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CSF proteome profiling across the Alzheimer’s disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels

Nature Aging volume 2, pages 1040–1053 (2022)Cite this article

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Abstract

Development of disease-modifying therapies against Alzheimer’s disease (AD) requires biomarkers reflecting the diverse pathological pathways specific for AD. We measured 665 proteins in 797 cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment with abnormal amyloid (MCI(Aβ+): n = 50), AD-dementia (n = 230), non-AD dementias (n = 322) and cognitively unimpaired controls (n = 195) using proximity ligation-based immunoassays. Here we identified >100 CSF proteins dysregulated in MCI(Aβ+) or AD compared to controls or non-AD dementias. Proteins dysregulated in MCI(Aβ+) were primarily related to protein catabolism, energy metabolism and oxidative stress, whereas those specifically dysregulated in AD dementia were related to cell remodeling, vascular function and immune system. Classification modeling unveiled biomarker panels discriminating clinical groups with high accuracies (area under the curve (AUC): 0.85–0.99), which were translated into custom multiplex assays and validated in external and independent cohorts (AUC: 0.8–0.99). Overall, this study provides novel pathophysiological leads delineating the multifactorial nature of AD and potential biomarker tools for diagnostic settings or clinical trials.

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Fig. 1: Study overview and differential abundance of CSF proteins across groups.
Fig. 2: Differentially regulated CSF proteins change along AD spectrum and reflect different biological processes.
Fig. 3: CSF biomarker panels for early and specific diagnosis of AD.

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Data availability

The data that support the findings of this study are available at https://www.synapse.org/PRIDE_AD. MS CSF results from previous studies have been provided by co-authors (J.J.L., N.T.S., E.B.D. and A.I.L.)13,25, and all data are available at https://www.synapse.org/consensus and https://www.synapse.org/#!Synapse:syn20821165/wiki/596086.

Code availability

The code that supports the findings of this study is available from the authors on reasonable request. All models were built using publicly available packages and functions in R.

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Acknowledgements

This research is part of the neurodegeneration research program of Amsterdam Neuroscience. This study was supported by Alzheimer Nederland (WE.03-2018-05, M.d.C. and C.E.T.) and Selfridges Group Foundation (NR170065, M.d.C. and C.E.T.). M.d.C. and G.H. are supported by the attraction talent fellowship of Comunidad de Madrid (2018-T2/BMD-11885) and San Pablo CEU University. D.A. acknowledges support from Institute of Health Carlos III (PI18/00435, INT19/00016) and the Department of Health Generalitat de Catalunya PERIS program (SLT006/17/125). Collection of patients samples and data from Penn University (A.C.-P. and D.J.I.) was supported by different funding sources, including National Institute on Aging NINDS R01-NS109260-01A1, P01-AG066597, P30-AG072979 (formerly P30-AG10124) and U19-AG062418-03 (formerly NINDSP50-NS053488-09). Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. The chair (W.M.v.d.F.) is supported by the Pasman stichting. The clinical database structure was developed with funding from Stichting Dioraphte. Research programs of W.M.v.d.F. have been funded by ZonMW, NWO, EU-FP7, EU-JPND, Alzheimer Nederland, Hersenstichting CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences & Health, stichting Dioraphte, Gieskes-Strijbisfonds, stichting Equilibrio, Edwin Bouw fonds, Pasman stichting, stichting Alzheimer & Neuropsychiatrie Foundation, Philips, Biogen MA, Novartis-NL, Life-MI, AVID, Roche BV, Fujifilm and Combinostics. W.M.v.d.F. holds the Pasman chair. W.M.v.d.F. is a recipient of ABOARD, which is a public-private partnership receiving funding from ZonMW (73305095007) and Health~Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). All funding is paid to her institution. Research of C.E.T. is supported by the European Commission (Marie Curie International Training Network, grant agreement No. 860197 (MIRIADE)), Innovative Medicines Initiatives 3TR (Horizon 2020, grant 831434), EPND (IMI 2 Joint Undertaking (JU) under grant agreement 101034344) and JPND (bPRIDE), National MS Society (Progressive MS alliance) and Health Holland, the Dutch Research Council (ZonMW), Alzheimer Drug Discovery Foundation, The Selfridges Group Foundation, Alzheimer Netherlands and Alzheimer Association. C.E.T. is a recipient of ABOARD, which is a public-private partnership receiving funding from ZonMW (3305095007) and Health~Holland, Topsector Life Sciences & Health (PPP allowance; LSHM20106). ABOARD also receives funding from Edwin Bouw Fonds and Gieskes-Strijbisfonds. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands

