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Comprehensive Analysis of Genetic Contributions to Alzheimer’s Disease and Frontotemporal Dementia in Admixed Latin American Populations
(Wiley, 2024-12)
Juliana Acosta‐Uribe
;
Stefanie Danielle Pina Escudero
;
J. Nicholas Cochran
;
Jared W Taylor
;
Caroline Warly Solsberg
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Most research initiatives have emerged from high‐income countries (HIC), leaving a gap in understanding the disease’s genetic basis in diverse populations like those in Latin American countries (LAC). ReDLat tackles this gap, focusing on LAC’s unique genetics and socioeconomic factors to identify specific Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) risk factors in Mexico, Colombia, Peru, Chile, Argentina, and Brazil.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>We employed a comprehensive genetic analysis approach, integrating Whole Genome Sequencing (WGS), Exome Sequencing, and SNP arrays to understand the cohort’s unique genetic architecture. We conducted ancestry analysis and searched for disease‐causing variants with mendelian inheritance, genome‐wide association studies (GWAS), rare variant enrichment, and evaluation of Polygenic Risk Scores (PRS).</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We recruited and genotyped an initial cohort of 1046 participants with AD, 423 with FTD, and 855 healthy controls (HC) between 2020 and 2023. Analysis is ongoing, and we expect to sequence ∼600 additional samples in the coming months. Ancestry analysis revealed tri‐continental admixture, except for Brazil, which showed an additional Asian component (Figure 1). Top candidate gene rare variant enrichment associations (SKAT p < 0.05) were <jats:italic>TREM2</jats:italic> for FTD and <jats:italic>ABCA7</jats:italic> and <jats:italic>ABCA1</jats:italic> for AD. GWAS identified a robust association with the <jats:italic>APOE</jats:italic> locus on chromosome 19 in AD vs. HC.. We tested an AD PRS developed in European populations by Bellenguez et al (2020). on our cohort using 83 single‐nucleotide polymorphisms.. The PRS modestly distinguishes between all patients and HC (p = 2.4 × 10^‐12), AD vs. HC (p = 2.2 × 10^‐12), and even FTD vs. HC (p = 4.3 × 10^‐5), albeit with modest separation between groups, as expected for its application in a genetically admixed population.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our findings represent a pivotal step in understanding the genetic landscape of AD and FTD in admixed populations. They underscore the importance of including diverse populations in genetic research, paving the way for future studies. These findings have the potential to inform more personalized approaches to the diagnosis and treatment of neurodegenerative diseases in diverse global populations, as well as identify novel targets for therapeutic development.</jats:p></jats:sec>
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The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds
(2023)
Pavel Prado
;
Vicente Medel
;
Raul Gonzalez-Gomez
;
Agustín Sainz-Ballesteros
;
Victor Vidal
The Latin American Brain Health Institute (BrainLat) has released a unique multimodal neuroimaging dataset of 780 participants from Latin American. The dataset includes 530 patients with neurodegenerative diseases such as Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), multiple sclerosis (MS), Parkinson’s disease (PD), and 250 healthy controls (HCs). This dataset (62.7 ± 9.5 years, age range 21–89 years) was collected through a multicentric effort across five Latin American countries to address the need for affordable, scalable, and available biomarkers in regions with larger inequities. The BrainLat is the first regional collection of clinical and cognitive assessments, anatomical magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), diffusion-weighted MRI (DWI), and high density resting-state electroencephalography (EEG) in dementia patients. In addition, it includes demographic information about harmonized recruitment and assessment protocols. The dataset is publicly available to encourage further research and development of tools and health applications for neurodegeneration based on multimodal neuroimaging, promoting the assessment of regional variability and inclusion of underrepresented participants in research.
30Scopus© Citations 22
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Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia
(2023)
Franco J. Ferrante
;
Joaquín Migeot
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Agustina Birba
;
Lucía Amoruso
;
Gonzalo Pérez
<jats:title>Abstract</jats:title><jats:sec><jats:title>INTRODUCTION</jats:title><jats:p>Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease‐specific semantic memory patterns. Here, we leveraged automated word‐property analysis to capture neurocognitive markers of AD vis‐à‐vis behavioral variant frontotemporal dementia (bvFTD).</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group‐level discrimination, patient‐level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group‐ and subject‐level discrimination only in AD, also predicting executive outcomes. Disease‐specific cortical thickness patterns were predicted by frequency in both disorders. Default‐mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD.</jats:p></jats:sec><jats:sec><jats:title>DISCUSSION</jats:title><jats:p>Word‐property analysis of fluency can boost AD characterization and diagnosis.</jats:p></jats:sec><jats:sec><jats:title>Highlights</jats:title><jats:p><jats:list list-type="bullet"> <jats:list-item><jats:p>We report novel word‐property analyses of verbal fluency in AD and bvFTD.</jats:p></jats:list-item> <jats:list-item><jats:p>Standard valid response counts captured deficits and brain patterns in both groups.</jats:p></jats:list-item> <jats:list-item><jats:p>Specific word properties (e.g., frequency, granularity) were altered only in AD.</jats:p></jats:list-item> <jats:list-item><jats:p>Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD.</jats:p></jats:list-item> <jats:list-item><jats:p>Word‐property analysis of fluency can boost AD characterization and diagnosis.</jats:p></jats:list-item> </jats:list></jats:p></jats:sec>
3Scopus© Citations 10
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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
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Sebastian Moguilner
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Odir Antonio Rodriguez-Villagra
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Felipe Botero-Rodriguez
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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
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Dementia in Latin America: Paving the way toward a regional action plan
(2020)
Mario Alfredo Parra
;
Sandra Baez
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Lucas Sedeño
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Cecilia Gonzalez Campo
;
Hernando Santamaría‐García
2Scopus© Citations 115
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Multi-feature computational framework for combined signatures of dementia in underrepresented settings
(2022)
Sebastian Moguilner
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Agustina Birba
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Sol Fittipaldi
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Cecilia Gonzalez-Campo
;
Enzo Tagliazucchi
<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. <jats:italic>Approach</jats:italic>. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). <jats:italic>Main results</jats:italic>. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). <jats:italic>Results</jats:italic>. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. <jats:italic>Significance</jats:italic>. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.</jats:p>
Scopus© Citations 19 1 3
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Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach
(2020)
M. Belen Bachli
;
Lucas Sedeño
;
Jeremi K. Ochab
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Olivier Piguet
;
Fiona Kumfor
16 1Scopus© Citations 56

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