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Precision Medicine Treatment of Alzheimer’s Disease: Successful Randomized Controlled Trial

2025· preprint· W7117711669 on OpenAlexaboutno aff
Kat Toups, Craig Tanio, Ann Hathaway, Nate Bergman, Kristine Burke, David Haase, Susan Guettel Cole, Stephen L. Aita, Cyrus A. Raji, Alan S. Boyd, Hans Frykman, Anna Mammel, Shannel H. K. Elhelou, Karen Miller, Varun B. Dwaraka, Sharon Hausman-Cohen, Mouna Attarha, Soheil Mohammadi, Kia Sanford, Lynn Killips, Mac Dodds, Benjamin Hill, Dale Bredesen

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Language
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialPrecision medicineDementiaClinical trialDiseaseAlternative medicineCognitionDrug trial

Abstract

fetched live from OpenAlex

Background: There is a critical need for effective therapeutics for Alzheimer’s disease. However, the majority of previous clinical trials have pre-determined a single treatment modality, such as a drug candidate or therapeutic procedure, which may be unrelated to the primary drivers of the neurodegenerative process. Therefore, a personalized, precision medicine approach, with increased data set size to include the potential contributors to cognitive decline for each patient, and treatment of the identified potential contributors, has emerged as a potentially more effective strategy. Recent proof-of-concept trials have provided clinical data that support this approach. Objective: To determine whether a precision medicine approach to Alzheimer’s disease at the mild cognitive impairment or early dementia stage is effective in a randomized controlled clinical trial. Methods: Seventy-three patients with mild cognitive impairment or early dementia, with Montreal Cognitive Assessment (MoCA) scores of 18 or higher, were evaluated for markers of inflammation, chronic infection, dysbiosis, immune dysfunction, insulin resistance, protein glycation, vascular disease, nocturnal hypoxemia, hormone insufficiency or dysregulation, nutrient deficiency, toxin or toxicant exposure, and other biochemical parameters associated with cognitive decline. Genetic and epigenetic evaluations were included, as well as Alzheimer’s-associated biomarkers. Brain magnetic resonance imaging with volumetrics was performed at baseline and study conclusion. Participants were randomly assigned to either a personalized, precision medicine protocol or standard of care treatment. Cognition and clinical symptoms were assessed at 0, 3, 6, and 9 months. Results: Relative to the standard of care protocol, statistically significant incremental effects of the precision medicine protocol were observed for broad neurocognitive functioning, composite memory (verbal plus visual), executive function, processing speed, cognitive symptom severity, and Alzheimer’s disease symptom severity. Furthermore, overall health was enhanced, with improvements in blood pressure, body mass index, glycemic index, lipid profiles, and methylation status. The treatment effect size for overall cognitive function was calculated to be greater than previously published clinical trials, seven times the effect size of the lecanemab trial and four times the effect size of the donanemab trial. Conclusion: A personalized, precision medicine approach represents an effective treatment for patients with mild cognitive impairment or early-stage dementia due to Alzheimer’s disease. In most cases, this treatment leads to cognitive improvement rather than simply retarding decline, and it does so without significant negative side effects such as brain edema, microhemorrhage, or atrophy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.118
GPT teacher head0.420
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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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