Person: Behrens, Maria Isabel
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Behrens
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Maria Isabel
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Publication 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, AgustínBackground 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.Publication Cancer History Avoids the Increase of Senescence Markers in Peripheral Cells of Amnestic Mild Cognitive Impaired Patients(2023) SanMartín, Carol D.; Salech, Felipe; Ponce, Daniela Paz; Concha-Cerda, Jorge; Romero-Hernández, Esteban; Liabeuf, Gianella; Rogers, Nicole K.; Murgas, Paola; Bruna, Bárbara; More, Jamileth; Behrens, Maria IsabelEpidemiological studies show that having a history of cancer protects from the development of Alzheimer’s Disease (AD), and vice versa, AD protects from cancer. The mechanism of this mutual protection is unknown. We have reported that the peripheral blood mononuclear cells (PBMC) of amnestic cognitive impairment (aMCI) and Alzheimer’s Disease (AD) patients have increased susceptibility to oxidative cell death compared to control subjects, and from the opposite standpoint a cancer history is associated with increased resistance to oxidative stress cell death in PBMCs, even in those subjects who have cancer history and aMCI (Ca + aMCI). Cellular senescence is a regulator of susceptibility to cell death and has been related to the pathophysiology of AD and cancer. Recently, we showed that cellular senescence markers can be tracked in PBMCs of aMCI patients, so we here investigated whether these senescence markers are dependent on having a history of cancer. Senescence-associated βeta-galactosidase (SA-β-Gal) activity, G0-G1 phase cell-cycle arrest, p16 and p53 were analyzed by flow cytometry; phosphorylated H2A histone family member X (γH2AX) by immunofluorescence; IL-6 and IL-8 mRNA by qPCR; and plasmatic levels by ELISA. Senescence markers that were elevated in PBMCs of aMCI patients, such as SA-β-Gal, Go-G1 arrested cells, IL-6 and IL-8 mRNA expression, and IL-8 plasmatic levels, were decreased in PBMCs of Ca + aMCI patients to levels similar to those of controls or of cancer survivors without cognitive impairment, suggesting that cancer in the past leaves a fingerprint that can be peripherally traceable in PBMC samples. These results support the hypothesis that the senescence process might be involved in the inverse association between cancer and AD.