Background/Purpose: Metabolomic profiling of patients with psoriatic disease (PsD) offers unparalleled opportunity to unravel the molecular and clinical interactions linking PsD with cardiovascular (CV) risk. Our objective was to evaluate the metabolomics profile in patients with PsD vs. non-psoriatic controls and to assess its association with carotid atherosclerosis progression.

Methods: Patients with psoriasis only or psoriatic arthritis from a longitudinal PsD cohort were enrolled. Non-psoriatic controls were recruited through advertisements and from hospital personnel. Baseline evaluation included clinical assessment of CV risk factors, joint and skin disease activity and medication use. Ultrasound assessment of the carotid arteries was performed only in patients with PsD. Total plaque area was measured at baseline and after 2-3 years. The average annual progression rate (APR) of atherosclerosis was calculated by subtracting the baseline from the follow-up TPA divided by the number of years between the visits. Atherosclerosis progression was defined as being in the top quartile of APR.  A high-throughput serum nuclear magnetic resonance metabolomics platform was used to quantify the baseline levels of 58 lipoprotein subclasses, fatty acid composition, amino acids glycolysis precursors and ketone bodies. Multivariable logistic regression analysis was performed to assess the association between metabolite levels and the following outcomes: 1) disease status (PsD vs. controls); 2) carotid atherosclerosis progression.  

Results: A total of 392 patients with PsD and 88 controls were included in the analysis. Their mean age was 54.1±11.7 years (53.4% men). 19 metabolite measures were found to be significantly associated with PsD compared to controls after adjusting for age, sex, BMI and medication use (Figure 1). The metabolites included lipoprotein subclasses, fatty acids, amino acids and intermediates of glycolysis. We then evaluated the incremental value of adding circulating metabolites to established CV risk factors for prediction of atherosclerosis progression using regression analysis. 13 metabolites, primarily atherogenic lipid particles, predicted atherosclerosis progression after adjusting for CV risk factors (Table 1).

Conclusion: Substantial differences were found in the metabolomic profile between patients with PsD and non-psoriatic controls. Atherogenic lipoprotein particles across the non-HDL-c spectrum predicted atherosclerosis progression in PsD independently of conventional CV risk factors.