Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults

A Latent Class Analysis of the Einstein Aging Study

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4 Citations (Scopus)

Abstract

Objectives: The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup’s characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. Methods: We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. Results: The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. Conclusions: LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer’s disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 23, 1–13)

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalJournal of the International Neuropsychological Society
DOIs
StateAccepted/In press - Jan 10 2018

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Independent Living
Vascular Dementia
Sex Education
Episodic Memory
Executive Function
Vulnerable Populations
Short-Term Memory
Dementia
Comorbidity
Alzheimer Disease
Cohort Studies
Language
Biomarkers
Demography
Education
Cognitive Dysfunction

Keywords

  • Cognitive aging
  • Cognitive heterogeneity
  • Cognitive subtypes
  • Individual differences

ASJC Scopus subject areas

  • Neuroscience(all)
  • Clinical Psychology
  • Clinical Neurology
  • Psychiatry and Mental health

Cite this

@article{40e815b048a5423cad13599010aeb475,
title = "Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults: A Latent Class Analysis of the Einstein Aging Study",
abstract = "Objectives: The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup’s characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. Methods: We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. Results: The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. Conclusions: LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer’s disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 23, 1–13)",
keywords = "Cognitive aging, Cognitive heterogeneity, Cognitive subtypes, Individual differences",
author = "Zammit, {Andrea R.} and Hall, {Charles B.} and Lipton, {Richard B.} and Katz, {Mindy Joy} and Graciela Muniz-Terrera",
year = "2018",
month = "1",
day = "10",
doi = "10.1017/S135561771700128X",
language = "English (US)",
pages = "1--13",
journal = "Journal of the International Neuropsychological Society",
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T1 - Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults

T2 - A Latent Class Analysis of the Einstein Aging Study

AU - Zammit, Andrea R.

AU - Hall, Charles B.

AU - Lipton, Richard B.

AU - Katz, Mindy Joy

AU - Muniz-Terrera, Graciela

PY - 2018/1/10

Y1 - 2018/1/10

N2 - Objectives: The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup’s characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. Methods: We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. Results: The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. Conclusions: LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer’s disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 23, 1–13)

AB - Objectives: The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup’s characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. Methods: We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. Results: The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. Conclusions: LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer’s disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 23, 1–13)

KW - Cognitive aging

KW - Cognitive heterogeneity

KW - Cognitive subtypes

KW - Individual differences

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