Cardiovascular diseases (CVDs) are notably prevalent among patients with obstructive sleep apnea (OSA), posing unique challenges in predicting CVD progression due to the intricate interactions of comorbidities. Traditional models typically lack the necessary dynamic and longitudinal scope to accurately forecast CVD trajectories in OSA patients. This study introduces a novel multi-level phenotypic model to analyze the progression and interplay of these conditions over time, utilizing data from the Wisconsin Sleep Cohort, which includes 1,123 participants followed for decades. Our methodology comprises three advanced steps: (1) Conducting feature importance analysis through tree-based models to underscore critical predictive variables like total cholesterol, low-density lipoprotein (LDL), and diabetes. (2) Developing a logistic mixed-effects model (LGMM) to track longitudinal transitions and pinpoint significant factors, which displayed a diagnostic accuracy of 0.9556. (3) Implementing t-distributed Stochastic Neighbor Embedding (t-SNE) alongside Gaussian Mixture Models (GMM) to segment patient data into distinct phenotypic clusters that reflect varied risk profiles and disease progression pathways. This phenotypic clustering revealed two main groups, with one showing a markedly increased risk of major adverse cardiovascular events (MACEs), underscored by the significant predictive role of nocturnal hypoxia and sympathetic nervous system activity from sleep data. Analysis of transitions and trajectories with t-SNE and GMM highlighted different progression rates within the cohort, with one cluster progressing more slowly towards severe CVD states than the other. This study offers a comprehensive understanding of the dynamic relationship between CVD and OSA, providing valuable tools for predicting disease onset and tailoring treatment approaches.

翻译：心血管疾病在阻塞性睡眠呼吸暂停患者中尤为普遍，由于共病之间复杂的相互作用，预测其进展面临独特挑战。传统模型通常缺乏必要的动态与纵向视角，难以准确预测OSA患者的心血管疾病发展轨迹。本研究引入一种新颖的多层次表型模型，利用威斯康星睡眠队列（包含1,123名受试者，随访数十年）的数据，分析这些疾病随时间的进展与相互作用。我们的方法包含三个进阶步骤：(1) 通过基于树的模型进行特征重要性分析，以突显关键预测变量，如总胆固醇、低密度脂蛋白和糖尿病。(2) 建立逻辑混合效应模型以追踪纵向转变并识别显著因素，该模型显示出0.9556的诊断准确度。(3) 结合t分布随机邻域嵌入与高斯混合模型，将患者数据划分为反映不同风险特征与疾病进展路径的独特表型聚类。此表型聚类揭示出两个主要群体，其中一组显示出主要不良心血管事件风险显著升高，睡眠数据中的夜间低氧与交感神经系统活动被证实具有重要预测作用。利用t-SNE与GMM对转变与轨迹的分析突显了队列内不同的进展速率，其中一个聚类向严重心血管疾病状态的进展速度较另一聚类更慢。本研究为理解心血管疾病与阻塞性睡眠呼吸暂停之间的动态关系提供了全面视角，并为预测疾病发作及定制治疗方案提供了有价值的工具。