Status of glycosylated hemoglobin and prediction of glycemic control among patients with insulin-treated type 2 diabetes in North China:a multicenter observational study  

作　　者：Jiao Wang Meng-Yang Wang Hui Wang Hong-Wei Liu Rui Lu Tong-Qing Duan Chang-Ping Li Zhuang Cui Yuan-Yuan Liu Yuan-Jun Lyu Jun Ma 

机构地区：[1]Department of Health Statistics,College of Public Health,Tianjin Medical University,Tianjin 300070,China [2]Department of Endocrinology,Tianjin Hospital,Tianjin 300211,China.

出　　处：《Chinese Medical Journal》2020年第1期17-24,共8页中华医学杂志（英文版）

基　　金：This study was supported by grants from the Ministry of Education of the Humanities and Social Science Project(No.17YJAZH048);the National Natural Science Foundation of China(No.81803333).

摘　　要：Background:Blood glucose control is closely related to type 2 diabetes mellitus(T2DM)prognosis.This multicenter study aimed to investigate blood glucose control among patients with insulin-treated T2DM in North China and explore the application value of combining an elastic network(EN)with a machine-learning algorithm to predict glycemic control.Methods:Basic information,biochemical indices,and diabetes-related data were collected via questionnaire from 2787 consecutive participants recruited from 27 centers in six cities between January 2016 and December 2017.An EN regression was used to address variable collinearity.Then,three common machine learning algorithms(random forest[RF],support vector machine[SVM],and back propagation artificial neural network[BP-ANN])were used to simulate and predict blood glucose status.Additionally,a stepwise logistic regression was performed to compare the machine learning models.Results:The well-controlled blood glucose rate was 45.82%in North China.The multivariable analysis found that hypertension history,atherosclerotic cardiovascular disease history,exercise,and total cholesterol were protective factors in glycosylated hemoglobin(HbAlc)control,while central adiposity,family history,T2DM duration,complications,insulin dose,blood pressure,and hypertension were risk factors for elevated HbAlc.Before the dimensional reduction in the EN,the areas under the curve of RF,SVM,and BP were 0.73,0.61,and 0.70,respectively,while these figures increased to 0.75,0.72,and 0.72,respectively,after dimensional reduction.Moreover,the EN and machine learning models had higher sensitivity and accuracy than the logistic regression models(the sensitivity and accuracy of logistic were 0.52 and 0.56;RF:0.79,0.70;SVM:0.84,0.73;BP-ANN:0.78,0.73,respectively).Conclusions:More than half of T2DM patients in North China had poor glycemic control and were at a higher risk of developing diabetic complications.The EN and machine learning algorithms are alternative choices,in addition to the traditional logistic

关 键 词：Type 2 diabetes Blood glucose HBALC Elastic network Machine learning 

分 类 号：R587.1[医药卫生—内分泌]