There is a paucity of literature on the relationship between pre-existing mental health conditions and coronavirus disease-2019 (COVID-19) outcomes. The aim was to examine the association between pre-existing mental health diagnosis and COVID-19 outcomes (positive screen, hospitalization, mortality).

Electronic medical record data for 30 976 adults tested for COVID-19 between March 2020 and 10th July 2020 was analyzed. COVID-19 outcomes included positive screen, hospitalization among screened positive, and mortality among screened positive and hospitalized. Primary independent variable, mental health disorders, was based on ICD-10 codes categorized as bipolar, internalizing, externalizing, and psychoses. Descriptive statistics were calculated, unadjusted and adjusted logistic regression and Cox proportional hazard models were used to investigate the relationship between each mental health disorder and COVID-19 outcomes.

Adults with externalizing (odds ratio (OR) 0.67, 95%CI 0.57–0.79) and internalizing disorders (OR 0.78, 95% CI 0.70–0.88) had lower odds of having a positive COVID-19 test in fully adjusted models. Adults with bipolar disorder had significantly higher odds of hospitalization in fully adjusted models (OR 4.27, 95% CI 2.06–8.86), and odds of hospitalization were significantly higher among those with externalizing disorders after adjusting for demographics (OR 1.71, 95% CI 1.23–2.38). Mortality was significantly higher in the fully adjusted model for patients with bipolar disorder (hazard ratio 2.67, 95% CI 1.07–6.67).

Adults with mental health disorders, while less likely to test positive for COVID-19, were more likely to be hospitalized and to die in the hospital. Study results suggest the importance of developing interventions that incorporate elements designed to address smoking cessation, nutrition and physical activity counseling and other needs specific to this population to improve COVID-19 outcomes.

Persistent psychological distress associated with the coronavirus disease 2019 (COVID-19) pandemic has been well documented. This study aimed to identify pre-COVID brain functional connectome that predicts pandemic-related distress symptoms among young adults.

Baseline neuroimaging studies and assessment of general distress using the Depression, Anxiety and Stress Scale were performed with 100 healthy individuals prior to wide recognition of the health risks associated with the emergence of COVID-19. They were recontacted for the Impact of Event Scale-Revised and the Posttraumatic Stress Disorder Checklist in the period of community-level outbreaks, and for follow-up distress evaluation again 1 year later. We employed the network-based statistic approach to identify connectome that predicted the increase of distress based on 136-region-parcellation with assigned network membership. Predictive performance of connectome features and causal relations were examined by cross-validation and mediation analyses.

The connectome features that predicted emergence of distress after COVID contained 70 neural connections. Most within-network connections were located in the default mode network (DMN), and affective network-DMN and dorsal attention network-DMN links largely constituted between-network pairs. The hippocampus emerged as the most critical hub region. Predictive models of the connectome remained robust in cross-validation. Mediation analyses demonstrated that COVID-related posttraumatic stress partially explained the correlation of connectome to the development of general distress.

Brain functional connectome may fingerprint individuals with vulnerability to psychological distress associated with the COVID pandemic. Individuals with brain neuromarkers may benefit from the corresponding interventions to reduce the risk or severity of distress related to fear of COVID-related challenges.

Major depressive disorder (MDD) is clinically documented to co-occur with multiple gastrointestinal disorders (GID), but the potential causal relationship between them remains unclear. We aimed to evaluate the potential causal relationship of MDD with 4 GID [gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), peptic ulcer disease (PUD), and non-alcoholic fatty liver disease (NAFLD)] using a two-sample Mendelian randomization (MR) design.

We obtained genome-wide association data for MDD from a meta-analysis (N = 480 359), and for GID from the UK Biobank (N ranges: 332 601–486 601) and FinnGen (N ranges: 187 028–218 792) among individuals of European ancestry. Our primary method was inverse-variance weighted (IVW) MR, with a series of sensitivity analyses to test the hypothesis of MR. Individual study estimates were pooled using fixed-effect meta-analysis.

Meta-analyses IVW MR found evidence that genetically predicted MDD may increase the risk of GERD, IBS, PUD and NAFLD. Additionally, reverse MR found evidence of genetically predicted GERD or IBS may increase the risk of MDD.

