Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety.

Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses.

Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10−27), sleep apnea (OR 1.71; p = 1 × 10−22), and type II diabetes (OR 1.74; p = 9 × 10−18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10−25), benign skin neoplasms (OR 1.61; p = 2 × 10−17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10−12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression.

While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.

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.

Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.

Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.

In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18–1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05–1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06–1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11–1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02–1.09), showing a genetic risk gradient across the conditions and the comorbidity.

This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy.

In 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment.

We identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption.

Data-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions – setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.

Genomewide association studies (GWASs) on antidepressant efficacy have yielded modest results. A possible reason is that response is influenced by other factors, which possibly interact with genetic variation. We used a GWAS model to predict antidepressant response, by including predictors previously known to affect response, such as quality of life (QoL). We also evaluated the association between genes, previously implicated in gene–environment (G × E) interactions, and response using an enrichment analysis.

We examined a sample of 1426 depressed patients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial: 774 responders, 652 non-responders and 418 865 single nucleotide polymorphisms (SNPs) were analysed. First, in a GWAS model, we investigated whether genetic variations interact with patients' levels of QoL to predict response, after controlling for demographic characteristics, severity and population stratification. Second, we conducted an enrichment analysis exploring whether candidate genes that have emerged from prior G × E interaction studies on depression are associated with treatment response.

The GWAS model, with QoL as a moderator, yielded one SNP (rs520210) associated with response in the NEDD4L gene (p = 3.64 × 10−8). In the Caucasian sample only, we observed a drop in significance for this SNP. The enrichment analysis showed that SNPs within serotonergic genes contained more significant markers that predicted response, compared with a random set of genes in the genome.

Our findings point to possible target genes, which are proposed for further independent replication. Our enrichment analysis provides further support, in a genomewide context, of the role of serotonergic genes in influencing antidepressant response.

People with severe mental illness (SMI) have more physical health conditions than the general population, resulting in higher rates of hospitalisations and mortality. In this study, we aimed to determine the rate of emergency and planned physical health hospitalisations in those with SMI, compared to matched comparators, and to investigate how these rates differ by SMI diagnosis.

We used Clinical Practice Research DataLink Gold and Aurum databases to identify 20,668 patients in England diagnosed with SMI between January 2000 and March 2016, with linked hospital records in Hospital Episode Statistics. Patients were matched with up to four patients without SMI. Primary outcomes were emergency and planned physical health admissions. Avoidable (ambulatory care sensitive) admissions and emergency admissions for accidents, injuries and substance misuse were secondary outcomes. We performed negative binomial regression, adjusted for clinical and demographic variables, stratified by SMI diagnosis.

Emergency physical health (aIRR:2.33; 95% CI 2.22–2.46) and avoidable (aIRR:2.88; 95% CI 2.60–3.19) admissions were higher in patients with SMI than comparators. Emergency admission rates did not differ by SMI diagnosis. Planned physical health admissions were lower in schizophrenia (aIRR:0.80; 95% CI 0.72–0.90) and higher in bipolar disorder (aIRR:1.33; 95% CI 1.24–1.43). Accident, injury and substance misuse emergency admissions were particularly high in the year after SMI diagnosis (aIRR: 6.18; 95% CI 5.46–6.98).

We found twice the incidence of emergency physical health admissions in patients with SMI compared to those without SMI. Avoidable admissions were particularly elevated, suggesting interventions in community settings could reduce hospitalisations. Importantly, we found underutilisation of planned inpatient care in patients with schizophrenia. Interventions are required to ensure appropriate healthcare use, and optimal diagnosis and treatment of physical health conditions in people with SMI, to reduce the mortality gap due to physical illness.

Maternal depressive symptoms (MDSs) are negatively associated with children's academic performance, with stronger effects sometimes reported in boys. However, few studies have tested the mechanisms of this association. We examined the mediating role of school engagement and peer victimization in this association and tested for sex differences.

Participants were 1173 families from a population-based longitudinal Canadian study. MDSs were self-reported annually using the Centre for Epidemiologic Studies Depression Scale (child's age: 5 months to 5 years). Data on mediators (peer victimization, cognitive, behavioral, and emotional school engagement) were reported annually from ages 6–10 by multiple informants including children, parents, and teachers using items from validated scales. Mathematics, reading, and writing exam scores at age 12 were obtained from standardized exams administered by Québec's Ministry of Education and Teaching. Structural equation modeling was used to test mediation by school experiences in boys and girls.

Exposure to MDSs was negatively associated with mathematics, reading, and writing scores in girls and with mathematics only in boys. Cognitive and behavioral engagement significantly mediated the association between MDSs and mathematics, reading, and writing scores in girls. There were no significant mediators for boys.

Prevention and intervention strategies aiming to improve school engagement might be beneficial for daughters of mothers experiencing depressive symptoms. Further research is needed to replicate these findings and to identify the mechanisms explaining this association in boys.

Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties.

We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features.

We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features.

SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.

To test the functional implications of impaired white matter (WM) connectivity among patients with schizophrenia and their relatives, we examined the heritability of fractional anisotropy (FA) measured on diffusion tensor imaging data acquired in Pittsburgh and Philadelphia, and its association with cognitive performance in a unique sample of 175 multigenerational non-psychotic relatives of 23 multiplex schizophrenia families and 240 unrelated controls (total = 438).

