Evidence map›Paper›PMID 42861867›Full record

ArticleDiabetes/metabolism research and reviews2026

Longitudinal Sleep Patterns and Risk of Type 2 Diabetes Mellitus.

Ying Zhang, Zhen'ge Chang, Shuang Zhang, Yingying Guo, Qian Guo, Feiyu Zhang, Yating Chen, Fang Luo, Kefei Dou, Caixia Guo

Abstract read
In one paragraph

Article in Diabetes/metabolism research and reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Ying ZhangCardiovascular Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Zhen'ge ChangCardiometabolic Medicine Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-2099-0887
Shuang ZhangCardiometabolic Medicine Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Yingying GuoCardiometabolic Medicine Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Qian GuoCenter for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-8377-2633
Feiyu ZhangCenter for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0009-0000-6697-3698
Yating ChenCenter for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Fang LuoCardiometabolic Medicine Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Kefei DouCardiometabolic Medicine Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-8372-6338
Caixia GuoCardiovascular Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0003-2313-7731

Funding

Beijing Municipal Natural Science Foundation JQ24039CAMS Innovation Fund for Medical Sciences 2024-I2M-3-020Noncommunicable Chronic Diseases-National Science and Technology Major Project 2025ZD0548201
6 · The paper itself

Abstract

aimsEvidence relating sleep characteristics to type 2 diabetes mellitus (T2DM) has largely been based on single assessments. We examined whether transitions in a multidimensional healthy sleep score between two assessments were associated with incident T2DM and explored the relationship of the triglyceride-glucose (TyG) index to this association. MATERIALS AND

methodsWe conducted a landmark analysis among 60,249 UK Biobank participants with sleep assessed at visits 1 and 2. Visit 2 was defined as time zero for follow-up. Sleep pattern transitions were classified according to poor, intermediate, or healthy sleep status at each assessment. Cox proportional hazards models were used to estimate associations with incident T2DM. TyG analysis was treated as exploratory.

resultsDuring a median follow-up of 4.2 years, 1284 incident T2DM cases occurred. Compared with stable healthy sleep, stable poor sleep was associated with higher risk (hazard ratio [HR], 2.07; 95% confidence interval [CI], 1.32-3.27). Baseline-stratified analyses did not show higher risk for improvement from poor or intermediate sleep relative to remaining in the same baseline category. The full-cohort spline showed evidence of nonlinearity (p for nonlinearity < 0.001), and TyG analyses were exploratory.

conclusionsSleep pattern transitions between the two assessments were associated with incident type 2 diabetes, and the association differed by baseline sleep status. Repeated sleep assessment may complement single-time-point evaluation in understanding the role of sleep in diabetes risk.

Indexed as

BiomarkersDiabetes Mellitus, Type 2SleepSleep Wake DisordersAgedBlood GlucoseFemaleFollow-Up StudiesHumansIncidenceLongitudinal StudiesMaleMiddle AgedPrognosisRisk FactorsSleep DurationBiomarkersBlood GlucoseTriglyceridespublic healthrepeated assessmentsleep pattern transitiontype 2 diabetes mellitus

Identifiers

PMID42861867
PMCPMC13656764

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.