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Supporting evidence for using biomarkers in the diagnosis of MCI due to AD

Journal of Neurology volume 260, pages 640–650 (2013)Cite this article

Abstract

The aim of this study is to support the use of biomarkers in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) according to the revised NIA-AA diagnostic criteria. We compared clinical features and conversion to AD and other dementias among groups of MCI patients with different abnormal biomarker profiles. In this study, we enrolled 58 patients with MCI, and for each of them AD biomarkers (CSF Abeta42 and tau, temporoparietal hypometabolism on 18F-FDG PET, and hippocampal volume) were collected. Patients were divided into three groups: (i) no abnormal biomarker, (ii) AD biomarker pattern (including three subgroups of early = only abnormal Abeta42, intermediate = abnormal Abeta42 and FDG PET or tau, and late = abnormal Abeta42, FDG PET or tau, and HV), and (iii) any other biomarker combination. MCI patients with AD biomarker pattern had lower behavioural disturbances than patients with any other biomarker combination (p < 0.0005). This group also showed lower performance on verbal and non-verbal memory than the other two groups (p = 0.07 and p = 0.004, respectively). Within the three subgroups with AD biomarker patterns we observed a significant trend toward a higher rate of conversion to dementia (p for trend = 0.006). With regard to dementia conversion, 100 % of patients with an AD biomarker pattern developed AD, but none of the patients with no abnormal biomarker and 27 % of patients with any other biomarker combination (p = 0.002) did so. We also described some clinical cases representative for each of these three groups. The results of this study provide evidence in favour of the use of biomarkers for the diagnosis of MCI due to AD, in line with recently published research criteria.

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References

  1. Albert MS, DeKosky ST, Dickson D et al (2011) The diagnosis of mild cognitive impairment 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 7:270–279

    PubMed  Article  Google Scholar 

  2. American Psychiatric Association (1994) Diagnostic and statistical manual of mental disorders, 4th edn. American Psychiatric Association, USA

    Google Scholar 

  3. Arai H, Terajima M, Miura M et al (1995) Tau in cerebrospinal fluid: a potential diagnostic marker in Alzheimer’s disease. Ann Neurol 38:649–652

    PubMed  Article  CAS  Google Scholar 

  4. Bennett DA, Wilson RS, Schneider JA et al (2002) Natural history of mild cognitive impairment in older persons. Neurology 59:198–205

    PubMed  Article  CAS  Google Scholar 

  5. Bian H, Van Swieten JC, Leight S et al (2008) CSF biomarkers in frontotemporal lobar degeneration with known pathology. Neurology 70:1827–1835

    PubMed  Article  CAS  Google Scholar 

  6. Bibl M, Mollenhauer B, Lewczuk P et al (2011) Cerebrospinal fluid tau, p-tau 181 and amyloid-β38/40/42 in frontotemporal dementias and primary progressive aphasias. Dement Geriatr Cogn Disord 31:37–44

    PubMed  Article  CAS  Google Scholar 

  7. Braak H, Braak E (1997) Frequency of stages of Alzheimer-related lesions in different age categories. Neurobiol Aging 18:351–357

    PubMed  Article  CAS  Google Scholar 

  8. Braak H, Del Tredici K (2011) The pathological process underlying Alzheimer’s disease in individuals under thirty. Acta Neuropathol 121:171–181

    PubMed  Article  Google Scholar 

  9. Caroli A, Prestia A, Chen K, EADC-PET Consortium, NEST-DD, and Alzheimer’s Disease Neuroimaging Initiative 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

    PubMed  Article  CAS  Google Scholar 

  10. Clark CM, Xie S, Chittams J et al (2003) Cerebrospinal fluid tau and beta-amyloid: how well do these biomarkers reflect autopsy-confirmed dementia diagnoses? Arch Neurol 60:1696–1702

    PubMed  Article  Google Scholar 

  11. Cummings JL, Mega M, Gray K et al (1994) The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology 44:2308–2314

    PubMed  Article  CAS  Google Scholar 

  12. De Leo D, Frisoni GB, Rozzini R, Trabucchi M (1993) Italian community norms for the brief symptom inventory in the elderly. Br J Clin Psychol 32:209–213

    PubMed  Article  Google Scholar 

  13. De Souza LC, Lamari F, Belliard S et al (2012) Cerebrospinal fluid biomarkers in the differential diagnosis of Alzheimer’s disease from other cortical dementias. J Neurol Neurosurg Psychiatry 82:240–246

