To examine the associations of pregnant women’s dietary and sedentary behaviours with their children’s birth weight.

Secondary data analysis was conducted using data from a randomised controlled trial, Communicating Healthy Beginnings Advice by Telephone, conducted in Australia. Information on mothers’ socio-demographics, dietary and sedentary behaviours during pregnancy was collected by telephone survey at the third trimester. Birth weight data were extracted from the child’s health record book. Multinomial logistic regression models were built to examine the associations of pregnant women’s dietary and sedentary behaviours with children’s birth weight.

Participating families.

Pregnant women and their children.

A total of 1132 mother–child dyads were included in the analysis. The majority of infants (87 %, n 989) were of normal birth weight (2500 g to <4000 g), 4 % (n 50) had low birth weight (<2500 g) and 8 % (n 93) had macrosomia (≥4000 g). Mothers who ate processed meat during pregnancy were more likely to have macrosomia (adjusted risk ratio (ARR) 1·80, 95 % CI (1·12, 2·89)). The risk of macrosomia decreased as the number of dietary recommendations met by mothers increased (ARR 0·84, 95 % CI (0·71, 0·99)). Children’s birth weight was not associated with mothers’ sedentary time. Children’s low birth weight was not associated with mothers’ dietary and sedentary behaviours during pregnancy.

Maternal consumption of processed meat during pregnancy was associated with an increased risk of macrosomia. Increasing number of dietary recommendations met by mothers was associated with a lower risk of macrosomia. The findings suggested encouraging pregnancy women to meet dietary recommendation will benefit children’s birth weight.

Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders.

Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed.

Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network.

These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

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.

Benzodiazepine receptor agonists (BZRAs) are commonly used clinically and data on their hazardous use from large populations of psychiatric patients is limited.

To assess the current status of hazardous BZRA use and related factors in Chinese out-patient psychiatric settings.

The study included out-patients with at least one BZRA prescription from five psychiatric settings in east, central and west China in 2018. Demographic and prescription information were extracted from the electronic prescription database. We defined the co-occurrence of overdose and long-term use as hazardous use, and patients whose recorded diagnoses did not meet any indications approved by the Chinese Food and Drug Administration as over-indication users. Additionally, 200 hazardous users were randomly selected for follow-up interview to confirm the actual situation.

Among 720 054 out-patients, 164 450 (22.8%) had at least one BZRA prescription; 55.9% of patients were prescribed over-indication and 3% were defined as hazardous users. Multilevel multivariate regression analysis with hospital as a random effect showed that factors associated with hazardous use were older age (18–64 years: β = 0.018; 95% CI 0.013–0.023; >65 years: β = 0.015; 95% CI 0.010–0.021), male (β = 0.005, 95% CI 0.003–0.007), over-indication (β = 0.013, 95% CI 0.012–0.015), more out-patient visits (β = 0.006, 95% CI 0.006–0.006) and more visits to different doctors (β = 0.007, 95% CI 0.007–0.008); 98.5% of hazardous users (197/200) could not be contacted.

BZRAs are commonly used and there is a relatively large proportion of over-indication users among Chinese psychiatric out-patients. However, only a small proportion of hazardous users were detected. The study highlights how to use prescription data to support improvements in clinical practice.

Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).

CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.

The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.

These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear.

To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age.

The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed.

The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014).

The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model.

We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia.

This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months.

This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients.

This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.

The purpose of this study was to evaluate the mid-term outcome of aortic valve replacement for bicuspid aortic valve and tricuspid aortic valve and the related risk factors.

From January 2014 to June 2019, 177 tricuspid aortic valve patients and 101 bicuspid aortic valve patients who underwent aortic valve replacement in our hospital were collected. 1:1 propensity score matching analysis was used to control the bias in patient selection. The perioperative and follow-up data between the two groups were compared. Independent risk factors which were associated with the continued dilatation of the ascending aorta were identified by univariate or multivariate logistic regression analysis.

After the matching procedure, 160 patients were included in the analysis (80 in each group). Baseline characteristics, intraoperative, and perioperative outcomes were similar between the two groups (all p > 0.05). Moreover, 67 patients in the tricuspid aortic valve group and 70 in the bicuspid aortic valve group completed the follow-up. The ascending aorta change, annual change rate, and the proportion of continuous dilation of ascending aorta in bicuspid aortic valve group were significantly higher than those in the tricuspid aortic valve group (p < 0.05). Multivariate logistic regression analysis showed that type 1 in bicuspid aortic valve (OR 5.173; 95% CI 1.772, 15.101; p = 0.003), aortic regurgitation (OR 3.673; 95% CI 1.133, 11.908; p = 0.030), and aortic valve stenosis with regurgitation (OR 6.489; 95% CI 1.726, 24.404; p = 0.006) were independent risk factors for the continued dilatation of the ascending aorta in all AV patients. Furthermore, the multivariate logistic regression analysis showed that type 1 in bicuspid aortic valve (OR 5.157; 95% CI 1.053, 25.272; p = 0.043), age ≥ 40 years (OR 6.956; 95% CI 1.228, 39.410; p = 0.028), and aortic regurgitation (OR 4.322; 95% CI 1.174, 15.911; p = 0.028) were independent risk factors for the continued dilatation of the ascending aorta in bicuspid aortic valve patients.

Compared with tricuspid aortic valve patients, the ascending aorta of bicuspid aortic valve patients is more likely to continue to enlarge after aortic valve replacement. Type 1 in bicuspid aortic valve, age ≥ 40 years, and aortic regurgitation were the independent risk factors.