Cluster analysis of ME/CFS symptoms in DecodeME reveals two subgroups and a link to onset type

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating, often infection-triggered illness with no cure and no effective treatment. Marked symptom heterogeneity hampers diagnosis, disease management, and trial design. Using phenotype data from the world's largest ME/CFS cohort, this study aimed to identify groups of patients with similar symptom profiles using cluster analysis, to assess the association between cluster membership and onset type, and to explore genetic associations with cluster membership. Methods: This study included 19,019 DecodeME participants, ages 16 and over, with ME/CFS in the UK, from 2022-2024. We performed a k-modes cluster analysis of individuals based on similar symptoms. Cluster metrics identified the optimal number of clusters, which were characterised and compared. A sex-stratified subgroup analysis explored differences between clusters among males and females. The association between ME/CFS onset type (infectious, non-infectious, or unknown) and cluster membership was assessed with logistic regression models, adjusting for sex, age, deprivation, and ethnicity. Genetic associations with cluster membership were assessed using a genome-wide association study. Results: We identified two clusters in our study population: a high symptom burden cluster (HSBC; 57% of participants) and a lower symptom burden cluster (LSBC; 43%). The HSBC was characterised by higher prevalence of symptoms across all domains, more comorbidities, and greater illness severity. Individuals with infectious and unknown onset had 1.24 times (95% CI: 1.15-1.35) and 1.30 times (95% CI: 1.18-1.43) higher adjusted odds of HSBC membership relative to non-infectious onset, respectively. A similar pattern was observed in the sex-stratified analyses, although it showed an overall higher symptom prevalence for females and a higher proportion of females in the HSBC compared to males. No genetic variant was significantly associated with cluster membership. Conclusions: This large-scale cluster analysis of DecodeME symptom data reinforces that ME/CFS is a heterogeneous condition with clinical subtypes. The identification of symptom-based phenotypes, along with sex-based differences in symptom burden and cluster characteristics, highlights the importance of incorporating symptom burden and sex in future research, clinical decision-making, and public health strategies. Tailoring future interventions to these subgroups could enhance patient management and improve outcomes.