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Updated: May 1, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Published on: December 9, 2015

Data-driven hypothesis discovery from disease trajectories in multiple sclerosis.

Niels Jodts¹, Lorin Werthen-Brabants¹, Sofie Aerts^2,3,4,5

¹IDLab, Universiteit Gent - Imec, Ghent, Belgium.

Frontiers in Immunology

|April 30, 2026

Summary

This summary is machine-generated.

This study introduces a novel statistical approach to identify multiple sclerosis (MS) progression patterns in patient data. The method reveals new hypotheses for biomarker discovery and optimizing treatments for MS patients.

Keywords:

clustering data-driven disease progression analysis disease trajectories hypothesis discovery longitudinal data analysis multiple sclerosis real-world cohort

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Area of Science:

Neurology
Biostatistics
Data Science

Background:

Multiple sclerosis (MS) is an incurable autoimmune disease with unpredictable progression.
Lack of reliable biomarkers hinders accurate prognosis and personalized treatment for MS patients.

Purpose of the Study:

To introduce a novel trajectory-based statistical approach for identifying patterns in multiple sclerosis patient histories.
To uncover previously unrecognized progression patterns and generate new hypotheses for MS research.

Main Methods:

Longitudinal clinical data from 1,025 MS patients were analyzed using a trajectory-based statistical approach.
Two analyses were performed: one on a large dataset (n=985) and another on a smaller cohort (n=83) to assess robustness.

Main Results:

The approach identified novel progression patterns in MS, suggesting potential effects of Alemtuzumab on bowel/bladder function and glatiramer acetate on relapse occurrence.
Confirmed known associations, such as the link between relapse activity and brain lesions.

Conclusions:

The trajectory-based method is robust across different dataset sizes and can reveal previously unseen relationships in MS.
Findings support hypothesis generation for biomarker discovery and therapeutic optimization in multiple sclerosis care.