Novel and emerging treatments for major depression.

Depression is common, costly, debilitating, and associated with increased risk of suicide. It is one of the leading global public health problems. Although existing available pharmacological treatments can be effective, their onset of action can take up to 6 weeks, side-effects are common, and recovery can require treatment with multiple different agents. Although psychosocial interventions might also be recommended, more effective treatments than those currently available are needed for people with moderate or severe depression. In the past 10 years, treatment trials have developed and tested many new targeted interventions. In this Review, we assess novel and emerging biological treatments for major depressive disorder, evaluate their putative brain and body mechanisms, and highlight how close each might be to clinical use.

Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis.

BACKGROUND: Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce. AIMS: To evaluate fairness in prediction models for development of psychosis and functional outcome. METHOD: Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes 'gender' and 'educational attainment' and compared them with the fairness of clinicians' judgements. RESULTS: Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians' judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians' predictions. CONCLUSIONS: Educational bias was present in algorithmic and clinicians' predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.

Protecting and promoting editorial independence.

We argue that editorial independence, through robust practice of publication ethics and research integrity, promotes good science and prevents bad science. We elucidate the concept of research integrity, and then discuss the dimensions of editorial independence. Best practice guidelines exist, but compliance with these guidelines varies. Therefore, we make recommendations for protecting and strengthening editorial independence.

Exercise4Psychosis: A randomised control trial assessing the effect of moderate-to-vigorous exercise on inflammatory biomarkers and negative symptom profiles in men with first-episode psychosis.

INTRODUCTION: First-Episode Psychosis (FEP) is a devastating mental health condition that commonly emerges during early adulthood, and is characterised by a disconnect in perceptions of reality. Current evidence suggests that inflammation and perturbed immune responses are involved in the pathology of FEP and may be associated specifically with negative symptoms. Exercise training is a potent anti-inflammatory stimulus that can reduce persistent inflammation, and can improve mood profiles in general populations. Therefore, exercise may represent a novel adjunct therapy for FEP. The aim of this study was to assess the effect of exercise on biomarkers of inflammation, negative symptoms of psychosis, and physiological health markers in FEP. METHODS: Seventeen young males (26.67 ± 6.64 years) were recruited from Birmingham Early Intervention in Psychosis Services and randomised to a 6-week exercise programme consisting of two-to-three sessions per week that targeted 60-70 % heart-rate max (HRMax), or a treatment as usual (TAU) condition. Immune T-helper (Th-) cell phenotypes and cytokines, symptom severity, functional wellbeing, and cognition were assessed before and after 6-weeks of regular exercise. RESULTS: Participants in the exercise group (n = 10) achieved 81.11 % attendance to the intervention, with an average exercise intensity of 67.54 % ± 7.75 % HRMax. This led to favourable changes in immune cell phenotypes, and a significant reduction in the Th1:Th2 ratio (-3.86 %) compared to the TAU group (p = 0.014). After the exercise intervention, there was also a significant reduction in plasma IL-6 concentration (-22.17 %) when compared to the TAU group (p = 0.006). IL-8, and IL-10 did not show statistically significant differences between the groups after exercise. Symptomatically, there was a significant reduction in negative symptoms after exercise (-13.54 %, Positive and Negative Syndrome Scale, (PANSS) Negative) when compared to the TAU group (p = 0.008). There were no significant change in positive or general symptoms, functional outcomes, or cognition (all p > 0.05). DISCUSSION: Regular moderate-to-vigorous physical activity is feasible and attainable in clinical populations. Exercise represents a physiological tool that is capable of causing significant inflammatory biomarker change and concomitant symptom improvements in FEP cohorts, and may be useful for treatment of symptom profiles that are not targeted by currently prescribed antipsychotic medication.

Measurement of brain glutathione with magnetic Resonance spectroscopy in Schizophrenia-Spectrum disorders - A systematic review and Meta-Analysis.

