Processed plasma lipidomic data matrices from the BECAME-HF study supporting the manuscript “Lipidomics Identifies HFpEF Phenogroups and a High-Risk Metabolic Signature.” Files include log2-transformed, normalized, and batch-corrected lipid intensities, lipid annotations, and cluster assignments for the Belgian and Canadian cohorts. These de-identified processed data support the lipidomic phenogroup discovery, cross-cohort comparison, and minimal lipid signature analyses presented in the study.

Processed plasma lipidomic data matrices from the BECAME-HF study supporting the manuscript “Lipidomics Identifies HFpEF Phenogroups and a High-Risk Metabolic Signature.” Files include log2-transformed, normalized, and batch-corrected lipid intensities, lipid annotations, and cluster assignments for the Belgian and Canadian cohorts. These de-identified processed data support the lipidomic phenogroup discovery, cross-cohort comparison, and minimal lipid signature analyses presented in the study.

Processed plasma lipidomic data matrices from the BECAME-HF study supporting the manuscript “Lipidomics Identifies HFpEF Phenogroups and a High-Risk Metabolic Signature.” Files include log2-transformed, normalized, and batch-corrected lipid intensities, lipid annotations, and cluster assignments for the Belgian and Canadian cohorts. These de-identified processed data support the lipidomic phenogroup discovery, cross-cohort comparison, and minimal lipid signature analyses presented in the study.

Processed plasma lipidomic data matrices from the BECAME-HF study supporting the manuscript “Lipidomics Identifies HFpEF Phenogroups and a High-Risk Metabolic Signature.” Files include log2-transformed, normalized, and batch-corrected lipid intensities, lipid annotations, and cluster assignments for the Belgian and Canadian cohorts. These de-identified processed data support the lipidomic phenogroup discovery, cross-cohort comparison, and minimal lipid signature analyses presented in the study.

Targeted metabolomics was employed to investigate the metabolic alterations in patients with heart failure with reduced ejection fraction (HFrEF). HFrEF is a complex cardiovascular condition where the heart is unable to pump blood effectively, failing to meet the body's metabolic demands. Despite optimal medical therapy, HFrEF patients remain at high risk for adverse outcomes. To characterize the metabolic phenotype of HFrEF, we conducted a targeted plasma metabolomics analysis on samples from a cross-sectional exploratory study within a cohort from the Montreal Heart Institute. This cohort included 132 subjects, comprising 61 HFrEF patients and 71 control subjects. A total of 71 metabolites were measured, including standard clinical markers such as glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, and free fatty acids. Energy-related metabolites, such as lactate, pyruvate, and intermediates of the citric acid cycle (citrate, fumarate, and malate), were also profiled. Furthermore, 53 acylcarnitines were quantified, covering short-chain (C3-C5), medium-chain (C6-C12), long-chain (C14-C18), hydroxylated, and dicarboxylic species. Detailed metadata including group labels, age, sex, and clinical variables for each sample is also provided.The raw metabolomics dataset is available upon request.

Targeted metabolomics was employed to investigate the metabolic alterations in patients with heart failure with reduced ejection fraction (HFrEF). HFrEF is a complex cardiovascular condition where the heart is unable to pump blood effectively, failing to meet the body's metabolic demands. Despite optimal medical therapy, HFrEF patients remain at high risk for adverse outcomes. To characterize the metabolic phenotype of HFrEF, we conducted a targeted plasma metabolomics analysis on samples from a cross-sectional exploratory study within a cohort from the Montreal Heart Institute. This cohort included 132 subjects, comprising 61 HFrEF patients and 71 control subjects. A total of 71 metabolites were measured, including standard clinical markers such as glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, and free fatty acids. Energy-related metabolites, such as lactate, pyruvate, and intermediates of the citric acid cycle (citrate, fumarate, and malate), were also profiled. Furthermore, 53 acylcarnitines were quantified, covering short-chain (C3-C5), medium-chain (C6-C12), long-chain (C14-C18), hydroxylated, and dicarboxylic species. Detailed metadata including group labels, age, sex, and clinical variables for each sample is also provided.The raw metabolomics dataset is available upon request.