Theracurmin may be a therapeutic option for elderly patients with Alzheimer’s disease: A 6‐month retrospective follow‐up study
Bibliographic record
Abstract
Abstract Background Therapeutic options for Alzheimer’s Disease (AD) treatment are highly limited and success rate in new molecules under investigated has been disappointing so that agents with various mechanisms represent a promising therapeutic opportunity. Among these, Theracurmin, a very highly absorbable curcumin formulation, was shown to improve memory and attention in non‐demented people. We aimed to investigate the effect of Theracurmin on the disease course in elderly patients with mild cognitive impairment (MCI) and AD. Method This is a retrospective follow up study on 93 consecutive elderly patients with MCI or AD. All patients underwent Comprehensive geriatric assessment (CGA), including Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), clock‐drawing test, trail making, activities of daily living (ADL), at baseline and end of the sixth month. Nineteen of 52 patients with AD and 17 of 41 patients with MCI were treated with Theracurmin 180 mg/day (PO). All AD patients were also treated with an acetylcholinesterase inhibitor. Result During the follow up period it was observed that MMSE or MOCA and instrumental ADL (IADL) scores declined in AD patients without treating with Theracurmin (p=0.001; 0.011; 0.004, respectively), whereas these scores remained stable in those with Theracurmin. Furthermore, this stabilization in the IADL was also observed in MCI patients treated with Theracurmin, but not in those without Theracurmin. During the follow‐up, three of MCI patients who did not receive Theracurmin progressed to AD, whereas it was only one patient in those who received it. No discontinuation of Theracurmin therapy was observed. Conclusion Theracurmin seems to be a therapeutic option for elderly patients with AD and MCI because of providing stabilization of the disease course by preventing progressive loss in cognitive functions and ADLs. Further prospective, multicenter, community‐based studies are needed to confirm these promising 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".