Alzheimer disease (AD) is the most common cause of dementia, with a worldwide prevalence of about 25 million in 2010, expected to be doubled by 2030 because of increased life expectancy.¹ The earliest recognizable pathological event in AD is cerebral amyloid-β aggregation.² This pathology may be present up to 20 years before the onset of dementia.^3,4 Novel research criteria for AD in individuals without dementia emphasize the presence of amyloid pathology to define the first stage of the disease.^5,6

The Montreal Cognitive Assessment (MoCA) was recently proposed as a cognitive screening test for milder forms of cognitive impairment, having surpassed the well-known limitations of the Mini-Mental State Examination (MMSE). This study aims to validate the MoCA for screening Mild Cognitive Impairment (MCI) and Alzheimer disease (AD) through an analysis of diagnostic accuracy and the proposal of cut-offs. Patients were classified into 2 clinical groups according to standard criteria: MCI (n=90) and AD (n=90). The 2 control groups (C-MCI: n=90; C-AD: n=90) consisted of cognitively healthy community dwellers selected to match patients in sex, age, and education. The MoCA showed consistently superior psychometric properties compared with the MMSE, and higher diagnostic accuracy to discriminate between MCI (area under the curve=0.856; 95% confidence interval, 0.796-0.904) and AD patients (area under the curve=0.980; 95% confidence interval, 0.947-0.995). At an optimal cut-off of below 22 for MCI and below 17 for AD, the MoCA achieved significantly superior values in comparison with MMSE for sensitivity, specificity, positive predictive value, negative predictive value, and classification accuracy. Furthermore, the MoCA revealed higher sensitivity to cognitive decline in longitudinal monitoring. This study provides robust evidence that the MoCA is a better cognitive tool than the widely used MMSE for the screening and monitoring of MCI and AD in clinical settings.

Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test.

Three sets of research criteria are available for diagnosis of Alzheimer’s disease in subjects with mild cognitive impairment: the International Working Group-1, International Working Group-2, and National Institute of Aging-Alzheimer Association criteria. We compared the prevalence and prognosis of Alzheimer’s disease at the mild cognitive impairment stage according to these criteria. Subjects with mild cognitive impairment (n = 1607), 766 of whom had both amyloid and neuronal injury markers, were recruited from 13 cohorts. We used cognitive test performance and available biomarkers to classify subjects as prodromal Alzheimer’s disease according to International Working Group-1 and International Working Group-2 criteria and in the high Alzheimer’s disease likelihood group, conflicting biomarker groups (isolated amyloid pathology or suspected non-Alzheimer pathophysiology), and low Alzheimer’s disease likelihood group according to the National Institute of Ageing-Alzheimer Association criteria. Outcome measures were the proportion of subjects with Alzheimer’s disease at the mild cognitive impairment stage and progression to Alzheimer’s disease-type dementia. We performed survival analyses using Cox proportional hazards models. According to the International Working Group-1 criteria, 850 (53%) subjects had prodromal Alzheimer’s disease. Their 3-year progression rate to Alzheimer’s disease-type dementia was 50% compared to 21% for subjects without prodromal Alzheimer’s disease. According to the International Working Group-2 criteria, 308 (40%) subjects had prodromal Alzheimer’s disease. Their 3-year progression rate to Alzheimer’s disease-type dementia was 61% compared to 22% for subjects without prodromal Alzheimer’s disease. According to the National Institute of Ageing-Alzheimer Association criteria, 353 (46%) subjects were in the high Alzheimer’s disease likelihood group, 49 (6%) in the isolated amyloid pathology group, 220 (29%) in the suspected non-Alzheimer pathophysiology group, and 144 (19%) in the low Alzheimer’s disease likelihood group. The 3-year progression rate to Alzheimer’s disease-type dementia was 59% in the high Alzheimer’s disease likelihood group, 22% in the isolated amyloid pathology group, 24% in the suspected non-Alzheimer pathophysiology group, and 5% in the low Alzheimer’s disease likelihood group. Our findings support the use of the proposed research criteria to identify Alzheimer’s disease at the mild cognitive impairment stage. In clinical settings, the use of both amyloid and neuronal injury markers as proposed by the National Institute of Ageing-Alzheimer Association criteria offers the most accurate prognosis. For clinical trials, selection of subjects in the National Institute of Ageing-Alzheimer Association high Alzheimer’s disease likelihood group or the International Working Group-2 prodromal Alzheimer’s disease group could be considered.

The Montreal Cognitive Assessment (MoCA) is a brief cognitive screening instrument with good psychometric features and an excellent sensitivity in the early detection of mild cognitive decline. The MoCA was applied to a community-based sample of cognitively healthy adults (n = 650), stratified according to sociodemographic variables (age, gender, educational level, geographic region, geographic localization, and residence area), with a distribution similar to that observed in the Portuguese population. The normative data were determined according to age and education as these were the sociodemographic variables that most significantly contributed to the prediction of the MoCA scores, explaining 49% of their variance.