Search

Childhood psychopathology traits are complex, being affected by a large number of genetic variants, each with a small effect. They are also associated with a number of other phenotypes, both psychiatric and non-psychiatric. These associations may be explained by different mechanisms, including pleiotropy, where the same genetic variant(s) influence multiple phenotypes, or shared biological pathways. Either way, polygenic risk scores (PRS) can and have been used to investigate these associations. When PRS of one trait significantly predict another, we can conclude that the traits are genetically correlated. In our previous study, we used PRS of various adult psychopathology, personality and functional outcome traits to predict childhood psychopathology in univariate analyses – using each PRS to predict each childhood psychopathology measure. Performing meta-analyses across 7 large European cohorts, we found that adult educational attainment (EA), major depressive disorder (MDD), schizophrenia, insomnia, subjective wellbeing, neuroticism, and BMI were genetically associated with symptoms of ADHD, internalizing, and social problems in childhood and adolescence. We also found that the association between adult EA PRS and childhood psychopathology became stronger with age. However, given the increased power of our analyses compared to others (sample size over 40,000 + some of the most powerful GWAS of the adult traits we investigated), the predictive power of the scores were still extremely small, accounting for less than 0.01% of variance. This automatically dampens optimism for any use of these PRS in prognostic research, though increased sample sizes in GWAS will eventually increase the predictive accuracy. Methodological advances, particularly the development of multivariate methods means that we can combine various PRS in joint analyses, leveraging the genetic correlation of the trait PRS, and childhood measures with each other, as well as the correlation between PRS and childhood measures, which should enable us to boost the predictive power. Taking a multivariate approach, we hope to understand the patterns of correlation/association between our traits of interest as well as investigate which adult traits show the biggest contribution to the association with childhood psychopathology.

Autism spectrum disorders (ASDs, from hereon: autism) are neurodevelopmental disorders characterized by profound difficulties in social communication and behavior routines. Recent findings suggest that symptoms and associated difficulties persist into adulthood (Billstedt, Gillberg, & Gillberg, 2005; Howlin, Goode, Hutton, & Rutter, 2004). Compared to the general population, autistic adults show higher risk for major chronic medical conditions as well as most major psychiatric disorders, with 54% having at least one diagnosed psychiatric condition (Croen et al., 2015). Transition to adulthood is a difficult time, especially for young autistic individuals (Friedman, Warfield, & Parish, 2013). This is due to multiple factors including the onset of most psychiatric disorders in early adulthood (age 16/17-24/25) (Birchwood & Singh, 2013; Jones, 2013); reluctance to seek help and a preference for self-reliance or reliance on family and friends instead of medical professionals (Crane, Adams, Harper, Welch, & Pellicano, 2019; Gulliver, Griffiths, & Christensen, 2010); and the complexity of the healthcare system (Nicolaidis et al., 2013). Autistic individuals may therefore find themselves deprived of relevant support and services when transitioning from childhood to adulthood mental health services (Nicolaidis et al., 2013) which warrants the close investigation of mental health problems in this age group. Young autistic women represent a particularly vulnerable group. They are usually diagnosed later in life compared to boys, which holds true especially for cognitively able women (Begeer et al., 2013; McDonnell et al., 2020). Delays in access to support due to late diagnosis make prognosis worse and contribute to the mental health problems reported in this population (Crane et al., 2019). Moreover, camouflaging, which negatively impacts mental health, is more prevalent in autistic women (Cage & Troxell-Whitman, 2019). Women may have an increased risk for mental health problems (e.g. psychiatric diagnoses, self-harm). The current study aims to characterize mental health and sex differences in mental health outcomes in young adults with autism, particularly while accounting for age of diagnosis and other neurodevelopmental disorders (ADHD and ID). By comparing autistic individuals, and particularly women with the general population, the study aims to differentiate between problems common to autistic individuals as a whole and to autistic women specifically. Moreover, age of diagnosis is included to move beyond describing differences and aiming at identifying why those differences exist. The study will primarily be based on Swedish registry data. In order to account for potential referral biases associated with registry data, we will include a sensitivity analysis with data from the Child and Adolescent Twin Study Sweden (CATSS). For the analysis in CATSS we are using screening diagnoses of autism at age 9, and questionnaire data on various psychiatric disorders, including amongst others anxiety, depression, eating disorders and substance misuse. Research Questions The overall aim of this project is to investigate whether adult women and men with clinical diagnoses of autism differ from each other and the general population with respect to mental health outcomes, using a cohort study design. The specific research questions are: 1. At what rate do young adults (up to 24 years of age) with a clinical diagnosis of autism experience mental health problems, and which psychiatric diagnoses do they receive? 2. Do women and men with clinical diagnoses of autism differ from each other, and women and men in the general population, with regard to their mental health outcomes? 3. Does age of diagnosis account for sex differences in mental health outcomes in young autistic adults?

