OP0221 TARGETED METABOLOMIC PROFILING AND PREDICTION OF CARDIOVASCULAR EVENTS: A PROSPECTIVE STUDY OF PATIENTS WITH PSORIATIC ARTHRITIS AND PSORIASIS

Abstract

Background:Psoriatic arthritis and psoriasis, collectively termed psoriatic disease (PsD), are associated with increased cardiovascular (CV) risk. Metabolites comprise biomarkers that may add predictive value over traditional CV risk factors.Objectives:We aimed to identify metabolites associated with CV events (CVEs) and to determine whether they could improve CV risk prediction beyond traditional CV risk factors.Methods:Patients from a longitudinal PsD cohort without a prior history of CVEs were included. In the first available serum sample, a targeted nuclear magnetic resonance (NMR) metabolomics platform was used to quantify 64 metabolite measures comprised of lipoprotein subclasses, fatty acids, glycolysis precursors, ketone bodies and amino acids. The study outcome included any of the following CVEs occurring within the first 10 years of biomarker assessment: angina, myocardial infarction, congestive heart failure, transient ischemic attack, cerebrovascular accident, revascularization procedures and CV death. The association of each metabolite with incident CVEs were analyzed separately using Cox proportional hazards regression models first adjusted for age and sex, and subsequently for traditional CV risk factors. Variable selection was performed using penalization with boosting after adjusting for age and sex. The added predictive value of the selected metabolites to improve risk prediction beyond traditional CV risk factors was assessed using the area under the receiver operator characteristic curve (AUC).Results:A total of 977 patients with PsD, followed between 2002 and 2019, were analyzed (mean age 49.1 ± 12.6 years, 45.1% female). During a mean follow-up of 7.1 years, 70 (7.2%) patients developed incident CVEs. In Cox regression models adjusted for CV risk factors, alanine, tyrosine, total high-density lipoprotein (HDL) cholesterol, medium and large HDL particles, and the degree of unsaturation of fatty acids were significantly associated with decreased CV risk. Glycoprotein acetyls, apolipoprotein B, remnant cholesterol, very low-density lipoprotein (VLDL) cholesterol, and very small VLDL particles were associated with an increased CV risk. In proportional sub-distribution hazards regression models adjusted for age and sex, 13 metabolites were selected (Table 1). The age- and sex-adjusted expanded model (base model + 13 metabolites) significantly improved prediction of CVEs beyond the base model (only age and sex) with an AUC of 79.9 vs. 72.6, respectively (p=0.019) (Figure 1).Table 1.Regression coefficients of the selected metabolites in a model adjusted for age and sex.CategoryMetaboliteModel adjusted for Age and SexAmino AcidsAlanine-0.1179Glycine-0.0339Tyrosine-0.1010Fatty acid ratios, relative to total fatty acidsDocosahexaenoic acid-0.0862Unsaturation degree, double bonds per fatty acid-0.1265Fluid BalanceAlbumin+0.0685GlyceridesTriglycerides in IDL cholesterol+0.1546Glycolysis precursorsGlucose+0.1391InflammationGlycoprotein acetyls+0.1478Ketone bodiesAcetoacetate+0.0464Lipoprotein subclassesHDL3 Cholesterol-0.0211Medium HDL-0.0296Large HDL-0.0309Figure 1.Predictive performance of a model with age and sex alone is compared to a model with age and sex plus selected metabolites.Conclusion:Using NMR metabolomics profiling, we identified a variety of metabolites associated with a lower and higher risk of developing CVEs in patients with PsD. Further study of their underlying association with CVEs is needed to clarify the clinical utility of these biomarkers to guide CV risk assessment in this population.