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The National Institute on Aging-Alzheimer’s Association research criteria for mild cognitive impairment due to Alzheimer’s disease: predicting the outcome

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Abstract

The National Institute on Aging-Alzheimer’s Association (NIA-AA) clinical research criteria for mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) incorporate the use of biomarkers to classify patients according to the likelihood of the presence of AD pathology. The aim of the study was to compare the risk of progression to AD dementia between the four NIA-AA MCI subgroups using data from the AD Neuroimaging Initiative. Patients with MCI were categorised according to the NIA-AA criteria into subgroups with high, intermediate, and low likelihood of the presence of AD pathology (MCI-high, MCI-intermediate, and MCI-unlikely, respectively) or into a group of patients that only met the MCI-core clinical criteria (MCI-core). Data of follow-up visits conducted 6–60 months after baseline were used to compare the relative risk of future AD dementia between the four subgroups employing a Cox regression model. The MCI-high subgroup (N = 22) had a 2.3 times higher risk of developing AD dementia compared with the MCI-core subgroup (N = 327; P = 0.002), while there was a trend for a higher risk in the MCI-high subgroup in contrast to the MCI-intermediate subgroup (N = 31, P = 0.08). No patients in the MCI-unlikely subgroup (N = 17) progressed to AD dementia. Patients with MCI-high have a higher risk for developing AD dementia. The new NIA-AA MCI criteria represent a valuable research instrument that could be incorporated into the diagnostic process of the MCI syndrome after optimisation and refinement.

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References

  1. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH (2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the national institute on ageing-Alzheimer’s Association Workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:270–279

    Article  PubMed  Google Scholar 

  2. Alexopoulos P, Grimmer T, Perneczky R, Domes G, Kurz A (2006) Do all patients with mild cognitive impairment progress to dementia? J Am Geriatr Soc 54:1008–1010

    Article  PubMed  Google Scholar 

  3. Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E (2011) Alzheimer’s disease. Lancet 377:1019–1031

    Article  PubMed  Google Scholar 

  4. Bertram L (2008) The genetics of alzheimer’s disease. Handb Clin Neurol 89:223–232

    Article  PubMed  Google Scholar 

  5. Busse A, Bischkopf J, Riedel-Heller SG, Angermeyer MC (2003) Mild cognitive impairment: Prevalence and incidence according to different diagnostic criteria. Results of the leipzig longitudinal study of the aged (leila75+). Br J Psychiatry 182:449–454

    Article  PubMed  Google Scholar 

  6. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980

    Article  PubMed  Google Scholar 

  7. Dickson DW, Kouri N, Murray ME, Josephs KA (2011) Neuropathology of frontotemporal lobar degeneration-tau (FTLD-tau). J Mol Neurosci 45:384–389

    Article  PubMed  CAS  Google Scholar 

  8. Drzezga A, Grimmer T, Riemenschneider M, Lautenschlager N, Siebner H, Alexopoulus P, Minoshima S, Schwaiger M, Kurz A (2005) Prediction of individual clinical outcome in MCI by means of genetic assessment and (18)F-FDG PET. J Nucl Med 46:1625–1632

    PubMed  CAS  Google Scholar 

  9. Engler H, Forsberg A, Almkvist O, Blomquist G, Larsson E, Savitcheva I, Wall A, Ringheim A, Langstrom B, Nordberg A (2006) Two-year follow-up of amyloid deposition in patients with Alzheimer’s disease. Brain 129:2856–2866

    Article  PubMed  Google Scholar 

  10. Falkai P, Moller HJ (2010) Understanding mental disorders from neuronal networks to glial cells and proteomics. Eur Arch Psychiatry Clin Neurosci 260:441–442

    Article  PubMed  Google Scholar 

  11. 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

    Article  PubMed  CAS  Google Scholar 

  12. Frisoni GB, Winblad B, O’Brien JT (2011) Revised NIA-AA criteria for the diagnosis of Alzheimer’s disease: a step forward but not yet ready for widespread clinical use. Int Psychogeriatr 23:1191–1196

