Latent Classes of Cognitive Functioning among Depressed Older Adults Without Dementia
(2019) In Journal of the International Neuropsychological Society 25(8). p.811-820- Abstract
Objective:Use latent class analysis (LCA) to identify patterns of cognitive functioning in a sample of older adults with clinical depression and without dementia and assess demographic, psychiatric, and neurobiological predictors of class membership.Method:Neuropsychological assessment data from 121 participants in the Alzheimer's Disease Neuroimaging Initiative-Depression project (ADNI-D) were analyzed, including measures of executive functioning, verbal and visual memory, visuospatial and language functioning, and processing speed. These data were analyzed using LCA, with predictors of class membership such as depression severity, depression and treatment history, amyloid burden, and APOE e4 allele also assessed.Results:A two-class... (More)
Objective:Use latent class analysis (LCA) to identify patterns of cognitive functioning in a sample of older adults with clinical depression and without dementia and assess demographic, psychiatric, and neurobiological predictors of class membership.Method:Neuropsychological assessment data from 121 participants in the Alzheimer's Disease Neuroimaging Initiative-Depression project (ADNI-D) were analyzed, including measures of executive functioning, verbal and visual memory, visuospatial and language functioning, and processing speed. These data were analyzed using LCA, with predictors of class membership such as depression severity, depression and treatment history, amyloid burden, and APOE e4 allele also assessed.Results:A two-class model of cognitive functioning best fit the data, with the Lower Cognitive Class (46.1% of the sample) performing approximately one standard deviation below the Higher Cognitive Class (53.9%) on most tests. When predictors of class membership were assessed, carrying an APOE e4 allele was significantly associated with membership in the Lower Cognitive Class. Demographic characteristics, age of depression onset, depression severity, history of psychopharmacological treatment for depression, and amyloid positivity did not predict class membership.Conclusion:LCA allows for identification of subgroups of cognitive functioning in a mostly cognitively intact late life depression (LLD) population. One subgroup, the Lower Cognitive Class, more likely to carry an APOE e4 allele, may be at a greater risk for subsequent cognitive decline, even though current performance on neuropsychological testing is within normal limits. These findings have implications for early identification of those at greatest risk, risk factors, and avenues for preventive intervention.
(Less)
- author
- Morin, Ruth T. ; Insel, Philip LU ; Nelson, Craig ; Butters, Meryl ; Bickford, David ; Landau, Susan ; Saykin, Andrew ; Weiner, Michael and Mackin, R. Scott
- organization
- publishing date
- 2019-06-24
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Aging, Cognitive functioning, Late life depression, Latent class analysis, Major depression, Neuropsychology
- in
- Journal of the International Neuropsychological Society
- volume
- 25
- issue
- 8
- pages
- 811 - 820
- publisher
- Cambridge University Press
- external identifiers
-
- scopus:85067987808
- pmid:31232250
- ISSN
- 1355-6177
- DOI
- 10.1017/S1355617719000596
- language
- English
- LU publication?
- yes
- id
- 1aadd23b-45b1-4521-8b67-9562dd74c7c2
- date added to LUP
- 2019-07-10 12:41:33
- date last changed
- 2024-04-16 16:13:46
@article{1aadd23b-45b1-4521-8b67-9562dd74c7c2,
abstract = {{<p>Objective:Use latent class analysis (LCA) to identify patterns of cognitive functioning in a sample of older adults with clinical depression and without dementia and assess demographic, psychiatric, and neurobiological predictors of class membership.Method:Neuropsychological assessment data from 121 participants in the Alzheimer's Disease Neuroimaging Initiative-Depression project (ADNI-D) were analyzed, including measures of executive functioning, verbal and visual memory, visuospatial and language functioning, and processing speed. These data were analyzed using LCA, with predictors of class membership such as depression severity, depression and treatment history, amyloid burden, and APOE e4 allele also assessed.Results:A two-class model of cognitive functioning best fit the data, with the Lower Cognitive Class (46.1% of the sample) performing approximately one standard deviation below the Higher Cognitive Class (53.9%) on most tests. When predictors of class membership were assessed, carrying an APOE e4 allele was significantly associated with membership in the Lower Cognitive Class. Demographic characteristics, age of depression onset, depression severity, history of psychopharmacological treatment for depression, and amyloid positivity did not predict class membership.Conclusion:LCA allows for identification of subgroups of cognitive functioning in a mostly cognitively intact late life depression (LLD) population. One subgroup, the Lower Cognitive Class, more likely to carry an APOE e4 allele, may be at a greater risk for subsequent cognitive decline, even though current performance on neuropsychological testing is within normal limits. These findings have implications for early identification of those at greatest risk, risk factors, and avenues for preventive intervention.</p>}},
author = {{Morin, Ruth T. and Insel, Philip and Nelson, Craig and Butters, Meryl and Bickford, David and Landau, Susan and Saykin, Andrew and Weiner, Michael and Mackin, R. Scott}},
issn = {{1355-6177}},
keywords = {{Aging; Cognitive functioning; Late life depression; Latent class analysis; Major depression; Neuropsychology}},
language = {{eng}},
month = {{06}},
number = {{8}},
pages = {{811--820}},
publisher = {{Cambridge University Press}},
series = {{Journal of the International Neuropsychological Society}},
title = {{Latent Classes of Cognitive Functioning among Depressed Older Adults Without Dementia}},
url = {{http://dx.doi.org/10.1017/S1355617719000596}},
doi = {{10.1017/S1355617719000596}},
volume = {{25}},
year = {{2019}},
}