    Marta del Campo, Lisa Vermunt, Yanaika S. Hok-A-Hin & Charlotte E. Teunissen

  2. Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Boadilla del Monte, Spain

    Marta del Campo & Gonzalo Herradon

  3. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain

    Marta del Campo

  4. Department of Epidemiology & Data Science, Amsterdam Public Health research institute, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands

    Carel F. W. Peeters, Mirrelijn van Nee & Wiesje M. van der Flier

  5. Mathematical & Statistical Methods group (Biometris), Wageningen University & Research, Wageningen, The Netherlands

    Carel F. W. Peeters

  6. Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA, USA

    Erik C. B. Johnson, James J. Lah, Nicholas T. Seyfried, Eric B. Dammer & Allan I. Levey

  7. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands

    Lisa Vermunt, Afina W. Lemstra, Yolande A. L. Pijnenburg, Pieter J. Visser, Betty M. Tijms & Wiesje M. van der Flier

  8. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Alice Chen-Plotkin & David J. Irwin

  9. Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA

    Alice Chen-Plotkin & David J. Irwin

  10. Rutgers-RWJ Medical School, Institute for Health, Health Care Policy, and Aging Research, Rutgers Biomedical and Health Sciences, New Brunswick, NJ, USA

    William T. Hu

  11. Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA

    William T. Hu, Nicholas T. Seyfried & Eric B. Dammer

  12. Department of Neurology and Alzheimer Center, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

    Lieke H. Meeter & John van Swieten

  13. Department of Neurology, Institut d’Investigacions Biomèdiques Sant Pau - Hospital de Sant Pau, Universitat Autònoma de Barcelona, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain

    Daniel Alcolea & Alberto Lleó

  14. Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands

    Pieter J. Visser

  15. Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden

    Pieter J. Visser

Authors
  1. Marta del Campo
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  2. Carel F. W. Peeters
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  4. Lisa Vermunt
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  7. Alice Chen-Plotkin
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  10. James J. Lah
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  11. Nicholas T. Seyfried
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  18. Allan I. Levey
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  19. Afina W. Lemstra
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  20. Yolande A. L. Pijnenburg
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  21. Pieter J. Visser
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  22. Betty M. Tijms
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  23. Wiesje M. van der Flier
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  24. Charlotte E. Teunissen
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Contributions

M.d.C. and C.T conceived the study. M.d.C., C.F.W.P., L.V. and M.v.N. performed the statistical analysis. L.V., A.C.-P., D.J.I., W.T.H., L.H.M., J.v.S., D.A., A.L., A.W.L, Y.A.L.P., P.J.V., B.T.M. and W.M.v.d.F. recruited participants and collected clinical data and samples. M.d.C. and Y.S.H. arranged and prepared samples for proteomics analysis. J.J.L., N.T.S., E.B.D. and A.I.L. provided the proteomic data (both Olink and MS) from previous published studies13,25. M.d.C. and C.E.T. drafted the manuscript. All authors contributed to revision and editing of the manuscript.

Corresponding author

Correspondence to Marta del Campo.

