Cognitive and imaging markers in non-demented subjects attending a memory clinic: study design and baseline findings of the MEMENTO cohort

Carole Dufouil, Bruno Dubois, Bruno Vellas, Florence Pasquier, Frédéric Blanc, Jacques Hugon, Olivier Hanon, Jean-François Dartigues, Sandrine Harston, Audrey Gabelle, Mathieu Ceccaldi, Olivier Beauchet, Pierre Krolak-Salmon, Renaud David, Olivier Rouaud, Olivier Godefroy, Catherine Belin, Isabelle Rouch, Nicolas Auguste, David Wallon, Athanase Benetos, Jérémie Pariente, Marc Paccalin, Olivier Moreaud, Caroline Hommet, François Sellal, Claire Boutoleau-Bretonniére, Isabelle Jalenques, Armelle Gentric, Pierre Vandel, Chabha Azouani, Ludovic Fillon, Clara Fischer, Helen Savarieau, Gregory Operto, Hugo Bertin, Marie Chupin, Vincent Bouteloup, Marie-Odile Habert, Jean-François Mangin, Geneviève Chêne, MEMENTO cohort Study Group, Carole Dufouil, Bruno Dubois, Bruno Vellas, Florence Pasquier, Frédéric Blanc, Jacques Hugon, Olivier Hanon, Jean-François Dartigues, Sandrine Harston, Audrey Gabelle, Mathieu Ceccaldi, Olivier Beauchet, Pierre Krolak-Salmon, Renaud David, Olivier Rouaud, Olivier Godefroy, Catherine Belin, Isabelle Rouch, Nicolas Auguste, David Wallon, Athanase Benetos, Jérémie Pariente, Marc Paccalin, Olivier Moreaud, Caroline Hommet, François Sellal, Claire Boutoleau-Bretonniére, Isabelle Jalenques, Armelle Gentric, Pierre Vandel, Chabha Azouani, Ludovic Fillon, Clara Fischer, Helen Savarieau, Gregory Operto, Hugo Bertin, Marie Chupin, Vincent Bouteloup, Marie-Odile Habert, Jean-François Mangin, Geneviève Chêne, MEMENTO cohort Study Group

Abstract

Background: The natural history and disease mechanisms of Alzheimer's disease and related disorders (ADRD) are still poorly understood. Very few resources are available to scrutinise patients as early as needed and to use integrative approaches combining standardised, repeated clinical investigations and cutting-edge biomarker measurements.

Methods: In the nationwide French MEMENTO cohort study, participants were recruited in memory clinics and screened for either isolated subjective cognitive complaints (SCCs) or mild cognitive impairment (MCI; defined as test performance 1.5 SD below age, sex and education-level norms) while not demented (Clinical Dementia Rating [CDR] <1). Baseline data collection included neurological and physical examinations as well as extensive neuropsychological testing. To be included in the MEMENTO cohort, participants had to agree to undergo both brain magnetic resonance imaging (MRI) and blood sampling. Cerebral 18F-fluorodeoxyglucose positon emission tomography and lumbar puncture were optional. Automated analyses of cerebral MRI included assessments of volumes of whole-brain, hippocampal and white matter lesions.

Results: The 2323 participants, recruited from April 2011 to June 2014, were aged 71 years, on average (SD 8.7), and 62% were women. CDR was 0 in 40% of participants, and 30% carried at least one apolipoprotein E ε4 allele. We observed that more than half (52%) of participants had amnestic mild cognitive impairment (17% single-domain aMCI), 32% had non-amnestic mild cognitive impairment (16.9% single-domain naMCI) and 16% had isolated SCCs. Multivariable analyses of neuroimaging markers associations with cognitive categories showed that participants with aMCI had worse levels of imaging biomarkers than the others, whereas participants with naMCI had markers at intermediate levels between SCC and aMCI. The burden of white matter lesions tended to be larger in participants with aMCI. Independently of CDR, all neuroimaging and neuropsychological markers worsened with age, whereas differences were not consistent according to sex.

Conclusions: MEMENTO is a large cohort with extensive clinical, neuropsychological and neuroimaging data and represents a platform for studying the natural history of ADRD in a large group of participants with different subtypes of MCI (amnestic or not amnestic) or isolated SCCs.

Trial registration: Clinicaltrials.gov, NCT01926249 . Registered on 16 August 2013.

Keywords: Alzheimer’s disease; Cognitive aging; Cohort studies; Natural history studies (prognosis); Neuroimaging.

