Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Profiling patients on a proposed ‘immunometabolic depression’ (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001–0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01–0.22, I2 = 23.91%), with a higher – but still small – effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Cross-sectional studies report high levels of depressive symptoms during the COVID-19 pandemic, especially in youth and females. However, longitudinal research comparing depressive symptoms before and during the pandemic is lacking. Little is known about how the pandemic affected individuals with familial history of mental illness. The present study examines the impact of the pandemic on youth depressive symptoms, including offspring of parents with major mood and psychotic disorders.

Between March 2018 and February 2020, we measured depressive symptoms in 412 youth aged 5–25 years. We measured depressive symptoms again in 371 (90%) of these youth between April 2020 and May 2022. Two thirds (249) participants had a biological parent with a major mood or psychotic disorder. We tested the effect of the pandemic by comparing depression symptoms before and after March 2020. We examined age, sex, and family history as potential moderators.

We found an overall small increase in youth depressive symptoms (b = 0.07, 95% CI −0.01 to 0.15, p = 0.062). This was driven by an increase in female youth without familial history of mental illness (b = 0.35, 95% CI 0.14 to 0.56, p = 0.001). There was no change in depressive symptoms among offspring of parents with mental illness or males.

Our results provide reassurance about the wellbeing of children of parents with mental illness during a period of restricted access to resources outside the family. Rather than increasing symptoms in established risk groups, the pandemic led to a redistribution of depression burden towards segments of the youth population that were previously considered to be low-risk.

Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.

In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.

A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.

A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Personality traits may predict antidepressant discontinuation and response. However, previous studies were rather small, only explored a few personality traits and did not include adverse drug effects nor the interdependency between antidepressant discontinuation patterns and response.

GENDEP included 589 patients with unipolar moderate-severe depression treated with escitalopram or nortriptyline for 12 weeks. Seven personality dimensions were measured using the self-reported 240-item Temperament and Character Inventory-Revised (TCI-R). We applied Cox proportional models to study discontinuation patterns, logistic and linear regression to investigate response and remission after 8 and 12 weeks, and mixed-effects linear models regarding time-varying treatment response and adverse drug reactions.

Low harm avoidance, low cooperativeness, high self-transcendence and high novelty seeking were associated with higher risks for antidepressant discontinuation, independent of depressed mood, adverse drug reactions, drug, sex and age. Regression analyses showed that higher novelty seeking and cooperativeness scores were associated with a greater likelihood of response and remission after 8 and 12 weeks, respectively, but we found no correlations with response in the mixed-effects models. Only high harm avoidance was associated with more self-reported adverse effects.

This study, representing the largest investigation between several personality traits and response to two different antidepressants, suggests that correlations between personality traits and antidepressant treatment response may be confounded by differential rates of discontinuation. Future trials on personality in the treatment of depression need to consider this interdependency and study whether interventions aiming at improving compliance for some personality types may improve response to antidepressants.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Patients with major depressive disorder (MDD) display cognitive deficits in acutely depressed and remitted states. Childhood maltreatment is associated with cognitive dysfunction in adults, but its impact on cognition and treatment related cognitive outcomes in adult MDD has received little consideration. We investigate whether, compared to patients without maltreatment and healthy participants, adult MDD patients with childhood maltreatment display greater cognitive deficits in acute depression, lower treatment-associated cognitive improvements, and lower cognitive performance in remission.

Healthy and acutely depressed MDD participants were enrolled in a multi-center MDD predictive marker discovery trial. MDD participants received 16 weeks of standardized antidepressant treatment. Maltreatment and cognition were assessed with the Childhood Experience of Care and Abuse interview and the CNS Vital Signs battery, respectively. Cognitive scores and change from baseline to week 16 were compared amongst MDD participants with (DM+, n = 93) and without maltreatment (DM−, n = 90), and healthy participants with (HM+, n = 22) and without maltreatment (HM−, n = 80). Separate analyses in MDD participants who remitted were conducted.

