Trajectories of depressive symptoms among community-dwelling Korean older adults: findings from the Korean longitudinal study of aging (2006-2016)

Depressive symptoms and cognitive decline in older adults

OBJECTIVES

Few studies have examined the impact of late-life depression trajectories on specific domains of cognitive function. This study aims to delineate how different depressive symptom trajectories specifically affect cognitive function in older adults.

DESIGN

Prospective longitudinal cohort study.

SETTING

Australia and the United States of America.

PARTICIPANTS

In total, 11,035 community-dwelling older adults with a mean age of 75 years.

MEASUREMENTS

Depressive trajectories were modelled from depressive symptoms according to annual Centre for Epidemiological Studies Depression Scale 10 (CES-D-10) surveys. Four trajectories of depressive symptoms were identified: low ("nondepressed"), consistently mild ("subthreshold depression"), consistently moderate ("persistent depression"), and initially low but increasing ("emerging depression"). Global cognition (Modified Mini-Mental State Examination [3MS]), verbal fluency (Controlled Oral Word Association Test [COWAT]), processing speed (Symbol Digit Modalities Test [SDMT]), episodic memory (Hopkins Verbal Learning Test - Revised [HVLT-R]), and a composite z-score were assessed over a subsequent median 2 years.

RESULTS

Subthreshold depression predicted impaired performance on the SDMT (Cohen's d -0.04) and composite score (-0.03); emerging depression predicted impaired performance on the SDMT (-0.13), HVLT-R (-0.09), 3 MS (-0.08) and composite score (-0.09); and persistent depression predicted impaired performance on the SDMT (-0.08), 3 MS (-0.11), and composite score (-0.09).

CONCLUSIONS

Depressive symptoms are associated with later impaired processing speed. These effects are small. Diverse depression trajectories have different impacts on cognitive function.

Factors Associated with Psychotic and Depressive Symptoms in Methamphetamine Users

Objective: Methamphetamine use has been recognized as a prominent public health issue, which is associated with psychotic and depressive symptoms. This study aimed to assess factors that show a significant relation with psychotic and depressive symptoms in adults who use methamphetamine. Method : We assessed 95 patients who had used methamphetamine within the last month and were admitted to the outpatient treatment clinic. Evaluation of all patients was carried out through face-to-face interviews, and their symptoms were evaluated using different scales. The Scale for the Assessment of Positive Symptoms and the Scale for the Assessment of Negative Symptoms were employed to assess positive and negative symptoms of psychosis, respectively. Depressive symptoms were measured using the Montgomery-Asberg Depression Rating Scale, while illness severity was evaluated using the Clinical Global Impression- Severity Scale. Additionally, functioning status was assessed using the Functioning Assessment Short Test, and withdrawal severity was measured by employing the Amphetamine Cessation Symptom Assessment Scale. Craving severity was evaluated using the Stimulant Craving Questionnaire, anxiety severity using the Hamilton Anxiety Rating Scale, and insight status using the Schedule for Assessment of Insight Expanded. Results: Among the demographic variables, working with family was associated with lower positive symptoms scores (OR = 6.31, P < 0.05). Parole/probation related admissions were associated with lower positive and depressive symptoms scores (OR = 15.06, P = 0.03; OR = 9.87, P = 0.02). Having suicide attempts, number of suicide attempts, and amount of methamphetamine used were found to show association with higher positive (OR = 13.59, P < 0.01; OR = 2.52, P < 0.05; OR = 3.48, P < 0.05, respectively) and depressive symptoms scores (OR = 10.35, P < 0.001; OR = 2.23, P < 0.01; OR = 2.3, P < 0.05). After adjusting for all variables, clinical impression and insight scores remained significantly associated with positive symptoms scores (AOR = 6.74, P < 0.05; AOR = 2.63, P < 0.001, respectively), while anxiety, amphetamine cessation, and positive symptoms scores remained associated with depressive symptoms scores (AOR = 0.48, P < 0.001; AOR = 0.11, P = 0.003; AOR = 0.36, P = 0.02, respectively). Conclusion: This study appears to be the first to examine the associations between clinical variables and both positive symptoms and depressive symptoms in methamphetamine users. Increased attention should be paid to suicide history, anxiety level, amount of methamphetamine use and loss of insight to provide effective treatment in patients with methamphetamine use.

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.

[An Investigation of the Cumulative Effects of Depressive Symptoms on the Cognitive Function in Community-Dwelling Older Adults: Analysis of the Korean Longitudinal Study of Aging]

PURPOSE

This study investigated the cumulative effects of depressive symptoms on cognitive function over time in community-dwelling older adults.

METHODS

Data were investigated from 2,533 community-dwelling older adults who participated in the Korean Longitudinal Study of Aging (KLoSA) from the 5th (2014) to the 8th wave (2020). The association between cumulative depressive symptoms and cognitive function was identified through multiple regression analysis.

RESULTS

When the multiple regression analysis was conducted from each wave, the current depressive symptoms scores and cognitive function scores were negatively associated, regardless of the waves (B5th = -0.26, B6th = -0.26, B7th = -0.26, and B8th = -0.27; all p < .001). Further, when all the previous depressive symptoms scores were added as explanatory variables in the 8th wave, the current one (B8th = -0.09, p < .001) and the previous ones (B5th = -0.11, B6th = -0.09, and B7th = -0.13; all p < .001) were also negatively associated with the cognitive function score. The delta R², which indicates the difference between the model's R² with and without the depressive symptoms scores, was greater in the model with all the previous and current depressive symptoms scores (6.4%) than in the model with only the current depressive symptoms score (3.6%).

