Nguyen Duc Phuc, Catcheside Peter, Lechat Bastien, Wittert Gary, Vakulin Andrew, Adams Robert, Appleton Sarah L
Flinders Health and Medical Research Institute-- Sleep Health (Adelaide Institute for Sleep Health), College of Medicine and Public Health, Flinders University, Bedford Park, SA, 5042, Australia.
Freemasons Centre for Male Health and Wellbeing, Level 7, South Australian Health and Medical Research Institute (SAHMRI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia.
Nat Sci Sleep. 2025 Aug 30;17:2013-2025. doi: 10.2147/NSS.S512262. eCollection 2025.
Type 2 diabetes (T2D) shows bidirectional relationships with polysomnographic measures. However, no studies have searched systematically for novel polysomnographic biomarkers of T2D. We therefore investigated if state-of-the-art explainable machine learning (ML) models could identify new polysomnographic biomarkers predictive of incident T2D.
We applied explainable ML models to longitudinal cohort study data from 536 males who were free of T2D at baseline and identified 52 cases of T2D at follow-up (mean 8.3, range 3.5-10.5 years). Beyond ranking biomarker importance, we explored how the explainable ML model approach can identify novel relationships, assist in hypothesis testing, and provide insights into risk factors.
The top five most predictive biomarkers included waist circumference, glucose, and three novel sleep biomarkers: the number of 3% desaturations in non-supine sleep, mean heart rate in supine sleep, and mean hypopnea duration. Explainable machine learning identified a significant association between the number of non-supine desaturation events (threshold of 19 events) and incident T2D (Odds ratio = 2.4 [95% CI 1.2-4.8], P = 0.013). No significant associations were found using continuous or quartiled versions of non-supine desaturation. Additionally, the model provided an individualized risk factor breakdown, supporting a more personalized approach to precision sleep medicine.
Explainable ML supports the role of established biomarkers and reveals novel biomarkers of T2D likely to help guide further hypothesis testing and validation of more robust and clinically useful biomarkers. Although further validation is needed, these proof-of-concept data support the benefits of explainable ML in prospective data analysis.
2型糖尿病（T2D）与多导睡眠图测量结果呈双向关系。然而，尚无研究系统地探寻T2D新的多导睡眠图生物标志物。因此，我们研究了先进的可解释机器学习（ML）模型是否能够识别预测新发T2D的新型多导睡眠图生物标志物。
我们将可解释ML模型应用于来自536名男性的纵向队列研究数据，这些男性在基线时无T2D，随访期间确诊52例T2D（平均8.3年，范围3.5 - 10.5年）。除了对生物标志物重要性进行排名外，我们还探讨了可解释ML模型方法如何识别新关系、协助假设检验以及深入了解风险因素。
预测性最强的前五个生物标志物包括腰围、血糖以及三个新的睡眠生物标志物：非仰卧睡眠中3%血氧饱和度下降次数、仰卧睡眠平均心率和平均呼吸暂停持续时间。可解释机器学习确定非仰卧血氧饱和度下降事件数量（阈值为19次）与新发T2D之间存在显著关联（优势比 = 2.4 [95% CI 1.2 - 4.8]，P = 0.013）。使用非仰卧血氧饱和度下降的连续或四分位数版本未发现显著关联。此外，该模型提供了个性化的风险因素分解，支持采用更个性化的方法进行精准睡眠医学。
可解释ML支持已确立生物标志物的作用，并揭示了T2D的新型生物标志物，可能有助于指导进一步的假设检验以及对更强大且临床有用的生物标志物进行验证。尽管需要进一步验证，但这些概念验证数据支持可解释ML在前瞻性数据分析中的益处。
