CoChat
Sign Up
Cited by 95

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

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

del Campo, Marta, Peeters, Carel F. W., Johnson, Erik C. B., Vermunt, Lisa, Hok-A-Hin, Yanaika S., van Nee, Mirrelijn, Chen-Plotkin, Alice, Irwin, David J., Hu, William T., Lah, James J., Seyfried, Nicholas T., Dammer, Eric B., Herradon, Gonzalo, Meeter, Lieke H., van Swieten, John, Alcolea, Daniel, Lleó, Alberto, Levey, Allan I., Lemstra, Afina W., Pijnenburg, Yolande A. L., Visser, Pieter J., Tijms, Betty M., van der Flier, Wiesje M., Teunissen, Charlotte E.·2022·Nature Aging
DOI 10.1038/s43587-022-00300-1PMC PMC10292920
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s43587-022-00300-1

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 54

  1. Jellinger, K. A. Neuropathology of dementia disorders.J. Alzheimers Dis. Parkinsonism 4, 135 (2014). · DOI
  2. Gauthier, S. et al. Why has therapy development for dementia failed in the last two decades? Alzheimers Dement. 12, 60–64 (2016). · DOI · Paper page
  3. Park, S. A., Han, S. M. & Kim, C. E. New fluid biomarkers tracking non-amyloid-β and non-tau pathology in Alzheimer’s disease. Exp. Mol. Med. 52, 556–568 (2020). · DOI
  4. Schoonenboom, N. S. M. et al. Cerebrospinal fluid markers for differential dementia diagnosis in a large memory clinic cohort. Neurology 78, 47–54 (2012). · DOI · Paper page
  5. Robinson, J. L. et al. Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated. Brain 141, 2181–2193 (2018). · DOI · Paper page
  6. Ewers, M. et al. CSF biomarkers for the differential diagnosis of Alzheimer’s disease: A large-scale international multicenter study. Alzheimers Dement. 11, 1306–1315 (2015). · DOI
  7. Enroth, S. et al. High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer. Commun. Biol. 2, 221 (2019). · DOI
  8. Fang, M. et al. The ER UDPase ENTPD5 promotes protein N-glycosylation, the Warburg effect, and proliferation in the PTEN pathway. Cell 143, 711–724 (2010). · DOI · Paper page
  9. Clements, C. M., Mcnally, R. S., Conti, B. J., Mak, T. W. & Ting, J. P. protein, stabilizes the antioxidant transcriptional master regulator Nrf2. Proc. Natl Acad. Sci. USA 103, 15091–15096 (2006). · DOI · Paper page
  10. Maes, M. B. et al. Kinetic investigation of human dipeptidyl peptidase II (DPPII)-mediated hydrolysis of dipeptide derivatives and its identification as quiescent cell proline dipeptidase (QPP)/dipeptidyl peptidase 7 (DPP7). Biochem. J 386, 315–324 (2005). · DOI
  11. Soisson, S. M. et al. Structural definition and substrate specificity of the S28 protease family: The crystal structure of human prolylcarboxypeptidase. BMC Struct. Biol. 10, 1–8 (2010). · DOI
  12. Patiño-Lopez, G. et al. Human class-I restricted T cell associated molecule is highly expressed in the cerebellum and is a marker for activated NKT and CD8+ T lymphocytes. J. Neuroimmunol. 171, 145–155 (2006). · DOI
  13. Derkinderen, P. et al. Tyrosine 394 is phosphorylated in Alzheimer’s paired helical filament tau and in fetal tau with c-Abl as the candidate tyrosine kinase. J. Neurosci. 25, 6584–6593 (2005). · DOI
  14. Erickson-Viitanen, S., Ruggieri, S., Natalini, P. & Horecker, B. L. Thymosin beta 10, a new analog of thymosin beta 4 in mammalian tissues. Arch. Biochem. Biophys. 225, 407–413 (1983). · DOI
  15. Johnson, E. C. B. et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat. Med. 26, 769–780 (2020). · DOI · Paper page
  16. Isgrò, M. A., Bottoni, P. & Scatena, R. Neuron-Specific enolase as a biomarker: Biochemical and clinical aspects. Adv. Exp. Med. Biol. 867, 125–143 (2015). · DOI
