ArticlebioRxiv : the preprint server for biology2024
Nomination of a novel plasma protein biomarker panel capable of classifying Alzheimer's disease dementia with high accuracy in an African American cohort.
Lindsey A Kuchenbecker, Kevin J Thompson, Cheyenne D Hurst, Bianca M Opdenbosch, Michael G Heckman, Joseph S Reddy, Thuy Nguyen, Heidi L Casellas, Katie D Sotelo, Delila J Reddy and 7 more
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In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 · The registryThe trial behind it
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5 · Who and what moneyAuthors and funding
17 authors.
Kevin J ThompsonDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Cheyenne D HurstDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
Bianca M OpdenboschDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
Michael G HeckmanDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Thuy NguyenDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
Katie D SoteloDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
Delila J ReddyDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
Nilufer Ertekin-TanerDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
Krishna R KalariDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Funding
Peripheral and Central Biomarkers of Alzheimer's Disease in Diverse CohortsU19AG074879 · NIA · MAYO CLINIC JACKSONVILLE · PI Minerva Maria Carrasquillo · 2023 to 2026
$42.0MResearch Education ComponentP30AG062677 · NIA · MAYO CLINIC ROCHESTER · PI NILUFER ERTEKIN-TANER · 2019 to 2026
$33.5MIntegrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's DiseaseU01AG046170 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI EHRLICH, MICHELLE E, GANDY, SAMUEL E. · 2013 to 2022
$26.0MIntegrating the exposome and methylome to inform brain molecular changes in ADRD across established diverse cohorts.U01AG046139 · NIA · UNIVERSITY OF FLORIDA · PI ERTEKIN-TANER, NILUFER, FUNK, CORY · 2013 to 2022
$24.6MMulti-omic network-directed proteoform discovery, dissection and functional validation to prioritize novel AD therapeutic targetsU01AG061356 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2018 to 2022
$13.7MUnderstanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer DiseaseR01AG067025 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FINKBEINER, STEVEN M, HAROUTUNIAN, VAHRAM · 2019 to 2023
$11.8MMetabolomic Signatures for Disease Sub-classification and Target Prioritization in AMP-ADU01AG061359 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KASTENMULLER, GABI · 2018 to 2022
$10.0MSupplement to AMP-AD Brain Proteomic Network Enhancement, Validation and Translation into CSF BiomarkersU01AG061357 · NIA · EMORY UNIVERSITY · PI LEVEY, ALLAN I, SEYFRIED, NICHOLAS THOMAS · 2018 to 2022
$9.1MNIA NIH HHS P30 AG062677NIA NIH HHS R01 AG067025NIA NIH HHS U01 AG046139NIA NIH HHS U01 AG046170NIA NIH HHS U01 AG061356NIA NIH HHS U01 AG061357NIA NIH HHS U01 AG061359NIA NIH HHS U19 AG074879
6 · The paper itselfAbstract
Introduction: African Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimer's disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD. Methods: Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in a cohort of AA clinically diagnosed as AD dementia (n=183) or cognitively unimpaired (CU, n=145). Machine learning approaches were implemented to identify the set of plasma proteins that yields the best classification accuracy. Results: A plasma protein panel achieved an area under the curve (AUC) of 0.91 to classify AD dementia vs CU. The reproducibility of this finding was observed in the ANMerge plasma and AMP-AD Diversity brain datasets (AUC=0.83; AUC=0.94). Discussion: This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning approaches. Our findings also highlight the potential importance of the matrisome and cerebrovascular dysfunction in AD pathophysiology.
Indexed as
African AmericanAlzheimer’s dementiaAlzheimer’s diseasebiomarkersbraincerebrovascularMachine learningmatrisomenetwork analysisplasmaSomaScan
Identifiers
PMID39131392
PMCPMC11312441
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