Illness and intelligence are comparatively strong predictors of individual differences in depressive symptoms following middle age

Cognitive and Emotional Factors Influencing the Incorporation of Advice Into Decision Making Across the Adult Lifespan

OBJECTIVES

The present study sought to investigate the influence of advice on decision making in older age, as well as the potential influence of depressive symptoms and age-related differences in the cognitively demanding emotion regulation on advice-taking.

METHOD

A nonclinical sample (N = 156; 50% female; 47 young: M age = 29.87, standard deviation [SD] = 5.58; 54 middle-aged: M age = 50.91, SD = 7.13; 55 older: M age = 72.51, SD = 5.33) completed a judge-advisor task to measure degree of advice-taking, as well as measures of fluid intelligence, depressive symptoms, confidence, perceived advice accuracy, and emotion regulation.

RESULTS

Age did not influence degree of advice-taking. Greater depressive symptoms were associated with more reliance on advice, but only among individuals who identified as emotion regulators. Interestingly, older age was associated with perceiving advice to be less accurate.

DISCUSSION

The study contributes to the sparse literature on advice-taking in older age. Cognitive and emotional factors influence the degree to which advice is incorporated into decision making in consistent ways across the adult lifespan. A key difference is that older adults take as much advice as younger adults despite perceiving the advice to be less accurate.

Why do Middle-Aged Adults Report Worse Mental Health and Wellbeing than Younger Adults? An Exploratory Network Analysis of the Swiss Household Panel Data

Despite the growing consensus that midlife appears to be a particularly vulnerable life phase for lower mental health and wellbeing, little is known about the potential reasons for this phenomenon or who the individuals at higher risk are. Our study used six waves (2013-2018) of the Swiss Household Panel (n = 5,315), to compare the distribution of mental health and wellbeing, as well as their key correlates, between midlife (40-55 years) and younger adults (25-39 years) in Switzerland. Moreover, using network analysis to investigate interrelationships across life domains, we describe the complex interrelations between multiple domain-specific correlates and indicators of both mental health and wellbeing across the two age groups. Middle-aged (age 40-55) individuals reported lower life satisfaction and joy, as well as higher anger, sadness, and worry than young adults (age 25-39), with the effect sizes reaching up to 0.20 Cohen's d. They also reported lower social support, relationships satisfaction, health satisfaction, and higher job demands and job insecurity. Relationships satisfaction and social support were the most consistent correlates across all three indicators of wellbeing in both age groups. Health satisfaction was more strongly, and directly, interrelated with energy and optimism in midlife compared with young adulthood (0.21 vs 0.12, p = 0.007). Job demands were more strongly linked with anger and sadness in midlife. The network model helped us to identify correlates or their clusters with direct and strong links to mental health and wellbeing. We hypothesised that health satisfaction, relationships satisfaction, social support, and job demands may help to explain worse mental health and wellbeing in midlife.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11482-024-10274-4.

Urban-rural disparities in the prevalence and trends of depressive symptoms among Chinese elderly and their associated factors

BACKGROUND

This study aimed to examine urban-rural disparities in the prevalence and trends of depressive symptoms (DS) among Chinese elderly and associated factors.

METHODS

A total of 8025, 7808, and 4887 respondents aged 60 years and above were selected from the China Family Panel Studies (CFPS) in 2016, 2018, and 2020, respectively. DS was assessed using a short version of Center for Epidemiologic Studies Depression Scale (CES-D). Twenty-two associated factors from six categories were included in random forest (RF) models. All urban-rural comparisons were conducted based on good model performance.

RESULTS

The DS prevalence among all rural elderly was significantly higher than corresponding urban elderly. This disparity continued to widen among younger elderly, while it continued to narrow among older elderly. The top 10 common leading factors were sleep quality, self-rated health, life satisfaction, memory ability, child relationship, IADL disability, marital status, educational level, and gender. Urban-rural disparities in sleep quality, interpersonal trust, and child relationship continued to widen, while disparities in multimorbidity, hospitalization status, and frequency of family dinner continued to narrow.

LIMITATION

This study may exist recall bias and lacks causal explanation.

CONCLUSIONS

Significant and continuing disparities in the DS prevalence were observed between urban and rural elderly in China, showing opposite trends in younger and older elderly. The top 10 leading associated factors for DS were nearly consistent across urban and rural elderly, with sleep quality having strongest contribution. Urban-rural disparities in associated factors also showed different trends. This study provides a reference for mental health promotion among Chinese elderly.

