Depression is a highly prevalent, multifactorial, complex disorder, its etiology is assumed to involve both genetic and environmental factors. Genetic factors, including biological clock genes such as CLOCK and SIRT1, have been linked to depression, particularly its symptom related sleep disturbances. Environmental factors also play a crucial role in the background of depression, particularly in interaction with genetic factors. Known environmental stress factors include stress caused negative life events or childhood adversities.

This study aims to delve into the chronotype-specific impacts of genes previously correlated with circadian functionality on the pathomechanism of depression in interaction with environmental stress factors.

A genome-wide association study on the ‘morning chronotype’ phenotype was conducted with Plink2, utilizing data from the UK Biobank discovery sample (N = 139135). Using LDPred2we derived a polygenic risk score (PRS) for the NewMood Hungarian dataset (N = 1820). We performed pathway-specific analyses including genes implicated within the genetic pathway, drawing on prior research findings. Specifically, we selected the top genes (with a false discovery rate-corrected p-value < 0.05) from the “responders vs. non-responders” analysis conducted by Jerome C. Foo et al.Transl Psychiatry 2019; 9 343). We performed a main effect analysis investigating the pathway specific PRS’s effect on BSI depression scores and interaction analyses using life course (number of negative life events in the past life) and recent (number of negative life events in the past year) stress scores to investigate how the interaction term predicts depression in our target sample.

Our primary analysis revealed a nominally significant protective effect (beta = -20.90938, p = 0.070218). Subsequently, in the context of our interaction analysis, we identified significant risk associations, both with lifetime stress (beta = 13.7416, p = 0.0171) and recent stress (beta = 24.6034, p = 0.0038)

Our study unveiled a protective role in our primary analysis, juxtaposed with risk associations in our interaction analyses. This intriguing dichotomy underscores that this genetic pathway, associated with circadian dysregulation, exerts a protective influence in association with the morning chronotype. However, it transitions into a predisposing factor for depression when influenced by environmental stress factors.

Considering these findings, our study substantiates the hypothesis that both circadian genes and chronotype contribute to the pathogenesis and clinical manifestation of depression. Additionally, it underscores the pivotal role of stress as a contributing factor in the intricate pathogenesis of depression.

None Declared

Diagnosis of acute ischemia typically relies on evidence of ischemic lesions on magnetic resonance imaging (MRI), a limited diagnostic resource. We aimed to determine associations of clinical variables and acute infarcts on MRI in patients with suspected low-risk transient ischemic attack (TIA) and minor stroke and to assess their predictive ability.

We conducted a post-hoc analysis of the Diagnosis of Uncertain-Origin Benign Transient Neurological Symptoms (DOUBT) study, a prospective, multicenter cohort study investigating the frequency of acute infarcts in patients with low-risk neurological symptoms. Primary outcome parameter was defined as diffusion-weighted imaging (DWI)-positive lesions on MRI. Logistic regression analysis was performed to evaluate associations of clinical characteristics with MRI-DWI-positivity. Model performance was evaluated by Harrel’s c-statistic.

In 1028 patients, age (Odds Ratio (OR) 1.03, 95% Confidence Interval (CI) 1.01–1.05), motor (OR 2.18, 95%CI 1.27–3.65) or speech symptoms (OR 2.53, 95%CI 1.28–4.80), and no previous identical event (OR 1.75, 95%CI 1.07–2.99) were positively associated with MRI-DWI-positivity. Female sex (OR 0.47, 95%CI 0.32–0.68), dizziness and gait instability (OR 0.34, 95%CI 0.14–0.69), normal exam (OR 0.55, 95%CI 0.35–0.85) and resolved symptoms (OR 0.49, 95%CI 0.30–0.78) were negatively associated. Symptom duration and any additional symptoms/symptom combinations were not associated. Predictive ability of the model was moderate (c-statistic 0.72, 95%CI 0.69–0.77).

Detailed clinical information is helpful in assessing the risk of ischemia in patients with low-risk neurological events, but a predictive model had only moderate discriminative ability. Patients with clinically suspected low-risk TIA or minor stroke require MRI to confirm the diagnosis of cerebral ischemia.

Geriatric depression (GD) is associated with cognitive impairment and brain atrophy. Tai-Chi-Chih (TCC) is a promising adjunct treatment to antidepressants. We previously found beneficial effects of TCC on resting state connectivity in GD. We now tested the effect of TCC on gray matter volume (GMV) change and the association between baseline GMV and clinical outcome.

Forty-nine participants with GD (>=60 y) underwent antidepressant treatment (38 women).

Participants completed 3 months of TCC (N = 26) or health and wellness education control (HEW; N = 23).

Depression and anxiety symptoms and MRI scans were acquired at baseline and 3-month follow-up. General linear models (GLMs) tested group-by-time interactions on clinical scores. Freesurfer 6.0 was used to process T1-weighted images and to perform voxel-wise whole-brain GLMs of group on symmetrized percent GMV change, and on the baseline GMV and symptom change association, controlling for baseline symptom severity. Age and sex served as covariates in all models.

