{"title":"Assessment of insulin resistance indexes with longitudinal trajectories of cardiometabolic multimorbidity: insights from the UK Biobank.","authors":"Yingdong Han, Juan Wu, Menghui Yao, Zhikai Li, Tiange Xie, Yun Zhang, Xuejun Zeng","doi":"10.1186/s12986-026-01098-0","DOIUrl":null,"url":null,"abstract":"<p><p>BACKGROUND: Insulin 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. METHODS: This 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. RESULTS: During 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. CONCLUSIONS: Insulin 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. </p>","PeriodicalId":19196,"journal":{"name":"Nutrition & Metabolism","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13104193/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutrition & Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12986-026-01098-0","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
引用次数: 0
Abstract
BACKGROUND: Insulin 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. METHODS: This 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. RESULTS: During 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. CONCLUSIONS: Insulin 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.
期刊介绍:
Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects.
The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases.
Key areas we wish to encourage submissions from include:
-how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes;
-the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components;
-how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved;
-how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.