TY - JOUR
T1 - Cognitive subtypes of probable Alzheimer's disease robustly identified in four cohorts
AU - Alzheimer's Disease Neuroimaging Initiative
AU - German Dementia Competence Network
AU - University of California San Francisco Memory and Aging Center
AU - Amsterdam Dementia Cohort
AU - Scheltens, Nienke M.E.
AU - Tijms, Betty M.
AU - Koene, Teddy
AU - Barkhof, Frederik
AU - Teunissen, Charlotte E.
AU - Wolfsgruber, Steffen
AU - Wagner, Michael
AU - Kornhuber, Johannes
AU - Peters, Oliver
AU - Cohn-Sheehy, Brendan I.
AU - Rabinovici, Gil D.
AU - Miller, Bruce L.
AU - Kramer, Joel H.
AU - Scheltens, Philip
AU - van der Flier, Wiesje M.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Introduction Patients with Alzheimer's disease (AD) show heterogeneity in profile of cognitive impairment. We aimed to identify cognitive subtypes in four large AD cohorts using a data-driven clustering approach. Methods We included probable AD dementia patients from the Amsterdam Dementia Cohort (n = 496), Alzheimer's Disease Neuroimaging Initiative (n = 376), German Dementia Competence Network (n = 521), and University of California, San Francisco (n = 589). Neuropsychological data were clustered using nonnegative matrix factorization. We explored clinical and neurobiological characteristics of identified clusters. Results In each cohort, a two-clusters solution best fitted the data (cophenetic correlation >0.9): one cluster was memory-impaired and the other relatively memory spared. Pooled analyses showed that the memory-spared clusters (29%–52% of patients) were younger, more often apolipoprotein E (APOE) ɛ4 negative, and had more severe posterior atrophy compared with the memory-impaired clusters (all P <.05). Conclusions We could identify two robust cognitive clusters in four independent large cohorts with distinct clinical characteristics.
AB - Introduction Patients with Alzheimer's disease (AD) show heterogeneity in profile of cognitive impairment. We aimed to identify cognitive subtypes in four large AD cohorts using a data-driven clustering approach. Methods We included probable AD dementia patients from the Amsterdam Dementia Cohort (n = 496), Alzheimer's Disease Neuroimaging Initiative (n = 376), German Dementia Competence Network (n = 521), and University of California, San Francisco (n = 589). Neuropsychological data were clustered using nonnegative matrix factorization. We explored clinical and neurobiological characteristics of identified clusters. Results In each cohort, a two-clusters solution best fitted the data (cophenetic correlation >0.9): one cluster was memory-impaired and the other relatively memory spared. Pooled analyses showed that the memory-spared clusters (29%–52% of patients) were younger, more often apolipoprotein E (APOE) ɛ4 negative, and had more severe posterior atrophy compared with the memory-impaired clusters (all P <.05). Conclusions We could identify two robust cognitive clusters in four independent large cohorts with distinct clinical characteristics.
KW - Alzheimer's disease
KW - Atypical
KW - Cognition
KW - Heterogeneity
KW - Neuropsychology
KW - Subtypes
UR - http://www.scopus.com/inward/record.url?scp=85019159802&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.jalz.2017.03.002
DO - https://doi.org/10.1016/j.jalz.2017.03.002
M3 - Article
C2 - 28427934
SN - 1552-5260
VL - 13
SP - 1226
EP - 1236
JO - Alzheimers & Dementia
JF - Alzheimers & Dementia
IS - 11
ER -