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M.d.C. has been an invited speaker at Eisai. L.V. received a grant for the CORAL consortium by Olink. B.M.T. and P.J.V. are inventors on a patent (WO2020197399A1; owned by Stichting VUmc). D.J.I. is a Scientific Advisory Board Member for Denali Therapeutics. D.A. participated in advisory boards from Fujirebio-Europe and Roche Diagnostics and received speaker honoraria from Fujirebio-Europe, Roche Diagnostics, Nutricia, Krka Farmacéutica S.L., Zambon S.A.U. and Esteve Pharmaceuticals S.A. D.A. declares a filed patent application (WO2019175379 A1 Markers of synaptopathy in neurodegenerative disease). W.M.v.d.F. has performed contract research for Biogen MA and Boehringer Ingelheim. W.M.v.d.F. has been an invited speaker at Boehringer Ingelheim, Biogen MA, Danone, Eisai, WebMD Neurology (Medscape) and Springer Healthcare. W.M.v.d.F. is consultant to Oxford Health Policy Forum CIC, Roche and Biogen MA. W.M.v.d.F. participated in advisory boards of Biogen MA and Roche. All funding is paid to her institution. W.M.v.d.F. is member of the steering committee of PAVE and Think Brain Health. W.M.v.d.F. was associate editor of Alzheimer’s Research & Therapy in 2020/2021. W.M.v.d.F. is associate editor at Brain. C.E.T. has a collaboration contract with ADx Neurosciences, Quanterix and Eli Lilly and performed contract research or received grants from AC-Immune, Axon Neurosciences, Bioconnect, Bioorchestra, Brainstorm Therapeutics, Celgene, EIP Pharma, Eisai, Grifols, Novo Nordisk, PeopleBio, Roche, Toyama and Vivoryon. She serves on editorial boards of Medidact Neurologie/Springer, Alzheimer’s Research & Therapy and Neurology: Neuroimmunology & Neuroinflammation and is editor of a Neuromethods book (Springer). She had speaker contracts for Roche, Grifols and Novo Nordisk. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Protein replicates measured through different PEA panels showed comparable differences.

a, Beta coefficients (log2 fold change) of the protein replicates from the comparisons between MCI(Aβ+) or AD and controls (yellow and red respectively) and AD and non-AD dementias (blue). Stars represent the corresponding significant q-value (* q < 0.05, ** q < 0.01, ***q < 0.001) in the discrimination between groups. b, Scatter plots show the correlation of the NPX values (protein abundance) between the protein replicates that showed significant differences across the groups of interest in the complete cohort (n = 797). Regression line and 95% confident intervals are depicted. Insert indicate the spearman correlation coefficient. CON, cognitively unimpaired controls; MCI(Aβ+), mild cognitive impairment with Aβ pathological changes; AD, Alzheimer’s disease; non-AD dem: non-AD dementias.

Extended Data Fig. 2 Sensitivity analysis AD versus control.

a, Volcano plot represent the proteins differentially regulated in the validation cohort (AD = 18, CON = 44)23,24 after two-sided nested F-test analysis with no adjustments for multiple comparisons. The beta coefficients (log2 fold change) are plotted versus p values (-log10-transformed). Scatter plots show the correlation between the p-values or beta coefficients obtained in the discovery and validation cohorts. Insert indicate the spearman correlation coefficient using all proteins. Proteins showing concordance between discovery and validation (that is, consistency regarding the proteins that are dysregulated and nondysregulated or show the same direction of changes between the groups of interest in both cohorts) are colored in green and the corresponding concordance % is indicated. b, Venn diagram depicts the number and % of overlapping proteins that are significantly dysregulated (p < 0.05) between AD and controls in the discovery and validation cohorts after applying two-sided nested F-test analysis with no adjustments for multiple comparisons. The name of the overlapping proteins is specified in extended data Table 1. AD, Alzheimer’s disease; CON, cognitively unimpaired controls.

Extended Data Fig. 3 Biological processes enriched with randomly selected markers.

Bar graphs depicting the biological pathways enriched when a random selection of markers was performed. The number of proteins randomly selected is the same as those in the unique protein subsets identified along AD stages (Fig. 2b). Functional enrichment was performed using Metascape selecting GO Biological Processes as ontology source. Terms with a p-value < 0.01, a minimum count of 3, and an enrichment factor > 1.5 were collected and grouped into clusters based on their membership similarities. p-values were calculated based on the accumulative hypergeometric distribution. Kappa scores are used as the similarity metric when performing hierarchical clustering on the enriched terms, and subtrees with a similarity of > 0.3 are considered a cluster. The most statistically significant term within a cluster is chosen to represent the cluster. The corresponding GO number and biological process is defined in the right side. Stronger colors represent higher significant enrichment. Vertical line represent the significant threshold (unadjusted p < 0.01).

Extended Data Fig. 4 Mean intensity values of the protein markers included in the CSF diagnostic protein panels for early and specific diagnosis of AD.

Violins represent the abundance (log2 NPX) of the CSF proteins that combined can accurately discriminate MCI(Aβ+) (n = 50) or AD (n = 230, orange) from controls (n = 195) and AD from other types of dementias (n = 322, blue). Horizontal black and dash lines indicate median and interquartile range of the protein abundance. aITGB2 and ABL1 are also markers within the AD versus non-AD dementia panel.*q < 0.05, **q < 0.01, ***q < 0.001. n.s: non-significant. MCI(Aβ+), mild cognitive impairment with Aβ pathological changes; AD, Alzheimer’s disease; non-ADdem: non-AD dementias; CON, cognitively unimpaired controls.

