Discrepancy in Metabolic Syndrome between Psoriatic Arthritis and Rheumatoid Arthritis: a Direct Comparison of Two Cohorts in One Center

Abstract

Objective We aimed to investigate the discrepancy in metabolic syndrome (MS) and cardiovascular disease (CVD) between patients with psoriatic arthritis (PsA) and those with rheumatoid arthritis (RA). Methods Patients with PsA and RA were enrolled between 1 December 2018 and 31 December 2021. Data on their demographics, height, weight, waist circumference, clinical and laboratory data, and comorbidities were collected. Disease activities of patients with RA and PsA were assessed. Prevalence was estimated by dividing cases (such as MS and CVD) of PsA and RA individually. Propensity score matching and inverse probability of treatment weighting were used for further validation. Results Consecutively, 197 patients with PsA and 279 patients with RA were enrolled in this study. Both MS [36.0% versus 23.3%, p = 0.002, OR 1.54 (1.16, 2.05)] and CVD [6.6% versus 1.1%, p = 0.001, OR 6.13 (1.77, 21.25)] were more frequently observed in patients with PsA compared with patients with RA. The frequency of abdominal obesity was also higher in patients with PsA [61.9% versus 33.0%, OR 1.87 (1.53, 2.29), p < 0.001]. After 1:1 propensity score matching for age, sex, smoking history, serum lipids, and disease activity, MS remained more common in 117 patients with PsA than in 117 patients with RA (37.6% versus 23.1%, p = 0.016) These findings remained after the inverse probability of treatment weighting in 196 patients with PsA and 288 patients with RA. A positive linear relationship between MS with disease activity was found in patients with PsA, but not in patients with RA. Conclusion Considerable discrepancies in MS and CVD were observed between patients with PsA and those with RA. The greater odds of MS and CVD emphasize the need to pay more attention to metabolic and cardiovascular conditions in patients with PsA. Supplementary Information The online version contains supplementary material available at 10.1007/s40744-022-00502-4.

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment

“…The body mass index (BMI) for all participants was calculated using the following equation using a universal height-weight scale to ascertain the subject’s height and weight. BMI (kg/m 2 ): weight (kg)/heigh2 (m 2 ), a global classification of BMI values based on a set of cut-off criteria for weight conditions: 18.5 lbs underweight; 18.5–25.0 lbs normal weight; 25.0 lbs overweight 5 .…”

“…The deposition of fat in the abdominal region, particularly visceral adipose tissue (VAT), is associated with an increased risk of obesity-related diseases and cardiometabolic disorders. VAT is known for its metabolic activity and pro-inflammatory properties, making it a prime target for therapeutic interventions 3 – 5 . Adipose tissue, primarily composed of adipocytes, serves essential functions such as energy storage in the form of lipids and thermal insulation.…”