Abstract
Original language | English |
---|---|
Pages (from-to) | 2535-2545 |
Number of pages | 11 |
Journal | Front. Neurol. |
Volume | 266 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2019 |
Keywords
- Alzheimer
- Biomarkers
- Cognition
- FDG-PET
- Imaging
- amyloid beta protein[1-42]
- biological marker
- tau protein
- adult
- Alzheimer disease
- Article
- brain size
- female
- hippocampus
- human
- major clinical study
- male
- mental deterioration
- middle aged
- mild cognitive impairment
- onset age
- priority journal
- prognosis
- receiver operating characteristic
- time
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Prognostic value of Alzheimer’s biomarkers in mild cognitive impairment: the effect of age at onset : Journal of Neurology. / Altomare, D.; Ferrari, C.; Caroli, A.; Galluzzi, S.; Prestia, A.; van der Flier, W.M.; Ossenkoppele, R.; Van Berckel, B.; Barkhof, F.; Teunissen, C.E.; Wall, A.; Carter, S.F.; Schöll, M.; Choo, I.H.; Grimmer, T.; Redolfi, A.; Nordberg, A.; Scheltens, P.; Drzezga, A.; Frisoni, G.B.; Initiative, for the Alzheimer's Disease Neuroimaging.
In: Front. Neurol., Vol. 266, No. 10, 2019, p. 2535-2545.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Prognostic value of Alzheimer’s biomarkers in mild cognitive impairment: the effect of age at onset
T2 - Journal of Neurology
AU - Altomare, D.
AU - Ferrari, C.
AU - Caroli, A.
AU - Galluzzi, S.
AU - Prestia, A.
AU - van der Flier, W.M.
AU - Ossenkoppele, R.
AU - Van Berckel, B.
AU - Barkhof, F.
AU - Teunissen, C.E.
AU - Wall, A.
AU - Carter, S.F.
AU - Schöll, M.
AU - Choo, I.H.
AU - Grimmer, T.
AU - Redolfi, A.
AU - Nordberg, A.
AU - Scheltens, P.
AU - Drzezga, A.
AU - Frisoni, G.B.
AU - Initiative, for the Alzheimer's Disease Neuroimaging
N1 - Export Date: 10 February 2020 CODEN: JNRYA Correspondence Address: Ferrari, C.; Service of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, via Pilastroni 4, Italy; email: cferrari@fatebenefratelli.eu Funding details: 283562 Funding details: National Institute on Aging, NIA Funding details: Takeda Pharmaceutical Company Funding details: Vetenskapsrådet, VR, 05817 Funding details: Karolinska Institutet, KI Funding details: Roche Funding details: GE Healthcare Funding details: DoD Alzheimer's Disease Neuroimaging Initiative, ADNI Funding details: Pfizer Funding details: Eli Lilly and Company Funding details: Merck Funding details: Seventh Framework Programme, FP7 Funding details: Swedish Brain Power, SBP Funding details: National Institute of Biomedical Imaging and Bioengineering, NIBIB Funding details: BioClinica Funding details: Bristol-Myers Squibb, BMS Funding details: Johnson and Johnson Pharmaceutical Research and Development, J&JPRD Funding details: Novartis Pharmaceuticals Corporation, NPC Funding details: Eisai Funding details: Alzheimer's Disease Neuroimaging Initiative, ADNI Funding details: Foundation for the National Institutes of Health, FNIH Funding details: National Institutes of Health, NIH, U01 AG024904 Funding details: U.S. Department of Defense, DOD, W81XWH-12-2-0012 Funding details: Biogen Idec Funding details: Servier Funding details: Synarc Funding details: Genentech Funding details: Canadian Institutes of Health Research, CIHR Funding details: UCLH Biomedical Research Centre, UCLH BRC Funding text 1: 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. Funding text 2: 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 . References: Dubois, B., Feldman, H.H., Jacova, C., Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria (2014) Lancet Neurol, 13, pp. 614-629; Jack, C.R.J., Albert, M.S., Knopman, D.S., Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (2011) Alzheimers Dement, 7, pp. 257-262; Duits, F.H., Martinez-Lage, P., Paquet, C., Performance and complications of lumbar puncture in memory clinics: results of the multicenter lumbar puncture feasibility study (2016) Alzheimers Dement, 12, pp. 154-163; Frisoni, G.B., Bocchetta, M., Chetelat, G., Imaging markers for Alzheimer disease: which vs how (2013) Neurology, 81, pp. 