Sleep as a Window: Auditable LLM Decoding of Aging Signatures from Integrated Sleep Profiles

Biological aging progresses at different rates across organs, creating patterns of vulnerability that chronological age alone cannot capture. Sleep offers an information-rich window into this heterogeneity, with each participant's full clinical record providing context for its interpretation. Existing sleep-age models usually yield a single estimate, whereas large language model (LLM) outputs can be difficult to trace to patient evidence. We introduce an evidence-auditable LLM framework that decodes integrated sleep profiles into an overall sleep age and ten organ-resolved age representations. A frozen registry curated by clinical sleep experts and trace-validated reports preserve input provenance. The framework also audits the accompanying physiological chains for major contradictions. We evaluated the framework in 9,123 participants from SHHS, MrOS, and the real-world XH longitudinal cohort. After adjustment for chronological age and conventional risk factors, mortality HRs per year of sleep gap were 1.054 (95% CI 1.031–1.079) in SHHS and 1.030 (1.008–1.052) in MrOS. Ten-year mortality was higher in the top than in the bottom gap decile (SHHS 25.4% versus 8.9%, MrOS 25.5% versus 13.0%). Models combining chronological age with endpoint-matched organ gaps outperformed those using non-matched gaps in C-index (mean proportion outperformed: SHHS 92%, MrOS 88%, XH 84%). This pattern supports LLM decoding of organ-specific aging signatures. In XH, overall sleep gaps tracked incident disease and cumulative disease burden, and endpoint-matched gaps added predictive value beyond multiple clinical baseline models. Together, these findings position integrated sleep profiles as an auditable window onto systemic aging heterogeneity and its future health consequences.