Evidence suggests a familial coaggregation of major psychiatric disorders, including schizophrenia, bipolar disorder, major depression (MDD), autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). Those disorders are further related to suicide and accidental death. However, whether death by suicide may coaggregate with accidental death and major psychiatric disorders within families remains unclear.

To clarify the familial coaggregation of deaths by suicide with accidental death and five major psychiatric disorders.

Using a database linked to the entire Taiwanese population, 68 214 first-degree relatives of individuals who died by suicide between 2003 and 2017 and 272 856 age- and gender-matched controls were assessed for the risks of death by suicide, accidental death and major psychiatric disorders.

A Poisson regression model showed that the first-degree relatives of individuals who died by suicide were more likely to die by suicide (relative risk RR = 4.61, 95% CI 4.02–5.29) or accident (RR = 1.62, 95% CI 1.43–1.84) or to be diagnosed with schizophrenia (RR = 1.53, 95% CI 1.40–1.66), bipolar disorder (RR = 1.99, 95% CI 1.83–2.16), MDD (RR = 1.98, 95% CI 1.89–2.08) or ADHD (RR = 1.34, 95% CI 1.24–1.44).

Our findings identified a familial coaggregation of death by suicide with accidental death, schizophrenia, major affective disorders and ADHD. Further studies would be required to elucidate the pathological mechanisms underlying this coaggregation.

Evidence has suggested that emotional dysregulation is a transdiagnostic feature in schizophrenia and major affective disorders. However, the relationship between emotional dysregulation and appetite hormone disturbance remains unknown in nonobese adolescents with first-episode schizophrenia, bipolar disorder, and major depressive disorder.

In total, 22 adolescents with schizophrenia; 31 with bipolar disorder; 33 with major depressive disorder; and 41 healthy age-, sex-, and body mass index (BMI)/BMI percentile-matched controls were enrolled for assessing levels of appetite hormones, namely leptin, ghrelin, insulin, and adiponectin. Emotional regulation symptoms were measured using the parent-reported Child Behavior Checklist―Dysregulation Profile.

Adolescents with first-episode schizophrenia, bipolar disorder, and major depressive disorder exhibited greater emotional dysregulation symptoms than the control group (P = .037). Adolescents with bipolar disorder demonstrated higher log-transformed levels of insulin (P = .029) and lower log-transformed levels of leptin (P = .018) compared with the control group. BMI (P < .05) and log-transformed ghrelin levels (P = .028) were positively correlated with emotional dysregulation symptoms.

Emotional dysregulation and appetite hormone disturbance may occur in the early stage of severe mental disorders. Further studies are required to clarify the unidirectional or bidirectional association of emotional dysregulation with BMI/BMI percentile and appetite hormones among patients with severe mental disorder.

The genetic load for major depressive disorder (MDD) may be higher in people who develop MDD earlier in life. This study aimed to investigate whether the parents of adolescents with MDD were more likely to have MDD, bipolar disorder (BD), schizophrenic disorder (SZ), alcohol use disorder, or substance use disorder than the parents of adolescents without MDD. We also examined whether the response to antidepressant treatment predicted the likelihood of parental psychiatric disorders.

In all, 1,758 adolescents aged 12–19 years with antidepressant-resistant depression, 7,032 (1:4) age-/sex-matched adolescents with antidepressant-responsive depression and 7,032 (1:4) age-/sex-matched controls were included. Parental psychiatric disorders of individuals enrolled were assessed.

The parents of the adolescents with MDD were more likely to be diagnosed with MDD, BD, SZ, alcohol use disorder, or substance use disorder than the parents of the control group. The parents of adolescents who were antidepressant resistant and the mothers of adolescents who were either treatment resistant or treatment responsive were more likely to be diagnosed with a psychiatric disorder.

Our study demonstrated that parents of adolescents with MDD may be more likely to be diagnosed with MDD, BD, SZ, alcohol use disorder, or substance use disorder than parents of adolescents without MDD, suggesting the within-disorder transmission and cross-disorder transmission of these psychiatric disorders. Furthermore, the parent’s sex and the response to antidepressant treatment may affect the within-disorder transmission of MDD.

