Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, China.
J Diabetes Investig. 2022 Aug;13(8):1374-1386. doi: 10.1111/jdi.13790. Epub 2022 Mar 28.
More than half of diabetes mellitus (DM) and pre-diabetes (pre-DM) cases remain undiagnosed, while existing risk assessment models are limited by focusing on diabetes mellitus only (omitting pre-DM) and often lack lifestyle factors such as sleep. This study aimed to develop a non-laboratory risk assessment model to detect undiagnosed diabetes mellitus and pre-diabetes mellitus in Chinese adults.
Based on a population-representative dataset, 1,857 participants aged 18-84 years without self-reported diabetes mellitus, pre-diabetes mellitus, and other major chronic diseases were included. The outcome was defined as a newly detected diabetes mellitus or pre-diabetes by a blood test. The risk models were developed using logistic regression (LR) and interpretable machine learning (ML) methods. Models were validated using area under the receiver-operating characteristic curve (AUC-ROC), precision-recall curve (AUC-PR), and calibration plots. Two existing diabetes mellitus risk models were included for comparison.
The prevalence of newly diagnosed diabetes mellitus and pre-diabetes mellitus was 15.08%. In addition to known risk factors (age, BMI, WHR, SBP, waist circumference, and smoking status), we found that sleep duration, and vigorous recreational activity time were also significant risk factors of diabetes mellitus and pre-diabetes mellitus. Both LR (AUC-ROC = 0.812, AUC-PR = 0.448) and ML models (AUC-ROC = 0.822, AUC-PR = 0.496) performed well in the validation sample with the ML model showing better discrimination and calibration. The performance of the models was better than the two existing models.
Sleep duration and vigorous recreational activity time are modifiable risk factors of diabetes mellitus and pre-diabetes in Chinese adults. Non-laboratory-based risk assessment models that incorporate these lifestyle factors can enhance case detection of diabetes mellitus and pre-diabetes.
超过一半的糖尿病（DM）和糖尿病前期（pre-DM）病例未被诊断，而现有的风险评估模型仅限于关注糖尿病（忽略 pre-DM），且往往缺乏睡眠等生活方式因素。本研究旨在开发一种非实验室风险评估模型，以检测中国成年人的未诊断糖尿病和糖尿病前期。
基于具有代表性的人群数据集，纳入了 1857 名年龄在 18-84 岁之间、无糖尿病、糖尿病前期和其他主要慢性疾病自述的参与者。结局定义为通过血液检查新发现的糖尿病或糖尿病前期。使用逻辑回归（LR）和可解释的机器学习（ML）方法开发风险模型。使用受试者工作特征曲线下面积（AUC-ROC）、精度-召回曲线（AUC-PR）和校准图验证模型。纳入两种现有的糖尿病风险模型进行比较。
新诊断的糖尿病和糖尿病前期的患病率为 15.08%。除了已知的风险因素（年龄、BMI、WHR、SBP、腰围和吸烟状况）外，我们还发现睡眠持续时间和剧烈娱乐活动时间也是糖尿病和糖尿病前期的显著风险因素。LR（AUC-ROC=0.812，AUC-PR=0.448）和 ML 模型（AUC-ROC=0.822，AUC-PR=0.496）在验证样本中表现良好，ML 模型的区分度和校准度更好。这些模型的性能优于两种现有的模型。
睡眠持续时间和剧烈娱乐活动时间是中国成年人糖尿病和糖尿病前期的可改变风险因素。基于非实验室的风险评估模型，纳入这些生活方式因素可以提高糖尿病和糖尿病前期的病例检出率。
