A Blood Biomarker‐guided Precision Medicine Approach for Individualized Neurodegenerative Disease Risk Reduction and Treatment: The Future of Preventive Neurology?
Bibliographic record
Abstract
BACKGROUND: This study investigates plasma proteins as potential markers for early detection and intervention of Alzheimer's Disease (AD) and other Neurodegenerative Diseases (NDDs). Participants with a family history of NDDs and minimal neurological symptoms, along with healthy controls, were recruited from five sites in the US and Canada. As of April 23, 2025, 198 participants were recruited, with 81 having longitudinal assessments analyzed. METHOD: Participants receiving preventive neurology or medicine care were divided into two groups: "Intervention 1" for those adhering to over 60% of risk reduction interventions, and "Intervention 2" for those adhering to less than 60%. These were compared to healthy controls and AD controls. NDD risk reduction included lifestyle changes, lipid-lowering agents, GLP1s, HRT, Statins, Zetia, and SSRIs. RESULTS: NULISA testing revealed significant changes in three ratios (Aβ42/40, pTau217/Aβ42, pTau181/Aβ42, Oligo-SNCA/SNCA) for Intervention 1 and two ratios for Intervention 2. Additionally, 34 individual biomarkers changed significantly in Intervention 1 and 26 in Intervention 2. Multi-modal interventions showed the highest number of significant changes. Lumipulse testing showed significant differences in the pTau181/AB42 ratio, Aβ40, Aβ42, pTau181, and pTau217 in Intervention 1, and changes in the pTau181/AB42 ratio, Aβ42, and pTau181 in Intervention 2. Controls showed no significant changes. CONCLUSION: The study concluded that higher compliance to interventions led to more significant changes in protein markers. Multi-modal interventions were most effective. Novel alpha-synuclein markers also changed, potentially aiding in evaluating interventions for Lewy Body Dementia and Parkinson's disease. These markers may serve as future outcome measures for preventive neurology care.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".