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Promotion of good mental health, prevention, and early intervention before/at the onset of mental disorders improve outcomes. However, the range and peak ages at onset for mental disorders are not fully established. To provide robust, global epidemiological estimates of age at onset for mental disorders, we conducted a PRISMA/MOOSE-compliant systematic review with meta-analysis of birth cohort/cross-sectional/cohort studies, representative of the general population, reporting age at onset for any ICD/DSM-mental disorders, identified in PubMed/Web of Science (up to 16/05/2020) (PROSPERO:CRD42019143015). Co-primary outcomes were the proportion of individuals with onset of mental disorders before age 14, 18, 25, and peak age at onset, for any mental disorder and across International Classification of Diseases 11 diagnostic blocks. Median age at onset of specific disorders was additionally investigated. Across 192 studies (n = 708,561) included, the proportion of individuals with onset of any mental disorders before the ages of 14, 18, 25 were 34.6%, 48.4%, 62.5%, and peak age was 14.5 years (k = 14, median = 18, interquartile range (IQR) = 11–34). For diagnostic blocks, the proportion of individuals with onset of disorder before the age of 14, 18, 25 and peak age were as follows: neurodevelopmental disorders: 61.5%, 83.2%, 95.8%, 5.5 years (k = 21, median=12, IQR = 7–16), anxiety/fear-related disorders: 38.1%, 51.8%, 73.3%, 5.5 years (k = 73, median = 17, IQR = 9–25), obsessive-compulsive/related disorders: 24.6%, 45.1%, 64.0%, 14.5 years (k = 20, median = 19, IQR = 14–29), feeding/eating disorders/problems: 15.8%, 48.1%, 82.4%, 15.5 years (k = 11, median = 18, IQR = 15–23), conditions specifically associated with stress disorders: 16.9%, 27.6%, 43.1%, 15.5 years (k = 16, median = 30, IQR = 17–48), substance use disorders/addictive behaviours: 2.9%, 15.2%, 48.8%, 19.5 years (k = 58, median = 25, IQR = 20–41), schizophrenia-spectrum disorders/primary psychotic states: 3%, 12.3%, 47.8%, 20.5 years (k = 36, median = 25, IQR = 20–34), personality disorders/related traits: 1.9%, 9.6%, 47.7%, 20.5 years (k = 6, median = 25, IQR = 20–33), and mood disorders: 2.5%, 11.5%, 34.5%, 20.5 years (k = 79, median = 31, IQR = 21–46). No significant difference emerged by sex, or definition of age of onset. Median age at onset for specific mental disorders mapped on a time continuum, from phobias/separation anxiety/autism spectrum disorder/attention deficit hyperactivity disorder/social anxiety (8-13 years) to anorexia nervosa/bulimia nervosa/obsessive-compulsive/binge eating/cannabis use disorders (17-22 years), followed by schizophrenia, personality, panic and alcohol use disorders (25-27 years), and finally post-traumatic/depressive/generalized anxiety/bipolar/acute and transient psychotic disorders (30-35 years), with overlap among groups and no significant clustering. These results inform the timing of good mental health promotion/preventive/early intervention, updating the current mental health system structured around a child/adult service schism at age 18.

Non-invasive brain stimulation (NIBS), including transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS), is a potentially effective treatment strategy for a number of mental conditions. However, no quantitative evidence synthesis of randomized controlled trials (RCTs) of TMS or tDCS using the same criteria including several mental conditions is available. Based on 208 RCTs identified in a systematic review, we conducted a series of random effects meta-analyses to assess the efficacy of NIBS, compared to sham, for core symptoms and cognitive functioning within a broad range of mental conditions. Outcomes included changes in core symptom severity and cognitive functioning from pre- to post-treatment. We found significant positive effects for several outcomes without significant heterogeneity including TMS for symptoms of generalized anxiety disorder (SMD = −1.8 (95% CI: −2.6 to −1), and tDCS for symptoms of substance use disorder (−0.73, −1.00 to −0.46). There was also significant effects for TMS in obsessive-compulsive disorder (−0.66, −0.91 to −0.41) and unipolar depression symptoms (−0.60, −0.78 to −0.42) but with significant heterogeneity. However, subgroup analyses based on stimulation site and number of treatment sessions revealed evidence of positive effects, without significant heterogeneity, for specific TMS stimulation protocols. For neurocognitive outcomes, there was only significant evidence, without significant heterogeneity, for tDCS for improving attention (−0.3, −0.55 to −0.05) and working memory (−0.38, −0.74 to −0.03) in individuals with schizophrenia. We concluded that TMS and tDCS can benefit individuals with a variety of mental conditions, significantly improving clinical dimensions, including cognitive deficits in schizophrenia which are poorly responsive to pharmacotherapy.

