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  1. Perna J, Bellato A, Ganapathy PS, Solmi M, Zampieri A, Faraone SV, et al.
    Mol Psychiatry, 2023 Dec;28(12):5011-5023.
    PMID: 37495888 DOI: 10.1038/s41380-023-02143-7
    AIM: To conduct a systematic review and meta-analysis assessing whether vision and/or eye disorders are associated with Autism Spectrum Disorder (ASD).

    METHOD: Based on a pre-registered protocol (PROSPERO: CRD42022328485), we searched PubMed, Web of Knowledge/Science, Ovid Medline, Embase and APA PsycINFO up to 5th February 2022, with no language/type of document restrictions. We included observational studies 1) reporting at least one measure of vision in people of any age with a diagnosis of ASD based on DSM or ICD criteria, or ADOS; or 2) reporting the prevalence of ASD 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 49 studies in the narrative synthesis and 46 studies in the meta-analyses (15,629,159 individuals distributed across multiple different measures). We found meta-analytic evidence of increased prevalence of strabismus (OR = 4.72 [95% CI: 4.60, 4.85]) in people with versus those without ASD (non-significant heterogeneity: Q = 1.0545, p = 0.7881). We also found evidence of increased accommodation deficits (Hedge's g = 0.68 [CI: 0.28, 1.08]) (non-significant heterogeneity: Q = 6.9331, p = 0.0741), reduced peripheral vision (-0.82 [CI: -1.32, -0.33]) (non-significant heterogeneity: Q = 4.8075, p = 0.4398), reduced stereoacuity (0.73 [CI: -1.14, -0.31]) (non-significant heterogeneity: Q = 0.8974, p = 0.3435), increased color discrimination difficulties (0.69 [CI: 0.27,1.10]) (non-significant heterogeneity: Q = 9.9928, p = 0.1890), reduced contrast sensitivity (0.45 [CI: -0.60, -0.30]) (non-significant heterogeneity: Q = 9.9928, p = 0.1890) and increased retinal thickness (=0.29 [CI: 0.07, 0.51]) (non-significant heterogeneity: Q = 0.8113, p = 0.9918) in ASD.

    DISCUSSION: ASD is associated with some self-reported and objectively measured functional vision problems, and structural alterations of the eye, even though we observed several methodological limitations in the individual studies included in our meta-analyses. Further research should clarify the causal relationship, if any, between ASD and problems of vision during early life.

    PROSPERO REGISTRATION: CRD42022328485.

  2. Radonjić NV, Bellato A, Khoury NM, Cortese S, Faraone SV
    CNS Drugs, 2023 May;37(5):381-397.
    PMID: 37166701 DOI: 10.1007/s40263-023-01005-8
    BACKGROUND: For some adults with Attention-Deficit/Hyperactivity Disorder (ADHD), nonstimulants need to be considered either as a monotherapy or as an adjunct to stimulants.

    OBJECTIVES: The objectives of this systematic review and meta-analysis were to assess the efficacy, acceptability, and tolerability of nonstimulants in adults with ADHD.

    METHODS: Data sources, searches, and study selection were based on a previously published network meta-analysis of randomized clinical trials (RCTs) by Cortese at al. (Lancet Psychiatry 5(9):727-738, 2018), which we updated in March 2022. Specifically, we searched PubMed, BIOSIS Previews, CINAHL, the Cochrane Central Register of Controlled Trials, EMBASE, ERIC, MEDLINE, PsycINFO, OpenGrey, Web of Science Core Collection, ProQuest Dissertations and Theses (UK and Ireland), ProQuest Dissertations and Theses (abstracts and international), and the WHO International Trials Registry Platform, including ClinicalTrials.gov for double-blind RCTs with a placebo arm, lasting at least one week, including adults with a diagnosis of ADHD based on DSM-III, DSM-III-R, DSM-IV(TR), DSM-5 or ICD-9- or 10, and reporting data on efficacy, tolerability (drop-out due to side effects) and acceptability (drop-out due to any cause) of guanfacine, clonidine, or atomoxetine. Additionally, we searched for RCTs of viloxazine extended release (ER), approved for ADHD in 2021. Random-effects meta-analyses were conducted, and the risk of bias for individual RCTs was assessed using the Cochrane Risk of Bias tool.

    RESULTS: We included 18 studies in the meta-analyses (4308 participants) plus one additional study in the narrative synthesis (374 participants). The meta-analysis showed that atomoxetine (15 RCTs) (Hedge's g = - 0.48, 95% CI [- 0.64; - 0.33]), guanfacine (two RCTs) (Hedge's g = - 0.66, 95% CI [- 0.94; - 0.38]) and viloxazine ER (one RCT) were significantly more efficacious than placebo. Atomoxetine was less well tolerated than placebo, while tolerability of guanfacine and viloxazine ER could not be meta-analysed, since only one study, for each medication, reported on it.

