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
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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.; Jovicich, J.; Albani, D.; Babiloni, C.; Cavaliere, L.; Didic, M.; Forloni, G.; Galluzzi, S.; Hoffmann, K.-T.; Molinuevo, J.L.; Nobili, F.; Parnetti, L.; Payoux, P.; Pizzini, F.; Rossini, P.M.; Salvatore, M.; Schönknecht, P.; Soricelli, A.; Del Percio, C.; Hensch, T.; Hegerl, U.; Tsolaki, M.; Visser, P.J.; Wiltfang, J.; Richardson, J.C.; Bordet, R.; Blin, O.; Frisoni, G.B.
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. 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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 -