https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.12369
Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging
Abstract
Introduction
Blood proteins are emerging as candidate biomarkers for Alzheimer's disease (AD). We systematically profiled the plasma proteome to identify novel AD blood biomarkers and develop a high-performance, blood-based test for AD.
Methods
We quantified 1160 plasma proteins in a Hong Kong Chinese cohort by high-throughput proximity extension assay and validated the results in an independent cohort. In subgroup analyses, plasma biomarkers for amyloid, tau, phosphorylated tau, and neurodegeneration were used as endophenotypes of AD.
Results
We identified 429 proteins that were dysregulated in AD plasma. We selected 19 “hub proteins” representative of the AD plasma protein profile, which formed the basis of a scoring system that accurately classified clinical AD (area under the curve = 0.9690–0.9816) and associated endophenotypes. Moreover, specific hub proteins exhibit disease stage-dependent dysregulation, which can delineate AD stages.
Discussion
This study comprehensively profiled the AD plasma proteome and serves as a foundation for a high-performance, blood-based test for clinical AD screening and staging.
1 INTRODUCTION
Evaluating the ATN biomarkers of Alzheimer's disease (AD) in the brain, including amyloid beta (Aβ) deposition (“A”), neurofibrillary tangles (pathologic tau, “T”) and neurodegeneration (“N”) requires invasive cerebrospinal fluid sampling for protein measurement and/or costly imaging by positron emission tomography (PET), greatly restricting their utility for population-scale AD screening.1-3 The recent discovery of blood-based AD biomarkers (i.e., plasma Aβ42/40 ratio, tau/phosphorylated tau [p-tau], and neurofilament light polypeptide [NfL]) raises the possibility of an alternative, less-invasive, blood-based test for AD.4-7 In particular, plasma p-tau181 and p-tau217 accurately classify AD and are associated with AD-specific brain pathologies including tau phosphorylation and Aβ deposition.6, 7 Moreover, cross-sectional and longitudinal studies have demonstrated that plasma p-tau and NfL can indicate disease progression.5, 7-9 Nonetheless, given their relatively constant changes during AD progression,5, 7-11 these blood biomarkers might not have clear stage-specific patterns to define AD stages. Moreover, as a few pilot screening studies identified alternative AD-associated blood proteins with predictive value,12-17 it remains unclear whether the existing AD blood biomarkers sufficiently capture the complete signatures of the AD blood proteome. Therefore, comprehensive protein profiling is needed to clarify the protein signatures of AD blood and delineate the disease pathways and stages.
Recent advances in ultrasensitive and high-throughput protein measurement technologies have enabled large-scale proteomic profiling of the blood,18, 19 which have been widely adopted to study cardiovascular diseases and aging, consequently identifying novel biomarkers and providing biological annotations for disease stages.20, 21 Accordingly, in this study, we used proximity extension assay (PEA) technology to systematically evaluate the protein profiles of AD plasma. Specifically, in a Hong Kong Chinese AD cohort (“discovery cohort” hereafter), consisting of 106 patients with AD and 74 healthy controls (HCs) for whom demographic data, cognitive measures, brain region volumes, and plasma biomarker levels (i.e., Aβ42/40 ratio, tau, p-tau181, and NfL) were available (Table S1 in supporting information), we quantified 1160 plasma proteins and revealed 429 plasma proteins that were dysregulated in patients with AD. We further identified a 19-protein biomarker panel representative of the plasma proteomic signature of AD and validated its high accuracy for classifying AD and associated endophenotypes in an independent cohort. In addition, we showed that certain plasma biomarker proteins are dysregulated in specific stages of AD. Thus, we determined a comprehensive profile of the AD plasma proteome and established a high-performance plasma biomarker panel for AD, which constitutes a critical foundation for developing a blood-based test for AD screening and staging.
2 METHODS
Subject recruitment
The discovery cohort comprised 180 Hong Kong Chinese people ≥60 years old, including 106 patients with AD and 74 HCs who visited the Specialist Outpatient Department of the Prince of Wales Hospital of the Chinese University of Hong Kong from April 2013 to February 2018. The clinical diagnosis of AD was established on the basis of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5).22All participants underwent medical history assessment, clinical assessment, cognitive and functional assessment using the Montreal Cognitive Assessment (MoCA), and neuroimaging assessment by magnetic resonance imaging (MRI).23, 24 Participants with any significant neurological disease other than AD or a psychiatric disorder were excluded. Age, sex, years of education, medical history, history of cardiovascular disease (i.e., heart disease, hypertension, diabetes mellitus, and hyperlipidemia), and white blood cell count were recorded. This study was approved by the Prince of Wales Hospital of the Chinese University of Hong Kong as well as the Hong Kong University of Science and Technology. All participants provided written informed consent for both study participation and sample collection.
The validation cohort comprised 97 Hong Kong Chinese people ≥60 years old, including 36 patients with AD and 14 HCs who visited Queen Elizabeth Hospital from February 2018 to March 2020 as well as 47 HCs who visited the Community CareAge Foundation or Haven of Hope Christian Service from October 2019 to January 2020. The participants recruited from Queen Elizabeth Hospital underwent medical history assessment, clinical assessment, cognitive and functional assessment using the MoCA, and neuroimaging assessment by MRI.23, 24 The clinical diagnosis of AD was based on the US National Institute on Aging and Alzheimer's Association (NIA-AA) workgroup 2011 revised criteria.1, 25 Participants with any significant neurological disease other than AD or a psychiatric disorder were excluded. The participants recruited from the Community CareAge Foundation or Haven of Hope Christian Service, representing population-level HCs, underwent medical history assessment as well as cognitive and functional assessment using the MoCA.23, 24 Age, sex, years of education, and medical history were recorded. This study was approved by Queen Elizabeth Hospital, the Community CareAge Foundation, Haven of Hope Christian Service, and the Hong Kong University of Science and Technology. All participants provided written informed consent for both study participation and sample collection.
The demographic data of both cohorts and the details of sample collection are presented in Table S1 and the Supplementary Methods section in supporting information.
Plasma protein measurement
The Aβ42/40 ratio, tau, p-tau181, and NfL levels were measured in 350 μL plasma by Quanterix Accelerator Lab using the Quanterix NF-light SIMOA Assay Advantage Kit (103186), the Neurology 3-Plex A Kit (101995), or the P-Tau 181 Advantage V2 Kit (103714) where appropriate. The levels of 1160 proteins were quantified in 20 μL plasma by Olink Proteomics using PEA technology (Supplementary Methods). The levels of the assayed plasma proteins are presented in normalized protein expression units. Selected plasma proteins were further validated by enzyme-linked immunosorbent assay (ELISA; Supplementary Methods).
Plasma proteome–AD association analysis
Prior to analysis, the proteomic data were subjected to rank-based normalization using the rntransform() function from the R GenABEL package (v1.8). AD-associated alterations in the plasma proteome were determined according to the associations between the normalized protein levels and AD phenotypes after adjusting for age, sex, history of cardiovascular disease (CVD; i.e., heart disease, hypertension, diabetes mellitus, and hyperlipidemia), and population structure (i.e., the top five principal components [PCs] obtained from the results of principal component analysis of whole-genome sequencing data; Supplementary Methods) using the following linear regression model:
