TY - JOUR
T1 - Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes
T2 - the FIBRO-TARGETS case-control analyses
AU - Ferreira, Joao Pedro
AU - Pizard, Anne
AU - Machu, Jean-Loup
AU - Bresso, Emmanuel
AU - Brunner-La Rocca, Hans-Peter
AU - Girerd, Nicolas
AU - Leroy, Celine
AU - Gonzalez, Arantxa
AU - Diez, Javier
AU - Heymans, Stephane
AU - Devignes, Marie-Dominique
AU - Rossignol, Patrick
AU - Zannad, Faiez
AU - FIBRO-TARGETS investigators
N1 - Funding Information:
JF, AP, JLM, PR, NG, MD D, EB and FZ are supported by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004), by the French PIA project???Lorraine Universit? d?Excellence???GEENAGE (ANR-15-IDEX-04-LUE) programs, and the Contrat de Plan Etat R?gion Lorraine and FEDER IT2MP. The authors thank the CRB Lorrain biobank for handling biosamples. The research leading to these results has received funding from the European Union Commission?s Seventh Framework programme under Grant agreement no. 602904 (FIBROTARGETS) and No. 261409 (MEDIA). SH acknowledge the support from the Netherlands Cardiovascular Research Initiative, an initiative with support of the Dutch Heart Foundation, CVON2016-Early HFPEF, 2015-10, and CVON She-PREDICTS, 2017-21. This research is co-financed as a PPP-allowance Research and Innovation by the Ministry of Economic Affairs within Top Sector Life sciences & Health in the Netherlands.
Publisher Copyright:
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2020/1
Y1 - 2020/1
N2 - 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.Graphic abstractPlasma 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.[GRAPHICS].
AB - 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.Graphic abstractPlasma 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.[GRAPHICS].
KW - Cardiovascular diseases
KW - Phenotypes
KW - Proteomics
KW - LASSO
KW - Decision tree
KW - Complex networks
KW - STEM-CELL FACTOR
KW - DIABETES-MELLITUS
KW - BLOOD-PRESSURE
KW - TASK-FORCE
KW - DISEASE
KW - RISK
KW - DATABASE
KW - VARIANT
KW - RATIONALE
KW - UPDATE
U2 - 10.1007/s00392-019-01480-4
DO - 10.1007/s00392-019-01480-4
M3 - Article
C2 - 31062082
SN - 1861-0684
VL - 109
SP - 22
EP - 33
JO - Clinical research in cardiology
JF - Clinical research in cardiology
IS - 1
ER -