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Prognostic value of Alzheimer’s biomarkers in mild cognitive impairment: the effect of age at onset

  • Daniele Altomare
  • Clarissa FerrariEmail author
  • Anna Caroli
  • Samantha Galluzzi
  • Annapaola Prestia
  • Wiesje M. van der Flier
  • Rik Ossenkoppele
  • Bart Van Berckel
  • Frederik Barkhof
  • Charlotte E. Teunissen
  • Anders Wall
  • Stephen F. Carter
  • Michael Schöll
  • IL Han Choo
  • Timo Grimmer
  • Alberto Redolfi
  • Agneta Nordberg
  • Philip Scheltens
  • Alexander Drzezga
  • Giovanni B. Frisoni
  • for the Alzheimer’s Disease Neuroimaging Initiative
Original Communication

Abstract

Objective

The aim of this study is to assess the impact of age at onset on the prognostic value of Alzheimer’s biomarkers in a large sample of patients with mild cognitive impairment (MCI).

Methods

We measured Aβ42, t-tau, hippocampal volume on magnetic resonance imaging (MRI) and cortical metabolism on fluorodeoxyglucose–positron emission tomography (FDG-PET) in 188 MCI patients followed for at least 1 year. We categorised patients into earlier and later onset (EO/LO). Receiver operating characteristic curves and corresponding areas under the curve (AUCs) were performed to assess and compar the biomarker prognostic performances in EO and LO groups. Linear Model was adopted for estimating the time-to-progression in relation with earlier/later onset MCI groups and biomarkers.

Results

In earlier onset patients, all the assessed biomarkers were able to predict cognitive decline (p < 0.05), with FDG-PET showing the best performance. In later onset patients, all biomarkers but t-tau predicted cognitive decline (p < 0.05). Moreover, FDG-PET alone in earlier onset patients showed a higher prognostic value than the one resulting from the combination of all the biomarkers in later onset patients (earlier onset AUC 0.935 vs later onset AUC 0.753, p < 0.001). Finally, FDG-PET showed a different prognostic value between earlier and later onset patients (p = 0.040) in time-to-progression allowing an estimate of the time free from disease.

Discussion

FDG-PET may represent the most universal tool for the establishment of a prognosis in MCI patients and may be used for obtaining an onset-related estimate of the time free from disease.

Keywords

Alzheimer Cognition Imaging Biomarkers FDG-PET 

Notes

Acknowledgements

Data used in this article were partially collected by Translational Outpatient Memory Clinic—TOMC—working group at IRCCS Centro San Giovanni di Dio Fatebenefratelli in Brescia, Italy. Contributors to the TOMC, involved in data collection, are: G Amicucci, S Archetti, L Benussi, G Binetti, L Bocchio-Chiavetto, C Bonvicini, E Canu, F Caobelli, E Cavedo, E Chittò, M Cotelli, M Gennarelli, S Galluzzi, C Geroldi, R Ghidoni, R Giubbini, UP Guerra, G Kuffenschin, G Lussignoli, D Moretti, B Paghera, M Parapini, C Porteri, M Romano, S Rosini, I Villa, R Zanardini, O Zanetti. FB is supported by the NIHR UCLH biomedical research centre. Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Funding

EU data collection and sharing: The work was supported by the Swedish Research Council (project 05817), the Strategic Research Program in Neuroscience at Karolinska Institutet, the Swedish Brain Power. This work was also supported by the grants: sottoprogetto finalizzato Strategico 2006: “Strumenti e procedure diagnostiche per le demenze utilizzabili nella clinica ai fini della diagnosi precoce e differenziale, della individuazione delle forme a rapida o lenta progressione e delle forme con risposta ottimale alle attuali terapie”; Programma Strategico 2006, Convenzione 71; Programma Strategico 2007, Convenzione PS39, Ricerca Corrente Italian Ministry of Health. Some of the costs related to patient assessment and imaging and biomarker detection were funded thanks to an ad hoc grant from the Fitness e Solidarieta‘2006 and 2007 campaigns. The analyses of MRI data presented in the paper have been performed thanks to the neuGRID platform, which has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 283562. Alzheimer’s Disease Neuroimaging Initiative (ADNI) data: Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Compliance with ethical standards

Conflicts of interest

The authors have no conflicts of interest to report.

