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Cited by 33Alzheimer's disease research and treatmentsDementia and Cognitive Impairment ResearchAdvanced Proteomics Techniques and Applications

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer’s Disease

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Tandon, Raghav, Levey, Allan I., Lah, James J., Seyfried, Nicholas T., Mitchell, Cassie S.·2023·Journal of Alzheimer's Disease
DOI 10.3233/jad-220683PMC PMC10041447
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Abstract

Background: The complex and not yet fully understood etiology of Alzheimer’s disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms. Objective: The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer’s disease (AsymAD), and AD. Methods: Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning. Results: A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.92) or Control (AUC = 0.92) from AD (AUC = 0.98). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PRKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, RABEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, ENPP6, HAPLN2, PSMD4, and CMAS. Conclusion: Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.

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  • Omics and large cohort dataset analyses have identified potential cell signatures and molecular mechanisms of resilience, including high processing of energetic pathways involving mitochondrial metabolism and glycolysis, axonal and dendritic growth, and a general increase in protein processing.

    “Advancements in omics and large cohort dataset analyses have enabled the identification of potential cell signatures and molecular mechanisms of resilience, including high processing of energetic pathways involving mitochondrial metabolism and glycolysis, axonal and dendritic growth, and a general increase in protein processing [11, 90, 91].”

    From Enhanced microglial dynamics and a paucity of tau seeding in the amyloid plaque microenvironment contribute to cognitive resilience in Alzheimer’s disease (2024) · cites this paper as [90]

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Cited by

Sorted by citations

Authors
Tandon, R., Levey, A. I., Lah, J. J., Seyfried, N. T., & Mitchell, C. S.
Affiliations
  • Georgia Institute of Technology (US)
  • Emory University (US)
Corresponding author
Mitchell, Cassie S.
ORCID
Venue
Journal of Alzheimer's Disease
Publisher
SAGE Publications
Published
2023-03-21
Volume
92
Issue
2
Pages
411-424
ISSN
  • 1387-2877 (ISSN-L)
  • 1875-8908
DOI
10.3233/jad-220683
PMC
PMC10041447
OpenAlex
W4319345240
Cited by
33
References
0
Citations by year
2026: 4 · 2025: 14 · 2024: 8 · 2023: 7
FWCI
5.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Alzheimer s Disease (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (1944247, P50 AG025688, CAREER)
  • Alzheimer's Association (P50 AG025688, 591014, 2018-AARGD-591014)
  • Emory University (P50AG025688, P30 AG066511, AG025688)
  • Goizueta Business School, Emory University
  • National Institute on Aging (P50AG025688, R01AG070937, U19AG065169, P30AG066511)
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Keywords
Alzheimer's disease, asymptomatic Alzheimer's disease
MeSH terms
Machine Learning, Sugars, Alzheimer Disease, Brain, Humans, Hydroxybutyrate Dehydrogenase, Proteins, Proteomics, HSP40 Heat-Shock Proteins

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