Identification of prognostic factors to predict cognitive decline of patients with early Alzheimer's disease in the Japanese Alzheimer's Disease Neuroimaging Initiative study - Takuya Yagi; Michio Kanekiyo; Junichi Ito; Ryoko Ihara; Kazushi Suzuki; Atsushi Iwata; Takeshi Iwatsubo; Ken Aoshima; Alzheimer's Disease Neuroimaging Initiative; Japanese Alzheimer's Disease Neuroimaging Initiative (Alzheimer’s & Dementia: Translational Research & Clinical Interventions)
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.