Precision Medicine Approach to Alzheimer’s Disease: Successful Proof-of-Concept Trial
Bibliographic record
Abstract
Abstract Importance Effective therapeutics for Alzheimer’s disease and mild cognitive impairment are needed. Objective To determine whether a precision medicine approach to Alzheimer’s disease and mild cognitive impairment, in which potential contributors to cognitive decline are identified and targeted therapeutically, is effective enough in a proof-of-concept trial to warrant a larger, randomized, controlled clinical trial. Rationale Previous clinical trials for Alzheimer’s disease have pre-determined a single treatment modality, such as a drug candidate or therapeutic procedure, that may be unrelated to the primary drivers of the neurodegenerative process. Therefore, increasing data set size to include the potential contributors to cognitive decline for each patient, and addressing the identified potential contributors, may represent a more effective therapeutic strategy. Hypothesis Alzheimer’s disease is a multi-factorial network dysfunction that results from a chronic or repeated insufficiency of support for a neuroplasticity network; thus factors that increase demand—such as infections or toxin exposure—or reduce support—such as reduced energetics or trophic support—may contribute to the neurodegenerative process. Rectifying this hypothesized network dysfunction represents a rational approach to the treatment of the cognitive decline associated with Alzheimer’s disease and mild cognitive impairment. Design Twenty-five patients with Alzheimer’s disease or mild cognitive impairment, with Montreal Cognitive Assessment (MoCA) scores of 19 or higher, were evaluated for markers of inflammation, chronic infection, dysbiosis, insulin resistance, protein glycation, vascular disease, nocturnal hypoxemia, hormone insufficiency or dysregulation, nutrient deficiency, toxin or toxicant exposure (metals, organic toxicants, and biotoxins), genetic predisposition to cognitive decline, and other biochemical parameters associated with cognitive decline. Brain magnetic resonance imaging with volumetrics was performed at baseline and study conclusion. Patients were treated for nine months with a personalized, precision medicine protocol that addressed each patient’s identified potentially contributory factors, and cognition was assessed at t = 0, 3, 6, and 9 months. Trial registration and IRB approval The clinical trial was registered at clinicaltrials.gov ( NCT03883633 ), 1 and approved by the Advarra IRB. Support for the trial The trial was supported by a grant from the Four Winds Foundation via Evanthea, LLC, and we are grateful to Diana Merriam and Gayle Brown for their interest, discussions, and support. Main Outcome Measures Trained external raters evaluated the study subjects with the Montreal Cognitive Assessment (MoCA), CNS Vital Signs (a computerized cognitive assessment battery), AQ-21 (a subjective scale completed by the significant other or study partner), and AQ-C change scale (a subjective scale of cognitive improvement or decline, completed by the significant other or study partner). Follow-up brain MRI with volumetrics was carried out at the completion of the trial. Results All outcome measures revealed improvement: statistically highly significant improvement in MoCA scores, CNS Vital Signs Neurocognitive Index, and AQ-C were documented. No serious adverse events were recorded. Conclusions and Relevance Based on the cognitive improvements observed in this study of patients with Alzheimer’s disease or mild cognitive impairment, a larger, randomized, controlled trial of the precision medicine therapeutic approach described herein is warranted.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".