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Updated: Jul 25, 2025

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
Published on: March 1, 2024
More than skin-deep: visceral fat is strongly associated with disease activity, function and metabolic indices in
Tim Blake1,2, Nicola J Gullick3,4, Charles E Hutchinson4,5
1Department of Rheumatology, University Hospitals Coventry and Warwickshire, Clifford Bridge Road, Coventry, CV2 2DX, UK. timblakewarwick@gmail.com.
Objective:
To compare body composition between patients with psoriatic disease (PsD), including cutaneous psoriasis (PsO) and psoriatic arthritis (PsA), and controls, and to explore associations between disease activity and measures of function and metabolic derangement.
Methods:
Body composition was assessed by air displacement plethysmography (ADP) and MRI-derived fat segmentation using an automated pipeline (FatSegNet). Function was assessed by Health Assessment Questionnaire (HAQ) and metabolic status by fasting lipid profile, insulin and adiponectin. Active and inactive PsO and PsA were defined by body surface area (BSA) and Psoriasis Area Severity Index (PASI) and minimal disease activity (MDA), respectively.
Results:
Thirty patients (median disease duration 15 years; median age 52 years) and 30 BMI-matched controls were enrolled. Compared with controls, all MRI-derived body composition parameters-whole-body volume, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), abdominal adipose tissue (AAT), VAT/AAT and VAT/SAT-were higher in the PsD group, specifically, those with active disease. Body mass, body fat, whole-body volume and whole-body VAT were correlated with higher triglycerides, cholesterol:HDL (high-density lipoprotein), insulin resistance and lower adiponectin as well as higher HAQ and lower MDA.
Conclusions:
In this pilot study, patients with PsD revealed excessive total adipose tissue and a greater volume of metabolically unfavourable ectopic fat, including VAT, compared with BMI-matched controls, which also correlated with HAQ, disease activity and overall dysmetabolism. We also provide the first evidence in patients with PsD for the clinical application of FatSegNet: a novel, automated and rapid deep learning pipeline for providing accurate MRI-based measurement of fat segmentation. Our findings suggest the need for a more integrated approach to the management of PsD, which considers both the metabolic and inflammatory burden of disease. More specifically, visceral fat is a surrogate marker of uncontrolled PsD and may be an important future target for both pharmacological and lifestyle interventions.
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