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Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk

Nature Aging volume 1, pages 473–489 (2021)Cite this article

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Abstract

The plasma proteomic changes that precede the onset of dementia could yield insights into disease biology and highlight new biomarkers and avenues for intervention. We quantified 4,877 plasma proteins in nondemented older adults in the Atherosclerosis Risk in Communities cohort and performed a proteome-wide association study of dementia risk over five years (n = 4,110; 428 incident cases). Thirty-eight proteins were associated with incident dementia after Bonferroni correction. Of these, 16 were also associated with late-life dementia risk when measured in plasma collected nearly 20 years earlier, during mid-life. Two-sample Mendelian randomization causally implicated two dementia-associated proteins (SVEP1 and angiostatin) in Alzheimer’s disease. SVEP1, an immunologically relevant cellular adhesion protein, was found to be part of larger dementia-associated protein networks, and circulating levels were associated with atrophy in brain regions vulnerable to Alzheimer’s pathology. Pathway analyses for the broader set of dementia-associated proteins implicated immune, lipid, metabolic signaling and hemostasis pathways in dementia pathogenesis.

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Fig. 1: Schematic overview of the study design.
Fig. 2: Proteome-wide associations with incident dementia, mid-life replication and external replication.
Fig. 3: Association of dementia-associated proteins with neuroimaging markers and gene coexpression analyses.
Fig. 4: Biological pathways and upstream regulators implicated in dementia risk.
Fig. 5: Protein networks identified among dementia-associated proteins.

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Data availability

All data generated in this study are either included in this article (and its Supplementary Information), available upon reasonable request or are available in an online public database. Consistent with a prespecified policy for access of ARIC and AGES data, requests may be submitted to the ARIC and AGES steering committees for review. Requests for clinical or proteomic data from individual investigators will be reviewed to ensure that data can be shared without compromising patient confidentiality or breaching intellectual property restrictions. Reasonable requests will be considered and promptly processed. Participant-level demographic, clinical and proteomic data may be partially restricted based on previously obtained participant consent. Data sharing restrictions may also be applied to ensure consistency with confidentiality or privacy laws and considerations. For information on how to access available data and study protocols, see www2.cscc.unc.edu/aric/. Data from the AGES-Reykjavik study used in this study are available through collaboration (AGES_data_request@hjarta.is) under a data usage agreement with the Icelandic Heart Association. Tissue-specific gene expression data are available at https://www.gtexportal.org/home/. Gene coexpression analyses were conducted using data available at http://www.explainbio.com. Brain gene expression data were derived from the Brain eQTL Almanac (http://www.braineac.org/) and the Functional Mapping and Annotation of Genome-Wide Association Studies105 platform (https://fuma.ctglab.nl/). eQTL gene enrichment was performed using data from the Molecular Signatures database (http://www.broadinstitute.org/msigdb) and the NHGRI-EBI Catalog of Published GWAS (https://www.ebi.ac.uk/gwas/). Functional enrichment of protein networks was conducted using the g:Profiler web tool (https://biit.cs.ut.ee/gprofiler/gost).

Code availability

All software used in this study are publicly available: R v.3.6.2 (https://www.r-project.org/); Stata, v.14 (https://www.stata.com/stata14/); IPA (https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/); ExplainBio (http://www.explainbio.com/); and GraphPad Prism v.8.4.3. (https://www.graphpad.com/scientific-software/prism/). The code used in this study can be made available from the corresponding author upon reasonable request.

Change history

  • 04 July 2022

    In the version of this article initially published, the Supplementary Tables 1–31 file was missing and has now been restored to the online version of the article.

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Acknowledgements

We thank the staff and participants of the ARIC study for their important contributions. We also thank B. Chen for her valuable assistance with aspects of the manuscript. The ARIC study has been funded in whole or in part with Federal funds from the National Heart, Lung and Blood Institute (NHLBI), NIH, Department of Health and Human Services (contract nos. HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, HHSN268201700005I, R01HL087641 and R01HL086694); National Human Genome Research Institute contract U01HG004402; and NIH contract HHSN268200625226C. Neurocognitive data are collected by U01 2U01HL096812, 2U01HL096814, 2U01HL096899, 2U01HL096902, 2U01HL096917 from the NIH (NHLBI, National Institute of Neurological Disorders and Stroke (NINDS), NIA and National Institute on Deafness and Other Communication Disorders (NIDCD)) and with previous brain MRI examinations funded by R01-HL70825 from the NHLBI. The ARIC-PET study is funded by the NIA (R01AG040282). Infrastructure was partly supported by grant no. UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. This study was also supported by contracts K23 AG064122 (to K.A.W.), K24 AG052573 (to R.F.G.) and U01-AG052409 (to M.F.) from NIA; and R01-HL134320 (to C.M.B.) from NHLBI. Avid Radiopharmaceuticals provided the florbetapir isotope for the study but had no role in the study design or interpretation of results. The AGES-Reykjavik study was supported by the Icelandic Heart Association, the NIA (N01-AG-12100 and HHSN271201200022C), the Intramural Program at the NIA, the Althingi (the Icelandic Parliament), the Icelandic Centre for Research grant 141101-051 and the Novartis Institute for Biomedical Research. SomaLogic provided assays as an in-kind contribution in a data flex change collaboration agreement. This research was supported in part by the Intramural Research Program of the NIH, NIA. Funders had no control over the publication.

