Cardiometabolic evalUation REgistry of Heart Failure

This is a combined retrospective-prospective observational cohort study aiming to elucidate the interplay between systemic/localized cardiometabolic risk factors and their impact on myocardial remodeling, cardiac function, and clinical trajectories in heart failure (HF) patients. By harmonizing retrospective clinical data spanning the past decade with a 5-year prospective longitudinal follow-up, the study encompasses all HF phenotypes, including reduced (HFrEF), mildly reduced (HFmrEF), preserved (HFpEF), and improved ejection fraction (HFimpEF), to capture the full spectrum of disease heterogeneity. Systemic metabolic dysregulation, such as dyslipidemia, impaired glucose metabolism, obesity and other metabolites assessed by mass spectrometry (MS), will be evaluated alongside localized factors, such as epicardial adipose tissue (EAT), perivascular adipose tissue (PVAT) quantified by advanced imaging modalities (cardiac MRI or CT). These factors will be correlated with changes in cardiac geometry (e.g., left ventricular mass, chamber dimensions, wall thickness) and function (e.g., ejection fraction, strain imaging, diastolic parameters), dynamic transitions between HF phenotypes (e.g., HFrEF to HFimpEF), as well as the occurrence of major adverse cardiovascular events (MACEs), defined as a composite of HF re-hospitalization and cardiovascular death. The prospective cohort will undergo standardized baseline assessments (blood biomarkers, echocardiography, cardiac CT or MRI) followed by routine clinical, biochemical and imaging evaluations at least 3-month intervals. Retrospective data will be extracted from electronic health records, including historical imaging studies, laboratory results, and event documentation, ensuring a robust sample size (target n≈3500 retrospective; n≈1200 prospective) for stratified analyses by HF phenotype, sex, and metabolic risk tertiles. Advanced statistical approaches, including multivariable regression analysis, multivariable Cox proportional hazards models and machine learning algorithms, will identify independent predictors for cardiac remodeling, functional alterations, HF phenotype transitions and MACEs. Ethical approval and informed consent are obtained for prospective participants, with retrospective data anonymized to ensure privacy. This study is expected to refine risk stratification tools by integrating metabolic imaging biomarkers and biochemical profiles, ultimately guiding personalized therapeutic cures for HF patients.