Heart failure proteomics: an epidemiology study

National Heart, Lung, And Blood Institute

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Abstract

We studied proteomic signatures associated with death in a HF community cohort and quantified 7,335 plasma proteins using an aptamer-based technology. With machine learning, we identified proteomics cluster signatures associated with mortality and examined how these signatures predicted death while adjusting extensively for clinical parameters included in the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) score, and comorbidity burden. We applied 10-fold cross-validation to optimize the generalizability of our clustering results, and explored mechanistic pathways with bioinformatics enrichment and pathway analyses. Among 1388 individuals with HF, machine learning identified 2 clusters based on 405 proteins associated with death. Cluster assignment was univariately associated with the risk of death with a large increased risk in Cluster 2 patients (HR 2.41, 95% confidence intervals CI 2.12 - 2.75; p <0.0001). This association was only slightly attenuated after adjustment for the MAGGIC score and comorbidity burden (HR: 1.82; 95% CI, 1.58 - 2.08, p<0.0001) and did not differ by ejection fraction. Mechanistic pathways were mainly related to matrix remodeling, immune response, inflammation, and angiogenesis. In summary, taken collectively, these results indicate that proteomics provides important information on the phenotypes of the HF syndrome and the proteomic signatures play an important role in risk stratification. Using machine learning, our initial studies led to the identification of proteomic signatures associated with the risk of death independently of clinical factors. Key mechanistic pathways were identified laying the foundation for mechanistic therapeutic approaches. To pursue this work on deep phenotyping of HF using proteomics, we are currently examining how proteomic signature differ by the presentation of the HF syndrome including specific etiologies (ischemic versus nonischemic) and critical comorbidities (kidney disease). Studies planned for year 2023 include collaboration with the Framingham Heart Study and deployment of the requisite infrastructure for the urgently needed expansion to diverse populations through partnership with the imaging program in place at MedStar. This work has been or will be presented at several national and international meetings listed below. Corresponding manuscripts are submitted or in preparation. Annual Meeting of the Society for Epidemiology Research in June 2022 Proteomic Signatures in Heart Failure: a population-based study Kayode O Kuku Hoyoung Park, Suzette J. Bielinski, Nicholas B. Larson Jungnam Joo, Veronique L. Roger 2022-Abstract-Book.pdf (epiresearch.org). European Society of Cardiology meeting in August 2022. Proteomic Signatures of Heart Failure Mortality in the Community Kayode O Kuku, Hoyoung Park, Brittany Dulek, Suzette J. Bielinski, Jungnam Joo, Veronique L. Roger American Heart Association Scientific Sessions in November 2022 o Proteomic Assessment of Novel Kidney Function Biomarkers in Heart Failure: A Community Study Joseph J. Shearer, Hoyoung Park, Jungnam Joo, Kayode O Kuku, Suzette J. Bielinski, Sheila M. Manemann, Veronique L. Roger o Proteomic Signatures Of Ischemic And Non-ischemic Heart Failure In A Community Cohort Kayode O Kuku, Hoyoung Park ,Joseph J. Shearer, Jungnam Joo, Brittany Dulek, Suzette, J. Bielinski MEd, Veronique L. Roger, MD, MPH American Heart Association Epidemiology Meeting, March 2023 o Proteomic Assessment of Progressive Chronic Renal Insufficiency Risk and Mortality in a Heart Failure Community Study Joseph J. Shearer, Christine P. Limonte, Kayode O. Kuku, Jungnam Joo, Nicholas B. Larson, Suzette J. Bielinski, Vronique L. Roger