Oxidative stress may contribute to declining course and poor outcomes in psychosis. However, in vivo Magnetic Resonance Spectroscopy studies yield disparate results due to clinical stage, sample demographics, neuroanatomical focus, sample size, and acquisition method variations. We investigated glutathione in brain regions from participants with psychosis, and the relation of glutathione to clinical features and spectroscopy protocols. Meta-analysis comprised 21 studies. Glutathione levels did not differ between total psychosis patients (N = 639) and controls (N = 704) in the Medial Prefrontal region (k = 21, d = -0.09, CI = -0.28 to 0.10, p = 0.37). Patients with stable schizophrenia exhibited a small but significant glutathione reduction compared to controls (k = 14, d = -0.20, CI = -0.40 to -0.00, p = 0.05). Meta-regression showed older studies had greater glutathione reductions, possibly reflecting greater accuracy related to spectroscopy advancements in more recent studies. No significant effects of methodological variables, such as voxel size or echo time were found. Reduced glutathione in patients with stable established schizophrenia may provide novel targets for precision medicine. Standardizing MRS acquisition methods in future studies may help address discrepancies in glutathione levels.

Changes in immunoglobulin levels during clozapine treatment in schizophrenia.

BACKGROUND AND HYPOTHESIS: Use of clozapine in treatment-resistant schizophrenia is often limited due to risk of adverse effects. Cross-sectional associations between clozapine treatment and low immunoglobulin levels have been reported, however prospective studies are required to establish temporal relationships. We tested the hypothesis that reductions in immunoglobulin levels would occur over the first 6 months following initiation of clozapine treatment. Relationships between immunoglobulin levels and symptom severity over the course of clozapine treatment were also explored. DESIGN: This prospective observational study measured immunoglobulin (Ig) levels (A, M and G) in 56 patients with treatment-resistant schizophrenia at 6-, 12- and 24-weeks following initiation with clozapine. Clinical symptoms were also measured at 12 weeks using the positive and negative syndrome scale (PANSS). RESULTS: IgA, IgG and IgM all decreased during clozapine treatment. For IgA and IgG the reduction was significant at 24 weeks (IgA: ß = -32.66, 95% CI = -62.38, -2.93, p = 0.03; IgG: ß = -63.96, 95% CI = -118.00, -9.31, p = 0.02). For IgM the reduction was significant at 12 and 24 weeks (12 weeks: ß = -23.48, 95% CI = -39.56, -7.42, p = 0.004; 24 weeks: ß = -33.12, 95 %CI = -50.30, -15.94, p = <0.001). Reductions in IgA and IgG during clozapine treatment were correlated with reductions in PANSS-total over 12 weeks (n = 32, IgA r = 0.59, p = 0.005; IgG r = 0.48, p = 0.03). CONCLUSIONS: The observed reductions in immunoglobulin levels over six months of clozapine treatment add further evidence linking clozapine to secondary antibody deficiency. Associations between Ig reduction and symptom improvement may however indicate that immune mechanisms contribute to both desirable and undesirable effects of clozapine.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Impact of anxiety and depression across childhood and adolescence on adverse outcomes in young adulthood: a UK birth cohort study.

BACKGROUND: Little is still known about the long-term impact of childhood and adolescent persistent depression and anxiety in adulthood. AIMS: To investigate the impact of persistent anxiety, depression, and comorbid anxiety and depression across childhood and adolescence on the development of multiple adverse outcomes in young adulthood. METHOD: This study used data from 8122 participants in the Avon Longitudinal Study of Parents and Children cohort. The Development and Well-Being Assessment (DAWBA) examined child anxiety and depression symptomatology. The DAWBA generalised anxiety and mood subscales at 8, 10 and 13 years were selected, and a measure of comorbid anxiety and depression symptoms was created at each time point. Further, several mental and physical health, substance misuse and education/employment problems were assessed at 24 years. Latent class growth analyses were used to detect trajectories of anxiety, depression and comorbid anxiety and depression; and logistic regression to examine how persistent anxiety, depression or both were associated with adverse outcomes at 24 years. RESULTS: All three classes with persistent anxiety, depression or both were significantly associated with presenting with any mental health problems and any education/employment problem. Persistent high levels of depression and high levels of comorbid anxiety and depression, but not persistent high anxiety, were significantly associated with any physical health problem. High levels of comorbid anxiety and depression was the only DAWBA domain significantly associated with substance misuse; and overall, this was the domain that exerted the greatest negative impact, as it presented the highest odd ratio values. CONCLUSIONS: Children and adolescents with comorbid anxiety and depression are at the highest risk for having more adverse outcomes at 24 years.