    Article  Google Scholar 

  14. Edison P, Rowe CC, Rinne JO et al (2008) Amyloid load in Parkinson’s disease dementia and Lewy body dementia measured with [11C] PIB positron emission tomography. J Neurol Neurosurg Psychiatry 79:1331–1338

    PubMed  Article  CAS  Google Scholar 

  15. Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental State: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198

    PubMed  Article  CAS  Google Scholar 

  16. 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

    PubMed  Article  Google Scholar 

  17. Frisoni GB, Prestia A, Zanetti O et al (2009) Markers of Alzheimer’s disease in a population attending a memory clinic. Alzheimers Dement 5:307–317

    PubMed  Article  Google Scholar 

  18. Galluzzi S, Geroldi C, Ghidoni R et al (2010) The new Alzheimer’s criteria in a naturalistic series of patients with mild cognitive impairment. J Neurol 257:2004–2014

    PubMed  Article  CAS  Google Scholar 

  19. Galluzzi S, Testa C, Boccardi M et al (2009) The Italian brain normative archive of structural MR scans: norms for medial temporal atrophy and white matter lesions. Aging Clin Exp Res 21:266–276

    PubMed  Google Scholar 

  20. Ghidoni R, Benussi L, Paterlini A et al (2011) Cerebrospinal fluid biomarkers for Alzheimer’s disease: the present and the future. Neurodegener Dis 8:413–420

    PubMed  Article  CAS  Google Scholar 

  21. Ghidoni R, Paterlini A, Albertini V et al (2011) A window into the heterogeneity of human cerebrospinal fluid Aβ peptides. J Biomed Biotechnol 2011:697036

    PubMed  Article  Google Scholar 

  22. Hampel H, Burger K, Teipel SJ et al (2008) Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzheimers Dement 4:38–48

    PubMed  Article  CAS  Google Scholar 

  23. Herholz K, Salmon E, Perani D et al (2002) Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 17:302–316

    PubMed  Article  CAS  Google Scholar 

  24. Hoffman JM, Welsh-Bohmer KA, Hanson M et al (2000) FDG PET imaging in patients with pathologically verified dementia. J Nucl Med 41:1920–1928

    PubMed  CAS  Google Scholar 

  25. Ingelsson M, Fukumoto H, Newell KL et al (2004) Early Abeta accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology 62:925–931

    PubMed  Article  CAS  Google Scholar 

  26. Jack CR Jr, Petersen RC, Xu Y et al (2000) Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology 55:484–489

    PubMed  Article  Google Scholar 

  27. Jack CR Jr, Lowe VJ, Weigand SD, Alzheimer’s Disease Neuroimaging Initiative et al (2009) Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease. Brain 132:1355–1365

    PubMed  Article  Google Scholar 

  28. Jack CR Jr, Knopman DS, Jagust WJ et al (2010) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9:119–128

    PubMed  Article  CAS  Google Scholar 

  29. Lawton MP, Brody EM (1969) Assessment of older people: self maintaining and instrumental activities of daily living. Gerontologist 9:179–186

    PubMed  Article  CAS  Google Scholar 

  30. Lezak M, Howieson D, Loring DW (2004) Neuropsychological assessment, 4th edn. University Press, Oxford

    Google Scholar 

  31. Mayeux R, Small SA, Tang M et al (2001) Memory performance in healthy elderly without Alzheimer’s disease: effects of time and apolipoprotein-E. Neurobiol Aging 22:683–689

    PubMed  Article  CAS  Google Scholar 

  32. McKeith IG, Galasko D, Kosaka K et al (1996) Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology 47:1113–1124

    PubMed  Article  CAS  Google Scholar 

  33. 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 34:939–944

    PubMed  Article  CAS  Google Scholar 

  34. McKhann GM, Albert MS, Grossman M et al (2001) Work group on frontotemporal dementia and pick’s disease clinical and pathological diagnosis of frontotemporal dementia: report of the work group on frontotemporal dementia and pick’s disease. Arch Neurol 58:1803–1809

    PubMed  Article  CAS  Google Scholar 

  35. McKhann GM, Knopman DS, Chertkow H et al (2011) 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 7:263–269

    PubMed  Article  Google Scholar 

  36. Seelaar H, Rohrer JD, Pijnenburg YA et al (2011) Clinical, genetic and pathological heterogeneity of frontotemporal dementia: a review. J Neurol Neurosurg Psychiatry 82:476–486

    PubMed  Article  Google Scholar 

  37. Sjogren M, Vanderstichele H, Agren H et al (2001) Tau and Abeta42 in cerebrospinal fluid from healthy adults 21–93 years of age: establishment of reference values. Clin Chem 47:1776–1781

