Lyn D. Ferguson

Key advances from the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) pilot research grant program were presented at the GRAPPA 2025 annual meeting. Areas of study included hypoxia-inducible factor 1α (HIF1A) as a potential factor in psoriatic arthritis (PsA), plasma extracellular vesicle cytokines as potential biomarkers for predicting response to tumor necrosis factor inhibitors in PsA, bone properties and biomechanics in psoriatic disease (PsD), better understanding of differences in body composition in PsD, and the effect of sleep on PsD severity.

Rheumatoid arthritis (RA) is a complex systemic disorder characterized primarily by articular inflammation and destruction with associated functional loss and reduced quality of life. RA is also associated with extra‐articular disease, such as that of the lung, with potentially devastating clinical consequences. The critical importance of comorbidities, and consequent multimorbidity, in determining outcomes in RA has now been recognized, not least as novel therapeutics have emerged with attendant increased life expectancy. The primary role of cytokine networks in mediating RA pathogenesis was established in extensive preclinical and clinical trials and in the adoption of cytokine‐targeted therapeutics in clinical care over three decades. Herein, we briefly review those pivotal cytokine pathways that are associated with RA articular disease and extend these insights to include extra‐articular RA and its common comorbidities.

Background: Differences in body fat distribution and muscle composition, beyond body mass index (BMI), may be functionally important in immune-mediated-inflammatory-diseases (IMIDs). We compared body composition in psoriasis, psoriatic arthritis (PsA), rheumatoid arthritis (RA), and gout, propensity to type 2 diabetes (T2DM) and coronary heart disease (CHD), and body composition predicted risk of incident psoriasis, RA, and gout. Methods: MRI body composition parameters were compared amongst 236 individuals with psoriasis, 61 with PsA, 281 with gout, and 308 with RA, each matched 1:5 on age, sex, and BMI to 1180, 305, 1405, and 1540 metabolic-disease-free controls from UK Biobank. Fat distribution-derived propensities to T2DM and CHD were calculated using adaptive k-nearest neighbours algorithm. Body composition predicted risks of incident psoriasis, RA, and gout were calculated using Cox regression, adjusted for age, sex, and BMI. Findings: Psoriasis, PsA, and gout displayed metabolically adverse body fat distribution with greater liver and muscle fat and 1.23, 1.39-, and 1.20-times greater propensity to T2DM compared to matched controls, respectively (p<0.05). RA displayed greater muscle fat and 1.18-times greater propensity to CHD compared to matched controls (p=0.002). 1 in 7 to 1 in 5 individuals with IMIDs displayed adverse muscle composition, significantly associated with greater T2DM and CHD propensity. During a mean follow-up of 3.9years (SD 2.2), adverse muscle composition (low muscle volume and high muscle fat) was associated with increased incidence of psoriasis (adj.HR 2.12, 95% CI (1.29, 3.50)), gout (adj.HR 1.66 (1.10, 2.50)), and RA (adj.HR 1.79 (1.14, 2.81)) compared to normal muscle composition. Greater visceral and liver and lower subcutaneous fat were associated with greater risk of incident gout (p<0.05). Interpretation: Psoriasis, PsA, gout, and RA display adverse body fat distributions and muscle compositions, which may help assess cardiometabolic risk and potential mechanistic insights into greater cardiometabolic disease susceptibility in IMIDs. Adverse muscle composition is also linked to greater susceptibility to these IMIDs. Effective weight management, targeting ectopic fat while maintaining muscle quality, should be integral to IMID management. Funding: GRAPPA Pilot Research Grant and Mason Medical Research Trust (grant number G4)

