Data-driven discovery of core sleep biomarkers for predicting early cardiometabolic risk in a healthy population using machine learning

Background Identifying robust biomarkers for future cardiometabolic risk within the crucial “ preventive window” in healthy individuals remains a major challenge. While numerous sleep metrics are linked to health, their hierarchical importance is unknown. This study aimed to leverage a data-driven machine learning paradigm to move beyond conventional metrics and objectively identify the core sleep-related physiological drivers for predicting the transition to early-stage cardiometabolic risk.

We conducted a longitudinal analysis on 447 initially healthy participants from the Sleep Heart Health Study (SHHS). A LASSO (L1-regularized) logistic regression model was trained on 16 high-quality clinical and polysomnographic features to perform data-driven biomarker selection, following a rigorous data quality audit where high-missingness variables (e.g., heart rate variability) were excluded. The performance of the final models was rigorously evaluated using 10-repeats of 10-fold cross-validation and compared using paired t-tests. Findings LASSO regression identified a parsimonious set of six core predictors. Notably, respiratory disturbance index (RDI) and minimum nocturnal oxygen saturation (min_spo2) emerged as the key biomarkers, superseding traditional sleep fragmentation metrics like the arousal index. In the primary cross-validation analysis, the lean LASSO model demonstrated the strongest predictive performance (mean AUC = 0.698), statistically outperforming a complex model with all 16 features (mean AUC = 0.669, p<0.0001). This superiority and robustness were maintained in high-risk subgroups. Interpretation Our data-driven approach reveals that physiological stress directly linked to sleep-disordered breathing and nocturnal hypoxemia, rather than general sleep fragmentation, are the primary drivers of the transition towards early cardiometabolic risk in healthy individuals. This finding provides specific, translatable targets for precision preventive medicine, points towards novel mechanisms for early risk development, and offers a blueprint for developing next-generation screening tools, potentially integrated into wearable technology. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study is a secondary analysis of de-identified data from the Sleep Heart Health Study (SHHS) cohort, which was made publicly available through the National Sleep Research Resource (NSRR, sleepdata.org). The original SHHS study was approved by the institutional review boards of all participating institutions, and all original participants provided written informed consent. As our research involved no interaction with human subjects and used only existing, publicly available, de-identified data, it is exempt from further institutional review board review. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The de-identified dataset used in this study was obtained from the Sleep Heart Health Study (SHHS) cohort, which is publicly available to researchers through the National Sleep Research Resource (NSRR). Access can be requested via the NSRR’s official website: https://sleepdata.org/. The full analysis code for this study is available from the corresponding author upon reasonable request.