■本研究旨在评估基线胰岛素抵抗(IR)代用品及其纵向轨迹与心血管疾病(CVD)的关系,为预防CVD提供有用的参考。
■本研究是在新疆兵团第三师第51团进行的前瞻性队列研究。2016年共招募了6362名参与者进行基线调查,以及2019年、2020年、2021年和2022年的后续调查。根据代谢胰岛素抵抗评分(METS-IR)和甘油三酸酯-葡萄糖(TyG)指数的基线IR替代指标,使用Kaplan-Meier方法估算CVD的累积发生率。Cox回归模型用于评估基线IR替代与CVD之间的关联。在排除测量的IR替代数据≤2次之后,分析了IR替代的纵向轨迹对CVD的影响。基于基于群体的轨迹模型(GBTM),确定红外替代的轨迹模式。采用Kaplan-Meier法估算METS-IR和TyG指数各轨迹组的CVD累积发生率。采用Cox回归模型分析各指标不同轨迹组与CVD的相关性。此外,Framingham模型用于评估基线IR替代指标的添加是否增加了模型的预测潜力.
■基线数据分析包括4712名参与者。在5.66年的中位随访期间,记录了572例CVD事件(平均年龄,39.42±13.67岁;男性,42.9%)。随着基线METS-IR和TyG指数四分位数(Q1-Q4)的上升,累积CVD发病率增加。METS-IR和TyG指数第4季度CVD风险的风险比和95%置信区间分别为1.79(1.25,2.58)和1.66(1.28,2.17),分别,与Q1相比。4343名参与者被纳入轨迹分析,基于METS-IR和TyG指数的纵向变化模式,确定了以下三个轨迹组:低增加,中等稳定,和增加的群体。多变量Cox回归显示,在METS-IR和TyG指数升高的轨迹组中,CVD风险的风险比(95%置信区间)为2.13(1.48,3.06)和2.63(1.68,4.13)。分别,与低上升组相比。C指数,综合歧视改进价值,在Framingham模型中加入基线METS-IR和TyG指数值后,净重新分类改善值增强(P<0.05)。
■在新疆农村地区,基线IR替代指标的升高及其较高的长期轨迹与CVD发病率的高风险密切相关。定期监测METS-IR和TyG指数有助于早期发现CVD风险人群。
This
study aimed to assess the association of baseline insulin resistance (IR) surrogates and their longitudinal trajectories with cardiovascular diseases (CVD) to provide a useful reference for preventing CVD.
This
study was a prospective cohort
study conducted in the 51st Regiment of the Third Division of Xinjiang Corps. A total of 6362 participants were recruited in 2016 to conduct the baseline survey, and the follow-up surveys in 2019, 2020, 2021, and 2022. The Kaplan-Meier method was used to estimate the cumulative incidence of CVD according to the baseline IR surrogates of metabolic insulin resistance score (METS-IR) and triglyceride-glucose (TyG) index. Cox regression models were used to assess the association between the baseline IR surrogates and CVD. The impact of the longitudinal trajectories of the IR surrogates on CVD was analyzed after excluding those with IR surrogate data measured ≤2 times. Based on the group-based trajectory model (GBTM), the trajectory patterns of IR surrogates were determined. The Kaplan-Meier method was used to estimate the cumulative incidence of CVD in each trajectory group of METS-IR and TyG index. Cox regression models were used to analyze the association between different trajectory groups of each index and CVD. In addition, the Framingham model was utilized to evaluate whether the addition of the baseline IR surrogates increased the predictive potential of the model.
Baseline data analysis included 4712 participants. During a median follow-up of 5.66 years, 572 CVD events were recorded (mean age, 39.42 ± 13.67 years; males, 42.9%). The cumulative CVD incidence increased with the ascending baseline METS-IR and TyG index quartiles (Q1-Q4). The hazard ratio and 95% confidence interval for CVD risk in Q4 of the METS-IR and TyG index were 1.79 (1.25, 2.58) and 1.66 (1.28, 2.17), respectively, when compared with Q1. 4343 participants were included in the trajectory analysis, based on the longitudinal change patterns of the METS-IR and TyG index, the following three trajectory groups were identified: low-increasing, moderate-stable, and elevated-increasing groups. Multivariate Cox regression revealed that the hazard ratio (95% confidence interval) for CVD risk in the elevated-increasing trajectory group of the METS-IR and TyG index was 2.13 (1.48, 3.06) and 2.63 (1.68, 4.13), respectively, when compared with the low-rising group. The C-index, integrated discrimination improvement value, and net reclassification improvement value were enhanced after adding the baseline METS-IR and TyG index values to the Framingham model (P<0.05).
Elevated baseline IR surrogates and their higher long-term trajectories were strongly associated with a high risk of CVD incidence in Xinjiang\'s rural areas. Regular METS-IR and TyG index monitoring can aid in the early detection of CVD-risk groups.