Evidence map›Paper›PMID 41832572›Full record

ArticleNutrition & metabolism2026

Assessment of insulin resistance indexes with longitudinal trajectories of cardiometabolic multimorbidity: insights from the UK Biobank.

Yingdong Han, Juan Wu, Menghui Yao, Zhikai Li, Tiange Xie, Yun Zhang, Xuejun Zeng

Abstract read
In one paragraph

Article in Nutrition & metabolism, 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

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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

7 authors.

Yingdong Han *Department of Family Medicine & Division of General Internal Medicine, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Beijing, 100730, China.
Juan Wu *Department of Family Medicine & Division of General Internal Medicine, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Beijing, 100730, China.
Menghui YaoDepartment of Family Medicine & Division of General Internal Medicine, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Beijing, 100730, China.
Zhikai LiDepartment of Family Medicine & Division of General Internal Medicine, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Beijing, 100730, China.
Tiange XieDepartment of Family Medicine & Division of General Internal Medicine, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Beijing, 100730, China.
Yun ZhangDepartment of Family Medicine & Division of General Internal Medicine, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Beijing, 100730, China. zhangyun10806@pumch.cn.
Xuejun ZengDepartment of Family Medicine & Division of General Internal Medicine, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Beijing, 100730, China. zxjpumch@126.com.

Funding

Peking Union Medical College Hospital Talent Cultivation Program UBJ10806
6 · The paper itself

Abstract

backgroundInsulin resistance is a key pathophysiological mechanism potentially linking cardiometabolic diseases, yet the comparative performance of various surrogate indexes in predicting disease trajectories remains unclear. Therefore, this study aimed to evaluate the associations between insulin resistance indexes and cardiometabolic disease trajectories, and to identify the most suitable index for clinical risk assessment.

methodsThis prospective cohort study included 357,759 participants free of cardiometabolic diseases at baseline from the UK Biobank. We assessed seven insulin resistance indexes (estimated glucose disposal rate [eGDR], triglyceride-glucose index [TyG], triglyceride-glucose-waist circumference index [TyG-WC], triglyceride-glucose-body mass index [TyG-BMI], triglyceride-glucose-waist-to-height ratio [TyG-WHtR], triglyceride to high-density lipoprotein cholesterol ratio [TG/HDL-C], and metabolic score for insulin resistance [METS-IR]) and applied multi-state models to analyze disease trajectories from baseline to first cardiometabolic disease (FCMD), cardiometabolic multimorbidity (CMM) and death.

resultsDuring follow-up, 47,125 participants developed FCMD, 5,326 progressed to CMM, and 26,074 died. Among all indexes, eGDR exhibited the highest predictive value for both FCMD (AUC = 0.690) and CMM (AUC = 0.754), followed by TyG-WHtR and TyG-WC. Higher eGDR was associated with lower risks of transitioning from baseline to FCMD (HR = 0.63, 95%CI:0.62–0.64) and from FCMD to CMM (HR = 0.78, 95%CI:0.74–0.81). Conversely, higher TyG-WC and TyG-WHtR increased risks of transitions from baseline to FCMD (HR = 1.52, 95%CI:1.48–1.56; HR = 1.55, 95%CI:1.52–1.59) and from FCMD to CMM (HR = 1.21, 95%CI:1.13–1.30; HR = 1.24, 95%CI:1.16–1.32). Disease-specific analysis revealed insulin resistance indexes exhibited strongest effects on type 2 diabetes-related transitions (eGDR: HR = 0.49, 95%CI:0.48–0.50; TyG-WC: HR = 2.37, 95%CI:2.28–2.45). These associations remained consistent across major demographic subgroups.

conclusionsInsulin resistance indexes, particularly eGDR, effectively predict cardiometabolic disease trajectories. These findings underscore insulin resistance’s role in disease progression and suggest eGDR as a valuable clinical risk stratification tool.

Indexed as

Cardiometabolic multimorbidityInsulin resistanceIschemic heart diseaseStrokeType 2 diabetes

Identifiers

PMID41832572
PMCPMC13104193

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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.