Predictors of depression among middle-aged and older men and women in Europe: A machine learning approach

Background: The high prevalence of depression in a growing aging population represents a critical public health issue. It is unclear how social, health, cognitive, and functional variables rank as risk/protective factors for depression among older adults and whether there are conspicuous differences among men and women. Methods: We used random forest analysis (RFA), a machine learning method, to compare 56 risk/protective factors for depression in a large representative sample of European older adults (N = 67,603; ages 45-105y; 56.1% women; 18 countries) from the Survey of Health, Ageing and Retirement in Europe (SHARE Wave 6). Depressive symptoms were assessed using the EURO-D questionnaire: Scores ≥ 4 indicated depression. Predictors included a broad array of sociodemographic, relational, health, lifestyle, and cognitive variables. Findings: Self-rated social isolation and self-rated poor health were the strongest risk factors, accounting for 22.0% (in men) and 22.3% (in women) of variability in depression. Odds ratios (OR) per +1SD in social isolation were 1.99x, 95% CI [1.90,2.08] in men; 1.93x, 95% CI [1.85,2.02] in women. OR for self-rated poor health were 1.93x, 95% CI [1.81,2.05] in men; 1.98x, 95% CI [1.87,2.10] in women. Difficulties in mobility (in both sexes), difficulties in instrumental activities of daily living (in men), and higher self-rated family burden (in women) accounted for an additional but small percentage of variance in depression risk (2.2% in men, 1.5% in women). Interpretation: Among 56 predictors, self-perceived social isolation and self-rated poor health were the most salient risk factors for depression in middle-aged and older men and women. Difficulties in instrumental activities of daily living (in men) and increased family burden (in women) appear to differentially influence depression risk across sexes

Enhanced response inhibition during intensive meditation training predicts improvements in self-reported adaptive socioemotional functioning.

We examined the impact of training-induced improvements in self-regulation, operationalized in terms of response inhibition, on longitudinal changes in self-reported adaptive socioemotional functioning. Data were collected from participants undergoing 3 months of intensive meditation training in an isolated retreat setting (Retreat 1) and a wait-list control group that later underwent identical training (Retreat 2). A 32-min response inhibition task (RIT) was designed to assess sustained self-regulatory control. Adaptive functioning (AF) was operationalized as a single latent factor underlying self-report measures of anxious and avoidant attachment, mindfulness, ego resilience, empathy, the five major personality traits (extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience), diffi-culties in emotion regulation, depression, anxiety, and psychological well-being. Participants in Retreat 1 improved in RIT performance and AF over time whereas the controls did not. The control participants later also improved on both dimensions during their own retreat (Retreat 2). These improved levels of RIT performance and AF were sustained in follow-up assessments conducted approximately 5 months after the training. Longitudinal dynamic models with combined data from both retreats showed that improvement in RIT performance during training influenced the change in AF over time, which is consistent with a key claim in the Buddhist literature that enhanced capacity for self-regulation is an important precursor of changes in emotional well-being

Self-reported mindfulness and cortisol during a Shamatha meditation retreat.

Objective: Cognitive perseverations that include worry and rumination over past or future events may prolong cortisol release, which in turn may contribute to predisease pathways and adversely affect physical health. Meditation training may increase self-reported mindfulness, which has been linked to reductions in cognitive perseverations. However, there are no reports that directly link self-reported mindfulness and resting cortisol output. Here, the authors investigate this link. Methods: In an observational study, we measured self-reported mindfulness and p.m. cortisol near the beginning and end of a 3-month meditation retreat (N = 57). Results: Mindfulness increased from pre- to post-retreat, F(1, 56) = 36.20, p < .001. Cortisol did not significantly change. However, mindfulness was inversely related to p.m. cortisol at pre-retreat, r(53) = −.31, p < .05, and post-retreat, r(53) = −.30, p < .05, controlling for age and body mass index. Pre to postchange in mindfulness was associated with pre to postchange in p.m. cortisol, β = −.37, t(49) = 2.30, p < .05: Larger increases in mindfulness were associated with decreases in p.m. cortisol, whereas smaller increases (or slight decreases) in mindfulness were associated with an increase in p.m. cortisol. Conclusions: These data suggest a relation between self-reported mindfulness and resting output of the hypothalamic-pituitary-adrenal system. Future work should aim to replicate this finding in a larger cohort and determine stronger inference about causality by using experimental designs that include control-group conditions

