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Statistically Derived Subtypes and Associations with Cerebrospinal Fluid and Genetic Biomarkers in Mild Cognitive Impairment: A Latent Profile Analysis

Published online by Cambridge University Press:  05 June 2017

Joel S. Eppig ,
Emily C. Edmonds ,
Laura Campbell ,
Mark Sanderson-Cimino ,
Lisa Delano-Wood ,
Mark W. Bondi  and
for the Alzheimer’s Disease Neuroimaging Initiative
Joel S. Eppig
Affiliation:
San Diego State University/University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego, California
Emily C. Edmonds
Affiliation:
Department of Psychiatry, University of California San Diego, School of Medicine, La Jolla, California Veterans Affairs San Diego Healthcare System, San Diego, California
Laura Campbell
Affiliation:
Veterans Affairs San Diego Healthcare System, San Diego, California
Mark Sanderson-Cimino
Affiliation:
San Diego State University/University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego, California
Lisa Delano-Wood
Affiliation:
Department of Psychiatry, University of California San Diego, School of Medicine, La Jolla, California Veterans Affairs San Diego Healthcare System, San Diego, California
Mark W. Bondi*
Affiliation:
Department of Psychiatry, University of California San Diego, School of Medicine, La Jolla, California Veterans Affairs San Diego Healthcare System, San Diego, California
*
Correspondence and reprint requests to: Mark W. Bondi, VA San Diego Healthcare System (116B), 3350 La Jolla Village Drive, San Diego, CA 92161. E-mail: mbondi@ucsd.edu
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Abstract

Objectives: Research demonstrates heterogeneous neuropsychological profiles among individuals with mild cognitive impairment (MCI). However, few studies have included visuoconstructional ability or used latent mixture modeling to statistically identify MCI subtypes. Therefore, we examined whether unique neuropsychological MCI profiles could be ascertained using latent profile analysis (LPA), and subsequently investigated cerebrospinal fluid (CSF) biomarkers, genotype, and longitudinal clinical outcomes between the empirically derived classes. Methods: A total of 806 participants diagnosed by means of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) MCI criteria received a comprehensive neuropsychological battery assessing visuoconstructional ability, language, attention/executive function, and episodic memory. Test scores were adjusted for demographic characteristics using standardized regression coefficients based on “robust” normal control performance (n=260). Calculated Z-scores were subsequently used in the LPA, and CSF-derived biomarkers, genotype, and longitudinal clinical outcome were evaluated between the LPA-derived MCI classes. Results: Statistical fit indices suggested a 3-class model was the optimal LPA solution. The three-class LPA consisted of a mixed impairment MCI class (n=106), an amnestic MCI class (n=455), and an LPA-derived normal class (n=245). Additionally, the amnestic and mixed classes were more likely to be apolipoprotein e4+ and have worse Alzheimer’s disease CSF biomarkers than LPA-derived normal subjects. Conclusions: Our study supports significant heterogeneity in MCI neuropsychological profiles using LPA and extends prior work (Edmonds et al., 2015) by demonstrating a lower rate of progression in the approximately one-third of ADNI MCI individuals who may represent “false-positive” diagnoses. Our results underscore the importance of using sensitive, actuarial methods for diagnosing MCI, as current diagnostic methods may be over-inclusive. (JINS, 2017, 23, 564–576)

Keywords

Mild cognitive impairment (MCI)Assessment of cognitive disorders/dementiaAlzheimer’s diseaseNeuropsychological profilesMultivariate mixture modelingLatent profile analysisBiomarkers
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 23 , Issue 7 , August 2017 , pp. 564 - 576
Copyright
Copyright © The International Neuropsychological Society 2017 

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Footnotes

*

Data used in preparation of this article were obtained from the ADNI database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in analysis or writing of this article. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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