João Pedro Ferreira1,2,3, Anne Pizard4,5, Jean-Loup Machu4,5, Emmanuel Bresso6,5, Hans-Peter Brunner-La Rocca7,5, Nicolas Girerd4,5, Céline Leroy4,5, Arantxa González8,9,5, Javier Diez8,9,10,5, Stephane Heymans10,11,12,5, Marie-Dominique Devignes6,5, Patrick Rossignol4,5, Faiez Zannad4,5. 1. Université de Lorraine, Centre d'Investigation Clinique- Plurithématique Inserm CIC-P 1433, and Inserm U1116, CHRU Nancy Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France. j.ferreira@chru-nancy.fr. 2. Department of Physiology, University of Porto, Porto, Portugal. j.ferreira@chru-nancy.fr. 3. The Netherlands Heart Institute, Nl-HI, Utrecht, The Netherlands. j.ferreira@chru-nancy.fr. 4. Université de Lorraine, Centre d'Investigation Clinique- Plurithématique Inserm CIC-P 1433, and Inserm U1116, CHRU Nancy Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France. 5. The Netherlands Heart Institute, Nl-HI, Utrecht, The Netherlands. 6. Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54500, France. 7. Department of Cardiology, CARIM, Maastricht University Medical Center, Maastricht, The Netherlands. 8. Program of Cardiovascular Diseases, Centre of Applied Medical Research, University of Navarra, Pamplona, Spain. 9. CIBERCV, Carlos III Institute of Health, Madrid, Spain. 10. Departments of Cardiology and Cardiac Surgery and Nephrology, Clinic University of Navarra, Pamplona, Spain. 11. Department of Cardiology, CARIM School for Cardiovascular Diseases Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands. 12. Department of Cardiovascular Sciences, Centre for Molecular and Vascular Biology, KU Leuven, Leuven, Belgium.
Abstract
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. CONCLUSION: Patients with "mutually exclusive" phenotypes display distinct bioprofiles that might underpin different biological pathways, potentially leading to fibrosis. Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO-TARGETS case-control analyses. Patients with "mutually exclusive" phenotypes (blue: obesity, hypertension and diabetes) display distinct protein bioprofiles (green: decreased expression; red: increased expression) that might underpin different biological pathways (orange arrow), potentially leading to fibrosis.
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. CONCLUSION:Patients with "mutually exclusive" phenotypes display distinct bioprofiles that might underpin different biological pathways, potentially leading to fibrosis. Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO-TARGETS case-control analyses. Patients with "mutually exclusive" phenotypes (blue: obesity, hypertension and diabetes) display distinct protein bioprofiles (green: decreased expression; red: increased expression) that might underpin different biological pathways (orange arrow), potentially leading to fibrosis.
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