Annals of Epidemiology最新文献

{"title":"Campus climate and intersectional inequities in eating disorders among U.S. college students: A multilevel analysis of individual heterogeneity and discriminatory accuracy","authors":"Ariel L. Beccia ,&nbsp;Vivienne M. Hazzard ,&nbsp;Rachel F. Rodgers ,&nbsp;Dougie Zubizarreta ,&nbsp;Lauren M. Schaefer ,&nbsp;Natasha L. Burke","doi":"10.1016/j.annepidem.2025.09.014","DOIUrl":"10.1016/j.annepidem.2025.09.014","url":null,"abstract":"<div><h3>Purpose</h3><div>To advance understanding of how contextual factors explain eating disorder (ED) inequities among college students, we examined associations between campus climate – i.e., the extent to which a given school is hostile vs. friendly to students of diverse social/cultural backgrounds – and ED prevalence across intersections of gender, sexual, and racialized identity.</div></div><div><h3>Method</h3><div>Cross-sectional data came from 15,544 students at colleges/universities that participated in the 2018/2019 Healthy Minds Study. We conducted a Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) by grouping participants into 35 intersectional social strata defined by gender, sexual, and racialized identity and fitting multilevel models to obtain stratum-specific prevalence estimates of probable EDs across the range of campus climate ratings (1 = “very hostile” to 5 = “very friendly”).</div></div><div><h3>Results</h3><div>Campus climate was inversely associated with probable EDs; specifically, for every 1-unit increase in ratings (i.e., more friendly climates), odds decreased by 8 %. There were differences in the magnitude of this association across strata, such that multiply marginalized students experienced the largest benefits from attending “very friendly” campuses, and especially those who were cisgender women and/or LGBQ+.</div></div><div><h3>Conclusions</h3><div>Results reveal a complex social patterning of EDs among college students across campus climate ratings and provide preliminary evidence suggesting that hostile campus climates may function as a driver of intersectional inequities in this population.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 94-101"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “The association of preterm birth and small for gestational age with recurrent multisite musculoskeletal pain during early and middle adulthood — The Northern Finland Birth Cohort 1966 Study” [Ann Epidemiol 111 (2025) 9979]","authors":"Sandra-Sofia Nieminen ,&nbsp;Jaro Karppinen ,&nbsp;Eero Kajantie ,&nbsp;Paulo Ferreira ,&nbsp;Eveliina Heikkala","doi":"10.1016/j.annepidem.2025.09.023","DOIUrl":"10.1016/j.annepidem.2025.09.023","url":null,"abstract":"","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Page 116"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic conditions, disability, and COVID-19 testing and vaccination: A national Rapid Acceleration of Diagnostics‐Underserved Populations analysis","authors":"Haley R. Fonseca ,&nbsp;Elizabeth Lydon ,&nbsp;Troy A. Stefano ,&nbsp;Eileen Fluney ,&nbsp;Lisa Wruck ,&nbsp;Susanna Stevens ,&nbsp;Krista M. Perreira ,&nbsp;David R. Brown ,&nbsp;Wensong Wu ,&nbsp;Marianna K. Baum","doi":"10.1016/j.annepidem.2025.09.019","DOIUrl":"10.1016/j.annepidem.2025.09.019","url":null,"abstract":"<div><h3>Purpose</h3><div>We investigated the impact of chronic conditions on COVID-19 testing, vaccination, and related challenges, with a focus on the interaction effect of disability.</div></div><div><h3>Methods</h3><div>This cross-sectional, cross-consortium analysis was conducted as part of the National Institutes of Health Rapid Acceleration of Diagnostics-Underserved Population (RADx-UP) initiative. Data were self-reported via standardized RADx-UP common data elements. Multivariable generalized estimating equation models with a logit link adjusted for sociodemographic variables, health insurance, health status, housing, and United States region were utilized.