Comorbidity patterns in people with multiple sclerosis: A latent class analysis of the Australian Multiple Sclerosis Longitudinal Study.

Abstract

This study was undertaken to identify clinically meaningful comorbidity patterns and their associations with the demographic/clinical characteristics of people with multiple sclerosis (MS). We conducted latent class analysis to identify clinically distinct comorbidity patterns in MS using the 15 most common comorbidities among 1518 Australian Multiple Sclerosis Longitudinal Study participants. The associations between demographic/clinical characteristics and comorbidity patterns were examined using log-binomial and multinomial logistic regression. Five distinct comorbidity patterns were identified: "minimally diseased class" (30.8%), consisting of participants with no or one comorbidity; "metabolic class" (22.7%); "mental health-allergy class" (21.7%); "nonmetabolic class" (7.6%); and "severely diseased class" (7.0%), consisting of participants with higher prevalence of these comorbidities. The relative probabilities of being assigned to comorbidity classes compared to the minimally diseased class were significantly increased for participants who were older (metabolic: relative risk ratio [RRR] = 1.09, 95% confidence interval [CI] = 1.06-1.11; nonmetabolic: RRR = 1.07, 95% CI = 1.04-1.11; severely diseased: RRR = 1.04, 95% CI = 1.01-1.08), female (nonmetabolic: RRR = 5.35, 95% CI = 1.98-14.42; severely diseased: RRR = 2.21, 95% CI = 1.02-4.77), and obese (metabolic: RRR = 4.06, 95% CI = 2.45-6.72; mental health-allergy: RRR = 1.57, 95% CI = 1.00-2.46; severely diseased: RRR = 4.53, 95% CI = 2.21-9.29) and who had moderate disability (mental health-allergy: RRR = 2.32, 95% CI = 1.47-3.64; severely diseased: RRR = 2.65, 95% CI = 1.16-6.04). Comorbidity patterns exist in MS. Women, people who were older, people who were obese, and people who had higher disability levels were more likely to be in classes with higher levels of comorbidity. These findings may offer opportunities for designing more personalised approaches to comorbidity prevention and treatment.