Comorbidities Poster Presentation

P0450 - Comorbidity patterns in people with multiple sclerosis:  A latent class analysis of the Australian Multiple Sclerosis Longitudinal Study (ID 1065)

  • L. Lo
  • L. Lo
  • B. Taylor
  • I. Van Der Mei
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Published studies are designed towards identifying the impact of the total number and individual comorbidities, and therefore limited knowledge exists on the comorbidity patterns and their influence on people with multiple sclerosis (MS).


To identify the comorbidity patterns and examine their association with the sociodemographic characteristics of people with MS.


We conducted latent class analysis (LCA) to identify clinically distinct comorbidity classes in PwMS using the 15 most common comorbidities among 1,518 Australian Multiple Sclerosis Longitudinal Study (AMSLS) participants. The associations between comorbidity classes and sociodemographic characteristics were explored using multinomial logistic regression.


Five classes with distinct comorbidity patterns were identified: “minimally-diseased class” (30.8%) for participants with no or one comorbidity; “metabolic class” (22.7%), “mental health-allergy class” (21.7%), “non-metabolic class” (7.6%) or “severely-diseased class” (7.0%) for participants with higher prevalence of comorbidities. The relative probability (relative risk ratios, 95%CI) of being assigned to other comorbidity classes over the “minimally-diseased class” were significantly increased for participants who were older (metabolic: 1.09 (1.06-1.11); non-metabolic: 1.07 (1.04-1.11); severely-diseased: 1.04 (1.01-1.08)), female (non-metabolic: 5.35 (1.98-14.42); severely-diseased: 2.21 (1.02-4.77)), obese (metabolic: 4.06 (2.45-6.72); mental health-allergy: 1.57 (1.00-2.46); severely-diseased: 4.53 (2.21-9.29)) and had moderate disability (mental health-allergy: 2.32 (1.47-3.64); severely-diseased: 2.65 (1.16-6.04)).


Comorbidities in MS tend to cluster into distinct disease patterns and are associated with some demographics and clinical characteristics. Understanding comorbidity patterns in MS may be used to design more appropriate comorbidity prevention and management strategies.