Taxifolin for Cognitive Preservation in Patients with Mild Cognitive Impairment or Mild Dementia
Bibliographic record
Abstract
BACKGROUND: The development of numerous disease-modifying drugs for age-related dementia has been attempted based on the amyloid-β (Aβ) hypothesis without much success. Taxifolin (TAX), a natural bioactive flavonoid, shows pleiotropic neuroprotective effects with inhibition of Aβ aggregation, production, and glycation, antiinflammatory effects, and amelioration of the waste clearance system. We hypothesized that TAX intake is associated with the suppression of cognitive deterioration. OBJECTIVE: To investigate associations between TAX intake and cognitive changes. METHODS: We retrospectively identified patients who orally took TAX 300 mg/day and regularly underwent Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog) and Montreal Cognitive Assessment (MoCA) and compared the temporal changes in ADAS-Cog and MoCA between the non-treatment (pre-TAX) period (180±100 days) and following treatment (on-TAX) period (180±100 days) from June 2020 to November 2021. Since some additional patients underwent the Mini-Mental State Examination (MMSE) instead of the MoCA at the beginning of the pre-TAX period, the same comparison was performed using the MoCA total score converted from MMSE as a sensitivity analysis. RESULTS: Sixteen patients were identified. TAX intake was associated with significantly higher interval changes in the MoCA subscale scores of visuospatial/executive function (p = 0.016), verbal fluency (p = 0.02), and the total score (p = 0.034), but not with ADAS-Cog (total score, p = 0.27). In the sensitivity analysis, 29 patients were included. TAX intake was associated with a significantly higher interval change in the total MoCA score (p = 0.004) but not with ADAS-Cog (p = 0.41). CONCLUSION: Our findings provide a basis for TAX as a novel strategy for maintaining brain health during aging. A prospective cohort study is required to confirm these findings.
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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) | 0.001 | 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 | 0.000 |
| 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".