Huang Li-Ying, Chen Fang-Yu, Jhou Mao-Jhen, Kuo Chun-Heng, Wu Chung-Ze, Lu Chieh-Hua, Chen Yen-Lin, Pei Dee, Cheng Yu-Fang, Lu Chi-Jie
Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan.
Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
J Clin Med. 2022 Jun 24;11(13):3661. doi: 10.3390/jcm11133661.
The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.
尿白蛋白肌酐比值（uACR）是2型糖尿病（T2D）患者肾功能恶化的一个预警指标。ACR的早期检测已成为一个重要问题。传统上，多元线性回归（MLR）被用于探索风险因素与终点之间的关系。近年来，机器学习（ML）方法在医学领域得到了广泛应用。在本研究中，使用了四种ML方法来预测T2D队列中的uACR。我们假设：（1）ML方法优于传统的MLR；（2）将获得不同排序的风险因素重要性。共有1147例T2D患者接受了四年的随访。使用了MLR、分类与回归树、随机森林、随机梯度提升和极端梯度提升方法。我们的研究结果表明，ML方法的预测误差小于MLR，这表明ML方法更准确。最重要的前六个因素是基线肌酐水平、收缩压和舒张压、糖化血红蛋白以及空腹血糖。总之，在预测T2D队列中的uACR方面，ML方法可能比传统的MLR更准确，在中国T2D患者中，基线肌酐水平是最重要的预测指标，其次是收缩压和舒张压、糖化血红蛋白以及空腹血糖。
