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.

To examine the thematic features of dignity therapy generativity documents of advanced cancer patients receiving chemotherapy in mainland China from the perspective of meaning-making during dignity therapy.

This is a qualitative descriptive study. Content analysis was used to analyze 24 dignity therapy generativity documents derived from a quasi-experimental trial of dignity therapy for advanced cancer patients receiving chemotherapy in a daycare center at a cancer hospital in northern China.

Among the 24 advanced cancer patients whose generativity documents were analyzed, 14 were male and ranged in age from 26 to 78 years (average = 50.3). Two dimensions emerged from the data: (1) the sources of meaning in life, which consisted of social relationships, things and circumstances providing meaning in life, and (2) the ultimate meaning in life, referring to finding one’s real self and realizing the purpose in life, including personal life goals, dignity and autonomy, and morality of patients.

The multidimensional constructs of meaning in life with distinctive traditional Chinese cultural characteristics could provide insights into dignity-conserving care for advanced cancer patients with Chinese culture and provide evidence for refining the implementation protocol of DT through intentionally addressing the ultimate meaning of patients in the therapeutic session.

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.

To investigate the association between folate levels and the risk of gestational diabetes mellitus (GDM) risk during the whole pregnancy.

In this retrospective cohort study of pregnant women, serum folate levels were measured before 24 gestational weeks (GW). GDM was diagnosed between 24th and 28th GW based on the criteria of the International Association of Diabetes and Pregnancy Study Groups. General linear models were performed to examine the association of serum folate with plasma glucose (i.e. linear regressions) and risk of GDM (i.e. log-binomial regressions) after controlling for confounders. Restricted cubic spline regression was conducted to test the dosage–response relationship between serum folate and the risk of GDM.

A sigle, urban hospital in Shanghai, China.

A total of 42 478 women who received antenatal care from April 2013 to March 2017 were included.

Consistent positive associations were observed between serum folate and plasma glucose levels (fasting, 1-h, 2-h). The adjusted relative risks (RR) and 95 % CI of GDM across serum folate quartiles were 1·00 (reference), 1·15 (95 % CI (1·04, 1·26)), 1·40 (95 % CI (1·27, 1·54)) and 1·54 (95 % CI (1·40, 1·69)), respectively (P-for-trend < 0·001). The positive association between serum folate and GDM remained when stratified by vitamin B12 (adequate v. deficient groups) and the GW of serum folate measurement (≤13 GW v. >13 GWs)

The findings of this study may provide important evidence for the public health and clinical guidelines of pregnancy folate supplementation in terms of GDM prevention.

Emotional dysregulation (ED) is a common characteristic of both attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD), especially in adolescents. However, whether ADHD and MDD may share the specific ED-related neural networks remains unknown.

In total, 43 adolescents with clinical ED (22 adolescents with ADHD and 21 with MDD) were recruited; in addition, 29 sex- and age-matched healthy controls (HCs) were included. Resting-state functional connectivity (RSFC) analysis, voxel-based morphometry, and diffusion tensor imaging analysis were performed for each patient. In addition, we determined the significant regions of interest in patients with ED due to ADHD and MDD as compared with HCs and tested their correlations with clinical rating scale scores.

Compared with HCs, patients with ED had greater RSFC in the cerebellum and supramarginal gyrus (SMG), especially between vermis VI and the SMG in the attention networks, and lower RSFC between the right supplementary motor area and right lateral parietal area. Lower gray matter (GM) volume in the SMG was also found. RSFC was significantly correlated with clinical rating scale scores for all patients with ED due to ADHD or MDD. GM change was correlated with ED and MDD rating scale scores.

The cerebellum and attention networks might play major roles in ED pathophysiology in adolescents with ADHD and MDD. Increased connectivity of the vermis to the SMG serves as a possible underlying neural network.

Evidence has demonstrated associations of bipolar disorder (BD) with cognitive impairment, dysregulated proinflammatory cytokines, and appetite hormones.

To compare executive dysfunction, proinflammatory cytokines, and appetite hormones between patients with first-episode and multiple-episode BDs.

This cross-sectional study included young adults aged 18 to 39 years who were diagnosed as having type 1 BD in the first or recurrent episode and a group of age-/sex-matched healthy controls. Data regarding patient characteristics, clinical symptoms, cytokines (C-reactive protein [CRP], interleukin-6, and tumor necrosis factor [TNF]-α), appetite hormones (leptin, adiponectin, ghrelin, and insulin), and executive function evaluated using the Wisconsin Card Sorting Test (WCST) were collected.

A total of 112 participants (38 patients in the multiple-episode BD group, 31 patients in the first-episode BD group, and 43 in the control group) were included. Multivariate analysis revealed that patients in the multiple-episode BD group performed significantly worse in the WCST (P < .05) and had higher levels of ghrelin (P = .002), and lower levels of CRP (P = .040) than those in the first-episode BD group. Patients with BD had significantly higher TNF-α and ghrelin levels compared with the healthy controls. No significant associations of CRP, TNF-α, and ghrelin levels with executive function were observed.