Previous studies have confirmed that miR-146a-5p overexpression suppresses neurogenesis, thereby enhancing depression-like behaviors. However, it remains unclear how miR-146a-5p dysregulation produces in vivo brain structural abnormalities in patients with major depressive disorder (MDD).

In this case–control study, we combined cortical morphology analysis of magnetic resonance imaging (MRI) and miR-146a-5p quantification to investigate the neuropathological effect of miR-146a-5p on cortical thickness in MDD patients. Serum-derived exosomes that were considered to readily cross the blood-brain barrier and contain miR-146a-5p were isolated for miRNA quantification. Moreover, follow-up MRI scans were performed in the MDD patients after 6 weeks of antidepressant treatment to further validate the clinical relevance of the relationship between miR-146a-5p and brain structural abnormalities.

In total, 113 medication-free MDD patients and 107 matched healthy controls were included. Vertex-vise general linear model revealed miR-146a-5p-dependent cortical thinning in MDD patients compared with healthy individuals, i.e., overexpression of miR-146a-5p was associated with reduced cortical thickness in the left orbitofrontal cortex (OFC), anterior cingulate cortex, bilateral lateral occipital cortices (LOCs), etc. Moreover, this relationship between baseline miR-146a-5p and cortical thinning was nonsignificant for all regions in the patients who had received antidepressant treatment, and higher baseline miR-146a-5p expression was found to be related to greater longitudinal cortical thickening in the left OFC and right LOC.

The findings of this study reveal a relationship between miR-146a-5p overexpression and cortical atrophy and thus may help specify the in vivo mediating effect of miR-146a-5p dysregulation on brain structural abnormalities in patients with MDD.

COVID-19 has long-term impacts on public mental health, while few research studies incorporate multidimensional methods to thoroughly characterise the psychological profile of general population and little detailed guidance exists for mental health management during the pandemic. This research aims to capture long-term psychological profile of general population following COVID-19 by integrating trajectory modelling approaches, latent trajectory pattern identification and network analyses.

Longitudinal data were collected from a nationwide sample of 18 804 adults in 12 months after COVID-19 outbreak in China. Patient Health Questionnaire-9, Generalised Anxiety Disorder-7 and Insomnia Severity Index were used to measure depression, anxiety and insomnia, respectively. The unconditional and conditional latent growth curve models were fitted to investigate trajectories and long-term predictors for psychological symptoms. We employed latent growth mixture model to identify the major psychological symptom trajectory patterns, and ran sparse Gaussian graphical models with graphical lasso to explore the evolution of psychopathological network.

At 12 months after COVID-19 outbreak, psychological symptoms generally alleviated, and five psychological symptom trajectories with different demographics were identified: normal stable (63.4%), mild stable (15.3%), mild-increase to decrease (11.7%), mild-decrease to increase (4.0%) and moderate/severe stable (5.5%). The finding indicated that there were still about 5% individuals showing consistently severe distress and approximately 16% following fluctuating psychological trajectories, who should be continuously monitored. For individuals with persistently severe trajectories and those with fluctuating trajectories, central or bridge symptoms in the network were mainly ‘motor abnormality’ and ‘sad mood’, respectively. Compared with initial peak and late COVID-19 phase, aftermath of initial peak might be a psychologically vulnerable period with highest network connectivity. The central and bridge symptoms for aftermath of initial peak (‘appetite change’ and ‘trouble of relaxing’) were totally different from those at other pandemic phases (‘sad mood’).

This research identified the overall growing trend, long-term predictors, trajectory classes and evolutionary pattern of psychopathological network of psychological symptoms in 12 months after COVID-19 outbreak. It provides a multidimensional long-term psychological profile of the general population after COVID-19 outbreak, and accentuates the essentiality of continuous psychological monitoring, as well as population- and time-specific psychological management after COVID-19. We believe our findings can offer reference for long-term psychological management after pandemics.

Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.

To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.

We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.

We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.

These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.