Dataset Information

Rationale of the FIBROTARGETS study designed to identify novel biomarkers of myocardial fibrosis.

ABSTRACT: AIMS:Myocardial fibrosis alters the cardiac architecture favouring the development of cardiac dysfunction, including arrhythmias and heart failure. Reducing myocardial fibrosis may improve outcomes through the targeted diagnosis and treatment of emerging fibrotic pathways. The European-Commission-funded 'FIBROTARGETS' is a multinational academic and industrial consortium with the main aims of (i) characterizing novel key mechanistic pathways involved in the metabolism of fibrillary collagen that may serve as biotargets, (ii) evaluating the potential anti-fibrotic properties of novel or repurposed molecules interfering with the newly identified biotargets, and (iii) characterizing bioprofiles based on distinct mechanistic phenotypes involving the aforementioned biotargets. These pathways will be explored by performing a systematic and collaborative search for mechanisms and targets of myocardial fibrosis. These mechanisms will then be translated into individualized diagnostic tools and specific therapeutic pharmacological options for heart failure. METHODS AND RESULTS:The FIBROTARGETS consortium has merged data from 12 patient cohorts in a common database available to individual consortium partners. The database consists of >12 000 patients with a large spectrum of cardiovascular clinical phenotypes. It integrates community-based population cohorts, cardiovascular risk cohorts, and heart failure cohorts. CONCLUSIONS:The FIBROTARGETS biomarker programme is aimed at exploring fibrotic pathways allowing the bioprofiling of patients into specific 'fibrotic' phenotypes and identifying new therapeutic targets that will potentially enable the development of novel and tailored anti-fibrotic therapies for heart failure.

SUBMITTER: Ferreira JP

PROVIDER: S-EPMC5793978 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Rationale of the FIBROTARGETS study designed to identify novel biomarkers of myocardial fibrosis.

Ferreira João Pedro JP Machu Jean-Loup JL Girerd Nicolas N Jaisser Frederic F Thum Thomas T Butler Javed J González Arantxa A Diez Javier J Heymans Stephane S McDonald Kenneth K Gyöngyösi Mariann M Firat Hueseyin H Rossignol Patrick P Pizard Anne A Zannad Faiez F

ESC heart failure 20171007 1

<h4>Aims</h4>Myocardial fibrosis alters the cardiac architecture favouring the development of cardiac dysfunction, including arrhythmias and heart failure. Reducing myocardial fibrosis may improve outcomes through the targeted diagnosis and treatment of emerging fibrotic pathways. The European-Commission-funded 'FIBROTARGETS' is a multinational academic and industrial consortium with the main aims of (i) characterizing novel key mechanistic pathways involved in the metabolism of fibrillary colla ...[more]

PMID: 28988439

Similar Datasets

Project description:BackgroundOsteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.MethodsSerum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography-tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.ResultsIn total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).ConclusionOur study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

Project description:BackgroundProteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals.MethodsWe used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651).ResultsIn the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β.ConclusionsWe demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

Project description:The optimal management of chronic severe primary degenerative mitral regurgitation (MR) is to repair the valve but identification of the optimal timing of surgery remains challenging. Current guidelines suggest 'watchful waiting' until the onset of symptoms or left ventricular (LV) dysfunction but these have been challenged as promoting 'rescue surgery'. Better predictors are required to inform decision-making in relation to the necessity and timing of surgery. Chronic volume overload is a stimulus for adverse adaptive LV remodelling. Subclinical reduction in LV strain before mitral repair predicts a fall in LV ejection fraction following surgery and is thought to reflect the development of myocardial fibrosis in response to chronic volume overload. Myocardial fibrosis can be detected non-invasively using cardiac magnetic resonance (CMR) imaging techniques as an expansion of the extracellular volume (ECV).This study investigates whether: 1) patients with above median ECV will have smaller reduction in end-systolic volume index (as a measure of the degree of reverse LV remodelling) on CMR following mitral valve repair, compared to those with below median ECV; and 2) higher ECV on CMR, validated through histology, adversely impacts upon post-operative complications and symptomatic improvement following surgery. This is a multi-centre, prospective, cross-sectional comparison of patients prior to and 9 months following surgery for chronic severe primary degenerative MR. To establish the natural history of ECV in MR, an additional cohort of patients with asymptomatic MR who do not wish to consider early repair will be followed. Investigations include CMR, cardiopulmonary exercise test, stress echocardiography, signal-averaged electrocardiogram, 24-h electrocardiogram monitoring, laboratory tests and patient-reported outcome measures. Patients undergoing surgery will have cardiac biopsies performed at the time of mitral valve repair for histological quantification of fibrosis.This study will advance our understanding of ventricular remodelling in MR, its impact on patient symptoms and ventricular response following surgery. Establishing the link between myocardial fibrosis (measured on CMR and validated through histology), with early ventricular dysfunction, will offer physicians a novel non-invasive biomarker that can further inform the timing of surgery.This trial was registered at ClinicalTrials.gov ( NCT02355418 ) on 30th November 2015.

Dataset Information

Rationale of the FIBROTARGETS study designed to identify novel biomarkers of myocardial fibrosis.

Publications

Rationale of the FIBROTARGETS study designed to identify novel biomarkers of myocardial fibrosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets