A transdiagnostic prodrome for severe mental disorders: an electronic health record study.

Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

Connectome dysfunction in patients at clinical high risk for psychosis and modulation by oxytocin.

Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all pFDR < 0.05) most localised to the frontoparietal network. Treatment effects were found in 7 regions (all pFDR < 0.05) predominantly within the ventral attention network. Our major finding was that many effects of oxytocin on network topology differ across CHR-P and healthy individuals, with significant interaction effects observed in numerous subcortical regions strongly implicated in psychosis onset, such as the thalamus, pallidum and nucleus accumbens, and cortical regions which localised primarily to the default mode network (12 regions, all pFDR < 0.05). Collectively, our findings provide new insights on aberrant functional brain network organisation associated with psychosis risk and demonstrate, for the first time, that oxytocin modulates network topology in brain regions implicated in the pathophysiology of psychosis in a clinical status (CHR-P vs healthy control) specific manner.

Individualized prediction models in ADHD: a systematic review and meta-regression.

There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (ß = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study.

BACKGROUND: The dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). METHODS: As primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21-50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. RESULTS: Sensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). CONCLUSION: GSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.

What is the duration of untreated psychosis worldwide? - A meta-analysis of pooled mean and median time and regional trends and other correlates across 369 studies.

Duration of untreated psychosis (DUP) has been associated with poor mental health outcomes. We aimed to meta-analytically estimate the mean and median DUP worldwide, evaluating also the influence of several moderating factors. This PRISMA/MOOSE-compliant meta-analysis searched for non-overlapping individual studies from inception until 9/12/2022, reporting mean ± s.d. or median DUP in patients with first episode psychosis (FEP), without language restrictions. We conducted random-effect meta-analyses, stratified analyses, heterogeneity analyses, meta-regression analyses, and quality assessment (PROSPERO:CRD42020163640). From 12 461 citations, 369 studies were included. The mean DUP was 42.6 weeks (95% confidence interval (CI) 40.6-44.6, k = 283, n = 41 320), varying significantly across continents (p < 0.001). DUP was (in descending order) 70.0 weeks (95% CI 51.6-88.4, k = 11, n = 1508) in Africa; 48.8 weeks (95% CI 43.8-53.9, k = 73, n = 12 223) in Asia; 48.7 weeks (95% CI 43.0-54.4, k = 36, n = 5838) in North America; 38.6 weeks (95% CI 36.0-41.3, k = 145, n = 19 389) in Europe; 34.9 weeks (95% CI 23.0-46.9, k = 11, n = 1159) in South America and 28.0 weeks (95% CI 20.9-35.0, k = 6, n = 1203) in Australasia. There were differences depending on the income of countries: DUP was 48.4 weeks (95% CI 43.0-48.4, k = 58, n = 5635) in middle-low income countries and 41.2 weeks (95% CI 39.0-43.4, k = 222, n = 35 685) in high income countries. Longer DUP was significantly associated with older age (ß = 0.836, p < 0.001), older publication year (ß = 0.404, p = 0.038) and higher proportion of non-White FEP patients (ß = 0.232, p < 0.001). Median DUP was 14 weeks (Interquartile range = 8.8-28.0, k = 206, n = 37 215). In conclusion, DUP is high throughout the world, with marked variation. Efforts to identify and intervene sooner in patients with FEP, and to promote global mental health and access to early intervention services (EIS) are critical, especially in developing countries.

Meta-analytic prevalence of comorbid mental disorders in individuals at clinical high risk of psychosis: the case for transdiagnostic assessment.