BACKGROUND/OBJECTIVES To compare muscle strength and muscle mass in individuals with various long-term conditions (LTCs) to those of healthy individuals, using data from the UK Biobank. SUBJECTS/METHODS This cross-sectional study of 444,420 UK Biobank participants (aged 37 – 73) examined 25 self-reported LTCs ( ≥ 1% prevalence). Multimorbidity was defined as a prevalent LTCs count ≥ 2. Muscle strength was assessed using a handgrip dynamometer, while muscle mass (fat-free mass as a percentage of body weight) was assessed using BIA, DXA, and MRI; all expressed as a percentage of total body weight. Linear regression analyses were conducted to examine how muscle strength and muscle mass varied by LTCs and multimorbidity. RESULTS The lowest muscle strength was observed in men with rheumatoid arthritis (RA), type 2 diabetes (T2D), stroke, gout, and deep venous thrombosis (-11.8% to -6.1%) lower than the reference group and in women with RA, gout, T2D, coronary heart disease (CHD), and stroke (-11.8% to -6.5%). Muscle mass was lowest in men with T2D, gout, CHD, hypertension, and high cholesterol (-4.7% to -3.1%), and in women with gout, T2D, hiatus hernia, CHD, and hypertension (-6.4% to -4.1%). Trends were consistent across measurements using DXA (n=4,719) and MRI (n=22,901). Muscle strength and muscle mass were lower in people with more LTCs, with a dose-response relationship. CONCLUSIONS This study demonstrates that muscle strength and muscle mass were lower in people with multiple LTCs and specific conditions. Emphasizing the need for targeted muscle interventions, particularly for those with T2D, RA, gout, and CHD.

Aim: Elevated C‐reactive protein (CRP), a marker of inflammation, is common in many chronic conditions. We aimed to examine to what extent elevated CRP in chronic conditions could be explained by concurrent adiposity. Materials and Methods: This cross‐sectional study analysed UK Biobank data on 10 chronic conditions reported at baseline. Linear regression models explored the extent to which CRP concentrations were elevated in each condition, unadjusted; adjusted for sociodemographic confounders and lifestyle and body mass index (BMI) in a series of models; or adjusted for BMI and waist circumference together or for adiposity alone. Results: After exclusion of participants with a potential acute infection at baseline, we tested the association in 292 772 UK Biobank participants. Linear regression showed that elevated CRP concentration was associated with all included conditions. After adjustment for sociodemographic confounders, lifestyle and BMI, chronic kidney disease, heart failure, liver disease, psoriasis, rheumatoid arthritis and chronic obstructive pulmonary disease were still associated with elevated CRP. In contrast, the association between prevalent diabetes, prior myocardial infarction (MI), hypertension and sleep apnoea and CRP could be mostly explained by adiposity alone. For example, the 42% higher CRP concentrations in diabetes compared to those without diabetes in the unadjusted model (lnCRP β: 0.35; 95% confidence interval [CI]: 0.32–0.37, p < 0.001) were completely attenuated after adjustment for BMI (lnCRP β: −0.07; 95% CI: −0.09−0.05, p < 0.001). Conclusions/Interpretation: In diabetes, MI, hypertension and sleep apnoea and elevated CRP appears to be accounted for by the greater adiposity typically evident in these conditions. However, for the other conditions, systemic inflammation cannot be explained by excess adiposity alone.

Introduction Early detection and treatment of diabetes as well as its prevention help lessen longer-term complications. We determined the prevalence of pre-diabetes and undiagnosed diabetes in the UK Biobank and standardized the results to the UK general population. Research design and methods This cross-sectional study analyzed baseline UK Biobank data on plasma glycated hemoglobin (HbA1c) to compare the prevalence of pre-diabetes and undiagnosed diabetes mellitus in white, South Asian, black, and Chinese participants. The overall and ethnic-specific results were standardized to the UK general population aged 40–70 years of age. Results Within the UK Biobank, the overall crude prevalence was 3.6% for pre-diabetes, 0.8% for undiagnosed diabetes, and 4.4% for either. Following standardization to the UK general population, the results were similar at 3.8%, 0.8%, and 4.7%, respectively. Crude prevalence was much higher in South Asian (11.0% pre-diabetes; 3.6% undiagnosed diabetes; 14.6% either) or black (13.8% pre-diabetes; 3.0% undiagnosed diabetes; 16.8% either) participants. Only six middle-aged or old-aged South Asian individuals or seven black would need to be tested to identify an HbA1c result that merits action. Conclusions Single-stage population screening for pre-diabetes or undiagnosed diabetes in middle-old or old-aged South Asian and black individuals using HbA1c could be efficient and should be considered.