Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study

Abstract

Background: Global brain health initiatives call for improving methods for the diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD) in underrepresented populations. However, diagnostic procedures in upper-middle-income countries (UMICs) and lower-middle income countries (LMICs), such as Latin American countries (LAC), face multiple challenges. These include the heterogeneity in diagnostic methods, lack of clinical harmonisation, and limited access to biomarkers.

Methods: This cross-sectional observational study aimed to identify the best combination of predictors to discriminate between AD and FTD using demographic, clinical and cognitive data among 1794 participants [904 diagnosed with AD, 282 diagnosed with FTD, and 606 healthy controls (HCs)] collected in 11 clinical centres across five LAC (ReDLat cohort).

Findings: A fully automated computational approach included classical statistical methods, support vector machine procedures, and machine learning techniques (random forest and sequential feature selection procedures). Results demonstrated an accurate classification of patients with AD and FTD and HCs. A machine learning model produced the best values to differentiate AD from FTD patients with an accuracy = 0.91. The top features included social cognition, neuropsychiatric symptoms, executive functioning performance, and cognitive screening; with secondary contributions from age, educational attainment, and sex.

Interpretation: Results demonstrate that data-driven techniques applied in archival clinical datasets could enhance diagnostic procedures in regions with limited resources. These results also suggest specific fine-grained cognitive and behavioural measures may aid in the diagnosis of AD and FTD in LAC. Moreover, our results highlight an opportunity for harmonisation of clinical tools for dementia diagnosis in the region.

Funding: This work was supported by the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by NIA/NIH (R01AG057234), Alzheimer's Association (SG-20-725707-ReDLat), Rainwater Foundation, Takeda (CW2680521), Global Brain Health Institute; as well as CONICET; FONCYT-PICT (2017-1818, 2017-1820); PIIECC, Facultad de Humanidades, Usach; Sistema General de Regalías de Colombia (BPIN2018000100059), Universidad del Valle (CI 5316); ANID/FONDECYT Regular (1210195, 1210176, 1210176); ANID/FONDAP (15150012); ANID/PIA/ANILLOS ACT210096; and Alzheimer's Association GBHI ALZ UK-22-865742.

Keywords: Alzheimer’s Disease; Frontotemporal dementia; Machine learning; Underrepresented samples.

Fig. 1

Approach to dementia diagnosis with standard and non-standard methodologies. (A) Data was combined from 1792 participants seen in eleven centres in five LAC, including 904 patients with AD, 282 patients with FTD, and 606 HCs. Data comprised cognitive screening (MMSE, MoCA, and ACE-III), executive function (IFS), social cognition (Mini-SEA), functional status (Pfeffer FAQ and Barthel index), and neuropsychiatric symptoms (NPI). Demographic data included year of birth, country, years of education, and sex. (B) Basic statistical comparisons included Welch's t-test (unequal variances t-test), Mann–Whitney U test, paired t-test, one-way and two-way ANOVA, Shapiro–Wilk test of normality, Levene test for equal variances, outliers' detection by standard deviation, percentiles and IQR methods. (C) Neuropsychological measurements and demographic variables were standardised and normalised. Cognitive screening total scores were converted (detailed in section 2.3). (D) Standard logistic regression models were applied. (E) Baseline machine learning models: support vector machines, and random forest were applied. (F) The stratified k-fold cross-validation scheme with standard grid-search approach was used to test different model's regularization. (G) Classification was obtained using out of sample testing sets. (H) ROC AUC, Precision-Recall curve, Accuracy, Sensitivity, specificity and F1-score were used for model evaluation. AD patients were set as the “negative” and FTD patients as the “positive” class. LAC, Latin American Countries; AD, Alzheimer's Disease; FTD, Frontotemporal Dementia; MMSE, Mini Mental State Examination; MoCA, Montreal Cognitive Assessment; ACE-III, Addenbrooke's Cognitive Examination; IFS, Ineco Frontal Screening; Mini-SEA, Mini-Social Cognition & Emotional Assessment; Pfeffer FAQ, Pfeffer Functional Activity Questionnaire; NPI, Neuropsychiatric Inventory; ANOVA, Analysis of Variance; IQR, Interquartile Range; ROC AUC, Receiver Operating Characteristics Area Under Curve.

Fig. 2

Mean Main results, including random forest model evaluation, CV validation curves, and feature importance test sets. (A) The upper left graph shows the confusion matrix in relative magnitudes. The upper central graph shows the ROC curves with confidence intervals and its AUC, the upper right graph shows the Precision-Recall curves for the original classifier (blue) and the scrambled one (red). (B) The central and lower graphs show the validation curves on five RF hyperparameters (regularization) on Stratified Cross Validation with k = 5, repeats = 20. The dots represent the average score for k ∗ n = 100 scores for each range of possible values shown on the x axis. The red and green areas around the lines represent the standard deviation for the results of training and testing for each of the 100 scores. (C) The bottom graph shows the importance of every feature of the original model. The most important ones were social cognition (Mini-SEA), executive functions (IFS), cognitive screening (measured with MMSE normalized via Van Steenoven method), age, neuropsychiatric symptoms (NPI), years of education and sex.