Comorbid mental disorders in subjects at clinical high risk for psychosis (CHR-P) may impact preventive care. We conducted a PRISMA/MOOSE-compliant systematic meta-analysis, searching PubMed/PsycInfo up to June 21st, 2021 for observational studies/randomized controlled trials reporting on comorbid DSM/ICD-mental disorders in CHR-P subjects ( protocol ). The primary and secondary outcomes were baseline and follow-up prevalence of comorbid mental disorders. We also explored the association of comorbid mental disorders compared with CHR-P versus psychotic/non-psychotic control groups, their impact on baseline functioning and transition to psychosis. We conducted random-effects meta-analyses, meta-regression, and assessed heterogeneity/publication bias/quality (Newcastle Ottawa Scale, NOS). We included 312 studies (largest meta-analyzed sample = 7834, any anxiety disorder, mean age = 19.98 (3.40), females = 43.88%, overall NOS > 6 in 77.6% of studies). The prevalence was 0.78 (95% CI = 0.73-0.82, k = 29) for any comorbid non-psychotic mental disorder, 0.60 (95% CI = 0.36-0.84, k = 3) for anxiety/mood disorders, 0.44 (95% CI = 0.39-0.49, k = 48) for any mood disorders, 0.38 (95% CI = 0.33-0.42, k = 50) for any depressive disorder/episode, 0.34 (95% CI = 0.30-0.38, k = 69) for any anxiety disorder, 0.30 (95% CI 0.25-0.35, k = 35) for major depressive disorders, 0.29 (95% CI, 0.08-0.51, k = 3) for any trauma-related disorder, 0.23 (95% CI = 0.17-0.28, k = 24) for any personality disorder, and <0.23 in other mental disorders (I2 > 50% in 71.01% estimates). The prevalence of any comorbid mental disorder decreased over time (0.51, 95% CI = 0.25-0.77 over 96 months), except any substance use which increased (0.19, 95% CI = 0.00-0.39, k = 2, >96 months). Compared with controls, the CHR-P status was associated with a higher prevalence of anxiety, schizotypal personality, panic, and alcohol use disorders (OR from 2.90 to 1.54 versus without psychosis), a higher prevalence of anxiety/mood disorders (OR = 9.30 to 2.02) and lower prevalence of any substance use disorder (OR = 0.41, versus psychosis). Higher baseline prevalence of alcohol use disorder/schizotypal personality disorder was negatively associated with baseline functioning (beta from -0.40 to -0.15), while dysthymic disorder/generalized anxiety disorder with higher functioning (beta 0.59 to 1.49). Higher baseline prevalence of any mood disorder/generalized anxiety disorder/agoraphobia (beta from -2.39 to -0.27) was negatively associated with transition to psychosis. In conclusion, over three-quarters of CHR-P subjects have comorbid mental disorders, which modulate baseline functionig and transition to psychosis. Transdiagnostic mental health assessment should be warranted in subjects at CHR-P.

Efficacy and acceptability of psychosocial interventions in schizophrenia: systematic overview and quality appraisal of the meta-analytic evidence.

Psychosocial interventions are recommended in schizophrenia and first-episode psychosis/early psychosis (EP). Nevertheless, literature is heterogeneous and often contradictory. We conducted an umbrella review of (network) meta-analyses of randomized controlled trials (RCTs) comparing psychosocial interventions vs treatment as usual (TAU)/active interventions(ACTIVE)/MIXED controls. Primary outcome was total symptoms (TS); secondary outcomes were positive/negative/depressive symptoms (PS/NS/DS), cognition, functioning, relapse, hospitalization, quality of life (QoL), treatment discontinuation. Standardized mean difference (SMD)/odds ratio (OR)/risk ratio (RR) vs TAU/ACTIVE/MIXED were summarized at end-of-treatment (EoT)/follow-up (FU). Quality was rated as high/medium/low (AMSTAR-PLUS). Eighty-three meta-analyses were included (RCTs = 1246; n = 84,925). Against TAU, regarding TS, Early Intervention Services (EIS) were superior EoT/FU in EP (SMD = -0.32/-0.21), cognitive behavioral therapy (CBT) in schizophrenia EoT/FU (SMD = -0.38/-0.19). Regarding secondary outcomes, in EP, EIS were superior for all outcomes EoT except cognition, and at FU for PS/NS/QoL, specific family interventions (FI-s) prevented relapse EoT; in schizophrenia, superiority emerged EoT for CBT for PS/NS/relapse/functioning/QoL; psychoeducation (EDU)/any FI for relapse; cognitive remediation therapy (CRT) for cognition/functioning; and hallucination-focused integrative treatment for PS. Against ACTIVE, in EP, mixed family interventions (FI-m) were superior at FU regarding TS (SMD = -0.61) and for functioning/relapse among secondary outcomes. In schizophrenia, regarding TS, mindfulness and social skills training (SST) were superior EoT, CBT at FU; regarding secondary outcomes superiority emerged at EoT for computerized cognitive drill-and-practice training for PS/DS, CRT for cognition/functioning, EDU for relapse, individual placement and support (IPS) for employment; and at FU CBT for PS/NS. Against MIXED, in schizophrenia, CRT/EDU were superior for TS EoT (d = -0.14/SMD = -0.33), CRT regarding secondary outcomes EoT for DS/social functioning, both EoT/FU for NS/cognition/global functioning; compensatory cognitive interventions for PS/functioning EoT/FU and NS EoT; CBT for PS at FU, and EDU/SST for relapse EoT. In conclusion, mental health services should consider prioritizing EIS/any FI in EP and CBT/CRT/any FI/IPS for schizophrenia, but other interventions may be helpful for specific outcomes.

