Symptoms of depression are transdiagnostic heterogenous features frequently assessed in psychiatric disorders, that impact the response to first-line treatment and are associated with higher suicide risk. This study assessed whether severe mental pain could characterize a specific phenotype of severely depressed high-risk psychiatric patients. We also aimed to analyze differences in treatments administered.

2,297 adult patients (1,404 females and 893 males; mean age = 43.25 years, SD = 15.15) treated in several Italian psychiatric departments. Patients were assessed for psychiatric diagnoses, mental pain, symptoms of depression, hopelessness, and suicide risk.

More than 23% of the patients reported high depression symptomatology and high mental pain (HI DEP/HI PAIN). Compared to patients with lower symptoms of depression, HI DEP/HI PAIN is more frequent among females admitted to an inpatient department and is associated with higher hopelessness and suicide risk. In addition, HI DEP/HI PAIN (compared to both patients with lower symptoms of depression and patients with higher symptoms of depression but lower mental pain) were more frequently diagnosed in patients with personality disorders and had different treatments.

Patients reporting severe symptoms of depression and high mental pain presented a mixture of particular dangerousness (high trait hopelessness and the presence of suicide ideation with more frequency and less controllability and previous suicide behaviors). The presence of severe mental pain may act synergically in expressing a clinical phenotype that is likewise treated with a more complex therapeutic regime than that administered to those experiencing symptoms of depression without mental pain.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

When facing a traumatic event, some people may experience positive changes, defined as posttraumatic growth (PTG).

Understanding the possible positive consequences of the pandemic on the individual level is crucial for the development of supportive psychosocial interventions. The present paper aims to: 1) evaluate the levels of PTG in the general population; 2) to identify predictors of each dimension of post-traumatic growth.

The majority of the sample (67%, N = 13,889) did not report any significant improvement in any domain of PTG. Participants reported the highest levels of growth in the dimension of “appreciation of life” (2.3 ± 1.4), while the lowest level was found in the “spiritual change” (1.2 ± 1.2). Female participants reported a slightly higher level of PTG in areas of personal strength (p < .002) and appreciation for life (p < .007) compared to male participants, while no significant association was found with age. At the multivariate regression models, weighted for the propensity score, only the initial week of lockdown (between 9-15 April) had a negative impact on the dimension of “relating to others” (B = −.107, 95% CI = −.181 to −.032, p < .005), while over time no other effects were found. The duration of exposure to lockdown measures did not influence the other dimensions of PTG.

The assessment of the levels of PTG is of great importance for the development of ad hoc supportive psychosocial interventions. From a public health perspective, the identification of protective factors is crucial for developing ad-hoc tailored interventions and for preventing the development of full-blown mental disorders in large scale.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

The Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented traumatic event influencing the healthcare, economic, and social welfare systems worldwide. In order to slow the infection rates, lockdown has been implemented almost everywhere. Italy, one of the countries most severely affected, entered the “lockdown” on March 8, 2020.

The COvid Mental hEalth Trial (COMET) network includes 10 Italian university sites and the National Institute of Health. The whole study has three different phases. The first phase includes an online survey conducted between March and May 2020 in the Italian population. Recruitment took place through email invitation letters, social media, mailing lists of universities, national medical associations, and associations of stakeholders (e.g., associations of users/carers). In order to evaluate the impact of lockdown on depressive, anxiety and stress symptoms, multivariate linear regression models were performed, weighted for the propensity score.

The final sample consisted of 20,720 participants. Among them, 12.4% of respondents (N = 2,555) reported severe or extremely severe levels of depressive symptoms, 17.6% (N = 3,627) of anxiety symptoms and 41.6% (N = 8,619) reported to feel at least moderately stressed by the situation at the DASS-21.

According to the multivariate regression models, the depressive, anxiety and stress symptoms significantly worsened from the week April 9–15 to the week April 30 to May 4 (p < 0.0001). Moreover, female respondents and people with pre-existing mental health problems were at higher risk of developing severe depression and anxiety symptoms (p < 0.0001).

Although physical isolation and lockdown represent essential public health measures for containing the spread of the COVID-19 pandemic, they are a serious threat for mental health and well-being of the general population. As an integral part of COVID-19 response, mental health needs should be addressed.