DM+ had lower baseline global cognition, processing speed, and memory v. HM−, with no significant baseline differences amongst DM−, HM+, and HM− groups. There were no significant between-group differences in cognitive change over 16 weeks. Post-treatment remitted DM+, but not remitted DM−, scored significantly lower than HM− in working memory and processing speed.

Childhood maltreatment was associated with cognitive deficits in depressed and remitted adults with MDD. Maltreatment may be a risk factor for more severe and persistent cognitive deficits in adult MDD.

Basic symptoms, defined as subjectively perceived disturbances in thought, perception and other essential mental processes, have been established as a predictor of psychotic disorders. However, the relationship between basic symptoms and family history of a transdiagnostic range of severe mental illness, including major depressive disorder, bipolar disorder and schizophrenia, has not been examined.

We sought to test whether non-severe mood disorders and severe mood and psychotic disorders in parents is associated with increased basic symptoms in their biological offspring.

We measured basic symptoms using the Schizophrenia Proneness Instrument – Child and Youth Version in 332 youth aged 8–26 years, including 93 offspring of control parents, 92 offspring of a parent with non-severe mood disorders, and 147 offspring of a parent with severe mood and psychotic disorders. We tested the relationships between parent mental illness and offspring basic symptoms in mixed-effects linear regression models.

Offspring of a parent with severe mood and psychotic disorders (B = 0.69, 95% CI 0.22–1.16, P = 0.004) or illness with psychotic features (B = 0.68, 95% CI 0.09–1.27, P = 0.023) had significantly higher basic symptom scores than control offspring. Offspring of a parent with non-severe mood disorders reported intermediate levels of basic symptoms, that did not significantly differ from control offspring.

Basic symptoms during childhood are a marker of familial risk of psychopathology that is related to severity and is not specific to psychotic illness.

None.

For patients with major depressive disorder (MDD) experiencing side-effects or non-response to their first antidepressant, little is known regarding the effect of switching between a tricyclic antidepressant (TCA) and a selective serotonin reuptake inhibitor (SSRI).

To compare the switch between the TCA nortriptyline and the SSRI escitalopram.

Among 811 adults with MDD treated with nortriptyline or escitalopram for up to 12 weeks, 108 individuals switched from nortriptyline to escitalopram or vice versa because of side-effects or non-response (trial registration: EudraCT No.2004-001723-38 (https://eudract.ema.europa.eu/) and ISRCTN No.03693000 (http://www.controlled-trials.com)). Patients were followed for up to 26 weeks after switching and response was measured with the Montgomery–Åsberg Depression Rating scale (MADRS). We performed adjusted mixed-effects linear regression models with full information maximum likelihood estimation reporting β-coefficients with 95% CIs.

Switching antidepressants resulted in a significant decrease in MADRS scores. This was present for switchers from escitalopram to nortriptyline (n = 36, β = −0.38, 95% CI −0.51 to −0.25, P<0.001) and from nortriptyline to escitalopram (n = 72, β = −0.34, 95% CI −0.41 to −0.26, P<0.001). Both switching options resulted in significant improvement among individuals who switched because of non-response or side-effects. The results were supported by analyses on other rating scales and symptom dimensions.

These results suggest that switching from a TCA to an SSRI or vice versa after non-response or side-effects to the first antidepressant may be a viable approach to achieve response among patients with MDD.

K.J.A. holds an Alberta Centennial Addiction and Mental Health Research Chair, funded by the Government of Alberta. K.J.A. has been a member of various advisory boards, received consultancy fees and honoraria, and has received research grants from various companies including Johnson and Johnson Pharmaceuticals Research and Development and Bristol-Myers Squibb Pharmaceuticals Limited. D.S. has served on advisory boards for, and received unrestricted grants from, Lundbeck and AstraZeneca. A.F. and P.M. have received honoraria for participating in expert panels for Lundbeck and GlaxoSmithKline.

