The Age of Onset of Anxiety Disorders Lijster, Jasmijn M. de; Dierckx, Bram; Utens, Elisabeth M.W.J. ...
Canadian journal of psychiatry,
04/2017, Volume:
62, Issue:
4
Journal Article
Peer reviewed
Open access
Objective:
The objective was to estimate the age of onset (AOO) for all anxiety disorders and for specific subtypes. Gender differences in the AOO of anxiety disorders were examined, as were the ...influence of study characteristics on reported AOOs.
Methods:
Seven electronic databases were searched up to October 2014, with keywords representing anxiety disorder subtypes, AOO, and study design. The inclusion criteria were studies using a general population sample that provided data on the AOO for all anxiety disorders, or specific anxiety disorders, according to DSM-III-R, DSM-IV, or ICD-10 criteria.
Results:
There were 1028 titles examined, which yielded 24 studies meeting the inclusion criteria. Eight studies reported the AOO and gender. Meta-analysis found a mean AOO of all anxiety disorders of 21.3 years (95% CI 17.46 to 25.07). Separation anxiety disorder, specific phobia, and social phobia had their mean onset before the age of 15 years, whereas the AOO of agoraphobia, obsessive-compulsive disorder, posttraumatic stress disorder, panic disorder, and generalized anxiety disorder began, on average, between 21.1 and 34.9 years. Meta-analysis revealed no difference in the AOO between genders. A prospective study design and higher developmental level of the study country were associated with an earlier AOO.
Conclusions:
Results from this meta-analysis indicate that anxiety disorder subtypes differ in the mean AOO, with onsets ranging from early adolescence to young adulthood. These findings suggest that prevention strategies of anxiety disorders should be directed towards factors associated with the development of anxiety disorder subtypes in the age groups with the greatest vulnerability for developing those disorders.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Anxiety disorders are highly prevalent during adolescence. Although literature points out that anxiety symptoms are related to problems in social and academic functioning, the extent of these ...problems among adolescents with clinical anxiety disorders has not been systematically reviewed before.
Electronic databases were searched up to October 2017, with keywords representing anxiety disorders, adolescents, and social or academic functioning. The inclusion criteria were studies with a sample of adolescents (10–19 years) with anxiety disorders that provided data regarding their social or academic functioning. 3431 studies were examined, of which 19 met the inclusion criteria.
Adolescents with anxiety disorders had a lower social competence relative to their healthy peers. They reported more negativity within interpersonal relationships, higher levels of loneliness, and victimization. Most adolescents with anxiety disorders felt impaired at school, however, findings of their average school results, compared to peers, were mixed. In addition, they had a higher risk for school refusal and entered higher education less often. Impairments in social and academic functioning differed across type and the number of anxiety disorders.
Most studies examined social phobia or anxiety disorders in general and methodological approaches varied widely between studies.
This systematic review indicates that adolescents with anxiety disorders experience a range of significant problems in both social and academic functioning. These findings suggest that the assessment and treatment of anxiety disorders in adolescence should focus on improving functioning across domains.
•Adolescents with anxiety disorders experience several social and academic problems.•Problems are low social competence, interpersonal difficulties, and victimization.•There is no clear evidence that their school results are lower compared to peers.•Adolescents with anxiety disorders, particularly with SOP, feel impaired at school.•Improving functioning across role domains is important for this specific subgroup.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Children with Autism Spectrum Disorders (ASDs) tend to be selective in their food intake, which may compromise their diet quality. While ASD diagnoses capture severe levels of impairment, autistic ...traits vary on a continuum throughout the population. Yet, little is known about how autistic traits relate to diet quality at the population level.
This study examines the association between autistic traits in early childhood and diet quality in mid-childhood and explores the mediating role of food selectivity.
Participants were children (n = 4092) from the population-based Generation R Study. Parents reported their child's autistic traits at 1.5, 3, and 6 years; food selectivity at 4 years; and food intake at 8 years, from which a diet quality score was derived. Associations of autistic traits and the autistic trait trajectory (identified using Latent Class Growth Modelling) with diet quality were examined using multiple linear regression models. The indirect effect of food selectivity in the association between autistic traits at 1.5 years and diet quality was examined using mediation analysis.
