Suboptimal treatment outcomes contribute to the high disease burden of mood, anxiety or psychotic disorders. Clinical prediction models could optimise treatment allocation, which may result in better outcomes. Whereas ample research on prediction models is performed, model performance in other clinical contexts (i.e. external validation) is rarely examined. This gap hampers generalisability and as such implementation in clinical practice.

Systematically appraise studies on externally validated clinical prediction models for estimated treatment outcomes for mood, anxiety and psychotic disorders by (1) reviewing methodological quality and applicability of studies and (2) investigating how model properties relate to differences in model performance.

The review and meta-analysis protocol was prospectively registered with PROSPERO (registration number CRD42022307987). A search was conducted on 8 November 2021 in the databases PubMED, PsycINFO and EMBASE. Random-effects meta-analysis and meta-regression were conducted to examine between-study heterogeneity in discriminative performance and its relevant influencing factors.

Twenty-eight studies were included. The majority of studies (n = 16) validated models for mood disorders. Clinical predictors (e.g. symptom severity) were most frequently included (n = 25). Low methodological and applicability concerns were found for two studies. The overall discrimination performance of the meta-analysis was fair with wide prediction intervals (0.72 [0.46; 0.89]). The between-study heterogeneity was not explained by number or type of predictors but by disorder diagnosis.

Few models seem ready for further implementation in clinical practice to aid treatment allocation. Besides the need for more external validation studies, we recommend close examination of the clinical setting before model implementation.

Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers).

Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs).

Smoking (HRs range 1.04–1.10) and physical inactivity (HRs range 1.01–1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03–1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01).

Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.

Depression is a highly recurrent disorder, with more than 50% of those affected experiencing a subsequent episode. Although there is relatively little stability in symptoms across episodes, some evidence indicates that suicidal ideation may be an exception. However, these findings warrant replication, especially over longer periods and across multiple episodes.

To assess the relative stability of suicidal ideation in comparison with other non-core depressive symptoms across episodes.

We examined 490 individuals with current major depressive disorder (MDD) at baseline and at least one subsequent episode during 9-year follow-up within the Netherlands Study of Depression and Anxiety (NESDA). The Inventory of Depressive Symptomatology (IDS) was used to assess DSM-5 non-core MDD symptoms (fatigue, appetite/weight change, sleep disturbance, psychomotor disturbance, concentration difficulties, worthlessness/guilt, suicidal ideation) at baseline and 2-, 4-, 6- and 9-year follow-up. We examined consistency in symptom presentation (i.e. whether the symptom met the diagnostic threshold, based on a binary categorisation of the IDS) using kappa (κ) and percentage agreement, and stability in symptom severity using Spearman correlation, based on the continuous IDS scores.

Out of all non-core depressive symptoms, insomnia appeared the most stable across episodes (r = 0.55–0.69, κ = 0.31–0.47) and weight decrease the least stable (r = 0.03–0.33, κ = 0.06–0.19). For suicidal ideation, correlations across episodes ranged from r = 0.36 to r = 0.55 and consistency ranged from κ = 0.28 to κ = 0.49.

Suicidal ideation is moderately stable in recurrent depression over 9 years. Contrary to prior reports, however, it does not exhibit substantially more stability than most other non-core symptoms of depression.

Depression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network perspective yields a more complete insight than single outcome-single predictor models.

We used data from the Netherlands Study of Depression and Anxiety (N = 2498) and leveraged networks capturing associations between 30 depressive symptoms (Inventory of Depressive Symptomatology) and 46 metabolites. Analyses involved 4 steps: creating a network with Mixed Graphical Models; calculating centrality measures; bootstrapping for stability testing; validating central, stable associations by extra covariate-adjustment; and validation using another data wave collected 6 years later.

The network yielded 28 symptom-metabolite associations. There were 15 highly-central variables (8 symptoms, 7 metabolites), and 3 stable links involving the symptoms Low energy (fatigue), and Hypersomnia. Specifically, fatigue showed consistent associations with higher mean diameter for VLDL particles and lower estimated degree of (fatty acid) unsaturation. These remained present after adjustment for lifestyle and health-related factors and using another data wave.

