Feng Yaning, Yin Liangying, Huang Haoran, Hu Yongheng, Lin Sitong
School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China.
Cardiovasc Diabetol. 2025 Mar 10;24(1):112. doi: 10.1186/s12933-025-02651-6.
Cardiovascular disease (CVD) is closely associated with Insulin Resistance (IR). However, there is limited research on the relationship between trajectories of IR and CVD incidence, considering both time-invariant and time-varying confounders. We employed advanced causal inference methods to evaluate the longitudinal impact of IR trajectories on CVD risk.
The data for this study were extracted from a Chinese nationwide cohort, named China Health and Retirement Longitudinal Study (CHARLS). Triglyceride-glucose (TyG) index and TyG body mass index (BMI) were used as surrogate markers for IR, and their changes were recorded as exposures. Longitudinal targeted maximum likelihood estimation (LTMLE) was used to study how dynamic shifts in IR trajectories (i.e., increase, decrease, etc.) influence long-term CVD risk, adjusting for both time-invariant and time-varying confounders.
A total of 3,966 participants were included in the analysis, with 2,152 (54.3%) being female. The average age at baseline was 58.28 years. Over the course of a 7-year follow-up period, 499 (12.6%) participants developed CVD. Four distinct trajectories of TyG index and TyG-BMI were identified: low stable, increasing, decreasing, and high stable. LTMLE analyses revealed individuals in the 'high stable' and 'increasing' groups had a significantly higher risk of developing CVD compared to those in the 'low stable' group, while the 'decreasing' group showed no significant differences. Specifically, when the exposure was set as TyG-BMI, the odds of CVD in the 'high stable' group were 1.694 (95% CI: 1.361-2.108) times higher than in the 'low stable' group. Similar trends were observed across other models, with ORs of 1.708 (95% CI: 1.367-2.134) in Model 2, 1.389 (1.083-1.782) in Model 3, 1.675 (1.185-2.366) in Model 4, and 1.375 (95% CI:1.07 - 1.768) in Model 5. When the exposure was changed to the TyG index, the results remained consistent, with a slightly lower magnitude of the odds ratios.
High stable and increasing TyG-BMI and TyG index trajectories were associated with the risk of CVD. TyG-BMI consistently exhibited higher odds ratios (ORs) of CVD risk when comparing with TyG index. Early identification of IR trajectories could provide insights for preventing CVD later in life.
心血管疾病（CVD）与胰岛素抵抗（IR）密切相关。然而，考虑到时间不变和随时间变化的混杂因素，关于IR轨迹与CVD发病率之间关系的研究有限。我们采用先进的因果推断方法来评估IR轨迹对CVD风险的纵向影响。
本研究的数据取自一项名为中国健康与养老追踪调查（CHARLS）的全国性队列研究。甘油三酯-葡萄糖（TyG）指数和TyG体重指数（BMI）被用作IR的替代标志物，并记录其变化作为暴露因素。纵向靶向最大似然估计（LTMLE）用于研究IR轨迹的动态变化（即增加、减少等）如何影响长期CVD风险，同时调整时间不变和随时间变化的混杂因素。
共有3966名参与者纳入分析，其中女性2152名（54.3%）。基线时的平均年龄为58.28岁。在7年的随访期内，499名（12.6%）参与者发生了CVD。确定了TyG指数和TyG-BMI的四种不同轨迹：低稳定、上升、下降和高稳定。LTMLE分析显示，与“低稳定”组相比，“高稳定”组和“上升”组的个体发生CVD的风险显著更高，而“下降”组无显著差异。具体而言，当暴露设定为TyG-BMI时，“高稳定”组发生CVD的几率比“低稳定”组高1.694倍（95%CI：1.361-2.108）。在其他模型中也观察到类似趋势，模型2中的比值比为1.708（95%CI：1.367-2.134），模型3中为1.389（1.083-1.782），模型4中为1.675（1.185-2.366），模型5中为1.375（95%CI：1.07-1.768）。当暴露改为TyG指数时，结果保持一致，比值比幅度略低。
TyG-BMI和TyG指数的高稳定及上升轨迹与CVD风险相关。与TyG指数相比，TyG-BMI在CVD风险方面始终表现出更高的比值比。早期识别IR轨迹可为预防晚年CVD提供见解。
