Mood and anxiety disorders are heterogeneous conditions with variable course. Knowledge on latent classes and transitions between these classes over time based on longitudinal disorder status information provides insight into clustering of meaningful groups with different disease prognosis.

Data of all four waves of the Netherlands Mental Health Survey and Incidence Study-2 were used, a representative population-based study of adults (mean duration between two successive waves = 3 years; N at T0 = 6646; T1 = 5303; T2 = 4618; T3 = 4007; this results in a total number of data points: 20 574). Presence of eight mood and anxiety DSM-IV disorders was assessed with the Composite International Diagnostic Interview. Latent class analysis and latent Markov modelling were used.

The best fitting model identified four classes: a healthy class (prevalence: 94.1%), depressed-worried class (3.6%; moderate-to-high proportions of mood disorders and generalized anxiety disorder (GAD)), fear class (1.8%; moderate-to-high proportions of panic and phobia disorders) and high comorbidity class (0.6%). In longitudinal analyses over a three-year period, the minority of those in the depressed-worried and high comorbidity class persisted in their class over time (36.5% and 38.4%, respectively), whereas the majority in the fear class did (67.3%). Suggestive of recovery is switching to the healthy class, this was 39.7% in the depressed-worried class, 12.5% in the fear class and 7.0% in the high comorbidity class.

People with panic or phobia disorders have a considerably more persistent and chronic disease course than those with depressive disorders including GAD. Consequently, they could especially benefit from longer-term monitoring and disease management.

Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers).

Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs).

Smoking (HRs range 1.04–1.10) and physical inactivity (HRs range 1.01–1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03–1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01).

Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.

Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and overlapping nature of symptom endorsement in a population-based sample across three of the most common categories of psychiatric disorders: depressive disorders, anxiety disorders, and sleep–wake disorders using unsupervised machine learning approaches.

We assessed a total of 43 symptoms in a discovery sample of 6,602 participants of the population-based Rotterdam Study between 2009 and 2013, and in a replication sample of 3,005 participants between 2016 and 2020. Symptoms were assessed using the Center for Epidemiologic Studies Depression Scale, the Hospital Anxiety and Depression Scale, and the Pittsburgh Sleep Quality Index. Hierarchical clustering analysis was applied on test items and participants to investigate common patterns of symptoms co-occurrence, and further quantitatively investigated with clustering methods to find groups that may represent similar psychiatric phenotypes.

First, clustering analyses of the questionnaire items suggested a three-cluster solution representing clusters of “mixed” symptoms, “depressed affect and nervousness”, and “troubled sleep and interpersonal problems”. A highly similar clustering solution was independently established in the replication sample. Second, four groups of participants could be separated, and these groups scored differently on the item clusters.

We identified three clusters of psychiatric symptoms that most commonly co-occur in a population-based sample. These symptoms clustered stable over samples, but across the topics of depression, anxiety, and poor sleep. We identified four groups of participants that share (sub)clinical symptoms and might benefit from similar prevention or treatment strategies, despite potentially diverging, or lack of, diagnoses.

Cerebrovascular disease is regarded as a potential cause of late-life depression. Yet, evidence for associations of neuroimaging markers of vascular brain disease with depressive symptoms is inconclusive. We examined the associations of neuroimaging markers and depressive symptoms in a large population-based study of middle-aged and elderly persons over time.

A total of 4943 participants (mean age = 64.6 ± 11.1 years, 55.7% women) from the Rotterdam Study were included. At baseline, total brain volume, gray matter volume, white matter volume, white matter hyperintensities volume, cortical infarcts, lacunar infarcts, microbleeds, white matter fractional anisotropy, and mean diffusivity (MD) were measured with a brain MRI (1.5T). Depressive symptoms were assessed twice with the Center for Epidemiologic Studies Depression scale (median follow-up time: 5.5 years, IQR = 0.9). To assess temporal associations of neuroimaging markers and depressive symptoms, linear mixed models were used.

A smaller total brain volume (β = −0.107, 95% CI −0.192 to −0.022), larger white matter hyperintensities volume (β = 0.047, 95% CI 0.010–0.084), presence of cortical infarcts (β = 0.194, 95% CI 0.047–0.341), and higher MD levels (β = 0.060, 95% CI 0.022–0.098) were cross-sectionally associated with more depressive symptoms. Longitudinal analyses showed that small total brain volume (β = −0.091, 95% CI −0.167 to −0.015) and presence of cortical infarcts (β = 0.168, 95% CI 0.022–0.314) were associated with increasing depressive symptoms over time. After stratification on age, effect sizes were more pronounced at older ages.

Neuroimaging markers of white matter microstructural damage were associated with depressive symptoms longitudinally in this study of middle-aged and elderly persons. These associations were more pronounced at older ages, providing evidence for the role of white matter structure in late-life depressive symptomatology.

