Evidence mapPaperPMID 41604412Full record

ArticlePloS one2026

The relationship between METS-IR and the risk of diabetes incidence in rural adults in China: A retrospective cohort study based on dynamic population.

Zihao Li, Xuejiao Chen, Wanli Hu, Gefei Li, Xiaoke Zhang, Datian Gao, Haiyun Gao, Songhe Shi

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Article in PloS one, 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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8 authors.

Zihao LiDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Xuejiao ChenDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Wanli HuDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Gefei LiDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Xiaoke ZhangDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Datian GaoDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Haiyun GaoDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Songhe ShiDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.ORCID https://orcid.org/0000-0002-2214-1440

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No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the longitudinal association between the Metabolic Score for Insulin Resistance (METS-IR) and the risk of diabetes mellitus in rural Chinese adults.

methodsThis retrospective cohort study included 53,120 participants aged ≥18 years from 2018 to 2023. Participants were stratified by quartiles of the METS-IR metrics. Cox proportional hazards models assessed the association between METS-IR and incident diabetes. Restricted cubic spline (RCS) models examined nonlinear trends. Subgroup analysis, interaction tests, and multiple sensitivity analyses were performed. Predictive ability was evaluated using time-dependent receiver operating characteristic (ROC) curves.

resultsDuring 176,413.4 person-years of follow-up (median 3.83 years), 14,397 participants developed diabetes. After multifactorial adjustment, METS-IR was significantly and positively associated with diabetes onset (hazard ratio (HR)=1.094,95% confidence interval (CI): 1.076-1.112, P < 0.001); those in the highest quartile group had a 1.435-fold higher risk compared to the lowest. RCS analysis revealed a nonlinear dose-response relationship. Kaplan-Meier curves confirmed increasing cumulative risk with higher METS-IR. Results remained robust across subgroups and sensitivity analyses. The area under the curve (AUC) for METS-IR predicting diabetes was 0.601 (1 year), 0.586 (3 years), and 0.599 (5 years).

conclusionMETS-IR is significantly correlated with the onset of diabetes, and the relationship is nonlinear. While it demonstrates limited discriminatory performance as a standalone screening tool, it remains suitable for initial risk stratification in primary health care institutions with limited resources.

Indexed as

Diabetes MellitusInsulin ResistanceAdultChinaFemaleHumansIncidenceMaleMiddle AgedProportional Hazards ModelsRetrospective StudiesRisk FactorsRural Population

Identifiers

PMID41604412
PMCPMC12851496

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