Cheri L. Suckow, Emily C. Chiang, and David J. Waters. 2024. "Comparison of Cranial Cruciate Ligament Rupture Incidence among Parous and Nulliparous Rottweiler Bitches: Evidence from a Lifetime Cohort Study Supporting a Paradigm of Pregnancy-Associated Protection against Subsequent Non-Reproductive Disease Outcomes" Animals 14, no. 17: 2608. https://doi.org/10.3390/ani14172608

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Front Endocrinol (Lausanne)
PMC11361993

Non-linear relationship between TyG index and the risk of prediabetes in young people: a 5-year retrospective cohort study in Chinese young adults

Jianhui xiao.

1 Department of Geriatrics, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China

2 Department of Emergency Medicine, the First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China

Zhenhua Huang

Xiang Hu, First Affiliated Hospital of Wenzhou Medical University, China

Associated Data

Publicly available datasets were analyzed in this study. This data can be found here: https://bmjopen.bmj.com/content/8/9/e021768 .

Given the limited evidence on the relationship between the triglyceride-glucose (TyG) index and the risk of prediabetes among young adults, our study aimed to investigate the potential impact of the TyG index on the future development of prediabetes in young individuals.

This retrospective cohort study included 125,327 healthy adults aged 20 to 45 years. We utilized Cox proportional hazards regression models, combined with cubic spline functions and smooth curve fitting, to assess the relationship between baseline TyG index and the risk of prediabetes among young adults, exploring its non-linear association. A series of sensitivity analyses and subgroup analyses were conducted to ensure the robustness of our findings.

After adjusting for covariates, the study found a positive correlation between the TyG index and the risk of prediabetes (HR=1.81, 95%CI: 1.54–2.13, p<0.0001). The risk of prediabetes increased progressively across quartiles of the TyG index (Q1 to Q4), with Q4 showing a significantly higher risk compared to Q1 (adjusted HR=2.33, 95% CI=1.72–3.16). Moreover, a non-linear relationship was identified between the TyG index and the risk of prediabetes, with an inflection point at 9.39. To the left of the inflection point, the HR was 2.04 (95% CI: 1.69 to 2.46), while to the right, the HR was 0.89 (95% CI: 0.48 to 1.65).

Our study reveals a non-linear relationship and a saturation effect between the TyG index and the development of prediabetes among young individuals in China, with an inflection point at 9.39. Understanding this non-linear relationship can assist clinicians in identifying young individuals at high risk and implementing targeted interventions to reduce their risk of progressing to diabetes.

Introduction

Prediabetes is characterized by elevated blood glucose levels below the diagnostic threshold for diabetes but is associated with a higher risk of developing diabetes ( 1 ). The standardized prevalence of prediabetes in Chinese adults was 35.2% (33.5% to 37.0%) ( 2 ). Approximately 70% of prediabetes patients will progress to diabetes within 10 years, and the incidence of diabetes exceeds 90% within 20 years ( 3 ). Prediabetes is also associated with a high burden of cardiometabolic risk factors and poor outcomes ( 4 ). In a large meta-analysis of prospective studies (53 studies, 1.6 million individuals, median follow-up duration 9.5 years) examining the risks of cardiovascular disease and death in persons with prediabetes compared with normal glycemia ( 5 ), prediabetes was associated with an increased risk of cardiovascular disease and all-cause mortality. Several cohort studies have shown that individuals with prediabetes have a higher all-cause mortality rate compared to those with normal glycemia ( 6 – 12 ). Similarly, individuals with prediabetes have a high risk of hospitalization ( 11 ). Age is a significant risk factor for prediabetes, with evidence showing a strong association between the increase in prediabetes and age. In a 2011–2012 National Health and Nutrition Examination Survey (NHANES) analysis, in adults, the prevalence of prediabetes was 38.0%, while among young individuals aged 20–44 years, it reached up to 28.2% ( 13 ). Prediabetes is commonly considered a warning sign; nevertheless, many prediabetic patients, particularly younger individuals, tend to neglect this metabolic abnormality and underestimate its importance.