Author information

Authors and Affiliations

  1. Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA

    Keenan A. Walker

  2. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Keenan A. Walker & Rebecca F. Gottesman

  3. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Jingsha Chen, Linda Zhou, Morgan E. Grams, Natalie Daya, Aozhou Wu, Rebecca F. Gottesman & Josef Coresh

  4. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Jingning Zhang, Scott L. Zeger & Nilanjan Chatterjee

  5. Brown Foundation Institute of Molecular Medicine, McGovern Medical School and Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA

    Myriam Fornage & Yunju Yang

  6. Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Morgan E. Grams

  7. MIND Center and Division of Nephrology, University of Mississippi Medical Center, Jackson, MS, USA

    Adrienne Tin

  8. Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA

    Ron C. Hoogeveen & Christie M. Ballantyne

  9. Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA

    Kevin J. Sullivan & Thomas H. Mosley

  10. Department of Medicine, University of California-San Francisco, San Francisco, CA, USA

    Peter Ganz

  11. Icelandic Heart Association, Kopavogur, Iceland

    Elias F. Gudmundsson, Valur Emilsson & Vilmundur Gudnason

  12. Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA

    Lenore J. Launer

  13. Novartis Institutes for Biomedical Research, Cambridge, MA, USA

    Lori L. Jennings

  14. University of Iceland, Reykjavik, Iceland

    Vilmundur Gudnason

  15. Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

    Eric Boerwinkle

  16. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Eric Boerwinkle

Authors
  1. Keenan A. Walker
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  2. Jingsha Chen
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Contributions

K.A.W., S.L.Z., R.F.G., T.H.M., E.B., C.M.B. and J. Coresh conceptualized and designed the study. M.G., N.D., R.C.H., C.M.B., V.E., L.J.L., L.L.J., V.G. and J. Coresh contributed to data acquisition. K.A.W., J. Chen, J.Z., M.F., Y.Y., L.Z., A.W. and E.F.G. analyzed data. K.A.W., M.F., Y.Y., M.G., A.T., P.G., N.C., R.F.G. and J. Coresh contributed to interpretation of data. K.A.W., M.F., Y.Y., M.G., A.T., R.C.H., A.W., K.J.S., P.G., L.J.L., R.F.G., C.M.B. and J. Coresh drafted and provided substantial revision of the manuscript.

Corresponding authors

Correspondence to Keenan A. Walker or Josef Coresh.

Ethics declarations

Competing interests

R.C.H. has received grants and consulting fees from Denka Seiken outside the scope of the current research study. A.W. received fees from the Analysis Group as a consultant outside the scope of the current research study. P.G. is a member of the SomaLogic Medical Advisory board, for which he receives no remuneration of any kind. L.L.J. is an employee and stockholder of Novartis. R.F.G. received fees from the American Academy of Neurology for her role as an Associate Editor for the journal Neurology. Proteomic assays in ARIC were conducted free of charge as part of a data exchange agreement with SomaLogic. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks Sebastian Palmqvist and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 P-values for dementia-associated proteins in mid-life replication and AGES-Reykjavik external replication analyses.

a, P-values (two-sided) for the mid-life replication analysis (index visit 1993-1995, ages 49-73) of the 38 dementia-associated proteins identified in older adults (y-axis) plotted against P-values for each dementia-associated protein derived from the primary analysis (index visit 2011-2013) on the x-axis. The horizontal dotted red line represents the Bonferroni-corrected threshold for statistical significance in the mid-life replication analysis (0.05/38; P < 0.0013). The vertical dotted black line represents the Bonferroni-corrected threshold for statistical significance in the primary analysis (0.05/4,877; P < 1.03×10-5). b, P-values (two-sided) for the AGES-Reykjavik (index visit 2002-2006) replication of proteins that were significantly associated with dementia risk in both the primary and the mid-life replication analysis (y-axis) plotted against P-values for each dementia-associated protein derived from the primary analysis (index visit 2011-2013) on the x-axis. The horizontal dotted red line represents the Bonferroni-corrected threshold for statistical significance in the AGES-Reykjavik replication analysis (0.05/13; P < 0.0038). The vertical dotted black line represents the Bonferroni-corrected threshold for statistical significance in the primary analysis (0.05/4,877; P < 1.03×10-5).

Extended Data Fig. 2 Prediction of incident dementia using proteins, demographic and clinical variables, and their combination measured at late-life baseline (2011-2013).

