Evidence mapPaperPMID 40731411Full record

ArticleDiabetology & metabolic syndrome2025

From the perspective of dynamic changes in BMI: the relationship between BMI trajectories and dysglycemia, all-cause mortality.

Buyu Guo, Yichen Yang, Na Wang, Yue Zhang, Caihong Jiao, Li Wang, Yanan Yan, Songbo Fu

Abstract read
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Article in Diabetology & metabolic syndrome, 2025. 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

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Buyu Guo *The First Clinical Medical College, Lanzhou University, Lanzhou, 730000, China.
Yichen Yang *The First Clinical Medical College, Lanzhou University, Lanzhou, 730000, China.
Na WangThe First Clinical Medical College, Lanzhou University, Lanzhou, 730000, China.
Yue ZhangThe First Clinical Medical College, Lanzhou University, Lanzhou, 730000, China.
Caihong JiaoDepartment of Endocrinology, Longnan People's Hospital, Longnan, Gansu, 746000, China.
Li WangDepartment of Geratology, The First People's Hospital of Lanzhou City, Lanzhou, Gansu, 730050, China.
Yanan YanDepartment of Geratology, The First People's Hospital of Lanzhou City, Lanzhou, Gansu, 730050, China.
Songbo FuGansu Provincial Endocrine Disease Clinical Medicine Research Center, Lanzhou, 730000, China. ery_fusp@lzu.edu.cn.

Funding

the Gansu Province Joint Research Foundation 23JRRA1490the Gansu Province Natural Science Foundation 22JR5RA914the Lanzhou Municipal Science and Technology Development Guidance Project 2019-ZD-38the National Key R&D Program of China 2023YFC3503400
6 · The paper itself

Abstract

objectivesThe aim was to explore the body mass index (BMI) trajectory over dynamic time and its relationship with the dysglycemia (including Type 2 Diabetes Mellitus and prediabetes), all-cause mortality, and insulin resistance.

methodsThe latent category trajectory model (LCTM) is used to identify the BMI trajectories. Logistic and Cox regression were fitted to assess the correlation between BMI trajectories/weight changes and Type 2 Diabetes Mellitus (T2DM)/prediabetes. Using linear regression to evaluate the correlation between the two and insulin resistance. Mediating role of inflammation was evaluated.

resultsFour BMI trajectories were identified, including "stable" (74.32%), "light increase" (17.18%), "rapid increase" (2.82%), and "increase-to-decrease" (5.67%). Compared to stable trajectories, participants with slight increase or increase-to-decrease trajectories had higher risks of T2DM, while participats with a slight increase or rapid increase trajectory had a higher probability of developing prediabetes. Early weight changes such as decrease, increase, overweight, or obesity were associated with higher T2DM, while recent weight changes like decrease or obesity-stable only impacted T2DM prevalence, and there were no significant associations for prediabetes.

conclusionThe findings underscore the critical impact of BMI trajectories and early/recent weight changes on T2DM and mortality risk.

Indexed as

BMI trajectoryPrediabetesRisk factorsType 2 diabetes

Identifiers

PMID40731411
PMCPMC12306124

What Socratic holds

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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 Socratic graph.