Social determinants of health and cognitive function: A cross-sectional study among men without dementia

Association between residential environment quality with mild cognitive impairment among middle and elderly adults in China

BACKGROUND

Most studies have focused on the effects of individual environmental risk factors on cognitive function; however, none have evaluated the association between residential environmental quality and cognitive impairment.

METHODS

Data from the China Health and Retirement Longitudinal Study (CHARLS) were used to include 12,801 participants in a cross-sectional study and 8781 participants in a cohort study. Residential environmental quality was assessed using indicators such as particulate matter, types of household fuel, water sources, indoor temperature, and building types. Based on the residential environment quality score, participants were classified into three groups: comfortable (0-1 points), moderate (2-3 points), and poor (4-6 points). To evaluate the association between residential environmental quality and cognitive scores in the cross-sectional study, as well as the development of mild cognitive impairment (MCI) in the cohort study, ordinary least squares (OLS) regression and logistic regression models were applied.

RESULTS

In the cross-sectional study, cognitive scores and performance across four dimensions-orientation, computation, memory, and drawing-showed a significant decline from the comfortable to the poor residential environment groups. In the fully adjusted OLS regression model, scores across these dimensions were significantly reduced in the moderate and poor groups compared to the comfortable group (P for trend <0.001). The incidence of MCI from 2011 to 2018 was 10.1 %, 16.8 %, and 18.8 % for participants living in comfortable, moderate, and poor environments, respectively, with statistically significant differences among groups (all P < 0.07). Logistic regression analysis revealed an odds ratio of 1.25 (95 % CI: 1.02-1.53) for the moderate group and 1.31 (95 % CI: 1.04-1.65) for the poor group, compared to the comfortable group (P for trend<0.05).

CONCLUSIONS

An inferior residential environment is associated with lower cognitive scores and a higher rik of developing MCI in middle-aged and older Chinese adults.

The Association Between Major Depression and Alzheimer's Disease Risk: Evidence from a 12-Year Longitudinal Study

Background: The relationship between depression, particularly major depression (MD), as a risk factor for Alzheimer's disease (AD) is well established; however, its precise role remains contested. Findings from the fourth wave of the ZARADEMP longitudinal study provide further insights into the association between MD and AD risk. Objectives: This study aimed to examine the association between MD and incident AD, controlling for established risk factors. Methods: The study analyzed 4803 participants, of whom 4057 were followed over a 12-year period as part of the ZARADEMP longitudinal study. Depression was assessed using the GMS-AGECAT, and dementia was diagnosed according to DSM-IV criteria. The association between MD and incident AD was evaluated using Cox proportional hazards regression models. Results: The incidence of AD was approximately twice as high in participants with MD compared to those without (relative risk = 2.07; 95% CI: 0.85-5.03; p = 0.123). This risk was nearly threefold higher in the fully adjusted model. Conclusions: These findings underscore a significant association between MD and an increased risk of AD, emphasizing the need for vigilant monitoring and potential early intervention among individuals diagnosed with MD.

Socioeconomic and medical determinants of state-level subjective cognitive decline in the United States

INTRODUCTION

It is important to understand the socioeconomic and medical determinants of subjective cognitive decline (SCD) at a population level in the United States.

METHODS

The primary outcomes are state-level rates of SCD and SCD-related functional impairment in adults aged ≥ 45, both measured in the Behavioral Risk Factor Surveillance System from 2016 to 2022. The exposures are state-level rates of poverty, unemployment, homelessness, college education, racial and ethnic minorities, uninsurance, smoking, hypertension, diabetes, and obesity as well as household income and physician density.

RESULTS

The strongest state-level associations with rates of SCD were the prevalence of diabetes (rho = 0.64), hypertension (rho = 0.59), and poverty (rho = 0.58; all p < 0.001), and with SCD-related functional impairment were prevalence of poverty (rho = 0.71), diabetes (rho = 0.68), and hypertension (rho = 0.53; all p < 0.001).

DISCUSSION

This study highlights critical links between SCD and socioeconomic and medical determinants in adults aged ≥ 45 in the United States, including the prevalence of poverty, diabetes, and hypertension.

HIGHLIGHTS

State-level analysis reveals socioeconomic and medical risk factors for subjective cognitive decline (SCD) at a population level. The prevalence of poverty is a critical contributor to the state-level prevalence of SCD. The prevalence of diabetes and hypertension are also strong state-level determinants of SCD. Addressing the burden of cognitive decline at the population level necessitates targeting socioeconomic and medical factors.

Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19

The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.