487-500; Molinuevo, J.L., Blennow, K., Dubois, B., The clinical use of cerebrospinal fluid biomarker testing for Alzheimer’s disease diagnosis: a consensus paper from the Alzheimer’s Biomarkers Standardization Initiative (2014) Alzheimers Dement, 10, pp. 808-817; Hansson, O., Zetterberg, H., Buchhave, P., Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study (2006) Lancet Neurol, 5, pp. 228-234; Mattsson, N., Zetterberg, H., Hansson, O., CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment (2009) JAMA, 302, pp. 385-393; Prestia, A., Caroli, A., Wade, S.K., Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics (2015) Alzheimers Dement, 11, pp. 1191-1201; Prestia, A., Caroli, A., Herholz, K., Diagnostic accuracy of markers for prodromal Alzheimer’s disease in independent clinical series (2013) Alzheimers Dement, 9, pp. 677-686; Shaffer, J.L., Petrella, J.R., Sheldon, F.C., Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers (2013) Radiology, 266, pp. 583-591; Yu, P., Dean, R.A., Hall, S.D., Enriching amnestic mild cognitive impairment populations for clinical trials: optimal combination of biomarkers to predict conversion to dementia (2012) J Alzheimers Dis, 32, pp. 373-385; Frisoni, G.B., Pievani, M., Testa, C., The topography of grey matter involvement in early and late onset Alzheimer’s disease (2007) Brain, 130, pp. 720-730; Moller, C., Vrenken, H., Jiskoot, L., Different patterns of gray matter atrophy in early- and late-onset Alzheimer’s disease (2013) Neurobiol Aging, 34, pp. 2014-2022; Bouwman, F.H., Schoonenboom, N.S.M., Verwey, N.A., CSF biomarker levels in early and late onset Alzheimer’s disease (2009) Neurobiol Aging, 30, pp. 1895-1901; Ossenkoppele, R., Zwan, M.D., Tolboom, N., Amyloid burden and metabolic function in early-onset Alzheimer’s disease: parietal lobe involvement (2012) Brain, 135, pp. 2115-2125; Schmand, B., Eikelenboom, P., van Gool, W.A., Value of neuropsychological tests, neuroimaging, and biomarkers for diagnosing Alzheimer’s disease in younger and older age cohorts (2011) J Am Geriatr Soc, 59, pp. 1705-1710; Matsunari, I., Samuraki, M., Chen, W.-P., Comparison of 18F-FDG PET and optimized voxel-based morphometry for detection of Alzheimer’s disease: aging effect on diagnostic performance (2007) J Nucl Med, 48, pp. 1961-1970; Mattsson, N., Rosen, E., Hansson, O., Age and diagnostic performance of Alzheimer disease CSF biomarkers (2012) Neurology, 78, pp. 468-476; Chiaravalloti, A., Koch, G., Toniolo, S., Belli, L., Di Lorenzo, F., Gaudenzi, S., Schillaci, O., Giuseppe Sancesario, A.M., Comparison between early-onset and late-onset Alzheimer’s disease patients with amnestic presentation: CSF and 18-F-FDG PET study (2016) Dement Geriatr Cogn Dis Extra, 6, pp. 108-119; Vanhoutte, M., Semah, F., Rollin Sillaire, A., 18F-FDG PET hypometabolism patterns reflect clinical heterogeneity in sporadic forms of early-onset Alzheimer’s disease (2017) Neurobiol Aging; Falgàs, N., Tort-Merino, A., Balasa, M., Clinical applicability of diagnostic biomarkers in early-onset cognitive impairment (2019) Eur J Neurol; Verclytte, S., Lopes, R., Lenfant, P., Cerebral hypoperfusion and hypometabolism detected by arterial spin labeling MRI and FDG-PET in early-onset Alzheimer’s disease (2016) J Neuroimaging; Li, K., Chan, W., Doody, R.S., Prediction of conversion to Alzheimer’s disease with longitudinal measures and time-to-event data (2017) J Alzheimers Dis; Petersen, R.C., Smith, G.E., Waring, S.C., Mild cognitive impairment: clinical characterization and outcome (1999) Arch Neurol, 56, pp. 303-308; O’Bryant, S.E., Humphreys, J.D., Smith, G.E., Detecting dementia with the mini-mental state examination in highly educated individuals (2008) Arch Neurol, 65, pp. 963-967; Hensel, A., Angermeyer, M.C., Riedel-Heller, S.G., Measuring cognitive change in older adults: reliable change indices for the mini-mental state examination (2007) J Neurol Neurosurg Psychiatry, 78, pp. 1298-1303; McKhann, G., Drachman, D., Folstein, M., 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 (1984) Neurology, 34, pp. 939-944; Herholz, K., Salmon, E., Perani, D., Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET (2002) Neuroimage, 17, pp. 302-316; Orimo, H., Ito, H., Suzuki, T., Reviewing the definition of “elderly (2006) Geriatr Gerontol Int, 6, pp. 149-158; Blagosklonny, M.V., Why human lifespan is rapidly increasing: solving “longevity riddle” with “revealed-slow-aging” hypothesis (2010) Aging (Albany NY); Jacobs, J.M., Maaravi, Y., Cohen, A., Changing profile of health and function from age 70 to 85 years (2012) Gerontology; Mendez, M.F., Early-onset Alzheimer disease (2017) Neurol Clin, 35, pp. 263-281; Prestia, A., Caroli, A., van der Flier, W.M., Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease (2013) Neurology, 80, pp. 1048-1056; DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach (1988) Biometrics, 44, pp. 837-845; Therneau, T., Grambsch, P.M., (2000) Modeling survival data: extending the Cox model, , Springer, New York; Schmand, B., Eikelenboom, P., van Gool, W.A., Value of diagnostic tests to predict conversion to Alzheimer’s disease in young and old patients with amnestic mild cognitive impairment (2012) J Alzheimers Dis, 29, pp. 641-648; van Rossum, I.A., Vos, S.J.B., Burns, L., Injury markers predict time to dementia in subjects with MCI and amyloid pathology (2012) Neurology, 79, pp. 1809-1816; Landau, S.M., Lu, M., Joshi, A.D., Comparing positron emission tomography imaging and cerebrospinal fluid measurements of β-amyloid (2013) Ann Neurol, 74, pp. 826-836; Zwan, M., van Harten, A., Ossenkoppele, R., Concordance between cerebrospinal fluid biomarkers and [11C]PIB PET in a memory clinic cohort (2014) J Alzheimers Dis, 41, pp. 801-807; Caroli, A., Prestia, A., Chen, K., Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison (2012) J Nucl Med, 53, pp. 592-600; Haense, C., Herholz, K., Jagust, W.J., Heiss, W.D., Performance of FDG PET for detection of Alzheimer’s disease in two independent multicentre samples (NEST-DD and ADNI) (2009) Dement Geriatr Cogn Disord, 28, pp. 259-266; Herholz, K., Westwood, S., Haense, C., Dunn, G., Evaluation of a calibrated (18)F-FDG PET score as a biomarker for progression in Alzheimer disease and mild cognitive impairment (2011) J Nucl Med, 52, pp. 1218-1226; Frisoni, G.B., Fox, N.C., Jack, C.R.J., The clinical use of structural MRI in Alzheimer disease (2010) Nat Rev Neurol, 6, pp. 67-77; Bobinski, M., Wegiel, J., Wisniewski, H.M., Neurofibrillary pathology—correlation with hippocampal formation atrophy in Alzheimer disease (1996) Neurobiol Aging, 17, pp. 909-919; Apostolova, L.G., Zarow, C., Biado, K., Relationship between hippocampal atrophy and neuropathology markers: a 7T MRI validation study of the EADC-ADNI harmonized hippocampal segmentation protocol (2015) Alzheimers Dement, 11, pp. 139-150; den Heijer, T., van der Lijn, F., Koudstaal, P.J., A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline (2010) Brain, 133, pp. 1163-1172; Palasí, A., Gutiérrez-Iglesias, B., Alegret, M., Differentiated clinical presentation of early and late-onset Alzheimer’s disease: is 65 years of age providing a reliable threshold? (2015) J Neurol, 262, pp. 1238-1246
PY - 2019
Y1 - 2019
N2 - 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. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
AB - 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. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
KW - Alzheimer
KW - Biomarkers
KW - Cognition
KW - FDG-PET
KW - Imaging
KW - amyloid beta protein[1-42]
KW - biological marker
KW - tau protein
KW - adult
KW - Alzheimer disease
KW - Article
KW - brain size
KW - female
KW - hippocampus
KW - human
KW - major clinical study
KW - male
KW - mental deterioration
KW - middle aged
KW - mild cognitive impairment
KW - onset age
KW - priority journal
KW - prognosis
KW - receiver operating characteristic
KW - time
U2 - 10.1007/s00415-019-09441-7
DO - 10.1007/s00415-019-09441-7
M3 - Article
VL - 266
SP - 2535
EP - 2545
JO - Front. Neurol.
JF - Front. Neurol.
SN - 1664-2295
IS - 10
ER -