The triglyceride-glucose (TyG) index is calculated as the product of fasting plasma glucose (FPG) levels and triglyceride (TG) ( 14 ). Due to its convenience and ease of calculation, the TyG index has been widely used in various clinical settings ( 15 – 20 ). Insulin resistance (IR) is a key factor in metabolic disorders such as metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), diabetes, and obesity ( 21 , 22 ). IR is a major contributor to the progression from normal glucose resistance to prediabetes and then to diabetes. The TyG index is considered a novel marker of IR that accurately and reliably reflects the extent of IR ( 23 , 24 ). Evidence suggests an association between the TyG index and the prevalence of prediabetes in middle-aged and older adults, but it is unclear whether this association exists in young adults ( 25 ).

Due to economic growth, lifestyle, and diet changes, diabetes rates are rising, with younger people increasingly affected. Young diabetics may show unusual symptoms and be overlooked, making early risk identification and intervention critical ( 26 , 27 ). This study used existing data to examine the TyG index and prediabetes risk in individuals aged 20–45, aiming to support its clinical use for early prediabetes detection.

Study design

This study utilized data from a previous retrospective cohort study conducted by Chinese researchers (Chen et al.) ( 28 ). The target independent variable was the TyG at baseline. The outcome variable was the development from normoglycemia to prediabetes at follow-up.

Data source

Access to the original dataset was granted at no cost through the DATADRYAD platform ( www.datadryad.org ), courtesy of Ying Chen et al. In accordance with Dryad’s usage policy, the data is available for academic and research purposes, allowing users to share, adapt, alter, and build upon the material, provided it is not for commercial use and proper attribution is given to the original authors and source. The dataset was sourced from a publicly accessible study published in 2018 titled “Association of body mass index and age with diabetes onset in Chinese adults: a population-based cohort study,” which can be found at http://dx.doi.org/10.1136/bmjopen-2018–021768 . For those interested, the dataset can be retrieved from the following link: https://doi.org/10.5061/dryad.ft8750v ( 28 ). Given that the current study involves a secondary analysis of existing data, there was no need for obtaining informed consent or additional ethical approval.

Research population

The primary research included 685,277 healthy individuals aged 20 years and above, all of whom had undergone a minimum of two health assessments. The study focused on participants who, during follow-up, had FPG levels ranging from 110 to 125 mg/dL without any prior diagnosis of diabetes. The initial selection excluded participants based on several factors (1): lack of detailed information regarding weight, height, or gender (2); BMI values outside the normal range (<15 kg/m 2 or >55 kg/m 2 ) (3); intervals between visits shorter than 2 years (4); missing fasting plasma glucose readings (5); individuals diagnosed with diabetes at the start or with uncertain diabetes status at the time of follow-up. Following these criteria, the study retained 211,833 participants.

Further analysis led to the exclusion of an additional 86,506 participants for reasons including: 1) absence of follow-up FPG readings, 2) baseline FPG levels ≥100 mg/dL, 3) diabetes diagnosis at follow-up, 4) had no available TG value, and 5) lack of TG values or being over 45 years of age. Young people are defined as those aged 45 years old or younger ( 29 ). Ultimately, the study included 125,327 healthy participants. The process of selecting participants for this study is depicted in Figure 1 .

An external file that holds a picture, illustration, etc.
Object name is fendo-15-1414402-g001.jpg

Flowchart illustrating the selection process of study participants.

Data collection

In this study, data collection included demographic information such as age, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, and weight, from which body mass index (BMI) was calculated. To ensure consistency in data collection, staff received specialized training focusing on demographic data and key measurements, including blood pressure. Tests were uniformly conducted in a standardized laboratory environment for FPG, serum creatinine (Scr), TG, total cholesterol (TC), blood urea nitrogen (BUN), alanine aminotransferase (ALT), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c). Additionally, the study collected information on the patients’ smoking and drinking histories, defining current drinking as 1, former drinking as 2, never drinking as 3. Similarly, current smoking was coded as 1, former smoking as 2, never smoking as 3.

Outcome measures

Our primary outcome was the occurrence of prediabetes, defined by FPG levels in the range of 100–125 mg/dL at follow-up without reported incident diabetes ( 30 ).

Statistical analysis

The TyG index, the primary exposure variable in this study, is defined as follows: TyG index = ln [FPG (mg/dL) × TG (mg/dL)/2] ( 31 ). We divided it into four quartiles and considered it as a continuous variable. For continuous variables following a normal distribution, we reported the mean and standard deviation; for non-normally distributed data, we provided the median. For categorical variables, we presented the frequency and proportion. To compare differences between TyG index groups, we used the Kruskal-Wallis H test (for skewed distributed data), one-way analysis of variance (for normally distributed data), or chi-square test (for categorical variables).