Neurocognitive skills and vulnerability for psychosis in depression and across the psychotic spectrum: findings from the PRONIA Consortium.

BACKGROUND: Neurocognitive deficits are a core feature of psychosis and depression. Despite commonalities in cognitive alterations, it remains unclear if and how the cognitive deficits in patients at clinical high risk for psychosis (CHR) and those with recent-onset psychosis (ROP) are distinct from those seen in recent-onset depression (ROD). AIMS: This study was carried out within the European project 'Personalized Prognostic Tools for Early Psychosis Management', and aimed to characterise the cognitive profiles of patients with psychosis or depression. METHOD: We examined cognitive profiles for patients with ROP (n = 105), patients with ROD (n = 123), patients at CHR (n = 116) and healthy controls (n = 372) across seven sites in five European countries. Confirmatory factor analysis identified four cognitive factors independent of gender, education and site: speed of processing, attention and working memory, verbal learning and spatial learning. RESULTS: Patients with ROP performed worse than healthy controls in all four domains (P < 0.001), whereas performance of patients with ROD was not affected (P > 0.05). Patients at CHR performed worse than healthy controls in speed of processing (P = 0.001) and spatial learning (P = 0.003), but better than patients with ROP across all cognitive domains (all P ≤ 0.01). CHR and ROD groups did not significantly differ in any cognitive domain. These findings were independent of comorbid depressive symptoms, substance consumption and illness duration. CONCLUSIONS: These results show that neurocognitive abilities are affected in CHR and ROP, whereas ROD seems spared. Although our findings may support the notion that those at CHR have a specific vulnerability to psychosis, future studies investigating broader transdiagnostic risk cohorts in longitudinal designs are needed.

Disentangling heterogeneity of psychosis expression in the general population: sex-specific moderation effects of environmental risk factors on symptom networks.

BACKGROUND: Psychosis expression in the general population may reflect a behavioral manifestation of the risk for psychotic disorder. It can be conceptualized as an interconnected system of psychotic and affective experiences; a so-called 'symptom network'. Differences in demographics, as well as exposure to adversities and risk factors, may produce substantial heterogeneity in symptom networks, highlighting potential etiological divergence in psychosis risk. METHODS: To explore this idea in a data-driven way, we employed a novel recursive partitioning approach in the 2007 English National Survey of Psychiatric Morbidity (N = 7242). We sought to identify 'network phenotypes' by explaining heterogeneity in symptom networks through potential moderators, including age, sex, ethnicity, deprivation, childhood abuse, separation from parents, bullying, domestic violence, cannabis use, and alcohol. RESULTS: Sex was the primary source of heterogeneity in symptom networks. Additional heterogeneity was explained by interpersonal trauma (childhood abuse and domestic violence) in women and domestic violence, cannabis use, ethnicity in men. Among women, especially those exposed to early interpersonal trauma, an affective loading within psychosis may have distinct relevance. Men, particularly those from minority ethnic groups, demonstrated a strong network connection between hallucinatory experiences and persecutory ideation. CONCLUSION: Symptom networks of psychosis expression in the general population are highly heterogeneous. The structure of symptom networks seems to reflect distinct sex-related adversities, etiologies, and mechanisms of symptom-expression. Disentangling the complex interplay of sex, minority ethnic group status, and other risk factors may help optimize early intervention and prevention strategies in psychosis.

The non-specific nature of mental health and structural brain outcomes following childhood trauma.