The impact of pharmacological and non-pharmacological interventions on physical health outcomes in people with mood disorders across the lifespan: An umbrella review of the evidence from randomised controlled trials.

OBJECTIVE: People with mood disorders have increased risk of comorbid medical diseases versus the general population. It is paramount to identify interventions to improve physical health in this population. METHODS: Umbrella review of meta-analyses of randomised controlled trials (RCTs) on pharmacological/non-pharmacological interventions for physical health outcomes/intolerability-related discontinuation in mood disorders (any age). RESULTS: Ninety-seven meta-analyses were included. Among youths, against placebo, in depression, antidepressants/antipsychotics had higher discontinuation rates; in bipolar depression, olanzapine+fluoxetine worsened total cholesterol (TC)/triglycerides/weight gain (WG) (large ES). In adults with bipolar disorder, olanzapine worsened HbA1c/TC/WG (moderate/large ES); asenapine increased fasting glucose (small ES); quetiapine/cariprazine/risperidone induced WG (small/moderate ES). In bipolar depression, lurasidone was metabolically neutral. In depression, psychological interventions improved physical health-related quality of life (PHQoL) (small ES), fasting glucose/HbA1c (medium/large ES); SSRIs improved fasting glucose/HbA1c, readmission for coronary disease, pain (small ES); quetiapine/aripiprazole/olanzapine induced WG (small to large ES). Exercise improved cardiorespiratory fitness (moderate ES). In the elderly, fluoxetine yielded more detrimental cardiovascular effects than sertraline/escitalopram (large ES); antidepressants were neutral on exercise tolerance and PHQoL. In mixed age groups, in bipolar disorder aripiprazole was metabolically neutral; in depression, SSRIs lowered blood pressure versus placebo and serotonin-noradrenaline reuptake inhibitors (small ES); brexpiprazole augmentation caused WG and was less tolerated (small ES); exercise improved PHQoL (moderate ES). CONCLUSIONS: Some interventions (psychological therapies, exercise and SSRIs) improve certain physical health outcomes in mood disorders, few are neutral, but various pharmacological interventions are associated with negative effects. Evidence from this umbrella review has limitations, should consider evidence from other disorders and should be integrated with recent evidence from individual RCTs, and observational evidence. Effective treatments with either beneficial or physically neutral profiles should be prioritized.

An umbrella review of candidate predictors of response, remission, recovery, and relapse across mental disorders.

