Article Text

other Versions

PDF
Research paper
The identification of cognitive subtypes in Alzheimer's disease dementia using latent class analysis
  1. Nienke M E Scheltens1,
  2. Francisca Galindo-Garre2,
  3. Yolande A L Pijnenburg1,
  4. Annelies E van der Vlies1,
  5. Lieke L Smits1,
  6. Teddy Koene3,
  7. Charlotte E Teunissen4,
  8. Frederik Barkhof5,
  9. Mike P Wattjes5,
  10. Philip Scheltens1,
  11. Wiesje M van der Flier1,2
  1. 1Department of Neurology, Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
  2. 2Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
  3. 3Department of Medical Psychology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
  4. 4Neurochemistry Laboratory and Biobank, Department of Clinical Chemistry, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
  5. 5Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
  1. Correspondence to Nienke Scheltens, Department of Neurology, Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, P.O. Box 7057, Amsterdam 1007 MB, the Netherlands; n.scheltens{at}vumc.nl

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.

  • ALZHEIMER'S DISEASE
  • COGNITION
  • COGNITIVE NEUROPSYCHOLOGY
  • DEMENTIA
  • STATISTICS

Statistics from Altmetric.com

Request permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.