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

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 (LMIC...

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Autores principales: Maito, Marcelo Adrián, Santamaría-García, Hernando, Moguilner, Sebastián, Possin, Katherine L., Godoy, María E., Avila-Funes, José Alberto, Behrens, María I., Brusco, Ignacio L., Bruno, Martín A., Cardona, Juan F., Custodio, Nilton, García, Adolfo M., 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794191/
https://www.ncbi.nlm.nih.gov/pubmed/36583137
http://dx.doi.org/10.1016/j.lana.2022.100387
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author Maito, Marcelo Adrián
Santamaría-García, Hernando
Moguilner, Sebastián
Possin, Katherine L.
Godoy, María E.
Avila-Funes, José Alberto
Behrens, María I.
Brusco, Ignacio L.
Bruno, Martín A.
Cardona, Juan F.
Custodio, Nilton
García, Adolfo M.
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
author_facet Maito, Marcelo Adrián
Santamaría-García, Hernando
Moguilner, Sebastián
Possin, Katherine L.
Godoy, María E.
Avila-Funes, José Alberto
Behrens, María I.
Brusco, Ignacio L.
Bruno, Martín A.
Cardona, Juan F.
Custodio, Nilton
García, Adolfo M.
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
author_sort Maito, Marcelo Adrián
collection PubMed
description 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 10.13039/100000049NIA/10.13039/100000002NIH (R01AG057234), 10.13039/100000957Alzheimer's Association (SG-20-725707-ReDLat), Rainwater Foundation, Takeda (CW2680521), 10.13039/100015442Global Brain Health Institute; as well as 10.13039/501100002923CONICET; FONCYT-PICT (2017-1818, 2017-1820); PIIECC, Facultad de Humanidades, 10.13039/100007194Usach; 10.13039/501100013409Sistema General de Regalías de Colombia (BPIN2018000100059), 10.13039/501100007329Universidad del Valle (CI 5316); 10.13039/501100020884ANID/FONDECYT Regular (1210195, 1210176, 1210176); 10.13039/501100020884ANID/10.13039/501100018735FONDAP (15150012); 10.13039/501100020884ANID/10.13039/501100021154PIA/ANILLOSACT210096; and 10.13039/100000957Alzheimer's Association GBHI ALZ UK-22-865742.
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spelling pubmed-97941912023-01-01 Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study Maito, Marcelo Adrián Santamaría-García, Hernando Moguilner, Sebastián Possin, Katherine L. Godoy, María E. Avila-Funes, José Alberto Behrens, María I. Brusco, Ignacio L. Bruno, Martín A. Cardona, Juan F. Custodio, Nilton García, Adolfo M. 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 Lancet Reg Health Am Articles 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 10.13039/100000049NIA/10.13039/100000002NIH (R01AG057234), 10.13039/100000957Alzheimer's Association (SG-20-725707-ReDLat), Rainwater Foundation, Takeda (CW2680521), 10.13039/100015442Global Brain Health Institute; as well as 10.13039/501100002923CONICET; FONCYT-PICT (2017-1818, 2017-1820); PIIECC, Facultad de Humanidades, 10.13039/100007194Usach; 10.13039/501100013409Sistema General de Regalías de Colombia (BPIN2018000100059), 10.13039/501100007329Universidad del Valle (CI 5316); 10.13039/501100020884ANID/FONDECYT Regular (1210195, 1210176, 1210176); 10.13039/501100020884ANID/10.13039/501100018735FONDAP (15150012); 10.13039/501100020884ANID/10.13039/501100021154PIA/ANILLOSACT210096; and 10.13039/100000957Alzheimer's Association GBHI ALZ UK-22-865742. Elsevier 2022-11-03 /pmc/articles/PMC9794191/ /pubmed/36583137 http://dx.doi.org/10.1016/j.lana.2022.100387 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Articles
Maito, Marcelo Adrián
Santamaría-García, Hernando
Moguilner, Sebastián
Possin, Katherine L.
Godoy, María E.
Avila-Funes, José Alberto
Behrens, María I.
Brusco, Ignacio L.
Bruno, Martín A.
Cardona, Juan F.
Custodio, Nilton
García, Adolfo M.
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
Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study
title Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study
title_full Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study
title_fullStr Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study
title_full_unstemmed Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study
title_short Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study
title_sort classification of alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794191/
https://www.ncbi.nlm.nih.gov/pubmed/36583137
http://dx.doi.org/10.1016/j.lana.2022.100387
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