The relationship between CSF biomarkers and cerebral metabolism in early-onset Alzheimer’s disease

  • Alice JaillardEmail author
  • Matthieu Vanhoutte
  • Aurélien Maureille
  • Susanna Schraen
  • Emilie Skrobala
  • Xavier Delbeuck
  • Adeline Rollin-Sillaire
  • Florence Pasquier
  • Stéphanie Bombois
  • Franck Semah
Original Article



One can reasonably suppose that cerebrospinal spinal fluid (CSF) biomarkers can identify distinct subgroups of Alzheimer’s disease (AD) patients. In order to better understand differences in CSF biomarker patterns, we used FDG PET to assess cerebral metabolism in CSF-based subgroups of AD patients.


Eighty-five patients fulfilling the criteria for probable early-onset AD (EOAD) underwent lumbar puncture, brain 18F-FDG PET and MRI. A cluster analysis was performed, with the CSF biomarkers for AD as variables. Vertex-wise, partial-volume-corrected metabolic maps were computed for the patients and compared between the clusters of patients. Linear correlations between each CSF biomarker and the metabolic maps were assessed.


Three clusters emerged. The “Aβ42” cluster contained 32 patients with low levels of Aβ42, while tau and p-tau remained within the normal range. The “Aβ42 + tau” cluster contained 41 patients with low levels of Aβ42 and high levels of tau and p-tau. Lastly, the “tau” cluster contained 12 patients with very high levels of tau and p-tau and low-normal levels of Aβ42. There were no inter-cluster differences in age, sex ratio, educational level, APOE genotype, disease duration or disease severity. The “Aβ42 + tau” and “tau” clusters displayed more marked frontal hypometabolism than the “Aβ42” cluster did, and frontal metabolism was significantly negatively correlated with the CSF tau level. The “Aβ42” and “Aβ42 + tau” clusters displayed more marked hypometabolism in the left occipitotemporal region than the “tau” cluster did, and metabolism in this region was significantly and positively correlated with the CSF Aβ42 level.


The CSF biomarkers can be used to identify metabolically distinct subgroups of patients with EOAD. Future research should seek to establish whether these biochemical differences have clinical consequences.


FDG-PET Alzheimer’s disease CSF biomarkers 



David Fraser: proofreading of documents in English.


Our study has not received any funding.

Compliance with ethical standards

Conflict of interest

- Alice Jaillard: Reports no disclosures.

- Matthieu Vanhoutte: Reports no disclosures.

- Stéphanie Bombois: Reports no disclosures.

- Aurélien Maureille: Reports no disclosures.

- Susanna Schraen: Reports no disclosures.

- Emilie Skrobala: Reports no disclosures.

- Xavier Delbeuck: Reports no disclosures.

- Adeline Rollin-Sillaire: Reports no disclosures.

- Florence Pasquier: Reports no disclosures.

- Franck Semah: Reports no disclosures.

Ethical approval

This research was an ancillary study of the COMAJ cohort, which has been approved by the corresponding local investigational review boards (CPP Nord-Ouest I, CPP Paris Pitié-Salpêtrière and CPP Ile-de-France II; reference: 110–05). All participants gave their written, informed consent to participation in the COMAJ study.



