. 2023;96(4):1767-1780.

doi: 10.3233/JAD-230145.

Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

Meghan I Short^{1

2

3}, Alison E Fohner^{4

5}, Håvard K Skjellegrind^{6

7}, Alexa Beiser^{8

9

10}, Mitzi M Gonzales^{1

11}, Claudia L Satizabal^{1

9

10

12}, Thomas R Austin^{4

5}, W T Longstreth^{4

13}, Joshua C Bis⁵, Oscar Lopez^{14

15}, Kristian Hveem^{6

16}, Geir Selbæk^{17

18}, Martin G Larson^{8

9}, Qiong Yang^{8

9}, Hugo J Aparicio^{9

10}, Emer R McGrath^{9

19

20}, Robert E Gerszten^{21

22}, Charles S DeCarli²³, Bruce M Psaty^{4

5

24

25}, Ramachandran S Vasan^{9

26

27

28}, Habil Zare^{1

29}, Sudha Seshadri^{1

9

10}

Affiliations

¹ Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA.
² Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
³ Department of Medicine, Tufts University School of Medicine, Boston, MA, USA.
⁴ Department of Epidemiology, University of Washington, Seattle, WA, USA.
⁵ Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.
⁶ HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Levanger, Norway.
⁷ Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.
⁸ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
⁹ Framingham Heart Study, Framingham, MA, USA.
¹⁰ Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
¹¹ Department of Neurology, University of Texas Health Science Center, San Antonio, TX, USA.
¹² Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA.
¹³ Department of Neurology, University of Washington, Seattle, WA, USA.
¹⁴ Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
¹⁵ Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
¹⁶ K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
¹⁷ Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.
¹⁸ Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
¹⁹ School of Medicine, National University of Ireland Galway, Galway, Ireland.
²⁰ HRB Clinical Research Facility, National University of Ireland Galway, Galway, Ireland.
²¹ Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²² Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
²³ Department of Neurology, School of Medicine and Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California, Davis, Sacramento, CA, USA.
²⁴ Department of Medicine, University of Washington, Seattle, WA, USA.
²⁵ Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
²⁶ Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
²⁷ Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
²⁸ Boston University Center for Computing and Data Science, Boston, MA, USA.
²⁹ Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, TX, USA.

PMID: 38007645
PMCID: PMC10741337
DOI: 10.3233/JAD-230145

Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

Meghan I Short et al. J Alzheimers Dis. 2023.

. 2023;96(4):1767-1780.

doi: 10.3233/JAD-230145.

Authors

Affiliations

¹ Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA.
² Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
³ Department of Medicine, Tufts University School of Medicine, Boston, MA, USA.
⁴ Department of Epidemiology, University of Washington, Seattle, WA, USA.
⁵ Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.
⁶ HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Levanger, Norway.
⁷ Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway.
⁸ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
⁹ Framingham Heart Study, Framingham, MA, USA.
¹⁰ Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
¹¹ Department of Neurology, University of Texas Health Science Center, San Antonio, TX, USA.
¹² Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA.
¹³ Department of Neurology, University of Washington, Seattle, WA, USA.
¹⁴ Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
¹⁵ Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
¹⁶ K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
¹⁷ Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.
¹⁸ Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
¹⁹ School of Medicine, National University of Ireland Galway, Galway, Ireland.
²⁰ HRB Clinical Research Facility, National University of Ireland Galway, Galway, Ireland.
²¹ Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²² Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
²³ Department of Neurology, School of Medicine and Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California, Davis, Sacramento, CA, USA.
²⁴ Department of Medicine, University of Washington, Seattle, WA, USA.
²⁵ Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
²⁶ Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
²⁷ Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
²⁸ Boston University Center for Computing and Data Science, Boston, MA, USA.
²⁹ Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, TX, USA.

PMID: 38007645
PMCID: PMC10741337
DOI: 10.3233/JAD-230145

Abstract

Background: Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults.

Objective: To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome.

Methods: Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up).

Results: Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities.

Conclusions: Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

Keywords: Alzheimer’s disease; biomarkers; dementia; endophenotypes; magnetic resonance imaging; proteomics.

PubMed Disclaimer

Conflict of interest statement

Dr. Psaty serves on the Steering Committee of the Yale Open Data Access Project, funded by Johnson & Johnson.

Dr. DeCarli serves as a consultant to Novartis on a safety trial of heart failure treatment, and as a consultant to Nova Nordisk on Alzheimer’s treatment.

Drs. Fohner, Satizabal, and McGrath are Associate Editors for this journal but were not involved in the peer-review process for this manuscript nor had access to any information regarding its peer-review.

All other authors have no conflicts of interest to disclose.

Figures

**Fig. 1**
Outline of analysis of protein data in FHS discovery cohort. Correlation in protein expression values is used to perform a weighted correlation network analysis (WGCNA), which identifies modules of correlated proteins. Each module is summarized as an eigenprotein, which is a weighted sum of the concentrations of proteins in a module for each participant. Eigenproteins are used as features in linear regressions to assess associations with structural brain MRIs and in Cox proportional hazards (PH) regressions for associations with incident ADRD. Pathway analysis with STRING identifies pathways that are over-represented in modules of interest.

**Fig. 2**
Associations between protein modules and demographic variables. Top panel summarizes Welch’s t statistics for two-sample t-tests for mean protein modules across levels of binary demographic variables. Bottom panel summarizes Spearman correlations between protein modules and continuous demographic variables. Asterisks denote associations for which p < 0.05.

**Fig. 3**
Subsets of hub proteins in M2 and M4 associated with TCBV in FHS. (a) p-values for association of M2 with TCBV, as M2 is gradually decreased from 165 to 2 proteins, with proteins removed in order of absolute weight (i.e., Module Membership) in the eigenprotein from least to greatest. The most significant association with TCBV corresponded to the top 43 M2 proteins. (b) p-values for association of M4 with TCBV, as M4 is gradually decreased from 42 to 2 proteins in a similar manner as in (a). The most significant association with all-cause TCBV corresponded to the top 17 M4 proteins. All p-values were adjusted for the four modules examined using Bonferroni corrections. Hub proteins identified from these analyses are listed in Supplementary Table 1.

See this image and copyright information in PMC

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Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

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Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

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