Background-Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events. Methods and Results-We performed mass-spectrometry-based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls ("initial"), and 140 CAD cases and 140 controls ("replication"). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined ("event" group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled ("eventreplication" group; nϭ63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis-derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial Pϭ0.002, replication Pϭ0.01), and 1 comprising urea cycle metabolites (factor 9, initial Pϭ0.0004, replication Pϭ0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; Pϭ0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; Pϭ0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; Pϭ0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; Pϭ0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; Pϭ0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; Pϭ0.01). Conclusions-Metabolite profiles are associated with CAD and subsequent cardiovascular events.(Circ Cardiovasc Genet. 2010;3:207-214.)Key Words: metabolism Ⅲ risk factors Ⅲ coronary artery disease C oronary artery disease (CAD) is the leading cause of death in industrialized countries. Many accepted risk factors for CAD are metabolic. However, there remains an incomplete mechanistic understanding of CAD risk and equally important, a need to refine our ability to identify individuals at highest risk of cardiovascular events. Given the complex nature of CAD, evaluation with more comprehensive tools may improve risk stratification and enhance our understanding of the disease process. Metabolomics, the study of small-molecule metabolites, may be particularly useful for the diagnosis of human disease. Studies have demonstrated heritability of metabolites in mice, 1 and we have shown that metabolite profiles are heritable in human families with early-onset CAD, 2 suggesting that the known heritability of CAD may be mediated at least in part through metabolic components measurable in peripheral blood. Clinical Perspective on p 214In this study, we performed quantitative profiling of 69 metabolites, including acylcarnitine species (byproducts of mitochondrial fatty aci...

Aims/hypothesis Insulin resistance (IR) improves with weight loss, but this response is heterogeneous. We hypothesised that metabolomic profiling would identify biomarkers predicting changes in IR with weight loss. Methods Targeted mass spectrometry-based profiling of 60 metabolites, plus biochemical assays of NEFA, β-hydroxybutyrate, ketones, insulin and glucose were performed in baseline and 6 month plasma samples from 500 participants who had lost ≥4 kg during Phase I of the Weight Loss Maintenance (WLM) trial. Homeostatic model assessment of insulin resistance (HOMA-IR) and change in HOMA-IR with weight loss (ΔHOMA-IR) were calculated. Principal components analysis (PCA) and mixed models adjusted for race, sex, baseline weight, and amount of weight loss were used; findings were validated in an independent cohort of patients (n=22). Results Mean weight loss was 8.67±4.28 kg; mean ΔHOMA-IR was −0.80±1.73, range −28.9 to 4.82). Baseline PCA-derived factor 3 (branched chain amino acids [BCAAs] and associated catabolites) correlated with baseline HOMA-IR (r=0.50, p<0.0001) and independently associated with ΔHOMA-IR (p<0.0001). ΔHOMA-IR increased in a linear fashion with increasing baseline factor 3 quartiles. Amount of weight loss was only modestly correlated with ΔHOMA-IR (r=0.24). These findings were validated in the independent cohort, with a factor composed of BCAAs and related metabolites predicting ΔHOMA-IR (p=0.007). Conclusions/interpretation A cluster of metabolites comprising BCAAs and related analytes predicts improvement in HOMA-IR independent of the amount of weight lost. These results may help identify individuals most likely to benefit from moderate weight loss and elucidate novel mechanisms of IR in obesity.

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