</div></div><div><h3>Results</h3><div>Participants were from 28 states (n = 8813), enrolled between February 2021-March 2022 with a mean age of 49 years, 60.4 % female, 30.8 % Hispanic, and 25.5 % Black, non-Hispanic. Over 30 % were living with three or more chronic conditions and 22.1 % reported some type of disability. Odds of COVID-19 testing (aOR:1.95; 95 %CI:1.75, 2.17), vaccination (aOR:1.63; 95 %CI:1.31, 2.03), food insecurity (aOR:1.43; 95 %CI:1.21, 1.68), housing insecurity (aOR:1.42; 95 %CI:1.10, 1.82), healthcare access challenges (aOR:1.60; 95 %CI:1.38, 1.86) and transportation challenges (aOR:1.48; 95 %CI:1.21, 1.81) increased as number of chronic conditions increased. The effect of chronic conditions on probability of COVID-19 testing (p = 0.157) and vaccination (p = 0.147) did not differ by disability, but the effect on probability of experiencing COVID-19-related challenges did differ by disability (p &lt; 0.001). For those with functional and employment disability, the more chronic conditions one had, the more likely they were to experience food insecurity (aOR:1.94; 95 %CI:1.33, 2.82) and issues accessing healthcare (aOR:2.21; 95 %CI:1.19, 4.14) and transportation (aOR:2.33; 95 %CI:1.11, 4.89).</div></div><div><h3>Conclusions</h3><div>Testing and vaccination sites may have been accessible to various populations and/or adults with chronic conditions may have had heightened awareness of potential vulnerability to COVID-19, which could have led to similar testing and vaccination behaviors across different disability statuses. However, disability may still exacerbate daily-life challenges in those living with chronic conditions during public health crises.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 107-115"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lung cancer mortality attributable to smoking: a multi-scenario analysis with variable lag periods","authors":"María Isolina Santiago-Pérez ,&nbsp;Carla Guerra-Tort ,&nbsp;Esther López-Vizcaíno ,&nbsp;Lucía Martín-Gisbert ,&nbsp;Ana Teijeiro ,&nbsp;Guadalupe García ,&nbsp;Julia Rey-Brandariz ,&nbsp;Alberto Ruano-Ravina ,&nbsp;Mónica Pérez-Ríos","doi":"10.1016/j.annepidem.2025.10.011","DOIUrl":"10.1016/j.annepidem.2025.10.011","url":null,"abstract":"<div><h3>Purpose</h3><div>The estimation of smoking-attributable mortality (SAM) is subject to the acceptance of different assumptions that may influence the estimates. We aimed to assess lung cancer mortality attributable to smoking by using both a prevalence-independent method (PIM) and a prevalence-dependent method (PDM) with different lags between exposure (smoking prevalence) and outcome (lung cancer mortality).</div></div><div><h3>Methods</h3><div>We estimated the population attributable fractions (PAF) and the lung cancer SAM by sex and age group (35–64, 65–84 years), year-by-year from 2011 to 2020, in four scenarios in Spain. In three of these scenarios, a PDM was applied using different lags: no lag, a 15-year lag and a 20-year lag. In the fourth scenario, a PIM was applied.</div></div><div><h3>Results</h3><div>In the period 2011–2020 in Spain, the SAM was higher when the 20-year lag PDM was considered (173,526 deaths) and lower when no lag PDM or a PIM was applied (161,249 and 157,390 deaths, respectively). In men, the PAFs were similar between the no lag PDM and the PIM (86.7 % and 87.3 %, respectively). However, when a PDM 15-year or 20-year lag was considered, the PAF increased to 91.0 % and 92.3 %, respectively. In women, the lowest PAF was obtained with the PIM (57.3 %), and the highest with the PDM 20-year lag (79.4 %).</div></div><div><h3>Conclusions</h3><div>SAM estimates differ depending on the methods and lags used. Applying a 15-year or 20-year lag PDM yields higher SAM estimates than when no lag PDM or a PIM is used. Therefore, when feasible, smoking prevalence data that incorporate a lag of 15 or 20 years between exposure and result should be used for accurate estimates.