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用于预测心血管疾病的九种胰岛素抵抗替代指标的比较:一项队列研究。

Comparison of nine insulin resistance surrogates for predicting cardiovascular disease: a cohort study.

作者信息

Zhou Haoqi, Shi Yu, Zhou Xiaohua

机构信息

Department of Biostatistics, School of Public Health, Peking University, 38 Xueyuan Rd, Haidian District, Beijing, 100191, China.

Inner Mongolia Brain Hospital (Third Hospital), Inner Mongolia, China.

出版信息

Diabetol Metab Syndr. 2025 Aug 31;17(1):365. doi: 10.1186/s13098-025-01933-z.

DOI:10.1186/s13098-025-01933-z
PMID:40887654
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12398982/
Abstract

BACKGROUND

Insulin resistance (IR) is an established independent risk factor for cardiovascular disease (CVD). Although numerous simple surrogate indicators for IR have been proposed, their comparative predictive utility for CVD remains unclear. This study aimed to evaluate the associations between nine IR surrogate indicators and incident CVD and to comparatively assess their predictive capacities using nationally representative data from China.

METHODS

7,662 participants without CVD from the China Health and Retirement Longitudinal Study (CHARLS) were included in the study. Nine IR surrogate measures including triglyceride-glucose (TyG) index, triglyceride to high-density lipoprotein ratio (TG/HDL), metabolic score for insulin resistance (METS-IR), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), atherogenic index of plasma (AIP), triglyceride glucose-body mass index (TyG-BMI), triglyceride glucose-waist circumference (TyG-WC), and triglyceride glucose-waist-to-height ratio (TyG-WHtR) were calculated. Cox model and restricted cubic spline model were used to estimate the relationships between distinct IR surrogates and incident CVD. We also computed the time-dependent Harrell's concordance index (C-index) to compare the predictive performance of IR surrogates.

RESULTS

After a mean follow-up duration of 8.2 years, a total of 1,906 individuals developed CVD. The full adjusted cox model revealed that per SD increase in all IR indicators was significantly associated with elevated CVD risk, with the hazard ratio (95%CI) of 1.09 (1.04-1.14) for TyG; 1.05 (1.01-1.10) for TG/HDL-C; 1.09 (1.03-1.14) for METS-IR; 1.13 (1.08-1.19) for CVAI; 1.07 (1.03-1.12) for LAP; 1.08 (1.03-1.13) for AIP; 1.10 (1.05-1.15) for TyG-BMI; 1.11 (1.06-1.16) for TyG-WC; and 1.11 (1.05-1.16) for TyG-WHtR. Predictive performance analysis showed TyG had the highest C-index of 0.742 (95% CI, 0.737-0.747).

CONCLUSIONS

Among nine IR surrogates, the TyG index exhibited the highest predictive performance for incident CVD in Chinese middle-aged and older adults. Acknowledging limitations such as the observational design and self-reported outcomes, our findings support the TyG index as a simple, powerful, and clinically accessible tool for early CVD risk prediction.

摘要

背景

胰岛素抵抗(IR)是心血管疾病(CVD)公认的独立危险因素。尽管已经提出了许多简单的IR替代指标,但其对CVD的比较预测效用仍不清楚。本研究旨在评估9种IR替代指标与CVD发病之间的关联,并使用来自中国的具有全国代表性的数据比较评估它们的预测能力。

方法

本研究纳入了中国健康与养老追踪调查(CHARLS)中7662名无CVD的参与者。计算了9种IR替代指标,包括甘油三酯-葡萄糖(TyG)指数、甘油三酯与高密度脂蛋白比值(TG/HDL)、胰岛素抵抗代谢评分(METS-IR)、中国内脏脂肪指数(CVAI)、脂质蓄积产物(LAP)、血浆致动脉粥样硬化指数(AIP)、甘油三酯葡萄糖-体重指数(TyG-BMI)、甘油三酯葡萄糖-腰围(TyG-WC)和甘油三酯葡萄糖-腰高比(TyG-WHtR)。采用Cox模型和受限立方样条模型估计不同IR替代指标与CVD发病之间的关系。我们还计算了时间依赖性Harrell一致性指数(C指数)以比较IR替代指标的预测性能。

结果

平均随访8.2年后,共有1906人发生CVD。完全调整后的Cox模型显示,所有IR指标每增加1个标准差均与CVD风险升高显著相关,TyG的风险比(95%CI)为1.09(1.04-1.14);TG/HDL-C为1.05(1.01-1.10);METS-IR为1.09(1.03-1.14);CVAI为1.13(1.08-1.19);LAP为1.07(1.03-1.12);AIP为1.08(1.03-1.13);TyG-BMI为1.10(1.05-1.15);TyG-WC为1.11(1.06-1.16);TyG-WHtR为1.11(1.05-1.16)。预测性能分析显示,TyG的C指数最高,为0.742(95%CI,0.737-0.747)。

结论

在9种IR替代指标中,TyG指数对中国中老年人群CVD发病的预测性能最高。尽管认识到本研究存在观察性设计和自我报告结局等局限性,但我们的研究结果支持TyG指数作为一种简单、强大且临床可及的早期CVD风险预测工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2817/12398982/f48d55a274eb/13098_2025_1933_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2817/12398982/1a678e07ce62/13098_2025_1933_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2817/12398982/975e4ea5a19f/13098_2025_1933_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2817/12398982/f48d55a274eb/13098_2025_1933_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2817/12398982/1a678e07ce62/13098_2025_1933_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2817/12398982/975e4ea5a19f/13098_2025_1933_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2817/12398982/f48d55a274eb/13098_2025_1933_Fig3_HTML.jpg

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