Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 49-58 |
| Number of pages | 10 |
| Journal | J. Alzheimer's Dis. |
| Volume | 69 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2019 |
Keywords
- Alzheimer's disease
- biomarker matrices
- clinical trial
- magnetic resonance imaging
- mild cognitive impairment
- amyloid beta protein[1-42]
- apolipoprotein E4
- biological marker
- tau protein
- aged
- Alzheimer disease
- Article
- brain size
- calculation
- cohort analysis
- controlled study
- dentate gyrus
- disease course
- disease exacerbation
- female
- follow up
- human
- lateral brain ventricle
- longitudinal study
- major clinical study
- male
- nuclear magnetic resonance imaging
- priority journal
- protein cerebrospinal fluid level
- sample size
- white matter lesion
Fingerprint
Dive into the research topics of 'Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease: Journal of Alzheimer's Disease'. Together they form a unique fingerprint.Cite this
- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS
Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease : Journal of Alzheimer's Disease. / Marizzoni, M.; Ferrari, C.; Macis, A. et al.
In: J. Alzheimer's Dis., Vol. 69, No. 1, 2019, p. 49-58.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease
T2 - Journal of Alzheimer's Disease
AU - Marizzoni, M.
AU - Ferrari, C.
AU - Macis, A.
AU - Jovicich, J.
AU - Albani, D.
AU - Babiloni, C.
AU - Cavaliere, L.
AU - Didic, M.
AU - Forloni, G.
AU - Galluzzi, S.
AU - Hoffmann, K.-T.
AU - Molinuevo, J.L.
AU - Nobili, F.
AU - Parnetti, L.
AU - Payoux, P.
AU - Pizzini, F.
AU - Rossini, P.M.
AU - Salvatore, M.
AU - Schönknecht, P.
AU - Soricelli, A.
AU - Del Percio, C.
AU - Hensch, T.
AU - Hegerl, U.
AU - Tsolaki, M.
AU - Visser, P.J.
AU - Wiltfang, J.
AU - Richardson, J.C.
AU - Bordet, R.
AU - Blin, O.
AU - Frisoni, G.B.
N1 - Cited By :1 Export Date: 10 February 2020 CODEN: JADIF Correspondence Address: Marizzoni, M.; Laboratory of Neuroimaging and Alzheimer's Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, via Pilastroni 4, Italy; email: mmarizzoni@fatebenefratelli.eu Funding details: Sapienza Università di Roma Funding details: Aristotle University of Thessaloniki Funding details: Australian Catholic University Funding details: Università degli Studi di Trento Funding details: Institut National de la Santé et de la Recherche Médicale Funding details: Università degli Studi di Genova Funding text 1: aLaboratory of Neuroimaging and Alzheimer’s Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy bUnit of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy cCenter for Mind/Brain Sciences, University of Trento, Italy dDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy eDepartment of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Italy fHospital San Raffaele Cassino, Cassino (FR), Italy gAix-Marseille Université, Inserm, INS UMR S 1106, Marseille, France hAPHM, Timone, Service de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, France jiDepartment′ of Neuroradiology, University of Leipzig, Leipzig, Germany Alzheimer s Disease Unit and Other Cognitive Disorders Unit, Hospital Clínic de Barcelona, and Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalunya, Spain kDepartment of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy lClinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy mClinica Neurologica, Università di Perugia, Ospedale Santa Maria della Misericordia, Perugia, Italy nINSERM; Imagerie cérébrale et handicaps neurologiques UMR 825, Toulouse, France oDepartment of Diagnostics and Pathology, Neuroradiology, Verona