Conflict of interest statement

Ethics approval and consent to participate

This study was performed in accordance with the guidelines of the Declaration of Helsinki. The MEMENTO study protocol has been approved by the local ethics committee (“Comité de Protection des Personnes Sud-Ouest et Outre Mer III”; approval number 2010-A01394-35). All participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

FB received speaker’s honoraria and travel expenses from Roche, Biogen Idec, Novartis and Merck Serono. MCe received consultant’s fees from GE Healthcare, Eli Lilly, MSD and Piramal. GC received research support from Avid Radiopharmaceuticals and GE Healthcare. JFD received research support from Ipsen and Roche. BD received consultant’s fees from Eli Lilly and Boehringer-Ingelheim, and he received funding for his institution from Merck, Pfizer and Roche. CD received research support from Avid Radiopharmaceuticals and GE Healthcare. FS received honoraria from Teva, Novartis Pharmaceuticals, Sanofi-Genzyme and Eisai. The other authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Number of included subjects by centre in the MEMENTO cohort

References

    1. Jacqmin-Gadda H, Alperovitch A, Montlahuc C, Commenges D, Leffondre K, Dufouil C, et al. 20-Year prevalence projections for dementia and impact of preventive policy about risk factors. Eur J Epidemiol. 2013;28:493–502. doi: 10.1007/s10654-013-9818-7.
    1. Jack CR, Jr, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12:207–16. doi: 10.1016/S1474-4422(12)70291-0.
    1. Casanova R, Varma S, Simpson B, Kim M, An Y, Saldana S, et al. Blood metabolite markers of preclinical Alzheimer’s disease in two longitudinally followed cohorts of older individuals. Alzheimers Dement. 2016;12:815–22. doi: 10.1016/j.jalz.2015.12.008.
    1. Winston CN, Goetzl EJ, Akers JC, Carter BS, Rockenstein EM, Galasko D, et al. Prediction of conversion from mild cognitive impairment to dementia with neuronally derived blood exosome protein profile. Alzheimers Dement (Amst) 2016;3:63–72.
    1. Nelson PT, Braak H, Markesbery WR. Neuropathology and cognitive impairment in Alzheimer disease: a complex but coherent relationship. J Neuropathol Exp Neurol. 2009;68:1–14. doi: 10.1097/NEN.0b013e3181919a48.
    1. Salloway S, Sperling R, Fox NC, Blennow K, Klunk W, Raskind M, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med. 2014;370:322–33. doi: 10.1056/NEJMoa1304839.
    1. Dong S, Duan Y, Hu Y, Zhao Z. Advances in the pathogenesis of Alzheimer’s disease: a re-evaluation of amyloid cascade hypothesis. Transl Neurodegener. 2012;1:18. doi: 10.1186/2047-9158-1-23.
    1. Jack CR, Jr, Holtzman DM. Biomarker modeling of Alzheimer’s disease. Neuron. 2013;80:1347–58. doi: 10.1016/j.neuron.2013.12.003.
    1. Nelson PT, Alafuzoff I, Bigio EH, Bouras C, Braak H, Cairns NJ, et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol. 2012;71:362–81. doi: 10.1097/NEN.0b013e31825018f7.
    1. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993;43:2412–4. doi: 10.1212/WNL.43.11.2412-a.
    1. Hugonot-Diner L. La consultation en gériatrie. Paris: Masson; 2001. MMS version consensuelle GRECO; pp. 13–20.
    1. Wechsler D. Wechsler Memory Scale. New York: Psychological Corp; 1975.
    1. Grober E, Buschke H, Crystal H, Bang S, Dresner R. Screening for dementia by memory testing. Neurology. 1988;38:900–3. doi: 10.1212/WNL.38.6.900.
    1. Barbeau E, Didic M, Tramoni E, Felician O, Joubert S, Sontheimer A, et al. Evaluation of visual recognition memory in MCI patients. Neurology. 2004;62:1317–22. doi: 10.1212/01.WNL.0000120548.24298.DB.
    1. Thurstone LL. Psychophysical analysis. By L. L. Thurstone, 1927. Am J Psychol. 1987;100:587–609. doi: 10.2307/1422696.
    1. Deloche G, Hannequin D, Dordain M, Perrier D, Pichard B, Quint S, et al. Picture confrontation oral naming: performance differences between aphasics and normals. Brain Lang. 1996;53:105–20. doi: 10.1006/brln.1996.0039.
    1. Peigneux P, Van der Liden M. Presentation d’une batterie neuropsychologique et cognitive pour l’evaluation de l’apraxie gestuelle. Rev Neuropsychol. 2000;10:311–62.