    Article  PubMed  Google Scholar 

  13. Ganguli M, Snitz BE, Saxton JA, Chang CC, Lee CW, Vander Bilt J, Hughes TF, Loewenstein DA, Unverzagt FW, Petersen RC (2011) Outcomes of mild cognitive impairment by definition: a population study. Arch Neurol 68:761–767

    Article  PubMed  Google Scholar 

  14. Gomar JJ, Bobes-Bascaran MT, Conejero-Goldberg C, Davies P, Goldberg TE (2011) Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to alzheimer disease in patients in the Alzheimer’s disease neuroimaging initiative. Arch Gen Psychiatry 68:961–969

    Article  PubMed  Google Scholar 

  15. Grundman M, Petersen RC, Ferris SH, Thomas RG, Aisen PS, Bennett DA, Foster NL, Jack CR Jr, Galasko DR, Doody R, Kaye J, Sano M, Mohs R, Gauthier S, Kim HT, Jin S, Schultz AN, Schafer K, Mulnard R, van Dyck CH, Mintzer J, Zamrini EY, Cahn-Weiner D, Thal LJ (2004) Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch Neurol 61:59–66

    Article  PubMed  Google Scholar 

  16. Heister D, Brewer JB, Magda S, Blennow K, McEvoy LK (2011) Predicting MCI outcome with clinically available MRI and CSF biomarkers. Neurology 77:1619–1628

    Article  PubMed  CAS  Google Scholar 

  17. Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, Whitwell JL, Ward C, Dale AM, Felmlee JP, Gunter JL, Hill DL, Killiany R, Schuff N, Fox-Bosetti S, Lin C, Studholme C, DeCarli CS, Krueger G, Ward HA, Metzger GJ, Scott KT, Mallozzi R, Blezek D, Levy J, Debbins JP, Fleisher AS, Albert M, Green R, Bartzokis G, Glover G, Mugler J, Weiner MW (2008) The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 27:685–691

    Article  PubMed  Google Scholar 

  18. Jack CR Jr, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM, Knopman DS, Boeve BF, Klunk WE, Mathis CA, Petersen RC (2008) 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain 131:665–680

    Article  PubMed  Google Scholar 

  19. Jagust WJ, Bandy D, Chen K, Foster NL, Landau SM, Mathis CA, Price JC, Reiman EM, Skovronsky D, Koeppe RA (2010) The Alzheimer’s disease neuroimaging initiative positron emission tomography core. Alzheimers Dement 6:221–229

    Article  PubMed  Google Scholar 

  20. Jagust WJ, Landau SM, Shaw LM, Trojanowski JQ, Koeppe RA, Reiman EM, Foster NL, Petersen RC, Weiner MW, Price JC, Mathis CA (2009) Relationships between biomarkers in aging and dementia. Neurology 73:1193–1199

    Article  PubMed  CAS  Google Scholar 

  21. Kim S, Swaminathan S, Shen L, Risacher SL, Nho K, Foroud T, Shaw LM, Trojanowski JQ, Potkin SG, Huentelman MJ, Craig DW, Dechairo BM, Aisen PS, Petersen RC, Weiner MW, Saykin AJ (2011) Genome-wide association study of CSF biomarkers a{beta}1–42, t-tau, and p-tau181p in the ADNI cohort. Neurology 76:69–79

    Article  PubMed  CAS  Google Scholar 

  22. Landau SM, Harvey D, Madison CM, Reiman EM, Foster NL, Aisen PS, Petersen RC, Shaw LM, Trojanowski JQ, Jack CR Jr, Weiner MW, Jagust WJ (2010) Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 75:230–238

    Article  PubMed  Google Scholar 

  23. Larrieu S, Letenneur L, Orgogozo JM, Fabrigoule C, Amieva H, Le Carret N, Barberger-Gateau P, Dartigues JF (2002) Incidence and outcome of mild cognitive impairment in a population-based prospective cohort. Neurology 59:1594–1599

    Article  PubMed  CAS  Google Scholar 

  24. Lopresti BJ, Klunk WE, Mathis CA, Hoge JA, Ziolko SK, Lu X, Meltzer CC, Schimmel K, Tsopelas ND, DeKosky ST, Price JC (2005) Simplified quantification of Pittsburgh compound b amyloid imaging pet studies: a comparative analysis. J Nucl Med 46:1959–1972