  17. Pollio, G. et al. Increased expression of the oligopeptidase THOP1 is a neuroprotective response to Aβ toxicity. Neurobiol. Dis. https://doi.org/10.1016/j.nbd.2008.04.004 (2008). · DOI
  18. Dayoub, H. et al. Dimethylarginine dimethylaminohydrolase regulates nitric oxide synthesis: genetic and physiological evidence. Circulation 108, 3042–3047 (2003). · DOI
  19. Kannan, L. et al. R-spondin3 prevents mesenteric ischemia/reperfusion-induced tissue damage by tightening endothelium and preventing vascular leakage. Proc. Natl Acad. Sci. USA 110, 14348–14353 (2013). · DOI
  20. Wesenhagen, K. E. J., Teunissen, C. E., Visser, P. J. & Tijms, B. M. Cerebrospinal fluid proteomics and biological heterogeneity in Alzheimer’s disease: A literature review. Crit. Rev. Clin. Lab. Sci. 57, 86–98 (2019). · DOI
  21. Boström, G. et al. Different inflammatory signatures in Alzheimer’s disease and frontotemporal dementia cerebrospinal fluid. J. Alzheimers Dis. 81, 629–640 (2021). · DOI
  22. Konijnenberg, E. et al. APOE ϵ4 genotype-dependent cerebrospinal fluid proteomic signatures in Alzheimer’s disease. Alzheimers Res. Ther. 12, 65 (2020). · DOI
  23. Duits, F. H. et al. Matrix metalloproteinases in Alzheimer’s disease and concurrent cerebral microbleeds. J. Alzheimers Dis. 48, 711–720 (2015). · DOI · Paper page
  24. Dayon, L. et al. Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimers Res. Ther. 10, 66 (2018). · DOI · Paper page
  25. Suhre, K., McCarthy, M. I. & Schwenk, J. M. Genetics meets proteomics: perspectives for large population-based studies. Nat. Rev. Genet. 22, 19–37 (2021). · DOI · Paper page
  26. Dammer, E. B. et al. Multi-platform proteomic analysis of Alzheimer’s disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome. Preprint at bioRxiv https://doi.org/10.1101/2022.06.20.494087 (2022). · DOI
  27. Wildsmith, K. R. et al. Identification of longitudinally dynamic biomarkers in Alzheimer’s disease cerebrospinal fluid by targeted proteomics. Mol. Neurodegener. 9, 22 (2014). · DOI
  28. Seyfried, N. T. et al. A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease. Cell Syst. 4, 60–72 (2017). · DOI · Paper page
  29. Gao, S., Casey, A. E., Sargeant, T. J. & Mäkinen, V.-P. Genetic variation within endolysosomal system is associated with late-onset Alzheimer’s disease. Brain 141, 2711–2720 (2018). · DOI
  30. Butkiewicz, M. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019). · DOI · Paper page
  31. Lepelletier, F. X., Mann, D. M. A., Robinson, A. C., Pinteaux, E. & Boutin, H. Early changes in extracellular matrix in Alzheimer’s disease. Neuropathol. Appl. Neurobiol. 43, 167–182 (2017). · DOI
  32. Philibert, K. D., Marr, R. A., Norstrom, E. M. & Glucksman, M. J. Identification and characterization of Aβ peptide interactors in Alzheimer’s disease by structural approaches. Front. Aging Neurosci. 6, 1–9 (2014). · DOI
  33. Lonskaya, I., Hebron, M. L., Desforges, N. M., Franjie, A. & Moussa, C. E.-H. Tyrosine kinase inhibition increases functional parkin‐Beclin‐1 interaction and enhances amyloid clearance and cognitive performance. EMBO Mol. Med. 5, 1247–1262 (2013). · DOI
  34. Siddiqui, T. J. et al. An LRRTM4-HSPG complex mediates excitatory synapse development on dentate gyrus granule cells. Neuron 79, 680–695 (2013). · DOI
  35. Gong, C.-X., Liu, F. & Iqbal, K. Perspective multifactorial hypothesis and multi-targets for Alzheimer’s disease. J. Alzheimers Dis. 64, 107–117 (2018). · DOI