The somatic symptoms Fatigue and Hypersomnia and cholesterol and fatty acid measures showed central, stable, and consistent relationships in our network. The present analyses showed how metabolic alterations are more consistently linked to specific symptom profiles.

Patients with schizophrenia experience cognitive impairment, which could be related to neuroinflammation in the hippocampus. The cause for such hippocampal inflammation is still unknown, but it has been suggested that herpes virus infection is involved. This study therefore aimed to determine whether add-on treatment of schizophrenic patients with the anti- viral drug valaciclovir would reduce hippocampal neuroinflammation and consequently improve cognitive symptoms.

We performed a double-blind monocenter study in 24 male and female patients with schizophrenia, experiencing active psychotic symptoms. Patients were orally treated with the anti-viral drug valaciclovir for seven consecutive days (8 g/day). Neuroinflammation was measured with Positron Emission Tomography using the translocator protein ligand [11C]-PK11195, pre-treatment and at seven days post-treatment, as were psychotic symptoms and cognition.

Valaciclovir treatment resulted in reduced TSPO binding (39%) in the hippocampus, as well as in the brainstem, frontal lobe, temporal lobe, parahippocampal gyrus, amygdala, parietal lobe, occipital lobe, insula and cingulate gyri, nucleus accumbens and thalamus (31–40%) when using binding potential (BPND) as an outcome. With total distribution volume (VT) as outcome we found essentially the same results, but associations only approached statistical significance (p = 0.050 for hippocampus). Placebo treatment did not affect neuroinflammation. No effects of valaciclovir on psychotic symptoms or cognitive functioning were found.

We found a decreased TSPO binding following antiviral treatment, which could suggest a viral underpinning of neuroinflammation in psychotic patients. Whether this reduced neuroinflammation by treatment with valaciclovir has clinical implications and is specific for schizophrenia warrants further research.

Mental health was only modestly affected in adults during the early months of the COVID-19 pandemic on the group level, but interpersonal variation was large.

We aim to investigate potential predictors of the differences in changes in mental health.

Data were aggregated from three Dutch ongoing prospective cohorts with similar methodology for data collection. We included participants with pre-pandemic data gathered during 2006–2016, and who completed online questionnaires at least once during lockdown in The Netherlands between 1 April and 15 May 2020. Sociodemographic, clinical (number of mental health disorders and personality factors) and COVID-19-related variables were analysed as predictors of relative changes in four mental health outcomes (depressive symptoms, anxiety and worry symptoms, and loneliness), using multivariate linear regression analyses.

We included 1517 participants with (n = 1181) and without (n = 336) mental health disorders. Mean age was 56.1 years (s.d. 13.2), and 64.3% were women. Higher neuroticism predicted increases in all four mental health outcomes, especially for worry (β = 0.172, P = 0.003). Living alone and female gender predicted increases in depressive symptoms and loneliness (β = 0.05–0.08), whereas quarantine and strict adherence with COVID-19 restrictions predicted increases in anxiety and worry symptoms (β = 0.07–0.11).Teleworking predicted a decrease in anxiety symptoms (β = −0.07) and higher age predicted a decrease in anxiety (β = −0.08) and worry symptoms (β = −0.10).

Our study showed neuroticism as a robust predictor of adverse changes in mental health, and identified additional sociodemographic and COVID-19-related predictors that explain longitudinal variability in mental health during the COVID-19 pandemic.

Previous research has suggested that statistical power is suboptimal in many biomedical disciplines, but it is unclear whether power is better in trials for particular interventions, disorders, or outcome types. We therefore performed a detailed examination of power in trials of psychotherapy, pharmacotherapy, and complementary and alternative medicine (CAM) for mood, anxiety, and psychotic disorders.