    PubMed  CAS  Google Scholar 

  25. Martins-de-Souza D (2010) Is the word ‘biomarker’ being properly used by proteomics research in neuroscience? Eur Arch Psychiatry Clin Neurosci 260:561–562

    Article  PubMed  Google Scholar 

  26. Mattsson N, Andreasson U, Persson S, Arai H, Batish SD, Bernardini S, Bocchio-Chiavetto L, Blankenstein MA, Carrillo MC, Chalbot S, Coart E, Chiasserini D, Cutler N, Dahlfors G, Duller S, Fagan AM, Forlenza O, Frisoni GB, Galasko D, Galimberti D, Hampel H, Handberg A, Heneka MT, Herskovits AZ, Herukka SK, Holtzman DM, Humpel C, Hyman BT, Iqbal K, Jucker M, Kaeser SA, Kaiser E, Kapaki E, Kidd D, Klivenyi P, Knudsen CS, Kummer MP, Lui J, Llado A, Lewczuk P, Li QX, Martins R, Masters C, McAuliffe J, Mercken M, Moghekar A, Molinuevo JL, Montine TJ, Nowatzke W, O’Brien R, Otto M, Paraskevas GP, Parnetti L, Petersen RC, Prvulovic D, de Reus HP, Rissman RA, Scarpini E, Stefani A, Soininen H, Schroder J, Shaw LM, Skinningsrud A, Skrogstad B, Spreer A, Talib L, Teunissen C, Trojanowski JQ, Tumani H, Umek RM, Van Broeck B, Vanderstichele H, Vecsei L, Verbeek MM, Windisch M, Zhang J, Zetterberg H, Blennow K (2011) The Alzheimer’s Association external quality control program for cerebrospinal fluid biomarkers. Alzheimers Dement 7:386-395 e386

    Google Scholar 

  27. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (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

    Article  PubMed  CAS  Google Scholar 

  28. Middleton LE, Grinberg LT, Miller B, Kawas C, Yaffe K (2011) Neuropathologic features associated with Alzheimer disease diagnosis: age matters. Neurology 77:1737–1744

    Article  PubMed  CAS  Google Scholar 

  29. Mormino EC, Kluth JT, Madison CM, Rabinovici GD, Baker SL, Miller BL, Koeppe RA, Mathis CA, Weiner MW, Jagust WJ (2009) Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain 132:1310–1323

    Article  PubMed  CAS  Google Scholar 

  30. Morris JC, McKeel DW Jr, Fulling K, Torack RM, Berg L (1988) Validation of clinical diagnostic criteria for Alzheimer’s disease. Ann Neurol 24:17–22

    Article  PubMed  CAS  Google Scholar 

  31. Orgogozo JM, Gilman S, Dartigues JF, Laurent B, Puel M, Kirby LC, Jouanny P, Dubois B, Eisner L, Flitman S, Michel BF, Boada M, Frank A, Hock C (2003) Subacute meningoencephalitis in a subset of patients with ad after abeta42 immunization. Neurology 61:46–54

    Article  PubMed  CAS  Google Scholar 

  32. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, Jack CR Jr, Jagust WJ, Shaw LM, Toga AW, Trojanowski JQ, Weiner MW (2010) Alzheimer’s disease neuroimaging initiative (ADNI): Clinical characterisation. Neurology 74:201–209

    Article  PubMed  Google Scholar 

  33. Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, Ritchie K, Rossor M, Thal L, Winblad B (2001) Current concepts in mild cognitive impairment. Arch Neurol 58:1985–1992

    Article  PubMed  CAS  Google Scholar 

  34. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E (1999) Mild cognitive impairment: clinical characterisation and outcome. Arch Neurol 56:303–308

    Article  PubMed  CAS  Google Scholar 

  35. Quigley H, Colloby SJ, O’Brien JT (2011) Pet imaging of brain amyloid in dementia: a review. Int J Geriatr Psychiatry 26:991–999