There were no group differences in baseline demographics or clinical scores, symptom change from baseline to follow-up, or treatment-related GMV change. However, whole-brain analysis revealed that lower baseline GMV in several clusters in the TCC, but not the HEW group, was associated with larger improvements in anxiety. This was similar for right precuneus GMV and depressive symptoms.

While we observed no effect on GMV due to the interventions, baseline regional GMV predicted symptom improvements with TCC but not HEW. Longer trials are needed to investigate the long-term effects of TCC on clinical symptoms and neuroplasticity.

Cigarette smoking prevalence is significantly higher among people with mental health problems than among the general population. Smoking accounts for much of the reduction in life expectancy associated with mental illness, why the high co-occurrence of smoking and mental health illness is a major public health concern. Persons belonging to socioeconomical disadvantaged groups have higher risk of mental health conditions and also higher smoking rates.

In this study we aim to examine smoking trajectories among adult smokers between 2012 and 2020. Furthermore, we aim to investigate differences in smoking trajectories by adult depression by taking into consideration participants intergenerational socioeconomic mobility (ISEM).

Analyses were based on data from CONSTANCES, a French general population cohort conducted from 2012 to 2020. In total were 107,734 participants included after exclusion of never smokers. Depression was measured by the CES-D scale, and depression was classified with a score ≥16. ISEM is based on childhood (maternal and parental occupational grade) and adult socioeconomic position (SEP), and low ISEM includes those with low SEP as child and adult and high ISEM those with consistent high SEP. Group-based trajectories modelling (GBTM) was used to determine smoking status trajectories. To address the association between ISEM and smoking trajectory class we used multinomial logistic regression with former smokers as reference class adjusted for depression, household income, sex and age.

We identified five smoking trajectories 1) Former smokers (56.6%), 2) Long-term smokers (26.4%), 3) Intermediate smokers (3.3%), 4) Early quitters (5.0%) and 5) Late quitters (8.7%). Preliminary results from multinomial logistic regression showed that persons with low ISEM had higher odds of depression (OR [95%CI]=1.91 [1.77;2.06]) than those with high ISEM. Participants with low ISEM had higher odds of being long-term smoking than former smokers compared to those with high ISEM (ORa [95%CI]=1.55 [1.43;1.67]). Furthermore, those with low ISEM had lower odds of being in any of the other smoking trajectory groups vs. former smokers compared to those with high ISEM (ORa [95%CI]=0.82 [0.69;0.97]) for intermediate smokers, ORa [95%CI]=0.75 [0.66;0.85]) for early quitters, and ORa [95%CI]=0.78 [0.70;0.87]) for late quitters).

Preliminary results showed an association between ISEM and smoking trajectories in our study. Persons with low ISEM are more likely to be long-term smokers. Future analysis should consider the effect of depression as a mediating factor on the association between ISEM and smoking trajectories.

None Declared

Geriatric depression (GD) is associated with significant medical comorbidity, cognitive impairment, brain atrophy, premature mortality, and suboptimal treatment response. While apathy and anxiety are common comorbidities, resilience is a protective factor. Understanding the relationships between brain morphometry, depression, and resilience in GD could inform clinical treatment. Only few studies have addressed gray matter volume (GMV) associations with mood and resilience.

Forty-nine adults aged >60 years (38 women) with major depressive disorder undergoing concurrent antidepressant treatment participated in the study.

Anatomical T1-weighted scans, apathy, anxiety, and resilience data were collected. Freesurfer 6.0 was used to preprocess T1-weighted images and qdec to perform voxel-wise whole-brain analyses. Partial Spearman correlations controlling for age and sex tested the associations between clinical scores, and general linear models identified clusters of associations between GMV and clinical scores, with age and sex as covariates. Cluster correction and Monte-Carlo simulations were applied (corrected alpha = 0.05).

Greater depression severity was associated with greater anxiety (r = 0.53, p = 0.0001), lower resilience (r = −0.33, p = 0.03), and greater apathy (r = 0.39, p = 0.01). Greater GMV in widespread, partially overlapping clusters across the brain was associated with reduced anxiety and apathy, as well as increased resilience.

Our results suggest that greater GMV in extended brain regions is a potential marker for resilience in GD, while GMV in more focal and overlapping regions may be markers for depression and anxiety. Interventions focused on improving symptoms in GD may seek to examine their effects on these brain regions.

The COVID-19 pandemic and associated preventive measures have an impact on the persons’ mental health, including increasing risk of symptoms of anxiety and depression in particular. Individual experiencing mental health difficulties in the past could be especially vulnerable during lockdown, however, few studies have tested this empirically considering preexisting mental health difficulties using longitudinal data.

The objective of this study is to examine the longitudinal association between preexisting symptoms of anxiety/depression and symptoms of anxiety/depression during lockdown due to the COVID-19 pandemic in a community sample.

