Zhang Yibo, Wang Meiping, Zuo Yingting, Su Xin, Wen Jing, Zhai Qi, He Yan
Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10 Xitoutiao, Youanmenwai, Fengtai District, Beijing, China.
School of Public Health, Baotou Medical College, Baotou, Inner Mongolia, China.
Hormones (Athens). 2022 Dec;21(4):683-690. doi: 10.1007/s42000-022-00398-3. Epub 2022 Sep 27.
The purpose of this study is to explore the association between adiposity indices and blood lipid indices and prediabetes. We compare the predictive value of new adiposity indices and traditional adiposity indices and blood lipid indices in the diagnosis of prediabetes.
This is a prospective cohort study of 7953 participants. The follow-up time was 3 years. The eight adiposity indices included the following: body mass index (BMI), waist circumference (WC), body roundness index (BRI), A Body Shape Index (ABSI), visceral adiposity index (VAI), lipid accumulation product (LAP), fatty liver index (FLI), and triglyceride-to-glucose fasting index (TyG), as well as four blood lipid indices as follows: total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL-C), and low-density lipoprotein (LDL-C).The association between adiposity indices and blood lipid indices for diagnosis of prediabetes was estimated using a logistic regression model to obtain the odds ratio (OR) and its 95% confidence interval (CI). We calculated the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis to measure the predictive value of adiposity indices and blood lipid indicators for the diagnosis of prediabetes in the general population stratified by gender.
The median age of the participants was 56 years old, men accounting for 35.3% of the final group. After adjusting for confounding factors, association of BMI, BRI, VAI, LAP, TyG, TC, TG, and LDL-C with prediabetes status was assessed at both baseline and follow-up. TyG (AUC, overall: 0.677 (95% CI, 0.665, 0.689), male: 0.645 (95% CI, 0.624-0.667), and female: 0.693 (95% CI, 0.678-0.708)) have better diagnostic value for prediabetes than VAI, LAP, FLI, TC, TG, HDL-C, and LDL-C. The predictive value of the combination of TyG, BRI, VAI, and TG significantly improves the power of any single index in the diagnosis of prediabetes. The AUC and corresponding 95% CI of TyG, BRI, VAI, and TG and the combination of these four indicators to diagnose prediabetes were 0.677 (0.665, 0.689), 0.630 (0.617, 0.643), 0.618 (0.606, 0.631), 0.622 (0.609, 0.635), and 0.728 (0.716, 0.739), respectively.
Among the eight adiposity indices and four blood lipid indices evaluated in the study, TyG had the highest diagnostic value for prediabetes in isolated indexes, and the combination of TyG, BRI, VAI, and TG significantly improved the diagnostic value for prediabetes of any single indicator.
本研究旨在探讨肥胖指数与血脂指标及糖尿病前期之间的关联。我们比较了新型肥胖指数、传统肥胖指数及血脂指标在糖尿病前期诊断中的预测价值。
这是一项针对7953名参与者的前瞻性队列研究。随访时间为3年。八项肥胖指数包括：体重指数（BMI）、腰围（WC）、身体圆度指数（BRI）、A体型指数（ABSI）、内脏脂肪指数（VAI）、脂质蓄积产物（LAP）、脂肪肝指数（FLI）以及甘油三酯与空腹血糖指数（TyG），还有四项血脂指标：总胆固醇（TC）、甘油三酯（TG）、高密度脂蛋白（HDL-C）和低密度脂蛋白（LDL-C）。采用逻辑回归模型评估肥胖指数与血脂指标对糖尿病前期诊断的关联，以获得比值比（OR）及其95%置信区间（CI）。我们计算受试者工作特征（ROC）曲线分析的曲线下面积（AUC），以衡量肥胖指数和血脂指标在按性别分层的普通人群中对糖尿病前期诊断的预测价值。
参与者的中位年龄为56岁，男性占最终研究组的35.3%。在调整混杂因素后，在基线和随访时均评估了BMI、BRI、VAI、LAP、TyG、TC、TG和LDL-C与糖尿病前期状态的关联。TyG（AUC，总体：0.677（95%CI，0.665，0.689），男性：0.645（95%CI，0.624 - 0.667），女性：0.693（95%CI，0.678 - 0.708））对糖尿病前期的诊断价值优于VAI、LAP、FLI、TC、TG、HDL-C和LDL-C。TyG、BRI、VAI和TG联合使用的预测价值显著提高了任何单一指标在糖尿病前期诊断中的效能。TyG、BRI、VAI、TG以及这四项指标联合诊断糖尿病前期的AUC及相应95%CI分别为0.677（0.665，0.689）、0.630（0.617，0.643）、0.618（0.606，0.631）、0.622（0.609，0.635）和0.728（0.716，0.739）。
在本研究评估的八项肥胖指数和四项血脂指标中，TyG在单一指标中对糖尿病前期的诊断价值最高，且TyG、BRI、VAI和TG联合使用显著提高了任何单一指标对糖尿病前期的诊断价值。
