Investigating the impact of obesity as an immunological instigator on young-onset arthritis using perturbation-based simulation

PurposeYoung-onset inflammatory arthritis (YOA; disease onset at age <25) incidence has risen since 1990, coinciding with the childhood obesity epidemic. Traditional epidemiology cannot easily quantify obesitys contribution because early-life exposures can precede clinical onset by years and may be poorly measured at diagnosis. We developed a perturbation-based simulation to estimate young-age inflammatory arthritis burden by calibrating to age-stratified RA estimates from GBD (GBD does not report JIA) under varying obesity scenarios - an approach that allows counterfactual testing, difficult to achieve in observational studies. MethodsWe built a Monte Carlo model generating arthritis prevalence estimates for 1,000,000 individuals. The model incorporated published odds ratios: BMI per SD, smoking, HLA-DR shared epitope allele dose (0/1/2), and interactions. We systematically perturbed average BMI in the <25 stratum (weighted towards adolescents and young adults) from 25-29 kg/m2 as a stress-test while holding other factors constant, then compared predicted prevalence against Global Burden of Disease data. Each scenario ran 2,500 iterations to propagate parameter uncertainty. ResultsOur model predicted the current YOA prevalence of 0.07% (observed: 0.06%, 95% CI: 0.05%-0.07%). Under perturbation analysis, each unit increase in average BMI yielded an additional 0.005% (95% CI: 0.0025%-0.0075%) in YOA cases - small but meaningful given the rarity of young-onset inflammatory arthritis. The relationship was locally linear. Significantly, the model saw that returning average BMI toward 1990 levels (<26) predicted around a 30% drop in BMI-attributable diagnoses and a 3% decrease in YOA prevalence. ConclusionsPerturbation modeling identifies childhood obesity as a potentially modifiable driver of young-onset RA, accounting for upwards of 5% of prevalence increases since 1990. This approach uniquely enables testing of prevention scenarios: our model predicts that lowering average BMI by one to two units over the next decade could prevent 3-5% of YOA cases in <25 under the modeled scenarios. These estimates provide a quantitative basis for incorporating arthritis prevention in childhood and adolescent obesity intervention cost-effectiveness analyses.