26 research outputs found
Child and adolescent psychiatric clinics of north America (ADHD : non-pharmacologic interventions), oktober 2014, vol 23, n 4./ Edit.: Stephen V. Faraone; Kevin M. Antshel
No full textxiv, p. 687-981 : tab.; 21 cm
Child and adolescent psychiatric clinics of north America (ADHD : non-pharmacologic interventions), oktober 2014, vol 23, n 4./ Edit.: Stephen V. Faraone; Kevin M. Antshel
No full textxiv, p. 687-981 : tab.; 21 cm
Sleep-associated adverse events during the methylphenidate treatment of attention-deficit/hyperactivity disorder: A meta-analysis
No full textObjective: Sleep disturbances are a feature of attention deficit/hyperactivity disorder (ADHD) and an adverse event (AE) of methylphenidate (MPH) treatment. We sought to clarify MPH-associated sleep problems and how studies are affected by confounding factors. Data Sources: Published studies in English collected via online databases and unpublished data from www.clinicaltrials.gov and FDA websites. Sources were searched from inception to August 2017. Study Selection: We included blinded placebo-controlled studies of youth with ADHD conducted in naturalistic settings. This led to 35 studies yielding 75 observations of sleep-related AEs. These studies comprised 3079 drug-exposed and 2606 placebo-treated patients. Data Extraction: Two PhD reviewers reviewed each study for inclusion. Four PhD/PharmD reviewers extracted data in duplicate. Discrepancies were resolved by discussion or, if needed, by the senior author. Results: We found increased pooled relative risks (RRs) for MPH-associated sleep-related AEs for insomnia, initial insomnia, middle insomnia, combined insomnia, and sleep disorder. Several sample or study design features were significantly associated with the RR for sleep-related AEs and the MPH formulation studied. After correcting for confounding, we found significant differences among drugs. We show that the RR, and its interpretation, is constrained by the placebo AE rate. Conclusions: Several types of insomnia and sleep problems are associated with MPH treatment. Study design and sample features influence the RR statistic. By showing that the rate of placebo AEs impacts the RR, we provide the field with a useful covariate for adjusting RR statistics
Meta-analysis of structural imaging findings in Attention-Deficit/Hyperactivity Disorder
No full textBackground\ud
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Although there are many structural neuroimaging studies of attention-deficit/hyperactivity disorder (ADHD) in children, there are inconsistencies across studies and no consensus regarding which brain regions show the most robust area or volumetric reductions relative to control subjects. Our goal was to statistically analyze structural imaging data via a meta-analysis to help resolve these issues.\ud
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Methods\ud
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We searched the MEDLINE and PsycINFO databases through January 2005. Studies must have been written in English, used magnetic resonance imaging, and presented the means and standard deviations of regions assessed. Data were extracted by one of the authors and verified independently by another author.\ud
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Results\ud
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Analyses were performed using STATA with metan, metabias, and metainf programs. A meta-analysis including all regions across all studies indicated global reductions for ADHD subjects compared with control subjects, standardized mean difference equal to .408, p less than .001. Regions most frequently assessed and showing the largest differences included cerebellar regions, the splenium of the corpus callosum, total and right cerebral volume, and right caudate. Several frontal regions assessed in only two studies also showed large significant differences.\ud
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Conclusions\ud
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This meta-analysis provides a quantitative analysis of neuroanatomical abnormalities in ADHD and information that can be used to guide future studies
The Multidimensional Anxiety Scale for Children : a further validation with Australian adolescents with and without ADHD
No full textTo examine the factor structure of the Multidimensional Anxiety Scale for Children (MASC) with Australian adolescents with and without Attention–Deficit/Hyperactivity Disorder (ADHD). The MASC was administered to 210 high school aged adolescents (109 males, 101 females), 115 of who were clinically diagnosed as ADHD (86 males, 29 females). The remaining 95 were non ADHD Community Comparisons. Results: Analyses supported a three-factor model, with a reduced item pool, which combined the Harm Avoidance and Separation Anxiety scales together. This model was invariant across younger and older participants, and across boys and girls. The model was largely invariant across ADHD and non-ADHD groups. The ADHD group had significantly higher Physical Symptom factor scores than the non-ADHD group. The MASC is useful for assessing anxiety in adolescents with and without ADHD but items reflecting the Harm Avoidance and Separation Anxiety scales may need revising
