The Clinnova-Multiple Sclerosis (MS) study is part of the Clinnova program (NCT06526364; NCT06235684 and NCT05733702), which seeks to advance precision medicine and the digitalization of healthcare through high-quality, interoperable health data.
This program focuses on people with multiple sclerosis (MS) and aims to identify objective surrogate markers derived from clinical, epidemiological, imaging, and omics data that can predict disease activity, such as progression or relapses.
By combining data science and artificial intelligence, the project seeks to improve patient stratification, support personalized therapeutic decisions, and provide insights into the mechanisms underlying treatment response and disease progression.
Although many therapies are available for MS, it remains challenging to determine the most appropriate strategy for each patient and to prevent long-term disability. Current treatments mainly target relapses and inflammation, with limited effects on chronic progression. Clinnova-MS will collect and analyze real-world and research data to better understand variability in disease activity and treatment outcomes, enabling more precise, evidence-based care within the standard of care. This study represents the first step toward the broader Clinnova objective: developing sustainable, personalized, and preventive healthcare for people living with MS.
Signed informed consent form
≥ 18 years of age
Willing and able to comply with the protocol for the duration of the study including data and samples collection as well as study visits and examinations.
Diagnosed with MS according to the revised McDonald criteria 2017 or revised McDonald criteria 2024, all clinical forms inclusive (CIS, RRMS, SPMS, PPMS) AND early disease stages (< 3 years), OR presenting at hospital for evaluation of a change in therapy (flare) OR transitioning phase to progressive disease as evaluated based on EDSS.
Diagnosis uncertain (no fulfilment of inclusion criteria)
Any condition that could potentially hamper the compliance with the study protocol, including study procedures and study visits such as mental disability that makes it difficult or impossible to answer questionnaires.
Not fluent in any of the following languages: French, English or German.
Known pregnancy before the inclusion into the study
Patients with MS as follows: Diagnosed with MS according to the revised McDonald criteria 2017 or revised McDonald criteria 2024, all clinical forms inclusive (CIS, RRMS, SPMS, PPMS) AND early disease stages (\< 3 years), OR presenting at hospital for evaluation of a change in therapy (flare) OR transitioning phase to progressive disease as evaluated based on EDSS.
Cohort
Prospective
Cohort
Other interventionParticipants will provide data and samples for analysis. In the first year after inclusion, demographics, lifestyle, labs, and physical exams will be collected at baseline, 6, and 12 months. Patient-Reported Outcomes (PROs) and challenges will be gathered between visits via the dreaMS app. Biological samples (blood required; saliva, urine, stool, CSF, hair optional), tissue from endoscopic biopsy, and imaging (if done as standard care) will be taken at baseline, 6, and 12 months. One unscheduled visit may occur for flares or treatment changes. From month 12 to 4 years later, yearly medical data, PROs every 6 months, and continuous smartwatch data will be collected.
Identification of Clinical, Imaging, and Omics Signatures for MS Subtype Stratification
Identify clinical, epidemiological, imaging and omics characteristics associated with changes of status for different subtypes of MS patients allowing the stratification of these patients according to similar patterns and disease courses.The primary endpoint will be the change of status of the patients' disease between the baseline and at Year 1. The status of the disease will be determined by using the No Evidence of Disease Activity (NEDA MS- 3).
Building Resources and Digital Tools to Advance Research and Healthcare in Multiple Sclerosis
* To identify clinical, imaging, epidemiological, omics and digital characteristics associated with MS disease activity triggering a treatment change. * To establish a sample and data bank to enable biomedical research. * To develop digital applications for improved interactions between patients and medical doctors, hence support improving healthcare. The secondary endpoints will be: 1. "Treatment change" (yes/no), a binary variable, defining if the current treatment has been changed at a time point/visit. The goal is to identify surrogate biomarkers for the clinician's decision to apply a treatment change. Treatment change is defined as either: * Change of drug dosage * Change of medication within the same treatment class * Change of treatment class 2. Change in participant reported outcomes and their evolution since baseline (improvement/worsening)
Unraveling Molecular, Cellular, and Clinical Determinants of MS Activity and Progression"
* Explore the therapies, biomarkers, health outcomes and their interaction with patient characteristics. * Derive and combine a set of biomarkers to better characterize the disease clinical phenotype and progression, the functional impairment of MS patients in different disease stages, and either associated with early MS or with transitioning phase to progressive MS, as an aid to assist clinicians in applying treatment change. * Identify on a granular level, novel metabolic and epigenetic (if available) drivers of the immune response in MS patients, and by doing so to understand the multiple molecular and cellular pathways underlying central nervous system pathology at the interface between inflammation and neural function.
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Luxembourg Institute of Health
Lead sponsor
Centre Hospitalier du Luxembourg
Collaborator
Luxembourg National Research Fund
Collaborator
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