University Hospital, Italy pDepartment of Gerontology, Area of Neuroscience, Neurosciences & Orthopedics, Catholic University, Policlinic A. Gemelli Foundation Rome, Italy qSDN Istituto di Ricerca Diagnostica e Nucleare, Napoli, Italy rDepartment of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany s3rd Neurologic Clinic, Medical School, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece tDepartment of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, The Netherlands References: Galluzzi, S., Marizzoni, M., Babiloni, C., Albani, D., Antelmi, L., Bagnoli, C., Bartres-Faz, D., Frisoni, G.B., (2016) Clinical and biomarker profiling of prodromal Alzheimer's disease in workpackage 5 of the Innovative Medicines Initiative PharmaCog project: A "European ADNI study. " J Intern Med, 279, pp. 576-591. , PharmaCog Consortium; Marizzoni, M., Ferrari, C., Galluzzi, S., Jovicich, J., Albani, D., Babiloni, C., Didic, M., Frisoni, G.B., CSF biomarkers, effect of apolipoprotein e genotype, age, sex on cut-off derivation in mild cognitive impairment (2018) Alzheimers Dement, 13, p. P1319; Marizzoni, M., Ferrari, C., Jovicich, J., Albani, D., Babiloni, C., Cavaliere, L., Didic, M., Frisoni, G.B., Predicting and tracking short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease: Structural brain biomarkers (2019) J Alzheimers Dis, 69, pp. 3-14; Albani, D., Marizzoni, M., Ferrari, C., Fusco, F., Boeri, L., Raimondi, I., Jovicich, J., Frisoni, G.B., Plasma A42 as a biomarker of prodromal Alzheimer's disease progression in patients with amnestic mild cognitive impairment: Evidence from the PharmaCog/E-ADNI Study (2019) J Alzheimers Dis, 69, pp. 37-48. , PharmaCog Consortium; Jovicich, J., Babiloni, C., Ferrari, C., Marizzoni, M., Moretti, D.V., Del Percio, C., Lizio, R., Frisoni, G.B., Two-year longitudinal monitoring of amnestic mild cognitive impairment patients with prodromal Alzheimer's disease using topographical biomarkers derived from functional magnetic resonance imaging and electroencephalographic activity (2019) J Alzheimers Dis, 69, pp. 15-35. , PharmaCog Consortium; Hsu, M.-J., Chang, Y.-C.I., Hsueh, H.-M., Biomarker selection for medical diagnosis using the partial area under the ROC curve (2014) BMC Res Notes, 7, p. 25; Kang, L., Liu, A., Tian, L., Linear combination methods to improve diagnostic/prognostic accuracy on future observations (2016) Stat Methods Med Res, 25, pp. 1359-1380; Pepe, M.S., Thompson, M.L., Combining diagnostic test results to increase accuracy (2000) Biostatistics, 1, pp. 123-140; Donohue, M.C., Sperling, R.A., Salmon, D.P., Rentz, D.M., Raman, R., Thomas, R.G., Weiner, M., Aisen, P.S., The preclinical Alzheimer cognitive composite: Measuring amyloid-related decline (2014) JAMA Neurol, 71, pp. 961-970; Mormino, E.C., Papp, K.V., Rentz, D.M., Donohue, M.C., Amariglio, R., Quiroz, Y.T., Chhatwal, J., Sperling, R.A., Early and late change on the preclinical Alzheimer's cognitive composite in clinically normal older individuals with elevated amyloid (2017) Alzheimers Dement, 13, pp. 1004-1012; Papp, K.V., Rentz, D.M., Orlovsky, I., Sperling, R.A., Mormino, E.C., Optimizing the preclinical Alzheimer's cognitive composite with semantic processing: The PACC5 (2017) Alzheimers Dement (N Y), 3, pp. 668-677; Wang, J., Logovinsky, V., Hendrix, S.B., Stanworth, S.H., Perdomo, C., Xu, L., Dhadda, S., Satlin, A., ADCOMS: A composite clinical outcome for prodromal Alzheimer's disease trials (2016) J Neurol Neurosurg Psychiatry, 87, pp. 993-999; Jutten, R.J., Harrison, J., De Jong, F.J., Aleman, A., Ritchie, C.W., Scheltens, P., Sikkes, S.A.M., A