对年轻男性未来糖尿病前期的危险因素的识别在很大程度上仍未被检查。这项研究纳入了6247名在基线(FPGbase)时空腹血糖正常的年轻华裔男性,并使用机器学习(Mach-L)方法预测5.8年后的糖尿病前期。本研究旨在实现以下目标:1.评估马赫-L是否优于传统的多元线性回归(MLR)。2.确定最重要的风险因素。基线数据包括人口统计,生物化学,和生活方式信息。建立了两个模型,其中模型1包括所有变量,模型2不包括FPGA,因为它对预测产生了最深远的影响。随机森林,随机梯度提升,极限梯度提升,使用了弹性网,并使用不同的误差度量比较了模型性能。所有马赫-L误差都比MLR小,因此,马赫-L提供了最准确的结果。按照重要性的降序,模型1的关键因素是FPGA,身体脂肪(BF),肌酐(Cr),促甲状腺激素(TSH),WBC,和年龄,而模型2的是BF,白细胞,年龄,TSH,TG,和LDL-C我们得出结论,FPGbase是预测未来糖尿病前期的最重要因素。然而,删除FPGA后,WBC,TSH,BF,HDL-C,年龄和年龄是5.8岁后的关键因素。
The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.