Browsing by Author "Matallana, Diana L."

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    Biomarkers for dementia in Latin American countries: Gaps andopportunities
    (2023) Parra, Mario A.; Orellana, Paulina; León, Tomas; Victoria, Cabello G.; Henriquez, Fernando; Gomez, Rodrigo; Avalos, Constanza; Damian, Andres; Slachevsky Chonchol, Andrea; Ibañez, Agustin; Zetterberg, Henrik; Tijms, Betty M.; Yokoyama, Jennifer S.; Piña-Escudero, Stefanie D.; Cochran, Nicholas; Matallana, Diana L.; Acosta, Daisy; Allegri, Ricardo; Arias-Suáres, Bianca P.; Barra, Bernardo; Behrens, María Isabel; Brucki, Sonia M.D.; Busatto, Geraldo; Caramelli, Paulo; Castro-Suarez, Sheila; Contreras, Valeria; Custodio, Nilton; Dansilio, Sergio; De la Cruz-Puebla, Myriam; Cruz de Souza, Leonado; Díaz, Monica M.; Duque, Lissette; Farias, Gonzalo A.; Ferreira, Sergio T.; Magrath Guimet, Nahuel; Kmaid, Ana; Lira, David; Lopera, Francisco; Mar Meza, Beatriz; Miotto, Eliane C.
    Limited knowledge on dementia biomarkers in Latin American and Caribbean (LAC)countries remains a serious barrier. Here, we reported a survey to explore the ongo-ing work, needs, interests, potential barriers, and opportunities for future studiesrelated to biomarkers. The results show that neuroimaging is the most used biomarker(73%), followed by genetic studies (40%), peripheral fluids biomarkers (31%), and cere-brospinal fluid biomarkers (29%). Regarding barriers in LAC, lack of funding appears toundermine the implementation of biomarkers in clinical or research settings, followedby insufficient infrastructure and training. The survey revealed that despite the abovebarriers, the region holds a great potential to advance dementia biomarkers research.Considering the unique contributions that LAC could make to this growing field,we highlight the urgent need to expand biomarker research. These insights allowedus to propose an action plan that addresses the recommendations for a biomarkerframework recently proposed by regional experts.
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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
    (2023) Maito, Marcelo Adrián; Santamaría-García, Hernando; Moguilner, Sebastián; Possin, Katherine L.; Godoy, María E.; Avila-Funes, José Alberto; Behrens, Maria Isabel; Brusco, Ignacio L. Maira Okada de Oliveira,b,r,s,ae Stefanie D. Pina-Escuder; 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; Slachevsky Chonchol, Andrea; Ana L Sosa Ortiz; Takada, Leonel T.; Tagliazuchi, Enzo; Valcour, Victor; Yokoyama, Jennifer S.; Ibañez, Agustín
    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.

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