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Background <p>Cardiometabolic multimorbidity (CMM) poses a mounting health challenge worldwide. Seven surrogate indexes of insulin resistance (IR)—the triglyceride-glucose index (TyG), TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), Chinese visceral adiposity Index (CVAI), Metabolic score for IR (METS-IR), Atherogenic index of plasma (AIP), and estimated glucose disposal rate (eGDR)—are well-established. However, comparative studies evaluating their predictive capacity for CMM incidence in Chinese middle-aged and older adults remain scarce. This study aimed to assess the associations between these seven IR indexes and CMM risk within this population and determine their relative predictive abilities.</p> Methods <p>Utilizing the China Health and Retirement Longitudinal Study (CHARLS) 2011–2020 datasets, this prospective cohort investigation assessed Chinese participants aged ≥ 45&#xa0;years. We applied Kaplan–Meier curve plotting, multivariable Cox proportional hazards models, and restricted cubic splines (RCS) to quantify associations of insulin resistance surrogate indexes with CMM risk. Predictive accuracy was appraised via time-dependent receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Subgroup evaluations further verified findings robustness.</p> Results <p>During a median follow-up of 9&#xa0;years, 1043 (14.49%) of the 7197 participants developed CMM. After adjusting for potential confounders, we observed that each standard deviation (SD) increase in eGDR was associated with a reduced risk of CMM, with an adjusted hazard ratio (HR) of 0.834 [95% confidence interval (CI): 0.781–0.891]. In contrast, each SD increase in TyG, TyG-BMI, TyG-WC, METS-IR, AIP and CVAI were associated with an increased risk of CMM. Restricted cubic spline analyses showed that TyG-BMI, TyG-WC, METS-IR, and eGDR were nonlinear associated with incident new-onset CMM (<i>P</i>-nonlinearity &lt; 0.05). eGDR showed a L-shaped association (<i>P</i>-nonlinearity &lt; 0.05), with CMM risk decreasing until the inflection point at 11.82 (HR = 0.758; 95% CI: 0.702–0.818). Conversely, TyG-WC displayed a U-shaped relationship (inflection point: 572.14). Moreover, the time-dependent AUC analysis revealed that eGDR exhibited superior predictive discrimination (AUC 0.68–0.76) during early follow-up and across all intervals. The optimal cut-off value for eGDR was determined to be 7.64.</p> Conclusion <p>All seven insulin resistance surrogate indexes independently demonstrated elevated CMM risk. In the context of Chinese middle-aged and elderly cohorts, eGDR demonstrated a notable predictive capacity for CMM. ROC-derived cut-offs (eGDR &lt; 7.64) are utilised for identifying high-risk individuals requiring intervention. Spline-derived thresholds (eGDR = 11.82) serve as therapeutic targets for risk reduction.</p> Graphical abstract <p></p>

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Assessment of seven insulin resistance surrogate indexes for predicting cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national prospective cohort study

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  • 发表日期：

    2026年4月5日

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  • Jun Lai,
  • Zongyan Liu,
  • Huajie Wang,
  • Xiaoqing Kong,
  • Yufeng Wei,
  • Yongxiao Cao

摘要

Background

Cardiometabolic multimorbidity (CMM) poses a mounting health challenge worldwide. Seven surrogate indexes of insulin resistance (IR)—the triglyceride-glucose index (TyG), TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), Chinese visceral adiposity Index (CVAI), Metabolic score for IR (METS-IR), Atherogenic index of plasma (AIP), and estimated glucose disposal rate (eGDR)—are well-established. However, comparative studies evaluating their predictive capacity for CMM incidence in Chinese middle-aged and older adults remain scarce. This study aimed to assess the associations between these seven IR indexes and CMM risk within this population and determine their relative predictive abilities.

Methods

Utilizing the China Health and Retirement Longitudinal Study (CHARLS) 2011–2020 datasets, this prospective cohort investigation assessed Chinese participants aged ≥ 45 years. We applied Kaplan–Meier curve plotting, multivariable Cox proportional hazards models, and restricted cubic splines (RCS) to quantify associations of insulin resistance surrogate indexes with CMM risk. Predictive accuracy was appraised via time-dependent receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Subgroup evaluations further verified findings robustness.

Results

During a median follow-up of 9 years, 1043 (14.49%) of the 7197 participants developed CMM. After adjusting for potential confounders, we observed that each standard deviation (SD) increase in eGDR was associated with a reduced risk of CMM, with an adjusted hazard ratio (HR) of 0.834 [95% confidence interval (CI): 0.781–0.891]. In contrast, each SD increase in TyG, TyG-BMI, TyG-WC, METS-IR, AIP and CVAI were associated with an increased risk of CMM. Restricted cubic spline analyses showed that TyG-BMI, TyG-WC, METS-IR, and eGDR were nonlinear associated with incident new-onset CMM (P-nonlinearity < 0.05). eGDR showed a L-shaped association (P-nonlinearity < 0.05), with CMM risk decreasing until the inflection point at 11.82 (HR = 0.758; 95% CI: 0.702–0.818). Conversely, TyG-WC displayed a U-shaped relationship (inflection point: 572.14). Moreover, the time-dependent AUC analysis revealed that eGDR exhibited superior predictive discrimination (AUC 0.68–0.76) during early follow-up and across all intervals. The optimal cut-off value for eGDR was determined to be 7.64.

Conclusion

All seven insulin resistance surrogate indexes independently demonstrated elevated CMM risk. In the context of Chinese middle-aged and elderly cohorts, eGDR demonstrated a notable predictive capacity for CMM. ROC-derived cut-offs (eGDR < 7.64) are utilised for identifying high-risk individuals requiring intervention. Spline-derived thresholds (eGDR = 11.82) serve as therapeutic targets for risk reduction.

Graphical abstract