Age-Related Trajectories of General Fluid Cognition and Functional Decline in the Health and Retirement Study: A Bivariate Latent Growth Analysis

There have been few studies on associations between age-related declines in fluid cognition and functional ability in population-representative samples of middle-aged and older adults. We used a two-stage process (longitudinal factor analysis followed by structural growth modeling) to estimate bivariate trajectories of age-related changes in general fluid cognition (numeracy, category fluency, executive functioning, and recall memory) and functional limitation (difficulties in daily activities, instrumental activities, and mobility). Data came from the Health and Retirement Study (Waves 2010-2016; N = 14,489; ages 50-85 years). Cognitive ability declined on average by -0.05 SD between ages 50-70 years, then -0.28 SD from 70-85 years. Functional limitation increased on average by +0.22 SD between ages 50-70 years, then +0.68 SD from 70-85 years. Significant individual variation in cognitive and functional changes was observed across age windows. Importantly, cognitive decline in middle age (pre-age 70 years) was strongly correlated with increasing functional limitation (r = -.49, p < .001). After middle age, cognition declined independently of change in functional limitation. To our knowledge, this is the first study to estimate age-related changes in fluid cognitive measures introduced in the HRS between 2010-2016.

Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques

OBJECTIVES

Our aim was to explore the possibility of using machine learning (ML) in predicting the onset and trajectories of depressive symptom in home-based older adults over a 7-year period.

METHODS

Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2650) recruited in the China Health and Retirement Longitudinal Study (CHARLS) were included in the current analysis. The latent class growth modeling (LCGM) and growth mixture modeling (GMM) were used to classify different trajectory classes. Based on the identified trajectory patterns, three ML classification algorithms (i.e. gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUC).

RESULTS

Four trajectories were identified for the depressive symptoms: no symptoms (63.9%), depressive symptoms onset {incident increasing symptoms [new-onset increasing (16.8%)], chronic symptoms [slowly decreasing (12.5%), persistent high (6.8%)]}. Among the analyzed baseline variables, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) score, cognition, sleep time, self-reported memory were the top five important predictors across all trajectories. The mean AUCs of the three predictive models had a range from 0.661 to 0.892.

CONCLUSIONS

ML techniques can be robust in predicting depressive symptom onset and trajectories over a 7-year period with easily accessible sociodemographic and health information.

Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2022.2031868.

Dietary Habit Is Associated with Depression and Intelligence: An Observational and Genome-Wide Environmental Interaction Analysis in the UK Biobank Cohort

Dietary habits have considerable impact on brain development and mental health. Despite long-standing interest in the association of dietary habits with mental health, few population-based studies of dietary habits have assessed depression and fluid intelligence. Our aim is to investigate the association of dietary habits with depression and fluid intelligence. In total, 814 independent loci were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habit-related traits. The individual genotype data were obtained from the UK Biobank cohort. Regression analyses were then conducted to evaluate the association of dietary habits with depression and fluid intelligence, respectively. PLINK 2.0 was utilized to detect the single nucleotide polymorphism (SNP) × dietary habit interaction effect on the risks of depression and fluid intelligence. We detected 22 common dietary habit-related traits shared by depression and fluid intelligence, such as red wine glasses per month, and overall alcohol intake. For interaction analysis, we detected that OLFM1 interacted with champagne/white wine in depression, while SYNPO2 interacted with coffee type in fluid intelligence. Our study results provide novel useful information for understanding how eating habits affect the fluid intelligence and depression.

Fluid Intelligence Predicts Change in Depressive Symptoms in Later Life: The Lothian Birth Cohort 1936

We examined reciprocal, time-ordered associations between age-related changes in fluid intelligence and depressive symptoms. Participants were 1,091 community-dwelling older adults from the Lothian Birth Cohort 1936 study who were assessed repeatedly at 3-year intervals between the ages of 70 and 79 years. On average, fluid intelligence and depressive symptoms worsened with age. There was also a dynamic-coupling effect, in which low fluid intelligence at a given age predicted increasing depressive symptoms across the following 3-year interval, whereas the converse did not hold. Model comparisons showed that this coupling parameter significantly improved overall fit and had a correspondingly moderately strong effect size, accounting on average for an accumulated 0.9 standard-deviation increase in depressive symptoms, following lower cognitive performance, across the observed age range. Adjustment for sociodemographic and health-related covariates did not significantly attenuate this association. This implies that monitoring for cognitive decrements in later life may expedite interventions to reduce related increases in depression risk.