Extended Data Fig. 5 Correlations between each biomarker panel composite score and AD CSF biomarkers and cognitive function.

Correlation matrix heatmaps represent the Spearman’s correlation coefficient in-between the biomarker panel composite score (PC) of each of the identified CSF panels, the classical AD CSF biomarkers (Aβ42, tTau, pTau181 and tTau/Aβ42 ratio) and MMSE score in the corresponding groups for which the specific panel was built. Positive correlations are depicted in red; negative correlations are depicted in blue. Correlation coefficients are indicated. AD, Alzheimer’s disease; non-AD dem: non-AD dementias; CON, cognitively unimpaired controls.

Extended Data Fig. 6 Additional sensitivity analysis for the CSF biomarker panels discriminating AD from controls or AD from non-AD dementias.

a, Receiver operating characteristic (ROC) curves showed that the performance of the CSF AD-diagnostic to discriminate AD from controls was similar between APOE4 + (AD = 133; CON = 44) and APOE4- (AD = 88; CON = 125) patients. b, ROC curve depicting the performance of the CSF AD-differential diagnostic discriminating AD from non-AD dementias in the subset of patients in which non-AD dementias was confirmed (AD = 230; cNon-AD dem = 101). Black line is the mean area under the curve (AUC) over all resamplings (1000 repeats of 5-fold cross-validation, gray lines). Inserts outline corresponding AUC and 95% CI. AD, Alzheimer’s disease; CON, cognitively unimpaired controls; cNon-Ad dem: confirmed non-AD dementias.

Extended Data Fig. 7 Performance of CSF biomarker panels discriminating other groups of interest.

Receiver operating characteristic (ROC) curves depicting the performance of the different panels (rows, Fig. 3, Table 2) discriminating other groups of interest (columns). Black line is the mean area under the curve (AUC) over all resamplings (1000 repeats of 5-fold cross-validation, gray lines). Inserts outline corresponding AUC and 95% CI. MCI(Aβ+), mild cognitive impairment with Aβ pathological changes; AD, Alzheimer’s disease; non-AD dem: non-AD dementias; CON, cognitively unimpaired controls.

Extended Data Fig. 8 CSF biomarker panel for MCI(Aβ+) was not fully replicated in the validation cohort using custom PEA assays.

a, Receiver operating characteristic (ROC) curve depicting the performance of CSF biomarker panel discriminating MCI(Aβ+) from controls (10 CSF proteins: ENTPD5, CRTAM, PARK7, DPP7, CLEC5A, CYR61, KYNU, PCSK9, DDAH1 and PRCP) in the discovery (a, CON = 195, MCI(Aβ+) = 50). Black line is the mean area under the curve (AUC) over all resamplings (1000 repeats of 5-fold cross-validation, gray lines). Inserts outline corresponding AUC and 95% CI. b) ROC curve depicting the performance of CSF MCI(Aβ+)-diagnostic panel in the validation cohort (CON = 110 and MCI(Aβ+) = 101) using the custom assays. CRTAM assay could not be developed due to technical difficulties and thus was not included in the analysis of the validation cohort. c) Scatter plots show the correlation between the beta-coefficients obtained in the discovery phase to those obtained with the custom assays in validation step 2. Insert indicate the spearman correlation coefficient.*ABL1 and ITGB2 are proteins that are present in both AD CSF panels. CLEC5A is present in both the AD and the MCI(Aβ+) diagnostic panel. MCI(Aβ+), mild cognitive impairment with Aβ pathological changes; AD, Alzheimer’s disease; non-AD dem: non-AD dementias; CON, cognitively unimpaired controls.

Supplementary information

Source data

Source Data Table 1

Table outlining the statistics of the CSF proteins differentially regulated across the different diagnostic groups

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del Campo, M., Peeters, C.F.W., Johnson, E.C.B. et al. CSF proteome profiling across the Alzheimer’s disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels. Nat Aging 2, 1040–1053 (2022). https://doi.org/10.1038/s43587-022-00300-1

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