    Article  PubMed  Google Scholar 

  36. Reiman EM, McKhann GM, Albert MS, Sperling RA, Petersen RC, Blacker D (2011) Alzheimer’s disease: implications of the updated diagnostic and research criteria. J Clin Psychiatry 72:1190–1196

    Article  PubMed  Google Scholar 

  37. Ritchie K, Artero S, Touchon J (2001) Classification criteria for mild cognitive impairment: a population-based validation study. Neurology 56:37–42

    Article  PubMed  CAS  Google Scholar 

  38. Saykin AJ, Shen L, Foroud TM, Potkin SG, Swaminathan S, Kim S, Risacher SL, Nho K, Huentelman MJ, Craig DW, Thompson PM, Stein JL, Moore JH, Farrer LA, Green RC, Bertram L, Jack CR Jr, Weiner MW (2010) Alzheimer’s disease neuroimaging initiative biomarkers as quantitative phenotypes: genetics core aims, progress, and plans. Alzheimers Dement 6:265–273

    Article  PubMed  CAS  Google Scholar 

  39. Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, Blennow K, Soares H, Simon A, Lewczuk P, Dean R, Siemers E, Potter W, Lee VM, Trojanowski JQ (2009) Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 65:403–413

    Article  PubMed  CAS  Google Scholar 

  40. Vanderstichele H, Bibl M, Engelborghs S, Le Bastard N, Lewczuk P, Molinuevo JL, Parnetti L, Perret-Liaudet A, Shaw LM, Teunissen C, Wouters D, Blennow K (2012) Standardization of preanalytical aspects of cerebrospinal fluid biomarker testing for Alzheimer’s disease diagnosis: a consensus paper from the Alzheimer’s Biomarkers Standardization Initiative. Alzheimers Dement 8:65–73

    Article  PubMed  CAS  Google Scholar 

  41. Viswanathan A, Greenberg SM (2011) Cerebral amyloid angiopathy in the elderly. Ann Neurol 70:871–880

    Article  PubMed  CAS  Google Scholar 

  42. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Liu E, Morris JC, Petersen RC, Saykin AJ, Schmidt ME, Shaw L, Siuciak JA, Soares H, Toga AW, Trojanowski JQ (2012) The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dement 8(1 Suppl):S1–S68

    Google Scholar 

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Acknowledgments

Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the NIA, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners including the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organisation is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at University of California San Diego. ADNI data are disseminated by the Laboratory for Neuroimaging at University of California Los Angeles. This research was also supported by NIH grants P30AG010129 and K01 AG030514 as well as the Dana Foundation. The sponsors did not have any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors wish to thank S. M. Landau PhD and Prof. W. J. Jagust from the Helen Wills Neuroscience Institute of the University of California, Berkeley, USA, for their invaluable advice concerning the application of the new MCI research criteria and for providing information about ADNI image analysis. They also express their gratitude to Dorottya Ruisz for proofreading.

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Authors and Affiliations

  1. Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar der Technischen Universität München, Ismaninger Str. 22, 81675, Munich, Germany

    Liang-Hao Guo, Panagiotis Alexopoulos, Tamara Eisele, Alexander Kurz & Robert Perneczky

  2. Institute of Medical Statistics and Epidemiology, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany

    Stefan Wagenpfeil

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  1. Liang-Hao Guo
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  2. Panagiotis Alexopoulos
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  3. Tamara Eisele
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  5. Alexander Kurz
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Correspondence to Panagiotis Alexopoulos or Robert Perneczky.

Additional information

Liang-Hao Guo and Panagiotis Alexopoulos contributed equally to the manuscript.

This study is conducted for the Alzheimer’s Disease Neuroimaging Initiative.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wpcontent/uploads/how_to_apply/ADNI_Authorship_List.pdf.

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Guo, LH., Alexopoulos, P., Eisele, T. et al. The National Institute on Aging-Alzheimer’s Association research criteria for mild cognitive impairment due to Alzheimer’s disease: predicting the outcome. Eur Arch Psychiatry Clin Neurosci 263, 325–333 (2013). https://doi.org/10.1007/s00406-012-0349-0

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