Seven waves of data collection were implemented from March-May 2020. Generalized estimation equations models were used to estimate the association between preexisting symptoms of anxiety/depression and symptoms of anxiety/depression during lockdown among 662 mid-aged individuals from the French TEMPO cohort.

We found an elevated odds ratio of symptoms of anxiety/depression (OR=6.73 95% [CI=4.45–10.17]) among individuals experiencing such symptoms prior lockdown. Furthermore, the odds of symptoms of anxiety/depression during lockdown was elevated among women (OR=2.07 [95% CI=1.32–3.25]), subjects with low household income (OR=2.28 [1.29–4.01]) and persons who reported loneliness (OR=3.94 [2.47–6.28]).

This study demonstrates a strong relationship between preexisting symptoms of anxiety/depression and anxiety/depression during the COVID-19 outbreak among mid-aged French adults. The findings underline the role of preexisting symptoms of anxiety/depression as a vulnerability factor of anxiety/depression during lockdown. Furthermore, the study shows that loneliness is independently associated with symptoms of anxious/depression, when controlling for prior anxiety/depression symptoms.

No significant relationships.

The coronavirus disease 2019 (COVID-19) pandemic might affect mental health. Data from population-representative panel surveys with multiple waves including pre-COVID data investigating risk and protective factors are still rare.

In a stratified random sample of the German household population (n = 6684), we conducted survey-weighted multiple linear regressions to determine the association of various psychological risk and protective factors assessed between 2015 and 2020 with changes in psychological distress [(PD; measured via Patient Health Questionnaire for Depression and Anxiety (PHQ-4)] from pre-pandemic (average of 2016 and 2019) to peri-pandemic (both 2020 and 2021) time points. Control analyses on PD change between two pre-pandemic time points (2016 and 2019) were conducted. Regularized regressions were computed to inform on which factors were statistically most influential in the multicollinear setting.

PHQ-4 scores in 2020 (M = 2.45) and 2021 (M = 2.21) were elevated compared to 2019 (M = 1.79). Several risk factors (catastrophizing, neuroticism, and asking for instrumental support) and protective factors (perceived stress recovery, positive reappraisal, and optimism) were identified for the peri-pandemic outcomes. Control analyses revealed that in pre-pandemic times, neuroticism and optimism were predominantly related to PD changes. Regularized regression mostly confirmed the results and highlighted perceived stress recovery as most consistent influential protective factor across peri-pandemic outcomes.

We identified several psychological risk and protective factors related to PD outcomes during the COVID-19 pandemic. A comparison of pre-pandemic data stresses the relevance of longitudinal assessments to potentially reconcile contradictory findings. Implications and suggestions for targeted prevention and intervention programs during highly stressful times such as pandemics are discussed.

To identify dietary self-monitoring implementation strategies in behavioural weight loss interventions.

We conducted a systematic review of eight databases and examined fifty-nine weight loss intervention studies targeting adults with overweight/obesity that used dietary self-monitoring.

NA.

NA.

We identified self-monitoring implementation characteristics, effectiveness of interventions in supporting weight loss and examined weight loss outcomes among higher and lower intensity dietary self-monitoring protocols. Included studies utilised diverse self-monitoring formats (paper, website, mobile app, phone) and intensity levels (recording all intake or only certain aspects of diet). We found the majority of studies using high- and low-intensity self-monitoring strategies demonstrated statistically significant weight loss in intervention groups compared with control groups.

Based on our findings, lower and higher intensity dietary self-monitoring may support weight loss, but variability in adherence measures and limited analysis of weight loss relative to self-monitoring usage limits our understanding of how these methods compare with each other.

(1) To investigate if gut microbiota can be a predictor of remission in geriatric depression and to identify features of the gut microbiota that is associated with remission. (2) To determine if changes in gut microbiota occur with remission in geriatric depression.

Secondary analysis of a parent randomized placebo-controlled trial (NCT02466958).

Los Angeles, CA, USA (2016-2018)

Seventeen subjects with major depressive disorder, over 60 years of age, 41.2% female.

Levomilacipran (LVM) or placebo.

Remission was defined by Hamilton Depression Rating Scale score of 6 or less at 12 weeks. 16S-ribosomal RNA sequencing based fecal microbiota composition and diversity were measured at baseline and 12 weeks. Differences in fecal microbiota were evaluated between remitters and non-remitters as well as between baseline and post-treatment samples. LVM and placebo groups were combined in all the analyses.

Baseline microbiota showed no community level α-diversity or β-diversity differences between remitters and non-remitters. At the individual taxa level, a random forest classifier created with nine genera from the baseline microbiota was highly accurate in predicting remission (AUC = .857). Of these, baseline enrichment of Faecalibacterium, Agathobacter and Roseburia relative to a reference frame was associated with treatment outcome of remission. Differential abundance analysis revealed significant genus level changes from baseline to post-treatment in remitters, but not in non-remitters.

This is the first study demonstrating fecal microbiota as a potential predictor of treatment response in geriatric depression. Our findings need to be confirmed in larger prospective studies.