Ethical standards

This study was approved by the ethics committee of each participating center and all participants were enrolled after written informed consent was obtained.

Supplementary material

415_2019_9441_MOESM1_ESM.docx (557 kb)
Supplementary material 1 (DOCX 557 kb)

References

  1. 1.
    Dubois B, Feldman HH, Jacova C et al (2014) Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol 13:614–629.  https://doi.org/10.1016/S1474-4422(14)70090-0 CrossRefGoogle Scholar
  2. 2.
    Jack CRJ, Albert MS, Knopman DS et al (2011) Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:257–262.  https://doi.org/10.1016/j.jalz.2011.03.004 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Duits FH, Martinez-Lage P, Paquet C et al (2016) Performance and complications of lumbar puncture in memory clinics: results of the multicenter lumbar puncture feasibility study. Alzheimers Dement 12:154–163.  https://doi.org/10.1016/j.jalz.2015.08.003 CrossRefPubMedGoogle Scholar
  4. 4.
    Frisoni GB, Bocchetta M, Chetelat G et al (2013) Imaging markers for Alzheimer disease: which vs how. Neurology 81:487–500.  https://doi.org/10.1212/WNL.0b013e31829d86e8 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Molinuevo JL, Blennow K, Dubois B et al (2014) The clinical use of cerebrospinal fluid biomarker testing for Alzheimer’s disease diagnosis: a consensus paper from the Alzheimer’s Biomarkers Standardization Initiative. Alzheimers Dement 10:808–817.  https://doi.org/10.1016/j.jalz.2014.03.003 CrossRefPubMedGoogle Scholar
  6. 6.
    Hansson O, Zetterberg H, Buchhave P et al (2006) Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 5:228–234.  https://doi.org/10.1016/S1474-4422(06)70355-6 CrossRefPubMedGoogle Scholar
  7. 7.
    Mattsson N, Zetterberg H, Hansson O et al (2009) CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302:385–393.  https://doi.org/10.1001/jama.2009.1064 CrossRefGoogle Scholar
  8. 8.
    Prestia A, Caroli A, Wade SK et al (2015) Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimers Dement 11:1191–1201.  https://doi.org/10.1016/j.jalz.2014.12.001 CrossRefPubMedGoogle Scholar
  9. 9.
    Prestia A, Caroli A, Herholz K et al (2013) Diagnostic accuracy of markers for prodromal Alzheimer’s disease in independent clinical series. Alzheimers Dement 9:677–686.  https://doi.org/10.1016/j.jalz.2012.09.016 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Shaffer JL, Petrella JR, Sheldon FC et al (2013) Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers. Radiology 266:583–591.  https://doi.org/10.1148/radiol.12120010 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Yu P, Dean RA, Hall SD et al (2012) Enriching amnestic mild cognitive impairment populations for clinical trials: optimal combination of biomarkers to predict conversion to dementia. J Alzheimers Dis 32:373–385.  https://doi.org/10.3233/JAD-2012-120832 CrossRefPubMedGoogle Scholar
  12. 12.
    Frisoni GB, Pievani M, Testa C et al (2007) The topography of grey matter involvement in early and late onset Alzheimer’s disease. Brain 130:720–730.  https://doi.org/10.1093/brain/awl377 CrossRefPubMedGoogle Scholar
  13. 13.
    Moller C, Vrenken H, Jiskoot L et al (2013) Different patterns of gray matter atrophy in early- and late-onset Alzheimer’s disease. Neurobiol Aging 34:2014–2022.  https://doi.org/10.1016/j.neurobiolaging.2013.02.013 CrossRefPubMedGoogle Scholar
  14. 14.
