RESUMO

BACKGROUND: Education influences brain health and dementia. However, its impact across regions, specifically Latin America (LA) and the United States (US), is unknown. METHODS: A total of 1412 participants comprising controls, patients with Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD) from LA and the US were included. We studied the association of education with brain volume and functional connectivity while controlling for imaging quality and variability, age, sex, total intracranial volume (TIV), and recording type. RESULTS: Education influenced brain measures, explaining 24%-98% of the geographical differences. The educational disparities between LA and the US were associated with gray matter volume and connectivity variations, especially in LA and AD patients. Education emerged as a critical factor in classifying aging and dementia across regions. DISCUSSION: The results underscore the impact of education on brain structure and function in LA, highlighting the importance of incorporating educational factors into diagnosing, care, and prevention, and emphasizing the need for global diversity in research. HIGHLIGHTS: Lower education was linked to reduced brain volume and connectivity in healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). Latin American cohorts have lower educational levels compared to the those in the United States. Educational disparities majorly drive brain health differences between regions. Educational differences were significant in both conditions, but more in AD than FTLD. Education stands as a critical factor in classifying aging and dementia across regions.

RESUMO

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 (N = 23,694; M = 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.

Assuntos

RESUMO

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 NIA/NIH (R01AG057234), Alzheimer's Association (SG-20-725707-ReDLat), Rainwater Foundation, Takeda (CW2680521), Global Brain Health Institute; as well as CONICET; FONCYT-PICT (2017-1818, 2017-1820); PIIECC, Facultad de Humanidades, Usach; Sistema General de Regalías de Colombia (BPIN2018000100059), Universidad del Valle (CI 5316); ANID/FONDECYT Regular (1210195, 1210176, 1210176); ANID/FONDAP (15150012); ANID/PIA/ANILLOS ACT210096; and Alzheimer's Association GBHI ALZ UK-22-865742.

RESUMO

Objective: To describe the demographic characteristics, initial psychiatric diagnoses, and the time to reach a diagnosis of probable behavioral variant frontotemporal dementia (bvFTD) in a public psychiatric hospital in Cali, Colombia. Methods: We retrospectively reviewed the medical records of 28 patients who were diagnosed with probable bvFTD based on a multidisciplinary evaluation that included a structural MRI, neuropsychological testing, functional assessment, and neurological exam. Prior to this evaluation, all patients were evaluated by a psychiatrist as part of their initial consultation at the hospital. The initial consultation included the Neuropsychiatric Inventory and diagnoses based on the DSM-V. Demographics, clinical features, and initial psychiatric misdiagnoses were extracted from clinical records and summarized in the full sample and by gender. Results: The study sample had a mean education of 10.0 years (SD = 4.9) and 68.0% were female. In the full sample, 28.6% were initially diagnosed with dementia, and 71.4% with a psychiatric disorder. The psychiatric diagnosis at initial consultation differed by gender. Women were most likely to be diagnosed with depression (26.3%) or bipolar disorder (26.3%), while the men were most likely to be diagnosed with anxiety (33.3%) or a psychotic disorder (22.2%). Psychotic symptoms were common (delusions, 60.7% and hallucinations, 39.3%), and the pattern of neuropsychiatric symptoms did not differ by gender. Conclusions: This is one of few case series of bvFTD in a Colombian population, where bvFTD is a recognizable and prevalent disorder. In this psychiatric hospital, the majority of patients with bvFTD were initially diagnosed with a primary psychiatric condition. There was a gender difference in psychiatric diagnosis, but not in neuropsychiatric symptoms. In this sample, the rate of psychiatric misdiagnosis, as well as the psychotic symptoms, were higher compared to rates described in other countries. These results highlight the need for interventions to improve bvFTD diagnosis in under-represented populations.

RESUMO

BACKGROUND: Rapid technological advances offer a possibility to develop cost-effective digital cognitive assessment tools. However, it is unclear whether these measures are suitable for application in populations from Low and middle-income countries (LMIC). OBJECTIVE: To examine the accuracy and validity of the Brain Health Assessment (BHA) in detecting cognitive impairment in a Cuban population. METHODS: In this cross-sectional study, 146 participants (cognitively healthy = 53, mild cognitive impairment (MCI) = 46, dementia = 47) were recruited at primary care and tertiary clinics. The main outcomes included: accuracy of the BHA and the Montreal Cognitive Assessment (MoCA) in discriminating between controls and cognitively impaired groups (MCI and dementia) and correlations between the BHA subtests of memory, executive functions, and visuospatial skills and criterion-standard paper-and-pencil tests in the same domains. RESULTS: The BHA had an AUC of 0.95 (95% CI: 0.91-0.98) in discriminating between controls and cognitively impaired groups (MCI and dementia, combined) with 0.91 sensitivity at 0.85 specificity. In discriminating between control and MCI groups only, the BHA tests had an AUC of 0.94 (95% CI: 0.90-0.99) with 0.71 sensitivity at 0.85 specificity. Performance was superior to the MoCA across all diagnostic groups. Concurrent and discriminant validity analyses showed moderate to strong correlations between the BHA tests and standard paper-and-pencil measures in the same domain and weak correlations with standard measures in unrelated domains. CONCLUSION: The BHA has excellent performance characteristics in detecting cognitive impairment including dementia and MCI in a Hispanic population in Cuba and outperformed the MoCA. These results support potential application of digital cognitive assessment for older adults in LMIC.

Assuntos