Background. Cardiovascular disease (CVD) remains the leading cause of global mortality, and conventional risk factors fail to identify a substantial proportion of individuals who develop early disease. Metabolomics, the systematic measurement of low-molecular-weight metabolites, captures the integrated output of genetic, environmental and lifestyle influences and may reveal biochemical perturbations that precede overt disease. Objective. To identify serum metabolites — spanning lipid, amino acid and gut-microbial pathways — that discriminate individuals with early or subclinical CVD from matched controls, and to evaluate whether a derived metabolite panel improves discrimination beyond conventional risk factors. Methods. A frequency matched case–control study is described. Cases with early CVD and controls without clinical or imaging evidence of disease provide fasting serum analysed by liquid chromatography–tandem mass spectrometry and targeted nuclear magnetic resonance. After quality control, univariable screening, multivariable logistic regression and penalised regression (LASSO) identify candidate biomarkers; discrimination is summarised by the area under the receiver-operating-characteristic curve (AUC) with internal validation by bootstrap resampling. Results. In the dataset, branched-chain amino acids, selected ceramides, and trimethylamine N-oxide were higher in cases, while certain phosphatidylcholines were lower. A parsimonious panel added to conventional risk factors raised the AUC from a 0.71 to 0.83. Conclusion. Serum metabolomic profiling is a biologically plausible strategy for early CVD detection and biomarker discovery.