Treatment-resistant depression (TRD) is the most problematic outcome of depression in terms of functional impairment, suicidal thoughts and decline in physical health.

To investigate the genetic predictors of TRD using a genome-wide approach to contribute to the development of precision medicine.

A sample recruited by the European Group for the Study of Resistant Depression (GSRD) including 1148 patients with major depressive disorder (MDD) was characterised for the occurrence of TRD (lack of response to at least two adequate antidepressant treatments) and genotyped using the Infinium PsychArray. Three clinically relevant patient groups were considered: TRD, responders and non-responders to the first antidepressant trial, thus outcomes were based on comparisons of these groups. Genetic analyses were performed at the variant, gene and gene-set (i.e. functionally related genes) level. Additive regression models of the outcomes and relevant covariates were used in the GSRD participants and in a fixed-effect meta-analysis performed between GSRD, STAR*D (n = 1316) and GENDEP (n = 761) participants.

No individual polymorphism or gene was associated with TRD, although some suggestive signals showed enrichment in cytoskeleton regulation, transcription modulation and calcium signalling. Two gene sets (GO:0043949 and GO:0000183) were associated with TRD versus response and TRD versus response and non-response to the first treatment in the GSRD participants and in the meta-analysis, respectively (corrected P = 0.030 and P = 0.027).

The identified gene sets are involved in cyclic adenosine monophosphate mediated signal and chromatin silencing, two processes previously implicated in antidepressant action. They represent possible biomarkers to implement personalised antidepressant treatments and targets for new antidepressants.

D.S. has received grant/research support from GlaxoSmithKline and Lundbeck; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen and Lundbeck. S.M. has been a consultant or served on advisory boards for: AstraZeneca, Bristol-Myers Squibb, Forest, Johnson & Johnson, Leo, Lundbeck, Medelink, Neurim, Pierre Fabre, Richter. S.K. has received grant/research support from Eli Lilly, Lundbeck, Bristol-Myers Squibb, GlaxoSmithKline, Organon, Sepracor and Servier; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Eli Lilly, Lundbeck, Pfizer, Organon, Schwabe, Sepracor, Servier, Janssen and Novartis; and has served on speakers' bureaus for AstraZeneca, Eli Lily, Lundbeck, Schwabe, Sepracor, Servier, Pierre Fabre, Janssen and Neuraxpharm. J.Z. has received grant/research support from Lundbeck, Servier, Brainsway and Pfizer, has served as a consultant or on advisory boards for Servier, Pfizer, Abbott, Lilly, Actelion, AstraZeneca and Roche and has served on speakers' bureaus for Lundbeck, Roch, Lilly, Servier, Pfizer and Abbott. J.M. is a member of the Board of the Lundbeck International Neuroscience Foundation and of Advisory Board of Servier. A.S. is or has been consultant/speaker for: Abbott, AbbVie, Angelini, Astra Zeneca, Clinical Data, Boehringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi and Servier. C.M.L. receives research support from RGA UK Services Limited.

In an effort to optimize patient outcomes, considerable attention is being devoted to identifying patient characteristics associated with major depressive disorder (MDD) and its responsiveness to treatment. In the current study, we extend this work by evaluating whether early change in these sensitivities is associated with response to antidepressant treatment for MDD.

Participants included 210 patients with MDD who were treated with 8 weeks of escitalopram and 112 healthy comparison participants. Of the original 210 patients, 90 non-responders received adjunctive aripiprazole for an additional 8 weeks. Symptoms of depression and anhedonia were assessed at the beginning of treatment and 8 weeks later in both samples. Reward and punishment sensitivity were assessed using the BIS/BAS scales measured at the initiation of treatment and 2 weeks later.

Individuals with MDD exhibited higher punishment sensitivity and lower reward sensitivity compared with healthy comparison participants. Change in reward sensitivity during the first 2 weeks of treatment was associated with improved depressive symptoms and anhedonia following 8 weeks of treatment with escitalopram. Similarly, improvement in reward responsiveness during the first 2 weeks of adjunctive therapy with aripiprazole was associated with fewer symptoms of depression at post-treatment.