composite measure of cognitive and functional progression in Alzheimer's disease: Design of the Capturing Changes in Cognition study (2017) Alzheimers Dement (N Y), 3, pp. 130-138; Coley, N., Gallini, A., Ousset, P.J., Vellas, B., Andrieu, S., Evaluating the clinical relevance of a cognitive composite outcome measure: An analysis of 1414 participants from the 5-year GuidAge Alzheimer's prevention trial (2016) Alzheimers Dement, 12, pp. 1216-1225; Insel, P.S., Mattsson, N., Mackin, R.S., Kornak, J., Nosheny, R., Tosun-Turgut, D., Donohue, M.C., Weiner, M.W., Biomarkers and cognitive endpoints to optimize trials in Alzheimer's disease (2015) Ann Clin Transl Neurol, 2, pp. 534-547; Nathan, P.J., Lim, Y.Y., Abbott, R., Galluzzi, S., Marizzoni, M., Babiloni, C., Albani, D., Frisoni, G.B., Association between CSF biomarkers, hippocampal volume and cognitive function in patients with amnestic mild cognitive impairment (MCI) (2017) Neurobiol Aging, 53, pp. 1-10; Folstein, M.F., Folstein, S.E., McHugh, P.R., Mini-Mental State: A practice method for grading the cognitive state of patients for the clinician (1975) J Psychiatr Res, 12, pp. 189-198; Morris, J.C., The Clinical Dementia Rating (CDR): Current version and scoring rules (1993) Neurology, 43, pp. 2412-2414; Woodard, J.L., Axelrod, B.N., Wechsler memory scale-revised (1987) Psychol Assess, 7, pp. 445-449; Brown, L.M., Schinka, J.A., Development of initial validation of a 15-item informant version of the Geriatric Depression Scale (2005) Int J Geriatr Psychiatry, 20, pp. 911-918; Richardson, J.T.E., Eta squared and partial eta squared as measures of effect size in educational research (2011) Educ Res Rev, 6, pp. 135-147; Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Van Der Kouwe, A., Dale, A.M., Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain (2002) Neuron, 33, pp. 341-355; Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B., Within-subject template estimation for unbiased longitudinal image analysis (2012) Neuroimage, 61, pp. 1402-1418; Iglesias, J.E., Augustinack, J.C., Nguyen, K., Player, C.M., Player, A., Wright, M., Roy, N., Van Leemput, K., A computational atlas of the hippocampal formation using ex vivo, ultra-high resolu tion MRI: Application to adaptive segmentation of in vivo MRI (2015) Neuroimage, 115, pp. 117-137; Wahlund, L.O., Barkhof, F., Fazekas, F., Bronge, L., Augustin, M., Sjögren, M., Wallin, A., Scheltens, P., A new rating scale for age-related white matter changes applicable to MRI and CT (2001) Stroke, 32, pp. 1318-1322; Liu, G., Liang, K.-Y., Sample size calculations for studies with correlated observations (1997) Biometrics, 53, p. 937; Preibisch, C., Castrillón, G.J.G., Bührer, M., Riedl, V., Evaluation of multiband EPI acquisitions for resting state fMRI (2015) PLoS One, 10, p. e0136961; Wig, G.S., Segregated systems of human brain networks (2017) Trends Cogn Sci, 21, pp. 981-996; De Pasquale, F., Corbetta, M., Betti, V., Della Penna, S., Cortical cores in network dynamics (2017) Neuroimage, 180, pp. 370-382; Nir, T.M., Jahanshad, N., Villalon-Reina, J.E., Toga, A.W., Jack, C.R., Weiner, M.W., Thompson, P.M., Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging (2013) Neuroimage Clin, 3, pp. 180-195; Kerchner, G.A., Ultra-high field 7T MRI: A new tool for studying Alzheimer's disease (2011) J Alzheimers Dis, 26, pp. 91-95; Nakamura, A., Kaneko, N., Villemagne, V.L., Kato, T., Doecke, J., Doré, V., Fowler, C., Yanagisawa, K., High performance plasma amyloid-biomarkers for Alzheimer's disease (2018) Nature, 554, pp. 249-254; Kaushik, A., Jayant, R.D., Tiwari, S., Vashist, A., Nair, M., Nano-biosensors