Identification of prognostic factors to predict cognitive decline of patients with early Alzheimer's disease in the Japanese Alzheimer's Disease Neuroimaging Initiative study. Issue 1 (7th August 2019)

• Record Type: Journal Article
• Authors: Yagi, Takuya; Kanekiyo, Michio; Ito, Junichi; Ihara, Ryoko; Suzuki, Kazushi; Iwata, Atsushi; Iwatsubo, Takeshi; Aoshima, Ken
• Is Part Of: Alzheimer's & dementia. Volume 5:Issue 1(2019)
• Page Start: 364
• Page End: 373
• Publication Date: 2019-08-07
• Subjects: J‐ADNI -- ADNI -- Alzheimer's disease assessment scale -- Mini‐Mental State Examination -- The clinical dementia rating -- Biomarker -- Amyloid PET imaging; Dementia -- Periodicals; Dementia -- Treatment -- Periodicals; Alzheimer's disease -- Treatment -- Periodicals; Alzheimer's disease -- Periodicals; 616.831
• DOI: 10.1016/j.trci.2019.06.004
• Languages: English
• ISSNs: 2352-8737

Abstract

Abstract: Introduction: The objective of this study was to determine the factors including neuropsychological test performances and cerebrospinal fluid (CSF) biomarkers which can predict disease progression of early Alzheimer's disease (AD) in a Japanese population. Methods: The group classification on early AD population in both Japanese Alzheimer's Disease Neuroimaging Initiative (J‐ADNI) and North American ADNI (NA‐ADNI) was performed using the inclusion criteria including brain amyloid positivity on positron emission tomography or CSF. Participants with early AD from each cohort were stratified into two groups based on a cutoff 1.0 of Clinical Dementia Rating Scale Sum of Boxes (CDR‐SB) change at month 24 (m24): participants in "progress group" have CDR‐SB change ≥ 1.0 and participants in "stable group" have CDR‐SB change < 1.0. Then, we performed identification of prognostic factors from baseline items including neuropsychological scores (Assessment Scale‐Cognitive Subscale[ADAS‐cog 13], Mini‐Mental State Examination (MMSE), CDR, FAQ, and Geriatric Depression Scale), CSF markers (t‐tau, p‐tau, and beta‐amyloid 1‐42), vital signs (body weight, pulse rate, etc., ), by using two statistical approaches, Welch's t‐test and simple linear regression by ordinary least squares. Comparisons between participants with J‐ADNI and participants with NA‐ADNI were also performed. Results: Trends of CDR‐SB changes were very similar between J‐ADNI and NA‐ADNI early AD population enrolled in this study. Baseline levels of CSF t‐tau, p‐tau, Mini‐Mental State Examination, FAQ, and ADAS‐cog13 were identified as prognostic factors in both J‐ADNI and NA‐ADNI. Based on a detailed subscale analysis on ADAS‐cog13, four subscales (Q1: word recall, Q3: construction, Q4: delayed word recall, and Q8: word recognition) were identified as prognostic factors in both J‐ADNI and NA‐ADNI. Discussion: Characterizing population with early AD can provide benefits for promoting efficiency in conducting AD clinical trials for disease‐modifying treatments. Thus, implementing these prognostic factors into clinical trials may be potentially a good method to enrich participants with early AD who are suitable for evaluating treatment effects.