Background: The association between body size, weight change and depression has not been systematically summarised, especially for individuals who are underweight.Aims: To conduct a systematic review and a meta-analysis to examine the association between indices of body size, weight change and depression.Method: A total of 183 studies were selected. Fully adjusted hazard ratios (HRs) or odds ratios (ORs) were extracted. A total of 76 studies contributed to data synthesis with a random-effect model, and subgroup analyses were conducted to evaluate the effect of potential moderators.Results: In cohort studies, underweight at baseline increased the risk of subsequent depression (OR = 1.16, 95% CI 1.08–1.24). Overweight (BMI 25–29.9 kg/m²) showed no statistically significant relationship with depression overall; however, the subgroup analyses found different results according to gender (men: OR = 0.84, 95% CI 0.72–0.97, women: OR = 1.16, 95% CI 1.07–1.25). In cross-sectional designs, obesity with BMI >40kg/m²showed a greater pooled odds ratio than obesity with BMI >30kg/m².Conclusions: Both underweight and obesity increase the risk of depression. The association between overweight and depression differs by gender.

Background: Public isolated due to the early quarantine regarding coronavirus disease 2019 (COVID-19) increasingly used more social media platforms. Contradictory claims regarding the effect of social media use on mental health needs to be resolved. The purpose of the study was to summarise the association between the time spent on social media platform during the COVID-19 quarantine and mental health outcomes (i.e., anxiety and depression). Methods: Studies were screened from the PubMed, Embase, and Cochrane Library databases. Regarding eligibility criteria, studies conducted after the declaration of the pandemic, studies that measured mental health symptoms with validated tools, and studies that presented quantitative results were eligible. The studies after retrieval evaluated the association between time spent on social media platform and mental health outcomes (i.e. anxiety and depression). The pooled estimates of retrieved studies were summarised in odds ratios (ORs). Data analyses included a random-effect model and an assessment of inter-study heterogeneity. Quality assessment was conducted by two independent researchers using the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS). This meta-analysis review was registered in PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, registration No CRD42021260223, 15 June 2021). Results: Fourteen studies were included. The increase in the time spent using social media platforms were associated with anxiety symptoms in overall studies (pooled OR = 1.55, 95% CI: 1.30–1.85), and the heterogeneity between studies was mild (I² = 26.77%). Similarly, the increase in social media use time was also associated with depressive symptoms (pooled OR = 1.43, 95% CI: 1.30–1.85), and the heterogeneity between studies was moderate (I² = 67.16%). For sensitivity analysis, the results of analysis including only the “High quality” studies after quality assessment were similar to those of the overall study with low heterogeneity (anxiety: pooled OR = 1.45, 95% CI: 1.21–1.96, I² = 0.00%; depression: pooled OR = 1.42, 95% CI: 0.69–2.90, I² = 0.00%). Conclusions: The analysis demonstrated that the excessive time spent on social media platform was associated with a greater likelihood of having symptoms of anxiety and depression.

Traditional epidemiological assessments, which mainly focused on evaluating the statistical association between two major components-the exposure and outcome-have recently evolved to ascertain the in-between process, which can explain the underlying causal pathway. Mediation analysis has emerged as a compelling method to disentangle the complex nature of these pathways. The statistical method of mediation analysis has evolved from simple regression analysis to causal mediation analysis, and each amendment refined the underlying mathematical theory and required assumptions. This short guide will introduce the basic statistical framework and assumptions of both traditional and modern mediation analyses, providing examples conducted with real-world data.

Although there are plausible mechanisms of female hormones in depression, epidemiological evidence has shown conflicting results. This study aimed to evaluate the associations between female hormonal events and post-menopausal depression and further investigate the relative association between the age of menarche, the age of menopause and depression. Among 111,589 women who took part in the Health Examinees (HEXA) Study, a total of 60,114 postmenopausal participants were included in the final analysis. Each participant provided information on depression and questions related to reproductive history. The outcome variable was self-reported by the history of depression diagnosed by physicians and the Center for Epidemiologic Studies Depression Scale (CES-D) score. With the multivariate logistic regression, odds ratios were calculated. Possible interactions between depression prevalence and the ages of menarche and menopause were assessed. A total of 2.2% (1342/60,114) women were diagnosed with depression after menopause, and 5.9% (500/8472) showed depressive symptoms. As the age of menopause and duration of reproductive years increased, the odds ratio of depression decreased (P-trend <0.001). As the age of menarche increased, the likelihood of physician-diagnosed depression also increased (P-trend 0.048). As the number of both spontaneous and induced abortions increased, the odds ratio of depression increased (P-trend <0.001). it is possible that women show inaccuracies in recalling their hormonal events and reporting other past mental disorders as depression. Both the ages of the initiation and the termination of menstruation were associated with the increased odds ratio of post-menopausal depression. However, the age of menopause seems to be more important.

There has been a growing interest in the relationship between sedative-hypnotics use and the risk of Alzheimer’s dementia (AD) risk. This study aimed to evaluate the risk of AD associated with the use of sedative-hypnotics. A retrospective cohort study was conducted with randomly selected 5% samples from ≥50 years old beneficiaries of National Health Insurance Service (NHIS) of Korea from January 2002 to December 2015. The exposure to sedative-hypnotics was defined when prescribed over 30 defined daily dose (DDD) after January 2004 and it was categorized by prescribed dosage, types and half-lives of benzodiazepines. Time-dependent Cox regression model with a lag period of 5-years was used to evaluate the association between use of sedative-hypnotics and the risk of subsequent AD. Sensitivity analysis was performed for restricting sedative-hypnotics only when prescribed with insomnia. A total of 268,170 subjects were identified and subjects exposed to sedative-hypnotics showed a higher risk of AD (HR: 1.79; 95% CI: 1.72–1.86) than those who were not. There was an increased risk of AD among subjects exposed to benzodiazepines or zolpidem (HR: 1.75; 95% CI: 1.67–1.82) and antidepressants or low-dose antipsychotics (HR: 1.63; 95% CI: 1.42–1.87). The risk of AD was increased regardless of dose of sedative-hypnotics and half-life among benzodiazepines, especially in exposure to more than 360 DDD of sedative-hypnotics (HR: 1.78; 95% CI: 1.60–1.99) and the long-acting benzodiazepine (HR:1.77; 95% CI: 1.65–1.89).