Thiamine pyrophosphokinase-1 deficiency in neurons drives Alzheimer’s multiple pathophysiological alterations
Bibliographic record
Abstract
ABSTRACT Background The mechanism driving multiple pathophysiological alterations in Alzheimer’s disease (AD) remains unclear. Thiamine deficiency, a well-known feature of AD, may contribute to these alterations. Methods The expressions of four known genes associated with thiamine metabolism were studied in brain samples from patients with AD and other neurodegenerative disorders. The results were further demonstrated in AD and diabetic mouse and cellular models. The phenotypes of mice with conditional Thiamine pyrophosphokinase-1 ( Tpk ) knockout in brain excitatory neurons were investigated. The therapeutic effects of thiamine diphosphate supplement and Tpk delivery on cellular and mouse models were explored. Phase 2 clinical trial of benfotiamine, a thiamine derivative, plus donepezil was performed. Results Only TPK expression was inhibited in brain samples of AD patients, while none of thiamine-associated genes were significantly changed in other neurodegenerative disorders. TPK inhibition in the brains and neurons was verified in AD and diabetic mouse and cellular models. Mice with Tpk deletion in neurons exhibited all major pathophysiological alterations of AD, including amyloid deposition, Tau hyperphosphorylation, and brain atrophy. TPK expression restoration and thiamine diphosphate supplement ameliorated the pathophysiological and behavioral phenotypes in mouse and cell models with Tpk insufficiency. Benfotiamine delayed cognitive decline in mild-to-moderate AD patients with a dose-effect relationship, particularly with a significant attenuation of the deterioration in moderate AD patients by post hoc analysis. Conclusions TPK deficiency and hence thiamine diphosphate reduction in neurons are a decisive factor driving multiple pathophysiologic alterations of AD, unveiling a new direction for the disease mechanism and treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".