We aimed to identify diagnosis-specific/transdiagnostic/transoutcome multivariable candidate predictors (MCPs) of key outcomes in mental disorders. We conducted an umbrella review (protocol  link ), searching MEDLINE/Embase (19/07/2022), including systematic reviews of studies reporting on MCPs of response, remission, recovery, or relapse, in DSM/ICD-defined mental disorders. From published predictors, we filtered MCPs, validating MCP criteria. AMSTAR2/PROBAST measured quality/risk of bias of systematic reviews/individual studies. We included 117 systematic reviews, 403 studies, 299,888 individuals with mental disorders, testing 796 prediction models. Only 4.3%/1.2% of the systematic reviews/individual studies were at low risk of bias. The most frequently targeted outcome was remission (36.9%), the least frequent was recovery (2.5%). Studies mainly focused on depressive (39.4%), substance-use (17.9%), and schizophrenia-spectrum (11.9%) disorders. We identified numerous MCPs within disorders for response, remission and relapse, but none for recovery. Transdiagnostic MCPs of remission included lower disease-specific symptoms (disorders = 5), female sex/higher education (disorders = 3), and quality of life/functioning (disorders = 2). Transdiagnostic MCPs of relapse included higher disease-specific symptoms (disorders = 5), higher depressive symptoms (disorders = 3), and younger age/higher anxiety symptoms/global illness severity/ number of previous episodes/negative life events (disorders = 2). Finally, positive trans-outcome MCPs for depression included less negative life events/depressive symptoms (response, remission, less relapse), female sex (response, remission) and better functioning (response, less relapse); for schizophrenia, less positive symptoms/higher depressive symptoms (remission, less relapse); for substance use disorder, marital status/higher education (remission, less relapse). Male sex, younger age, more clinical symptoms and comorbid mental/physical symptoms/disorders were poor prognostic factors, while positive factors included social contacts and employment, absent negative life events, higher education, early access/intervention, lower disease-specific and comorbid mental and physical symptoms/conditions, across mental disorders. Current data limitations include high risk of bias of studies and extraction of single predictors from multivariable models. Identified MCPs can inform future development, validation or refinement of prediction models of key outcomes in mental disorders.

Major Depression as a Disorder of the Narrative Self: A Qualitative Study.

INTRODUCTION: Abnormal self-experiences are a common feature of major depression despite their absence from current diagnostic manuals. Current diagnostic criteria leave us with an impoverished conception of depressive disorders, and they fail to exploit the diverse experiential alterations that might be useful for understanding and diagnosing patients, and last but not least for explaining the aetiology of these disorders. Although some phenomenological descriptions of abnormal self-experiences in major depression are available, further research is needed to validate these through detailed clinical interviews. METHODS: To characterize these phenomena in more detail and to verify and consolidate previous accounts, we conducted a qualitative study using the Consensual Qualitative Research method. RESULTS: Our findings identified three categories of abnormal self-experiences: (1) impossibility to project oneself forward, (2) not recognizing one's self, and (3) losing control on one's self. CONCLUSION: Before delving into these results, we briefly described how the self is conceptualized in phenomenological psychopathology and explored in the literature on the self-experience in major depression. After discussing our results in the light of recent and contemporary phenomenological literature, we suggest that the inability to recognize otherness as part of oneself - which is the core of depressive experiences - ends in specific symptoms of depersonalization that differ from schizophrenic ones. We conclude that the self-experience, and in particular narrative identity, is central to the development and maintenance of depression.

Development and validation of an 8-item version of the Real Relationship Inventory-Client form.

OBJECTIVE: To develop and validate a very brief version of the 24-item Real Relationship Inventory-Client (RRI-C) form. METHOD: Two independent samples of individual psychotherapy patients (Nsample1 = 700, Nsample2 = 434) completed the RRI-C along with other measures. Psychometric scale shortening involved exploratory factor analysis, item response theory analysis, confirmatory factor analysis (CFA), and multigroup CFA. Reliability and convergent and discriminant validity of the scale and subscales were also assessed. RESULTS: The 8-item RRI-C (RRI-C-SF) preserves the two-factor structure: Genuineness (k = 4, α = .86) and Realism (k = 4, α = .87), which were correlated at r = .74. CFA provided the following fit indices for the bifactor model: X2/df = 2.16, CFI = .99, TLI = .96, RMSEA = .07, and SRMR = .03. Multigroup CFA showed that the RRI-C-SF was invariant across in-person and remote session formats. The RRI-C-SF demonstrated high reliability (α = .91); high correlation with the full-length scale (r = .96); and excellent convergent and discriminant validity with measures of other elements of the therapeutic relationship, personality characteristics, current mental health state, and demographic-clinical variables. Clinical change benchmarks were calculated to serve as valuable tools for both research and clinical practice. CONCLUSION: The RRI-C-SF is a reliable measure that can be used for both research and clinical purposes. It enables a nuanced assessment of the genuineness and the realism dimensions of the real relationship.

