The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations

Social and health disparities associated with healthy brain ageing in Brazil and in other Latin American countries

BACKGROUND

Latin American countries present major health-related inequities due to historical, cultural, and social aspects. Recent evidence highlights that factors related to social and health disparities outweigh classic demographic factors in determining healthy brain aging in these populations. However, these analyses have not been conducted with the Brazilian population, the largest and most ethnically diverse population in Latin America.

METHODS

Here, we evaluated demographic, social, and health factors for healthy brain ageing using a machine learning model in a Brazilian population-based cohort (n=9412) and in additional cohorts from other Latin American countries, including Colombia (n=23 694), Chile (n=1301), Ecuador (n=5235), and Uruguay (n=1450).

FINDINGS

In the Brazilian population and other Latin American countries, social and health disparities were more influential than demographic factors for cognition and functional ability. Uniquely in Brazil, education emerged as the primary risk factor impacting cognitive outcomes, diverging from other Latin American countries where mental health symptoms played more prominent roles. In terms of functional ability, Brazil displayed a distinct pattern, with mental health symptoms identified as the primary contributing factor.

INTERPRETATION

Our findings indicate that Brazil converges with other Latin American countries to show that heterogeneous factors impacted more than demographic factors, but also showed a unique set of health factors when compared with other Latin American countries. Therefore, our study emphasises that social and health disparity factors are relevant predictors of healthy brain ageing in Latin America, but population-specific analyses are necessary to identify the specific risk profiles of each country.

FUNDING

None.

TRANSLATIONS

For the Portuguese and Spanish translations of the abstract see Supplementary Materials section.

Evidence of validity and reliability of the Colombian version of Addenbroke's Cognitive Examination Revised (ACE-R)

OBJECTIVE

The purpose of this study is to provide evidence that supports the validity and reliability of the Colombian version of the Addenbrooke's Cognitive Examination Revised (ACE-R) in comparison to the MMSE at assessing and finding patients with Mild Cognitive Impairment (MCI). Additionally, the study aims to determine the optimal cut-off scores based on the age of a population with a low education level.

METHOD

This study included 314 individuals (235 participants diagnosed with MCI and 79 cognitively healthy) who live in two different rural departments (states) in Colombia. The participants were recruited for this study through community clubs for the older adults. Most of the individuals were female (236), the average age was 65.95 years of age (SD= 7.8), and the average education level was of 3.78 years (SD = 1.79). It is important to note that the sample only included people with a maximum of 6 years of schooling.

RESULTS

A ROC analysis indicated that the ACE-R is more effective than the MMSE at evaluating and finding MCI individuals within the three groups. The cut-off points for the Under 60 years of age group was 83.50 (sensitivity 0.880% and specificity 0.632%); 61-69 years of age 80.50 (sensitivity 0.714% and specificity 0.677%); and Over 70 years of age was 79.50 (sensitivity 0.750% and specificity 0.659%). The internal consistency analysis with MacDonald's Ω determined reliability indicators ≥70 in the ACE-R, except for the age range of 61 to 69 years.

CONCLUSION

The Colombian version of the ACE-R demonstrates to be a valid and reliable global cognitive screening tool. It is effective at discerning MCI individuals from healthy within a group of participants with a low education level.

Factors associated with healthy aging in Latin American populations

Latin American populations may present patterns of sociodemographic, ethnic and cultural diversity that can defy current universal models of healthy aging. The potential combination of risk factors that influence aging across populations in Latin American and Caribbean (LAC) countries is unknown. Compared to other regions where classical factors such as age and sex drive healthy aging, higher disparity-related factors and between-country variability could influence healthy aging in LAC countries. We investigated the combined impact of social determinants of health (SDH), lifestyle factors, cardiometabolic factors, mental health symptoms and demographics (age, sex) on healthy aging (cognition and functional ability) across LAC countries with different levels of socioeconomic development using cross-sectional and longitudinal machine learning models (n = 44,394 participants). Risk factors associated with social and health disparities, including SDH (β > 0.3), mental health (β > 0.6) and cardiometabolic risks (β > 0.22), significantly influenced healthy aging more than age and sex (with null or smaller effects: β < 0.2). These heterogeneous patterns were more pronounced in low-income to middle-income LAC countries compared to high-income LAC countries (cross-sectional comparisons), and in an upper-income to middle-income LAC country, Costa Rica, compared to China, a non-upper-income to middle-income LAC country (longitudinal comparisons). These inequity-associated and region-specific patterns inform national risk assessments of healthy aging in LAC countries and regionally tailored public health interventions.

Fear of Falling as a Behavioral Symptom in Neurocognitive Impaired Patients: Evidence from an Underrepresented Population

BACKGROUND

Fear of falling (FoF) is a condition associated with falls, multi-morbidity, and functional impairment. To date it remains unknow which clinical, somatic, socio-demographic, behavioral, and emotional factors are associated with FoF and how these factors interact in people with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD).

OBJECTIVE

Identify the association of FoF with clinical, socio-demographic, and neuropsychiatric factors in patients with AD and bvFTD.

METHODS

We evaluated 98 participants, 58 with AD and 40 with bvFTD at mild or moderate stages and assess FoF using the Falls Efficacy Scale-International. Additionally, we analyzed cognitive, physical performance variables, functional impairment, and affective and behavioral symptoms associated with FoF using standardized scales and a regression model analysis.

RESULTS

The prevalence of FoF in AD and bvFTD was 51% and 40%, respectively. In the AD group, physical performance [F (3, 53) = 4.318, p = 0.009], the behavioral symptoms model [F (19, 38) = 3.314, p = 0.001], and the anxiety model [F (1, 56) = 13.4, p≤0.01] showed statistically significant values. In addition, the presence of hallucinations assessed with the Neuropsychiatric Inventory and social behavior assessed with the Mild Behavioral Impairment Checklist were significant. In contrast, in the bvFTD group, a homologous group of models was evaluated but we did not find any significant results.

CONCLUSION

FoF in people with AD was related to physical performance, neuropsychiatric symptoms such as apathy and hallucinations, and affective symptoms such as anxiety. However, this pattern was not seen in the bvFTD group, and therefore further studies are required.

Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples

BACKGROUND

Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines.

METHODS

We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations.

FINDINGS

Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility.

INTERPRETATION

The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples.

FUNDING

The specific funding of this article is provided in the acknowledgements section.