We constructed several models to assess the relationship between the TyG index and prediabetes risk: a baseline model without any adjustments, a simplified model adjusting for gender and age only (Model I), and a comprehensive model adjusting for multiple covariates (Model II: including age, gender, BMI, systolic blood pressure, diastolic blood pressure, alanine aminotransferase, urea nitrogen, aspartate aminotransferase, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, serum creatinine, family history of diabetes, alcohol consumption, and smoking status). We recorded the effect size (hazard ratio, HR) and its 95% confidence interval (CI) for each model.

We adjusted for potential confounding factors based on clinical experience, literature review, and univariate analysis results. Additionally, we used a multivariable Cox proportional hazards model and introduced cubic spline functions and smooth curve fitting to explore the possible nonlinear relationship between the TyG index and prediabetes risk. We also used a segmented Cox proportional hazards model to further clarify this nonlinear relationship. Unmeasured confounding between TyG and Prediabetes risk was assessed by calculating E-values.

To validate our findings, we conducted a series of sensitivity analyses. By incorporating continuous variables into a generalized additive model (GAM) in curve form, we further confirmed the robustness of the results. Additionally, we conducted analyses using stratified Cox proportional hazards models in different subgroups (such as age, gender, blood pressure, smoking, and drinking status). Finally, we used likelihood ratio tests to examine whether there were interactions in the model, both in models including interaction terms and those without. All analyses were performed using Empower Stats (X&Y Solutions, Inc., Boston, MA, http://www.empowerstats.com ), with a statistical significance level set at a two-sided P value less than 0.05.

The characteristics of participants

We conducted a comprehensive analysis of the baseline characteristics of participants to investigate the relationship between the TyG index and the risk of prediabetes. The results are summarized in Table 1 , demonstrating significant differences between normal and prediabetes groups across various parameters. The median follow-up duration of our study was 3.00 years, ranging from 2.00 to 6.20 years. The prediabetes group exhibited a slightly higher mean age (35.70 years) compared to the normal group (34.31 years), along with increased height, weight, and BMI. SBP and DBP were also notably elevated in the prediabetes group. The prediabetes group displayed higher levels of FPG, TG, and the TyG index, indicating a potential link between these parameters and the development of prediabetes. Additionally, AST, ALT, BUN, and Scr levels were higher in the prediabetes group. In addition, higher percentage of males, current smokers, and current drinkers were observed in the prediabetes group, along with a greater prevalence of family history of diabetes. Furthermore, the average follow-up duration was slightly longer in the prediabetes group compared to the normal group. As illustrated in Figure 2 , a detailed Distribution of TyG index was conducted. It presented a normal distribution, ranging from 4.65 to 11.78, with a mean of 8.23.

Table 1

The baseline characteristics of participants.

TyG index	Normal	prediabetes	P-value
Participants	114,999	10,328
Age (years)	34.31 ± 5.31	35.70 ± 5.32	<0.001
Height (cm)	166.98 ± 8.30	168.75 ± 8.13	<0.001
Weight (kg)	63.15 ± 12.39	68.90 ± 13.03	<0.001
BMI (kg/m )	22.52 ± 3.27	24.08 ± 3.57	<0.001
SBP (mmHg)	114.74 ± 13.85	120.30 ± 14.59	<0.001
DBP (mmHg)	71.66 ± 9.77	75.11 ± 10.50	<0.001
FPG at baseline (mg/dL)	84.96 ± 8.59	90.10 ± 7.65	<0.001
TG (mg/dL)	102.15 ± 73.75	130.38 ± 98.60	<0.001
TyG index	8.21 ± 0.56	8.48 ± 0.61	<0.001
ALT (U/L)	16.50 (12.00–26.00)	22.00 (14.10–35.80)	<0.001
AST (U/L)	21.00 (18.00–25.20)	22.30 (19.00–28.00)	<0.001
BUN (mmol/L)	4.46 ± 1.11	4.65 ± 1.12	<0.001
Scr (μmol/L)	68.90 ± 15.10	72.65 ± 14.81	<0.001
TC (mmol/L)	4.52 ± 0.83	4.65 ± 0.85	<0.001
HDL-c (mmol/L)	1.38 ± 0.30	1.32 ± 0.29	<0.001
LDL-c (mmol/L)	2.63 ± 0.63	2.72 ± 0.63	<0.001
Sex			<0.001
Male	59,197 (51.48%)	7,039 (68.15%)
Female	55,802 (48.52%)	3,289 (31.85%)
Smoking status			<0.001
Current smoker	4,684 (14.19%)	620 (19.40%)
Ever smoker	1,448 (4.39%)	169 (5.29%)
Never	26,882 (81.43%)	2,407 (75.31%)
Drinking status			<0.001
Current drinker	410 (1.24%)	60 (1.88%)
Ever drinker	4,801 (14.54%)	602 (18.84%)
Never	27,803 (84.22%)	2,534 (79.29%)
Family history of diabetes			<0.001
No	112,693 (97.99%)	10,031 (97.12%)
Yes	2306 (2.01%)	297 (2.88%)
Follow-up (year)	3.12 ± 0.93	3.23 ± 0.96	<0.001

