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Author: search.filters.author.Cecilia AlbalaSubject: search.filters.subject.human

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    Item type:Publication,
    The first genome‐wide association study in the Argentinian and Chilean populations identifies shared genetics with Europeans in Alzheimer's disease
    (2023)
    Maria Carolina Dalmasso
    ;
    Itziar de Rojas
    ;
    Natividad Olivar
    ;
    Carolina Muchnik
    ;
    Bárbara Angel
    <jats:title>Abstract</jats:title><jats:sec><jats:title>INTRODUCTION</jats:title><jats:p>Genome‐wide association studies (GWAS) are fundamental for identifying loci associated with diseases. However, they require replication in other ethnicities.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>We performed GWAS on sporadic Alzheimer's disease (AD) including 539 patients and 854 controls from Argentina and Chile. We combined our results with those from the European Alzheimer and Dementia Biobank (EADB) in a meta‐analysis and tested their genetic risk score (GRS) performance in this admixed population.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>We detected apolipoprotein E ε4 as the single genome‐wide significant signal (odds ratio  = 2.93 [2.37–3.63], <jats:italic>P</jats:italic> = 2.6 × 10<jats:sup>−23</jats:sup>). The meta‐analysis with EADB summary statistics revealed four new loci reaching GWAS significance. Functional annotations of these loci implicated endosome/lysosomal function. Finally, the AD‐GRS presented a similar performance in these populations, despite the score diminished when the Native American ancestry rose.</jats:p></jats:sec><jats:sec><jats:title>DISCUSSION</jats:title><jats:p>We report the first GWAS on AD in a population from South America. It shows shared genetics modulating AD risk between the European and these admixed populations.</jats:p></jats:sec><jats:sec><jats:title>Highlights</jats:title><jats:p><jats:list list-type="bullet"> <jats:list-item><jats:p>This is the first genome‐wide association study on Alzheimer's disease (AD) in a population sample from Argentina and Chile.</jats:p></jats:list-item> <jats:list-item><jats:p>Trans‐ethnic meta‐analysis reveals four new loci involving lysosomal function in AD.</jats:p></jats:list-item> <jats:list-item><jats:p>This is the first independent replication for <jats:italic>TREM2L</jats:italic>, <jats:italic>IGH‐gene‐cluster</jats:italic>, and <jats:italic>ADAM17</jats:italic> loci.</jats:p></jats:list-item> <jats:list-item><jats:p>A genetic risk score (GRS) developed in Europeans performed well in this population.</jats:p></jats:list-item> <jats:list-item><jats:p>The higher the Native American ancestry the lower the GRS values.</jats:p></jats:list-item> </jats:list></jats:p></jats:sec>
    Scopus© Citations 3  1
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    The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations
    (2023)
    Hernando Santamaria-Garcia
    ;
    Sebastian Moguilner
    ;
    Odir Antonio Rodriguez-Villagra
    ;
    Felipe Botero-Rodriguez
    ;
    Stefanie Danielle Pina-Escudero
    <jats:title>Abstract</jats:title><jats:p>Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (<jats:italic>N</jats:italic> = 23,694;<jats:italic>M</jats:italic> = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.</jats:p>
    Scopus© Citations 10  1