SEARCHCOHORTcardiology, internal medicine

cohort·cardiology, internal medicine, biomedical research, epidemiology·PMC10367269

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

BMC Medicine · 10 authors, 8 centres

AI SUMMARY

FIDELITY 100%

POPULATION29,047 heart failure patients, including 8062 with HFpEF and 6585 with HFrEF

INTERVENTIONRetrospective analysis of 569 comorbidities using network medicine and machine learning

COMPARISONHFpEF versus HFrEF

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AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

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TL;DR

This retrospective cohort study analyzed comorbidity profiles in 29,047 heart failure patients, finding that HFpEF is characterized by a more diverse comorbidity profile than HFrEF and predicted novel candidate genes for HFpEF from these clinical patterns.

Full summary

983 CHARS

This retrospective systems-level study analyzed 569 comorbidities in 29,047 heart failure (HF) patients from a German university hospital. Using multiple correspondence analysis and machine learning, the researchers found HFpEF patients had a more diverse comorbidity profile than HFrEF patients, including neoplastic, osteologic, and rheumatoid disorders. A comorbidity network identified nine disease clusters associated with HF subtypes, age, and sex. The study predicted novel gene candidates for HFpEF, such as genes involved in fibrosis, hypertrophy, oxidative stress, and endoplasmic reticulum stress. These predictions were partially corroborated in a murine HFpEF transcriptomic model. Limitations include the retrospective, single-center design and reliance on ICD-10 codes, which may introduce bias. The findings suggest HFpEF is a systemic, comorbidity-driven syndrome and the derived gene candidates may inform future research into pathophysiology and treatment targets.

PICO

PPOPULATION

29,047 heart failure patients, including 8062 with HFpEF and 6585 with HFrEF

IINTERVENTION

Retrospective analysis of 569 comorbidities using network medicine and machine learning

OOUTCOME

Differences in comorbidity profiles and predicted gene candidates for HFpEF

STUDY TYPE

cohort

SPECIALTY

cardiology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10367269

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

BMC Medicine

READ THE FULL PAPER

ABSTRACT

Background 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. Methods 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. Results 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. Conclusions 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. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s12916-023-02922-7.

CITATION EXPORT

Jan D. Lanzer, Alberto Valdeolivas, Mark Pepin et al.. A network medicine approach to study comorbidities in heart failure with preserved ejection fraction. BMC Medicine. (2023). https://doi.org/10.1186/s12916-023-02922-7

AUTHORS

10 · 8 CENTRES

Jan D. Lanzer, Alberto Valdeolivas, Mark Pepin, Hauke Hund, Johannes Backs, Norbert Frey, Hans-Christoph Friederich, Jobst-Hendrik Schultz, Julio Saez-Rodriguez, Rebecca T. Levinson

1grid.7700.00000 0001 2190 4373Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Bioquant, Heidelberg, Germany
2grid.5253.10000 0001 0328 4908Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany
3grid.7700.00000 0001 2190 4373Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
4Informatics for Life, Heidelberg, Germany
5grid.417570.00000 0004 0374 1269Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
6grid.7700.00000 0001 2190 4373Institute of Experimental Cardiology, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
7grid.452396.f0000 0004 5937 5237DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Im Neuenheimer Feld 669, 69120 Heidelberg, Germany
8grid.5253.10000 0001 0328 4908Department of Cardiology, Internal Medicine III, Heidelberg University Hospital, Heidelberg, Germany