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TyG 指数在预测中国老年男性糖尿病发病中的作用:一项 20 年回顾性研究。

The role of TyG index in predicting the incidence of diabetes in Chinese elderly men: a 20-year retrospective study.

机构信息

Department of Geriatric Endocrinology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Department of Geriatric Endocrinology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.

出版信息

Front Endocrinol (Lausanne). 2023 Jun 23;14:1191090. doi: 10.3389/fendo.2023.1191090. eCollection 2023.

DOI:10.3389/fendo.2023.1191090
PMID:37424876
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10327477/
Abstract

BACKGROUND

The triglyceride glucose index (TyG index) has been regarded as a reliable surrogate marker of insulin resistance and an independent predictor of diabetes. However, few studies have reported the association between the TyG index and diabetes in the elderly population. Accordingly, this study aimed to investigate the association between the TyG index and diabetes progression in elderly Chinese.

METHODS

Baseline medical history, fasting plasma glucose (FPG), glucose levels during the oral glucose tolerance test (OGTT) after 1-hour (1h-PG) and 2-hour (2h-PG), and triglyceride (TG) were obtained from a cohort of 862 elderly (aged ≥ 60 years) Chinese in the Beijing urban area between 1998 and 1999. A follow-up visit was conducted between 1998 and 2019 to assess incident diabetes. TyG index was calculated by the following formula ln[TG (mg/dL) × FPG (mg(dL)/2]. The predictive values of TyG index, lipids, and glucose levels during OGTT were assessed alone and also in a clinical prediction model comprising traditional risk factors using concordance index (C-index). Areas under the receiver operating characteristics curves (AUC) and 95% CIs were calculated.

RESULTS

After 20 years of follow-up, there were 544 cases of incident type 2 diabetes mellitus (63.1% of incidence). The multivariable HRs (95% CI) for TyG index, FPG, 1h-PG and 2h-PG, high-density lipoprotein-cholesterol (HDL-c), and TG were 1.525 (1.290-1.804), 1.350 (1.181-1.544), 1.337 (1.282-1.395), 1.401 (1.327-1.480), 0.505 (0.375-0.681), and 1.120 (1.053-1.192), respectively. The corresponding C-index were 0.623, 0.617, 0.704, 0.694, 0.631, and 0.610, respectively. The AUC (95% CI) for the TyG index, FPG, 1h-PG, 2h-PG, HDL-c, and TG were 0.608 (0.569-0.647), 0.587 (0.548-0.625), 0.766 (0.734-0.797), 0.713 (0.679-0.747), 0.397 (0.358-0.435), and 0.588 (0.549-0.628). The AUC of the TyG index was higher than that of TG but did not differ with FPG and HDL-c. In addition, the AUCs of 1h-PG and 2h-PG were higher than that of the TyG index.

CONCLUSIONS

Elevated TyG index is independently correlated with an increased risk of incident diabetes in the elderly male population, but it is not superior to OGTT 1h-PG and 2h-PG in predicting the risk of diabetes.

摘要

背景

甘油三酯-葡萄糖指数(TyG 指数)已被视为胰岛素抵抗的可靠替代标志物,也是糖尿病的独立预测因子。然而,很少有研究报告 TyG 指数与老年人群糖尿病之间的关联。因此,本研究旨在探讨 TyG 指数与中国老年人群糖尿病进展之间的关系。

方法

本研究纳入了 1998 年至 1999 年期间北京城区 862 名年龄≥60 岁的老年人的队列研究,收集了基线病史、空腹血糖(FPG)、口服葡萄糖耐量试验(OGTT)后 1 小时(1h-PG)和 2 小时(2h-PG)的血糖水平以及甘油三酯(TG)。在 1998 年至 2019 年期间进行随访,以评估新发糖尿病。TyG 指数通过以下公式计算:ln[TG(mg/dL)× FPG(mg/dL)/2]。使用一致性指数(C-index)评估 TyG 指数、脂质和 OGTT 期间血糖水平的单独预测值,以及包括传统危险因素的临床预测模型。计算接受者操作特征曲线(ROC)的曲线下面积(AUC)和 95%置信区间(CI)。

结果

经过 20 年的随访,共有 544 例发生 2 型糖尿病(发病率为 63.1%)。TyG 指数、FPG、1h-PG、2h-PG、高密度脂蛋白胆固醇(HDL-c)和 TG 的多变量 HR(95%CI)分别为 1.525(1.290-1.804)、1.350(1.181-1.544)、1.337(1.282-1.395)、1.401(1.327-1.480)、0.505(0.375-0.681)和 1.120(1.053-1.192)。相应的 C-index 分别为 0.623、0.617、0.704、0.694、0.631 和 0.610。TyG 指数、FPG、1h-PG、2h-PG、HDL-c 和 TG 的 AUC(95%CI)分别为 0.608(0.569-0.647)、0.587(0.548-0.625)、0.766(0.734-0.797)、0.713(0.679-0.747)、0.397(0.358-0.435)和 0.588(0.549-0.628)。TyG 指数的 AUC 高于 TG,但与 FPG 和 HDL-c 无差异。此外,1h-PG 和 2h-PG 的 AUC 均高于 TyG 指数。

结论

升高的 TyG 指数与老年男性人群新发糖尿病的风险增加独立相关,但在预测糖尿病风险方面并不优于 OGTT 1h-PG 和 2h-PG。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d8e/10327477/853aa467f6fb/fendo-14-1191090-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d8e/10327477/853aa467f6fb/fendo-14-1191090-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d8e/10327477/853aa467f6fb/fendo-14-1191090-g001.jpg

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