Large-Scale Metabolomics and the Incidence of Cardiovascular Disease

Abstract

Background The study aimed to show the relationship between a large number of circulating metabolites and subsequent cardiovascular disease (CVD) and subclinical markers of CVD in the general population. Methods and Results In 2278 individuals free from CVD in the EpiHealth study (aged 45-75 years, mean age 61 years, 50% women), 790 annotated nonxenobiotic metabolites were measured by mass spectroscopy (Metabolon). The same metabolites were measured in the PIVUS (Prospective Investigation of Vasculature in Uppsala Seniors) study (n=603, all aged 80 years, 50% women), in which cardiac and carotid artery pathologies were evaluated by ultrasound. During a median follow-up of 8.6 years, 107 individuals experienced a CVD (fatal or nonfatal myocardial infarction, stroke, or heart failure) in EpiHealth. Using a false discovery rate of 0.05 for age- and sex-adjusted analyses and P<0.05 for adjustment for traditional CVD risk factors, 37 metabolites were significantly related to incident CVD. These metabolites belonged to multiple biochemical classes, such as amino acids, lipids, and nucleotides. Top findings were dimethylglycine and N-acetylmethionine. A lasso selection of 5 metabolites improved discrimination when added on top of traditional CVD risk factors (+4.0%, P=0.0054). Thirty-five of the 37 metabolites were related to subclinical markers of CVD evaluated in the PIVUS study. The metabolite 1-carboxyethyltyrosine was associated with left atrial diameter as well as inversely related to both ejection fraction and the echogenicity of the carotid artery. Conclusions Several metabolites were discovered to be associated with future CVD, as well as with subclinical markers of CVD. A selection of metabolites improved discrimination when added on top of CVD risk factors.

Keywords: amino acids; cardiovascular disease; epidemiology; mass spectroscopy; metabolomics.

Figure 1. Overview of the 37 significant metabolites related to incident cardiovascular disease in the EpiHealth sample.

The intensity of the red color is proportional to the estimate (hazard ratio) so that the pale dots correspond to hazard ratios <1.0. Subpathway and metabolite names for these 37 metabolites, with the start from 1‐stearoyl‐2‐oleoyl‐GPE (18:0/18:1) at the top of the figure and then in clockwise order, are given in Table S2. GPE indicates glycero‐3‐phosphoethanolamine; and SAM, S‐adenosylmethionine.

Figure 2. Area under the curve for traditional risk factors vs traditional risk factors plus metabolites on the outcome incident cardiovascular disease in the EpiHealth study.

The ROC curve (old ROC area) for the traditional risk factors of importance (systolic blood pressure, low‐density lipoprotein and high‐density lipoprotein cholesterol, and current smoking) is given in red (denoted old ROC curve), whereas the curve in black (denoted new ROC curve) also includes the 5 metabolites 5‐methylthioadenosine, dimethylglycine, pregnenediol sulfate, imidazole propionate, and 1‐carboxyethyltyrosine. P=0.0054 for the difference between curves. ROC indicates receiver operating characteristic.

Figure 3. Hierarchically clustered heat map of the relationships (given as regression coefficients) between the 37 metabolites found to be related to incident CVD and markers of subclinical CVD in the PIVUS study following adjustment for age and sex.

Red filling indicates positive relationships, whereas blue filling indicates inverse relationships. A star denotes that the relationships showed P<0.05. The heat map was created in R 4.2 using the package pheatmap. CVD indicates cardiovascular disease; EF, ejection fraction; GPE, glycero‐3‐phosphoethanolamine; IMGSM, echogenicity of the intima‐media complex; IMT, carotid artery intima‐media thickness; LA, left atrial diameter; LVMI, left ventricular mass index; and PIVUS, Prospective Investigation of Vasculature in Uppsala Seniors.