Effect of Memantine Treatment and Combination with Vitamin D Supplementation on Body Composition in the APP/PS1 Mouse Model of Alzheimer’s Disease Following Chronic Vitamin D Deficiency
Bibliographic record
Abstract
BACKGROUND: Vitamin D deficiency and altered body composition are common in Alzheimer's disease (AD). Memantine with vitamin D supplementation can protect cortical axons against amyloid-β exposure and glutamate toxicity. OBJECTIVE: To study the effects of vitamin D deprivation and subsequent treatment with memantine and vitamin D enrichment on whole-body composition using a mouse model of AD. METHODS: Male APPswe/PS1dE9 mice were divided into four groups at 2.5 months of age: the control group (n = 14) was fed a standard diet throughout; the remaining mice were started on a vitamin D-deficient diet at month 6. The vitamin D-deficient group (n = 14) remained on the vitamin D-deficient diet for the rest of the study. Of the remaining two groups, one had memantine (n = 14), while the other had both memantine and 10 IU/g vitamin D (n = 14), added to their diet at month 9. Serum 25(OH)D levels measured at months 6, 9, 12, and 15 confirmed vitamin D levels were lower in mice on vitamin D-deficient diets and higher in the vitamin D-supplemented mice. Micro-computed tomography was performed at month 15 to determine whole-body composition. RESULTS: In mice deprived of vitamin D, memantine increased bone mineral content (8.7% increase, p < 0.01) and absolute skeletal tissue mass (9.3% increase, p < 0.05) and volume (9.2% increase, p < 0.05) relative to controls. This was not observed when memantine treatment was combined with vitamin D enrichment. CONCLUSION: Combination treatment of vitamin D and memantine had no negative effects on body composition. Future studies should clarify whether vitamin D status impacts the effects of memantine treatment on bone physiology in people with AD.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".