Genetically predicted MDD may increase the risk of GERD, IBS, PUD and NAFLD. Genetically predicted GERD or IBS may increase the risk of MDD. The findings may help elucidate the mechanisms underlying the co-morbidity of MDD and GID. Focusing on GID symptoms in patients with MDD and emotional problems in patients with GID is important for the clinical management.

Cognitive Bias Modification for paranoia (CBM-pa) is a novel, theory-driven psychological intervention targeting the biased interpretation of emotional ambiguity associated with paranoia. Study objectives were (i) test the intervention's feasibility, (ii) provide effect size estimates, (iii) assess dose–response and (iv) select primary outcomes for future trials.

In a double-blind randomised controlled trial, sixty-three outpatients with clinically significant paranoia were randomised to either CBM-pa or an active control (text reading) between April 2016 and September 2017. Patients received one 40 min session per week for 6 weeks. Assessments were given at baseline, after each interim session, post-treatment, and at 1- and 3-months post-treatment.

A total of 122 patients were screened and 63 were randomised. The recruitment rate was 51.2%, with few dropouts (four out of 63) and follow-up rates were 90.5% (1-month) and 93.7% (3-months). Each session took 30–40 min to complete. There was no statistical evidence of harmful effects of the intervention. Preliminary data were consistent with efficacy of CBM-pa over text-reading control: patients randomised to the intervention, compared to control patients, reported reduced interpretation bias (d = −0.48 to −0.76), improved symptoms of paranoia (d = −0.19 to −0.38), and lower depressed and anxious mood (d = −0.03 to −0.29). The intervention effect was evident after the third session.

CBM-pa is feasible for patients with paranoia. A fully powered randomised control trial is warranted.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.

We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.

BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI.

We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

A subgroup of patients with anorexia nervosa (AN) undergoing involuntary treatment (IT) seems to account for most of the IT events. Little is known about these patients and their treatment including the temporal distribution of IT events and factors associated with subsequent utilization of IT. Hence, this study explores (1) utilization patterns of IT events, and (2) factors associated with subsequent utilization of IT in patients with AN.

In this nationwide Danish register-based retrospective exploratory cohort study patients were identified from their first (index) hospital admission with an AN diagnosis and followed up for 5 years. We explored data on IT events including estimated yearly and total 5-year rates, and factors associated with subsequent increased IT rates and restraint, using regression analyses and descriptive statistics.

IT utilization peaked in the initial few years starting at or following the index admission. A small percentage (1.0%) of patients accounted for 67% of all IT events. The most frequent measures reported were mechanical and physical restraint. Factors associated with subsequent increased IT utilization were female sex, lower age, previous admissions with psychiatric disorders before index admission, and IT related to those admissions. Factors associated with subsequent restraint were lower age, previous admissions with psychiatric disorders, and IT related to these.

High IT utilization in a small percentage of individuals with AN is concerning and can lead to adverse treatment experiences. Exploring alternative approaches to treatment that reduce the need for IT is an important focus for future research.

Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value.

Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses.

SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy.

SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.

Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated.

Using structural magnetic resonance imaging data from the UK Biobank (n = 33 293), a brain age predictor was trained on non-smoking healthy groups and tested on smokers to obtain the BrainAge Gap (BAG). The cumulative effect of multiple common genetic variants associated with smoking was then calculated to acquire a polygenic risk score (PRS). The relationship between PRS, BAG, total gray matter volume (tGMV), and smoking parameters was explored and further genes included in the PRS were annotated to identify potential molecular mechanisms affected by smoking.

The BrainAge in smokers was predicted with very high accuracy (r = 0.725, MAE = 4.16). Smokers had a greater BAG (Cohen's d = 0.074, p < 0.0001) and higher PRS (Cohen's d = 0.63, p < 0.0001) than non-smokers. A higher PRS was associated with increased amount of smoking, mediated by BAG and tGMV. Several neurotransmitters and ion channel pathways were enriched in the group of smoking-related genes involved in addiction, brain synaptic plasticity, and some neurological disorders.

By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.

While adult outcome in autism spectrum disorder (ASD) is generally measured using socially valued roles, it could also be understood in terms of aspects related to health status – an approach that could inform on potential gender differences.

