Creatine Supplementation Increases Muscle Branched‐Chain Amino Acids in an Alzheimer Mouse Model
Bibliographic record
Abstract
Besides mental dysfunctions, Alzheimer's disease (AD) may impair muscle function. Creatine supplementation (CR) can enhance skeletal muscle mass and function in sarcopenia and muscular dystrophies, but has yet to be studied in AD. We examined the effect of oral CR on muscle amino acids (AA) level in an AD model. 24 triple transgenic (3×Tg, ~8 mo old) AD mice were randomly assigned to control (CON; 6 males (M)/6 females (F)) or CR (3% w/w; 4M/8F) diet. Bodyweights and feed intakes were measured throughout the 8‐week study. Quadriceps muscle (QM) was collected at the end to analyze for levels of creatine and AA and measured by high‐performance liquid‐chromatography. Data (mean±SEM) were analyzed by 2‐way ANOVAs. Feed intakes and changes in bodyweight were similar among groups (p>0.05). Creatine content in QM was higher (p=0.067) in CR (M: 0.38±0.04, F: 0.38±0.02 nmol/μl) vs. CON (M: 0.33±0.01, F: 0.35±0.01 nmol/μl). Total branched‐chain AA (BCAA) level in QM was greater for CR (M: 111.10±8.51, F: 87.26±4.31 nmol/mg tissue) vs. CON (M: 78.56±4.46, F: 66.07±4.35 nmol/mg tissue). BCAA/non‐BCAA ratio was higher (p=0.002) in CR (M: 0.87±0.17, F: 0.58±0.03) vs. CON (M: 0.54±0.04, F: 0.45±0.03). Particularly, leucine was dramatically increased (p<0.0001) in CR (M: 61.15±5.64, F: 47.28±2.55 nmol/mg) vs. CON (M: 34.21±2.64, F: 25.49±2.38 nmol/mg). CR supplementation enhances muscle AA contents, increasing BCAA levels in the 3×Tg AD mouse model, which may promote muscle function in AD. Funded by CIHR and Everett Endowment Fund
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".