Autistic traits were associated with diet quality (e.g., 1.5 years: β = −0.09; 95% CI: −0.13 to −0.06). Two classes captured the autistic trait trajectories from 1.5 to 6 years: children with “low and stable” (95%) and “high and increasing” (5%) mean scores. Children in the high and increasing group had poorer diet quality than those in the low and stable group (β = −0.28; 95% CI: −0.44 to −0.11). Food selectivity mediated the association between autistic traits at 1.5 years and diet quality at 8 years (βindirect = −0.03; 95% CI: −0.03 to −0.02).
Autistic traits in early childhood are associated with poorer diet quality in mid-childhood, and food selectivity appears to mediate this association. Interventions intended to optimize nutrition in children with elevated autistic traits may integrate behavioral strategies to support parents’ responding to their child's food selectivity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Neurodevelopmental disorders such as attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable and influenced by many single nucleotide polymorphisms ...(SNPs). SNPs can be used to calculate individual polygenic risk scores (PRS) for a disorder. We aim to explore the association between the PRS for ADHD, ASD and for Schizophrenia (SCZ), and ADHD and ASD diagnoses in a clinical child and adolescent population. Based on the most recent genome wide association studies of ADHD, ASD and SCZ, PRS of each disorder were calculated for individuals of a clinical child and adolescent target sample (N = 688) and for adult controls (N = 943). We tested with logistic regression analyses for an association with (1) a single diagnosis of ADHD (N = 280), (2) a single diagnosis of ASD (N = 295), and (3) combining the two diagnoses, thus subjects with either ASD, ADHD or both (N = 688). Our results showed a significant association of the ADHD PRS with ADHD status (OR 1.6,
P
= 1.39 × 10
−07
) and with the combined ADHD/ASD status (OR 1.36,
P
= 1.211 × 10
−05
), but not with ASD status (OR 1.14,
P
= 1). No associations for the ASD and SCZ PRS were observed. In sum, the PRS of ADHD is significantly associated with the combined ADHD/ASD status. Yet, this association is primarily driven by ADHD status, suggesting disorder specific genetic effects of the ADHD PRS.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Reports of increasing incidence rates of delirium in critically ill children are reason for concern. We evaluated the measurement properties of the pediatric delirium component (PD-scale) of the ...Sophia Observation Withdrawal Symptoms scale Pediatric Delirium scale (SOS-PD scale).
In a multicenter prospective observational study in four Dutch pediatric ICUs (PICUs), patients aged ≥ 3 months and admitted for ≥ 48 h were assessed with the PD-scale thrice daily. Criterion validity was assessed: if the PD-scale score was ≥ 4, a child psychiatrist clinically assessed the presence or absence of PD according to the Diagnostic and statistical manual of mental disorders (DSM)-IV. In addition, the child psychiatrist assessed a randomly selected group to establish the false-negative rate. The construct validity was assessed by calculating the Pearson coefficient (r
) for correlation between the PD-scale and Cornell Assessment Pediatric Delirium (CAP-D) scores. Interrater reliability was determined by comparing paired nurse-researcher PD-scale assessments and calculating the intraclass correlation coefficient (ICC).
Four hundred eighty-five patients with a median age of 27.0 months (IQR 8-102) were included, of whom 48 patients were diagnosed with delirium by the child psychiatrist. The PD-scale had overall sensitivity of 92.3% and specificity of 96.5% compared to the psychiatrist diagnosis for a cutoff score ≥4 points. The r
between the PD-scale and the CAP-D was 0.89 (CI 95%, 0.82-0.93; p < 0.001). The ICC of 75 paired nurse-researcher observations was 0.99 (95% CI, 0.98-0.99).
The PD-scale has good reliability and validity for early screening of PD in critically ill children. It can be validly and reliably used by nurses to this aim.
