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Blanken AE, Jang JY, Ho JK, et al. Distilling Heterogeneity of Mild Cognitive Impairment in the National Alzheimer Coordinating Center Database Using Latent Profile Analysis. JAMA Netw Open. 2020;3(3):e200413. doi:10.1001/jamanetworkopen.2020.0413
Clinical trials aimed at improving mild cognitive impairment (MCI) and preventing dementia increasingly focus on biomarkers associated with risk, yet risk varies among individuals with MCI who have biomarker anomalies.1 Accurate cognitive phenotyping of MCI may improve prognostic accuracy. Recent studies have applied cluster analyses to neuropsychological data, identifying MCI cognitive endophenotypes.2-5 Observations across cohorts consistently identify cluster-derived mixed or dysnomic, dysexecutive, and amnestic groups, and a subgroup of individuals with MCI showing intact neuropsychological performance and attenuated dementia risk.2-5
Latent profile analysis (LPA) is a flexible, person-centered, and model-based clustering technique that enables probabilistic identification of neuropsychologically defined MCI subgroups. This cohort study aimed to extend prior work by applying LPA to a large, heterogeneous sample from the National Alzheimer Coordinating Center (NACC) database to delineate data-driven subgroups of persons with clinically diagnosed MCI and determine differential long-term risk for all-cause dementia.
The NACC is a standardized, large-scale data set with clinical and neuropathological data from 39 Alzheimer Disease Centers. Each NACC site’s institutional review board reviewed and approved the study protocol before it could contribute data. Participants provided written informed consent according to Alzheimer Disease Centers Institutional Review Boards and completed semiannual physician and neurologic examination, medical history, and neuropsychological testing. Per NACC guidelines, an experienced clinician or consensus conference determined cognitive status. This cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Baseline data were analyzed from 5155 participants with MCI and 12 490 participants who were cognitively unimpaired; participants were older than 60 years, had at least 1 follow-up visit between September 1, 2005, and March 15, 2019, and had results for at least 3 neuropsychological tests. Neuropsychological tests covering 3 cognitive domains were chosen, including memory (measured using the Logical Memory tests I and II), executive function (measured using the Trail Making Test parts A and B), and language (measured using the Animal Fluency Test and 30-item Boston Naming Test).
Analyses used Mplus version 8 (Muthén & Muthén) and SPSS version 24 (IBM Corp). We derived LPA clusters using raw neuropsychological scores. Participants who were cognitively unimpaired were included for comparison. Missing data were handled using missing-at-random maximum likelihood estimation. Akaike information criterion and bayesian information criterion were used to determine the optimal number of groups. The bootstrapped parametric likelihood ratio test compared models with 2 to 5 groups, based on previous literature.2,4,5 Cox regression and mean annual conversion rate (ACR) compared groups on rate of progression to dementia. Covariates included age, sex, self-reported race/ethnicity, and education. P values were 2-tailed, and statistical significance was set at .05. Data analyses were conducted from May 1, 2019, to August 21, 2019.
Data from 12 490 participants who were cognitively unimpaired (mean [SD] age, 73.2 [8.2] years; 5171 [41.2%] men; mean [SD] education, 15.3 [3.3] years) and 5155 participants with MCI (mean [SD] age, 75.8 [15.3] years; 2643 [51.3%] men; mean [SD] education, 15.3 [3.4] years) were analyzed. Fit criteria supported a 4-group solution. Our LPA revealed mixed dysexecutive and dysnomic (214 participants [4.2%]), dysexecutive (947 participants [18.4%]), and amnestic (2231 participants [43.3%]) MCI subgroups and a large, statistically determined neuropsychologically intact subgroup (1763 participants [34.2%]). The Table displays mean z scores and demographic characteristics. Notably, compared with other groups, the mixed MCI subgroup had the oldest mean (SD) age (amnestic: 76.1 [7.7] years; dysexecutive: 78.0 [8.3] years; neuropsychologically intact: 73.8 [7.8] years; cognitively unimpaired: 73.2 [8.2] years; mixed: 78.6 [8.5] years) and lowest mean (SD) years of education (amnestic: 15.5 [3.1] years; dysexecutive: 13.8 [3.7] years; neuropsychologically intact: 16.3 [2.8] years; cognitively unimpaired: 15.3 [3.3] years; mixed: 11.9 [4.6] years). Cox regression models (Figure) revealed significantly higher dementia risk among amnestic (hazard ratio [HR], 18.0 [95% CI, 15.2-20.0]; P < .001), dysexecutive (HR, 23.6 [95% CI, 20.7-26.9]; P < .001), and mixed (HR, 27.0 [95% CI, 21.9-33.4]; P < .001) MCI subgroups compared with the neuropsychologically intact subgroup (HR, 6.5 [95% CI, 5.7-7.4]; P < .001). Participants who were cognitively unimpaired demonstrated lower dementia risk (ACR, 1.6% [95% CI, 1.6%-1.6%]) than participants in the MCI subgroups (amnestic: ACR, 7.1% [95% CI, 6.9%-7.4%]; dysexecutive: ACR, 6.8% [95% CI, 6.3%-7.3%]; mixed: ACR, 6.7% [95% CI, 5.9%-7.8%]; neuropsychologically intact: ACR, 4.2% [95% CI, 4.1%-4.3%]; P < .001).
