Circadian rhythmicity of symptomatic phenotypes in multiple sclerosis: the CircaMS study protocol and feasibility of biomarker collection
Bibliographic record
Abstract
Introduction: Multiple Sclerosis (MS) is a chronic autoimmune neurological disease with a variable prognosis and unpredictable course. Fatigue, pain, and low mood are common symptoms that tend to fluctuate in people with MS (pwMS). Disrupted circadian rhythms may have a role in the symptoms variability. Distinguishing inter-individual differences and temporal daily fluctuations in MS symptoms may help to define specific symptomatic phenotypes. Understanding how these phenotypes are associated with quality-of-life and their immunological underpinnings (immune profiles) could shape new MS management strategies. Our primary aim is to document ongoing fluctuations in fatigue, pain, and mood in a cohort of pwMS to determine whether symptom variability is associated with differential quality of life. Our secondary aim is to evaluate the feasibility of our study design to identify immune profiles of circadian rhythmicity in MS. Methods and analysis: This observational cohort study examines individual temporal fluctuations in MS symptomatology via self-report questionnaires in a cohort of pwMS. All participants complete 1) a baseline battery of questionnaires; and 2) electronic symptom-tracking diaries to rate fatigue, pain intensity, and mood on a 0-10 scale at 3 time-points (08:00, 14:00, 20:00) for 10 days. A subgroup of ~20 participants (feasibility study) will also complete blood sample collection twice within 24 hours to study immune profiles and molecular markers of circadian rhythmicity in MS. Participants will be grouped into symptomatic phenotypes based on longitudinal data from e-diaries. We will assess whether exhibiting a specific phenotype is associated with certain baseline measures. Flow cytometry, whole blood RNA sequencing, and plasma analyses will be applied to determine changes in immune profiles indicative of circadian rhythmicity. This work has the potential to reduce the burden of this complex disease on a global scale. Future studies will build on our work to understand individual variability in MS symptomatology, including disease severity; identification of biomarkers underlying the association between rhythmic symptomatology profiles and symptomatic phenotypes in MS; and designing personalized interventions focused on inter-individual differences in symptomatology and circadian rhythmicity. Ethics and dissemination: The CircaMS project and its associated procedures have been reviewed and approved by the Queens University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board (File number: 6039383). Participants provide informed consent to participate, and their data will not be identifiable in any publication or report. All documents are stored securely and only accessible by study staff and authorized personnel. Results will be presented to academic and lay audiences via national/international conferences, publications in peer-reviewed journals, social media, and through an official website created to engage pwMS, caregivers, clinicians, and researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".