Treating negative symptoms of schizophrenia: current approaches and future perspectives.

Negative symptoms are core symptoms of schizophrenia which are common throughout the course of the illness. We outline their functional impact, before reviewing the latest research and guidelines on their assessment and treatment. Finally, we discuss conceptual issues related to measurement of negative symptoms and approaches to address these.

Negative symptoms in children and adolescents with early-onset psychosis and at clinical high-risk for psychosis: systematic review and meta-analysis.

BACKGROUND: Early-onset psychosis (EOP) refers to the development of a first episode of psychosis before 18 years of age. Individuals at clinical high risk for psychosis (CHR-P) include adolescents and young adults, although most evidence has focused on adults. Negative symptoms are important prognostic indicators in psychosis. However, research focusing on children and adolescents is limited. AIMS: To provide meta-analytical evidence and a comprehensive review of the status and advances in the diagnosis, prognosis and treatment of negative symptoms in children and adolescents with EOP and at CHR-P. METHOD: PRISMA/MOOSE-compliant systematic review (PROSPERO: CRD42022360925) from inception to 18 August 2022, in any language, to identify individual studies conducted in EOP/CHR-P children and adolescents (mean age <18 years) providing findings on negative symptoms. Findings were systematically appraised. Random-effects meta-analyses were performed on the prevalence of negative symptoms, carrying out sensitivity analyses, heterogeneity analyses, publication bias assessment and quality assessment using the Newcastle-Ottawa Scale. RESULTS: Of 3289 articles, 133 were included (n = 6776 EOP, mean age 15.3 years (s.d. = 1.6), males = 56.1%; n = 2138 CHR-P, mean age 16.1 years (s.d. = 1.0), males = 48.6%). There were negative symptoms in 60.8% (95% CI 46.4%-75.2%) of the children and adolescents with EOP and 79.6% (95% CI 66.3-92.9%) of those at CHR-P. Prevalence and severity of negative symptoms were associated with poor clinical, functional and intervention outcomes in both groups. Different interventions were piloted, with variable results requiring further replication. CONCLUSIONS: Negative symptoms are common in children and adolescents at early stages of psychosis, particularly in those at CHR-P, and are associated with poor outcomes. Future intervention research is required so that evidence-based treatments will become available.

Psychosis and substance abuse increase the COVID-19 mortality risk.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has been a global challenge. High mortality rates have been reported in some risk groups, including patients with pre-existing mental disorders. METHODS: We used electronic health records to retrospectively identify people infected due to COVID-19 (between March 2020 and March 2021) in the three territories of the Basque Country. COVID-19 cases were defined as individuals who had tested positive on a reverse transcription-polymerase chain reaction (PCR) test. Univariate and multivariate logistic regression models and multilevel analyses with generalized estimated equations were used to determine factors associated with COVID-19-related mortality and hospital admission. RESULTS: The COVID-19 mortality rate was increased for patients with psychotic disorders [odds ratio (OR) adjusted: 1.45, 95% confidence interval (CI) (1.09-1.94), p = 0.0114] and patients with substance abuse [OR adjusted: 1.88, 95% CI (1.13-3.14, p < 0.0152)]. The mortality rate was lower for patients with affective disorders [OR adjusted: 0.80, 95% CI (0.61-0.99), p = 0.0407]. Hospital admission rates due to COVID-19 were higher in psychosis [OR adjusted: 2.90, 95% CI (2.36-3.56), p < 0.0001] and anxiety disorder groups [OR adjusted: 1.54, 95% CI (1.37-1.72), p < 0.0001]. Among admitted patients, COVID-19 mortality rate was decreased for those with affective disorders rate [OR adjusted: 0.72, 95% CI (0.55-0.95), p = 0.0194]. CONCLUSIONS: COVID-19-related mortality and hospitalizations rates were higher for patients with a pre-existing psychotic disorder.

Impact of data extraction errors in meta-analyses on the association between depression and peripheral inflammatory biomarkers: an umbrella review.

