ReCODE: A Personalized, Targeted, Multi-Factorial Therapeutic Program for Reversal of Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is the major cause of age-associated cognitive decline, and in the absence of effective therapeutics is progressive and ultimately fatal, creating a dire need for successful prevention and treatment strategies. We recently reported results of a successful proof-of-concept trial, using a personalized, precision medicine protocol, but whether such an approach is readily scalable is unknown. OBJECTIVE: In the case of AD, there is not a single therapeutic that exerts anything beyond a marginal, unsustained, symptomatic effect. This suggests that the monotherapeutic approach of drug development for AD may not be an optimal one, at least when used alone. Using a novel, comprehensive, and personalized therapeutic system called ReCODE (reversal of cognitive decline), which proved successful in a small, proof-of-concept trial, we sought to determine whether the program could be scaled to improve cognitive and metabolic function in individuals diagnosed with subjective cognitive impairment, mild cognitive impairment, and early-stage AD. METHODS: 255 individuals submitted blood samples, took the Montreal Cognitive Assessment (MoCA) test, and answered intake questions. Individuals who enrolled in the ReCODE program had consultations with clinical practitioners, and explanations of the program were provided. Participants had follow-up visits that included education regarding diet, lifestyle choices, medications, supplements, repeat blood sample analysis, and MoCA testing between 2 and 12 months after participating in the ReCODE program. Pre- and post-treatment measures were compared using the non-parametric Wilcoxon signed rank test. RESULTS AND CONCLUSIONS: By comparing baseline to follow-up testing, we observed that MoCA scores either significantly improved or stabilized in the entire participant pool-results that were not as successful as those in the proof-of-concept trial, but more successful than anti-amyloid therapies-and other risk factors including blood glucose, high-sensitivity C-reactive protein, HOMA-IR, and vitamin D significantly improved in the participant pool. Our findings provide evidence that a multi-factorial, comprehensive, and personalized therapeutic program designed to mitigate AD risk factors can improve risk factor scores and stabilize or reverse the decline in cognitive function. Since superior results were obtained in the proof-of-concept trial, which was conducted by a small group of highly trained and experienced physicians, it is possible that results from the use of this personalized approach would be enhanced by further training and experience of the practicing physicians. Nonetheless, the current results provide further support indicating the potential of such an approach for the prevention and reversal of cognitive decline.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".