Study population characteristics.

The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative screening approaches. This study investigates the potential of using a voice-based algorithm to predict T2D status in adults, as the first step towards developing a non-invasive and scalable screening method. We analyzed pre-specified text recordings from 607 US participants from the Colive Voice study registered on ClinicalTrials.gov (NCT04848623). Using hybrid BYOL-S/CvT embeddings, we constructed gender-specific algorithms to predict T2D status, evaluated through cross-validation based on accuracy, specificity, sensitivity, and Area Under the Curve (AUC). The best models were stratified by key factors such as age, BMI, and hypertension, and compared to the American Diabetes Association (ADA) score for T2D risk assessment using Bland-Altman analysis. The voice-based algorithms demonstrated good predictive capacity (AUC = 75% for males, 71% for females), correctly predicting 71% of male and 66% of female T2D cases. Performance improved in females aged 60 years or older (AUC = 74%) and individuals with hypertension (AUC = 75%), with an overall agreement above 93% with the ADA risk score. Our findings suggest that voice-based algorithms could serve as a more accessible, cost-effective, and noninvasive screening tool for T2D. While these results are promising, further validation is needed, particularly for early-stage T2D cases and more diverse populations.全球范围内亟需降低未确诊2型糖尿病（type 2 diabetes, T2D）的疾病负担，这一需求催生了创新型筛查方法的研发契机。本研究探讨了基于语音算法预测成人2型糖尿病患病状态的应用潜力，以此作为开发无创且可规模化筛查手段的第一步。我们对注册于ClinicalTrials.gov（注册号：NCT04848623）的Colive Voice研究中607名美国参与者的预先指定语音转写文本进行了分析。本研究采用混合BYOL-S/CvT嵌入特征，构建了分性别2型糖尿病患病状态预测算法，并以准确率、特异度、灵敏度及曲线下面积（Area Under the Curve, AUC）为评估指标，通过交叉验证对模型性能开展检验。我们针对年龄、身体质量指数（Body Mass Index, BMI）、高血压等关键因素对最优模型进行分层分析，并采用布兰德-奥特曼分析（Bland-Altman analysis），将其与美国糖尿病协会（American Diabetes Association, ADA）的2型糖尿病风险评估评分进行对比。该语音算法展现出良好的预测性能：男性受试者的曲线下面积为75%，女性为71%，可正确识别71%的男性2型糖尿病患者与66%的女性2型糖尿病患者。在60岁及以上女性受试者（曲线下面积为74%）以及高血压患者群体（曲线下面积为75%）中，模型预测性能有所提升，且与美国糖尿病协会风险评分的总体一致性超过93%。本研究结果表明，基于语音的算法可作为2型糖尿病一种更具可及性、成本效益更高且无创的筛查工具。尽管上述研究结果颇具前景，但仍需开展进一步验证，尤其针对早期2型糖尿病患者与更多样化的人群队列。