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.

利用密集睡眠期患者的数据，本研究引入一种新型的多层次表型模型，分析这些疾病随时间的进展和相互作用，并通过树状模型进行特征重要性分析，发展混合效应模型，应用 t 分布随机邻居嵌入和高斯混合模型对患者数据进行分群，阐明了心血管疾病和阻塞性睡眠呼吸暂停的动态关系，为预测疾病发生和个体化治疗提供有价值的工具。