Continuous variables were summarized as mean (SD) or medians (quartile interval); categorical variables were displayed as percentage (%).

BMI, body mass index; SBP, systolic blood pressure; DBP; diastolic blood pressure; TG triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; AST aspartate aminotransferase; ALT, alanine aminotransferase; BUN, blood urea nitrogen; Scr, serum creatinine; FPG, fasting plasma glucose; TyG index, triglyceride glucose index.

An external file that holds a picture, illustration, etc.
Object name is fendo-15-1414402-g002.jpg

Distribution of TyG index. It presented a normal distribution, ranging from4.65 to 11.78, with a mean of 8.23.

Incidence of prediabetes in participants

Table 2 and Figure 3 describe the incidence rates of prediabetes. Among the participants, 10,328 (8.24%) developed prediabetes. Participants were divided into subgroups based on the quartiles of the TyG index. The incidence rates of prediabetes per 1000 person-years were 14.426, 20.012, 27.066, and 44.490 for each TyG index quartile. The incidence rates of prediabetes in each TyG index quartile were as follows: Q1: 4.68%, Q2: 6.27%, Q3: 8.33%, and Q4: 13.69%. Participants with the highest TyG index (Q4) had a higher risk of developing prediabetes compared to those with the lowest TyG index (Q1) (trend p < 0.001).

Table 2

The Incidence rate of prediabetes (% or Per 1,000 person-year).

TyG index (quartile)	Participants (n)	Prediabetes events (n)	Incidence rate (95%CI) (%)	Per 1,000 person-year
Total	125327	10328	8.24 (8.09–8.39)	26.355
Q1	31,328	1466	4.68 (4.45–4.91)	14.426
Q2	31,317	1962	6.27 (6.00–6.53)	20.012
Q3	31,349	2611	8.33 (8.02–8.64)	27.066
Q4	31,332	4289	13.69 (13.31–14.07)	44.490
P for trend				<0.001

An external file that holds a picture, illustration, etc.
Object name is fendo-15-1414402-g003.jpg

Kaplan–Meier event-free survival curve. Kaplan–Meier analysis of incident prediabetes based on two group (log-rank, P < 0.0001).

Multivariable analysis using Cox proportional hazards regression model

In Table 3 , the relationship between the TyG index and the risk of prediabetes is presented across different models. The results are as follows: In the crude model, the HR for the TyG index was 2.17 (95%CI 2.11–2.24, P < 0.0001). After adjusting for age and sex (Model I), the HR decreased to 1.94 (95%CI 1.88–2.00, P < 0.0001). Further adjustments in Model II, which included age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status, resulted in an HR of 1.81 (95%CI 1.54–2.13, P < 0.0001).

Table 3

Relationship between TyG index and risk of prediabetes in different models.

Exposure	Crude model (HR,95%CI) P	Model I (HR,95%CI) P	Model II (HR,95%CI) P	Model III (HR,95%CI) P
TyG index	2.17 (2.11, 2.24) <0.0001	1.94 (1.88, 2.00) <0.0001	1.81 (1.54, 2.13) <0.0001	1.86 (1.57, 2.19) <0.0001
(TyG index quartiles)
Q1	Ref	Ref	Ref	Ref
Q2	1.49 (1.39, 1.59) <0.0001	1.40 (1.31, 1.50) <0.0001	1.29 (0.95, 1.75) 0.1065	1.30 (0.95, 1.77) 0.0998
Q3	2.11 (1.98, 2.25) <0.0001	1.86 (1.74, 1.99) <0.0001	1.75 (1.30, 2.34) 0.0002	1.81 (1.35, 2.44) <0.0001
Q4	3.56 (3.36, 3.78) <0.0001	2.90 (2.72, 3.09) <0.0001	2.33 (1.72, 3.16) <0.0001	2.44 (1.78, 3.34) <0.0001
P for trend	<0.0001	<0.0001	<0.0001	<0.0001

Crude model: we did not adjust other covariates.