    Bouwman FH, Schoonenboom NSM, Verwey NA et al (2009) CSF biomarker levels in early and late onset Alzheimer’s disease. Neurobiol Aging 30:1895–1901.  https://doi.org/10.1016/j.neurobiolaging.2008.02.007 CrossRefPubMedGoogle Scholar
  15. 15.
    Ossenkoppele R, Zwan MD, Tolboom N et al (2012) Amyloid burden and metabolic function in early-onset Alzheimer’s disease: parietal lobe involvement. Brain 135:2115–2125.  https://doi.org/10.1093/brain/aws113 CrossRefPubMedGoogle Scholar
  16. 16.
    Schmand B, Eikelenboom P, van Gool WA (2011) Value of neuropsychological tests, neuroimaging, and biomarkers for diagnosing Alzheimer’s disease in younger and older age cohorts. J Am Geriatr Soc 59:1705–1710.  https://doi.org/10.1111/j.1532-5415.2011.03539.x CrossRefPubMedGoogle Scholar
  17. 17.
    Matsunari I, Samuraki M, Chen W-P et al (2007) Comparison of 18F-FDG PET and optimized voxel-based morphometry for detection of Alzheimer’s disease: aging effect on diagnostic performance. J Nucl Med 48:1961–1970.  https://doi.org/10.2967/jnumed.107.042820 CrossRefPubMedGoogle Scholar
  18. 18.
    Mattsson N, Rosen E, Hansson O et al (2012) Age and diagnostic performance of Alzheimer disease CSF biomarkers. Neurology 78:468–476.  https://doi.org/10.1212/WNL.0b013e3182477eed CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Chiaravalloti Agostino, Koch Giacomo, Toniolo Sofia, Belli Lorena, Di Lorenzo Francesco, Gaudenzi Sara, Schillaci Orazio, Bozzali Marco, Giuseppe Sancesario AM (2016) Comparison between early-onset and late-onset Alzheimer’s disease patients with amnestic presentation: CSF and 18-F-FDG PET study. Dement Geriatr Cogn Dis Extra 6:108–119.  https://doi.org/10.1159/000441776 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Vanhoutte M, Semah F, Rollin Sillaire A et al (2017) 18F-FDG PET hypometabolism patterns reflect clinical heterogeneity in sporadic forms of early-onset Alzheimer’s disease. Neurobiol Aging.  https://doi.org/10.1016/j.neurobiolaging.2017.08.009 CrossRefPubMedGoogle Scholar
  21. 21.
    Falgàs N, Tort-Merino A, Balasa M et al (2019) Clinical applicability of diagnostic biomarkers in early-onset cognitive impairment. Eur J Neurol.  https://doi.org/10.1111/ene.13945 CrossRefPubMedGoogle Scholar
  22. 22.
    Verclytte S, Lopes R, Lenfant P et al (2016) Cerebral hypoperfusion and hypometabolism detected by arterial spin labeling MRI and FDG-PET in early-onset Alzheimer’s disease. J Neuroimaging.  https://doi.org/10.1111/jon.12264 CrossRefPubMedGoogle Scholar
  23. 23.
    Li K, Chan W, Doody RS et al (2017) Prediction of conversion to Alzheimer’s disease with longitudinal measures and time-to-event data. J Alzheimers Dis.  https://doi.org/10.3233/JAD-161201 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Petersen RC, Smith GE, Waring SC et al (1999) Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 56:303–308CrossRefGoogle Scholar
  25. 25.
    O’Bryant SE, Humphreys JD, Smith GE et al (2008) Detecting dementia with the mini-mental state examination in highly educated individuals. Arch Neurol 65:963–967.  https://doi.org/10.1001/archneur.65.7.963 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Hensel A, Angermeyer MC, Riedel-Heller SG (2007) Measuring cognitive change in older adults: reliable change indices for the mini-mental state examination. J Neurol Neurosurg Psychiatry 78:1298–1303.  https://doi.org/10.1136/jnnp.2006.109074 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    McKhann G, Drachman D, Folstein M et al (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34:939–944CrossRefGoogle Scholar