BACKGROUND: Childhood trauma (CT) is associated with an increased risk of mental health disorders; however, it is unknown whether this represents a diagnosis-specific risk factor for specific psychopathology mediated by structural brain changes. Our aim was to explore whether (i) a predictive CT pattern for transdiagnostic psychopathology exists, and whether (ii) CT can differentiate between distinct diagnosis-dependent psychopathology. Furthermore, we aimed to identify the association between CT, psychopathology and brain structure. METHODS: We used multivariate pattern analysis in data from 643 participants of the Personalised Prognostic Tools for Early Psychosis Management study (PRONIA), including healthy controls (HC), recent onset psychosis (ROP), recent onset depression (ROD), and patients clinically at high-risk for psychosis (CHR). Participants completed structured interviews and self-report measures including the Childhood Trauma Questionnaire, SCID diagnostic interview, BDI-II, PANSS, Schizophrenia Proneness Instrument, Structured Interview for Prodromal Symptoms and structural MRI, analyzed by voxel-based morphometry. RESULTS: (i) Patients and HC could be distinguished by their CT pattern with a reasonable precision [balanced accuracy of 71.2% (sensitivity = 72.1%, specificity = 70.4%, p ≤ 0.001]. (ii) Subdomains 'emotional neglect' and 'emotional abuse' were most predictive for CHR and ROP, while in ROD 'physical abuse' and 'sexual abuse' were most important. The CT pattern was significantly associated with the severity of depressive symptoms in ROD, ROP, and CHR, as well as with the PANSS total and negative domain scores in the CHR patients. No associations between group-separating CT patterns and brain structure were found. CONCLUSIONS: These results indicate that CT poses a transdiagnostic risk factor for mental health disorders, possibly related to depressive symptoms. While differences in the quality of CT exposure exist, diagnostic differentiation was not possible suggesting a multi-factorial pathogenesis.

Cognitive function in early-phase schizophrenia-spectrum disorder: IQ subtypes, brain volume and immune markers.

BACKGROUND: Evidence suggests that cognitive subtypes exist in schizophrenia that may reflect different neurobiological trajectories. We aimed to identify whether IQ-derived cognitive subtypes are present in early-phase schizophrenia-spectrum disorder and examine their relationship with brain structure and markers of neuroinflammation. METHOD: 161 patients with recent-onset schizophrenia spectrum disorder (<5 years) were recruited. Estimated premorbid and current IQ were calculated using the Wechsler Test of Adult Reading and a 4-subtest WAIS-III. Cognitive subtypes were identified with k-means clustering. Freesurfer was used to analyse 3.0 T MRI. Blood samples were analysed for hs-CRP, IL-1RA, IL-6 and TNF-α. RESULTS: Three subtypes were identified indicating preserved (PIQ), deteriorated (DIQ) and compromised (CIQ) IQ. Absolute total brain volume was significantly smaller in CIQ compared to PIQ and DIQ, and intracranial volume was smaller in CIQ than PIQ (F(2, 124) = 6.407, p = 0.002) indicative of premorbid smaller brain size in the CIQ group. CIQ had higher levels of hs-CRP than PIQ (F(2, 131) = 5.01, p = 0.008). PIQ showed differentially impaired processing speed and verbal learning compared to IQ-matched healthy controls. CONCLUSIONS: The findings add validity of a neurodevelopmental subtype of schizophrenia identified by comparing estimated premorbid and current IQ and characterised by smaller premorbid brain volume and higher measures of low-grade inflammation (CRP).

Prevalence of cognitive impairments and strengths in the early course of psychosis and depression.

