基于Logistic回归分析及ROC曲线评估LAP对PCOS糖代谢状态的预测价值

Evaluation of Predictive Value of LAP on Glucose Metabolism of PCOS based on Logistic Regression Analysis and ROC Curve

目的探讨多囊卵巢综合征(polycystic ovary syndrome,PCOS)患者不同糖代谢状态下一般临床特征、脂代谢及新型体脂指数的差异性,并基于Logistic回归分析及ROC曲线评估脂质蓄积指数(LAP)对PCOS患者糖代谢状态的预测价值。方法回顾性分析黑龙江中医药大学附属第一医院妇科门诊临床科研信息一体化系统PCOS病例800例,时间节点为2015年7月~2019年5月,根据1999年WHO制定的糖代谢状态分类标准分为3组,即正常糖代谢组(NGT组)497例、糖代谢受损组(IGT组)248例及糖尿病组(T2DM组)55例。结果 T2DM组及IGT组的年龄、BMI、WC、HC及WHR显著高于NGT组,T2DM组的BMI及HC显著高于IGT组(P<0.05);T2DM组及IGT组的HOMA-IR、TG、TC、LDL、Apo-B、LAP及VAI均显著高于NGT组,HDL显著低于NGT组,T2DM组的HOMA-IR、TG、TC、LDL、Apo-B、LAP及VAI显著高于IGT组(P<0.05);经多因素Logistic回归分析,最终确定PCOS患者IGT的回归方程为Logit(P)=-1.518+0.012 LAP,对Logistic回归分析模型中的LAP值为检验变量做ROC曲线,曲线下面积为0.714,约登指数为0.329,临界值为41.905。经多因素Logistic回归分析,确定PCOS患者T2DM的回归方程为Logit(P)=-3.606+0.014 LAP,对Logistic回归分析模型中的LAP值为检验变量做ROC曲线,曲线下面积为0.797,约登指数为0.463,临界值为43.43。结论 PCOS患者不同糖代谢状态下一般临床特征及糖脂代谢存在差异性,且伴随糖代谢状态的进展而加重;基于Logistic回归分析及ROC曲线评估LAP对PCOS患者糖代谢状态的预测价值,建立回归方程,提高对PCOS患者糖代谢状态的预测效能及诊断的敏感度和特异性,临床应用价值较高。

Objective To explore the differences in general clinical characteristics, lipid metabolism and new body fat index of patients with polycystic ovary syndrome under different glucose metabolism status, and to evaluate the predictive value of LAP on glucose metabolism status in PCOS patients based on Logistic regression and ROC curve. Methods A retrospective analysis of 800 cases of PCOS cases in the integrated clinical research information system of gynecology clinic of the First Affiliated Hospital of Heilongjiang University of Chinese Medicine, time node is from July 2015 to May 2019, according to the glucose metabolism status classification standard established by the WHO in 1999, they were divided into three groups: 497 in the normal glucose metabolism group, 248 in the impaired glucose metabolism group, and 55 in the diabetes group. Results The Age, BMI, WC, HC and WHR of the T2 DM group and IGT group were significantly higher than those of NGT group, and the BMI and HC of T2 DM group were significantly higher than those of IGT group(P<0.05); The HOMA-IR, TG, TC, LDL, Apo-B, LAP, and VAI of the T2 DM group and the IGT group were significantly higher than the NGT group, and the HDL was significantly lower than the NGT group. HOMA-IR, TG, TC, LDL, Apo-B, LAP, and VAI were significantly higher in the T2 DM group than in the IGT group(P<0.05); After multivariate Logistic regression analysis, the regression equation of IGT in PCOS patients was finally determined as: Logit(P)=-1.518+0.012 LAP. The ROC curve of the LAP value in the Logistic regression analysis model was used as the test variable, the area under the curve is 0.714, the Jordan Index is 0.329, and the critical value is 41.905; After multivariate Logistic regression analysis, the regression equation of T2 DM in PCOS patients was determined as: Logit(P)=-3.606 + 0.014 LAP. The ROC curve of the LAP value in the Logistic regression analysis model was used as the test variable, the area under the curve is 0.797, the Jordan Index is 0.463, and the critical value is 43.43. Conclusion There are differences in the general clinical characteristics and glucose and lipid metabolism of patients with PCOS under different glucose metabolism states, and they are aggravated with the progress of glucose metabolism states. Logistic regression and ROC curve were used to evaluate the predictive value of LAP on the glucose metabolism status of PCOS patients, and a regression equation was established, in order to improve the predictive power of glucose metabolism status in PCOS patients and the sensitivity and specificity of diagnosis, it has higher clinical application value.