  36. Johnstone, D., Milward, E. A., Berretta, R. & Moscato, P. Multivariate protein signatures of pre-clinical Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset. PLoS ONE 7, e34341 (2012). · DOI
  37. Kim, Y. et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat. Commun. 7, 11906 (2016). · DOI
  38. Teunissen, C. E., Otto, M., Engelborghs, S., Herukka, S.-K. & Lehmann, S. White paper by the Society for CSF Analysis and Clinical Neurochemistry: Overcoming barriers in biomarker development and clinical translation. Alzheimers Res. Ther. 12, 23 (2018).
  39. Teunissen, C. E. et al. Novel diagnostic cerebrospinal fluid protein biomarkers for pathologic subtypes of frontotemporal dementia identified by proteomics. Alzheimers Dement. 9, 86–94 (2016).
  40. Teunissen, C. E. et al. Blood-based biomarkers for Alzheimer’s disease: Towards clinical implementation. Lancet Neurol. 21, 66–77 (2021). · DOI · Paper page
  41. van der Flier, W. M. & Scheltens, P. Amsterdam Dementia Cohort: Performing research to optimize care. J. Alzheimers Dis. 62, 1091–1111 (2018). · DOI
  42. Lleó, A. et al. A 2-step cerebrospinal algorithm for the selection of frontotemporal lobar degeneration subtypes. JAMA Neurol. 75, 738–745 (2018). · DOI · Paper page
  43. Del Campo, M. et al. Recommendations to standardize preanalytical confounding factors in Alzheimer’s and Parkinson’s disease cerebrospinal fluid biomarkers: An update. Biomark Med. 6, 419–430 (2012). · DOI · Paper page
  44. Dubois, B. et al. Research criteria for the diagnosis of Alzheimer’s disease: Revising the NINCDS-ADRDA criteria. Lancet Neurol. 6, 734–746 (2007). · DOI · Paper page
  45. McKeith, I. G. et al. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 89, 88–100 (2017). · DOI · Paper page
  46. Rascovsky, K. et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134, 2456–2477 (2011). · DOI · Paper page
  47. Armstrong, M. J. et al. Criteria for the diagnosis of corticobasal degeneration. Neurology 80, 496–503 (2013). · DOI · Paper page
  48. Duits, F. H. et al. The cerebrospinal fluid ‘alzheimer profile’: Easily said, but what does it mean? Alzheimers Dement. 10, 713–723 (2014). · DOI
  49. Alcolea, D. et al. The Sant Pau Initiative on Neurodegeneration (SPIN) cohort: A data set for biomarker discovery and validation in neurodegenerative disorders. Alzheimers Dement. 5, 597–609 (2019). · DOI
  50. Tijms, B. M. et al. Unbiased approach to counteract upward drift in cerebrospinal fluid amyloid-β 1–42 analysis results. Clin. Chem. 64, 576–585 (2018). · DOI
  51. Jessen, F. et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement. 10, 844–852 (2014). · DOI · Paper page
  52. Leeuw, F. A. et al. Blood‐based metabolic signatures in Alzheimer’s disease. Alzheimers Dement. 8, 196–207 (2017).
  53. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995). · DOI · Paper page
  54. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010). · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
del Campo, M., Peeters, C. F. W., Johnson, E. C. B., Vermunt, L., Hok-A-Hin, Y. S., van Nee, M., Chen-Plotkin, A., Irwin, D. J., Hu, W. T., Lah, J. J., Seyfried, N. T., Dammer, E. B., Herradon, G., Meeter, L. H., van Swieten, J., Alcolea, D., Lleó, A., Levey, A. I., Lemstra, A. W., Pijnenburg, Y. A. L., Visser, P. J., Tijms, B. M., van der Flier, W. M., & Teunissen, C. E.
ORCID
Venue
Nature Aging
Publisher
Springer Science and Business Media LLC
Published
2022-11-10
Volume
2
Issue
11
Pages
1040-1053
ISSN
2662-8465
DOI
10.1038/s43587-022-00300-1
PMC
PMC10292920
Cited by
95
References
19
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review