We examined polygenic inheritance (h2r) of FA in 24 WM tracts bilaterally, and also pleiotropy to test whether heritability of FA in multiple WM tracts is secondary to genetic correlation among tracts using the Sequential Oligogenic Linkage Analysis Routines. Partial correlation tests examined the correlation of FA with performance on eight cognitive domains on the Penn Computerized Neurocognitive Battery, controlling for age, sex, site and mother's education, followed by multiple comparison corrections.

Significant total additive genetic heritability of FA was observed in all three-categories of WM tracts (association, commissural and projection fibers), in total 33/48 tracts. There were significant genetic correlations in 40% of tracts. Diagnostic group main effects were observed only in tracts with significantly heritable FA. Correlation of FA with neurocognitive impairments was observed mainly in heritable tracts.

Our data show significant heritability of all three-types of tracts among relatives of schizophrenia. Significant heritability of FA of multiple tracts was not entirely due to genetic correlations among the tracts. Diagnostic group main effect and correlation with neurocognitive performance were mainly restricted to tracts with heritable FA suggesting shared genetic effects on these traits.

One aim of personalized medicine is to determine which treatment is to be preferred for an individual patient, given all patient information available. Particularly in mental health, however, there is a lack of a single objective, reliable measure of outcome that is sensitive to crucial individual differences among patients.

We examined the feasibility of quantifying the total clinical value provided by a treatment (measured by both harms and benefits) in a single metric. An expert panel was asked to compare 100 pairs of patients, one from each treatment group, who had participated in a randomized clinical trial (RCT) involving interpersonal psychotherapy (IPT) and escitalopram, selecting the patient with the preferred outcome considering both benefits and harms.

From these results, an integrated preference score (IPS) was derived, such that the differences between any two patients' IPSs would predict the clinicians' preferences. This IPS was then computed for all patients in the RCT. A second set of 100 pairs was rated by the panel. Their preferences were highly correlated with the IPS differences (r=0.84). Finally, the IPS was used as the outcome measure comparing IPT and escitalopram. The 95% confidence interval (CI) for the effect size comparing treatments indicated clinical equivalence of the treatments.

A metric that combines benefits and harms of treatments could increase the value of RCTs by making clearer which treatments are preferable and, ultimately, for whom. Such methods result in more precise estimation of effect sizes, without increasing the required sample size.

Several multivariate algorithms have been developed for predicting psychosis, as attempts to obtain better prognosis prediction than with current clinical high-risk (CHR) criteria. The models have typically been based on samples from specialized clinics. We evaluated the generalizability of 19 prediction models to clinical practice in an unselected adolescent psychiatric sample.

In total, 153 adolescent psychiatric patients in the Helsinki Prodromal Study underwent an extensive baseline assessment including the SIPS interview and a neurocognitive battery, with 50 participants (33%) fulfilling CHR criteria. The adolescents were followed up for 7 years using comprehensive national registers. Assessed outcomes were (1) any psychotic disorder diagnosis (n = 18, 12%) and (2) first psychiatric hospitalization (n = 25, 16%) as an index of overall deterioration of functioning.

Most models improved the overall prediction accuracy over standard CHR criteria (area under the curve estimates ranging between 0.51 and 0.82), although the accuracy was worse than that in the samples used to develop the models, also when applied only to the CHR subsample. The best models for transition to psychosis included the severity of positive symptoms, especially delusions, and negative symptoms. Exploratory models revealed baseline negative symptoms, low functioning, delusions, and sleep problems in combination to be the best predictor of psychiatric hospitalization in the upcoming years.

Including the severity levels of both positive and negative symptomatology proved beneficial in predicting psychosis. Despite these advances, the applicability of extended psychosis-risk models to general psychiatric practice appears limited.

Studies have criticized the low level of agreement between the various methods of personality disorder (PD) assessment. This is an important issue for research and clinical purposes.

Seven hundred and forty-two participants in the Hopkins Epidemiology of Personality Disorders Study (HEPS) were assessed on two occasions using the Personality Disorder Schedule (PDS) and the International Personality Disorder Examination (IPDE). The concordance between the two diagnostic methods for all DSM-IV PDs was assessed using standard methods and also two item response analytic approaches designed to take account of measurement error: a latent trait-based approach and a generalized estimating equations (GEE)-based approach, with post-hoc adjustment.

Raw criteria counts, using the intraclass correlation coefficient (ICC), κ and odds ratio (OR), showed poor concordance. The more refined statistical methods showed a moderate to moderately high level of concordance between the methods for most PDs studied. Overall, the PDS produced lower prevalences of traits but higher precision of measurement than the IPDE. Specific criteria within each PD showed varying endorsement thresholds and precision for ascertaining the disorder.

Concordance in the raw measurement of the individual PD criteria between the two clinical methods is lacking. However, based on two statistical methods that adjust for differential endorsement thresholds and measurement error in the assessments, we deduce that the PD constructs themselves can be measured with a moderate degree of confidence regardless of the clinical approach used. This may suggest that the individual criteria for each PD are, in and of themselves, less specific for diagnosis, but as a group the criteria for each PD usefully identify specific PD constructs.