to detect beta-amyloid for Alzheimer's disease management (2016) Biosens Bioelectron, 80, pp. 273-287; Jack, C.R., Knopman, D.S., Jagust, W.J., Petersen, R.C., Weiner, M.W., Aisen, P.S., Shaw, L.M., Trojanowski, J.Q., Update on hypothetical model of Alzheimer's disease biomarkers (2013) Lancet Neurol, 12, pp. 207-216; Nestor, S.M., Rupsingh, R., Borrie, M., Smith, M., Accomazzi, V., Wells, J.L., Fogarty, J., Bartha, R., Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database (2008) Brain, 131, pp. 2443-2454; Gutman, B.A., Hua, X., Rajagopalan, P., Chou, Y.Y., Wang, Y., Yanovsky, I., Toga, A.W., Thompson, P.M., Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features (2013) Neuroimage, 70, pp. 386-401. , Alzheimer's Disease Neuroimaging Initiative; Leung, K.K., Bartlett, J.W., Barnes, J., Manning, E.N., Ourselin, S., Fox, N.C., Cerebral atrophy in mild cognitive impairment and Alzheimer disease: Rates and acceleration (2013) Neurology, 80, pp. 648-654; Jack, C.R., Weigand, S.D., Shiung, M.M., Przybelski, S.A., O'Brien, P.C., Gunter, J.L., Knopman, D.S., Petersen, R.C., Atrophy rates accelerate in amnestic mild cognitive impairment (2008) Neurology, 70, pp. 1740-1752; Frankó, E., Joly, O., Evaluating Alzheimer's disease progression using rate of regional hippocampal atrophy (2013) PLoS One, 8, p. e71354. , Alzheimer's Disease Neuroimaging Initiative; Pluta, J., Yushkevich, P., Das, S., Wolk, D., In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI (2012) J Alzheimers Dis, 31, pp. 85-99; Edland, S.D., Ard, M.C., Sridhar, J., Cobia, D., Martersteck, A., Mesulam, M.M., Rogalski, E.J., Proof of concept demonstration of optimal composite MRI endpoints for clinical trials (2016) Alzheimers Dement (N Y), 2, pp. 177-181; Rogalski, E., Cobia, D., Martersteck, A., Rademaker, A., Wieneke, C., Weintraub, S., Mesulam, M.M., Asymmetry of cortical decline in subtypes of primary progressive aphasia (2014) Neurology, 83, pp. 1184-1191; Vellas, B., Andrieu, S., Sampaio, C., Coley, N., Wilcock, G., Endpoints for trials in Alzheimer's disease:AEuropean task force consensus (2008) Lancet Neurol, 7, pp. 436-450; Wirth, M., Madison, C.M., Rabinovici, G.D., Oh, H., Landau, S.M., Jagust, W.J., Alzheimer's disease neurodegenerative biomarkers are associated with decreased cognitive function but not beta-amyloid in cognitively normal older individuals (2013) J Neurosci, 33, pp. 5553-5563; Dubois, B., Feldman, H.H., Jacova, C., Hampel, H., Molinuevo, J.L., Blennow, K., DeKosky, S.T., Cummings, J.L., Advancing research diagnostic criteria for Alzheimer's disease: The IWG-2 criteria (2014) Lancet Neurol, 13, pp. 614-629
PY - 2019
Y1 - 2019
N2 - Background: Assessment of human brain atrophy in temporal regions using magnetic resonance imaging (MRI), resting state functional MRI connectivity in the left parietal cortex, and limbic electroencephalographic (rsEEG) rhythms as well as plasma amyloid peptide 42 (Aβ42) has shown that each is a promising biomarker of disease progression in amnestic mild cognitive impairment (aMCI) patients with prodromal Alzheimer's disease (AD). However, the value of their combined use is unknown. Objective: To evaluate the association with cognitive decline and the effect on sample size calculation when using a biomarker composite matrix in prodromal AD clinical trials. Methods: Multicenter longitudinal study with follow-up of 2 years or until development of incident dementia. APOE ϵ4-specific cerebrospinal fluid (CSF) Aβ42 /P-tau cut-offs were used to identify aMCI with prodromal AD. Linear mixed models were performed 1) with