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 175-179"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between adverse childhood experiences and obesity among young US adults","authors":"Kiran Thapa PhD ,&nbsp;Ye Shen PhD ,&nbsp;José F. Cordero MD, MPH ,&nbsp;Emily Anne Vall PhD ,&nbsp;Janani Rajbhandari-Thapa PhD","doi":"10.1016/j.annepidem.2025.09.009","DOIUrl":"10.1016/j.annepidem.2025.09.009","url":null,"abstract":"<div><h3>Purpose</h3><div>We examined whether adverse childhood experiences (ACEs) are associated with obesity in young adulthood, and whether these associations differ by sex.</div></div><div><h3>Methods</h3><div>We used data from the National Longitudinal Study of Adolescent to Adult Health, a nationally representative cohort of U.S. adolescents followed into adulthood (age 33–43 years) across five waves. Our sample included 5193 participants with measured anthropometrics at wave V (2016–18). Modified Poisson regression estimated risk ratios (RR) for general obesity (body mass index ≥ 30 kg/m²) and abdominal obesity (waist circumference &gt;102 cm for males, &gt;88 cm for females) associated with individual and cumulative ACEs, adjusting for baseline BMI, co-occurring ACEs, and sociodemographic covariates. Sex-stratified models assessed heterogeneity in effects.</div></div><div><h3>Results</h3><div>Childhood physical abuse was independently associated with higher risk of general obesity, particularly among females (aRR: 1.23; 95 % CI: 1.05–1.45). Exposure to ≥ 4 ACEs was associated with increased risk of both general (aRR: 1.32; 95 % CI: 1.15–1.52) and abdominal obesity (aRR: 1.18; 95 % CI: 1.02–1.37), independent of childhood obesity.</div></div><div><h3>Conclusions</h3><div>ACEs, especially physical abuse and cumulative exposure, were linked to higher risk of obesity, suggesting that traumatic events may play an important role in young adulthood obesity, especially in females.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 51-57"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods for estimating public transit travel times to healthcare services as a measure of equitable healthcare access","authors":"Noah Mancuso ,&nbsp;Patrick S. Sullivan","doi":"10.1016/j.annepidem.2025.09.010","DOIUrl":"10.1016/j.annepidem.2025.09.010","url":null,"abstract":"<div><h3>Purpose</h3><div>To introduce an equity-based method for assessing public transit access to health services and apply it to pre-exposure prophylaxis (PrEP) clinics in metro-Atlanta.</div></div><div><h3>Methods</h3><div>Census block groups (CBGs) were analyzed with PrEP clinics identified via <em>PrEP Locator</em>. One-way public transit times were estimated using the <em>Google Maps Distance Matrix</em> API. CBGs were classified as public transit deserts if transit options were unavailable or if travel time was &gt; 30 min. T-tests compared sociodemographic characteristics of CBGs with and without public transit. Linear regression assessed the association of a 5 % increase in priority populations with transit times.</div></div><div><h3>Results</h3><div>Among 2466 CBGs, one-quarter lacked public transit access to PrEP and two-thirds were transit deserts. Median travel time was 32 min. CBGs with transit access had significantly higher proportions of Black, Hispanic/Latinx, young men (aged 25–34), and residents living below the poverty line (P &lt; .001). Increases in the proportion of Hispanic/Latinx residents, young men, and residents living under the poverty line were associated with shorter transit times, with no association for Black residents.</div></div><div><h3>Conclusions</h3><div>Public transit access to PrEP was low in Atlanta, and overall public transit times were long. Current PrEP locations are aligned with priority populations, but additional work is needed to ensure equity is met for Black and Hispanic/Latinx residents.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 24-29"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model","authors":"Hans Moen ,&nbsp;Vishnu Raj ,&nbsp;Andrius Vabalas ,&nbsp;Markus Perola ,&nbsp;Samuel Kaski ,&nbsp;Andrea Ganna ,&nbsp;Pekka Marttinen","doi":"10.1016/j.annepidem.2025.08.025","DOIUrl":"10.1016/j.annepidem.2025.08.025","url":null,"abstract":"<div><h3>Purpose:</h3><div>Health registers provide valuable insights into individuals’ health trajectories. This study explores the use of deep learning to model and analyze these trajectories using a nationwide longitudinal dataset containing coded features such as clinical codes, procedures, and drug purchases.