Model I: we adjusted age, sex.

Model II: we adjusted age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status.

Model III: we adjusted age(smooth), sex, SBP (smooth), DBP (smooth), BMI (smooth), BUN (smooth), Scr (smooth), ALT (smooth), AST (smooth), TC (smooth), LDL-C(smooth), HDL-c(smooth), smoking status, drinking status, family history of diabetes.

HR, Hazard ratios; CI, confidence, Ref, reference.

After adjustments, similar trends were observed across all quartiles, indicating a significant association between the TyG index quartiles and the risk of prediabetes (P for trend < 0.0001 for all models).

Sensitivity analysis

We performed a series of sensitivity analyses to confirm the reliability of our conclusions. Initially, the generalized additive model (GAM) in Model III, which included additional smooth terms for various variables, showed an HR of 1.86 (95%CI 1.57–2.19, P < 0.0001) ( Table 3 ). Secondly, participants with a BMI ≥ 28 kg/m 2 were excluded ( 32 ). After controlling for confounding variables, the results demonstrated a persistent positive association between the TyG index and the risk of prediabetes (HR= 1.82, 95% CI: 1.52–2.17, p < 0.0001). Furthermore, we performed a sensitivity analysis excluding patients aged ≥ 40 years. After adjusting for confounding variables, the results continued to demonstrate a positive association between the TyG index and the risk of prediabetes (HR = 1.99, 95% CI: 1.64–2.42, p < 0.0001). Additionally, we conducted an analysis on participants without a family history of diabetes, revealing a risk ratio of 1.83 (95% confidence interval: 1.54–2.16, p < 0.0001). Based on all sensitivity analyses, we concluded that our findings are reliable ( Table 4 ).

Table 4

Relationship between TyG index and the risk of prediabetes in different sensitivity analyses.

Exposure	Model I (HR,95%CI) P	Model II (HR,95%CI) P	Model III (HR,95%CI) P
TyG index	1.82 (1.52, 2.17) <0.0001	1.99 (1.64, 2.42) <0.0001	1.83 (1.54, 2.16) <0.0001
(TyG index quartiles)
Q1	Ref	Ref	Ref
Q2	1.33 (0.97, 1.82) 0.0792	1.18 (0.84, 1.65) 0.3502	1.21 (0.88, 1.67) 0.2386
Q3	1.77 (1.31, 2.40) 0.0002	1.59 (1.15, 2.20) 0.0053	1.67 (1.23, 2.26) 0.0010
Q4	2.39 (1.73, 3.29) <0.0001	2.14 (1.52, 3.01) <0.0001	2.25 (1.64, 3.09) <0.0001
P for trend	<0.0001	<0.0001	<0.0001

Model I was a sensitivity analysis performed after excluding participants with BMI≥ 28 kg/m 2 (N= 8,595). we adjusted age, sex, BMI, SBP, DBP, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status.

Model II was a sensitivity analysis performed after excluding participants with age≥ 40 years (N= 25,255). we adjusted age, sex, BMI, SBP, DBP, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status.

Model III was a sensitivity analysis performed on participants without family of diabetes (N= 2,603). We adjusted age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, drinking status, and smoking status. HR, Hazard ratios; CI, confidence, Ref, reference.

Results of the two-piece Cox proportional hazards regression model

We identified a non-linear relationship between the TyG index and the risk of prediabetes ( Figure 4 and Table 5 ). Firstly, we used a Cox proportional hazards regression model with cubic spline functions to evaluate the relationship between the TyG index and prediabetes risk. The result showed that the relationship between the TyG index and prediabetes risk was non-linear. We employed a two-piecewise Cox proportional hazards regression model to investigate the relationship between the TyG index and the risk of developing prediabetes. The standard Cox regression model revealed a HR of 1.81 (95% CI: 1.54, 2.13) with a P value of <0.0001, indicating a significant association between the TyG index and prediabetes risk. In addition, we identified an inflection point in the TyG index at 9.39. Below this inflection point (<9.39), there is a significant positive correlation between the TyG index and the risk of prediabetes (HR: 2.04, 95% CI: 1.69, 2.46, P <0.0001). Conversely, Conversely, when the TyG index is 9.39 or higher, their relationship is not statistically significant (HR: 0.89, 95% CI: 0.48, 1.65, P=0.7019).

