TY - JOUR
T1 - Parsing heterogeneity within dementia with Lewy bodies using clustering of biological, clinical, and demographic data
AU - Abdelnour, Carla
AU - Ferreira, Daniel
AU - van de Beek, Marleen
AU - Cedres, Nira
AU - Oppedal, Ketil
AU - Cavallin, Lena
AU - Blanc, Frédéric
AU - Bousiges, Olivier
AU - Wahlund, Lars-Olof
AU - Pilotto, Andrea
AU - Padovani, Alessandro
AU - Boada, Mercè
AU - Pagonabarraga, Javier
AU - Kulisevsky, Jaime
AU - Aarsland, Dag
AU - Lemstra, Afina W.
AU - Westman, Eric
N1 - Funding Information: This study was supported by The Swedish Foundation for Strategic Research (SSF), The Swedish Research Council (VR), Center for Innovative Medicine (CIMED), The Foundation for Geriatric Diseases at Karolinska Institutet, Research Funding at Karolinska Institutet, Swedish Dementia Funding, The Strategic Research Programme in Neuroscience at Karolinska Institutet (StratNeuro), The Åke Wiberg foundation, Swedish Brain Funding, Swedish Alzheimer's Funding, Stiftelsen Olle Engkvist Byggmästare, Birgitta och Sten Westerberg, Stiftelsen För Gamla Tjänarinnor, Gun och Bertil Stohnes Stiftelse, and The Regional Agreement on Medical Training and Clinical Research (ALF) between the Stockholm County Council and Karolinska Institutet. Funding Information: The authors are thankful to all members of the E-DLB consortium. This research study was performed as part of the Medicine Doctoral Program of Carla Abdelnour at Universitat Aut?noma de Barcelona (Barcelona, Spain). Publisher Copyright: © 2022, The Author(s).
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Background: Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer’s disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features. Methods: We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions. Results: We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-β and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores. Conclusions: This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials.
AB - Background: Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer’s disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features. Methods: We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions. Results: We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-β and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores. Conclusions: This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials.
KW - Alzheimer’s disease
KW - Biomarkers
KW - Dementia with Lewy bodies
KW - Factorial analysis
KW - Heterogeneity
KW - Hierarchical clustering
UR - http://www.scopus.com/inward/record.url?scp=85123443437&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13195-021-00946-w
DO - https://doi.org/10.1186/s13195-021-00946-w
M3 - Article
C2 - 35063023
SN - 1758-9193
VL - 14
JO - Alzheimer's Research and Therapy
JF - Alzheimer's Research and Therapy
IS - 1
M1 - 14
ER -