fluorine-18 fluorodeoxyglucose


amyloid-beta 1–42


Alzheimer’s disease


Clinical Dementia Rating Scale - sum of boxes


cerebrospinal fluid


early-onset Alzheimer’s disease


Frontal Assessment Battery


field of view


late-onset Alzheimer’s disease


lumbar puncture


Mini Mental State Evaluation


magnetic resonance imaging




ordered subset expectation maximization


positron emission tomography


tau phosphorylated at threonine 181


partial volume effect


standard deviation


Visual Association Test


Visual Object and Space Perception


  1. 1.
    Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol. 2006;5:228–34.CrossRefGoogle Scholar
  2. 2.
    van der Vlies AE, Verwey NA, Bouwman FH, Blankenstein MA, Klein M, Scheltens P, et al. CSF biomarkers in relationship to cognitive profiles in Alzheimer disease. Neurology. 2009;72:1056–61.CrossRefGoogle Scholar
  3. 3.
    Wallin AK, Blennow K, Zetterberg H, Londos E, Minthon L, Hansson O. CSF biomarkers predict a more malignant outcome in Alzheimer disease. Neurology. 2010;74:1531–7.CrossRefGoogle Scholar
  4. 4.
    Ossenkoppele R, Tolboom N, Foster-Dingley JC, Adriaanse SF, Boellaard R, Yaqub M, et al. Longitudinal imaging of Alzheimer pathology using [11C]PIB, [18F]FDDNP and [18F]FDG PET. Eur J Nucl Med Mol Imaging. 2012;39:990–1000.CrossRefGoogle Scholar
  5. 5.
    Edison P, Archer HA, Hinz R, Hammers A, Pavese N, Tai YF, et al. Amyloid, hypometabolism, and cognition in Alzheimer disease: an [11C]PIB and [18F]FDG PET study. Neurology. 2007;68:501–8.CrossRefGoogle Scholar
  6. 6.
    Jagust WJ, Landau SM, Shaw LM, Trojanowski JQ, Koeppe RA, Reiman EM, et al. Relationships between biomarkers in aging and dementia. Neurology. 2009;73:1193–9.CrossRefGoogle Scholar
  7. 7.
    Chiaravalloti A, Martorana A, Koch G, Toniolo S, di Biagio D, di Pietro B, et al. Functional correlates of t-tau, p-tau and Aβ1-42 amyloid cerebrospinal fluid levels in Alzheimer’s disease: a 18F-FDG PET/CT study. Nucl Med Commun. 2015;36:461–8.CrossRefGoogle Scholar
  8. 8.
    Dumurgier J, Gabelle A, Vercruysse O, Bombois S, Laplanche J-L, Peoc’h K, et al. Exacerbated CSF abnormalities in younger patients with Alzheimer’s disease. Neurobiol Dis. 2013;54:486–91.CrossRefGoogle Scholar
  9. 9.
    Mendez MF, Lee AS, Joshi A, Shapira JS. Nonamnestic presentations of early-onset Alzheimer’s disease. Am J Alzheimers Dis Other Demen. 2012;27:413–20.CrossRefGoogle Scholar
  10. 10.
    Kim EJ, Cho SS, Jeong Y, Park KC, Kang SJ, Kang E, et al. Glucose metabolism in early onset versus late onset Alzheimer’s disease: an SPM analysis of 120 patients. Brain. 2005;128:1790–801.CrossRefGoogle Scholar
  11. 11.
    Chen Y, Sillaire AR, Dallongeville J, Skrobala E, Wallon D, Dubois B, et al. Low prevalence and clinical effect of vascular risk factors in early-onset Alzheimer’s disease. J Alzheimers Dis. 2017;60:1045–54.CrossRefGoogle Scholar
  12. 12.
    Vanhoutte M, Semah F, Rollin Sillaire A, Jaillard A, Petyt G, Kuchcinski G, et al. (18)F-FDG PET hypometabolism patterns reflect clinical heterogeneity in sporadic forms of early-onset Alzheimer’s disease. Neurobiol Aging. 2017;59:184–96.CrossRefGoogle Scholar
  13. 13.
    McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:263–9.CrossRefGoogle Scholar
  14. 14.
    Folstein M, Anthony JC, Parhad I, Duffy B, Gruenberg EM. The meaning of cognitive impairment in the elderly. J Am Geriatr Soc. 1985;33:228–35.CrossRefGoogle Scholar
  15. 15.
