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代谢性胰岛素抵抗评分(METS-IR)可预测普通人群的全因和心血管死亡率:来自 NHANES 2001-2018 的证据。

Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001-2018.

机构信息

Henan Key Laboratory of Hereditary Cardiovascular Diseases, Department of Cardiology, Cardiovascular Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.

出版信息

Cardiovasc Diabetol. 2024 Jul 10;23(1):243. doi: 10.1186/s12933-024-02334-8.

DOI:10.1186/s12933-024-02334-8
PMID:38987779
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11238348/
Abstract

BACKGROUND

The prevalence of obesity-associated insulin resistance (IR) is increasing along with the increase in obesity rates. In this study, we compared the predictive utility of four alternative indexes of IR [triglyceride glucose index (TyG index), metabolic score for insulin resistance (METS-IR), the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and homeostatic model assessment of insulin resistance (HOMA-IR)] for all-cause mortality and cardiovascular mortality in the general population based on key variables screened by the Boruta algorithm. The aim was to find the best replacement index of IR.

METHODS

In this study, 14,653 participants were screened from the National Health and Nutrition Examination Survey (2001-2018). And TyG index, METS-IR, TG/HDL-C and HOMA-IR were calculated separately for each participant according to the given formula. The predictive values of IR replacement indexes for all-cause mortality and cardiovascular mortality in the general population were assessed.

RESULTS

Over a median follow-up period of 116 months, a total of 2085 (10.23%) all-cause deaths and 549 (2.61%) cardiovascular disease (CVD) related deaths were recorded. Multivariate Cox regression and restricted cubic splines analysis showed that among the four indexes, only METS-IR was significantly associated with both all-cause and CVD mortality, and both showed non-linear associations with an approximate "U-shape". Specifically, baseline METS-IR lower than the inflection point (41.33) was negatively associated with mortality [hazard ratio (HR) 0.972, 95% CI 0.950-0.997 for all-cause mortality]. In contrast, baseline METS-IR higher than the inflection point (41.33) was positively associated with mortality (HR 1.019, 95% CI 1.011-1.026 for all-cause mortality and HR 1.028, 95% CI 1.014-1.043 for CVD mortality). We further stratified the METS-IR and showed that significant associations between METS-IR levels and all-cause and cardiovascular mortality were predominantly present in the nonelderly population aged < 65 years.

CONCLUSIONS

In conjunction with the results of the Boruta algorithm, METS-IR demonstrated a more significant association with all-cause and cardiovascular mortality in the U.S. population compared to the other three alternative IR indexes (TyG index, TG/HDL-C and HOMA-IR), particularly evident in individuals under 65 years old.

摘要

背景

随着肥胖率的增加,肥胖相关胰岛素抵抗(IR)的患病率也在增加。在这项研究中,我们根据 Boruta 算法筛选的关键变量,比较了四种替代 IR 指标[三酰甘油葡萄糖指数(TyG 指数)、代谢评分胰岛素抵抗(METS-IR)、三酰甘油/高密度脂蛋白胆固醇(TG/HDL-C)比值和稳态模型评估的胰岛素抵抗(HOMA-IR)]对普通人群全因死亡率和心血管死亡率的预测价值。目的是寻找最佳的 IR 替代指标。

方法

本研究从全国健康和营养检查调查(2001-2018 年)中筛选出 14653 名参与者。根据给定的公式,分别为每个参与者计算 TyG 指数、METS-IR、TG/HDL-C 和 HOMA-IR。评估普通人群中 IR 替代指标对全因死亡率和心血管死亡率的预测价值。

结果

在中位随访 116 个月期间,共记录到 2085 例(10.23%)全因死亡和 549 例(2.61%)心血管疾病(CVD)相关死亡。多变量 Cox 回归和限制立方样条分析表明,在这四个指标中,只有 METS-IR 与全因和 CVD 死亡率均显著相关,且均呈非线性相关,近似呈“U 形”。具体而言,低于拐点(41.33)的基线 METS-IR 与死亡率呈负相关[全因死亡率的危险比(HR)0.972,95%置信区间(CI)0.950-0.997]。相比之下,高于拐点(41.33)的基线 METS-IR 与死亡率呈正相关(全因死亡率的 HR 1.019,95%CI 1.011-1.026;CVD 死亡率的 HR 1.028,95%CI 1.014-1.043)。我们进一步对 METS-IR 进行分层,结果表明,METS-IR 水平与全因和心血管死亡率之间的显著关联主要存在于年龄<65 岁的非老年人群中。

结论

与 Boruta 算法的结果相结合,METS-IR 与美国人群的全因和心血管死亡率的相关性比其他三种替代 IR 指标(TyG 指数、TG/HDL-C 和 HOMA-IR)更显著,在年龄<65 岁的人群中尤为明显。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f758/11238348/e441aede3a1b/12933_2024_2334_Fig6_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f758/11238348/8cf41966d7f5/12933_2024_2334_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f758/11238348/e441aede3a1b/12933_2024_2334_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f758/11238348/9eea576a3edd/12933_2024_2334_Fig1_HTML.jpg
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