BACKGROUND: Accumulating evidence suggests that alterations in inflammatory biomarkers are important in depression. However, previous meta-analyses disagree on these associations, and errors in data extraction may account for these discrepancies. METHODS: PubMed/MEDLINE, Embase, PsycINFO, and the Cochrane Library were searched from database inception to 14 January 2020. Meta-analyses of observational studies examining the association between depression and levels of tumor necrosis factor-α (TNF-α), interleukin 1-ß (IL-1ß), interleukin-6 (IL-6), and C-reactive protein (CRP) were eligible. Errors were classified as follows: incorrect sample sizes, incorrectly used standard deviation, incorrect participant inclusion, calculation error, or analysis with insufficient data. We determined their impact on the results after correction thereof. RESULTS: Errors were noted in 14 of the 15 meta-analyses included. Across 521 primary studies, 118 (22.6%) showed the following errors: incorrect sample sizes (20 studies, 16.9%), incorrect use of standard deviation (35 studies, 29.7%), incorrect participant inclusion (7 studies, 5.9%), calculation errors (33 studies, 28.0%), and analysis with insufficient data (23 studies, 19.5%). After correcting these errors, 11 (29.7%) out of 37 pooled effect sizes changed by a magnitude of more than 0.1, ranging from 0.11 to 1.15. The updated meta-analyses showed that elevated levels of TNF- α, IL-6, CRP, but not IL-1ß, are associated with depression. CONCLUSIONS: These findings show that data extraction errors in meta-analyses can impact findings. Efforts to reduce such errors are important in studies of the association between depression and peripheral inflammatory biomarkers, for which high heterogeneity and conflicting results have been continuously reported.

Global population attributable fraction of potentially modifiable risk factors for mental disorders: a meta-umbrella systematic review.

Numerous risk factors for mental disorders have been identified. However, we do not know how many disorders we could prevent and to what extent by modifying these risk factors. This study quantifies the Population Attributable Fraction (PAF) of potentially modifiable risk factors for mental disorders. We conducted a PRISMA 2020-compliant (Protocol: https://osf.io/hk2ag ) meta-umbrella systematic review (Web of Science/PubMed/Cochrane Central Register of Reviews/Ovid/PsycINFO, until 05/12/2021) of umbrella reviews reporting associations between potentially modifiable risk factors and ICD/DSM mental disorders, restricted to highly convincing (class I) and convincing (class II) evidence from prospective cohorts. The primary outcome was the global meta-analytical PAF, complemented by sensitivity analyses across different settings, the meta-analytical Generalised Impact Fraction (GIF), and study quality assessment (AMSTAR). Seven umbrella reviews (including 295 meta-analyses and 547 associations) identified 28 class I-II risk associations (23 risk factors; AMSTAR: 45.0% high-, 35.0% medium-, 20.0% low quality). The largest global PAFs not confounded by indication were 37.84% (95% CI = 26.77-48.40%) for childhood adversities and schizophrenia spectrum disorders, 24.76% (95% CI = 13.98-36.49%) for tobacco smoking and opioid use disorders, 17.88% (95% CI = not available) for job strain and depression, 14.60% (95% CI = 9.46-20.52%) for insufficient physical activity and Alzheimer's disease, 13.40% (95% CI = 7.75-20.15%) for childhood sexual abuse and depressive disorders, 12.37% (95% CI = 5.37-25.34%) for clinical high-risk state for psychosis and any non-organic psychotic disorders, 10.00% (95% CI = 5.62-15.95%) for three metabolic factors and depression, 9.73% (95% CI = 4.50-17.30%) for cannabis use and schizophrenia spectrum disorders, and 9.30% (95% CI = 7.36-11.38%) for maternal pre-pregnancy obesity and ADHD. The GIFs confirmed the preventive capacity for these factors. Addressing several potentially modifiable risk factors, particularly childhood adversities, can reduce the global population-level incidence of mental disorders.

Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges.

Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study's risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whose potential utility in clinical practice was formally assessed. Our findings illustrated significant growth in precision psychiatry with promising progress towards real-world application. Nevertheless, these efforts have been inhibited by a preponderance of bias and overfitting, while the generalizability and clinical utility of many published models has yet to be formally established. Through improved methodological rigor during initial development, robust evaluations of reproducibility via independent validation, and evidence-based implementation frameworks, future research has the potential to generate risk prediction tools capable of enhancing clinical decision-making in psychiatric care.