The coronavirus disease-2019 (COVID-19) pandemic and accompanying lockdown restrictions impacted social life significantly. We studied associations of sociodemographic factors, mental and social health markers, and brain structure with social health trajectories during the COVID-19 pandemic.

Prospective longitudinal population-based cohort study.

Community-dwelling inhabitants of Rotterdam, the Netherlands.

Repeated questionnaires including questions on social health were sent to Rotterdam Study participants from April 2020 onwards. Social health data at study baseline were available for 5017 participants (mean age: 68.7 ± 11.3; 56.9% women).

Determinants were assessed in routine Rotterdam Study follow-up (1990–2020), including global brain volumes in a subset of participants (N = 1720). We applied linear mixed models and generalized estimating equations to quantify associations between determinants and trajectories of loneliness, perceived social isolation and social connectedness over three time points from April 22nd to July 31st 2020.

Loneliness prevalence was 27.9% in April 2020 versus 12.6% prepandemic. Social isolation (baseline mean 4.7 ± 2.4) and loneliness scores (baseline mean 4.9 ± 1.5) decreased over time, whereas social connectedness trajectories remained stable. Depressive symptoms, female sex, prepandemic loneliness, living alone, and not owning a pet were independently associated with lower social connectedness and higher social isolation and loneliness at COVID-19 baseline, but recovery of social health was similar for all determinants. Larger intracranial volume was associated with higher social connectedness.

Despite baseline differences for specific determinants, older adults showed similar recovery of loneliness and social isolation alongside stable social connectedness over time during the pandemic. Social health is multidimensional, especially during a global health crisis.

In older populations disturbed 24-h activity rhythms, poor sleep, and depressive symptoms are often lingering and co-morbid, making treatment difficult. To improve insights into these commonly co-occurring problems, we assessed the bidirectional association of sleep and 24-h activity rhythms with depressive symptoms in middle-aged and elderly persons.

In 1734 participants (mean age: 62.3 ± 9.3 years, 55% women) from the prospective Rotterdam Study, 24-h activity rhythms and sleep were estimated with actigraphy (mean duration: 146 ± 19.6 h), sleep quality with the Pittsburgh Sleep Quality Index, and depressive symptoms with the Center for Epidemiological Studies Depression scale. Repeated measures were available for 947 participants (54%) over a median follow-up of 6 years (interquartile range = 5.6–6.3). Linear-mixed models were used to assess temporal associations of 24-h activity rhythms and sleep with depressive symptoms in both directions.

High 24-h activity rhythm fragmentation (IV) (B = 1.002, 95% confidence interval (CI) = 0.641–1.363), long time in bed (TIB) (B = 0.111, 95% CI = 0.053–0.169), low sleep efficiency (SE) (B = −0.015, 95% CI = −0.020 to −0.009), long sleep onset latency (SOL) (B = 0.009, 95% CI = 0.006–0.012), and low self-rated sleep quality (B = 0.112, 95% CI = 0.0992–0.124) at baseline were associated with increasing depressive symptoms over time. Conversely, more depressive symptoms at baseline were associated with an increasing 24-h activity rhythm fragmentation (B = 0.002, 95% CI = 0.001–0.003) and TIB (B = 0.009, 95% CI = 0.004–0.015), and a decreasing SE (B = −0.140, 95% CI = −0.196 to −0.084), SOL (B = 0.013, 95% CI = 0.008–0.018), and self-rated sleep quality (B = 0.193, 95% CI = 0.171–0.215) over time.

This study demonstrates a bidirectional association of 24-h activity rhythms, actigraphy-estimated sleep, and self-rated sleep quality with depressive symptoms over a time frame of multiple years in middle-aged and elderly persons.

Insomnia and depression are highly comorbid and mutually exacerbate clinical trajectories and outcomes. Cognitive behavioral therapy for insomnia (CBT-I) effectively reduces both insomnia and depression severity, and can be delivered digitally. This could substantially increase the accessibility to CBT-I, which could reduce the health disparities related to insomnia; however, the efficacy of digital CBT-I (dCBT-I) across a range of demographic groups has not yet been adequately examined. This randomized placebo-controlled trial examined the efficacy of dCBT-I in reducing both insomnia and depression across a wide range of demographic groups.

Of 1358 individuals with insomnia randomized, a final sample of 358 were retained in the dCBT-I condition and 300 in the online sleep education condition. Severity of insomnia and depression was examined as a dependent variable. Race, socioeconomic status (SES; household income and education), gender, and age were also tested as independent moderators of treatment effects.

The dCBT-I condition yielded greater reductions in both insomnia and depression severity than sleep education, with significantly higher rates of remission following treatment. Demographic variables (i.e. income, race, sex, age, education) were not significant moderators of the treatment effects, suggesting that dCBT-I is comparably efficacious across a wide range of demographic groups. Furthermore, while differences in attrition were found based on SES, attrition did not differ between white and black participants.

Results provide evidence that the wide dissemination of dCBT-I may effectively target both insomnia and comorbid depression across a wide spectrum of the population.