Changes in Fat Mass Following Creatine Supplementation and Resistance Training in Adults ≥50 Years of Age: A Meta-Analysis
Bibliographic record
Abstract
Aging is associated with an increase in fat mass which increases the risk for disease, morbidity and premature mortality. Creatine supplementation in combination with resistance training has been shown to increase lean tissue mass in adults ≥50 years of age; however, the synergetic effects of creatine and resistance training on fat mass in this population are unclear. Creatine metabolism plays an important role in adipose tissue bioenergetics and energy expenditure. Thus, the combination of creatine supplementation and resistance training may decrease fat mass more than resistance training alone. The purpose of this review is two-fold: (1) to perform meta-analyses on studies involving creatine supplementation during resistance training on fat mass in adults ≥50 years of age, and (2) to discuss possible mechanistic actions of creatine on reducing fat mass. Nineteen studies were included in our meta-analysis with 609 participants. Results from the meta-analyses showed that adults ≥50 years of age who supplemented with creatine during resistance training experienced a greater reduction in body fat percentage (0.55%, p = 0.04) compared to those on placebo during resistance training. Despite no statistical difference (p = 0.13), adults supplementing with creatine lost ~0.5 kg more fat mass compared to those on placebo. Interestingly, there are studies which have linked mechanism(s) explaining how creatine may influence fat mass, and these data are also discussed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".