CONCLUSION

Depressive symptoms in older adults have a long-term impact. This results in an accumulated adverse effect on the cognitive function. Therefore, to prevent cognitive decline in older adults, we suggest detecting their depressive symptoms early and providing continuous intervention to reduce exposure to long-term depressive symptoms.

Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model

BACKGROUND

Given the rapidly rising proportion of the older population in China and the relatively high prevalence of depressive symptoms among this population, this study aimed to identify the trajectories of depressive symptoms and the factors associated with the trajectory class to gain a better understanding of the long-term course of depressive symptoms in this population.

METHODS

Data were obtained from four wave's survey of the China Health and Retirement Longitudinal Study (CHARLS). A total of 3646 participants who aged 60 years or older during baseline survey, and completed all follow-ups were retained in this study. Depressive symptoms were measured using the 10-item version of the Center for Epidemiologic Studies Depression Scale (CES-D-10). Growth mixture modelling (GMM) was adopted to identify the trajectory classes of depressive symptoms, and both linear and quadratic functions were considered. A multivariate logistic regression model was used to calculate the adjusted odds ratios (ORs) of the associated factors to predict the trajectory class of participants.

RESULTS

A four-class quadratic function model was the best-fitting model for the trajectories of depressive symptoms in the older Chinese population. The four trajectories were labelled as increasing (16.70%), decreasing (12.31%), high and stable (7.30%), and low and stable (63.69%), according to their trends. Except for the low and stable trajectory, the other trajectories were almost above the threshold for depressive symptoms. The multivariate logistic regression model suggested that the trajectories of chronic depressive symptoms could be predicted by being female, living in a village (rural area), having a lower educational level, and having chronic diseases.

CONCLUSIONS

This study identified four depressive symptom trajectories in the older Chinese population and analysed the factors associated with the trajectory class. These findings can provide references for prevention and intervention to reduce the chronic course of depressive symptoms in the older Chinese population.

Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults:Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

BACKGROUND

Falls are the most common adverse outcome of depression in older adults, yet a accurate risk prediction model for falls stratified by distinct long-term trajectories of depressive symptoms is still lacking.

METHODS

We collected the data of 1617 participants from the China Health and Retirement Longitudinal Study register, spanning between 2011 and 2018. The 36 input variables included in the baseline survey were regarded as candidate features. The trajectories of depressive symptoms were classified by the latent class growth model and growth mixture model. Three data balancing technologies and four machine learning algorithms were utilized to develop predictive models for fall classification of depressive prognosis.

RESULTS

Depressive symptom trajectories were divided into four categories, i.e., non-symptoms, new-onset increasing symptoms, slowly decreasing symptoms, and persistent high symptoms. The random forest-TomekLinks model achieved the best performance among the case and incident models with an AUC-ROC of 0.844 and 0.731, respectively. In the chronic model, the gradient boosting decision tree-synthetic minority oversampling technique obtained an AUC-ROC of 0.783. In the three models, the depressive symptom score was the most crucial component. The lung function was a common and significant feature in both the case and the chronic models.

CONCLUSIONS

This study suggests that the ideal model has a good chance of identifying older persons with a high risk of falling stratified by long-term trajectories of depressive symptoms. Baseline depressive symptom score, lung function, income, and injury experience are influential factors associated with falls of depression evolution.

Multimorbidity and Mental Health Trajectories Among Middle-Aged and Older U.S. Adults During the COVID-19 Pandemic: Longitudinal Findings From the COVID-19 Coping Study

Background and Objectives

This study aimed to examine the associations between multimorbidity at the COVID-19 pandemic onset and subsequent longitudinal trajectories of depressive symptoms, anxiety symptoms, and loneliness in middle-aged and older adults over a 12-month follow-up.

Research Design and Methods

Data were from monthly online questionnaires in the COVID-19 Coping Study of U.S. adults aged ≥55 from April/May 2020 through April/May 2021 (N = 4,024). Multimorbidity was defined as having ≥2 versus <2 chronic conditions at baseline. Mental health outcomes were assessed monthly as depressive symptoms (8-item Center for Epidemiologic Studies Depression scale), anxiety symptoms (5-item Beck Anxiety Inventory), and loneliness (3-item UCLA Loneliness Scale). We used multivariable-adjusted population- and attrition-weighted mixed-effects linear models to examine the longitudinal associations between multimorbidity and mental health symptoms.

Results

Multimorbidity at the pandemic onset was associated with elevated depressive (b = 0.37; 95% CI: 0.16–0.59) and anxiety (b = 0.39; 95% CI: 0.15–0.62) symptoms at baseline. Changes in symptoms for all three mental health outcomes were nonlinear over time, with worsening symptoms over the first 6 months of the pandemic (April/May to September/October 2020), followed by improvement in symptoms over the subsequent 6 months (September/October 2020 to April/May 2021). Middle-aged and older adults with multimorbidity experienced faster rates of change in anxiety symptoms and loneliness than those without multimorbidity, with persistently elevated mental health symptomatology throughout the follow-up.

Discussion and Implications

Results highlight the unique and persistent mental health risks experienced by middle-aged and older adults with multimorbidity during the COVID-19 pandemic. The observed improvements in symptoms underscore the mental resilience of these individuals, indicating their adaptation to the ongoing pandemic.