Background The knowledge of how the separate Attention-Deficit/Hyperactivity Disorder (ADHD) subdimensions (impulsivity, hyperactivity, and inattention) are associated with nonsuicidal self-injury (NSSI) and suicidal behavior (SB) is limited. The objective of this study was to investigate the associations of childhood ADHD subdimensions with NSSI and SB in children at risk of neurodevelopmental disorders (NDDs; including ADHD). Methods The sample (N = 391) included twin pairs where at least one twin screened positive for at least one NDD or common comorbidity at age 9 or 12. Data on ADHD subdimensions was collected through a telephone interview with a caregiver/legal guardian at age 9 or 12, and data on NSSI and SB was collected through an in-person clinical assessment at age 15. The associations between the ADHD subdimensions and NSSI or SB were tested in three different models: (1) univariable, (2) together with the other ADHD subdimensions, and (3) in a confounder-adjusted model including other NDD symptoms in addition to ADHD subdimensions, for NSSI and SB separately. Results A total of 32 (8.2%) adolescents reported life-time engagement of NSSI, and 18 (4.6%) SB. Childhood impulsivity was associated with SB and childhood inattention with NSSI, in all models. Hyperactivity was not meaningfully associated with any of the outcomes. Conclusion Impulsivity and inattention, but not hyperactivity, may be of particular importance in understanding SB and NSSI. Brief screening for impulsivity and inattention in childhood could facilitate detection of children vulnerable to NSSI and SB and indicate valuable information for preventive and intervention strategies.

Supplementary Material 1. Supportive information

Additional file 1.

Supplemental data:Limited Genetic Overlap Between Hashimoto's thyroiditis and Graves' disease in Twins: A Population-based Study.The Journal of Clinical Endocrinology and Metabolism

A female advantage in social cognition (SoC) might contribute to women’s underrepresentation in autism spectrum disorder (ASD). The latter could be underpinned by sex differences in social brain structure. This study investigated the relationship between structural social brain networks and SoC in females and males in relation to ASD and autistic traits in twins. We used a co-twin design in 77 twin pairs (39 female) aged 12.5 to 31.0 years. Twin pairs were discordant or concordant for ASD or autistic traits, discordant or concordant for other neurodevelopmental disorders or concordant for neurotypical development. They underwent structural magnetic resonance imaging and were assessed for SoC using the naturalistic Movie for the Assessment of Social Cognition. Autistic traits predicted reduced SoC capacities predominantly in male twins, despite a comparable extent of autistic traits in each sex, although the association between SoC and autistic traits did not differ significantly between the sexes. Consistently, within-pair associations between SoC and social brain structure revealed that lower SoC ability was associated with increased cortical thickness of several brain regions, particularly in males. Our findings confirm the notion that sex differences in SoC in association with ASD are underpinned by sex differences in brain structure.

Additional file 2: Table S7A. Within-pair associations between cortical volume, surface area and thickness of neocortical regions of interest (ROIs) and RRB symptoms from SRS-2. Estimates (B), standard deviations (SD) and q-values (q) for the within pair associations between all surface-based neocortical measures (volume, surface area and thickness) and RRBs as assessed by the SRS-2. A positive estimate indicates an increase in brain estimate related to more repetitive behavior symptoms. Bold text indicates significant associations (FDR-corrected q-value

Additional file 1: Figure S1. Within Pair distributions of ADI-R C scores. A within-pair difference per pair consists of the difference in score for RRBIs on ADI-R between 2 twins in a pair. The within-pair difference ranged from 0 to 5 points (B panel) in the cohort. Figure S2. The figure illustrates the within-pair difference model that was implemented in the analyses. The example shows within-pair difference associations in 3 twin pairs. Each line connects 2 individuals from one twin pair. In these examples, the individuals with a higher RRBI score on ADI-R compared to their co-twin, also had smaller right cerebellar cortex volumes. Figure S3. Within-pair association between RRBIs on ADI-R scores and thickness of the right intraparietal sulcus in males and females. Each dot represents one twin pair. For females (light-grey), there was a significant positive within-pair association between RRBIs on ADI-R and thickness of the right intraparietal sulcus.

Additional file 11: Figure S10. Attributable fraction of suicidal ideation in individuals within 'borderline' and 'abnormal' anxiety categories at age 15 in self−report and parental report. Note. Dashed lines represent 95% confidence intervals. For representational purposes, figures were cut at age 21.

Additional file 10: Figure S9. Attributable fraction of depressive disorders in individuals within 'borderline' and 'abnormal' anxiety categories at age 15 in self−report and parental report. Note. Dashed lines represent 95% confidence intervals. For representational purposes, figures were cut at age 21.

Additional file 8: Figure S7. Attributable fraction of anxiety disorders in individuals within 'borderline' and 'abnormal' anxiety categories at age 15 in self−report and parental report. Note. Dashed lines represent 95% confidence intervals. For representational purposes, figures were cut at age 21.