BACKGROUND: Studies investigating cognitive impairments in psychosis and depression have typically compared the average performance of the clinical group against healthy controls (HC), and do not report on the actual prevalence of cognitive impairments or strengths within these clinical groups. This information is essential so that clinical services can provide adequate resources to supporting cognitive functioning. Thus, we investigated this prevalence in individuals in the early course of psychosis or depression. METHODS: A comprehensive cognitive test battery comprising 12 tests was completed by 1286 individuals aged 15-41 (mean age 25.07, s.d. 5.88) from the PRONIA study at baseline: HC (N = 454), clinical high risk for psychosis (CHR; N = 270), recent-onset depression (ROD; N = 267), and recent-onset psychosis (ROP; N = 295). Z-scores were calculated to estimate the prevalence of moderate or severe deficits or strengths (>2 s.d. or 1-2 s.d. below or above HC, respectively) for each cognitive test. RESULTS: Impairment in at least two cognitive tests was as follows: ROP (88.3% moderately, 45.1% severely impaired), CHR (71.2% moderately, 22.4% severely impaired), ROD (61.6% moderately, 16.2% severely impaired). Across clinical groups, impairments were most prevalent in tests of working memory, processing speed, and verbal learning. Above average performance (>1 s.d.) in at least two tests was present for 40.5% ROD, 36.1% CHR, 16.1% ROP, and was >2 SDs in 1.8% ROD, 1.4% CHR, and 0% ROP. CONCLUSIONS: These findings suggest that interventions should be tailored to the individual, with working memory, processing speed, and verbal learning likely to be important transdiagnostic targets.

Early neutrophil trajectory following clozapine may predict clozapine response - Results from an observational study using electronic health records.

BACKGROUND: Clozapine has unique effectiveness in treatment-resistant schizophrenia and is known to cause immunological side-effects. A transient spike in neutrophils commonly occurs in the first weeks of clozapine therapy. There is contradictory evidence in the literature as to whether neutrophil changes with clozapine are linked to treatment response. AIMS: The current study aims to further examine the neutrophil changes in response to clozapine and explore any association between neutrophil trajectory and treatment response. METHODS: A retrospective cohort study of patients undergoing their first treatment with clozapine and continuing for at least 2 years identified 425 patients (69% male/31% female). Neutrophil counts at baseline, 3 weeks and 1 month were obtained predominantly by linkage with data from the clozapine monitoring service. Clinical Global Impression- Severity (CGI-S) was rated from case notes at the time of clozapine initiation and at 2 years. Latent class growth analysis (LCGA) was performed to define distinct trajectories of neutrophil changes during the first month of treatment. Logistic regression was then conducted to investigate for association between the trajectory of neutrophil count changes in month 1 and clinical response at 2 years as well as between baseline neutrophil count and response. RESULTS: Of the original cohort, 397 (93%) patients had useable neutrophil data during the first 6 weeks of clozapine treatment. LCGA revealed significant differences in neutrophil trajectories with a three-class model being the most parsimonious. The classes had similar trajectory profiles but differed primarily on overall neutrophil count: with low, high-normal and high neutrophil classes, comprising 52%, 40% and 8% of the sample respectively. Membership of the high-normal group was associated with significantly increased odds of a positive response to clozapine, as compared to the low neutrophil group [Odds ratio (OR) = 2.10, p-value = 0.002; 95% confidence interval (95% CI) = 1.31-3.36]. Baseline neutrophil count was a predictor of response to clozapine at 2 years, with counts of ≥5 × 109/l significantly associated with positive response (OR = 1.60, p-value = 0.03; 95% CI = 1.03-2.49). CONCLUSIONS: Our data are consistent with the hypothesis that patients with low-level inflammation, reflected in a high-normal neutrophil count, are more likely to respond to clozapine, raising the possibility that clozapine exerts its superior efficacy via immune mechanisms.

Inflammatory subgroups of schizophrenia and their association with brain structure: A semi-supervised machine learning examination of heterogeneity.