repeated matrix values and time as factors to explain the longitudinal changes in ADAS-cog13, 2) with CSF Aβ42 /P-tau status, time, and CSF Aβ42 /P-tau status×time interaction as factors to explain the longitudinal changes in matrix measures, and 3) to compute sample size estimation for a trial implemented with the selected matrices. Results: The best composite matrix included the MRI volumes of hippocampal dentate gyrus and lateral ventricle. This matrix showed the best sensitivity to track disease progression and required a sample size 31% lower than that of the best individual biomarker (i.e., volume of hippocampal dentate gyrus). Conclusion: Optimal matrices improved the statistical power to track disease development and to measure clinical progression in the short-term period. This might contribute to optimize the design of future clinical trials in MCI. © 2019 - IOS Press and the authors. All rights reserved.
AB - Background: Assessment of human brain atrophy in temporal regions using magnetic resonance imaging (MRI), resting state functional MRI connectivity in the left parietal cortex, and limbic electroencephalographic (rsEEG) rhythms as well as plasma amyloid peptide 42 (Aβ42) has shown that each is a promising biomarker of disease progression in amnestic mild cognitive impairment (aMCI) patients with prodromal Alzheimer's disease (AD). However, the value of their combined use is unknown. Objective: To evaluate the association with cognitive decline and the effect on sample size calculation when using a biomarker composite matrix in prodromal AD clinical trials. Methods: Multicenter longitudinal study with follow-up of 2 years or until development of incident dementia. APOE ϵ4-specific cerebrospinal fluid (CSF) Aβ42 /P-tau cut-offs were used to identify aMCI with prodromal AD. Linear mixed models were performed 1) with repeated matrix values and time as factors to explain the longitudinal changes in ADAS-cog13, 2) with CSF Aβ42 /P-tau status, time, and CSF Aβ42 /P-tau status×time interaction as factors to explain the longitudinal changes in matrix measures, and 3) to compute sample size estimation for a trial implemented with the selected matrices. Results: The best composite matrix included the MRI volumes of hippocampal dentate gyrus and lateral ventricle. This matrix showed the best sensitivity to track disease progression and required a sample size 31% lower than that of the best individual biomarker (i.e., volume of hippocampal dentate gyrus). Conclusion: Optimal matrices improved the statistical power to track disease development and to measure clinical progression in the short-term period. This might contribute to optimize the design of future clinical trials in MCI. © 2019 - IOS Press and the authors. All rights reserved.
KW - Alzheimer's disease
KW - biomarker matrices
KW - clinical trial
KW - magnetic resonance imaging
KW - mild cognitive impairment
KW - amyloid beta protein[1-42]
KW - apolipoprotein E4
KW - biological marker
KW - tau protein
KW - aged
KW - Alzheimer disease
KW - Article
KW - brain size
KW - calculation
KW - cohort analysis
KW - controlled study
KW - dentate gyrus
KW - disease course
KW - disease exacerbation
KW - female
KW - follow up
KW - human
KW - lateral brain ventricle
KW - longitudinal study
KW - major clinical study
KW - male
KW - nuclear magnetic resonance imaging
KW - priority journal
KW - protein cerebrospinal fluid level
KW - sample size
KW - white matter lesion
U2 - 10.3233/JAD-181016
DO - 10.3233/JAD-181016
M3 - Article
VL - 69
SP - 49
EP - 58
JO - J. Alzheimer's Dis.
JF - J. Alzheimer's Dis.
SN - 1387-2877
IS - 1
ER -