Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO‑TARGETS case–control analyses

Joao Pedro Ferreira (1, 2, 3, 4, 5) , Anne Pizard (1, 2, 3, 4) , Jean-Loup Machu (1, 2, 3, 4) , Emmanuel Bresso (6) , Hans-Peter Brunner-Larocca (7) , Nicolas Girerd (1, 2, 3, 4) , Céline Leroy (1, 2, 3, 4) , Arantxa González (8) , Javier Díez (8) , Stephane Heymans (8, 7) , Marie-Dominique Devignes (6) , Patrick Rossignol (1, 2, 3, 4) , Faiez Zannad (1, 2, 3, 4)

1 DCAC - Défaillance Cardiovasculaire Aiguë et Chronique
2 CIC-P - Centre d'investigation clinique plurithématique Pierre Drouin [Nancy]
3 INI-CRCT - Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy]
4 Cardiovascular & Renal Clinical Trialists - CRCT - French-Clinical Research Infrastructure Network - F-CRIN [Paris]
5 Universidade do Porto = University of Porto
6 CAPSID - Computational Algorithms for Protein Structures and Interactions
7 CARIM - Cardiovascular Research Institute Maastricht
8 UNAV - Universidad de Navarra [Pamplona]

Background Hypertension, obesity and diabetes are major and potentially modifiable "risk factors" for cardiovascular diseases. Identification of biomarkers specific to these risk factors may help understanding the underlying pathophysiological pathways, and developing individual treatment. Methods The FIBRO-TARGETS (targeting cardiac fibrosis for heart failure treatment) consortium has merged data from 12 patient cohorts in 1 common database of > 12,000 patients. Three mutually exclusive main phenotypic groups were identified ("cases"): (1) "hypertensive"; (2) "obese"; and (3) "diabetic"; age-sex matched in a 1:2 proportion with "healthy controls" without any of these phenotypes. Proteomic associations were studied using a biostatistical method based on LASSO and confronted with machine-learning and complex network approaches. Results The case:control distribution by each cardiovascular phenotype was hypertension (50:100), obesity (50:98), and diabetes (36:72). Of the 86 studied proteins, 4 were found to be independently associated with hypertension: GDF-15, LEP, SORT-1 and FABP-2; 3 with obesity: CEACAM-8, LEP and PRELP; and 4 with diabetes: GDF-15, REN, CXCL-1 and SCF. GDF-15 (hypertension + diabetes) and LEP (hypertension + obesity) are shared by 2 different phenotypes. A machine-learning approach confirmed GDF-15, LEP and SORT-1 as discriminant biomarkers for the hypertension group, and LEP plus PRELP for the obesity group. Complex network analyses provided insight on the mechanisms underlying these disease phenotypes where fibrosis may play a central role.ConclusionPatients with “mutually exclusive” phenotypes display distinct bioprofiles that might underpin different biological pathways, potentially leading to fibrosis.

Mots clés: Proteomics; Complex networks; Cardiovascular diseases; Decision tree; Phenotypes; LASSO

Domaines: Cardiologie et système cardiovasculaire; Bio-Informatique, Biologie Systémique [q-bio.QM]; Médecine humaine et pathologie

Joao Pedro Ferreira, Anne Pizard, Jean-Loup Machu, Emmanuel Bresso, Hans-Peter Brunner-Larocca, et al.. Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO‑TARGETS case–control analyses. Clinical Research in Cardiology, 2020, 109 (1), pp.22-33. ⟨10.1007/s00392-019-01480-4⟩. ⟨hal-02138814⟩