Aim To conduct the first systematic review and meta-analysis assessing whether attention-deficit/hyperactivity disorder (ADHD) is associated with disorders of the eye, and/or altered measures of visual function. Method Based on a pre-registered protocol (PROSPERO: CRD42021256352), we searched PubMed, Web of Knowledge/Science, Ovid Medline, Embase and APA PsycINFO up to 16th November 2021, with no language/type of document restrictions. We included observational studies reporting at least one measure of vision in people of any age meeting DSM/ICD criteria for ADHD and in people without ADHD; or the prevalence of ADHD in people with and without vision disorders. Study quality was assessed with the Appraisal tool for Cross-Sectional Studies (AXIS). Random effects meta-analyses were used for data synthesis. Results We included 42 studies in the narrative synthesis and 35 studies in the meta-analyses (3,250,905 participants). We found meta-analytic evidence of increased risk of astigmatism (OR = 1.79 [CI: 1.50, 2.14]), hyperopia and hypermetropia (OR = 1.79 [CI: 1.66, 1.94]), strabismus (OR = 1.93 [CI: 1.75, 2.12]), unspecified vision problems (OR = 1.94 [CI: 1.38, 2.73]) and reduced near point of convergence (OR = 5.02 [CI: 1.78, 14.11]); increased lag (Hedge’s g  = 0.63 [CI: 0.30, 0.96]) and variability (Hedge’s g  = 0.40 [CI: 0.17, 0.64]) of the accommodative response; and increased self-reported vision problems (Hedge’s g  = 0.63 [CI: 0.44, 0.82]) in people with ADHD compared to those without ADHD (with no significant heterogeneity). We also found meta-analytic evidence of no differences between people with and without ADHD on retinal nerve fiber layer thickness (Hedge’s g  = −0.19 [CI: −0.41, 0.02]) and refractive error (Hedge’s g  = 0.08 [CI: −0.26, 0.42]) (with no significant heterogeneity). Discussion ADHD is associated with some self-reported and objectively ascertained functional vision problems, but not with structural alterations of the eye. Further studies should clarify the causal relationship, if any, between ADHD and problems of vision. Trial registration PROSPERO registration: CRD42021256352.

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.

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.

Background There is now evolving data exploring the relationship between depression and various individual lifestyle factors such as diet, physical activity, sleep, alcohol intake, and tobacco smoking. While this data is compelling, there is a paucity of longitudinal research examining how multiple lifestyle factors relate to depressed mood, and how these relations may differ in individuals with major depressive disorder (MDD) and those without a depressive disorder, as ‘healthy controls’ (HC). Methods To this end, we assessed the relationships between 6 key lifestyle factors (measured via self-report) and depressed mood (measured via a relevant item from the Patient Health Questionnaire) in individuals with a history of or current MDD and healthy controls (HCs). Cross-sectional analyses were performed in the UK Biobank baseline sample, and longitudinal analyses were conducted in those who completed the Mental Health Follow-up. Results Cross-sectional analysis of 84,860 participants showed that in both MDD and HCs, physical activity, healthy diet, and optimal sleep duration were associated with less frequency of depressed mood (all p  < 0.001; ORs 0.62 to 0.94), whereas screen time and also tobacco smoking were associated with higher frequency of depressed mood (both p  < 0.0001; ORs 1.09 to 1.36). In the longitudinal analysis, the lifestyle factors which were protective of depressed mood in both MDD and HCs were optimal sleep duration (MDD OR = 1.10; p  < 0.001, HC OR = 1.08; p  < 0.001) and lower screen time (MDD OR = 0.71; p  < 0.001, HC OR = 0.80; p  < 0.001). There was also a significant interaction between healthy diet and MDD status ( p  = 0.024), while a better-quality diet was indicated to be protective of depressed mood in HCs (OR = 0.92; p  = 0.045) but was not associated with depressed mood in the MDD sample. In a cross-sectional (OR = 0.91; p  < 0.0001) analysis, higher frequency of alcohol consumption was surprisingly associated with reduced frequency of depressed mood in MDD, but not in HCs. Conclusions Our data suggest that several lifestyle factors are associated with depressed mood, and in particular, it calls into consideration habits involving increased screen time and a poor sleep and dietary pattern as being partly implicated in the germination or exacerbation of depressed mood.

