Association of Autistic Traits With Depression From Childhood to Age 18 Years

1Autism spectrum disorders (ASD) are characterized by impairments in reciprocal social interaction and by repetitive and stereotyped interests and behaviors. The autism spectrum is a heterogeneous construct, and its component traits are distributed across the population,, with potentially distinct etiologies, and outcomes. Despite increasing recognition in recent years, there are substantial gaps in our understanding of the outcomes of children with ASD as they transition into adulthood.

2Depression is disabling and is common in children with ASD, but few longitudinal population-based studies have followed the natural history of depression in ASD or its component traits. Because family members of children with ASD also have an increased risk of depression,,, a genetic overlap between ASD and depression is possible. However, depression in family members could also relate to difficulties associated with having a child with greater needs or behavioral difficulties.

3Regardless of a genetic basis, it is possible that there are modifiable factors that could be targeted by interventions to reduce the risk of depression in individuals with autism. In clinical practice, individuals with autism seen with depression often report histories of traumatic experiences, particularly bullying. Bullying is strongly associated with depression, an effect that may endure into adulthood, and could thus be important in the association between autism and depression. For instance, bullying could be a mediator on the causal pathway between autism and depression. It is also possible that the negative effect of bullying on depression may be amplified in the context of the social impairments in autism. To our knowledge, no longitudinal studies have explored these potential mechanisms.

4This study used data from a large population-based cohort in England. Our objectives were to (1) compare trajectories of depressive symptoms from ages 10 to 18 years for children with or without ASD or high scores on autistic trait measures, (2) assess whether children with ASD and autistic traits were at increased risk of depression at age 18 years, (3) explore the role of genetic confounding in these associations, and (4) explore the importance of bullying in any associations.

5The Avon Longitudinal Study of Parents and Children (ALSPAC) is a birth cohort study that enrolled mothers in early pregnancy in Bristol and surrounding areas in 1990 to 1992 in England., It has detailed information on parents and children, collected prospectively at multiple times during pregnancy and throughout childhood. Data sources include self-report questionnaires, clinical assessments, biological samples, and birth, medical, and educational records. The study website contains details of all the data available in a fully searchable format (http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/). We used all available data for each combination of exposure and outcome, and we imputed values for missing covariate data using multiple imputation (details are shown in eFigure 1 in the Supplement).

6Ethical approval for all data collected in the ALSPAC was obtained from the ALSPAC Ethics and Law Committee and the local research ethics committees. Participants provided written informed consent for all clinic assessments, and consent was implied if questionnaires were returned. Participants were followed up through age 18 years. Data analysis was conducted from January to November 2017.

7We identified children with ASD using a multisource approach, including a review of clinical records of all children who had multidisciplinary assessment for a developmental disorder (validated against International Statistical Classification of Diseases, 10th Revision [ICD-10] criteria by a consultant pediatrician), educational records of special education support provided for ASD, and parental reports of an autism or Asperger syndrome diagnosis. The ASD cases have been cross-validated against the ASD trait measures,, and as reported below in the Results section, they are associated with a polygenic risk score (PRS) for autism.

8By age 11 years, the ALSPAC had collected 93 measures related to autistic features. Of these, the following 4 individual measures were the strongest predictors of ASD: the Social Communication Disorders Checklist (SCDC) at 7 years, the coherence subscale of the Children’s Communication Checklist at 9 years, a repetitive behavior scale at 5 years, and the sociability subscale of the Emotionality Activity and Sociability temperament measure at 3 years. To define high-risk groups for these autistic traits, we dichotomized individuals closest to the worst 10% of distributions of each ASD trait measure.

9The Short Mood and Feelings Questionnaire (SMFQ), designed to measure depressive symptoms in children and adolescents, was administered at 6 time points between ages 10 and 18 years via postal questionnaires or in clinics. It has 13 items relating to low mood during the past 2 weeks, each with scores of 0 to 2. Individual item scores were summed, producing a 0 to 26 score range.

10The computerized version of the Clinical Interview Schedule–Revised (CIS-R) is a fully structured psychiatric interview widely used in community samples. It was administered at age 18 years to identify individuals with an ICD-10 diagnosis of depression.