Findings highlight the predictive utility of early change in reward sensitivity during antidepressant treatment for major depression. In a clinical setting, a lack of change in early reward processing may signal a need to modify a patient's treatment plan with alternative or augmented treatment approaches.

The long-term outcomes of bipolar disorder range from lasting remission to chronic course or frequent recurrences requiring admissions. The distinction between bipolar I and II disorders has limited utility in outcome prediction. It is unclear to what extent the clinical course of bipolar disorder predicts long-term outcomes.

A representative sample of 191 individuals diagnosed with bipolar I or II disorder was recruited and followed for up to 5 years using a life-chart method. We previously described the clinical course over the first 18 months with dimensional course characteristics and latent classes. Now we test if these course characteristics predict long-term outcomes, including time ill (time with any mood symptoms) and hospital admissions over a second non-overlapping follow-up period in 111 individuals with available data from both 18 months and 5 years follow-ups.

Dimensional course characteristics from the first 18 months prospectively predicted outcomes over the following 3.5 years. The proportion of time depressed, the severity of depressive symptoms and the proportion of time manic predicted more time ill. The proportion of time manic, the severity of manic symptoms and depression-to-mania switching predicted a greater likelihood of hospital admissions. All predictions remained significant after controlling for age, sex and bipolar I v. II disorder.

Differential associations with long-term outcomes suggest that course characteristics may facilitate care planning with greater predictive validity than established types of bipolar disorders. A clinical course dominated by depressive symptoms predicts a greater proportion of time ill. A clinical course characterized by manic episodes predicts hospital admissions.

Attention-deficit hyperactivity disorder (ADHD) and anxiety disorders have been proposed as precursors of bipolar disorder, but their joint and relative roles in the development of bipolar disorder are unknown.

To test the prospective relationship of ADHD and anxiety with onset of bipolar disorder.

We examined the relationship between ADHD, anxiety disorders and bipolar disorder in a birth cohort of 2 409 236 individuals born in Denmark between 1955 and 1991. Individuals were followed from their sixteenth birthday or from January 1995 to their first clinical contact for bipolar disorder or until December 2012. We calculated incidence rates per 10 000 person-years and tested the effects of prior diagnoses on the risk of bipolar disorder in survival models.

Over 37 394 865 person-years follow-up, 9250 onsets of bipolar disorder occurred. The incidence rate of bipolar disorder was 2.17 (95% CI 2.12–2.19) in individuals with no prior diagnosis of ADHD or anxiety, 23.86 (95% CI 19.98–27.75) in individuals with a prior diagnosis of ADHD only, 26.05 (95% CI 24.47–27.62) in individuals with a prior diagnosis of anxiety only and 66.16 (95% CI 44.83–87.47) in those with prior diagnoses of both ADHD and anxiety. The combination of ADHD and anxiety increased the risk of bipolar disorder 30-fold (95% CI 21.66–41.40) compared with those with no prior ADHD or anxiety.

Early manifestations of both internalising and externalising psychopathology indicate liability to bipolar disorder. The combination of ADHD and anxiety is associated with a very high risk of bipolar disorder.

None.

Depression and obesity are highly prevalent, and major impacts on public health frequently co-occur. Recently, we reported that having depression moderates the effect of the FTO gene, suggesting its implication in the association between depression and obesity.

To confirm these findings by investigating the FTO polymorphism rs9939609 in new cohorts, and subsequently in a meta-analysis.

The sample consists of 6902 individuals with depression and 6799 controls from three replication cohorts and two original discovery cohorts. Linear regression models were performed to test for association between rs9939609 and body mass index (BMI), and for the interaction between rs9939609 and depression status for an effect on BMI. Fixed and random effects meta-analyses were performed using METASOFT.

