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

• Autores: Maito, Marcelo Adrián; Santamaria Garcia, Hernando; Moguilner, Sebastián; Possin, Katherine L.; Godoy, María Eugenia; Avila Funes, José Alberto; Behrens, María I.; Brusco, Luis Ignacio; Bruno, Martin; Cardona, Juan F.; Custodio, Nilton; García, Adolfo Martín; Javandel, Shireen; Lopera, Francisco; Matallana, Diana L.; Miller, Bruce; Okada de Oliveira, Maira; Pina-Escudero, Stefanie D.; Slachevsky, Andrea; Sosa Ortiz, Ana L.; Takada, Leonel T.; Tagliazuchi, Enzo; Valcour, Victor; Yokoyama, Jennifer S.; Ibañez, Agustín
• Año de publicación: 2023
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: ALZHEIMER'S DISEASE; FRONTOTEMPORAL DEMENTIA; MACHINE LEARNING; UNDERREPRESENTED SAMPLES
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Maito, Marcelo Adrián. Universidad de San Andrés; Argentina
Fil: Santamaria Garcia, Hernando. Pontificia Universidad Javeriana; Colombia. Hospital Universitario San Ignacio; Colombia. University of California; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Moguilner, Sebastián. Universidad de San Andrés; Argentina
Fil: Possin, Katherine L.. University of California; Estados Unidos
Fil: Godoy, María Eugenia. Universidad de San Andrés; Argentina
Fil: Avila Funes, José Alberto. Inserm; Francia. Instituto Nacional de la Nutrición Salvador Zubiran; México. Universite de Bordeaux; Francia
Fil: Behrens, María I.. Universidad de Chile; Chile
Fil: Brusco, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Bruno, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Cardona, Juan F.. Universidad del Valle; Colombia
Fil: Custodio, Nilton. Peruvian National Institute Of Health; Perú
Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Santiago de Chile; Chile. Universidad de San Andrés; Argentina. University of California; Estados Unidos
Fil: Javandel, Shireen. University of California; Estados Unidos
Fil: Lopera, Francisco. Universidad de Antioquia; Colombia
Fil: Matallana, Diana L.. Pontificia Universidad Javeriana; Colombia
Fil: Miller, Bruce. University of California; Estados Unidos
Fil: Okada de Oliveira, Maira. Hospital Santa Marcelina; Brasil. Universidade de Sao Paulo; Brasil. University of California; Estados Unidos
Fil: Pina-Escudero, Stefanie D.. University of California; Estados Unidos
Fil: Slachevsky, Andrea. Universidad del Desarrollo; Chile. Brain Health And Metabolism; Chile. Universidad de Chile; Chile
Fil: Sosa Ortiz, Ana L.. Instituto Nacional de Neurología y Neurocirugía; México
Fil: Takada, Leonel T.. Universidade de Sao Paulo; Brasil
Fil: Tagliazuchi, Enzo. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Valcour, Victor. University of California; Estados Unidos
Fil: Yokoyama, Jennifer S.. University of California; Estados Unidos
Fil: Ibañez, Agustín. Trinity College Dublin; Irlanda. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chile