</div></div><div><h3>Methods:</h3><div>We introduce <span>Evolve</span>, a transformer-based deep learning model designed to provide continuous multi-label predictions over time. The model predicts disease onsets at each time step conditioned on the health history up to that time step and the time until a given 5-year forecast window. <span>Evolve</span> is evaluated against several baseline models for basic prediction performance. Additionally, we analyze health trajectories by tracking changes in prediction probabilities and in the latent embedding neighborhood to identify important events.</div></div><div><h3>Results:</h3><div><span>Evolve</span> performed comparably to baseline models in disease onset prediction while offering unique trajectory modeling capabilities. The model identified early predictive events and demonstrated that changes in embedding space could indicate shifts in health trajectories. Visualization of evolving health trajectories showed how individuals may become most similar to others with similar profiles and outcomes over time.</div></div><div><h3>Conclusions:</h3><div>The <span>Evolve</span> model seems promising at enabling continuous health monitoring, early disease detection, and retrospective analysis, making it a promising tool for personalized healthcare interventions.</div><div>Code available at: <span><span>https://github.com/hansmoen/evolvehealth</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 30-43"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of machine learning and deep learning approaches for prediction modeling with time-to-event outcomes in clinical epidemiology. Methods comparison and practical considerations for generalizability and interpretability","authors":"Siona Prasad,&nbsp;Sabina A. Murphy,&nbsp;David A. Morrow,&nbsp;Benjamin S. Scirica,&nbsp;Marc S. Sabatine,&nbsp;David D. Berg,&nbsp;Andrea Bellavia","doi":"10.1016/j.annepidem.2025.10.012","DOIUrl":"10.1016/j.annepidem.2025.10.012","url":null,"abstract":"<div><h3>Purpose</h3><div>Clinical prediction models (CPM) are essential tools for diagnosis and prognosis in clinical epidemiology. Machine learning (ML) and deep learning (DL) approaches provide flexible methods that can complement regression-based methods for CPM when complex predictors such as clinical biomarkers are of interest. However, concerns have been raised on the ability of ML and DL to address desired properties of CPMs such as parsimony, generalizability, and interpretability.</div></div><div><h3>Methods</h3><div>In this study, we evaluated and applied selected regression-based, ML and DL approaches for time-to-event outcomes in a clinical study integrating protein biomarkers and lipids in an existing CPM for cardiovascular risk.</div></div><div><h3>Results</h3><div>We observed considerable advantages from the application of gradient boosting machines (GBM: C-statistic=0.72; Brier Score=0.052), which provided the best balance between model flexibility, discrimination, calibration, and parsimony, the latter being directly related to one of the model parameters (shrinking rate). Further, GBM results can be used for individual risk prediction, providing an interpretable tool for CPM implementation.</div></div><div><h3>Conclusions</h3><div>We compared ML and DL methods for CPM with time-to-event outcomes and discussed practical aspects of their implementation in clinical epidemiology including generalizability and interpretability. Adequately trained ML approaches can provide advantages in prediction modeling, especially when integrating complex predictors.