    Gardner R, Oliver-Muñoz S, Fisher L, Empting L. Mattis dementia rating scale: internal reliability study using a diffusely impaired population. J Clin Neuropsychol. 1981;3:271–5.CrossRefGoogle Scholar
  16. 16.
    Lindeboom J, Schmand B, Tulner L, Walstra G, Jonker C. Visual association test to detect early dementia of the Alzheimer type. J Neurol Neurosurg Psychiatry. 2002;73:126–33.CrossRefGoogle Scholar
  17. 17.
    Lebert F, Pasquier F, Souliez L, Petit H. Frontotemporal behavioral scale. Alzheimer Dis Assoc Disord. 1998;12:335–9.CrossRefGoogle Scholar
  18. 18.
    Quental NBM, Brucki SMD, Bueno OFA. Visuospatial function in early Alzheimer’s disease--the use of the visual object and space perception (VOSP) battery. PLoS One. 2013;8:e68398.CrossRefGoogle Scholar
  19. 19.
    Deloche, G, Hannequin D.. DO 80, Epreuve de dénomination orale d'images [DO80: Eighty pictures: confrontation oral naming battery], 1997 Paris Les Editions du Centre de Psychologie.Google Scholar
  20. 20.
    Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL. A new clinical scale for the staging of dementia. Br J Psychiatry. 1982;140:566–72.CrossRefGoogle Scholar
  21. 21.
    Vercruysse O, Paquet C, Gabelle A, Delbeuck X, Blanc F, Wallon D, et al. Relevance of Follow-Up in Patients with Core Clinical Criteria for Alzheimer Disease and Normal CSF biomarkers. Curr Alzheimer Res. 2018.Google Scholar
  22. 22.
    Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I Segmentation and surface reconstruction. NeuroImage. 1999;9:179–94.CrossRefGoogle Scholar
  23. 23.
    Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp. 1999;8:272–84.CrossRefGoogle Scholar
  24. 24.
    Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage. 2009;48:63–72.CrossRefGoogle Scholar
  25. 25.
    Rousset OG, Ma Y, Evans AC. Correction for partial volume effects in PET: principle and validation. J Nucl Med. 1998;39:904–11.PubMedGoogle Scholar
  26. 26.
    Quarantelli M, Berkouk K, Prinster A, Landeau B, Svarer C, Balkay L, et al. Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction. J Nucl Med. 2004;45:192–201.PubMedGoogle Scholar
  27. 27.
    Petrie EC, Cross DJ, Galasko D, Schellenberg GD, Raskind MA, Peskind ER, et al. Preclinical evidence of Alzheimer changes: convergent cerebrospinal fluid biomarker and Fluorodeoxyglucose positron emission tomography findings. Arch Neurol. 2009;66:632–7.CrossRefGoogle Scholar
  28. 28.
    Okamura N, Arai H, Higuchi M, Tashiro M, Matsui T, Itoh M, et al. Cerebrospinal fluid levels of amyloid beta-peptide1-42, but not tau have positive correlation with brain glucose metabolism in humans. Neurosci Lett. 1999;273:203–7.CrossRefGoogle Scholar
  29. 29.
    Arlt S, Brassen S, Jahn H, Wilke F, Eichenlaub M, Apostolova I, et al. Association between FDG uptake, CSF biomarkers and cognitive performance in patients with probable Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2009;36:1090–100.CrossRefGoogle Scholar
  30. 30.
    Vukovich R, Perneczky R, Drzezga A, Förstl H, Kurz A, Riemenschneider M. Brain metabolic correlates of cerebrospinal fluid beta-amyloid 42 and tau in Alzheimer’s disease. Dement Geriatr Cogn Disord. 2009;27:474–80.CrossRefGoogle Scholar
  31. 31.
    Chiaravalloti A, Barbagallo G, Ricci M, Martorana A, Ursini F, Sannino P, et al. Brain metabolic correlates of CSF tau protein in a large cohort of Alzheimer’s disease patients: a CSF and FDG PET study. Brain Res. 2018;1678:116–22.CrossRefGoogle Scholar