We extracted data from the Cochrane Database of Systematic Reviews (Mental Health). We focused on continuous efficacy outcomes and estimated power to detect predetermined effect sizes (standardized mean difference [SMD] = 0.20–0.80, primary SMD = 0.40) and meta-analytic effect sizes (ESMA). We performed meta-regression to estimate the influence of including underpowered studies in meta-analyses.

We included 256 reviews with 10 686 meta-analyses and 47 384 studies. Statistical power for continuous efficacy outcomes was very low across intervention and disorder types (overall median [IQR] power for SMD = 0.40: 0.32 [0.19–0.54]; for ESMA: 0.23 [0.09–0.58]), only reaching conventionally acceptable levels (80%) for SMD = 0.80. Median power to detect the ESMA was higher in treatment-as-usual (TAU)/waitlist-controlled (0.49–0.63) or placebo-controlled (0.12–0.38) trials than in trials comparing active treatments (0.07–0.13). Adequately-powered studies produced smaller effect sizes than underpowered studies (B = −0.06, p ⩽ 0.001).

Power to detect both predetermined and meta-analytic effect sizes in psychiatric trials was low across all interventions and disorders examined. Consistent with the presence of reporting bias, underpowered studies produced larger effect sizes than adequately-powered studies. These results emphasize the need to increase sample sizes and to reduce reporting bias against studies reporting null results to improve the reliability of the published literature.

Intravenous infusion of ketamine can produce rapid and large symptom reduction in patients with treatment-resistant depression (TRD) but presents major obstacles to clinical applicability, especially in community settings. Oral esketamine may be a promising addition to our TRD treatment armamentarium.

To explore the safety, tolerability and potential clinical effectiveness of a 3-week treatment with repeated, low-dose oral esketamine.

Seven patients with chronic and severe TRD received 1.25 mg/kg generic oral esketamine daily, over 21 consecutive days. Scores on the Systematic Assessment for Treatment Emergent Events (SAFTEE), Community Assessment of Psychic Experiences (CAPE), Clinician Administered Dissociative States Scale (CADSS) and Hamilton Rating Scale for Depression (HRSD) instruments, as well as blood pressure and heart rate, were repeatedly assessed.

Treatment with oral esketamine was well-tolerated. No serious side-effects occurred, and none of the participants discontinued treatment prematurely. Psychotomimetic effects were the most frequently reported adverse events. Mean HDRS score decreased by 16.5%, from 23.6 to 19.7. Three participants showed reductions in HDRS scores above the minimum clinically important difference (eight-point change), of whom two showed partial response. No participants showed full response or remission.

These results strengthen the idea that oral esketamine is a safe and well-tolerated treatment for patients with chronic and severe TRD, but therapeutic effects were modest. Results were used to design a randomised controlled trial that is currently in progress.

Observational studies suggest that hormonal contraceptive use may increase depressive symptoms in women, but it is unclear whether the effect is causal.

To quantitatively examine the evidence from randomised clinical trials for the link between hormonal contraceptive use and depressive symptoms.

We performed a systematic review and network meta-analysis of randomised clinical trials comparing women randomised to any form of a hormonal contraceptive with women randomised to any other form of a (non-)hormonal contraceptive or placebo. We searched the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Web of Science, PsycINFO, EMCare and EMBASE, from inception to 1 May 2020. Certainty of the evidence was assessed with the Grading of Recommendations Assessment, Development and Evaluation approach. A random-effect Bayesian network meta-analysis was conducted, with change in depressive symptoms between baseline and three cycles as outcome.

This review identified 3492 records, of which 14 trials were eligible and 12 could be included in the network meta-analysis. These trials included 5833 participants (mean age per study range: 16.8–32.4 years) and compared 10 different interventions. Compared with placebo, hormonal contraceptive use did not cause worsening of depressive symptoms (standardised mean difference: median, −0.04; range, −0.17 [95% credible interval −0.46 to 0.13] to 0.13 [95% credible interval −0.28 to 0.56]).

This study suggests that hormonal contraceptive use does not lead to an increase in depressive symptoms in adult women. Future studies should include first-time users, to confirm the results in young women.

