糖耐量受损和2型糖尿病患者脂联素水平的相关性研究

目的探讨在糖耐量受损及2型糖尿病患者中血脂血糖、胰岛素抵抗和血清脂联素之间的关系。方法 166例分为正常对照组（29例）、糖耐量受损组（41例）和糖尿病组（96例）,分别检测血脂、血糖、血胰岛素水平和血清脂联素,采用HOMA-IR法计算胰岛素抵抗指数。分别对正常组是否进展为糖耐量受损,以及糖耐量受损是否进展为糖尿病进行二分类Logistic回归分析。结果正常对照组、糖耐量受损组和糖尿病组血清脂联素水平分别为（15.8±3.6）、（10.7±2.4）和（6.3±2.4）mg/L,3组间差异有显著统计学意义（P〈0.05）。糖耐量受损组的体质指数、总胆固醇、胰岛素水平和胰岛素抵抗显著高于正常组（P均〈0.05）。Logistic回归分析表明体质指数、高密度脂蛋白胆固醇、空腹胰岛素、胰岛素抵抗指数为正常对照进展为糖耐量受损的危险因素。当糖耐量受损组进展为糖尿病组时,空腹胰岛素是唯一有统计学差异的危险因素。脂联素在两个阶段中都为保护因素。结论在糖耐量受损时胰岛素抵抗水平升高,并已伴随脂联素水平下降。血清脂联素测定,可作为2型糖尿病的辅助诊断、及早干预的指标。

Evaluation of adiponectin level in association with impaired glucose tolerance and type 2 diabetes

Objective To explore the relationship between plasma level of lipoprotein and insulin resistance in type 2 diabetes（T2D） patients with impaired glucose tolerance（IGT）.Methods Patients with IGT（n=41） and T2D（n=96） were recruited for this study.Subjects with normal glucose tolerance（n=29） served as controls.Plasma level of lipoprotein,glucose,insulin and adiponectin was measured.Insulin resistance index was calculated by using HOMA-IR.The differences between groups（IGT,T2D and controls） were analyzed by using Logistic regression.Results In controls,IGT and T2D,the plasma adiponectin levels were found to be significantly different at 15.8±3.6 mg/L（mean±SD）,10.7±2.4 mg/L and 6.3±2.4 mg/L respectively（P0.05）.BMI,plasma total cholesterol level and insulin resistance index of IGT patients were significantly higher than those of the controls（P0.05）.Logistic regression analysis showed that BMI,plasma level of high density lipoprotein,total cholesterol and fasting insulin,together with insulin resistance index were risk factors for the deterioration to IGT in controls.Fasting plasma insulin level was the only risk factor in our study in prediction of IGT to T2D.Plasma level of adiponectin was a protective factor in both glycemia deteriorations.Conclusion Plasma adiponectin level decreases with the increase of insulin resistance index in IGT patients who are on an early stage of T2D.Hypoadiponectinaemia can be used as a biomarker for diagnosis and prediction in T2D.