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 SoricelliClaudio 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

8 Citations (Scopus)


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
Number of pages10
JournalJournal of Alzheimer's Disease
Issue number1
Publication statusPublished - 1 Jan 2019


  • Alzheimer's disease
  • biomarker matrices
  • clinical trial
  • magnetic resonance imaging
  • mild cognitive impairment

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