Body Composition and Its Outcomes and Management in Multiple Sclerosis: Narrative Review
Bibliographic record
Abstract
Background: There is emerging interest in obesity and its prevalence, outcomes, and management in people with multiple sclerosis (MS). Body mass index (BMI) is the traditional marker of obesity in MS, whereas body composition, inclusive of specific body tissue compartments (e.g., fat, bone, and muscle), is often overlooked despite its relevance. Objective: This narrative review (a) underscored the use and utility of dual-energy X-ray absorptiometry (DEXA) as an accurate and reliable measure of body composition; (b) thematically analyzed and synthesized the current evidence regarding body composition (using DEXA); and (c) determined gaps to be addressed in future research. Methods: The structure and reporting of this narrative review followed the guiding criteria outlined in the Scale for the Assessment of Narrative Review Articles (SANRA). The relevant literature for this narrative review was identified via a PubMed search utilizing combined search terms such as ‘body composition’ and ‘multiple sclerosis’. The identified research was then organized by the authors into major themes and sub-themes. The articles described within the narrative review were based on saturation of the identified themes and sub-themes. Results: Three major themes were identified, namely (1) comparison of body composition between people with MS and non-MS controls (2 meta-analyses); (2) examination of the relationships between body composition and a range of outcomes (14 cross-sectional studies); and (3) interventions that report and/or target body composition in MS (11 clinical trials). Conclusions: This narrative review mapped the existing evidence regarding body composition in MS, and posits body composition as a novel, informative, and targeted concept for this population. The narrative review underscores the importance of randomized controlled trials that focus on body composition as a significant and modifiable outcome. Such research could improve the understanding of obesity and poor body composition in MS and identify useful clinical recommendations for diagnosis and management.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".