Development of a robust blood‐based multi‐pathway biomarker assay for early screening and classification of Alzheimer’s disease

Abstract Background Alzheimer's disease (AD) is a devastating neurodegenerative disease with delayed diagnosis until the manifestation of symptoms. Although the emergence of blood‐based biomarkers offers hope for easy detection of AD, existing AD‐associated blood biomarkers, known as the “blood ATN biomarkers”, mainly capture the pathological hallmarks of AD, overlooking other AD‐associated biological processes such as inflammation and vascular dysfunctions. Therefore, developing a blood‐based biomarker assay that captures dysregulation beyond the ATN biomarkers may help advance early detection and staging of AD, enabling a comprehensive examination of the disease status Method We leveraged ultrasensitive proteomic technology to develop a blood‐based, multiplex biomarker assay for AD. This assay simultaneously measures the levels of 21 blood proteins associated with different biological pathways, including neurodegeneration, inflammation, innate immunity, vascular functions, and metabolic activities. Moreover, we developed an AD risk scoring system by integrating the level changes of these 21 proteins in a machine learning‐based model. The performance of this 21‐protein biomarker assay in AD classification and indication of AD‐related endophenotypes was evaluated in three independent cohorts Result We showed that this 21‐protein biomarker assay accurately classifies AD (AUC = 0.9407–0.9867) and mild cognitive impairment (MCI) (AUC = 0.8434–0.8945). It also indicates amyloid pathology in Chinese and European populations. Moreover, this assay simultaneously evaluates changes in five biological processes, providing comprehensive assessment of disease status and revealing heterogeneity of AD among individuals. Notably, dysregulations of biological processes upon AD progression are different between Chinese and European populations, particularly in biological pathways related to inflammation and vascular functions. Conclusion Our findings demonstrate the utility of a blood‐based multi‐pathway biomarker assay for early screening and staging of AD in clinical settings and provide insights for patient stratification and the development of precision medicine.

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Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.086053
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Development of a robust blood‐based multi‐pathway biomarker assay for early screening and classification of Alzheimer’s disease

Ronnie M.N. Lo, Elaine Cheng, Aida Fernández‐Lebrero, Xiaoyun Cao et al.
Alzheimer s & Dementia
Computational Drug Discovery Methods
article

Development of a robust blood‐based multi‐pathway biomarker assay for early screening and classification of Alzheimer’s disease

Ronnie M.N. Lo, Elaine Cheng, Aida Fernández‐Lebrero, Xiaoyun Cao, José Contador, John Hardy, Vincent Mok, Chunyan Tan, Fanny C.F. Ip, Nancy Y. Ip, Paula Ortiz‐Romero, Amy K.Y. Fu, Yuanbing Jiang, Henrik Zetterberg, Timothy Kwok, Marc Suárez‐Calvet, Albert Puig‐Pijoan, Kin Ying Mok, Hyebin Uhm, Ouyang Li, Brian C.H. Law
article en

Abstract

Abstract Background Alzheimer's disease (AD) is a devastating neurodegenerative disease with delayed diagnosis until the manifestation of symptoms. Although the emergence of blood‐based biomarkers offers hope for easy detection of AD, existing AD‐associated blood biomarkers, known as the “blood ATN biomarkers”, mainly capture the pathological hallmarks of AD, overlooking other AD‐associated biological processes such as inflammation and vascular dysfunctions. Therefore, developing a blood‐based biomarker assay that captures dysregulation beyond the ATN biomarkers may help advance early detection and staging of AD, enabling a comprehensive examination of the disease status Method We leveraged ultrasensitive proteomic technology to develop a blood‐based, multiplex biomarker assay for AD. This assay simultaneously measures the levels of 21 blood proteins associated with different biological pathways, including neurodegeneration, inflammation, innate immunity, vascular functions, and metabolic activities. Moreover, we developed an AD risk scoring system by integrating the level changes of these 21 proteins in a machine learning‐based model. The performance of this 21‐protein biomarker assay in AD classification and indication of AD‐related endophenotypes was evaluated in three independent cohorts Result We showed that this 21‐protein biomarker assay accurately classifies AD (AUC = 0.9407–0.9867) and mild cognitive impairment (MCI) (AUC = 0.8434–0.8945). It also indicates amyloid pathology in Chinese and European populations. Moreover, this assay simultaneously evaluates changes in five biological processes, providing comprehensive assessment of disease status and revealing heterogeneity of AD among individuals. Notably, dysregulations of biological processes upon AD progression are different between Chinese and European populations, particularly in biological pathways related to inflammation and vascular functions. Conclusion Our findings demonstrate the utility of a blood‐based multi‐pathway biomarker assay for early screening and staging of AD in clinical settings and provide insights for patient stratification and the development of precision medicine.

Alzheimer s & DementiaVol. 20(S2)
Universitat Autònoma de Barcelona (ES), University of Wisconsin–Madison (US), Universitat Pompeu Fabra (ES), Chinese University of Hong Kong (HK), Hong Kong University of Science and Technology (HK), Hong Kong Science and Technology Parks Corporation (HK), Instituto de Salud Carlos III (ES), HKUST Shenzhen Research Institute (CN), Pasqual Maragall Foundation (ES), Prince of Wales Hospital (CN), UK Dementia Research Institute (GB), Barcelonaβeta Brain Research Center (ES), Hospital Del Mar (ES), Municipal Institute for Medical Research (ES), Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (ES), National Hospital for Neurology and Neurosurgery (GB), Hospital del Mar Research Institute (ES), University College London (GB), University of Gothenburg (SE), University of Hong Kong (HK)
Openalex Percentile: Top 70%
Computational Drug Discovery Methods
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