Predicting Amyloid Positivity Through Proteomic and Machine Learning Approaches

Introduction

Alzheimer’s disease is a progressive neurodegenerative disorder where early detection remains difficult. To address this challenge, we analysed a large proteomics dataset from older adults, including individuals diagnosed through clinical and imaging confirmation of brain amyloid deposition. We hypothesized that amyloid positivity could be detected using blood-based proteomic profiles combined with statistical and machine learning methods.

Methods

We applied descriptive and inferential statistical analyses alongside supervised classification approaches, and group comparisons between amyloid positive and amyloid negative individuals were conducted. Classification methods, including random forests, gradient boosting, and neural networks, were used to evaluate prediction of amyloid status. All data were normalized and privacy compliant.

Results

Distinct proteomic signatures were associated with disease status. Significant protein expression differences were observed between amyloid positive ( n =337) and amyloid negative ( n =651) groups. Classification models reached balanced performance with prediction accuracy up AUC of 0.80. Eight proteins (i.e. SERPINA1, C3, CRP, APOE4, CFH, VTN, C1QTNF5, and PON1) emerged as strong predictors from the best-performing classifiers, representing potential biomarker candidates.:

Discussion and Conclusions

Combining statistical and machine learning methods enabled robust identification of patterns distinguishing amyloid profiles. This strategy supports biomarker discovery and development of accessible blood-based diagnostic and therapeutic targets.

Significance Statement

This study leverages high-throughput proteomic profiling and machine learning to identify peripheral blood-based protein signatures associated with cerebral amyloid pathology, a hallmark of Alzheimer’s disease. By integrating clinical data with proteomic biomarkers, we aimed to develop a non-invasive, scalable predictive tool that can support early detection and risk stratification of Alzheimer’s disease, potentially improving screening efficiency and guiding future therapeutic strategies. Furthermore, this analysis allowed for mechanistic insight into the biology of amyloid in Alzheimer’s disease.

Highlights

• Developed a machine learning-based proteomic model to predict amyloid positivity in Alzheimer’s disease using plasma blood samples.

• Identified a protein machine learning generated signature linked to central amyloid pathology.

• Suggested the potential for scalable, early detection tools to support precise and targeted diagnosis and treatment planning in Alzheimer’s disease using machine learning.