We investigated gender differences in two aspects of outcome related to health-status, i.e. general functioning and self-perceived health status, and co-occurring health conditions in a large multi-center sample of autistic adults. Three hundred and eighty-three participants were consecutively recruited from the FondaMental Advanced Centers of Expertise for ASD cohort (a French network of seven expert centers) between 2013 and 2020. Evaluation included a medical interview, standardized scales for autism diagnosis, clinical and functional outcomes, self-perceived health status and verbal ability. Psychosocial function was measured using the Global Assessment of Functioning scale.

While autistic women in this study were more likely than men to have socially valued roles, female gender was associated with poorer physical and mental health (e.g. a 7-fold risk for having three or more co-occurring physical health conditions) and a poorer self-perceived health status. Psychosocial function was negatively associated with depression and impairment in social communication. Half of the sample had multiple co-occurring health conditions but more than 70% reported that their visit at the Expert Center was their first contact with mental health services.

To improve objective and subjective aspects of health outcome, gender differences and a wide range of co-occurring health conditions should be taken into account when designing healthcare provision for autistic adults.

The treatment of depression in patients with somatic disorders is crucial, given its negative impact on quality of life (QoL), functioning, and even on the somatic disease prognosis. We aimed to examine the most updated evidence on the effects of psychotherapy in patients with depression and somatic disorders, including HIV, oncological, cardiometabolic, and neurological disorders.

We conducted a meta-analysis of 75 randomized trials (8209 participants) of psychotherapy for adults with somatic disorders and a diagnosis or elevated symptoms of depression. Outcomes included depression, QoL, somatic health-related outcomes, and mortality.

Psychotherapy significantly reduced the severity of depression at post-treatment across all categories of somatic disorders (Hedges'g = 0.65; 95% CI 0.52–0.79), with sustained effects at 6–11 months (g = 0.38; 95% CI 0.22–0.53) and at 12 months follow-up or longer (g = 0.13; 95% CI 0.04–0.21). Psychotherapy also showed significant effects on QoL (g = 0.26; 95% CI 0.17–0.35), maintained up to 11 months follow-up (g = 0.25; 95% CI 0.16–0.34). No significant effects were observed on the most frequently reported somatic health-related outcomes (glycemic control, pain), and neither on mortality. Heterogeneity in most analyses was very high, and only 29 (38%) trials were rated at low risk of bias (RoB).

Psychotherapy may be an effective treatment option for patients with depression and somatic disorders, with long-term effects on depression severity and QoL. However, these results should be interpreted with caution due to heterogeneity and RoB.

Traumatic brain injury (TBI) can cause long-lasting sequelae that may increase the risk of suicidal or criminal behaviour, but large-scale longitudinal studies are lacking on the link between TBI and events of suicide attempt and violent crime. This study examined the incidence of suicide attempt and violent crime following hospital contact for TBI in a nationwide cohort study.

We used nationwide register data covering all individuals aged 10+ living in Denmark during 1980–2016 (n = 7 783 951). Of these, 587 522 individuals had a hospital contact for TBI. Incidence rate ratios (IRR) were calculated by Poisson regression analyses while adjusted for relevant covariates including other fractures and psychiatric diagnoses.

Individuals with TBI had higher rates of suicide attempt (females IRR, 2.78; 95% CI 2.71–2.85; males IRR, 3.00; 95% CI 2.93–3.08) compared to individuals without TBI in adjusted analyses. Multiple TBI and temporal proximity to TBI were associated with higher rates of suicide attempt. Individuals with TBI had higher rates of violent crime (females IRR, 2.43; 95% CI 2.36–2.49; males IRR, 1.80, 95% CI 1.78–1.82) compared with individuals without TBI. Higher rates of violent crime were found after multiple TBI and temporal proximity to TBI.

This nationwide cohort study found higher rates of suicide attempt and violent crime among individuals with prior hospital diagnosed TBI, compared with individuals without TBI. This emphasises the need for preventive efforts immediately after TBI diagnosis, which might mitigate the risks of a trajectory toward suicidal or violent behaviours.