Most epidemiological studies show a decrease of internalizing disorders at older ages, but it is unclear how the prevalence exactly changes with age, and whether there are different patterns for internalizing symptoms and traits, and for men and women. This study investigates the impact of age and sex on the point prevalence across different mood and anxiety disorders, internalizing symptoms, and neuroticism.

We used cross-sectional data on 146 315 subjects, aged 18–80 years, from the Lifelines Cohort Study, a Dutch general population sample. Between 2012 and 2016, five current internalizing disorders – major depression, dysthymia, generalized anxiety disorder, social phobia, and panic disorder – were assessed according to DSM-IV criteria. Depressive symptoms, anxiety symptoms, neuroticism, and negative affect (NA) were also measured. Generalized additive models were used to identify nonlinear patterns across age, and to investigate sex differences.

The point prevalence of internalizing disorders generally increased between the ages of 18 and 30 years, stabilized between 30 and 50, and decreased after age 50. The patterns of internalizing symptoms and traits were different. NA and neuroticism gradually decreased after age 18. Women reported more internalizing disorders than men, but the relative difference remained stable across age (relative risk ~1.7).

The point prevalence of internalizing disorders was typically highest between age 30 and 50, but there were differences between the disorders, which could indicate differences in etiology. The relative gap between the sexes remained similar across age, suggesting that changes in sex hormones around the menopause do not significantly influence women's risk of internalizing disorders.

Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results.

Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes.

The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased.

SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients.

Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach.

In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs).

At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features.

The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.

Although symptomatic remission is considered the optimal outcome in depression, this is not always achieved. Furthermore, symptom indicators do not fully capture patients’ and clinicians’ perspectives on remission. Broader indicators of (partial) remission from depression should be considered.

To investigate relevant outcomes of depression treatment in specialist care from patients’ and clinicians’ perspectives and to investigate whether these perspectives differ from each other.

Three focus groups with 11 patients with depression and seven semi-structured interviews with clinicians were conducted exploring their perspectives on remission. All interviews were audio-recorded and transcribed verbatim. We analysed the transcripts thematically using the phenomenologist approach.

Independently, both patients and clinicians perceived the following outcomes relevant: restoring social functioning and interpersonal relations, regaining quality of life and achieving personal goals. All clinicians emphasised symptom reduction and satisfaction with treatment as relevant outcomes, whereas the former was not an obvious theme in patients. Unlike clinicians, patients made a clear distinction between treatment outcomes in first versus recurrent/chronic depression.

Classically defined study outcomes based on symptom resolution only partly reflect issues considered important by patients and clinicians in specialist depression treatment. Incorporating patients’ and clinicians’ perspectives in the development of measurable end-points makes them more suitable for use in trials and subsequent translation to clinical practice. Furthermore, evaluating patients’ perspectives on treatment outcomes helps in the development of tailored interventions according to patients’ needs.

Reviews of the relative efficacy of psychotherapy and combined therapy (psychotherapy with pharmacotherapy) for depression have yielded contradicting conclusions. This may be explained by the clinical heterogeneity of the studies reviewed.

To conduct a meta-analysis with an acceptable level of homogeneity in order to investigate the relative efficacy of psychotherapy and combined therapy in the acute treatment of depression.

A systematic search was performed for RCTs published between 1980 and 2005 comparing psychotherapy and combined therapy in adult psychiatric outpatients with non-psychotic unipolar major depressive disorder. The studies were classified according to the chronicity and severity of the depression. Data were pooled by means of meta-analysis and statistical tests were conducted to measure heterogeneity.

