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Subtypes Based on Neuropsychological Performance Predict Incident Dementia: Findings from the Rush Memory and Aging Project.

ABSTRACT: BACKGROUND:In a previous report, we used latent class analysis (LCA) to identify natural subgroups of older adults in the Einstein Aging Study (EAS) based on neuropsychological performance. These subgroups differed in demographics, genetic profile, and prognosis. Herein, we assess the generalizability of these findings to an independent sample, the Rush Memory and Aging Project (MAP), which used an overlapping, but distinct neuropsychological battery. OBJECTIVE:Our aim was to identify the association of natural subgroups based on neuropsychological performance in the MAP cohort with incident dementia and compare them with the associations identified in the EAS. METHODS:MAP is a community-dwelling cohort of older adults living in the northeastern Illinois, Chicago. Latent class models were applied to baseline scores of 10 neuropsychological measures across 1,662 dementia-free MAP participants. Results were compared to prior findings from the EAS. RESULTS:LCA resulted in a 5-class model: Mixed-Domain Impairment (n?=?71, 4.3%), Memory-specific-Impairment (n?=?274, 16.5%), Average (n?=?767, 46.1%), Frontal Impairment (n?=?222, 13.4%), and a class of Superior Cognition (n?=?328, 19.7%). Similar to the EAS, the Mixed-Domain Impairment, the Memory-Specific Impairment, and the Frontal Impairment classes had higher risk of incident Alzheimer's disease when compared to the Average class. By contrast, the Superior Cognition had a lower risk of Alzheimer's disease when compared to the Average class. CONCLUSIONS:Natural cognitive subgroups in MAP are similar to those identified in EAS. These similarities, despite study differences in geography, sampling strategy, and cognitive tests, suggest that LCA is capable of identifying classes that are not limited to a single sample or a set of cognitive tests.

SUBMITTER: Zammit AR

PROVIDER: S-EPMC6335582 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Subtypes Based on Neuropsychological Performance Predict Incident Dementia: Findings from the Rush Memory and Aging Project.

Zammit Andrea R AR Muniz-Terrera Graciela G Katz Mindy J MJ Hall Charles B CB Ezzati Ali A Bennett David A DA Lipton Richard B RB

Journal of Alzheimer's disease : JAD 20190101 1

<h4>Background</h4>In a previous report, we used latent class analysis (LCA) to identify natural subgroups of older adults in the Einstein Aging Study (EAS) based on neuropsychological performance. These subgroups differed in demographics, genetic profile, and prognosis. Herein, we assess the generalizability of these findings to an independent sample, the Rush Memory and Aging Project (MAP), which used an overlapping, but distinct neuropsychological battery.<h4>Objective</h4>Our aim was to iden ...[more]

PMID: 30507576

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Project description:ObjectiveThis study aimed to evaluate the value of odors in the olfactory identification (OI) test and other known risk factors for predicting incident dementia in the prospective Shanghai Aging Study.MethodsAt baseline, OI was assessed using the Sniffin' Sticks Screening Test 12, which contains 12 different odors. Cognition assessment and consensus diagnosis were conducted at both baseline and follow-up to identify incident dementia. Four different multivariable logistic regression (MLR) models were used for predicting incident dementia. In the no-odor model, only demographics, lifestyle, and medical history variables were included. In the single-odor model, we further added one single odor to the first model. In the full model, all 12 odors were included. In the stepwise model, the variables were selected using a bidirectional stepwise selection method. The predictive abilities of these models were evaluated by the area under the receiver operating characteristic curve (AUC). The permutation importance method was used to evaluate the relative importance of different odors and other known risk factors.ResultsSeventy-five (8%) incident dementia cases were diagnosed during 4.9 years of follow-up among 947 participants. The full and the stepwise MLR model (AUC = 0.916 and 0.914, respectively) have better predictive abilities compared with those of the no- or single-odor models. The five most important variables are Mini-Mental State Examination (MMSE) score, age, peppermint detection, coronary artery disease, and height in the full model, and MMSE, age, peppermint detection, stroke, and education in the stepwise model. The combination of only the top five variables in the stepwise model (AUC = 0.901 and sensitivity = 0.880) has as a good a predictive ability as other models.ConclusionThe ability to smell peppermint might be one of the useful indicators for predicting dementia. Combining peppermint detection with MMSE, age, education, and history of stroke may have sensitive and robust predictive value for dementia in older adults.

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Subtypes Based on Neuropsychological Performance Predict Incident Dementia: Findings from the Rush Memory and Aging Project.

Publications

Subtypes Based on Neuropsychological Performance Predict Incident Dementia: Findings from the Rush Memory and Aging Project.

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