Nutrients and Drugs in Alzheimer’s Disease: Harnessing Antioxidants
Bibliographic record
Abstract
Alzheimer’s disease (AD) has been associated with various types of food and drugs. First, we review the nutrients commonly associated with AD in the literature. Then, we query PubMed citations for the co-occurrence of AD with foods and drugs, using a list of 217,776 molecules. Significantly, AD is found to be associated with prescription drugs, potential therapies, diagnostic agents of amyloid β (Aβ) and tau, potential biomarkers, inducers of AD in animal models, as well as drug scaffold moieties. Prescription drugs include rivastigmine (70.7%), donepezil (67.9%), galantamine (56.5%), tacrine (55.8%), physostigmine (6.5%), memantine (46.2%), selegiline (8.8%), and flurbiprofen (4.5%). Potential therapies include docosahexaenoic acid (3.5%), curcumin (4.6%), rosmarinic acid (3.6%), epigallocatechin (3.1%), resveratrol (3.3%), choline (4.5%), D-serine (4.4%), and D-aspartic acid (4.0%). Diagnostic agents include flortaucipir (39.5%) and exametazime (8.8%). Potential biomarkers include Aβ (81.1%), 24-hydroxycholesterol (38.1%), hydroxynonenal (6.2%), homocysteine (3.7%), and 3-nitrotyrosine (3.0%). Disease inducers include scopolamine (9.3%), mecamylamine (3.2%), α-bungarotoxin (3.0%), ibotenic acid (6.4%), okadaic acid (5.5%), quinolinic acid (4.9%), and streptozocin (3.8%). Drug scaffolds include aniline (5.1%), piperidine (4.9%), stilbene (3.9%), and benzofurane (3.0%). Notably, we classify the molecules according to their role in AD. Our study emphasizes the nutritional value of antioxidants, choline, D-serine, and D-aspartic acid in the prevention of AD. Furthermore, our study promulgates vitamin B, which lowers high homocysteine levels associated with dementia. This study is important because it shows how the foods we eat and the medicines we take influence the risk and progression of AD. By scanning huge amounts of medical research, we identified nutrients (like plant compounds such as those in green tea and turmeric) that help protect the brain, as well as drugs that can be repurposed for treatment. Since Alzheimer’s has no cure and is becoming more common worldwide, these findings highlight that diet, together with smarter drug use, can play a big role in preventing or slowing the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".