Restless legs syndrome severity and neurological disability are the strongest predictors of poor sleep quality in patients with multiple sclerosis
In a cross-sectional study of 173 patients with MS, 48.0% were poor sleepers (PSQI > 5) with a mean PSQI of 6.06. XGBoost machine learning identified restless legs syndrome severity (IRLS) as the strongest predictor of PSQI, followed by EDSS (disability) and depression, while cardiometabolic and inflammatory factors contributed inconsistently.
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Restless legs syndrome severity and neurological disability are the strongest predictors of poor sleep quality in patients with multiple sclerosis The current body of evidence comprises 1 study. EvidenceHub rates the overall confidence at 34/100 (low).
The Claim
Restless legs syndrome severity and neurological disability are the strongest predictors of poor sleep quality in patients with multiple sclerosis
This conclusion is most relevant to: Adult patients with multiple sclerosis (mean age 39.66 years, 69.9% female, 90.2% RRMS).
What the Research Shows
The conclusion draws on 1 linked study. Highlights from the cited literature:
- ▸Machine Learning-Based Prediction of Sleep Quality in Patients with Multiple Sclerosis. (Medical sciences (Basel, Switzerland), 2026) —
How It Works
The proposed biological pathway:
- ▸Restless legs syndrome severity (IRLS) is the strongest predictor of PSQI
- ▸Neurological disability (EDSS) and depression also contribute significantly
- ▸Cardiometabolic and inflammatory factors show inconsistent and sometimes opposite effects
- ▸Disease-related and symptomatic factors outweigh cardiometabolic and inflammatory contributions
Who Might Benefit
Evidence fit by population:
- ▸Adult patients with multiple sclerosis (mean age 39.66 years, 69.9% female, 90.2% RRMS)
Recommended Dose
N/A
Limitations & Caveats
Important context when interpreting this evidence:
- ▸Cross-sectional design prevents causal inference
- ▸Modest sample size (n=173) and single-center study
- ▸Exploratory machine learning approach requires confirmation in larger independent cohorts
Frequently Asked Questions
What is the most important predictor of poor sleep in MS patients?▼
Restless legs syndrome severity (IRLS) was the strongest predictor of PSQI across all machine learning models.
How common is poor sleep in this MS cohort?▼
48.0% of the 173 patients were classified as poor sleepers (PSQI > 5), with a mean PSQI of 6.06.
Do cardiometabolic and inflammatory factors predict sleep quality in MS?▼
They contributed inconsistently and some showed effects opposite to physiological expectation, suggesting they are less important than disease-related and symptomatic factors.
What machine learning model performed best?▼
XGBoost achieved the best predictive performance with a test R2 of 0.451.
References
- 1.Cucu LE, Baciu LC, Onicescu OM, Ignat BE, Săcărescu A, Oancea A, Grosu C, Chirica C, Popescu G, Maștaleru A, Bîlcu RV, Mustață A, Roca M, Leon MM. “Machine Learning-Based Prediction of Sleep Quality in Patients with Multiple Sclerosis..” Medical sciences (Basel, Switzerland), 2026. PMID: 42646617 DOI: 10.3390/medsci14040483