Year: 2026
Type: preprint
Abstract: Introduction: Phosphorylated tau-217 (p-tau 217) is widely used as a plasma-based biomarker for Alzheimer's Disease (AD) detection, demonstrating superior accuracy for detecting brain amyloid pathology. However, 30-50% of patients fall within an intermediate diagnostic "gray zone" where biomarker results are indeterminate, often decreasing physician confidence and requiring subsequent diagnostic workup. To address this, we developed a two-stage machine learning algorithm 'GRAD: Gatekeeper & Reflex for Alzheimer's Disease' to increase clinical confidence and reduce the AD health economic burden. Methods: We initially analyzed 320 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with plasma biomarkers and amyloid PET. We then built a two-stage machine learning classifier mimicking real clinical workflow where the stage 1 'Gatekeeper' used the gold-standard marker: p-tau217 with respective 25%/75% probability thresholds. The stage 2 'Reflex' step applied Random Forest multi-marker classification (p-tau 217, AB42/40, NFL, GFAP) for difficult-to-diagnose gray zone cases. To ensure statistical robustness, leave-one-out cross-validation with bootstrap confidence intervals was used. We externally validated the GRAD architecture on 1,644 A4 Study participants, with MRI enhancement analysis in 1,044 gray zone cases. To measure cost-effectiveness we compared our GRAD-staged testing to universal PET. Results: The model's 'Gatekeeper' resolved 55.6% of ADNI cases with 88.8% accuracy (NPV 91.8%, PPV 85.0%). The complete pipeline achieved AUC 0.867 (95% CI: 0.825-0.904), with 80.6% sensitivity, 80.0% specificity, LR+ 4.03, LR- 0.24. For the difficult-to-diagnose gray zone cases, the 'Reflex' machine learning model achieved AUC 0.755. In our A4 validation, the predictions correlated strongly with centiloid (r= 0.693). Expanding beyond plasma biomarkers, MRI integration improved gray zone classification from AUC 0.829 to 0.853 (p=0.014). The cost modeling analysis projected a 67% reduction in spending versus the current standard of universal PET. Discussion: Our clinically-staged diagnostic algorithm, 'GRAD', provides actionable classifications for the majority of patients while routing uncertain cases for additional workup. The GRAD framework offers a practical, cost-effective approach for implementing plasma biomarkers in clinical practice. Future iterations of this framework, with integration of novel biomarkers like MTBR-tau243 present a significant opportunity to alleviate the AD health-economic burden and eliminate expensive but unnecessary diagnostic measures.
Source: medRxiv
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G104586243, G1557667123, G2105457377, G2189010064, G3146424788, G3710456697, G4119534169, G4475667458, G5207404921, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", Alzheimer's Disease Neuroimaging Initiative, Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) Open-Label Extension Study, G5850682332, G764699806, G781725486, G8228018387, G8895907669, G8957659042, G9000014521, G9000039018, G9000122335
OpenAlex ID: W7128441898