    1. Benton AL, Varney NR, Hamsher KD. Visuospatial judgment: a clinical test. Arch Neurol. 1978;35:364–7. doi: 10.1001/archneur.1978.00500300038006.
    1. Reischies FM, Neu P. Comorbidity of mild cognitive disorder and depression – a neuropsychological analysis. Eur Arch Psychiatry Clin Neurosci. 2000;250:186–93. doi: 10.1007/s004060070023.
    1. Tombaugh TN. Trail Making Test A and B: normative data stratified by age and education. Arch Clin Neuropsychol. 2004;19:203–14. doi: 10.1016/S0887-6177(03)00039-8.
    1. Dubois B, Slachevsky A, Litvan I, Pillon B. The FAB: a Frontal Assessment Battery at bedside. Neurology. 2000;55:1621–6. doi: 10.1212/WNL.55.11.1621.
    1. Slachevsky A, Villalpando JM, Sarazin M, Hahn-Barma V, Pillon B, Dubois B. Frontal Assessment Battery and differential diagnosis of frontotemporal dementia and Alzheimer disease. Arch Neurol. 2004;61:1104–7. doi: 10.1001/archneur.61.7.1104.
    1. de Medeiros K, Robert P, Gauthier S, Stella F, Politis A, Leoutsakos J, et al. The Neuropsychiatric Inventory-Clinician rating scale (NPI-C): reliability and validity of a revised assessment of neuropsychiatric symptoms in dementia. Int Psychogeriatr. 2010;22:984–94. doi: 10.1017/S1041610210000876.
    1. Hagstromer M, Oja P, Sjostrom M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9:755–62. doi: 10.1079/PHN2005898.
    1. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function. JAMA. 1963;185:914–9. doi: 10.1001/jama.1963.03060120024016.
    1. Lawton MP. Scales to measure competence in everyday activities. Psychopharmacol Bull. 1988;24:609–14.
    1. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49:M85–94. doi: 10.1093/geronj/49.2.M85.
    1. Operto G, Chupin M, Batrancourt B, Habert MO, Colliot O, Benali H, et al. CATI: a large distributed infrastructure for the neuroimaging of cohorts. Neuroinformatics. 2016;14:253–64. doi: 10.1007/s12021-016-9295-8.
    1. Habert MO, Marie S, Bertin H, Reynal M, Martini JB, Diallo M, et al. Optimization of brain PET imaging for a multicentre trial: the French CATI experience. EJNMMI Phys. 2016;3:6. doi: 10.1186/s40658-016-0141-8.
    1. Varrone A, Asenbaum S, Vander Borght T, Booij J, Nobili F, Nagren K, et al. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging. 2009;36:2103–10. doi: 10.1007/s00259-009-1264-0.
    1. Prieto E, Marti-Climent JM, Arbizu J, Garrastachu P, Dominguez I, Quincoces G, et al. Evaluation of spatial resolution of a PET scanner through the simulation and experimental measurement of the recovery coefficient. Comput Biol Med. 2010;40:75–80. doi: 10.1016/j.compbiomed.2009.11.002.
    1. Chupin M, Hammers A, Liu RS, Colliot O, Burdett J, Bardinet E, et al. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. 2009;46:749–61. doi: 10.1016/j.neuroimage.2009.02.013.
    1. Chupin M, Mukuna-Bantumbakulu AR, Hasboun D, Bardinet E, Baillet S, Kinkingnehun S, et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer’s disease. Neuroimage. 2007;34:996–1019. doi: 10.1016/j.neuroimage.2006.10.035.
    1. Scheltens P, Launer LJ, Barkhof F, Weinstein HC, van Gool WA. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: interobserver reliability. J Neurol. 1995;242:557–60. doi: 10.1007/BF00868807.
    1. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14:11–22. doi: 10.1093/cercor/bhg087.
    1. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–80. doi: 10.1016/j.neuroimage.2006.01.021.
    1. Mangin JF, Jouvent E, Cachia A. In-vivo measurement of cortical morphology: means and meanings. Curr Opin Neurol. 2010;23:359–67.
    1. Perrot M, Riviere D, Mangin JF. Cortical sulci recognition and spatial normalization. Med Image Anal. 2011;15:529–50. doi: 10.1016/j.media.2011.02.008.
    1. Samaille T, Fillon L, Cuingnet R, Jouvent E, Chabriat H, Dormont D, et al. Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS One. 2012;7:e48953. doi: 10.1371/journal.pone.0048953.
    1. Fazekas F, Barkhof F, Wahlund LO, Pantoni L, Erkinjuntti T, Scheltens P, et al. CT and MRI rating of white matter lesions. Cerebrovasc Dis. 2002;13(Suppl 2):31–6. doi: 10.1159/000049147.
