Multidimensional neuropsychological heterogeneity in obstructive sleep apnea: deep clinical phenotyping in a single-center cohort, population proteomic context, and depressive-burden risk stratification

Obstructive sleep apnea (OSA) is associated with substantial cognitive and affective burden, yet conventional respiratory severity does not fully explain this heterogeneity. We investigated whether neuropsychological vulnerability in OSA shows differentiated clinical association patterns, whether cognition, depression, and anxiety share general-population proteomic associations, and whether a separate depressive-burden model can support pragmatic risk stratification. Methods: We conducted a clinically anchored study integrating a single-center clinically phenotyped adult OSA cohort, plasma proteomics in the UK Biobank Olink subset, machine-learning-based candidate prioritization, a translational extension based on TyG-WHtR-anchored depressive-burden stratification, and longitudinal human PBMC transcriptomic contextualization. In the single-center cohort, multivariable regression and exploratory cross-sectional decomposition were used to evaluate associations among respiratory burden, sleep fragmentation, metabolic dysregulation, cognition, and affective symptoms. In the UK Biobank proteomic dataset, proteins associated with cognition, depression, and anxiety were identified after full-proteome false discovery rate correction. The risk model was developed in available-source OSA and clinically evaluated, without refitting, in a single-center polysomnography-defined OSA cohort. Results: In the clinically phenotyped cohort, cognition was associated with both apnea burden and TyG, whereas affective burden was more strongly associated with TyG; the cross-sectional decompositions do not establish causal mediation. Of 2,922 estimable proteins per outcome, 886, 152, and 45 were FDR-significant for cognition, PHQ-2, and GAD-2, respectively, with 21 shared across all three outcomes and showing non-random adverse-direction concordance. Strict mutual-outcome and CRP/SII adjustment attenuated cross-outcome significance; in OSA-context analyses, directional concordance remained common, although individual associations did not survive FDR correction. Of 117 machine-learning workflows, 103 were evaluable; the formally selected workflow achieved locked-validation AUROC 0.694. The primary full-spectrum risk model achieved nested out-of-fold AUROC 0.726 and clinical-evaluation AUROC 0.809; calibration was less stable across settings (Brier score 0.356), indicating that absolute probabilities may require local recalibration. In the PBMC analysis, pathway-level changes involved immune, lipid/lysosomal, mitochondrial/redox, and vascular/barrier processes; predefined candidate-gene testing was not FDR-significant. Conclusion: The findings support a multidimensional framework integrating differentiated clinical associations, a non-random general-population proteomic context, human functional transcriptomic context, and encouraging cross-setting depressive-burden risk ranking, while absolute probabilities remained less stable; OSA-specific molecular validation and prospective calibration remain priorities.