Background
Psychiatric traits are heritable, highly comorbid and genetically correlated, suggesting that genetic effects that are shared across disorders are at play. The aim of the present study is ...to quantify the predictive capacity of common genetic variation of a variety of traits, as captured by their PRS, to predict case‐control status in a child and adolescent psychiatric sample including controls to reveal which traits contribute to the shared genetic risk across disorders.
Method
Polygenic risk scores (PRS) of 14 traits were used as predictor phenotypes to predict case‐control status in a clinical sample. Clinical cases (N = 1,402), age 1–21, diagnostic categories: Autism spectrum disorders (N = 492), Attention‐deficit/ hyperactivity disorders (N = 471), Anxiety (N = 293), disruptive behaviors (N = 101), eating disorders (N = 97), OCD (N = 43), Tic disorder (N = 50), Disorder of infancy, childhood or adolescence NOS (N = 65), depression (N = 64), motor, learning and communication disorders (N = 59), Anorexia Nervosa (N = 48), somatoform disorders (N = 47), Trauma/stress (N = 39) and controls (N = 1,448, age 17–84) of European ancestry. First, these 14 PRS were tested in univariate regression analyses. The traits that significantly predicted case‐control status were included in a multivariable regression model to investigate the gain in explained variance when leveraging the genetic effects of multiple traits simultaneously.
Results
In the univariate analyses, we observed significant associations between clinical status and the PRS of educational attainment (EA), smoking initiation (SI), intelligence, neuroticism, alcohol dependence, ADHD, major depression and anti‐social behavior. EA (p‐value: 3.53E‐20, explained variance: 3.99%, OR: 0.66), and SI (p‐value: 4.77E‐10, explained variance: 1.91%, OR: 1.33) were the most predictive traits. In the multivariable analysis with these eight significant traits, EA and SI, remained significant predictors. The explained variance of the PRS in the model with these eight traits combined was 5.9%.
Conclusion
Our study provides more insights into the genetic signal that is shared between childhood and adolescent psychiatric disorders. As such, our findings might guide future studies on psychiatric comorbidity and offer insights into shared etiology between psychiatric disorders. The increase in explained variance when leveraging the genetic signal of different predictor traits supports a multivariable approach to optimize precision accuracy for general psychopathology.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Risperidone, aripiprazole, and pipamperone are antipsychotic drugs frequently prescribed for the treatment of comorbid behavioral problems in children with autism spectrum disorders. Therapeutic drug ...monitoring (TDM) could be useful to decrease side effects and to improve patient outcome. Dried blood spot (DBS) sample collection seems to be an attractive technique to develop TDM of these drugs in a pediatric population. The aim of this work was to develop and validate a DBS assay suitable for TDM and home sampling.
Risperidone, 9-OH risperidone, aripiprazole, dehydroaripiprazole, and pipamperone were extracted from DBS and analyzed by ultra-high-performance liquid chromatography-tandem mass spectrometry using a C18 reversed-phase column with a mobile phase consisting of ammonium acetate/formic acid in water or methanol. The suitability of DBS for TDM was assessed by studying the influence of specific parameters: extraction solution, EDTA carryover, hematocrit, punching location, spot volume, and hemolysis. The assay was validated with respect to conventional guidelines for bioanalytical methods.
The method was linear, specific without any critical matrix effect, and with a mean recovery around 90%. Accuracy and imprecision were within the acceptance criteria in samples with hematocrit values from 30% to 45%. EDTA or hemolysis did not skew the results, and no punching carryover was observed. No significant influence of the spot volume or the punch location was observed. The antipsychotics were all stable in DBS stored 10 days at room temperature and 1 month at 4 or -80°C. The method was successfully applied to quantify the 3 antipsychotics and their metabolites in patient samples.
A UHPLC-MS/MS method has been successfully validated for the simultaneous quantification of risperidone, 9-OH risperidone, aripiprazole, dehydroaripiprazole, and pipamperone in DBS. The assay provided good analytical performances for TDM and clinical research applications.