  28. 28.
    Herholz K, Salmon E, Perani D et al (2002) Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 17:302–316CrossRefGoogle Scholar
  29. 29.
    Orimo H, Ito H, Suzuki T et al (2006) Reviewing the definition of “elderly”. Geriatr Gerontol Int 6:149–158.  https://doi.org/10.1111/j.1447-0594.2006.00341.x CrossRefGoogle Scholar
  30. 30.
    Blagosklonny MV (2010) Why human lifespan is rapidly increasing: solving “longevity riddle” with “revealed-slow-aging” hypothesis. Aging (Albany NY).  https://doi.org/10.18632/aging.100139 CrossRefPubMedCentralGoogle Scholar
  31. 31.
    Jacobs JM, Maaravi Y, Cohen A et al (2012) Changing profile of health and function from age 70 to 85 years. Gerontology.  https://doi.org/10.1159/000335238 CrossRefPubMedGoogle Scholar
  32. 32.
    Mendez MF (2017) Early-onset Alzheimer disease. Neurol Clin 35:263–281.  https://doi.org/10.1016/j.ncl.2017.01.005 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Prestia A, Caroli A, van der Flier WM et al (2013) Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease. Neurology 80:1048–1056.  https://doi.org/10.1212/WNL.0b013e3182872830 CrossRefPubMedGoogle Scholar
  34. 34.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefGoogle Scholar
  35. 35.
    Therneau T, Grambsch PM (2000) Modeling survival data: extending the Cox model. Springer, New YorkCrossRefGoogle Scholar
  36. 36.
    Schmand B, Eikelenboom P, van Gool WA (2012) Value of diagnostic tests to predict conversion to Alzheimer’s disease in young and old patients with amnestic mild cognitive impairment. J Alzheimers Dis 29:641–648.  https://doi.org/10.3233/JAD-2012-111703 CrossRefPubMedGoogle Scholar
  37. 37.
    van Rossum IA, Vos SJB, Burns L et al (2012) Injury markers predict time to dementia in subjects with MCI and amyloid pathology. Neurology 79:1809–1816.  https://doi.org/10.1212/WNL.0b013e3182704056 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Landau SM, Lu M, Joshi AD et al (2013) Comparing positron emission tomography imaging and cerebrospinal fluid measurements of β-amyloid. Ann Neurol 74:826–836.  https://doi.org/10.1002/ana.23908 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Zwan M, van Harten A, Ossenkoppele R et al (2014) Concordance between cerebrospinal fluid biomarkers and [11C]PIB PET in a memory clinic cohort. J Alzheimers Dis 41:801–807.  https://doi.org/10.3233/JAD-132561 CrossRefPubMedGoogle Scholar
  40. 40.
    Caroli A, Prestia A, Chen K et al (2012) Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison. J Nucl Med 53:592–600.  https://doi.org/10.2967/jnumed.111.094946 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Haense C, Herholz K, Jagust WJ, Heiss WD (2009) Performance of FDG PET for detection of Alzheimer’s disease in two independent multicentre samples (NEST-DD and ADNI). Dement Geriatr Cogn Disord 28:259–266.  https://doi.org/10.1159/000241879 CrossRefPubMedGoogle Scholar
  42. 42.
    Herholz K, Westwood S, Haense C, Dunn G (2011) Evaluation of a calibrated (18)F-FDG PET score as a biomarker for progression in Alzheimer disease and mild cognitive impairment. J Nucl Med 52:1218–1226.  https://doi.org/10.2967/jnumed.111.090902 CrossRefPubMedGoogle Scholar
  43. 43.
