Dataset Information

Type 2 Diabetes Interacts With Alzheimer Disease Risk Factors to Predict Functional Decline.

ABSTRACT:

Objective

The current study examined the interactive effect of type 2 diabetes and Alzheimer disease (AD) risk factors on the rate of functional decline in cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative.

Methods

Participants underwent annual assessments that included the Functional Activities Questionnaire, an informant-rated measure of everyday functioning. Multilevel modeling, controlling for demographic variables and ischemic risk, examined the interactive effects of diabetes status (diabetes, n=69; no diabetes, n=744) and AD risk factors in the prediction of 5-year longitudinal change in everyday functioning. One model was run for each AD risk factor, including: objectively-defined subtle cognitive decline (Obj-SCD), and genetic susceptibility [apolipoprotein E ?4 (APOE ?4) as well as cerebrospinal fluid ?-amyloid (A?), total tau (tau), and hyperphosphorylated tau (p-tau).

Results

The 3-way diabetes×AD risk factor×time interaction predicted increased rates of functional decline in models that examined Obj-SCD, APOE ?4, tau, and p-tau positivity, but not A? positivity.

Conclusions

Participants with both diabetes and at least 1 AD risk factor (ie, Obj-SCD, APOE ?4, tau, and p-tau positivity) demonstrated faster functional decline compared with those without both risk factors (diabetes or AD). These findings have implications for early identification of, and perhaps earlier intervention for, diabetic individuals at risk for future functional difficulty.

SUBMITTER: Thomas KR

PROVIDER: S-EPMC6952586 | biostudies-literature | 2020 Jan-Mar

REPOSITORIES: biostudies-literature

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Publications

Type 2 Diabetes Interacts With Alzheimer Disease Risk Factors to Predict Functional Decline.

Thomas Kelsey R KR Bangen Katherine J KJ Weigand Alexandra J AJ Edmonds Emily C EC Sundermann Erin E Wong Christina G CG Eppig Joel J Werhane Madeleine L ML Delano-Wood Lisa L Bondi Mark W MW

Alzheimer disease and associated disorders 20200101 1

<h4>Objective</h4>The current study examined the interactive effect of type 2 diabetes and Alzheimer disease (AD) risk factors on the rate of functional decline in cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative.<h4>Methods</h4>Participants underwent annual assessments that included the Functional Activities Questionnaire, an informant-rated measure of everyday functioning. Multilevel modeling, controlling for demographic variables and ischemic risk, examined ...[more]

PMID: 31305320

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Project description:ObjectiveWe constructed random forest classifiers employing either the traditional method of scoring semantic fluency word lists or new methods. These classifiers were then compared in terms of their ability to diagnose Alzheimer disease (AD) or to prognosticate among individuals along the continuum from cognitively normal (CN) through mild cognitive impairment (MCI) to AD.MethodSemantic fluency lists from 44 cognitively normal elderly individuals, 80 MCI patients, and 41 AD patients were transcribed into electronic text files and scored by four methods: traditional raw scores, clustering and switching scores, "generalized" versions of clustering and switching, and a method based on independent components analysis (ICA). Random forest classifiers based on raw scores were compared to "augmented" classifiers that incorporated newer scoring methods. Outcome variables included AD diagnosis at baseline, MCI conversion, increase in Clinical Dementia Rating-Sum of Boxes (CDR-SOB) score, or decrease in Financial Capacity Instrument (FCI) score. Receiver operating characteristic (ROC) curves were constructed for each classifier and the area under the curve (AUC) was calculated. We compared AUC between raw and augmented classifiers using Delong's test and assessed validity and reliability of the augmented classifier.ResultsAugmented classifiers outperformed classifiers based on raw scores for the outcome measures AD diagnosis (AUC .97 vs. .95), MCI conversion (AUC .91 vs. .77), CDR-SOB increase (AUC .90 vs. .79), and FCI decrease (AUC .89 vs. .72). Measures of validity and stability over time support the use of the method.ConclusionLatent information in semantic fluency word lists is useful for predicting cognitive and functional decline among elderly individuals at increased risk for developing AD. Modern machine learning methods may incorporate latent information to enhance the diagnostic value of semantic fluency raw scores. These methods could yield information valuable for patient care and clinical trial design with a relatively small investment of time and money.

Dataset Information

Type 2 Diabetes Interacts With Alzheimer Disease Risk Factors to Predict Functional Decline.

Objective

Methods

Results

Conclusions

Publications

Type 2 Diabetes Interacts With Alzheimer Disease Risk Factors to Predict Functional Decline.

Similar Datasets

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