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Background <p>Cardiovascular-kidney-metabolic (CKM) syndrome imposes a substantial global health burden, with most adults clustered in early stages 0–3. Insulin resistance (IR), as a core manifestation of metabolic dysfunction, is thought to play a pivotal role in CKM progression and cardiovascular disease (CVD) development, but the relative impact of diverse IR surrogates and the mediating role of biological ageing acceleration remain unclear.</p> Method <p>This prospective analysis included 6318 participants with CKM syndrome stages 0–3 from the China Health and Retirement Longitudinal Study (CHARLS). We evaluated twelve insulin resistance surrogates in relation to incident CVD using multivariable-adjusted logistic regression, restricted cubic splines (RCS), and quantile-based models. Mediation analyses assessed whether biological aging acceleration mediated the association between IR indices and new-onset CVD.</p> Results <p>1231 (19.5%) of 6318 participants with CKM stages 0–3 developed new-onset CVD. All IR surrogates demonstrated significant associations with CVD risk, with elevated TyG-derived indices, METS-IR, CTI, and TG/HDL-C showing positive associations whereas eGDR exhibited an inverse relationship (all <i>P-trend</i> &lt; 0.05). RCS analyses revealed nonlinear relationships for METS-IR, CTI, and eGDR. Significant modification effects were observed by biological ageing acceleration, gender, and CKM stage. Mediation analyses indicated that biological aging acceleration accounted for 14.9–16.4% of the TyG-ABSI–CVD association and 1.3–4.2% of other IR–CVD relationships.</p> Conclusions <p>Multiple IR surrogate indices independently predict cardiovascular disease in CKM stages 0–3, with biological aging acceleration mediating this association. Integrating these measures into risk stratification could enable early identification and targeted intervention for high-risk individuals.</p>

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Association of various insulin resistance surrogate indices with aging acceleration and future risk of cardiovascular disease in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3: insights from CHARLS 2011–2020 data

  • 期刊论文

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  • 发表日期：

    2026年2月4日

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  • Shu-Shu Han,
  • Qin Liu,
  • Zhi-Ming Zeng,
  • Ying Li,
  • Ping-Wei Li,
  • Fang-Xiao Cheng,
  • Pian Zhong,
  • Jiang-Bo Li

摘要

Background

Cardiovascular-kidney-metabolic (CKM) syndrome imposes a substantial global health burden, with most adults clustered in early stages 0–3. Insulin resistance (IR), as a core manifestation of metabolic dysfunction, is thought to play a pivotal role in CKM progression and cardiovascular disease (CVD) development, but the relative impact of diverse IR surrogates and the mediating role of biological ageing acceleration remain unclear.

Method

This prospective analysis included 6318 participants with CKM syndrome stages 0–3 from the China Health and Retirement Longitudinal Study (CHARLS). We evaluated twelve insulin resistance surrogates in relation to incident CVD using multivariable-adjusted logistic regression, restricted cubic splines (RCS), and quantile-based models. Mediation analyses assessed whether biological aging acceleration mediated the association between IR indices and new-onset CVD.

Results

1231 (19.5%) of 6318 participants with CKM stages 0–3 developed new-onset CVD. All IR surrogates demonstrated significant associations with CVD risk, with elevated TyG-derived indices, METS-IR, CTI, and TG/HDL-C showing positive associations whereas eGDR exhibited an inverse relationship (all P-trend < 0.05). RCS analyses revealed nonlinear relationships for METS-IR, CTI, and eGDR. Significant modification effects were observed by biological ageing acceleration, gender, and CKM stage. Mediation analyses indicated that biological aging acceleration accounted for 14.9–16.4% of the TyG-ABSI–CVD association and 1.3–4.2% of other IR–CVD relationships.

Conclusions

Multiple IR surrogate indices independently predict cardiovascular disease in CKM stages 0–3, with biological aging acceleration mediating this association. Integrating these measures into risk stratification could enable early identification and targeted intervention for high-risk individuals.