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Updated: May 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Revisiting the Montreal Cognitive Assessment in a European cohort of elderly living with type 2 diabetes
Natasa Popovic1, Noemi Lois2, Santiago Pérez-Hoyos3
1Department of Medical Physiology, Faculty of Medicine, University of Montenegro, Podgorica, Montenegro.
Abstract:
BackgroundIndividuals with type 2 diabetes have an increased risk of developing both vascular and Alzheimer's dementia.ObjectiveThis prospective cross-sectional study assessed the screening ability of the standard Montreal Cognitive Assessment (MoCA) score suggestive of mild cognitive impairment (<26) in a European cohort of individuals ≥65 of age with type 2 diabetes.MethodsParticipants of RECOGNISED, a European prospective EU-funded cohort study, were screened using MoCA. In addition, a 13-item Neuropsychological Test Battery (NTB) with the Clinical Dementia Rating was undertaken to categorize participants as normocognitive (NC, n = 128) or mild cognitive impaired (MCI, n = 185). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of MoCA cut-off scores to categorize patients as having MCI or not.ResultsThe standard MoCA cut-off of 25/26 demonstrated a sensitivity of 88% and a specificity of 51%, resulting in a false positive rate of 20%. ROC analysis showed that a MoCA cut-off of 24/25 has a better balance between sensitivity (81%) and specificity (62%), with a lower false positive rate of 16%. NTB results showed that the MCI group had the lowest norm-referenced percentile scores in the visuo-construction domain, a known early feature of Alzheimer's disease and a significant predictor of a rapid rate of disease progression.ConclusionsMoCA as a screening tool in individuals ≥65 with type 2 diabetes, overestimates the prevalence of MCI, even when applying lower cut-offs. More specific screening strategies are necessary, particularly targeting the visuo-construction domain, to effectively identify cognitive impairment in individuals with type 2 diabetes.
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