Onset Symptom Clusters in Multiple Sclerosis: Characteristics, Comorbidities, and Risk Factors

Vladeta Ajdacic-Gross; Nina Steinemann; Gábor Horváth; Stephanie Rodgers; Marco Kaufmann; Yanhua Xu; Christian P Kamm; Jürg Kesselring; Zina-Mary Manjaly; Chiara Zecca; Pasquale Calabrese; Milo A Puhan; Viktor von Wyl

doi:10.3389/fneur.2021.693440

Onset Symptom Clusters in Multiple Sclerosis: Characteristics, Comorbidities, and Risk Factors

Front Neurol. 2021 Jul 6:12:693440. doi: 10.3389/fneur.2021.693440. eCollection 2021.

Authors

Affiliations

¹ Swiss MS Registry, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
² Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, Zurich, Switzerland.
³ Department of Neurology, Inselspital, University Hospital Bern and University of Bern, Bern, Switzerland.
⁴ Neurocentre, Luzerner Kantonsspital, Lucerne, Switzerland.
⁵ Department of Neurology and Neurorehabilitation, Rehabilitation Centre Kliniken Valens, Valens, Switzerland.
⁶ Department of Neurology, Schulthess Clinic, Zurich, Switzerland.
⁷ Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
⁸ Department of Neurology, Multiple Sclerosis Center (MSC), Neurocenter of Southern Switzerland, Lugano, Switzerland.
⁹ Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland.
¹⁰ Division of Molecular and Cognitive Neuroscience, University of Basel, Basel, Switzerland.

Abstract

Background: Multiple sclerosis (MS) symptoms are expected to aggregate in specific patterns across different stages of the disease. Here, we studied the clustering of onset symptoms and examined their characteristics, comorbidity patterns and associations with potential risk factors. Methods: Data stem from the Swiss Multiple Sclerosis Registry, a prospective study including 2,063 participants by November 2019. MS onset symptoms were clustered using latent class analysis (LCA). The latent classes were further examined using information on socio-demographic characteristics, MS-related features, potential risk factors, and comorbid diseases. Results: The LCA model with six classes (frequencies ranging from 12 to 24%) was selected for further analyses. The latent classes comprised a multiple symptoms class with high probabilities across several symptoms, contrasting with two classes with solitary onset symptoms: vision problems and paresthesia. Two gait classes emerged between these extremes: the gait-balance class and the gait-paralysis class. The last class was the fatigue-weakness-class, also accompanied by depression symptoms, memory, and gastro-intestinal problems. There was a moderate variation by sex and by MS types. The multiple symptoms class yielded increased comorbidity with other autoimmune disorders. Similar to the fatigue-weakness class, the multiple symptoms class showed associations with angina, skin diseases, migraine, and lifetime prevalence of smoking. Mononucleosis was more frequently reported in the fatigue-weakness and the paresthesia class. Familial aggregation did not differ among the classes. Conclusions: Clustering of MS onset symptoms provides new perspectives on the heterogeneity of MS. The clusters comprise different potential risk factors and comorbidities. They point toward different risk mechanisms.

Keywords: comorbidity; epidemiology; latent class analysis; multiple sclerosis; onset symptoms; risk factors.