Nienke M E Scheltens1, Francisca Galindo-Garre2, Yolande A L Pijnenburg1, Annelies E van der Vlies1, Lieke L Smits1, Teddy Koene3, Charlotte E Teunissen4, Frederik Barkhof5, Mike P Wattjes5, Philip Scheltens1, Wiesje M van der Flier6. 1. Department of Neurology, Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. 2. Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. 3. Department of Medical Psychology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. 4. Neurochemistry Laboratory and Biobank, Department of Clinical Chemistry, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. 5. Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. 6. Department of Neurology, Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Abstract
OBJECTIVE: Alzheimer's disease (AD) is a heterogeneous disorder with complex underlying neuropathology that is still not completely understood. For better understanding of this heterogeneity, we aimed to identify cognitive subtypes using latent class analysis (LCA) in a large sample of patients with AD dementia. In addition, we explored the relationship between the identified cognitive subtypes, and their demographical and neurobiological characteristics. METHODS: We performed LCA based on neuropsychological test results of 938 consecutive probable patients with AD dementia using Mini-Mental State Examination as the covariate. Subsequently, we performed multinomial logistic regression analysis with cluster membership as dependent variable and dichotomised demographics, APOE genotype, cerebrospinal fluid biomarkers and MRI characteristics as independent variables. RESULTS: LCA revealed eight clusters characterised by distinct cognitive profile and disease severity. Memory-impaired clusters-mild-memory (MILD-MEM) and moderate-memory (MOD-MEM)-included 43% of patients. Memory-spared clusters mild-visuospatial-language (MILD-VILA), mild-executive (MILD-EXE) and moderate-visuospatial (MOD-VISP) -included 29% of patients. Memory-indifferent clusters mild-diffuse (MILD-DIFF), moderate-language (MOD-LAN) and severe-diffuse (SEV-DIFF) -included 28% of patients. Cognitive clusters were associated with distinct demographical and neurobiological characteristics. In particular, the memory-spared MOD-VISP cluster was associated with younger age, APOE e4 negative genotype and prominent atrophy of the posterior cortex. CONCLUSIONS: Using LCA, we identified eight distinct cognitive subtypes in a large sample of patients with AD dementia. Cognitive clusters were associated with distinct demographical and neurobiological characteristics. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
OBJECTIVE:Alzheimer's disease (AD) is a heterogeneous disorder with complex underlying neuropathology that is still not completely understood. For better understanding of this heterogeneity, we aimed to identify cognitive subtypes using latent class analysis (LCA) in a large sample of patients with AD dementia. In addition, we explored the relationship between the identified cognitive subtypes, and their demographical and neurobiological characteristics. METHODS: We performed LCA based on neuropsychological test results of 938 consecutive probable patients with AD dementia using Mini-Mental State Examination as the covariate. Subsequently, we performed multinomial logistic regression analysis with cluster membership as dependent variable and dichotomised demographics, APOE genotype, cerebrospinal fluid biomarkers and MRI characteristics as independent variables. RESULTS: LCA revealed eight clusters characterised by distinct cognitive profile and disease severity. Memory-impaired clusters-mild-memory (MILD-MEM) and moderate-memory (MOD-MEM)-included 43% of patients. Memory-spared clusters mild-visuospatial-language (MILD-VILA), mild-executive (MILD-EXE) and moderate-visuospatial (MOD-VISP) -included 29% of patients. Memory-indifferent clusters mild-diffuse (MILD-DIFF), moderate-language (MOD-LAN) and severe-diffuse (SEV-DIFF) -included 28% of patients. Cognitive clusters were associated with distinct demographical and neurobiological characteristics. In particular, the memory-spared MOD-VISP cluster was associated with younger age, APOE e4 negative genotype and prominent atrophy of the posterior cortex. CONCLUSIONS: Using LCA, we identified eight distinct cognitive subtypes in a large sample of patients with AD dementia. Cognitive clusters were associated with distinct demographical and neurobiological characteristics. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
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