    CONCLUSIONS: All investigated nonstimulants were more efficacious in the treatment of ADHD in adults, than placebo, while the placebo had better acceptability and tolerability.

    PROTOCOL: https://osf.io/5vnmt/?view_only=2bf87ed12ba94645babedceeee4c0120 .

  3. Bellato A, Perna J, Ganapathy PS, Solmi M, Zampieri A, Cortese S, et al.
    Mol Psychiatry, 2023 Jan;28(1):410-422.
    PMID: 35931758 DOI: 10.1038/s41380-022-01699-0
    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.

  4. Psychiatric GWAS Consortium Coordinating Committee, Cichon S, Craddock N, Daly M, Faraone SV, Gejman PV, et al.
    Am J Psychiatry, 2009 May;166(5):540-56.
    PMID: 19339359 DOI: 10.1176/appi.ajp.2008.08091354
    OBJECTIVE: The authors conducted a review of the history and empirical basis of genomewide association studies (GWAS), the rationale for GWAS of psychiatric disorders, results to date, limitations, and plans for GWAS meta-analyses.

    METHOD: A literature review was carried out, power and other issues discussed, and planned studies assessed.

    RESULTS: Most of the genomic DNA sequence differences between any two people are common (frequency >5%) single nucleotide polymorphisms (SNPs). Because of localized patterns of correlation (linkage disequilibrium), 500,000 to 1,000,000 of these SNPs can test the hypothesis that one or more common variants explain part of the genetic risk for a disease. GWAS technologies can also detect some of the copy number variants (deletions and duplications) in the genome. Systematic study of rare variants will require large-scale resequencing analyses. GWAS methods have detected a remarkable number of robust genetic associations for dozens of common diseases and traits, leading to new pathophysiological hypotheses, although only small proportions of genetic variance have been explained thus far and therapeutic applications will require substantial further effort. Study design issues, power, and limitations are discussed. For psychiatric disorders, there are initial significant findings for common SNPs and for rare copy number variants, and many other studies are in progress.

    CONCLUSIONS: GWAS of large samples have detected associations of common SNPs and of rare copy number variants with psychiatric disorders. More findings are likely, since larger GWAS samples detect larger numbers of common susceptibility variants, with smaller effects. The Psychiatric GWAS Consortium is conducting GWAS meta-analyses for schizophrenia, bipolar disorder, major depressive disorder, autism, and attention deficit hyperactivity disorder. Based on results for other diseases, larger samples will be required. The contribution of GWAS will depend on the true genetic architecture of each disorder.

  5. Hartman CA, Larsson H, Vos M, Bellato A, Libutzki B, Solberg BS, et al.
    Neurosci Biobehav Rev, 2023 Aug;151:105209.
    PMID: 37149075 DOI: 10.1016/j.neubiorev.2023.105209
    Knowledge on psychiatric comorbidity in adult ADHD is essential for prevention, detection, and treatment of these conditions. This review (1) focuses on large studies (n > 10,000; surveys, claims data, population registries) to identify (a) overall, (b) sex- and (c) age-specific patterns of comorbidity of anxiety disorders (ADs), major depressive disorder (MDD), bipolar disorder (BD) and substance use disorders (SUDs) in adults with ADHD relative to adults without ADHD; and (2) describes methodological challenges relating to establishing comorbidity in ADHD in adults as well as priorities for future research. Meta-analyses (ADHD: n = 550,748; no ADHD n = 14,546,814) yielded pooled odds ratios of 5.0(CI:3.29-7.46) for ADs, 4.5(CI:2.44-8.34) for MDD, 8.7(CI:5.47-13.89) for BD and 4.6(CI:2.72-7.80) for SUDs, indicating strong differences in adults with compared to adults without ADHD. Moderation by sex was not found: high comorbidity held for both men and women with sex-specific patterns as in the general population: higher prevalences of ADs, MDD and BD in women and a higher prevalence of SUDs in men. Insufficient data on different phases of the adult lifespan prevented conclusions on developmental changes in comorbidity. We discuss methodological challenges, knowledge gaps, and future research priorities.
  6. Salazar de Pablo G, Iniesta R, Bellato A, Caye A, Dobrosavljevic M, Parlatini V, et al.
    Mol Psychiatry, 2024 Dec;29(12):3865-3873.
    PMID: 38783054 DOI: 10.1038/s41380-024-02606-5
    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 (β = 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.
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