The meta-analysis included seven studies looking at a total of 903 patients. None of the heterogeneity tests established significance. This indicates a lack of evidence for the heterogeneity of the results. The dropout rates did not differ significantly between the two treatment modalities (25% in combined therapy and 24% in psychotherapy, p = 0.77). At treatment termination, the intention-to-treat remission rate for combined therapy (46%) was better than for psychotherapy (34%) (p = 0.0007); Relative Risk 1.32 (95% CI: 1.12–1.56), Odds Ratio 1.59 (95% CI: 1.22–2.09). In moderate depression, the difference between the remission rate for combined therapy and psychotherapy was statistically significant (47% compared to 34% respectively, p = 0.001). This was not the case in mild major depression (42% compared to 37% respectively, p = 0.29). The difference was also statistically significant in chronic major depression (48% compared to 32%, p < 0.001), but not in non-chronic major depression (43% compared to 37%, p = 0.22). On a more specific level, no differences were found in the remission rates for the treatment modalities in mild or moderate non-chronic depression. Combined therapy led to significantly better results than psychotherapy in moderate chronic depression only (48% compared to 32%, p < 0.001).

In the acute treatment of adult psychiatric outpatients with major depressive disorder, patient compliance with combined therapy matches compliance with psychotherapy alone. Combined therapy is more efficacious than psychotherapy alone. However, these results depend on severity and chronicity. Combined therapy outperformed psychotherapy in moderate chronic depression only. No differences were found in mild and moderate non-chronic depression. No data were found for mild chronic depression and for severe depression.

The output of many healthy physiological systems displays fractal fluctuations with self-similar temporal structures. Altered fractal patterns are associated with pathological conditions. There is evidence that patients with bipolar disorder have altered daily behaviors.

To test whether fractal patterns in motor activity are altered in patients with bipolar disorder, we analyzed 2-week actigraphy data collected from 106 patients with bipolar disorder type I in a euthymic state, 73 unaffected siblings of patients, and 76 controls. To examine the link between fractal patterns and symptoms, we analyzed 180-day actigraphy and mood symptom data that were simultaneously collected from 14 patients.

Compared to controls, patients showed excessive regularity in motor activity fluctuations at small time scales (<1.5 h) as quantified by a larger scaling exponent (α1 > 1), indicating a more rigid motor control system. α1 values of siblings were between those of patients and controls. Further examinations revealed that the group differences in α1 were only significant in females. Sex also affected the group differences in fractal patterns at larger time scales (>2 h) as quantified by scaling exponent α2. Specifically, female patients and siblings had a smaller α2 compared to female controls, indicating more random activity fluctuations; while male patients had a larger α2 compared to male controls. Interestingly, a higher weekly depression score was associated with a lower α1 in the subsequent week.

Our results show sex- and scale-dependent alterations in fractal activity regulation in patients with bipolar disorder. The mechanisms underlying the alterations are yet to be determined.

In a large and comprehensively assessed sample of patients with bipolar disorder type I (BDI), we investigated the prevalence of psychotic features and their relationship with life course, demographic, clinical, and cognitive characteristics. We hypothesized that groups of psychotic symptoms (Schneiderian, mood incongruent, thought disorder, delusions, and hallucinations) have distinct relations to risk factors.

In a cross-sectional study of 1342 BDI patients, comprehensive demographical and clinical characteristics were assessed using the Structured Clinical Interview for DSM-IV (SCID-I) interview. In addition, levels of childhood maltreatment and intelligence quotient (IQ) were assessed. The relationships between these characteristics and psychotic symptoms were analyzed using multiple general linear models.

A lifetime history of psychotic symptoms was present in 73.8% of BDI patients and included delusions in 68.9% of patients and hallucinations in 42.6%. Patients with psychotic symptoms showed a significant younger age of disease onset (β = −0.09, t = −3.38, p = 0.001) and a higher number of hospitalizations for manic episodes (F11 338 = 56.53, p < 0.001). Total IQ was comparable between groups. Patients with hallucinations had significant higher levels of childhood maltreatment (β = 0.09, t = 3.04, p = 0.002).

In this large cohort of BDI patients, the vast majority of patients had experienced psychotic symptoms. Psychotic symptoms in BDI were associated with an earlier disease onset and more frequent hospitalizations particularly for manic episodes. The study emphasizes the strength of the relation between childhood maltreatment and hallucinations but did not identify distinct subgroups based on psychotic features and instead reported of a large heterogeneity of psychotic symptoms in BD.