11We included the following variables in our models: (1) child sex, (2) parity (≤1 child vs ≥2 children), (3) maternal occupational class (manual vs nonmanual), (4) mother’s highest educational attainment, (5) financial problems (occurrence vs nonoccurrence of major financial problems), (6) maternal age at delivery (in years), (7) maternal Crown-Crisp anxiety score at 18 weeks’ gestation and 8 weeks after delivery, (8) maternal antenatal (18 and 32 weeks’ gestation) and postnatal (8 weeks and 8 months) depression measured with the Edinburgh Postnatal Depression Scale (EPDS score ≥13), and (9) accommodation type (detached house vs semidetached house vs flat). We included these variables because they are associated with both autism and depression, apart from being predictors of attrition in the ALSPAC.

12Relational and overt bullying was assessed as separate yes or no items at ages 8, 10, and 13 years using the modified Bullying and Friendship Interview Schedule. We created a latent construct of bullying based on 6 binary measures (relational and overt bullying assessed at ages 8, 10, and 13 years) using factor analysis to identify the common variance in the items. Conceptually, this latent construct represents the tendency of children to be bullied persistently throughout childhood or adolescence and was used for the mediation analysis described below in the Statistical Analysis subsection. We also created a binary variable to capture no vs any overt or relational bullying at any time, which we used for testing interactions described below in the Statistical Analysis subsection.

13We examined potential genetic confounding of associations between ASD and depression using autism PRSs, calculated for genotyped ALSPAC children using summary data from the Psychiatric Genomics Consortium autism discovery genome-wide association study (GWAS) (eMethods 1 in the Supplement). We created a set of scores based on single-nucleotide polymorphisms (SNPs) that were associated with an ASD diagnosis at a range of GWAS P value thresholds (.5 to 1e−7) and used PRSs generated using SNPs meeting a 0.05 GWAS P value threshold in our main analysis because it maximally captured autism liability within our sample (eFigure 2 in the Supplement).

14We conducted analyses using Stata/MP (version 14; StataCorp) and Mplus (version 8; Muthén & Muthén). We examined trajectories of depressive symptoms (continuous SMFQ scores) between ages 10 and 18 years among those with or without ASD and each autistic trait using mixed-effects linear growth models. To accommodate individual differences in trends of depressive symptoms with age, we included random effects for intercept and slope coefficients and added quadratic and cubic terms to accommodate potential nonlinear trends.

15We then used modified Poisson regression to estimate the relative risk (RR) of an ICD-10 depression diagnosis at age 18 years in individuals with ASD and each autistic trait vs those without, with robust 95% CIs. We estimated crude risks, followed by adjustment for all potential confounders. We further adjusted these models for the autism PRS in the sample with genetic data.

16We used path analysis to assess mediation of associations between autistic traits and depression at age 18 years by the experience of being bullied in late childhood or early adolescence using latent constructs of bullying and depression. Details are provided in eMethods 2 and eMethods 3 in the Supplement.

17Finally, to separate the association of ASD diagnosis or traits with depression within and outside the context of bullying, we created categories representing 4 groups by the presence or absence of ASD or ASD traits and the presence or absence of any experiencing of bullying; we compared trajectories of depressive traits between ages 10 and 18 years using mixed-effects linear growth models as described above in the Statistical Analysis subsection. To statistically test moderating associations of bullying, we compared models that included the ASD and bullying variables with those that included only the ASD variable; we then compared models that included the statistical interaction between the ASD and bullying variables with models that included only the main effects of these variables using likelihood ratio tests.

18Missing data in our trajectory and age 18 years analysis are listed in eTable 4 in the Supplement. We imputed missing data for covariates and outcome using multiple imputation (eFigure 1, eMethods 4 in the Supplement). The availability of extensive auxiliary socioeconomic and clinical data (including 7 measures of depression between ages 10 and 18 years) enabled us to account for factors that may explain attrition, providing support to the missing-at-random assumption. We repeated our analyses, estimating average associations across 100 imputed data sets and calculated standard errors using the rule by Rubin.