Prognostic accuracy and clinical utility of psychometric instruments for individuals at clinical high-risk of psychosis: a systematic review and meta-analysis.

Accurate prognostication of individuals at clinical high-risk for psychosis (CHR-P) is an essential initial step for effective primary indicated prevention. We aimed to summarise the prognostic accuracy and clinical utility of CHR-P assessments for primary indicated psychosis prevention. Web of Knowledge databases were searched until 1st January 2022 for longitudinal studies following-up individuals undergoing a psychometric or diagnostic CHR-P assessment, reporting transition to psychotic disorders in both those who meet CHR-P criteria (CHR-P + ) or not (CHR-P-). Prognostic accuracy meta-analysis was conducted following relevant guidelines. Primary outcome was prognostic accuracy, indexed by area-under-the-curve (AUC), sensitivity and specificity, estimated by the number of true positives, false positives, false negatives and true negatives at the longest available follow-up time. Clinical utility analyses included: likelihood ratios, Fagan's nomogram, and population-level preventive capacity (Population Attributable Fraction, PAF). A total of 22 studies (n = 4 966, 47.5% female, age range 12-40) were included. There were not enough meta-analysable studies on CHR-P diagnostic criteria (DSM-5 Attenuated Psychosis Syndrome) or non-clinical samples. Prognostic accuracy of CHR-P psychometric instruments in clinical samples (individuals referred to CHR-P services or diagnosed with 22q.11.2 deletion syndrome) was excellent: AUC = 0.85 (95% CI: 0.81-0.88) at a mean follow-up time of 34 months. This result was driven by outstanding sensitivity (0.93, 95% CI: 0.87-0.96) and poor specificity (0.58, 95% CI: 0.50-0.66). Being CHR-P + was associated with a small likelihood ratio LR + (2.17, 95% CI: 1.81-2.60) for developing psychosis. Being CHR-P- was associated with a large LR- (0.11, 95%CI: 0.06-0.21) for developing psychosis. Fagan's nomogram indicated a low positive (0.0017%) and negative (0.0001%) post-test risk in non-clinical general population samples. The PAF of the CHR-P state is 10.9% (95% CI: 4.1-25.5%). These findings consolidate the use of psychometric instruments for CHR-P in clinical samples for primary indicated prevention of psychosis. Future research should improve the ability to rule in psychosis risk.

Differential expression of MicroRNAs in Alzheimer's disease: a systematic review and meta-analysis.

Alzheimer's disease (AD) results in progressive cognitive decline owing to the accumulation of amyloid plaques and hyperphosphorylated tau. MicroRNAs (miRNAs) have attracted attention as a putative diagnostic and therapeutic target for neurodegenerative diseases. However, existing meta-analyses on AD and its association with miRNAs have produced inconsistent results. The primary objective of this study is to evaluate the magnitude and consistency of differences in miRNA levels between AD patients, mild cognitive impairment (MCI) patients and healthy controls (HC). Articles investigating miRNA levels in blood, brain tissue, or cerebrospinal fluid (CSF) of AD and MCI patients versus HC were systematically searched in PubMed/Medline from inception to February 16th, 2021. Fixed- and random-effects meta-analyses were complemented with the I2 statistic to measure the heterogeneity, assessment of publication bias, sensitivity subgroup analyses (AD severity, brain region, post-mortem versus ante-mortem specimen for CSF and type of analysis used to quantify miRNA) and functional enrichment pathway analysis. Of the 1512 miRNAs included in 61 articles, 425 meta-analyses were performed on 334 miRNAs. Fifty-six miRNAs were significantly upregulated (n = 40) or downregulated (n = 16) in AD versus HC and all five miRNAs were significantly upregulated in MCI versus HC. Functional enrichment analysis confirmed that pathways related to apoptosis, immune response and inflammation were statistically enriched with upregulated pathways in participants with AD relative to HC. This study confirms that miRNAs' expression is altered in AD and MCI compared to HC. These findings open new diagnostic and therapeutic perspectives for this disorder.