Association between SYP with attention-deficit/hyperactivity disorder in Chinese Han subjects: Differences among subtypes and genders
No full textDysfunction of neurotransmitters has been suggested to be involved in the etiology of attention-deficit/hyperactivity disorder (ADHD). Hence, genes encoding proteins involved in the vesicular release process of those neurotransmitters are attractive candidates in ADHD genetics. One of these genes is SYP, which encodes synaptophysin, a protein known to participate in regulating neurotransmitter release and synaptic plasticity. Several studies have reported an association between SYP and ADHD, but more work is needed to refine the association. In the present study, we attempt to investigate their association in Chinese Han subjects by family-based and case-control studies. Transmission disequilibrium tests (TDTs) in 1112 trios found significant association between SYP and the predominantly inattentive subtype (ADHD-I), especially for males with ADHD-I, both from single nucleotide polymorphism (SNP) and haplotypic analyses. Chi-square tests in 1682 ADHD probands and 957 comparison subjects indicated possible association of SYP with female ADHD and female ADHD-I. However, the associated alleles and haplotypes between males and females were reversed. In conclusion, our results suggested that SYP may be primarily associated with ADHD-I and its genetic mechanism may be gender-specific. Thus, it is necessary to take subtype and gender into account in ADHD genetic studies. (C) 2013 Elsevier Ireland Ltd. All rights reserved.PsychiatrySCI(E)PubMedSSCI1ARTICLE1308-31421
Adrenergic neurotransmitter system transporter and receptor genes associated with atomoxetine response in attention-deficit hyperactivity disorder children
No full textAtomoxetine, a selective inhibitor of the norepinephrine transporter, exerts its therapeutic effect for attention-deficit hyperactivity disorder (ADHD) by increasing the concentration of synaptic norepinephrine. The objective of this study was to evaluate the association of the genetic variants of multiple genes of the noradrenergic neurotransmitter system with atomoxetine response. One hundred and eleven ADHD children and adolescents were enrolled in a prospective, open-label study of atomoxetine for 8-12 weeks. The dose was titrated to 1.2-1.4 mg/kg per day and maintained for at least 4 weeks. The primary efficacy measure was the investigator-rated ADHD Rating Scale-IV. Two categorical evaluations of treatment effects (defined as response and remission) were used. Twelve SNPs in SLC6A2, ADRA2A, and ADRA1A were genotyped to analyze their association with response or remission status. rs3785143 in SLC6A2 was associated with responder status (nominal P = 0.0048; corrected by multiple test, P = 0.0416; OR 2.66, 95 % confidence interval (CI) 1.35-5.26). rs2279805 of SLC6A2 was nominally significantly associated with the remission status. (P = 0.0221, OR 2.32, 95 % CI 1.13-4.75, multiple test P = 0.2130). The GG haplotype of rs1800544 and rs553668 in ADRA2A achieved nominal significance for association with non-remission (P = 0.0219, OR 2.82, 95 % CI 1.16-6.85, multiple test, P = 0.2076). The results of this study suggest that DNA variants of both SLC6A2 and ADRA2A in the adrenergic neurotransmitter system might alter the response to atomoxetine, though further replication study in larger sample for validation of these findings is still needed.Clinical NeurologyNeurosciencesSCI(E)PubMed5ARTICLE71127-113312
Attention Deficit Hyperactivity Disorder comorbid oppositional defiant disorder and its predominately inattentive type: evidence for an association with COMT but not MAOA in a Chinese sample
No full textAbstract Background There are three childhood disruptive behavior disorders (DBDs), attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD). The most common comorbid disorder in ADHD is ODD. DSM-IV describes three ADHD subtypes: predominantly inattentive type (ADHD-IA), predominantly hyperactive-impulsive type (ADHD-HI), and combined type (ADHD-C). Prior work suggests that specific candidate genes are associated with specific subtypes of ADHD in China. Our previous association studies between ADHD and functional polymorphisms of COMT and MAOA, consistently showed the low transcriptional activity alleles were preferentially transmitted to ADHD-IA boys. Thus, the goal of the present study is to test the hypothesis that COMT Val158Met and MAOA-uVNTR jointly contribute to the ODD phenotype among