The efficacy of the unified protocol of the transdiagnostic treatment for emotional disorders (UP) has been poorly studied in patients with depressive disorders. This study aimed to examine the efficacy of UP for improving depressive symptoms in patients with depressive and/or anxiety-related disorders.

This assessor-blinded, randomized, 20-week, parallel-group, superiority study compared the efficacy of the UP with treatment-as-usual (UP-TAU) v. wait-list with treatment-as-usual (WL-TAU). Patients diagnosed with depressive and/or anxiety disorders and with depressive symptoms participated. The primary outcome was depressive symptoms assessed by GRID-Hamilton depression rating scale (GRID-HAMD) at 21 weeks. The secondary outcomes included assessor-rated anxiety symptoms, severity and improvement of clinical global impression, responder and remission status, and loss of principal diagnosis.

In total, 104 patients participated and were subjected to intention-to-treat analysis [mean age = 37.4, s.d. = 11.5, 63 female (61%), 54 (51.9%) with a principal diagnosis of depressive disorders]. The mean GRID-HAMD scores in the UP-TAU and WL-TAU groups were 16.15 (s.d. = 4.90) and 17.06 (s.d. = 6.46) at baseline and 12.14 (s.d. = 5.47) and 17.34 (s.d. = 5.78) at 21 weeks, with a significant adjusted mean change difference of −3.99 (95% CI −6.10 to −1.87). Patients in the UP-TAU group showed significant superiority in anxiety and clinical global impressions. The improvement in the UP-TAU group was maintained in all outcomes at 43 weeks. No serious adverse events were observed in the UP-TAU group.

The UP is an effective approach for patients with depressive and/or anxiety disorders.

Adverse childhood experiences (ACEs) are associated with multimorbidity in adulthood. This link may be mediated by psychosocial and biological factors, but evidence is lacking. The current study evaluates this mediation model.

We analyzed data from the Canadian Longitudinal Study of Aging (N = 27 170 community participants). Participants were 45–85 years at recruitment, when allostatic load and social engagement data were collected, and 3 years older at follow-up, when ACEs and multimorbidity data were collected. Structural equation modeling was used to test for mediation in the overall sample, and in sex- and age-stratified subsamples, all analyses adjusted for concurrent lifestyle confounds.

In the overall sample, ACEs were associated with multimorbidity, directly, β = 0.12 (95% confidence interval 0.11–0.13) and indirectly. Regarding indirect associations, ACEs were related to social engagement, β = −0.14 (−0.16 to −0.12) and social engagement was related to multimorbidity, β = −0.10 (−0.12 to −0.08). ACEs were related to allostatic load, β = 0.04 (0.03–0.05) and allostatic load was related to multimorbidity, β = 0.16 (0.15–0.17). The model was significant for males and females and across age cohorts, with qualifications in the oldest stratum (age 75–85).

ACEs are related to multimorbidity, directly and via social engagement and allostatic load. This is the first study to show mediated pathways between early adversity and multimorbidity in adulthood. It provides a platform for understanding multimorbidity as a lifespan dynamic informing the co-occurrence of the varied disease processes represented in multimorbidity.

Conspiracy beliefs are associated with detrimental health attitudes during the coronavirus disease 2019 (Covid-19) pandemic. Most prior research on these issues was cross-sectional, however, and restricted to attitudes or behavioral intentions. The current research was designed to examine to what extent conspiracy beliefs predict health behavior and well-being over a longer period of time.

In this preregistered multi-wave study on a large Dutch research panel (weighted to provide nationally representative population estimates), we examined if conspiracy beliefs early in the pandemic (April 2020) would predict a range of concrete health and well-being outcomes eight months later (December 2020; N = 5745).

The results revealed that Covid-19 conspiracy beliefs prospectively predicted a decreased likelihood of getting tested for corona; if tested, an increased likelihood of the test coming out positive; and, an increased likelihood of having violated corona regulations, deteriorated economic outcomes (job loss; reduced income), experiences of social rejection, and decreased overall well-being. Most of these effects generalized to a broader susceptibility to conspiracy theories (i.e. conspiracy mentality).

These findings suggest that conspiracy beliefs are associated with a myriad of negative life outcomes in the long run. Conspiracy beliefs predict how well people have coped with the pandemic over a period of eight months, as reflected in their health behavior, and their economic and social well-being.

