TyG及其衍生指数与2型糖尿病发病关系的巢式病例对照研究
A nested case-control study on relationship of TyG and its derivative indexes with incidence of T2DM
背景 我国是世界上糖尿病患者人数最多的国家。胰岛素抵抗形成了2型糖尿病的主要发病机制，有研究表明胰岛素抵抗替代标志物甘油三酯-葡萄糖指数（Triglyceride and Glucose Index, TyG）及其衍生指数可能与2型糖尿病的发展具有一定相关性，但现有研究以单独横断面研究或纵向研究居多，在我国东北地区相关研究较少。目的 探究TyG及其衍生指数甘油三酯-葡萄糖-身体质量指数（Triglyceride Glucose-body mass index, TyG-BMI）与2型糖尿病之间的关系，同时比较TyG、TyG-BMI的预测能力，为2型糖尿病高危人群的早期筛查提供科学依据。方法 采用巢式病例对照研究方法，以2018年1月-2021年4月随访期间209例新发2型糖尿病患者为病例组；在同期随访过程中未新发2型糖尿病、内分泌系统疾病及恶性肿瘤者中，按照性别相同、年龄±2岁采用倾向性评分进行1:2匹配，选取418例设为对照组，最终共627人纳入本次巢式病例对照研究。收集其基线资料，包括流行病学信息、体格检查信息、生化检测信息、TyG、TyG-BMI。采用Cox拟合条件logistic回归模型进行TyG、TyG-BMI与2型糖尿病之间关系的研究。采用限制性立方样条回归模型分析TyG、TyG-BMI不同水平与2型糖尿病的剂量-反应关系。通过受试者工作特征曲线（Receiver Operating Characteristic Curve, ROC）分析TyG、TyG-BMI对2型糖尿病的诊断价值。结果 与对照组相比，新发病例组的BMI、AC、SBP、DBP、FPG、TG、TC、LDL-C、TyG、TyG-BMI均高于对照组、HDL-C低于对照组（P0.05）,剂量反应关系呈递增形状,当TyG、TyG-BMI高于8.838、229.364时，2型糖尿病发病风险逐渐升高。TyG、TyG-BMI的ROC曲线下面积（Area under the Curve, AUC）分别为0.696(95%CI：0.658-0.732)、0.725(95%CI：0.688-0.760)。TyG、TyG-BMI预测2型糖尿病的最佳切点分别为8.650、224.859。结论 TyG、TyG-BMI的高水平状态是2型糖尿病的独立危险因素，其与2型糖尿病呈线性剂量-反应关系，二者对2型糖尿病均具有预测价值。若综合考虑风险关联强度、AUC、筛查结果的临床影响等方面TyG-BMI或许是较优的预测指标。
内科学医学研究方法
TyGTyG-BMI2型糖尿病巢式病例对照研究限制性立方样条
葛旭红, 胡洁怡, 白云瑞, 王露, 冷松.TyG及其衍生指数与2型糖尿病发病关系的巢式病例对照研究[EB/OL].(2022-10-28)[2025-11-03].https://chinaxiv.org/abs/202211.00054.点此复制
BackgroundChina is the country with the largest number of diabetics in the world. Insulin resistance has formed the main pathogenesis of type 2 diabetes mellitus, and studies have shown that the triglyceride and Glucose Index (TyG) and its derivative index may have a certain correlation with the development of type 2 diabetes, but the existing studies are mostly cross-sectional studies or longitudinal studies alone, and there are fewer related studies in northeast China. ObjectiveTo explore the relationship between TyG and its derivative index triglyceride-glucose-body mass index (TyG-BMI) and type 2 diabetes mellitus, and to compare the predictive power of TyG and TyG-BMI, to provide a scientific basis for early screening of people at high risk of type 2 diabetes. MethodsUsing a nested case-control study method, 209 patients with new-onset type 2 diabetes mellitus were enrolled in the follow-up period from January 2018 to April 2021; Among those who did not have new type 2 diabetes, endocrine system diseases and malignant tumors during the same period of follow-up, a tendency score was matched according to the same sex, age 2 years old, and 418 cases were selected as the control group, and a total of 627 people were finally included in the nested case control study. Collect baseline data, including epidemiological information, physical examination information, biochemical testing information, TyG, TyG-BMI. The relationship between TyG, TyG-BMI and type 2 diabetes mellitus was studied using cox-fitted conditional logistic regression model. The dose-response relationship between different levels of TyG and TyG-BMI and type 2 diabetes was analyzed by restrictive cubic spline regression model. The diagnostic value of TyG and TyG-BMI for type 2 diabetes was analyzed by the Receiver Operating Characteristic Curve (ROC). ResultsCompared with the control group, the BMI, AC, SBP, DBP, FPG, TG, TC, LDL-C, TyG, TyG-BMI of the new case group were higher than those of the control group, and the HDL-C was lower than that of the control group (P0.05), and the dose-response relationship showed an increasing shape, and the risk of developing type 2 diabetes gradually increased when TyG and TyG-BMI were higher than 8.838 and 229.364. The area under the Curve (AUC) of TyG and TyG-BMI was 0.696 (95% CI: 0.658-0.732) and 0.725 (95% CI: 0.688-0.760), respectively. The best cut points for TyG and TyG-BMI to predict type 2 diabetes were 8.650 and 224.859, respectively. ConclusionsThe high levels of TyG and TyG-BMI are independent risk factors for type 2 diabetes mellitus, and they have a linear dose-response relationship with type 2 diabetes mellitus, and both have predictive value for type 2 diabetes. TYG-BMI may be a better predictor if the risk-related intensity, AUC, and clinical impact of screening results are taken into account.
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