背景:胰岛素抵抗与虚弱风险增加有关,然而,甘油三酯葡萄糖-体重指数(TyG-BMI)之间的综合关系,这反映了体重,和脆弱,尚不清楚。在这项研究中调查了这种关系。
方法:分析了中国健康与退休纵向研究(2011-2020)9135名参与者的数据。基线TyG-BMI,基线至2015年之间的TyG-BMI和累积TyG-BMI的变化,以及九年来的虚弱指数(FI),被计算。使用K均值聚类,根据TyG-BMI变化将参与者分为不同类别。使用基于组的轨迹模型评估FI轨迹。使用Logistic和Cox回归模型来分析TyG-BMI和FI轨迹与虚弱发生率之间的关联。使用受限三次样条探索了非线性关系,并使用线性混合效应模型来评估FI发展速度。加权分位数回归用于确定主要影响因素。
结果:确定了TyG-BMI的四类变化和两种FI轨迹。基线TyG-BMI的第三(OR=1.25,95%CI:1.10-1.42)和第四(OR=1.83,95%CI:1.61-2.09)四分位数的个体,那些始终排名第二(OR=1.49,95%CI:1.32-1.70)和最高(OR=2.17,95%CI:1.84-2.56)的TyG-BMI变化,而在累积TyG-BMI的第3(OR=1.20,95%CI:1.05-1.36)和第4(OR=1.94,95%CI:1.70-2.22)四分位数的患者经历快速FI轨迹的可能性更大.在基线TyG-BMI的第四个四分位数中,观察到较高的脆弱风险(HR=1.42,95%CI:1.28-1.58),始终排名第二(HR=1.23,95%CI:1.12-1.34),TyG-BMI变化最高(HR=1.58,95%CI:1.42-1.77),以及累积TyG-BMI的第三四分位数(HR=1.10,95%CI:1.00-1.21)和第四四分位数(HR=1.46,95%CI:1.33-1.60)。TyG-BMI变化持续第二低到最高的参与者(分别为β=0.15、0.38和0.76)和经历第三至第四累积TyG-BMI(分别为β=0.25和0.56)的参与者表现出加速的FI进展。在TyG-BMI水平与快速FI轨迹和较高的虚弱风险之间观察到U形关联。BMI是主要因素。
结论:较高的TyG-BMI与FI轨迹的快速发展和较高的虚弱风险相关。然而,过低的TyG-BMI水平似乎也有助于虚弱的发育。保持健康的TyG-BMI,尤其是健康的BMI,可能有助于预防或延缓虚弱的发作。
BACKGROUND: Insulin resistance is linked to an increased risk of frailty, yet the comprehensive relationship between the triglyceride glucose-body mass index (TyG-BMI), which reflects weight, and frailty, remains unclear. This relationship is investigated in this study.
METHODS: Data from 9135 participants in the China Health and Retirement Longitudinal Study (2011-2020) were analysed. Baseline TyG-BMI, changes in the TyG-BMI and cumulative TyG-BMI between baseline and 2015, along with the frailty index (FI) over nine years, were calculated. Participants were grouped into different categories based on TyG-BMI changes using K-means clustering. FI trajectories were assessed using a group-based trajectory model. Logistic and Cox regression models were used to analyse the associations between the TyG-BMI and FI trajectory and frail incidence. Nonlinear relationships were explored using restricted cubic splines, and a linear mixed-effects model was used to evaluate FI development speed. Weighted quantile regression was used to identify the primary contributing factors.
RESULTS: Four classes of changes in the TyG-BMI and two FI trajectories were identified. Individuals in the third (OR = 1.25, 95% CI: 1.10-1.42) and fourth (OR = 1.83, 95% CI: 1.61-2.09) quartiles of baseline TyG-BMI, those with consistently second to highest (OR = 1.49, 95% CI: 1.32-1.70) and the highest (OR = 2.17, 95% CI: 1.84-2.56) TyG-BMI changes, and those in the third (OR = 1.20, 95% CI: 1.05-1.36) and fourth (OR = 1.94, 95% CI: 1.70-2.22) quartiles of the cumulative TyG-BMI had greater odds of experiencing a rapid FI trajectory. Higher frail risk was noted in those in the fourth quartile of baseline TyG-BMI (HR = 1.42, 95% CI: 1.28-1.58), with consistently second to highest (HR = 1.23, 95% CI: 1.12-1.34) and the highest TyG-BMI changes (HR = 1.58, 95% CI: 1.42-1.77), and those in the third (HR = 1.10, 95% CI: 1.00-1.21) and fourth quartile of cumulative TyG-BMI (HR = 1.46, 95% CI: 1.33-1.60). Participants with persistently second-lowest to the highest TyG-BMI changes (β = 0.15, 0.38 and 0.76 respectively) and those experiencing the third to fourth cumulative TyG-BMI (β = 0.25 and 0.56, respectively) demonstrated accelerated FI progression. A U-shaped association was observed between TyG-BMI levels and both rapid FI trajectory and higher frail risk, with BMI being the primary factor.
CONCLUSIONS: A higher TyG-BMI is associated with the rapid development of FI trajectory and a greater frail risk. However, excessively low TyG-BMI levels also appear to contribute to frail development. Maintaining a healthy TyG-BMI, especially a healthy BMI, may help prevent or delay the frail onset.