Consensus Risk Modeling and Uncertainty Quantification of Alzheimers Disease Using 5ADCSI Plasma Biomarkers and Multiple External Machine-Learning Frameworks

Background: Blood-based biomarkers are increasingly used to identify Alzheimer's disease (AD)-related pathology, but differences in p217tau assay methodology, training cohorts, and model-development context can substantially influence machine-learning (ML) predictions. Whether emerging biomarker platforms preserve biologically meaningful AD-related information across independently developed ML frameworks remains incompletely understood. Objective: To evaluate the biological coherence and translational consistency of plasma biomarker measurements generated using the 5ADCSI platform by applying multiple externally trained ML frameworks and developing a consensus-risk approach that integrates framework predictions while quantifying prediction uncertainty. Methods: Plasma biomarker measurements from 472 participants in the Louisville Twins Study were analyzed using three independently trained ML frameworks: an A4-derived model using the Lilly p217tau MSD assay and two ADNI-derived models using Quanterix Simoa p217tau measured with either the AlzPath or Janssen antibody. Framework-specific predictions of amyloid positivity probability and predicted centiloid burden were integrated into consensus amyloid risk, consensus centiloid burden, and composite consensus AD-risk scores. Prediction uncertainty and rank instability were used to characterize framework agreement and participant-level classification stability. Results: All three frameworks recognized biologically coherent AD-related signal despite differences in training cohort and assay methodology. Agreement was strongest between the A4-MSD and ADNI-AlzPath frameworks, whereas agreement involving the ADNI-Jan framework was weaker. Consensus-risk modeling identified a reproducibly high-risk subgroup characterized by elevated consensus-risk scores, low prediction uncertainty, and low rank instability. Participants prioritized by the consensus framework were enriched for APOE {varepsilon}4 burden, p-tau217, p-tau217/A{beta}42, and GFAP, while discordant high-risk participants exhibited substantially greater framework disagreement. Conclusions: Plasma biomarker measurements generated using the 5ADCSI platform preserve biologically meaningful AD-related information that is consistently recognized across multiple independent ML frameworks. Consensus-risk modeling provides a practical strategy for integrating complementary information from external biological reference models while explicitly characterizing prediction uncertainty, thereby supporting evaluation of emerging blood-based biomarker platforms when direct pathological validation is unavailable.