Diet and disease-related outcomes in multiple sclerosis: A systematic review of clinical trials
Bibliographic record
Abstract
Background: A growing number of clinical trials have investigated the role of diet in multiple sclerosis (MS) patients. We systematically reviewed the literature for clinical trials to assess the impact of different kinds of diets on MS-related outcomes. Methods: We searched MEDLINE, EMBASE, and Web of Science for relevant studies published before July 2019. The clinical trials included a defined dietary intervention and MS outcomes, including fatigue, relapse rate (RR), quality of life (QOL), and disability. Results: In the present review, 15 trials on 669 MS patients were included. The 2 plant-based diet trials, 1 was low-fat and the other was low-calorie, included in the review showed a large effect (ES: 0.6 to 0.7) on fatigue compared to the regular diet. The other plant-based diet was a low-protein diet and showed moderate to large effects on disability and RR compared to the Western diet. Moreover, 2 studies showed the clinically meaningful effects of the ketogenic diet (KD) on QOL and disability compared to the regular diet. In addition, 2 studies compared fish oil (FO) to placebo and found a small effect on disability (ES: 0.1 to 0.3). There were 2 studies that evaluated evening primrose oil and hemp seed oil and showed medium to large effect (ES: 0.7 to 1.5) on RR compared to olive oil. Finally, we found 2 studies that showed high flavonoid cocoa had a moderate effect (ES: 0.4) on fatigue and a small effect (ES: 0.04) on QOL compared to low flavonoid cocoa. Conclusion: Plant-based diet is a backbone for dietary recommendations in MS patients although low-fat, low-calorie, and KD diets with the addition of fish oil, vegetable oil, and flavonoids could be helpful.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".