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

Quantitative Methods in Psychological Aging Research: A Mini-Review

As research on psychological aging moves forward, it is increasingly important to accurately assess longitudinal changes in psychological processes and to account for their (often complex) associations with sociodemographic, lifestyle, and health-related variables. Traditional statistical methods, though time tested and well documented, are not always satisfactory for meeting these aims. In this mini-review, we therefore focus the discussion on recent statistical advances that may be of benefit to researchers in psychological aging but that remain novel in our area of study. We first compare two methods for the treatment of incomplete data, a common problem in longitudinal research. We then discuss robust statistics, which address the question of what to do when critical assumptions of a standard statistical test are not met. Next, we discuss two approaches that are promising for accurately describing phenomena that do not unfold linearly over time: nonlinear mixed-effects models and (generalized) additive models. We conclude by discussing recursive partitioning methods, as these are particularly well suited for exploring complex relations among large sets of variables

Memory Deficits Precede Increases in Depressive Symptoms in Later Adulthood

Objectives: We examined bidirectional, time-ordered associations between agerelated changes in depressive symptoms and memory. Method: Data came from 107,599 community-dwelling adults, aged 49–90 years, who participated in the Survey of Health, Ageing, and Retirement in Europe (SHARE). Depressive symptoms were measured with the EURO-D inventory, and memory was evaluated as delayed recall of a 10-word list. Participants were assessed up to 5 times at 2-year intervals. Dynamic structural equation models were used to estimate longitudinal and time-ordered (lead-lag) relations between depressive symptoms and memory performance. Results: Depressive symptoms increased and memory scores decreased across the observed age range, with worsening mostly evident after age 62 years. These long-term changes were moderately negatively correlated (r = -.53, p < .001). A time-ordered effect emerged such that age-specific memory deficits preceded shorter-term increases in depression symptoms. This effect can be translated such that each 1-point decrement on a 10-point memory scale at a given age predicted a 14.5% increased risk for depression two years later. Statistical adjustment for covariates (sex, education, re-test, smoking, and body mass index) had little influence on these associations. Conclusion: In later adulthood, lower memory performance at a given age predicts subsequent 2-year increases in depressive symptoms

Lifespan Decrements in Fluid Intelligence and Processing Speed Predict Mortality Risk

We examined lifespan changes in five domains of cognitive performance as predictive of mortality risk. Data came from the Manchester Longitudinal Study of Cognition, a 20-plus year investigation of 6203 individuals aged 42–97 years. Cognitive domains included crystallized intelligence, fluid intelligence, verbal memory, visual memory, and processing speed. Lifespan decrements were evident across these domains, controlling for baseline performance at age 70 and adjusting for retest effects. Survival analyses stratified by sex and conducted independently by cognitive domain showed that lower baseline performance levels in all domains—and larger lifespan decrements in fluid intelligence and processing speed—were predictive of increased mortality risk for both females and males. Critically, analyses of the combined predictive power of cognitive performance variables showed that baseline levels of processing speed (in females) and fluid intelligence (in males), and decrements in processing speed (in females and in males) and fluid intelligence (in females), accounted for most of the explained variation in mortality risk. In light of recent evidence from brain imaging studies, we speculate that cognitive abilities closely linked to cerebral white matter integrity (such as processing speed and fluid intelligence) may represent particularly sensitive markers of mortality risk. Additionally, we presume that greater complexity in cognition-survival associations observed in females (in analyses incorporating all cognitive predictors) may be a consequence of longer and more variable cognitive declines in females relative to males