阻塞性睡眠呼吸暂停中的多维神经心理异质性：单中心队列的深度临床表型分析、人群蛋白质组学背景及抑郁负担风险分层

文献信息

中文摘要

未标注： 阻塞性睡眠呼吸暂停（OSA）与显著的认知和情感负担相关，但常规呼吸严重程度并不能完全解释这种异质性。我们研究了OSA中的神经心理易感性是否表现出差异化的临床关联模式，认知、抑郁和焦虑是否共享一般人群蛋白质组学关联，以及一个独立的抑郁负担模型是否能够支持实用的风险分层。
方法： 我们开展了一项以临床为锚定的研究，整合了一个单中心临床表型成人OSA队列、英国生物银行Olink子集中的血浆蛋白质组学、基于机器学习的候选优先排序、基于TyG-WHtR锚定抑郁负担分层的转化扩展，以及纵向人类PBMC转录组学背景化分析。在单中心队列中，使用多变量回归和探索性横断面分解来评估呼吸负担、睡眠碎片化、代谢失调、认知和情感症状之间的关联。在英国生物银行蛋白质组学数据集中，在全蛋白质组错误发现率校正后识别与认知、抑郁和焦虑相关的蛋白质。风险模型在可获得的来源OSA中开发，并在单中心多导睡眠图定义的OSA队列中进行临床评估，未重新拟合。
结果： 在临床表型队列中，认知与呼吸暂停负担和TyG均相关，而情感负担与TyG的关联更强；横断面分解未确立因果中介。在每个结局的2,922种可估计蛋白质中，分别有886、152和45种对认知、PHQ-2和GAD-2具有FDR显著性，其中21种在三个结局中共享，并显示出非随机的不良方向一致性。严格的相互结局和CRP/SII调整减弱了跨结局显著性；在OSA背景分析中，方向一致性仍然常见，尽管个体关联未通过FDR校正。在117个机器学习工作流程中，103个可评估；正式选择的工作流程实现了锁定验证AUROC 0.694。主要全谱风险模型实现了嵌套折外AUROC 0.726和临床评估AUROC 0.809；校准在不同环境中较不稳定（Brier评分0.356），表明绝对概率可能需要局部重新校准。在PBMC分析中，通路水平变化涉及免疫、脂质/溶酶体、线粒体/氧化还原和血管/屏障过程；预定义的候选基因检测未达到FDR显著性。
结论： 研究结果支持一个多维框架，整合了差异化的临床关联、非随机的一般人群蛋白质组学背景、人类功能性转录组学背景以及令人鼓舞的跨环境抑郁负担风险排序，而绝对概率仍较不稳定；OSA特异性分子验证和前瞻性校准仍是优先事项。

英文摘要

UNLABELLED: 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.