Chinese ADHD boys. Methods 171 Chinese boys between 6 and 17.5 years old (mean = 10.3, SD = 2.6) with complete COMT val158met and MAOA-uVNTR genotyping information were studied. We used logistic regression with genotypes as independent variables and the binary phenotype as the dependent variable. We used p Results Our results highlight the potential etiologic role of COMT in the ADHD with comorbid ODD and its predominately inattentive type in male Chinese subjects. ADHD with comorbid ODD was associated with homozygosity of the high-activity Val allele, while the predominantly inattentive ADHD subtype was associated with the low-activity Met allele. We found no evidence of association between the MAOA-uVNTR variant and ADHD with comorbid ODD or the ADHD-IA subtype. Conclusion Our study of attention deficit hyperactivity disorder comorbid oppositional defiant disorder and its predominately inattentive type highlights the potential etiologic role of COMT for ADHD children in China. But we failed to observe an interaction between COMT and MAOA, which suggests that epistasis between COMT and MAOA genes does not influence the phenotype of ADHD-IA with comorbid ODD in a clinical sample of Chinese male subjects. To confirm our findings further studies with a larger number of subjects and healthy controls are needed.</p
Predicting suicide attempt or suicide death following a visit to psychiatric specialty care : A machine learning study using Swedish national registry data
Full text linkBackground: Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior. Methods and findings: The study sample consisted of 541,300 inpatient and outpatient visits by 126,205 Sweden-born patients (54% female and 46% male) aged 18 to 39 (mean age at the visit: 27.3) years to psychiatric specialty care in Sweden between January 1, 2011 and December 31, 2012. The most common psychiatric diagnoses at the visit were anxiety disorders (20.0%), major depressive disorder (16.9%), and substance use disorders (13.6%). A total of 425 candidate predictors covering demographic characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as family history of disease and crime were extracted from the Swedish registry data. The sample was randomly split into an 80% training set containing 433,024 visits and a 20% test set containing 108,276 visits. Models were trained separately for suicide attempt/death within 90 and 30 days following a visit using multiple machine learning algorithms. Model discrimination and calibration were both evaluated. Among all eligible visits, 3.5% (18,682) were followed by a suicide attempt/death within 90 days and 1.7% (9,099) within 30 days. The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87-0.89) and 0.89 (95% CI = 0.88-0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). Sensitivity, specificity, and predictive values were reported at different risk thresholds. A limitation of our study is that our models have not yet been externally validated, and thus, the generalizability of the models to other populations remains unknown. Conclusions: By combining the ensemble method of multiple machine learning algorithms and high-quality data solely from the Swedish registers, we developed prognostic models to predict short-term suicide attempt/death with good discrimination and calibration. Whether novel predictors can improve predictive performance requires further investigation. Author summary Why was this study done? Suicidal behavior is overrepresented in people with mental illness and contributes to the substantial public health burden of psychiatric conditions. Accurately predicting suicidal behavior has long been challenging. The potential of applying machine learning to linked national datasets to predict suicidal behavior remains unknown. What did the researchers do and find? We identified a sample of 541,300 inpatient and outpatient visits to psychiatric specialty care in Sweden during 2011 and 2012. The sample was then divided into a training dataset and a test dataset. We first trained prediction models separately for suicide attempt/death within 90 days and 30 days following a visit to psychiatric specialty care, using 4 different machine learning algorithms. We then used an ensemble method to combine the performance of the trained models with the intention to achieve an overall performance superior than each individual model. The final model based on the ensemble method achieved the best predictive performance. This model was applied to test dataset and showed good model discrimination and calibration for both the 90-day and 30-day outcomes. What do these findings mean? Our findings suggest that combining machine learning with registry data has the potential to accurately predict short-term suicidal behavior. An approach combining 4 machine learning methods showed an overall predictive performance slightly better than each individual model