Elastic net machine learning with Cox proportional hazards regression was used to select the best combination of proteins from the top 50 proteins in each model. This table shows results from 10-fold cross validated analyses. Two-sided P-values were calculated for the C statistic comparisons. No corrections for multiple comparisons were performed. a Protein combination defined using elastic net machine learning algorithm. b Includes age, sex, race-center, education, and APOEε4. c Includes body mass index, diabetes, hypertension, smoking status, and eGFR-creatinine. Abbreviations: C stat. Δ, change in C statistic with the addition of elastic net proteins.

Extended Data Fig. 3 Gene expression in whole blood, brain, heart, kidney, spleen, and adipose tissue of genes coding for dementia-associated proteins.

Using gene expression data available from postmortem samples in the GTEx database, this heatmap shows the expression of genes coding for dementia-associated proteins (in transcripts per million) in whole blood, select brain regions, and other selected tissue. Hierarchical cluster analysis was used to group dementia-associated proteins based on gene expression across multiple tissues.

Extended Data Fig. 4 Functional profiling of protein Network 1 identified among the set of dementia-associated proteins.

Protein networks were assembled based on evidence of known gene/molecule interactions in the Ingenuity Knowledge Base. The g:Profiler toolkit106 was used to analyze the proteins in each dementia-associated protein network for functional enrichment. We defined the biological pathways/processes associated with each protein set using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways databases. Images were generated using the g:Profiler web tool: https://biit.cs.ut.ee/gprofiler/. Abbreviations: GO:BP, gene ontology biological process; GO:MF, gene ontology molecular function.

Extended Data Fig. 5 Functional profiling of protein Network 2 and Network 3 identified among the set of dementia-associated proteins.

Protein networks were assembled based on evidence of known gene/molecule interactions in the Ingenuity Knowledge Base. The g:Profiler toolkit106 was used to analyze the proteins in each dementia-associated protein network for functional enrichment. We defined the biological pathways/processes associated with each protein set using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways databases. Images were generated using the g:Profiler web tool: https://biit.cs.ut.ee/gprofiler/. Abbreviations: GO:BP, gene ontology biological process; GO:MF, gene ontology molecular function.

Extended Data Fig. 6 Functional profiling of protein Network 4, Network 5, and Network 6 identified among the set of dementia-associated proteins.

Protein networks were assembled based on evidence of known gene/molecule interactions in the Ingenuity Knowledge Base. The g:Profiler toolkit106 was used to analyze the proteins in each dementia-associated protein network for functional enrichment. We defined the biological pathways/processes associated with each protein set using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways databases. Images were generated using the g:Profiler web tool: https://biit.cs.ut.ee/gprofiler/. Abbreviations: GO:BP, gene ontology biological process; GO:MF, gene ontology molecular function.

Extended Data Fig. 7 Functional profiling of protein Network 7, Network 8, and Network 10 identified among the set of dementia-associated proteins.

Protein networks were assembled based on evidence of known gene/molecule interactions in the Ingenuity Knowledge Base. The g:Profiler toolkit106 was used to analyze the proteins in each dementia-associated protein network for functional enrichment. We defined the biological pathways/processes associated with each protein set using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways databases. Images were generated using the g:Profiler web tool: https://biit.cs.ut.ee/gprofiler/. No enriched biological pathways/processes were found for Network 9. Abbreviations: GO:BP, gene ontology biological process; GO:MF, gene ontology molecular function.

Extended Data Fig. 8 Functional profiling of protein Network 11 and Network 12 identified among the set of dementia-associated proteins.

Protein networks were assembled based on evidence of known gene/molecule interactions in the Ingenuity Knowledge Base. The g:Profiler toolkit106 was used to analyze the proteins in each dementia-associated protein network for functional enrichment. We defined the biological pathways/processes associated with each protein set using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways databases. Images were generated using the g:Profiler web tool: https://biit.cs.ut.ee/gprofiler/. Abbreviations: GO:BP, gene ontology biological process; GO:MF, gene ontology molecular function.

Extended Data Fig. 9 PEER factors associated with incident dementia after visit 5.

a, Volcano plot showing the hazard ratio (x-axis) and two-sided P-value (y-axis) for the association of 110 PEER factors with incident dementia. PEER factors above the horizontal dotted line were significantly associated with incident dementia after Bonferroni correction (0.05/110; P < 0.00045). P-values for dementia-associated PEER factors were 1.52E-06, 2.24E-05, and 2.00E-04 for PEER factors 9, 88, and 3, respectively. b, Spearman correlations between dementia-associated protein level and each PEER factors associated with dementia risk.

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Supplementary Methods and Figs. 1 and 2.

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Walker, K.A., Chen, J., Zhang, J. et al. Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk. Nat Aging 1, 473–489 (2021). https://doi.org/10.1038/s43587-021-00064-0

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