This study was led by @jan_lanzer with supervision from @rtlevinson, we thank all authors that contributed to this project @alvaldeolivas, @MarkEPepin, @hhund, @JohannesBacks, @saezlab, and our partners in Innere II department J. H. Schultz and H.C. Friederich and @I4L_org

Insights were leveraged to predict novel gene candidates for HFpEF based on proximity to comorbidity profiles. Biological relevance in a murine HFpEF model suggests that comorbidities in HFpEF associate with recurrent genetic associations relevant for ventricular remodeling!

We connected the HFnet to multiple layers of gene networks and internally validated predicted disease-gene associations via the multi-layer random walk.

We built a comorbidity network from HF patients that yielded 9 disease clusters reflecting co-occurring disease groups. These groups also could be linked to patient characteristics such as age, sex or HF-subtype.

We found that #HFpEF and #HFrEF patients could be separated based on comorbidity profiles alone. In general, #HFpEF patients suffered from more diverse comorbidities than #HFrEF!

Comorbidities are expected to be influential factors to the #HFpEF syndrome, we analyzed the comorbidity profiles of a total of 29k HF patients visiting @uniklinik_hd using network based methods. Now out in @BMCMedicine https://t.co/TEWJO39x8r