  32. 32.
    Strozyk D, Blennow K, White LR, Launer LJ. CSF Abeta 42 levels correlate with amyloid-neuropathology in a population-based autopsy study. Neurology. 2003;60:652–6.CrossRefGoogle Scholar
  33. 33.
    Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82:239–59.CrossRefGoogle Scholar
  34. 34.
    Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW. The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer’s disease. Cereb Cortex 1991. 1991;1:103–16.CrossRefGoogle Scholar
  35. 35.
    Tapiola T, Overmyer M, Lehtovirta M, Helisalmi S, Ramberg J, Alafuzoff I, et al. The level of cerebrospinal fluid tau correlates with neurofibrillary tangles in Alzheimer’s disease. Neuroreport. 1997;8:3961–3.CrossRefGoogle Scholar
  36. 36.
    Ossenkoppele R, Schonhaut DR, Schöll M, Lockhart SN, Ayakta N, Baker SL, et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain. 2016;139:1551–67.CrossRefGoogle Scholar
  37. 37.
    Braak H, Braak E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol Aging. 1995;16:271–84.CrossRefGoogle Scholar
  38. 38.
    Dickson DW, Ahmed Z, Algom AA, Tsuboi Y, Josephs KA. Neuropathology of variants of progressive supranuclear palsy. Curr Opin Neurol. 2010;23:394–400.CrossRefGoogle Scholar
  39. 39.
    Renard D, Collombier L, Castelnovo G, Labauge P. Teaching NeuroImages: FDG-PET in progressive supranuclear palsy. Neurology. 2010;74:e60.CrossRefGoogle Scholar
  40. 40.
    Foster NL, Heidebrink JL, Clark CM, Jagust WJ, Arnold SE, Barbas NR, et al. FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain. 2007;130:2616–35.CrossRefGoogle Scholar
  41. 41.
    Niethammer M, Tang CC, Feigin A, Allen PJ, Heinen L, Hellwig S, et al. A disease-specific metabolic brain network associated with corticobasal degeneration. Brain. 2014;137:3036–46.CrossRefGoogle Scholar
  42. 42.
    Jack CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9:119–28.CrossRefGoogle Scholar
  43. 43.
    Blennow K, Zetterberg H, Minthon L, Lannfelt L, Strid S, Annas P, et al. Longitudinal stability of CSF biomarkers in Alzheimer’s disease. Neurosci Lett. 2007;419:18–22.CrossRefGoogle Scholar
  44. 44.
    Brier MR, Gordon B, Friedrichsen K, McCarthy J, Stern A, Christensen J, et al. Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease. Sci Transl Med. 2016;8:338–66.CrossRefGoogle Scholar
  45. 45.
    Alvarez JA, Emory E. Executive function and the frontal lobes: a meta-analytic review. Neuropsychol Rev. 2006;16:17–42.Google Scholar
  46. 46.
    Hagler DJ, Saygin AP, Sereno MI. Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. NeuroImage. 2006;33:1093–103.CrossRefGoogle Scholar
  47. 47.
    Tucholka A, Fritsch V, Poline J-B, Thirion B. An empirical comparison of surface-based and volume-based group studies in neuroimaging. NeuroImage. 2012;63:1443–53.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Alice Jaillard
    • 1
    • 2
    Email author
  • Matthieu Vanhoutte
    • 1
  • Aurélien Maureille
    • 3
  • Susanna Schraen
    • 4
  • Emilie Skrobala
    • 3
  • Xavier Delbeuck
    • 3
  • Adeline Rollin-Sillaire
    • 3
  • Florence Pasquier
    • 2
    • 3
  • Stéphanie Bombois
    • 2
    • 3
  • Franck Semah
    • 1
    • 2
  1. 1.Nuclear Medicine DepartmentCHU LilleLilleFrance
  2. 2.Inserm, U1171LilleFrance
  3. 3.Neurology DepartmentCHU LilleLilleFrance
  4. 4.Department of Biology and PathologyCHU LillLilleFrance

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