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The non-linear relationship between TyG index and the risk of prediabetes in participants. We used a Cox proportional hazards regression model with cubic spline functions to evaluate the relationship between the TyG index and prediabetes risk. We adjusted age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status. The result showed that the relationship between the TyG index and prediabetes risk was non-linear, with the inflection point of TyG index being 9.39.

Table 5

The result of the two-piecewise Cox proportional hazards regression model.

Outcome: prediabetes	HR, 95% CI	P-value
Fitting model by standard Cox regression	1.81 (1.54, 2.13)	<0.0001
Fitting model by two-piecewise Cox regression
Inflection points of TyG index	9.39
<9.39	2.04 (1.69, 2.46)	<0.0001
≥9.39	0.89 (0.48, 1.65)	0.7019
P for log likelihood ratio test	0.011

We adjusted age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status.

Subgroup analysis results

As illustrated in Table 6 , a detailed subgroup analysis was conducted. Gender, age, BMI, systolic and diastolic blood pressures, smoking and drinking habits, and a family history of diabetes did not alter the association between the TyG index and the risk of prediabetes. Thus, no significant interactions were observed between these variables and the TyG index (all interaction P > 0.05).

Table 6

Effect size of TyG index on prediabetes in prespecified and exploratory subgroups.

Variable	HR (95% CI)	P-value	P for interaction
			0.4260
20–30	2.14 (1.40, 3.25)	0.0004
30–40	1.90 (1.57, 2.30)	<0.0001
>40	1.66 (1.32, 2.08)	<0.0001
			0.7626
<18	2.73 (0.55, 13.53)	0.2186
18–24	1.95 (1.56, 2.42)	<0.0001
24–28	1.71 (1.38, 2.14)	<0.0001
≥28	1.95 (1.39, 2.76)	0.0001
			0.1758
Male	1.74 (1.46, 2.06)	<0.0001
Female	2.19 (1.59, 3.03)	<0.0001
			0.660
<140	1.79 (1.52, 2.12)	<0.0001
≥140	2.00 (1.25, 3.20)	0.0040
			0.3484
<90	1.79 (1.52, 2.11)	<0.0001
≥90	2.22 (1.42, 3.47)	0.0005
			0.3930
Current drinker	2.93 (1.20, 7.14)	0.0183
Ever drinker	1.66 (1.30, 2.12)	<0.0001
Never	1.87 (1.55, 2.24)	<0.0001
			0.2937
Current smoker	1.58 (1.22, 2.06)	0.0006
Ever smoker	2.38 (1.43, 3.96)	0.0008
Never	1.86 (1.55, 2.23)	<0.0001
			0.2497
Yes	1.86 (1.57, 2.19)	<0.0001
No	1.49 (1.03, 2.16)	0.0360

Above model adjusted for age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status. In each case, the model is not adjusted for the stratification variable when the stratification variable was a categorical variable.

This retrospective cohort study revealed that a higher TyG index independently predicts an increased risk of prediabetes in Chinese adults aged 20–45. Furthermore, a nonlinear relationship was observed, indicating that the risk of prediabetes tends to rise with an increasing TyG index. Individuals in the highest TyG index quartile had a 2.33-fold increased risk of developing prediabetes compared to those in the lowest quartile. Additionally, a threshold effect curve was identified, revealing varying relationships between the TyG index and prediabetes risk across the inflection point.

Initially, the TyG index has been widely validated as an indicator of IR in various epidemiological studies. A cross-sectional study conducted in Mexico involving 99 individuals identified the TyG index as an optimal tool for assessing IR, showing a high sensitivity (96.5%) and good specificity (85.0%) compared to the gold standard, HIEC ( 23 ). Research involving 82 Brazilian subjects corroborated the TyG index as a more precise predictor of IR compared to HOMA-IR ( 33 ). Numerous studies have indicated an association between the TyG index and cardiovascular disease (CVD) risk ( 34 – 37 ). A study involving 4,340 American patients under the age of 65 with prediabetes or diabetes found that an increase in the TyG index is associated with a higher incidence of CVD, including congestive heart failure (CHF), coronary heart disease (CHD), atherosclerotic cardiovascular disease (ASCVD), heart attack, angina, and stroke ( 34 ). Additionally, several studies have found a close relationship between the TyG index and coronary artery disease ( 35 – 37 ). Furthermore, recent studies have identified a significant correlation between the TyG index and both cognitive and physical impairments in elderly individuals with prefrail hypertension ( 38 ).

