Clustering executive functions yields MCI profiles that significantly predict conversion to AD dementia

Junquera, Almudena and García-Zamora, Estefanía and Parra, Mario Alfredo and Fernández-Guinea, S (2019) Clustering executive functions yields MCI profiles that significantly predict conversion to AD dementia. In: Alzheimer's Association International Conference, 2019-07-14 - 2019-07-18, Los Angeles Convention Centre.

[img]
Preview
Text (Junquera-etal-AAIC-2019-Clustering-executive-functions-yields-MCI-profiles-that-significantly-predict)
Junquera_etal_AAIC_2019_Clustering_executive_functions_yields_MCI_profiles_that_significantly_predict.pdf
Accepted Author Manuscript

Download (1MB)| Preview

    Abstract

    Background: It has been acknowledged that executive dysfunctions hold potential as early predictors of progression from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD) dementia. Executive deficits have a significant impact on the ability to perform activities of daily living (ADL), and as such, can lead to the transition from MCI to AD dementia. However, the extent to which executive impairments can yield identifiable cognitive profiles which can increase the risk of MCI to AD dementia progression has not been well investigated to date. Method: We analysed 152 patients (52 healthy controls and 100 MCI) who were subsequently divided based on the traditional classification of MCI (single domain amnestic MCI, multidomain amnestic MCI and non-amnestic MCI). Eight tests, assessing executive functions were administered at baseline and at 1-year-follow-up. Screening tests of cognitive and functional abilities were also used. A new dysexecutive MCI classification was developed relying on k-means cluster analysis through which three clusters were identified. Baseline data entered simple lineal regression models to examine whether such a classification based on executive profiles could significantly predict progression to AD dementia a year later. Results: The dysexecutive classification accounted for 63% of the variance linked to MCI to AD conversion even when controlling for the severity of disease at baseline (F(1, 68) = 116.25, p=0.000, R2=0.63). Such a prediction power was not observed when the classical MCI classification based on memory profiles alone entered the model as a predictor (F(1, 68) = 5.09, p=0.955, R2=0.07). Conclusion: Considering dysexecutive profiles of MCI patients may increase the accuracy of prediction models aimed at detecting risk of progressing to AD dementia. MCI patients with worse performance on executive tests seem to hold a higher risk of conversion and such a risk seems to be accounted for neither by memory impairments nor by the severity of the disease at baseline.