Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease

Moira Marizzoni, Clarissa Ferrari, Ambra Macis, Jorge Jovicich, Diego Albani, Claudio Babiloni, Libera Cavaliere, Mira Didic, Gianluigi Forloni, Samantha Galluzzi, Karl-Titus Hoffmann, José Luis Molinuevo, Flavio Nobili, Lucilla Parnetti, Pierre Payoux, Francesca Pizzini, Paolo Maria Rossini, Marco Salvatore, Peter Schönknecht, Andrea Soricelli & 10 others Claudio del Percio, Tilman Hensch, Ulrich Hegerl, Magda Tsolaki, Pieter Jelle Visser, Jens Wiltfang, Jill C. Richardson, R. gis Bordet, Olivier Blin, Giovanni B. Frisoni

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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 (Aβ 42 ) 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) Aβ 42 /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 Aβ 42 /P-tau status, time, and CSF Aβ 42 /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.
Original languageEnglish
Pages (from-to)49-58
JournalJournal of Alzheimer's Disease
Volume69
Issue number1
DOIs
Publication statusPublished - 2019

Cite this

Marizzoni, Moira ; Ferrari, Clarissa ; Macis, Ambra ; Jovicich, Jorge ; Albani, Diego ; Babiloni, Claudio ; Cavaliere, Libera ; Didic, Mira ; Forloni, Gianluigi ; Galluzzi, Samantha ; Hoffmann, Karl-Titus ; Molinuevo, José Luis ; Nobili, Flavio ; Parnetti, Lucilla ; Payoux, Pierre ; Pizzini, Francesca ; Rossini, Paolo Maria ; Salvatore, Marco ; Schönknecht, Peter ; Soricelli, Andrea ; del Percio, Claudio ; Hensch, Tilman ; Hegerl, Ulrich ; Tsolaki, Magda ; Visser, Pieter Jelle ; Wiltfang, Jens ; Richardson, Jill C. ; Bordet, R. gis ; Blin, Olivier ; Frisoni, Giovanni B. / Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease. In: Journal of Alzheimer's Disease. 2019 ; Vol. 69, No. 1. pp. 49-58.
@article{4db6119697284852bf77078323c2aa75,
title = "Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease",
abstract = "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 (Aβ 42 ) 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) Aβ 42 /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 Aβ 42 /P-tau status, time, and CSF Aβ 42 /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.",
author = "Moira Marizzoni and Clarissa Ferrari and Ambra Macis and Jorge Jovicich and Diego Albani and Claudio Babiloni and Libera Cavaliere and Mira Didic and Gianluigi Forloni and Samantha Galluzzi and Karl-Titus Hoffmann and Molinuevo, {Jos{\'e} Luis} and Flavio Nobili and Lucilla Parnetti and Pierre Payoux and Francesca Pizzini and Rossini, {Paolo Maria} and Marco Salvatore and Peter Sch{\"o}nknecht and Andrea Soricelli and {del Percio}, Claudio and Tilman Hensch and Ulrich Hegerl and Magda Tsolaki and Visser, {Pieter Jelle} and Jens Wiltfang and Richardson, {Jill C.} and Bordet, {R. gis} and Olivier Blin and Frisoni, {Giovanni B.}",
year = "2019",
doi = "10.3233/JAD-181016",
language = "English",
volume = "69",
pages = "49--58",
journal = "Journal of Alzheimer's Disease",
issn = "1387-2877",
publisher = "IOS Press",
number = "1",

}

Marizzoni, M, Ferrari, C, Macis, A, Jovicich, J, Albani, D, Babiloni, C, Cavaliere, L, Didic, M, Forloni, G, Galluzzi, S, Hoffmann, K-T, Molinuevo, JL, Nobili, F, Parnetti, L, Payoux, P, Pizzini, F, Rossini, PM, Salvatore, M, Schönknecht, P, Soricelli, A, del Percio, C, Hensch, T, Hegerl, U, Tsolaki, M, Visser, PJ, Wiltfang, J, Richardson, JC, Bordet, RG, Blin, O & Frisoni, GB 2019, 'Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease' Journal of Alzheimer's Disease, vol. 69, no. 1, pp. 49-58. https://doi.org/10.3233/JAD-181016

Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease. / Marizzoni, Moira; Ferrari, Clarissa; Macis, Ambra; Jovicich, Jorge; Albani, Diego; Babiloni, Claudio; Cavaliere, Libera; Didic, Mira; Forloni, Gianluigi; Galluzzi, Samantha; Hoffmann, Karl-Titus; Molinuevo, José Luis; Nobili, Flavio; Parnetti, Lucilla; Payoux, Pierre; Pizzini, Francesca; Rossini, Paolo Maria; Salvatore, Marco; Schönknecht, Peter; Soricelli, Andrea; del Percio, Claudio; Hensch, Tilman; Hegerl, Ulrich; Tsolaki, Magda; Visser, Pieter Jelle; Wiltfang, Jens; Richardson, Jill C.; Bordet, R. gis; Blin, Olivier; Frisoni, Giovanni B.

In: Journal of Alzheimer's Disease, Vol. 69, No. 1, 2019, p. 49-58.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Biomarker matrix to track short term disease progression in amnestic mild cognitive impairment patients with prodromal Alzheimer's disease

AU - Marizzoni, Moira

AU - Ferrari, Clarissa

AU - Macis, Ambra

AU - Jovicich, Jorge

AU - Albani, Diego

AU - Babiloni, Claudio

AU - Cavaliere, Libera

AU - Didic, Mira

AU - Forloni, Gianluigi

AU - Galluzzi, Samantha

AU - Hoffmann, Karl-Titus

AU - Molinuevo, José Luis

AU - Nobili, Flavio

AU - Parnetti, Lucilla

AU - Payoux, Pierre

AU - Pizzini, Francesca

AU - Rossini, Paolo Maria

AU - Salvatore, Marco

AU - Schönknecht, Peter

AU - Soricelli, Andrea

AU - del Percio, Claudio

AU - Hensch, Tilman

AU - Hegerl, Ulrich

AU - Tsolaki, Magda

AU - Visser, Pieter Jelle

AU - Wiltfang, Jens

AU - Richardson, Jill C.

AU - Bordet, R. gis

AU - Blin, Olivier

AU - Frisoni, Giovanni B.

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 (Aβ 42 ) 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) Aβ 42 /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 Aβ 42 /P-tau status, time, and CSF Aβ 42 /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.

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 (Aβ 42 ) 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) Aβ 42 /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 Aβ 42 /P-tau status, time, and CSF Aβ 42 /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.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065677389&origin=inward

UR - https://www.ncbi.nlm.nih.gov/pubmed/30958351

U2 - 10.3233/JAD-181016

DO - 10.3233/JAD-181016

M3 - Article

VL - 69

SP - 49

EP - 58

JO - Journal of Alzheimer's Disease

JF - Journal of Alzheimer's Disease

SN - 1387-2877

IS - 1

ER -