# Cardiometabolic Candidate Endotypes in Psoriatic Disease: Integration of Clinical, Metabolic, and Immunogenetic Data Across Psoriasis and Psoriatic Arthritis

## TL;DR

This study identifies distinct cardiometabolic profiles in psoriatic disease patients by integrating clinical, genetic, and metabolic data, revealing differing risk patterns in psoriasis and psoriatic arthritis.

## Contribution

The novel integration of HLA-Cw6 immunogenetic markers with clinical data reveals distinct cardiometabolic endotypes and disease-specific risk patterns in psoriatic disease.

## Key findings

- Four cardiometabolic phenotypes were identified, including a high-risk subgroup with cardiovascular disease, hypertension, diabetes, and dyslipidemia.
- HLA-Cw6 was enriched in high-risk psoriasis but depleted in high-risk psoriatic arthritis, indicating disease-specific genetic associations.
- Disease-stratified analyses showed that high-risk profiles exist in both psoriasis and psoriatic arthritis, suggesting differing cardiometabolic implications.

## Abstract

Background/objectives: Psoriatic disease (PsD) encompasses psoriasis (PsO) and psoriatic arthritis (PsA) and is associated with heterogeneous cardiometabolic risk. Integrating immunogenetic markers such as HLA-Cw6 into data-driven analyses may refine phenotyping and uncover clinically meaningful endotypes. We aimed to identify cardiometabolic phenotypes across PsD, integrating HLA-Cw6 and exploring disease-specific heterogeneity and predictors of high-risk profiles. Methods: In a cross-sectional study of 572 PsD patients (401 PsO, 171 PsA), eight demographic and clinical variables, including HLA-Cw6, were entered into k-means clustering (k = 4). Cardiometabolic risk factors were profiled post hoc. Cluster validity was assessed by Gaussian Mixture Models and principal component analysis (PCA). Stratified analyses (k = 3) were conducted separately for PsO and PsA. Predictors of the high-risk phenotype were examined using bootstrap-resampled logistic regression. Results: Four cardiometabolic phenotypes were identified, ranging from younger patients with active PsO and low cardiometabolic burden to a small, high-risk subgroup (~6%) combining older age, universal cardiovascular disease, and a clustering of hypertension, diabetes, and dyslipidemia. Disease-stratified analyses showed that high-risk phenotypes were present in both PsO and PsA. In stratified analyses, HLA-Cw6 showed opposite associations—enriched in high-risk PsO (OR 2.0, 95% CI 1.3–3.1) but depleted in high-risk PsA (OR 0.24, 95% CI 0.11–0.52). Conclusions: Incorporating HLA-Cw6 into clustering identified reproducible cardiometabolic phenotypes with distinct genetic signatures. The inverse HLA-Cw6 risk patterns in PsO and PsA suggest disease-specific patterns that may have differing cardiometabolic implications, which should be tested in longitudinal studies.