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Latent Classes of Cognitive Functioning Among Depressed Older Adults Without Dementia

Published online by Cambridge University Press:  24 June 2019

Ruth T. Morin Open the ORCID record for Ruth T. Morin [Opens in a new window] ,
Philip Insel ,
Craig Nelson ,
Meryl Butters ,
David Bickford ,
Susan Landau ,
Andrew Saykin Open the ORCID record for Andrew Saykin [Opens in a new window] ,
Michael Weiner ,
R. Scott Mackin  and
the ADNI Depression Project
Ruth T. Morin*
Affiliation:
San Francisco VA Medical Center, Mental Health Service, San Francisco, USA
Philip Insel
Affiliation:
San Francisco VA Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, USA Lund University, Faculty of Medicine, Clinical Memory Research Unit, Lund, Sweden
Craig Nelson
Affiliation:
University of California San Francisco, Department of Psychiatry, San Francisco, USA
Meryl Butters
Affiliation:
University of Pittsburgh, Department of Psychiatry, Pittsburgh, Pennsylvania
David Bickford
Affiliation:
University of California San Francisco, Department of Psychiatry, San Francisco, USA
Susan Landau
Affiliation:
University of California Berkeley, Helen Wills Neuroscience Institute, Berkeley, USA
Andrew Saykin
Affiliation:
Indiana University School of Medicine, Department of Radiology and Imaging Sciences, Indianapolis, USA
Michael Weiner
Affiliation:
University of California San Francisco, Department of Psychiatry, San Francisco, USA
R. Scott Mackin
Affiliation:
San Francisco VA Medical Center, Mental Health Service, San Francisco, USA San Francisco VA Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, USA University of California San Francisco, Department of Psychiatry, San Francisco, USA
the ADNI Depression Project
Affiliation:
San Francisco VA Medical Center, Mental Health Service, San Francisco, USA San Francisco VA Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, USA Lund University, Faculty of Medicine, Clinical Memory Research Unit, Lund, Sweden University of California San Francisco, Department of Psychiatry, San Francisco, USA University of Pittsburgh, Department of Psychiatry, Pittsburgh, Pennsylvania University of California Berkeley, Helen Wills Neuroscience Institute, Berkeley, USA Indiana University School of Medicine, Department of Radiology and Imaging Sciences, Indianapolis, USA
*
*Correspondence and reprint requests to: Ruth T. Morin, San Francisco VA Medical Center, 4150 Clement Street, Mental Health Service, San Francisco, CA 94121. E-mail: [email protected]; (415) 221-4810 x23114.
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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 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.

Keywords

Late life depressionCognitive functioningLatent class analysisAgingNeuropsychologyMajor depression
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 25 , Issue 8 , September 2019 , pp. 811 - 820
Copyright
Copyright © INS. Published by Cambridge University Press, 2019. 

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