Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Bioquant, Heidelberg, Germany.
Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany.
BMC Med. 2023 Jul 24;21(1):267. doi: 10.1186/s12916-023-02922-7.
Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles.
We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature.
We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance.
We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.
合并症预计会影响射血分数保留的心力衰竭（HFpEF）的病理生理学。然而，合并症的情况通常会简化为少数几种合并症。系统医学方法可以对表型广泛的合并症情况进行建模，以提高我们对 HFpEF 的认识，并推断出相关的遗传情况。
我们回顾性地研究了德国一所大学医院 29047 名 HF 患者中的 569 种合并症，其中包括 8062 名 HFpEF 和 6585 名射血分数降低的心力衰竭（HFrEF）患者。我们通过多元对应分析评估了 HF 亚型之间合并症情况的差异。然后，我们使用机器学习分类器来识别 HFpEF 和 HFrEF 患者的独特合并症情况。此外，我们构建了一个合并症网络（HFnet），以识别总结表型广泛合并症的主要疾病群。最后，我们通过将 HFnet 与整合了多个数据库的多层基因网络相连接，预测 HFpEF 的新基因候选物。为了证实 HFpEF 的候选基因，我们在 HFpEF 小鼠模型中收集了转录组数据。我们将预测的基因与小鼠疾病特征以及文献进行了比较。
我们发现 HFpEF 和 HFrEF 之间的合并症情况差异很大，而它们各自与 HFmrEF 更为相似。HFpEF 患者的合并症比 HFrEF 患者更多样化，包括肿瘤、骨和类风湿性疾病。HFnet 中的疾病群捕获了 HF 患者的重要合并症概念，这些概念可以分配给 HF 亚型、年龄组和性别。基于 HFpEF 的合并症情况，我们预测并恢复了基因候选物，包括参与纤维化的基因（COL3A1、LOX、SMAD9、PTHL）、肥大的基因（GATA5、MYH7）、氧化应激的基因（NOS1、GSST1、XDH）和内质网应激的基因（ATF6）。最后，预测的基因在小鼠转录组疾病特征中显著过表达，这为它们的相关性提供了额外的可能性。
我们应用系统医学的概念来分析 HF 患者队列中的合并症情况。我们能够识别有助于描述 HF 患者的疾病群。我们为 HFpEF 确定了独特的合并症情况，并通过网络传播来提出新的候选基因。从常规临床数据中识别出独特的合并症情况和候选基因，为改善 HFpEF 患者的诊断和确定治疗靶点提供了新的思路。
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