Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer's disease biomarkers
Journal article, 2025

Purpose
This study explores the application of machine learning to high-dimensional proteomics datasets for identifying Alzheimer’s disease (AD) biomarkers. AD, a neurodegenerative disorder affecting millions worldwide, necessitates early and accurate diagnosis for effective management.

Methods
We leverage Tandem Mass Tag (TMT) proteomics data from the cerebrospinal fluid (CSF) samples from the frontal cortex of patients with idiopathic normal pressure hydrocephalus (iNPH), a condition often comorbid with AD, with rare access to both lumbar and ventricular samples. Our methodology includes extensive data preprocessing to address batch effects and missing values, followed by the use of the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation to overcome the small sample size. We apply linear, and non-linear machine learning models, and ensemble methods, to compare iNPH patients with and without biomarker evidence of AD pathology ( Aβ−T − or Aβ+T +) in a classification task.

Results
We present a machine learning workflow for working with high-dimensional TMT proteomics data that addresses their inherent data characteristics. Our results demonstrate that batch effect correction has no or minor impact on the models’ performance and robust feature selection is critical for model stability and performance, especially in the high-dimensional proteomics data setting for AD diagnostics. The results further indicated that removing features with missing values produced stronger models than imputing them, and the batch effect had minimal impact on the models Our best-performing disease-progression detection model, a random forest, achieves an AUC of 0.84 (± 0.03).

Conclusion
We identify several novel protein biomarkers candidates, such as FABP3 and GOT1, with potential diagnostic value for AD pathology detection, suggesting the necessity of different biomarkers for AD diagnoses for patients with iNPH, and considering different biomarkers for ventricular and lumbar CSF samples. This work underscores the importance of a meticulous machine learning process in enhancing biomarker discovery. Our study also provides insights in translating biomarkers from other central nervous system diseases like iNPH, and both ventricular and lumbar CSF samples for biomarker discovery, providing a foundation for future research and clinical applications.

Proteomics

High-dimensional data

Alzheimer's disease

Biomarkers

Machine learning

Mass spectrometry

Feature selection

Author

Christoffer Ivarsson Orrelid

University of Gothenburg

Oscar Rosberg

University of Gothenburg

Sophia Weiner

University of Gothenburg

Fredrik Johansson

Data Science and AI 3

University of Gothenburg

Johan Gobom

Sahlgrenska University Hospital

University of Gothenburg

Henrik Zetterberg

University of Wisconsin Madison

Hong Kong Center for Neurodegenerative Diseases

Sahlgrenska University Hospital

University College London (UCL)

Newton Mwai Kinyanjui

University of Gothenburg

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Lena Stempfle

University of Gothenburg

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Fluids and Barriers of the CNS

20458118 (eISSN)

Vol. 22 1 23

Subject Categories (SSIF 2025)

Other Clinical Medicine

Other Medical Engineering

Artificial Intelligence

DOI

10.1186/s12987-025-00634-z

PubMed

40033432

More information

Latest update

3/21/2025