Background Individuals with depression are often found to perform worse on cognitive tests and to have an increased risk of dementia. The causes and the direction of these associations are however not well understood. We looked at two specific hypotheses, the aetiological risk factor hypothesis and the reverse causality hypothesis. Method We analysed observational data from two cohorts, English Longitudinal Study of Ageing (ELSA) and Health and Retirement Study (HRS), using cross-lagged panel models with unit fixed effects. Each model was run once with depression and repeated with cognition as the dependent variable and the other variable as the main explanatory variable. All models were estimated separately for contemporaneous effects and lagged effects up to 8 years in the past. We contrasted the results with models making the random effects assumption. Results Evidence from the fixed effects models is mixed. We find no evidence for the reverse causality hypothesis in ELSA and HRS. While there is no evidence for the aetiological risk factors hypothesis in ELSA, results from HRS indicate some effects. Conclusion Our findings suggest that current levels of cognitive function do not influence future levels of depression. Results in HRS provide some evidence that current levels of depressive symptoms influence future cognition.

Background: Experiences of chronic stress and trauma are major risk factors for psychiatric illness. Evidence suggests that adversity-related changes in brain structure and function accelerate this vulnerability. It is yet to be determined whether neuroendocrine effects on the brain are a result of the interference with neural development during sensitive periods or a consequence of cumulative lifetime adversity. To address this question, the present study investigated the associations between brain structure and self-reported data of childhood and adult adversity using machine learning techniques and structural equation models (SEM). Methods: The UK Biobank resource was used to access Imaging Derived Phenotypes (IDPs) of grey matter and white matter tract integrity of 7003 participants, together with selected childhood and adult adversity data. Latent measures of adversity and imaging phenotypes were estimated to evaluate their associations using SEM. Results: We demonstrated that increased incidence of childhood adversity events may be associated with smaller grey matter in frontal, insular, subcallosal and cerebellar regions of the brain. There were no significant associations between brain phenotypes and negative experiences during adulthood. Conclusions: Using a large population cohort dataset, this study contributes to the suggestion that childhood adversity may determine grey matter reductions in brain regions, which are putatively sensitive to the neurotoxic effects of chronic stress. Furthermore, it provides novel evidence to support the "sensitive periods" model though which adversity affects the brain.

Background Conceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as chronic and, although they may be pathologically related, they may also act independently 1. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition. Objectives To examine whether anxiety and/or depression are important longitudinal predictors of cognitive change. Methods UK Biobank participants used at three time points (n= 502,664): baseline, 1st follow-up (n= 20,257) and 1st imaging study (n=40,199). Participants with no missing data were 1,175 participants aged 40 to 70 years, 41% female. Machine learning (ML) was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used. Findings Using the area under the Receiver Operating Characteristic (ROC) curve, the anxiety model achieves the best performance with an Area Under the Curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively. Conclusions Outcomes suggest psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline. Clinical implications Important predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.