    PubMed  CAS  Google Scholar 

  38. Strozyk D, Blennow K, White LR et al (2003) CSF Abeta 42 levels correlate with amyloid-neuropathology in a population-based autopsy study. Neurology 60:652–656

    PubMed  Article  CAS  Google Scholar 

  39. Wahlund LO, Barkhof F, Fazekas F et al (2001) European task force on age-related white matter changes. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 32:1318–1322

    PubMed  Article  CAS  Google Scholar 

  40. Whitwell JL, Wiste HJ, Weigand SD, for the Alzheimer Disease Neuroimaging Initiative et al (2012) Comparison of imaging biomarkers in the Alzheimer disease neuroimaging initiative and the mayo clinic study of aging. Arch Neurol 69:614–622

    PubMed  Article  Google Scholar 

  41. Womack KB, Diaz-Arrastia R, Aizenstein HJ et al (2011) Temporoparietal hypometabolism in frontotemporal lobar degeneration and associated imaging diagnostic errors. Arch Neurol 68:329–337

    PubMed  Article  Google Scholar 

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Acknowledgments

We wish to thank Chiara Barattieri di San Pietro for editing the English language of the manuscript. This work was 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, and was also supported in part by a grant from the Associazione Fatebenefratelli per la Ricerca (AFaR): “Proteomica Clinica nelle Malattie Neurodegenerative ad esordio tardivo”. Some of the costs related to patient assessment and imaging and biomarker detection were paid by an ad hoc grant from the Fitness e Solidarietà 2006 and 2007 campaigns.

Conflicts of interest

Dr.S Galluzzi, Geroldi, Amicucci, Bocchio-Chiavetto, Bonetti, Bonvicini, Cotelli, Ghidoni, Paghera, report no disclosures. Dr. Zanetti has acted as a speaker for Pfizer, Novartis, Bracco, and Lunbeck. Dr. Frisoni has acted as a consultant for Lilly, BMS, Bayer, Lundbeck, Elan, Astra Zeneca, Pfizer, Taurx, Wyeth.

Ethical standard

All human studies must state that they have been approved by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki.

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Affiliations

  1. Laboratory of Epidemiology, Neuroimaging and Telemedicine (LENITEM), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, via Pilastroni 4, 25125, Brescia, Italy

    Samantha Galluzzi & Giovanni B. Frisoni

  2. Alzheimer’s Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Cristina Geroldi & Orazio Zanetti

  3. Service of Anesthesiology, Azienda Ospedaliera Mellino Mellini di Chiari, Brescia, Italy

    Giovanni Amicucci

  4. Neuropsychopharmacology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Luisella Bocchio-Chiavetto

  5. Neuroradiology Service, Istituto Clinico Città di Brescia, Brescia, Italy

    Matteo Bonetti

  6. Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Cristian Bonvicini

  7. Neuropsychology Unit, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Maria Cotelli

  8. Proteomics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Roberta Ghidoni

  9. Nuclear Medicine Service, Spedali Civili of Brescia, Brescia, Italy

    Barbara Paghera

Authors
  1. Samantha Galluzzi
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  2. Cristina Geroldi
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  5. Matteo Bonetti
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  6. Cristian Bonvicini
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  7. Maria Cotelli
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  8. Roberta Ghidoni
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  9. Barbara Paghera
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  10. Orazio Zanetti
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  11. Giovanni B. Frisoni
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Consortia

Translational Outpatient Memory Clinic Working Group

Corresponding author

Correspondence to Giovanni B. Frisoni.

Additional information

The contributors of the Translational Outpatient Memory Clinic Working Group are listed in the appendix.

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Supplementary material 1 (PPT 477 kb)

Supplementary material 2 (DOC 28 kb)

Appendix

Appendix

Translational Outpatient Memory Clinic Working Group contributors: S Archetti, L Benussi, G Binetti, E Canu, F Caobelli, E Cavedo, E Chitò, D Costardi, M Gennarelli, R Giubbini, UP Guerra, G Kuffenschin, G Lussignoli, D Moretti, A Orlandini, M Parapini, D Paternicò, C Porteri, M Romano, S Rosini, C Scarpazza, I Villa, R Zanardini.

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Galluzzi, S., Geroldi, C., Amicucci, G. et al. Supporting evidence for using biomarkers in the diagnosis of MCI due to AD. J Neurol 260, 640–650 (2013). https://doi.org/10.1007/s00415-012-6694-0

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  • DOI: https://doi.org/10.1007/s00415-012-6694-0

Keywords

  • Alzheimer’s disease
  • Mild cognitive impairment
  • Biomarkers
  • Dynamic model