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March 17, 2026Open Access

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

Key Points

Evaluate the associations between insulin resistance indexes and cardiometabolic disease trajectories and identify the most suitable index for risk assessment.
Prospective cohort study with 357,759 participants from the UK Biobank.
Assessment of seven insulin resistance indexes during follow-up.
Multi-state models applied to analyze transitions to cardiometabolic disease and multimorbidity.
47,125 participants developed first cardiometabolic disease (FCMD) during follow-up.
eGDR showed highest predictive value for FCMD (AUC = 0.690) and multimorbidity (AUC = 0.754).
Higher eGDR linked to lower risks of transitioning to FCMD and multimorbidity, while higher TyG-WC increased risks.

Abstract

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. 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. 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. 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.

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