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Record W4416810647 · doi:10.4103/ijnpnd.ijnpnd_123_25

Nutrients and Drugs in Alzheimer’s Disease: Harnessing Antioxidants

2025· article· en· W4416810647 on OpenAlexaff
Tehila Nissim, Karin Ben Zaken, Ibrahim O. Sawaid, Lior Segev, Samuel Mesfin, Pnina Frankel, Rahaf Ezzy, Trishna Saha, Naamah Bloch, Baruh Polis, Abraham O. Samson

Bibliographic record

VenueInternational journal of Nutrition Pharmacology Neurological Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsGalantamineNutraceuticalDrugTacrineQuinolinic acidRivastigmineResveratrolVitamin EBioproduction

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.378
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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