OBJECTIVE: Immune system dysfunction is hypothesised to contribute to structural brain changes through aberrant synaptic pruning in schizophrenia. However, evidence is mixed and there is a lack of evidence of inflammation and its effect on grey matter volume (GMV) in patients. We hypothesised that inflammatory subgroups can be identified and that the subgroups will show distinct neuroanatomical and neurocognitive profiles. METHODS: The total sample consisted of 1067 participants (chronic patients with schizophrenia n = 467 and healthy controls (HCs) n = 600) from the Australia Schizophrenia Research Bank (ASRB) dataset, together with 218 recent-onset patients with schizophrenia from the external Benefit of Minocycline on Negative Symptoms of Psychosis: Extent and Mechanism (BeneMin) dataset. HYDRA (HeterogeneitY through DiscRiminant Analysis) was used to separate schizophrenia from HC and define disease-related subgroups based on inflammatory markers. Voxel-based morphometry and inferential statistics were used to explore GMV alterations and neurocognitive deficits in these subgroups. RESULTS: An optimal clustering solution revealed five main schizophrenia groups separable from HC: Low Inflammation, Elevated CRP, Elevated IL-6/IL-8, Elevated IFN-γ, and Elevated IL-10 with an adjusted Rand index of 0.573. When compared with the healthy controls, the IL-6/IL-8 cluster showed the most widespread, including the anterior cingulate, GMV reduction. The IFN-γ inflammation cluster showed the least GMV reduction and impairment of cognitive performance. The CRP and the Low Inflammation clusters dominated in the younger external dataset. CONCLUSIONS: Inflammation in schizophrenia may not be merely a case of low vs high, but rather there are pluripotent, heterogeneous mechanisms at play which could be reliably identified based on accessible, peripheral measures. This could inform the successful development of targeted interventions.

Psychosocial functioning in the balance between autism and psychosis: evidence from three populations.

Functional impairment is a core feature of both autism and schizophrenia spectrum disorders. While diagnostically independent, they can co-occur in the same individual at both the trait and diagnostic levels. The effect of such co-occurrence is hypothesized to worsen functional impairment. The diametric model, however, suggests that the disorders are etiologically and phenotypically diametrical, representing the extreme of a unidimensional continuum of cognition and behavior. A central prediction of this model is that functional impairment would be attenuated in individuals with mixed symptom expressions or genetic liability to both disorders. We tested this hypothesis in two clinical populations and one healthy population. In individuals with chronic schizophrenia and in individuals with first episode psychosis we evaluated the combined effect of autistic traits and positive psychotic symptoms on psychosocial functioning. In healthy carriers of alleles of copy number variants (CNVs) that confer risk for both autism and schizophrenia, we also evaluated whether variation in psychosocial functioning depended on the combined risk conferred by each CNV. Relative to individuals with biased symptom/CNV risk profiles, results show that functional impairments are attenuated in individuals with relatively equal levels of positive symptoms and autistic traits-and specifically stereotypic behaviors-, and in carriers of CNVs with relatively equal risks for either disorder. However, the pattern of effects along the "balance axis" varied across the groups, with this attenuation being generally less pronounced in individuals with high-high symptom/risk profile in the schizophrenia and CNV groups, and relatively similar for low-low and high-high individuals in the first episode psychosis group. Lower levels of functional impairments in individuals with "balanced" symptom profile or genetic risks would suggest compensation across mechanisms associated with autism and schizophrenia. CNVs that confer equal risks for both disorders may provide an entry point for investigations into such compensatory mechanisms. The co-assessment of autism and schizophrenia may contribute to personalized prognosis and stratification strategies.

The role of inflammation in the prospective associations between early childhood sleep problems and ADHD at 10 years: findings from a UK birth cohort study.

BACKGROUND: Several underlying mechanisms potentially account for the link between sleep and attention deficit and hyperactivity disorder (ADHD), including inflammation. However, studies so far have been cross sectional. We investigate (a) the association between early childhood sleep and probable ADHD diagnosis in childhood and (b) whether childhood circulating inflammatory markers mediate these prospective associations. METHODS: Data from the Avon Longitudinal Study of Parents and Children were available for 7,658 10-year-old children. Parent-reported sleep duration, night awakening frequency and regular sleep routines were collected at 3.5 years. The Development and Wellbeing Assessment was administered to capture children with clinically relevant ADHD symptoms, or probable ADHD diagnosis. Blood samples were collected at 9 years, from which two inflammatory markers were obtained [interleukin-6 (IL-6) and C-reactive protein (CRP)]. Logistic regression analyses were applied to investigate the associations between sleep variables at 3.5 years and probable ADHD diagnosis at 10 years. Further, path analysis was applied to examine the potential mediating role of inflammation at 9 years (as measured by CRP and IL-6) in the associations between early sleep and ADHD at 10 years. RESULTS: Less regular sleep routines (OR = 0.51, 95% CI = 0.28-0.93, p = .029), shorter nighttime sleep (OR = 0.70, 95% CI = 0.56-0.89, p = .004) and higher night awakening frequency (OR = 1.27, 95% CI = 1.06-1.52, p = .009) at 3.5 years were associated with higher odds of ADHD at 10 years. Further, IL-6 at 9 years, but not CRP, mediated the association between irregular sleep routines and ADHD (bias-corrected estimate, -0.002; p = .005) and between night awakening and ADHD (bias-corrected estimate, 0.002; p = .003). CONCLUSIONS: Several sleep problems in early childhood constitute a risk factor for probable ADHD diagnosis at 10 years. Further, these associations are partially mediated by IL-6 at 9 years. These results open a new research vista to the pathophysiology of ADHD and highlight sleep and inflammation as potential preventative targets for ADHD.