Recently, there has been a growing body of research exploring the relationship between the TyG index and the risk of diabetes ( 39 – 42 ). A prospective cohort study conducted in rural China suggests that an increasing TyG index is associated with a higher cumulative risk of developing incident T2DM among individuals with normal weight ( 43 ). Another retrospective cohort study demonstrated a link between elevated TyG index levels and an augmented risk of diabetes within the Chinese population ( 44 ). Additionally, recent studies have indicated that there is a positive correlation between the TyG index and the risk of diabetes ( 45 ). It is noteworthy that prediabetes is a critical period for promoting, preventing, or delaying the development of diabetes mellitus (DM).

But research on the TyG index’s association with prediabetes is limited. An initial study demonstrated that the TyG index is comparable to HbA1C as a diagnostic marker for prediabetes ( 46 ). Findings from a case-control study conducted in Palembang, Indonesia, involving 570 participants without a family history of T2DM (265 prediabetes cases and an equal number of age-matched controls), indicated that the TyG index was the primary risk predictor for prediabetes ( 47 ). A prospective cohort study in China indicated that the predictive capacity of the TyG index in forecasting prediabetes surpassed that of obesity, lipid profiles, and other non-insulin-based IR indices ( 48 ). A cross-sectional study conducted in the United States, involving 25,159 participants, found that 23.88% had prediabetes and 16.22% were diagnosed with diabetes. The study revealed a positive relationship between the TyG index and the prevalence of prediabetes and diabetes ( 49 ). Interestingly, a cohort study conducted in China indicated that, among prediabetic patients, an elevated TyG-BMI might reduce the likelihood of returning to normal glucose levels in the future ( 50 ). However, no specific studies have investigated the association between TyG index and prediabetes in a young population.

Therefore, we hypothesize that the TyG index is positively correlated with the risk of prediabetes in young populations. Therefore, our study investigates the TyG index’s role in identifying prediabetes risk factors among young individuals, aiming to enhance prevention strategies for diabetes onset and progression within this demographic. Initially, our findings indicate the TyG index as a reliable predictor of prediabetes and an independent risk factor, consistent with existing research [45–46]. Furthermore, our study has shown a positive correlation between the TyG index and prediabetes, consistent with our research hypothesis. That is for every 1-unit increase in the TyG index, the risk of prediabetes will increase by 81% (HR: 1.81, 95% CI 1.54–2.13, P < 0.0001). Similarly, a cohort study conducted in China also identified a positive correlation between TyG and the risk of prediabetes after adjusting for variables ( 48 ). However, there are significant differences between the two studies in terms of subjects, outcomes, and adjusted variables. Their study sample is relatively small compared to ours. They also adjusted for different variables, such as age, cigarette smoking, alcohol consumption, education level, family history of diabetes, hypertension, and cardiovascular disease history, which differs slightly from our study. Additionally, they did not use the Cox proportional hazards regression model combined with cubic splines and smoothing curve fits to examine the nonlinear association between the TyG index and the risk of prediabetes. A study in the United States also found a nonlinear relationship between the TyG index and the prevalence of prediabetes and diabetes, identifying an inflection point at 8 ( 49 ), which is very similar to our conclusion but with some notable differences. Firstly, their study was cross-sectional, which has limitations such as a smaller sample size, unclear causality, and selection bias. Secondly, there are differences in the age distribution of the study populations between the two studies. Additionally, variations in the adjusted variables between the two studies may impact the research outcomes. Although both studies confirm a nonlinear relationship, we identified a critical threshold of 9.39. Our study focused on prediabetes as the outcome, while their study included both prediabetes and diabetes, which could be the primary reason for the earlier inflection point in their findings.

