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Leveraging Routine Blood Tests, Plasma pTau‐217, and Machine Learning for Enhanced Detection and Monitoring of Preclinical Alzheimer's Disease

Year: 2025

Type: article

Abstract: BACKGROUND: Preclinical Alzheimer's disease (AD) develops 15-20 years before symptom onset. This study aimed to develop models to enhance the detection and monitoring of preclinical AD. METHOD: A retrospective cohort study was conducted based on data from the A4 study, spanning February 28, 2014, to June 8, 2023. A total of 420 A4 participants with preclinical AD were analyzed to examine associations between hematology/serum biochemistry markers and brain amyloid-PET SUVR or plasma pTau-217 levels. Multivariable linear regression models were used for these analyses, and machine learning models were developed to predict these AD biomarkers over a 4.5-year period. RESULT: Increased monocyte count was associated with elevated brain amyloid-PET SUVR, while higher mean corpuscular volume (MCV) was linked to elevated plasma pTau-217 levels. Additionally, elevated AST-to-ALT ratios and creatinine levels were associated with increased amyloid-PET SUVR and plasma pTau-217 levels. Hematology and serum biochemistry markers notably improved the predictive performance of models for plasma pTau-217 levels. However, monocyte count was not a strong predictor of amyloid-PET SUVR. CONCLUSION: The machine learning models demonstrated promising potential for predicting AD biomarkers, emphasizing the need for future research to evaluate their applicability in both clinical and research contexts.

Source: Alzheimer s & Dementia

DOI: 10.1002/alz70860_097106