    1. Guevara P, Duclap D, Poupon C, Marrakchi-Kacem L, Fillard P, Le Bihan D, et al. Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. Neuroimage. 2012;61:1083–99. doi: 10.1016/j.neuroimage.2012.02.071.
    1. Duclap D, Lebois A, Scmitt B, Riff O, Guevara P, Marrakchi-Kacem L, et al. Connectomist-2.0: a novel diffusion analysis toolbox for BrainVISA. In: 29th ESMRMB Congress, Lisbon, Portugal, 2012.
    1. Buchert R, Wilke F, Chakrabarti B, Martin B, Brenner W, Mester J, et al. Adjusted scaling of FDG positron emission tomography images for statistical evaluation in patients with suspected Alzheimer’s disease. J Neuroimaging. 2005;15:348–55. doi: 10.1111/j.1552-6569.2005.tb00335.x.
    1. Toussaint PJ, Perlbarg V, Bellec P, Desarnaud S, Lacomblez L, Doyon J, et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer’s disease using conjoint univariate and independent component analyses. Neuroimage. 2012;63:936–46. doi: 10.1016/j.neuroimage.2012.03.091.
    1. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. 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. 1984;34:939–44. doi: 10.1212/WNL.34.7.939.
    1. American Psychiatric Association . Diagnostic and statistical manual of mental disorders. 4. Washington, DC: American Psychiatric Association; 1994.
    1. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256:183–94. doi: 10.1111/j.1365-2796.2004.01388.x.
    1. Langbaum JB, Chen K, Lee W, Reschke C, Bandy D, Fleisher AS, et al. Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Neuroimage. 2009;45:1107–16. doi: 10.1016/j.neuroimage.2008.12.072.
    1. Weuve J, Proust-Lima C, Power MC, Gross AL, Hofer SM, Thiebaut R, et al. Guidelines for reporting methodological challenges and evaluating potential bias in dementia research. Alzheimers Dement. 2015;11:1098–109. doi: 10.1016/j.jalz.2015.06.1885.
    1. Cavedo E, Lista S, Khachaturian Z, Aisen P, Amouyel P, Herholz K, et al. The road ahead to cure Alzheimer’s disease: development of biological markers and neuroimaging methods for prevention trials across all stages and target populations. J Prev Alzheimers Dis. 2014;1:181–202.
    1. Schneider JA, Bennett DA. Where vascular meets neurodegenerative disease. Stroke. 2010;41(10 Suppl):S144–146. doi: 10.1161/STROKEAHA.110.598326.
    1. Visser PJ, Verhey F, Knol DL, Scheltens P, Wahlund LO, Freund-Levi Y, et al. Prevalence and prognostic value of CSF markers of Alzheimer’s disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: a prospective cohort study. Lancet Neurol. 2009;8:619–27. doi: 10.1016/S1474-4422(09)70139-5.
    1. Murray ME, Graff-Radford NR, Ross OA, Petersen RC, Duara R, Dickson DW. Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. Lancet Neurol. 2011;10:785–96. doi: 10.1016/S1474-4422(11)70156-9.
    1. Barnes J, Dickerson BC, Frost C, Jiskoot LC, Wolk D, van der Flier WM. Alzheimer’s disease first symptoms are age dependent: evidence from the NACC dataset. Alzheimers Dement. 2015;11:1349–57. doi: 10.1016/j.jalz.2014.12.007.
    1. Farias ST, Mungas D, Reed BR, Harvey D, DeCarli C. Progression of mild cognitive impairment to dementia in clinic- vs community-based cohorts. Arch Neurol. 2009;66:1151–7. doi: 10.1001/archneurol.2009.106.
    1. Yesavage JA, O’Hara R, Kraemer H, Noda A, Taylor JL, Ferris S, et al. Modeling the prevalence and incidence of Alzheimer’s disease and mild cognitive impairment. J Psychiatr Res. 2002;36:281–6. doi: 10.1016/S0022-3956(02)00020-1.
    1. Mattsson N, Zetterberg H, Hansson O, Andreasen N, Parnetti L, Jonsson M, et al. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA. 2009;302:385–93. doi: 10.1001/jama.2009.1064.
    1. Dickerson BC, Sperling RA, Hyman BT, Albert MS, Blacker D. Clinical prediction of Alzheimer disease dementia across the spectrum of mild cognitive impairment. Arch Gen Psychiatry. 2007;64:1443–50. doi: 10.1001/archpsyc.64.12.1443.
    1. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology. 2010;74:201–9. doi: 10.1212/WNL.0b013e3181cb3e25.
    1. Sperling RA, Rentz DM, Johnson KA, Karlawish J, Donohue M, Salmon DP, Aisen P. The A4 Study: Stopping AD before Symptoms Begin. Sci Transl Med. 2014;6(228):228fs13.

Source: PubMed

3
订阅