    Frisoni GB, Fox NC, Jack CRJ et al (2010) The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 6:67–77.  https://doi.org/10.1038/nrneurol.2009.215 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Bobinski M, Wegiel J, Wisniewski HM et al (1996) Neurofibrillary pathology—correlation with hippocampal formation atrophy in Alzheimer disease. Neurobiol Aging 17:909–919PubMedGoogle Scholar
  45. 45.
    Apostolova LG, Zarow C, Biado K et al (2015) Relationship between hippocampal atrophy and neuropathology markers: a 7T MRI validation study of the EADC-ADNI harmonized hippocampal segmentation protocol. Alzheimers Dement 11:139–150.  https://doi.org/10.1016/j.jalz.2015.01.001 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    den Heijer T, van der Lijn F, Koudstaal PJ et al (2010) A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 133:1163–1172.  https://doi.org/10.1093/brain/awq048 CrossRefGoogle Scholar
  47. 47.
    Palasí A, Gutiérrez-Iglesias B, Alegret M et al (2015) Differentiated clinical presentation of early and late-onset Alzheimer’s disease: is 65 years of age providing a reliable threshold? J Neurol 262:1238–1246.  https://doi.org/10.1007/s00415-015-7698-3 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory of Neuroimaging of Aging (LANVIE)University of GenevaGenevaSwitzerland
  2. 2.Service of StatisticsIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
  3. 3.Medical Imaging UnitIstituto di Ricerche Farmacologiche Mario Negri IRCCSBergamoItaly
  4. 4.Laboratory of Alzheimer’s Neuroimaging and Epidemiology (LANE)IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
  5. 5.Alzheimer Center Amsterdam, Department of Neurology, Amsterdam NeuroscienceVrije Universiteit Amsterdam, Amsterdam UMCAmsterdamThe Netherlands
  6. 6.Department of Epidemiology and Biostatistics, Amsterdam NeuroscienceVrije Universiteit Amsterdam, Amsterdam UMCAmsterdamThe Netherlands
  7. 7.Department of Radiology and Nuclear Medicine, Amsterdam NeuroscienceVrije Universiteit Amsterdam, Amsterdam UMCAmsterdamThe Netherlands
  8. 8.Clinical Memory Research Unit, Department of Clinical SciencesLund UniversityMalmöSweden
  9. 9.Institute of NeurologyUCLLondonUK
  10. 10.Institute of Healthcare EngineeringUCLLondonUK
  11. 11.Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam NeuroscienceVrije Universiteit Amsterdam, Amsterdam UMCAmsterdamThe Netherlands
  12. 12.Section of Nuclear Medicine and PET, Department of Surgical SciencesUppsala UniversityUppsalaSweden
  13. 13.Alzheimer Neurobiology CenterKarolinska InstitutetStockholmSweden
  14. 14.Wolfson Molecular Imaging CentreUniversity of ManchesterManchesterUK
  15. 15.Department of Neuropsychiatry, School of MedicineChosun UniversityGwangjuRepublic of Korea
  16. 16.Department of Psychiatry and Psychotherapy, Klinikum rechts der IsarTechnische Universität MünchenMunichGermany
  17. 17.Center for Alzheimer Research, Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and SocietyKarolinska InstitutetStockholmSweden
  18. 18.Aging ThemeKarolinska University HospitalStockholmSweden
  19. 19.Department of Nuclear MedicineUniversity of CologneCologneGermany
  20. 20.Memory ClinicUniversity Hospital of GenevaGenevaSwitzerland
  21. 21.Wallenberg Centre for Molecular and Translational Medicine, Department of Psychiatry and NeurochemistryUniversity of GothenburgGothenburgSweden
  22. 22.Dementia Research Centre, Department of Neurodegenerative Disease, Queen Square Institute of NeurologyUniversity College LondonLondonUK

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