This cohort study found that the mixed and dysexecutive MCI subgroups were at the greatest risk for dementia, and the neuropsychologically intact MCI subgroup was at lowest risk despite showing a higher ACR than the cognitively unimpaired control group. The population MCI-to-dementia ACR is estimated to be between 5% and 10%,6 consistent with the ACRs we found for the mixed, dysexecutive, and amnestic MCI subgroups (6.7%-7.1%) but higher than the neuropsychologically intact MCI subgroup (4.2%). Notably, the neuropsychologically intact MCI subgroup constituted greater than one-third of the MCI sample, as observed in prior studies.2,4 Although neuropsychologically intact MCI may include persons with noncognitive (eg, neuropsychiatric) symptoms, many of these individuals may represent diagnostic errors contributing to MCI heterogeneity. Differences in demographic characteristics, symptom severity (eg, late vs early MCI), and symptom fluctuation may also contribute to differential dementia risk.2-5 Our findings extend prior work by demonstrating consistency of MCI phenotypes in a large, heterogeneous sample of participants with long-term follow-up.2-5 Our study has some limitations, such as that the cross-sectional examination of neuropsychological performance precludes us from making any conclusions about group differences in Alzheimer disease progression or phenotypic stability. Future research that includes biomarker data could improve characterization of dementia risk in MCI subgroups.2
Accepted for Publication: January 13, 2020.
Published: March 6, 2020. doi:10.1001/jamanetworkopen.2020.0413
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Blanken AE et al. JAMA Network Open.
Corresponding Author: Daniel A. Nation, PhD, Department of Psychological Science, University of California, Irvine, 4201 Social & Behavioral Sciences Gateway, Irvine, CA 92697-7085 (email@example.com).
Author Contributions: Dr Nation had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Blanken, Edmonds, Nation.
Acquisition, analysis, or interpretation of data: Blanken, Jang, Ho, Han, Bangen, Nation.
Drafting of the manuscript: Blanken, Han, Nation.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Blanken, Jang, Nation.
Obtained funding: Nation.
Administrative, technical, or material support: Blanken, Ho, Han, Nation.
Conflict of Interest Disclosures: Dr Nation reported receiving grants from the National Institute on Aging during the conduct of the study. No other disclosures were reported.
Funding/Support: This work was generously supported by grants from the National Institutes of Health (R01 AG049810 [principal investigator, Mark W. Bondi, PhD]; R01 AG064228, R01 AG060049, P01 AG052350, and P50 AG016573 [principal investigator, Dr Nation]) and Alzheimer’s Association (AARG-17-532905 [Dr Nation]).
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: Mark Bondi, PhD (VA San Diego Healthcare System, Department of Psychiatry, University of California San Diego); Lisa Delano-Wood, PhD (VA San Diego Healthcare System, Department of Psychiatry, University of California San Diego); and David Libon, PhD (Departments of Geriatrics, Gerontology, and Psychology, New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University) contributed to editing this article. They were not compensated for this contribution.
Additonal Information: The NACC database is funded by National Insitute on Aging and National Insitutes of Health grant U01 AG016976. The NACC data are contributed by the National Insitute on Aging–funded Alzheimer’s Disease Centers: P30 AG019610 (principal investigator [PI], Eric Reiman, MD), P30 AG013846 (PI, Neil Kowall, MD), P30 AG062428-01 (PI, James Leverenz, MD) P50 AG008702 (PI, Scott Small, MD), P50 AG025688 (PI, Allan Levey, MD, PhD), P50 AG047266 (PI, Todd Golde, MD, PhD), P30 AG010133 (PI, Andrew Saykin, PsyD), P50 AG005146 (PI, Marilyn Albert, PhD), P30 AG062421-01 (PI, Bradley Hyman, MD, PhD), P30 AG062422-01 (PI, Ronald Petersen, MD, PhD), P50 AG005138 (PI, Mary Sano, PhD), P30 AG008051 (PI, Thomas Wisniewski, MD), P30 AG013854 (PI, Robert Vassar, PhD), P30 AG008017 (PI, Jeffrey Kaye, MD), P30 AG010161 (PI, David Bennett, MD), P50 AG047366 (PI, Victor Henderson, MD, MS), P30 AG010129 (PI, Charles DeCarli, MD), P50 AG016573 (PI, Frank LaFerla, PhD), P30 AG062429-01 (PI, James Brewer, MD, PhD), P50 AG023501 (PI, Bruce Miller, MD), P30 AG035982 (PI, Russell Swerdlow, MD), P30 AG028383 (PI, Linda Van Eldik, PhD), P30 AG053760 (PI, Henry Paulson, MD, PhD), P30 AG010124 (PI, John Trojanowski, MD, PhD), P50 AG005133 (PI, Oscar Lopez, MD), P50 AG005142 (PI, Helena Chui, MD), P30 AG012300 (PI, Roger Rosenberg, MD), P30 AG049638 (PI, Suzanne Craft, PhD), P50 AG005136 (PI, Thomas Grabowski, MD), P30 AG062715-01 (PI, Sanjay Asthana, MD, FRCP), P50 AG005681 (PI, John Morris, MD), and P50 AG047270 (PI, Stephen Strittmatter, MD, PhD).
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