Tetrahydrobiopterin Improves Recognition Memory in the Triple-Transgenic Mouse Model of Alzheimer’s Disease, Without Altering Amyloid-β and Tau Pathologies
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a multifactorial disease, implying that multi-target treatments may be necessary to effectively cure AD. Tetrahydrobiopterin (BH4) is an enzymatic cofactor required for the synthesis of monoamines and nitric oxide that also exerts antioxidant and anti-inflammatory effects. Despite its crucial role in the CNS, the potential of BH4 as a treatment in AD has never been scrutinized. OBJECTIVE: Here, we investigated whether BH4 peripheral administration improves cognitive symptoms and AD neuropathology in the triple-transgenic mouse model of AD (3xTg-AD), a model of age-related tau and amyloid-β (Aβ) neuropathologies associated with behavior impairment. METHODS: Non-transgenic (NonTg) and 3xTg-AD mice were subjected to a control diet (5% fat - CD) or to a high-fat diet (35% fat - HFD) from 6 to 13 months to exacerbate metabolic disorders. Then, mice received either BH4 (15 mg/kg/day, i.p.) or vehicle for ten consecutive days. RESULTS: This sub-chronic administration of BH4 rescued memory impairment in 13-month-old 3xTg-AD mice, as determined using the novel object recognition test. Moreover, the HFD-induced glucose intolerance was completely reversed by the BH4 treatment in 3xTg-AD mice. However, the HFD or BH4 treatment had no significant impact on Aβ and tau neuropathologies. CONCLUSION: Overall, our data suggest a potential benefit from BH4 administration against AD cognitive and metabolic deficits accentuated by HFD consumption in 3xTg-AD mice, without altering classical neuropathology. Therefore, BH4 should be considered as a candidate for drug repurposing, at least in subtypes of cognitively impaired patients experiencing metabolic disorders.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".