Novel Gyrification Networks Reveal Links with Psychiatric Risk Factors in Early Illness.

Adult gyrification provides a window into coordinated early neurodevelopment when disruptions predispose individuals to psychiatric illness. We hypothesized that the echoes of such disruptions should be observed within structural gyrification networks in early psychiatric illness that would demonstrate associations with developmentally relevant variables rather than specific psychiatric symptoms. We employed a new data-driven method (Orthogonal Projective Non-Negative Matrix Factorization) to delineate novel gyrification-based networks of structural covariance in 308 healthy controls. Gyrification within the networks was then compared to 713 patients with recent onset psychosis or depression, and at clinical high-risk. Associations with diagnosis, symptoms, cognition, and functioning were investigated using linear models. Results demonstrated 18 novel gyrification networks in controls as verified by internal and external validation. Gyrification was reduced in patients in temporal-insular, lateral occipital, and lateral fronto-parietal networks (pFDR < 0.01) and was not moderated by illness group. Higher gyrification was associated with better cognitive performance and lifetime role functioning, but not with symptoms. The findings demonstrated that gyrification can be parsed into novel brain networks that highlight generalized illness effects linked to developmental vulnerability. When combined, our study widens the window into the etiology of psychiatric risk and its expression in adulthood.

Five-year illness trajectories across racial groups in the UK following a first episode psychosis.

PURPOSE: Psychosis disproportionally affects ethnic minority groups in high-income countries, yet evidence of disparities in outcomes following intensive early intervention service (EIS) for First Episode Psychosis (FEP) is less conclusive. We investigated 5-year clinical and social outcomes of young people with FEP from different racial groups following EIS care. METHOD: Data were analysed from the UK-wide NIHR SUPEREDEN study. The sample at baseline (n = 978) included White (n = 750), Black (n = 71), and Asian (n = 157) individuals, assessed during the 3 years of EIS, and up to 2 years post-discharge (n = 296; Black [n = 23]; Asian [n = 52] and White [n = 221]). Outcome trajectories were modelled for psychosis symptoms (positive, negative, and general), functioning, and depression, using linear mixed effect models (with random intercept and slopes), whilst controlling for social deprivation. Discharge service was also explored across racial groups, 2 years following EIS. RESULTS: Variation in linear growth over time was accounted for by racial group status for psychosis symptoms-positive (95% CI [0.679, 1.235]), negative (95% CI [0.315, 0.783]), and general (95% CI [1.961, 3.428])-as well as for functioning (95% CI [11.212, 17.677]) and depressive symptoms (95% CI [0.261, 0.648]). Social deprivation contributed to this variance. Black individuals experienced greater levels of deprivation (p < 0.001, 95% CI [0.187, 0.624]). Finally, there was a greater likelihood for Asian (OR = 3.04; 95% CI [2.050, 4.498]) and Black individuals (OR = 2.47; 95% CI [1.354, 4.520]) to remain in secondary care by follow-up. CONCLUSION: Findings suggest variations in long-term clinical and social outcomes following EIS across racial groups; social deprivation contributed to this variance. Black and Asian individuals appear to make less improvement in long-term recovery and are less likely to be discharged from mental health services. Replication is needed in large, complete data, to fully understand disparities and blind spots to care.

The potential of precision psychiatry: what is in reach?

Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.