A proportion of patients with bipolar disorder (BD) manifests with only unipolar mania (UM). This study examined relevant clinical features and psychosocial characteristics in UM compared with depressive-manic (D-M) subgroups. Moreover, comorbidity patterns of physical conditions and psychiatric disorders were evaluated between the UM and D-M groups.

This clinical retrospective study (N = 1015) analyzed cases with an average of 10 years of illness duration and a nationwide population-based cohort (N = 8343) followed up for 10 years in the Taiwanese population. UM was defined as patients who did not experience depressive episodes and were not prescribed adequate antidepressant treatment during the disease course of BD. Logistic regression models adjusted for relevant covariates were used to evaluate the characteristics and lifetime comorbidities in the two groups.

The proportion of UM ranged from 12.91% to 14.87% in the two datasets. Compared with the D-M group, the UM group had more psychotic symptoms, fewer suicidal behaviors, a higher proportion of morningness chronotype, better sleep quality, higher extraversion, lower neuroticism, and less harm avoidance personality traits. Substantially different lifetime comorbidity patterns were observed between the two groups.

Patients with UM exhibited distinct clinical and psychosocial features compared with patients with the D-M subtype. In particular, a higher risk of comorbid cardiovascular diseases and anxiety disorders is apparent in patients with D-M. Further studies are warranted to investigate the underlying mechanisms for diverse presentations in subgroups of BDs.

This study leveraged machine learning to evaluate the contribution of information from multiple developmental stages to prospective prediction of depression and anxiety in mid-adolescence.

A community sample (N = 374; 53.5% male) of children and their families completed tri-annual assessments across ages 3–15. The feature set included several important risk factors spanning psychopathology, temperament/personality, family environment, life stress, interpersonal relationships, neurocognitive, hormonal, and neural functioning, and parental psychopathology and personality. We used canonical correlation analysis (CCA) to reduce the large feature set to a lower dimensional space while preserving the longitudinal structure of the data. Ablation analysis was conducted to evaluate the relative contributions to prediction of information gathered at different developmental periods and relative to previous disorder status (i.e. age 12 depression or anxiety) and demographics (sex, race, ethnicity).

CCA components from individual waves predicted age 15 disorder status better than chance across ages 3, 6, 9, and 12 for anxiety and 9 and 12 for depression. Only the components from age 12 for depression, and ages 9 and 12 for anxiety, improved prediction over prior disorder status and demographics.

These findings suggest that screening for risk of adolescent depression can be successful as early as age 9, while screening for risk of adolescent anxiety can be successful as early as age 3. Assessing additional risk factors at age 12 for depression, and going back to age 9 for anxiety, can improve screening for risk at age 15 beyond knowing standard demographics and disorder history.

The degree to which suicide risk aggregates in US families is unknown. The authors aimed to determine the familial risk of suicide in Utah, and tested whether familial risk varies based on the characteristics of the suicides and their relatives.

A population-based sample of 12 160 suicides from 1904 to 2014 were identified from the Utah Population Database and matched 1:5 to controls based on sex and age using at-risk sampling. All first through third- and fifth-degree relatives of suicide probands and controls were identified (N = 13 480 122). The familial risk of suicide was estimated based on hazard ratios (HR) from an unsupervised Cox regression model in a unified framework. Moderation by sex of the proband or relative and age of the proband at time of suicide (<25 v. ⩾25 years) was examined.

Significantly elevated HRs were observed in first- (HR 3.45; 95% CI 3.12–3.82) through fifth-degree relatives (HR 1.07; 95% CI 1.02–1.12) of suicide probands. Among first-degree relatives of female suicide probands, the HR of suicide was 6.99 (95% CI 3.99–12.25) in mothers, 6.39 in sisters (95% CI 3.78–10.82), and 5.65 (95% CI 3.38–9.44) in daughters. The HR in first-degree relatives of suicide probands under 25 years at death was 4.29 (95% CI 3.49–5.26).

Elevated familial suicide risk in relatives of female and younger suicide probands suggests that there are unique risk groups to which prevention efforts should be directed – namely suicidal young adults and women with a strong family history of suicide.