Profiles in proinflammatory cytokines and appetite hormones as well as executive function significantly differed between patients with first-episode and multiple-episode BDs and controls, which may suggest their potential roles in the clinical stages and pathophysiology of type 1 BD.

Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy.

The resting functional Magnetic Resonance Imaging (fMRI) dataset comprised 220 patients with schizophrenia and 220 healthy controls. Brain-wise FCs was calculated for each participant and linear SVMs were developed for automatic classification of patients and controls. First, we evaluated the SVMs based on all participants and homogeneous subsamples of men, women, younger (18–30 years), and older (31–50 years) participants by 10-fold nested cross-validation. Then, we hold out a fixed test set of 40 participants (20 patients and 20 controls) and evaluated the SVMs based on incremental training sample sizes (N = 40, 80, …, 400).

We found that the SVMs based on all participants had accuracy of 85.05%. The SVMs based on male, female, young, and older participants yielded accuracy of 84.66, 81.56, 80.50, and 86.13%, respectively. Although the SVMs based on older subsamples had better performance than those based on all participants, they generalized poorly to younger participants (77.24%). For incremental training sizes, the classification accuracy increased stepwise from 72.6 to 83.3%, with >80% accuracy achieved with sample size >240.

The findings indicate that SVMs based on a large dataset yield high classification accuracy and establish models using a large sample size with heterogeneous properties are recommended for single subject prediction of schizophrenia.

Long-acting injectable (LAI) antipsychotics improve medication adherence in patients with schizophrenia and extend the duration of therapeutic drug levels but with administration of an increased dose. Real-world mortality data in patients prescribed LAIs are lacking. We conducted a population-based cohort study to estimate and compare the incidence rates of all-cause death and completed suicide in patients with schizophrenia/schizoaffective disorder exposed to LAIs and oral antipsychotics.

Patients with a diagnosis of schizophrenia/schizoaffective disorder between January 1, 2015 and November 30, 2019 were enrolled from the Taiwan National Health Insurance Research Database and linked to Death Registry records. Eligible patients were new antipsychotic users. Relative risks of death for each antipsychotic compared with oral paliperidone were evaluated using a Cox proportional hazard model adjusted for age, sex, Charlson Comorbidity Index, index year, bipolar or major depressive or other mood disorders, mental disorders due to drug use, and baseline hospitalization frequency.

There were 228,791.08 person-years of follow-up (mean 2.48 years). The incidence rates of all-cause death in users of LAI paliperidone administered monthly (PP1M) and every 3 months (PP3M) were 7.40/1,000 person-years (95% confidence interval 5.94–9.11) and 9.93 (5.88–15.79), respectively. The incidences of completed suicide were 2.03/1,000 person-years (1.32–2.99) and 3.10 (1.14–6.88), respectively. No significant associations were observed between PP1M and PP3M compared to oral paliperidone in incidences of all-cause death or for completed suicide.

No increased risk of all-cause death or completed suicide was observed in users of antipsychotic LAIs, including PP1M and PP3M.

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.

Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.

Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.

Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.

MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.

Cognitive impairment is common in late-life depression, which may increase Alzheimer disease (AD) risk. Therefore, we aimed to investigate whether late-life major depressive disorder (MDD) has worse cognition and increases the characteristic AD neuropathology. Furthermore, we carried out a comparison between treatment-resistant depression (TRD) and non-TRD. We hypothesized that patients with late-life depression and TRD may have increased β-amyloid (Aβ) deposits in brain regions responsible for global cognition.

We recruited 81 subjects, including 54 MDD patients (27 TRD and 27 non-TRD) and 27 matched healthy controls (HCs). Neurocognitive tasks were examined, including Mini-Mental State Examination and Montreal Cognitive Assessment to detect global cognitive functions. PET with Pittsburgh compound-B and fluorodeoxyglucose were used to capture brain Aβ pathology and glucose use, respectively, in some patients.

MDD patients performed worse in Montreal Cognitive Assessment (p = 0.003) and had more Aβ deposits than HCs across the brain (family-wise error-corrected p < 0.001), with the most significant finding in the left middle frontal gyrus. Significant negative correlations between global cognition and prefrontal Aβ deposits existed in MDD patients, whereas positive correlations were noted in HCs. TRD patients had significantly more deposits in the left-sided brain regions (corrected p < 0.001). The findings were not explained by APOE genotypes. No between-group fluorodeoxyglucose difference was detected.

Late-life depression, particularly TRD, had increased brain Aβ deposits and showed vulnerability to Aβ deposits. A detrimental role of Aβ deposits in global cognition in patients with late-onset or non-late-onset MDD supported the theory that late-life MDD could be a risk factor for AD.