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 186-192"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint spatial modelling of COVID-19 severity among seniors: A Bayesian shared component approach using health administrative data from Ontario, Canada","authors":"Nushrat Nazia,&nbsp;Charmaine Dean","doi":"10.1016/j.annepidem.2025.10.004","DOIUrl":"10.1016/j.annepidem.2025.10.004","url":null,"abstract":"<div><h3>Purpose</h3><div>Jointly monitoring adverse COVID-19 outcomes among seniors is critical for assessing outbreak severity. These outcomes are often influenced by socioeconomic and demographic conditions and may co-occur in space, indicating shared structural risks that inform targeted responses.</div></div><div><h3>Methods</h3><div>We analyzed severe COVID-19 outcomes among adults aged 65 + in Ontario (January 2020–March 2022) using data from the Ontario Health Data Platform supported by ICES. A Bayesian shared component model with Integrated Nested Laplace Approximation at the forward sortation area level included socioeconomic and demographic covariates.</div></div><div><h3>Results</h3><div>The shared component explained ∼75 % of the total modeled spatial variability. High risks clustered in southern Ontario, while lower risks occurred in central and northern regions. Material deprivation was positively associated with death (RR 1.12, 95 % CrI: 1.04–1.21) and multiple hospitalizations (RR 1.20, 95 % CrI: 1.13–1.29). Racialized/newcomer population concentration was positively associated with death (RR 1.25, 95 % CrI: 1.14–1.38) and with single hospitalizations (RR 1.18, 95 % CrI: 1.11–1.24). The percentage of seniors was inversely associated with hospitalization (RR 0.98, 95 % CrI: 0.96–0.99) but not death.</div></div><div><h3>Conclusions</h3><div>Findings highlight structural inequities in pandemic severity and suggest targeted, equity-oriented strategies in guiding pandemic preparedness and response.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 120-128"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal triglyceride and HDL cholesterol, but not LDL cholesterol associated with the risk of incident type 2 diabetes: Evidence from a multi-trajectory analysis","authors":"Xue Tian ,&nbsp;Shuohua Chen ,&nbsp;Xue Xia ,&nbsp;Qin Xu ,&nbsp;Shouling Wu ,&nbsp;Anxin Wang","doi":"10.1016/j.annepidem.2025.10.007","DOIUrl":"10.1016/j.annepidem.2025.10.007","url":null,"abstract":"<div><h3>Purpose</h3><div>There are inconsistent findings regarding the associations between lipids and type 2 diabetes mellitus (T2DM), partially due to ignoring the joint effects of longitudinal patterns in lipids simultaneously. This study aimed to investigate the association of joint multi-trajectory of different lipids with the risk of type 2 diabetes.</div></div><div><h3>Methods</h3><div>We enrolled 71,043 participants free of T2DM from the Kailuan study. Using group-based multi-trajectory modeling, joint multi-trajectory of triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) during 2006–2010 was developed to predict the risk of T2DM during 2010–2019.</div></div><div><h3>Results</h3><div>Five distinct multi-trajectory groups were identified over 4-year exposure, and 6473 (9.11 %) cases of incident T2DM occurred during a median follow-up of 8.97 years. The highest risk of T2DM was observed in Group 5 with the highest level of TG, optimal-increasing LDL, and high-increasing HDL-C (hazard ratio [HR], 2.14; 95 % confidence interval [CI], 1.89–2.41), followed by Group 3 with the lowest level of HDL-C and an optimal TG and LDL-C (HR, 1.39; 95 % CI, 1.11–1.43), and Group 4 with the highest level of LDL-C, optimal-increasing TG and high-increasing HDL-C (HR, 1,26; 95 % CI, 1.11–1.43), compared to Group 2 with the lowest level of TG and optimal-increasing LDL-C and high-increasing HDL-C. The observed associations existed regardless of baseline lipid levels.</div></div><div><h3>Conclusion</h3><div>Our results showed the important role of high-increasing TG and low-decreasing HDL-C, rather than high-increasing LDL-C in the development of T2DM, which would help better understand the heterogeneous risk of T2DM and facilitate targeted prevention programs.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 138-145"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}