Zou Xiantong, Luo Yingying, Huang Qi, Zhu Zhanxing, Li Yufeng, Zhang Xiuying, Zhou Xianghai, Ji Linong
Peking University People's Hospital, Beijing, China.
School of Mathematical Sciences, Peking University, Beijing, China.
Diabetes Obes Metab. 2024 Jan;26(1):97-107. doi: 10.1111/dom.15291. Epub 2023 Oct 1.
To investigate whether stratifying participants with prediabetes according to their diabetes progression risks (PR) could affect their responses to interventions.
We developed a machine learning-based model to predict the 1-year diabetes PR (ML-PR) with the least predictors. The model was developed and internally validated in participants with prediabetes in the Pinggu Study (a prospective population-based survey in suburban Beijing; n = 622). Patients from the Beijing Prediabetes Reversion Program cohort (a multicentre randomized control trial to evaluate the efficacy of lifestyle and/or pioglitazone on prediabetes reversion; n = 1936) were stratified to low-, medium- and high-risk groups using ML-PR. Different effect of four interventions within subgroups on prediabetes reversal and diabetes progression was assessed.
Using least predictors including fasting plasma glucose, 2-h postprandial glucose after 75 g glucose administration, glycated haemoglobin, high-density lipoprotein cholesterol and triglycerides, and the ML algorithm XGBoost, ML-PR successfully predicted the 1-year progression of participants with prediabetes in the Pinggu study [internal area under the curve of the receiver operating characteristic curve 0.80 (0.72-0.89)] and Beijing Prediabetes Reversion Program [external area under the curve of the receiver operating characteristic curve 0.80 (0.74-0.86)]. In the high-risk group pioglitazone plus intensive lifestyle therapy significantly reduced diabetes progression by about 50% at year l and the end of the trial in the high-risk group compared with conventional lifestyle therapy with placebo. In the medium- or low-risk group, intensified lifestyle therapy, pioglitazone or their combination did not show any benefit on diabetes progression and prediabetes reversion.
This study suggests personalized treatment for prediabetes according to their PR is necessary. ML-PR model with simple clinical variables may facilitate personal treatment strategies in participants with prediabetes.
研究根据糖尿病前期参与者的糖尿病进展风险（PR）进行分层是否会影响他们对干预措施的反应。
我们开发了一种基于机器学习的模型，以最少的预测指标预测1年糖尿病进展风险（ML-PR）。该模型在平谷研究（北京郊区一项基于人群的前瞻性调查；n = 622）的糖尿病前期参与者中进行开发和内部验证。来自北京糖尿病前期逆转项目队列（一项多中心随机对照试验，以评估生活方式和/或吡格列酮对糖尿病前期逆转的疗效；n = 1936）的患者使用ML-PR分层为低、中、高风险组。评估了亚组内四种干预措施对糖尿病前期逆转和糖尿病进展的不同影响。
使用包括空腹血糖、75克葡萄糖负荷后2小时餐后血糖、糖化血红蛋白、高密度脂蛋白胆固醇和甘油三酯在内最少的预测指标，以及ML算法XGBoost，ML-PR成功预测了平谷研究[受试者工作特征曲线的内部曲线下面积为0.80（0.72 - 0.89）]和北京糖尿病前期逆转项目[受试者工作特征曲线的外部曲线下面积为0.80（0.74 - 0.86）]中糖尿病前期参与者的1年进展情况。在高风险组中，与使用安慰剂的传统生活方式治疗相比，吡格列酮加强化生活方式治疗在第1年和试验结束时使高风险组的糖尿病进展显著降低约50%。在中、低风险组中，强化生活方式治疗、吡格列酮或其联合使用对糖尿病进展和糖尿病前期逆转均未显示出任何益处。
本研究表明，根据糖尿病前期患者的进展风险进行个性化治疗是必要的。具有简单临床变量的ML-PR模型可能有助于制定糖尿病前期参与者的个性化治疗策略。