Since the discovery of chlorpromazine (CPZ) in 1952, first-generation antipsychotics (FGAs) have revolutionized psychiatric care in terms of facilitating discharge from hospital and enabling large numbers of patients with severe mental illness (SMI) to be treated in the community. Second-generation antipsychotics (SGAs) ushered in a progressive shift from the paternalistic management of SMI symptoms to a patient-centered approach, which emphasized targets important to patients - psychosocial functioning, quality of life, and recovery. These drugs are no longer limited to specific ( ) categories. Evidence indicates that SGAs show an improved safety and tolerability profile compared with FGAs. The incidence of treatment-emergent extrapyramidal side effects is lower, and there is less impairment of cognitive function and treatment-related negative symptoms. However, treatment with SGAs has been associated with a wide range of untoward effects, among which treatment-emergent weight gain and metabolic abnormalities are of notable concern. The present clinical review aims to summarize the safety and tolerability profile of selected FGAs and SGAs and to link treatment-related adverse effects to the pharmacodynamic profile of each drug. Evidence, predominantly derived from systematic reviews, meta-analyses, and clinical trials of the drugs amisulpride, aripiprazole, asenapine, brexpiprazole, cariprazine, clozapine, iloperidone, lurasidone, olanzapine, paliperidone, quetiapine, risperidone, sertindole, ziprasidone, CPZ, haloperidol, loxapine, and perphenazine, is summarized. In addition, the safety and tolerability profiles of antipsychotics are discussed in the context of the \"behavioral toxicity\" conceptual framework, which considers the longitudinal course and the clinical and therapeutic consequences of treatment-emergent side effects. In SMI, SGAs with safer metabolic profiles should ideally be prescribed first. However, alongside with safety, efficacy should also be considered on a patient-tailored basis.

Background Depression is a prevalent and disabling mental disorder that frequently co-occurs with a wide range of chronic conditions. Evidence has suggested that depression could be associated with excess all-cause mortality across different settings and populations, although the causality of these associations remains unclear. Methods We conducted an umbrella review of systematic reviews and meta-analyses of observational studies. PubMed, PsycINFO, and Embase electronic databases were searched through January 20, 2018. Systematic reviews and meta-analyses that investigated associations of depression and all-cause and cause-specific mortality were selected for the review. The evidence was graded as convincing, highly suggestive, suggestive, or weak based on quantitative criteria that included an assessment of heterogeneity, 95% prediction intervals, small-study effects, and excess significance bias. Results A total of 26 references providing 2 systematic reviews and data for 17 meta-analytic estimates met inclusion criteria (19 of them on all-cause mortality); data from 246 unique studies ( N  = 3,825,380) were synthesized. All 17 associations had P  < 0.05 per random effects summary effects, but none of them met criteria for convincing evidence. Associations of depression and all-cause mortality in patients after acute myocardial infarction, in individuals with heart failure, in cancer patients as well as in samples from mixed settings met criteria for highly suggestive evidence. However, none of the associations remained supported by highly suggestive evidence in sensitivity analyses that considered studies employing structured diagnostic interviews. In addition, associations of depression and all-cause mortality in cancer and post-acute myocardial infarction samples were supported only by suggestive evidence when studies that tried to adjust for potential confounders were considered. Conclusions Even though associations between depression and mortality have nominally significant results in all assessed settings and populations, the evidence becomes weaker when focusing on studies that used structured interviews and those that tried to adjust for potential confounders. A causal effect of depression on all-cause and cause-specific mortality remains unproven, and thus interventions targeting depression are not expected to result in lower mortality rates at least based on current evidence from observational studies.

Systematic reviews are a cornerstone for synthesizing the available evidence on a given topic. They simultaneously allow for gaps in the literature to be identified and provide direction for future research. However, due to the ever‐increasing volume and complexity of the available literature, traditional methods for conducting systematic reviews are less efficient and more time‐consuming. Numerous artificial intelligence (AI) tools are being released with the potential to optimize efficiency in academic writing and assist with various stages of the systematic review process including developing and refining search strategies, screening titles and s for inclusion or exclusion criteria, extracting essential data from studies and summarizing findings. Therefore, in this article we provide an overview of the currently available tools and how they can be incorporated into the systematic review process to improve efficiency and quality of research synthesis. We emphasize that authors must report all AI tools that have been used at each stage to ensure replicability as part of reporting in methods.

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.