Off-label ketamine treatment has shown acute antidepressant effects that offer hope for patients with therapy-resistant depression. However, its potential for integration into treatment algorithms is controversial, not least because the evidence base for maintenance treatment with repeated ketamine administration is currently weak. Ketamine is also a drug of misuse, which has raised concerns regarding the target population. Little is known about which patients would seek ketamine treatment if it were more widely available.

To explore some of the characteristics of the patients actively seeking ketamine treatment.

An online survey containing questions about duration of current depressive episode, number of antidepressants used and other comments was completed by patients who were exploring the internet regarding the possibility of ketamine for depression.

Of the 1088 people who registered their interest, 93.3% reported depression, 64.3% reported a chronic course of their symptoms and in the past 10 years, 86.3% had tried at least two antidepressants. Desperation was a common theme, but this appeared to be competently expressed. A small minority (<8%) reported experience of illegal ketamine use.

It cannot be ruled out that patients with different degrees of treatment resistance and comorbidities will seek treatment with ketamine. This stresses the urgency to perform larger randomised controlled trials as well as to systematically monitor outcomes and adverse effects of ketamine, that is currently prescribed off-label for patients in need.

R.M. is consulting and is Principal Investigator for Janssen trials of esketamine and is consulting for Eleusis.

Etiological research of depression and anxiety disorders has been hampered by diagnostic heterogeneity. In order to address this, researchers have tried to identify more homogeneous patient subgroups. This work has predominantly focused on explaining interpersonal heterogeneity based on clinical features (i.e. symptom profiles). However, to explain interpersonal variations in underlying pathophysiological mechanisms, it might be more effective to take biological heterogeneity as the point of departure when trying to identify subgroups. Therefore, this study aimed to identify data-driven subgroups of patients based on biomarker profiles.

Data of patients with a current depressive and/or anxiety disorder came from the Netherlands Study of Depression and Anxiety, a large, multi-site naturalistic cohort study (n = 1460). Thirty-six biomarkers (e.g. leptin, brain-derived neurotrophic factor, tryptophan) were measured, as well as sociodemographic and clinical characteristics. Latent class analysis of the discretized (lower 10%, middle, upper 10%) biomarkers were used to identify different patient clusters.

The analyses resulted in three classes, which were primarily characterized by different levels of metabolic health: ‘lean’ (21.6%), ‘average’ (62.2%) and ‘overweight’ (16.2%). Inspection of the classes’ clinical features showed the highest levels of psychopathology, severity and medication use in the overweight class.

The identified classes were strongly tied to general (metabolic) health, and did not reflect any natural cutoffs along the lines of the traditional diagnostic classifications. Our analyses suggested that especially poor metabolic health could be seen as a distal marker for depression and anxiety, suggesting a relationship between the ‘overweight’ subtype and internalizing psychopathology.

For patients with severe mental illness (SMI) in residential facilities, adopting a healthy lifestyle is hampered by the obesity promoting (obesogenic) environment.

To determine the effectiveness of a 12-month lifestyle intervention addressing the obesogenic environment with respect to diet and physical activity to improve waist circumference and cardiometabolic risk factors v. care as usual (Dutch Trial Registry: NTR2720).

In a multisite cluster randomised controlled pragmatic trial, 29 care teams were randomised into 15 intervention (365 patients) and 14 control teams (371 patients). Intervention staff were trained to improve the obesogenic environment.

Waist circumference decreased 1.51 cm (95% CI −2.99 to −0.04) in the intervention v. control group after 3 months and metabolic syndrome z-score decreased 0.22 s.d. (95% CI −0.38 to −0.06). After 12 months, the decrease in waist circumference was no longer statistically significantly different (–1.28 cm, 95% CI −2.79 to 0.23, P = 0.097).

Targeting the obesogenic environment of residential patients with SMI has the potential to facilitate reduction of abdominal adiposity and cardiometabolic risk, but maintaining initial reductions over the longer term remains challenging.