A Systematic Review and Meta-Analysis of the Effect of Caloric Restriction on Skeletal Muscle Mass in Individuals with, and without, Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Severe caloric restriction interventions (such as very-low-calorie diets) are effective for inducing significant weight loss and remission of type 2 diabetes (T2DM). However, suggestions of associated significant muscle mass (MM) loss create apprehension regarding their widespread use. We conducted a systematic review and meta-analysis to provide a quantitative assessment of their effect on measures of MM in individuals with, or without, T2DM. METHODS: EMBASE, Medline, Pubmed, CINAHL, CENTRAL and Google Scholar were systematically searched for studies involving caloric restriction interventions up to 900 kilocalories per day reporting any measure of MM, in addition to fat mass (FM) or body weight (BW). RESULTS: < 0.0001). A similar pattern was observed across studies involving individuals without T2DM. MM constituted approximately 25.5% of overall weight loss in individuals with T2DM, and 27.5% in individuals without T2DM. Subgroup analysis paradoxically revealed greater BW and FM reductions with less restrictive interventions. CONCLUSIONS: Our review suggests that caloric restriction interventions up to 900 kilocalories per day are associated with a significant reduction in MM, albeit in the context of a significantly greater reduction in FM. Furthermore, MM constituted approximately a quarter of the total weight loss. Finally, our data support the use of less restrictive interventions, which appear to be more beneficial for BW and FM loss.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".