pr1c00966_si_001.pdf (60.87 kB)
Download fileExposing the Brain Proteomic Signatures of Alzheimer’s Disease in Diverse Racial Groups: Leveraging Multiple Data Sets and Machine Learning
journal contribution
posted on 2022-03-11, 23:14 authored by Heather Desaire, Kaitlyn E. Stepler, Renã A. S. RobinsonRecent
studies have highlighted that the proteome can be used to
identify potential biomarker candidates for Alzheimer’s disease
(AD) in diverse cohorts. Furthermore, the racial and ethnic background
of participants is an important factor to consider to ensure the effectiveness
of potential biomarkers for representative populations. A promising
approach to survey potential biomarker candidates for diagnosing AD
in diverse cohorts is the application of machine learning to proteomics
data sets. Herein, we leveraged six existing bottom-up proteomics
data sets, which included non-Hispanic White, African American/Black,
and Hispanic participants, to study protein changes in AD and cognitively
unimpaired participants. Machine learning models were applied to these
data sets and resulted in the identification of amyloid-β precursor
protein (APP) and heat shock protein β-1 (HSPB1) as two proteins
that have high ability to distinguish AD; however, each protein’s
performance varied based upon the racial and ethnic background of
the participants. HSPB1 particularly was helpful for generating high
areas under the curve (AUCs) for African American/Black participants.
Overall, HSPB1 improved the performance of the machine learning models
when combined with APP and/or participant age and is a potential candidate
that should be further explored in AD biomarker discovery efforts.
History
Usage metrics
Categories
Keywords
brain proteomic signaturesβ precursor proteinstudy protein changesgenerating high areasproteomics data setsmachine learning modelscognitively unimpaired participantsdiverse racial groupsmachine learningdata setsprotein ’high abilitydiverse cohortstwo proteinsrepresentative populationspromising approachpotential candidatepotential biomarkersparticipant ageincluded nonimportant factorhispanic whitehispanic participantsethnic backgroundalzheimer ’african american