Explainable Machine Learning Assists in Revealing Associations Between Polysomnographic Biomarkers and Incident Type 2 Diabetes in Men

Introduction: 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. 

Methods: 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. 

Results: 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. 

Conclusion: 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.