19The maximum sample available with complete data on exposures, outcomes, and covariates was 6091 for the trajectory analysis (48.8% male) and 3168 for analysis of depression diagnosis at age 18 years (44.4% male) (eFigure 1 in the Supplement). The characteristics of our study sample by the presence of ASD and autistic traits are listed in eTable 1 in the Supplement (with an abridged version in Table 1). Mothers of children scoring highest on all autistic trait measures except sociability had a greater prevalence of screening positive for depression and had higher mean anxiety scores in pregnancy and the early postnatal period than the general population, although this pattern was not observed in children with ASD. Children with ASD and those scoring highest on all the autistic traits had a higher prevalence of depressive symptoms at age 10 years, a pattern that was also observed at other time points, albeit inconsistently (eTable 2 in the Supplement). Children with ASD and those scoring highest on the autistic trait measures had a consistently greater prevalence of overt and relational bullying at ages 8, 10, and 13 years than the comparison population, although the statistical evidence for such differences varied (eTable 3 in the Supplement).

20The autism PRS was associated with the ASD diagnosis and with measures of social communication and repetitive behavior (eFigure 2 in the Supplement), while being in the top decile of the autism PRS was associated with ASD and all 4 autism trait measures, with the exception of coherence (eFigure 3 in the Supplement). Results were generally consistent when using autism PRSs generated using SNP inclusion P value thresholds exceeding.001. There was no evidence of associations between the autism PRS and depression or bullying variables (eFigure 4 and eFigure 5 in the Supplement).

21Examining trajectories, children with ASD and autistic traits had higher average SMFQ depressive symptom scores than the general population at age 10 years (eg, for social communication 5.55 [95% CI, 5.16-5.95] vs 3.73 [95% CI, 3.61-3.85], for ASD 7.31 [95% CI, 6.22-8.40] vs 3.94 [95% CI, 3.83-4.05], remaining elevated in an upward trajectory until age 18 years (eg, for social communication 7.65 [95% CI, 6.92-8.37] vs 6.50 [95% CI, 6.29-6.71], for ASD 7.66 [95% CI, 5.96-9.35] vs 6.62 [95% CI, 6.43-6.81]) (Figure 1). Most pronounced were differences between those with or without social communication difficulties. Analyses using imputed data sets led to similar but more precise estimates (eFigure 6 in the Supplement).

22Children with social communication impairments at age 7 years were at increased risk of a diagnosis of depression at age 18 years (adjusted RR, 1.68; 95% CI, 1.05-2.70). These associations were almost unchanged after adjustment for the autism PRS (Table 2) and were estimated with greater precision in the sample without genetic data (eTable 5 in the Supplement) and after multiple imputation (eTable 6 in the Supplement). No evidence of an association between ASD and a depression diagnosis at age 18 years was observed, although the 95% CIs were wide in our main (adjusted RR, 0.50; 95% CI, 0.08-3.38) and imputed (adjusted RR, 0.80; 95% CI, 0.23-2.81) analyses.

23Children with ASD and autistic traits who also reported being bullied had the highest depression symptom scores at age 10 years, which remained elevated throughout adolescence (Figure 2). There was statistical evidence that the model that included the ASD and bullying variables explained the data better than one that included only ASD diagnosis (likelihood ratio χ2 = 454.75, P <.001) and that models with interaction terms for ASD and bullying explained the data better than models that included only the main effects of these variables (likelihood ratio χ2 = 5.71, P =.017). These different trajectories were most apparent for children with social communication difficulties and were least apparent for worst scores on sociability temperament. In the absence of bullying, the depressive symptom trajectories of children with or without ASD or autistic traits appeared broadly similar. Analyses using imputed data sets led to similar results (eFigure 7 in the Supplement).

24Finally, we assessed for mediation of associations between social communication difficulties at 7 years and diagnosed depression at age 18 years by the experience of being bullied in late childhood and early adolescence (Table 3). Both before and after adjustment for potential confounders, there was strong evidence of an indirect pathway leading from social communication difficulties in early childhood to being bullied in late childhood or early adolescence to a depression diagnosis at age 18 years. We estimated that this indirect association accounted for 50.5% (95% CI, 5.5%-95.5%) of the total association of social communication difficulties with risk of depression after accounting for potential confounders. Furthermore, there was no evidence of a direct association of social communication difficulties with depression risk after accounting for the indirect association via bullying. Repeating these analyses using imputed data led to similar results (eTable 7 in the Supplement), with a more precise estimate of the indirect association accounted for by bullying (31.5%; 95% CI, 17.3%-45.7%).