In the replication cohorts, we observed a significant interaction between FTO, BMI and depression with fixed effects meta-analysis (β=0.12, P = 2.7 × 10−4) and with the Han/Eskin random effects method (P = 1.4 × 10−7) but not with traditional random effects (β = 0.1, P = 0.35). When combined with the discovery cohorts, random effects meta-analysis also supports the interaction (β = 0.12, P = 0.027) being highly significant based on the Han/Eskin model (P = 6.9 × 10−8). On average, carriers of the risk allele who have depression have a 2.2% higher BMI for each risk allele, over and above the main effect of FTO.

This meta-analysis provides additional support for a significant interaction between FTO, depression and BMI, indicating that depression increases the effect of FTO on BMI. The findings provide a useful starting point in understanding the biological mechanism involved in the association between obesity and depression.

It has been suggested that offspring of parents with bipolar disorder are at increased risk for disruptive mood dysregulation disorder (DMDD), but the specificity of this association has not been established.

We examined the specificity of DMDD to family history by comparing offspring of parents with (a) bipolar disorder, (b) major depressive disorder and (c) a control group with no mood disorders.

We established lifetime diagnosis of DMDD using the Schedule for Affective Disorders and Schizophrenia for School Aged Children for DSM-5 in 180 youth aged 6–18 years, including 58 offspring of parents with bipolar disorder, 82 offspring of parents with major depressive disorder and 40 control offspring.

Diagnostic criteria for DMDD were met in none of the offspring of parents with bipolar disorder, 6 of the offspring of parents with major depressive disorder and none of the control offspring. DMDD diagnosis was significantly associated with family history of major depressive disorder.

Our results suggest that DMDD is not specifically associated with a family history of bipolar disorder and may be associated with parental depression.

Little is known about the impact of insulin resistance on bipolar disorder.

To examine the relationships between insulin resistance, type 2 diabetes and clinical course and treatment outcomes in bipolar disorder.

We measured fasting glucose and insulin in 121 adults with bipolar disorder. We diagnosed type 2 diabetes and determined insulin resistance. The National Institute of Mental Health Life Chart was used to record the course of bipolar disorder and the Alda scale to establish response to prophylactic lithium treatment.

Patients with bipolar disorder and type 2 diabetes or insulin resistance had three times higher odds of a chronic course of bipolar disorder compared with euglycaemic patients (50% and 48.7% respectively v. 27.3%, odds ratio (OR) = 3.07, P = 0.007), three times higher odds of rapid cycling (38.5% and 39.5% respectively v. 18.2%, OR = 3.13, P = 0.012) and were more likely to be refractory to lithium treatment (36.8% and 36.7% respectively v. 3.2%, OR = 8.40, P<0.0001). All associations remained significant after controlling for antipsychotic exposure and body mass index in sensitivity analyses.

Comorbid insulin resistance may be an important factor in resistance to treatment in bipolar disorder.

In general, mothers with depression experience more environmental and family risk factors, and lead riskier lifestyles, than mothers who are not depressed.

To test whether the exposure of a child to risk factors associated with mental health adds to the prediction of child psychopathology beyond exposure to maternal depression.

In 7429 mother–offspring pairs participating in the Avon Longitudinal Study of Parents and Children in the UK, maternal depression was assessed when the children were aged 1.5 years; multiple risk factor exposures were examined between birth and 2 years of age; and DSM-IV-based externalising and internalising diagnoses were evaluated when the children were 7.5 years of age.

Children of clinically depressed mothers were exposed to more risk factors associated with maternal mental health. Maternal depression increased diagnoses of externalising and internalising disorders, but a substantial portion of these associations was explained by increased risk factor exposure (41% for externalising and 37% for internalising disorders). At the same time, these risk exposures significantly increased the odds of both externalising and internalising diagnoses, over and above the influence of maternal depression.

Children of clinically depressed mothers are exposed to both maternal psychopathology and risks that are associated with maternal mental health. These results may explain why treating mothers with depression shows beneficial effects for children, but does not completely neutralise the increased risk of psychopathology and impairment.