Our study has significant clinical implications. We have identified a non-linear relationship with a saturation effect between the TyG index and the risk of prediabetes in young individuals. Monitoring TyG index values can facilitate early interventions in young individuals to decrease the occurrence of prediabetes. Specifically, when the TyG index is below 9.39, by lowering FBG and TG, we can significantly reduce the risk of prediabetes. However, when the TyG index exceeds 9.39, merely controlling FBG and TG is insufficient to reduce the risk of prediabetes. Thus, it is necessary to manage other related risk factors such as BMI, smoking, and alcohol consumption to better prevent the onset of the disease. Consequently, it is advisable for young people to start making lifestyle changes early, including reducing the intake of high-fat foods, increasing physical activity, and controlling blood sugar levels. These measures can effectively help reduce the risk of developing prediabetes.

This study has some advantages. Firstly, we first identified a nonlinear relationship between the TyG index and prediabetes risk in young individuals. Furthermore, this study included a large sample of 125,327 young adults, minimizing potential biases. Sensitivity analysis was conducted to ensure the reliability and robustness of the results, and a two-stage Cox proportional hazards regression model was applied to determine the inflection point of the TyG index. These findings underscore the critical threshold between the TyG index and diabetes risk, emphasizing the importance of TyG index monitoring and early intervention strategies in diabetes prevention efforts. Additionally, subgroup analysis and interaction tests were performed. The results revealed variations in the impact of the TyG index on prediabetes risk across different subgroups, yet interaction analysis did not indicate significant interactions between the TyG index and factors such as drinking status, smoking status, or family history of diabetes. This further validates the stability of the results.

This study has several limitations. Firstly, as all participants were of Chinese descent, further research is needed to determine the relationship between the TyG index and the risk of prediabetes in individuals with different genetic backgrounds. Secondly, since this large-scale cohort study only focused on young adults in China, these findings may not be applicable to other racial groups and specific populations, such as children and the elderly. Thirdly, the average follow-up time for participants was relatively short. Future research could benefit from extending follow-up duration to mitigate potential misinterpretations or chance findings from short-term observations. Fourthly, there was no data available on serum insulin levels to compare the predictive value of the TyG index and HOMA-IR; additionally, there was also no data on the use of lipid-lowering medications, which could potentially impact the results. Fively, this report represents a secondary analysis of existing databases. Although adjustments were made for many confounding factors, variables not included in the database were not adjusted for. However, we calculated the E value to quantify the potential impact of unmeasured confounders and found that unmeasured confounders were unlikely to explain the results. sixly, The current diagnostic criteria for prediabetes have some limitations, primarily relying on impaired fasting glucose and not considering oral glucose tolerance tests, glycated hemoglobin, and multiple fasting glucose measurements. This single-index approach may lead to underdiagnosis of prediabetes. Therefore, future studies evaluating the incidence of prediabetes should consider measuring additional variables, including oral glucose tolerance tests, glycated hemoglobin, and multiple fasting glucose measurements, to improve diagnostic accuracy and comprehensiveness. Additionally, this retrospective observational study provides an associative inference rather than establishing a causal relationship between the TyG index and the risk of prediabetes. Lastly, this study only measured the TyG index and other parameters at baseline and did not consider changes in the TyG index over time. Therefore, in the future, we should collect as much information as possible, including information on changes in the TyG index.

This study reveals a nonlinear relationship and a saturation effect between the TyG index and the risk of prediabetes among individuals aged 20 to 45 in China. There is a significant positive correlation between the TyG index and the risk of prediabetes to the left of the inflection point at a TyG index of 9.39. Reducing FBG and TG levels can significantly decrease the risk of prediabetes. Conversely, when the TyG index reaches or exceeds 9.39, its relationship with the risk of prediabetes is no longer statistically significant; merely lowering the TyG index is not enough to reduce the risk of prediabetes. It is also necessary to comprehensively control other factors such as BMI, smoking, and alcohol consumption. Understanding this non-linear relationship can help clinicians identify high-risk young individuals and implement focused interventions to reduce the risk of developing diabetes.

Funding Statement

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The Guangdong Provincial Medical Science and Technology Research Fund project (NO: A2024429).

Data availability statement

Ethics statement.

The studies involving humans were approved by The Rich Healthcare Group Review Board. The ethics committee waived the need for written informed consent. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

JX: Data curation, Writing – original draft, Writing – review & editing. LZ: Formal analysis, Writing – original draft, Writing – review & editing. CL: Data curation, Formal analysis, Writing – review & editing. YH: Formal analysis, Writing – review & editing. ZH: Formal analysis, Writing – original draft, Writing – review & editing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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