Anorexia nervosa (AN) entails many uncertainties regarding the clinical outcome, due to large heterogeneity in the disease course. AN is associated with global decrease in brain volumes and altered ...brain functioning during acute illness. However, it is unclear whether structural and functional brain alterations can predict clinical outcome. We aimed to systematically review the predictive value of volumetric and functional brain outcome measures of structural and functional brain magnetic resonance imaging (MRI) on the disease course of AN. Four databases (Embase, Medline, Psycinfo, and Cochrane Central Register) were systematically searched. A total of 15 studies (structural MRI: n = 6, functional MRI: n = 9) were reviewed. In total 464 unique AN patients, and 328 controls were included. Follow-up time ranged between 1 and 43 months. Structural neuroimaging studies showed that lower brain volumes of the cerebellum, subcortical grey matter, and cortical white matter at admission predicted a worse clinical outcome. A smaller increase of the anterior cingulate cortex volume in the early phase of the disease predicted a worse clinical outcome. Lower overall gyrification, and a higher clustering coefficient predicted a worse clinical outcome. Functional MRI studies showed that frontal, parietal and temporal activity during task-based algorithms predicted follow-up body mass index, although results were bidirectional possibly due to the large heterogeneity in methodological approaches. Neuroimaging measures may predict the clinical outcome of AN. However, there is a lack of replication studies. Future studies are needed to validate the prognostic utility of neuroimaging measures in AN patients, and should harmonize demographic, clinical and neuroimaging features in order to enhance comparability.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The first aim of this study was to construct/validate a subscale—with cut-offs considering gender/age differences—for the school-age Child Behavior CheckList (CBCL) to screen for Autism Spectrum ...Disorder (ASD) applying both data-driven (
N
= 1666) and clinician-expert (
N
= 15) approaches. Further, we compared these to previously established CBCL ASD profiles/subscales and DSM-oriented subscales. The second aim was to cross-validate results in two truly independent samples (
N
= 2445 and 886). Despite relatively low discriminative power of all subscales in the cross-validation samples, results indicated that the data-driven subscale had the best potential to screen for ASD and a similar screening potential as the DSM-oriented subscales. Given beneficial implications for pediatric/clinical practice, we encourage colleagues to continue the validation of this CBCL ASD subscale.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Aim
Risperidone is the most commonly prescribed antipsychotic drug to children and adolescents worldwide, but it is associated with serious side effects, including weight gain. This study assessed ...the relationship of risperidone and 9‐hydroxyrisperidone trough concentrations, maximum concentrations and 24‐hour area under the curves (AUCs) with body mass index (BMI) z‐scores in children and adolescents with autism spectrum disorder (ASD) and behavioural problems. Secondary outcomes were metabolic, endocrine, extrapyramidal and cardiac side effects and effectiveness.
Methods
Forty‐two children and adolescents (32 males) aged 6‐18 years were included in a 24‐week prospective observational trial. Drug plasma concentrations, side effects and effectiveness were measured at several time points during follow‐up. Relevant pharmacokinetic covariates, including medication adherence and CYP2D6, CYP3A4, CYP3A5 and P‐glycoprotein (ABCB1) genotypes, were measured. Nonlinear mixed‐effects modelling (NONMEM®) was used for a population pharmacokinetic analysis with 205 risperidone and 205 9‐hydroxyrisperidone concentrations. Subsequently, model‐based trough concentrations, maximum concentrations and 24‐hour AUCs were analysed to predict outcomes using generalized and linear mixed‐effects models.
Results
A risperidone two‐compartment model combined with a 9‐hydroxyrisperidone one‐compartment model best described the measured concentrations. Of all the pharmacokinetic parameters, higher risperidone sum trough concentrations best predicted higher BMI z‐scores during follow‐up (P < .001). Higher sum trough concentrations also predicted more sedation (P < .05), higher prolactin levels (P < .001) and more effectiveness measured with Aberrant Behavior Checklist irritability score (P < .01).
Conclusion
Our results indicate a therapeutic window exists, which suggests that therapeutic drug monitoring of risperidone might increase safety and effectiveness in children and adolescents with ASD and behavioural problems.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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