Additional file 9: Figure S8. Attributable fraction of bipolar/psychotic disorders in individuals within 'borderline' and 'abnormal' anxiety categories at age 15 in self−report and parental report. Note. Dashed lines represent 95% confidence intervals. For representational purposes, figures were cut at age 20.

Additional file 1: Figures S1–S3 and Table S1. Supplementary figures and tables.

Additional file 7: Figure S6. Attributable fraction of alcohol and drug misuse disorders in individuals within 'borderline' and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. Dashed lines represent 95% confidence intervals. For representational purposes, figures were cut at age 21.

Supplemental material, Supplement_figure_1_Flow-chart for The Quantified Behavioral Test Failed to Differentiate ADHD in Adolescents With Neurodevelopmental Problems by Viktoria Johansson, Eva Norén Selinus, Ralf Kuja-Halkola, Sebastian Lundström, Natalie Durbeej, Henrik Anckarsäter, Paul Lichtenstein and Clara Hellner in Journal of Attention Disorders

Supplemental material, Supplement_table_2 for The Quantified Behavioral Test Failed to Differentiate ADHD in Adolescents With Neurodevelopmental Problems by Viktoria Johansson, Eva Norén Selinus, Ralf Kuja-Halkola, Sebastian Lundström, Natalie Durbeej, Henrik Anckarsäter, Paul Lichtenstein and Clara Hellner in Journal of Attention Disorders

Additional file 4: Table S4. Cross-trait, cross-twin correlation.

Additional file 3: Table S3. Additional demographic findings on the sample.

Additional file 2: Table S2. Minor physical anomalies checklist.

Additional file 1: Table S1. Description of instruments from RATSS used in this study.

Additional file 6: Table S6. Description of differences in MPAs found in monozygotic pairs discordant for ASD and ADHD.

Additional file 1: Table S1. Overlap of psychiatric outcomes for individuals in the Swedish National Patient Register (NPR). Table S2. Associations between anxiety category at baseline and psychiatric outcome in self-report and parental report after multiple imputation for variables ADHD, ASD, DCD, and anxiety self- and parental report. Table S3 Prevalence and distribution of psychiatric outcomes in individuals whose parents only responded at CATSS-9/12 but not at CATSS-15, versus those who have responded at both assessment waves. Table S4 Prevalence and distribution of psychiatric outcomes divided by sex. Table S5 Associations between anxiety category at baseline and psychiatric outcome in CATSS, estimated from within-twin analyses conducted on monozygotic twins. Table S6 E-values for the associations between anxiety category at baseline and psychiatric outcome in self-report and parental report. Table S7. Prevalence and distribution of psychiatric outcomes in the Netherlands Twin Register (NTR) among anxiety categories ‘normal’, ‘borderline’, and ‘abnormal’. Table S8. Associations between anxiety category at baseline and psychiatric outcome in the NTR.

Additional file 3: Figure S2. Cumulative incidence of anxiety disorders in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 3: Figure S2. Cumulative incidence of anxiety disorders in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 2: Figure S1. Cumulative incidence of alcohol and drug misuse disorders in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 4: Figure S3. Cumulative incidence of bipolar/psychotic disorders in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 5: Figure S4. Cumulative incidence of depressive disorders in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 6: Figure S5. Cumulative incidence of suicidal ideation in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 6: Figure S5. Cumulative incidence of suicidal ideation in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 2: Figure S1. Cumulative incidence of alcohol and drug misuse disorders in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 1: Table S1. Overlap of psychiatric outcomes for individuals in the Swedish National Patient Register (NPR). Table S2. Associations between anxiety category at baseline and psychiatric outcome in self-report and parental report after multiple imputation for variables ADHD, ASD, DCD, and anxiety self- and parental report. Table S3 Prevalence and distribution of psychiatric outcomes in individuals whose parents only responded at CATSS-9/12 but not at CATSS-15, versus those who have responded at both assessment waves. Table S4 Prevalence and distribution of psychiatric outcomes divided by sex. Table S5 Associations between anxiety category at baseline and psychiatric outcome in CATSS, estimated from within-twin analyses conducted on monozygotic twins. Table S6 E-values for the associations between anxiety category at baseline and psychiatric outcome in self-report and parental report. Table S7. Prevalence and distribution of psychiatric outcomes in the Netherlands Twin Register (NTR) among anxiety categories ‘normal’, ‘borderline’, and ‘abnormal’. Table S8. Associations between anxiety category at baseline and psychiatric outcome in the NTR.

Additional file 5: Figure S4. Cumulative incidence of depressive disorders in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.

Additional file 4: Figure S3. Cumulative incidence of bipolar/psychotic disorders in individuals within 'normal', 'borderline', and 'abnormal' anxiety categories at age 15 in self-report and parental report. Note. For representational purposes, figures were cut at age 21.