Targeted metabolomic profiling and prediction of cardiovascular events: a prospective study of patients with psoriatic arthritis and psoriasis

Objective In patients with psoriatic disease (PsD), we sought serum metabolites associated with cardiovascular (CV) events and investigated whether they could improve CV risk prediction beyond traditional risk factors and the Framingham Risk Score (FRS). Methods Nuclear magnetic resonance metabolomics identified biomarkers for incident CV events in patients with PsD. The association of each metabolite with incident CV events was analysed using Cox proportional hazards regression models first adjusted for age and sex, and subsequently for traditional CV risk factors. Variable selection was performed using penalisation with boosting after adjusting for age and sex, and the FRS. Results Among 977 patients with PsD, 70 patients had incident CV events. In Cox regression models adjusted for CV risk factors, alanine, tyrosine, degree of unsaturation of fatty acids and high-density lipoprotein particles were associated with decreased CV risk. Glycoprotein acetyls, apolipoprotein B and cholesterol remnants were associated with increased CV risk. The age-adjusted and sex-adjusted expanded model with 13 metabolites significantly improved prediction of CV events beyond the model with age and sex alone, with an area under the receiver operator characteristic curve (AUC) of 79.9 versus 72.6, respectively (p=0.02). Compared with the FRS alone (AUC=73.9), the FRS-adjusted expanded model with 11 metabolites (AUC=75.0, p=0.72) did not improve CV risk discrimination. Conclusions We identify novel metabolites associated with the development of CV events in patients with PsD. Further study of their underlying causal role may clarify important pathways leading to CV events in this population.