25In this detailed longitudinal study, we found that children with ASD and those with higher scores on all autistic trait measures had more depressive symptoms at age 10 years than the general population, and these remained elevated in an upward trajectory until age 18 years. Social communication impairments had the strongest association with a depression diagnosis at age 18 years. Findings were robust to adjustment for a range of confounders, including maternal depression and anxiety and the child’s polygenic risk for autism. We found evidence of a substantial role of bullying in contributing to and explaining a higher risk of depression in individuals with ASD and autistic symptoms.

26Previous reports on this topic have been contradictory, with results of some studies, suggesting an improvement in depressive symptoms in children with autism over time and other findings suggesting worsening, possibly because of selected and heterogeneous populations and different methods. The lack of a general population group in previous studies makes it difficult to conclude whether the trajectories of depressive symptoms in the autistic population differ from those of the general population,,,, a limitation in the literature that our study attempted to address. Our findings suggesting that difficulties in social communication may have stronger associations with future depression than other autistic traits have also been reported for outcomes of suicidal thoughts and behaviors and are consistent with the concept of fractionation of component features of the autism spectrum. However, although social communication difficulties are an important feature of autism, they may occur independently in the population or within the context of other psychiatric diagnoses. Therefore, the association between social communication difficulties and depression may be important within and outside the context of ASD.

27We report a significant contribution of bullying as a potential environmental intermediary between childhood autistic features and later depression. Previous work has shown strong links between the experience of bullying and later depression,; although confounding could have a role, the association is considered to be at least partially causal. In our study, children with social communication impairments were more likely to report being bullied, and the mediation analysis suggests that this explained a substantial proportion of the variance of depression at age 18 years, possibly due to reduced self-esteem or social isolation after the bullying. The risk of depression in children with greater autistic symptoms may also be amplified in the context of bullying because of preexisting underlying vulnerabilities in children with autistic features, such as impaired social skills and decreased ability to adapt to adverse or stressful events, such as being bullied. This could explain the elevated trajectories of depressive symptoms in children with ASD and autistic traits who reported being bullied. In the absence of bullying, these children appeared to follow trajectories of depressive symptoms similar to those of the general population. However, such interactions could simply suggest that bullying and autism sometimes co-occur in causal models of depression, as might be expected for any outcome of multifactorial etiology.

28Although it is impossible to identify the exact nature of the underlying mechanisms, our results highlight the need for further research on the role of bullying in this association and the potential for preventive interventions. Furthermore, other relevant characteristics, including comorbidities with neurodevelopmental conditions (eg, attention-deficit/hyperactivity disorder) and classroom placement could be important in this association within or outside the context of bullying and warrant future study.

29The main strengths of this study were the population-based design with prospectively collected data and repeated measures of depressive symptoms, reducing the possibility of selection and recall bias and allowing us to model longitudinal trajectories. The rich covariate information enabled us to minimize the possibility of confounding bias.

30This study has limitations. Like all cohort studies, there was significant attrition, and we used multiple imputation to limit any potential bias; however, selection bias related to missing data remains a possibility. While the use of PRSs was an advantage, they only capture common variation and were based on a small GWAS, so genetic confounding in the associations is still possible. We had insufficient numbers with an ASD diagnosis also meeting the diagnostic criteria for depression at 18 years, possibly due to selective attrition of individuals with autism with more severe depressive symptoms. This is likely to have led to the imprecise result because of a lack of statistical power. Furthermore, atypical presentations of depression are common in ASD, and our study has the potential for outcome measurement error because we used scales (eg, the CIS-R) that have not been adapted for autism. Individuals with ASD may also have difficulties in expressing and communicating their emotions and may not have sufficient verbal skills to express changes in their mood or feelings.

31Our results suggest that ASD and autistic traits are associated with higher depressive symptom scores by age 10 years, which persist to age 18 years, particularly in the context of bullying. Social communication impairments are important in relation to a later diagnosis of depression, and bullying in adolescence could have an important role in this association. These findings add to the evidence highlighting a higher burden of depression, and also suggest a potentially modifiable pathway, through bullying. However, gaps remain in our understanding of the measurement and phenomenology of depression in individuals with autism, which could be a priority for future research. Further work could also focus on improvements in psychological and pharmacological management of depression in ASD. Finally, further research into the role of traumatic experiences, such as bullying, and the utility of interventions to reduce bullying or address its adverse effects could have the potential to reduce the burden of depression in this population.