Variable Emergence of Autism Spectrum Disorder Symptoms From Childhood to Early Adulthood
Publication: American Journal of Psychiatry
Abstract
Objective:
Autism spectrum disorder (ASD) is currently considered an early-onset neurodevelopmental condition. Follow-up studies of clinic-ascertained autism suggest that autistic symptoms typically decline with age, although symptom improvement is limited for some. To date there have been no population-based prospective studies investigating the natural history of autistic symptoms from childhood to adulthood. The aim of this study was to characterize the development and heterogeneity of autistic symptoms in a population-based cohort from childhood to age 25.
Methods:
Data were analyzed in a prospective U.K. population-based cohort (ALSPAC). Trajectories were derived using five assessments of the parent-rated Social and Communication Disorders Checklist (SCDC) spanning ages 7–25. Additional measures were used to validate symptom trajectories.
Results:
Three distinct SCDC symptom trajectory classes were identified: low (88.5%), declining (5.0%), and late-emerging (6.5%). Both the declining and late-emerging trajectory classes were associated with child and adult ASD measures, low IQ, communication problems, peer problems, and worse adult functioning compared with the low trajectory class. Male sex was associated with a higher likelihood of being in the declining trajectory class (odds ratio=2.84, 95% CI=2.19, 3.69). This sex difference was not observed in the late-emerging class (odds ratio=1.00, 95% CI=0.80, 1.24) compared with the low trajectory class.
Conclusions:
ASD symptom levels that emerged early tended to decline across development, although impairment was still present in adulthood for some. For others, autistic symptoms emerged across adolescence and adulthood. This challenges our current understanding that ASD symptoms inevitably first manifest early in development.
Autism spectrum disorder (ASD) is currently considered an early-onset neurodevelopmental condition characterized by social communication impairments and repetitive, restrictive behaviors (1). Although defined categorically for clinical purposes, its genetic architecture and epidemiological profile suggest that ASD lies at the end of a continuously distributed continuum (2–4). It is well established that childhood ASD shows a very high degree of phenotypic and etiological heterogeneity (5), which includes marked variation in both its short-term developmental trajectories (6, 7) and its later clinical course (8).
Follow-up studies of clinic-ascertained autism into adulthood suggest that autistic symptoms typically decline with age (9), although one school-ascertained study observed little symptom improvement (10), and broader, global outcomes for ASD are highly variable (11, 12). To date, virtually all follow-up studies into adulthood have been conducted in patients referred to clinic during childhood, which precludes the study of individuals who may not present with high symptom levels until adolescence or adulthood. Adolescence and early adulthood represent a time of heightened social challenges that include establishing romantic partnerships, transitioning to employment, and managing independent living. One population-based study that followed individuals to age 17 (13) observed an increase in social communication symptoms among females in adolescence, a finding that appears to be at odds with much of the clinical literature. However, there is growing appreciation that some affected individuals, especially females, may present in clinic with autistic symptoms in adolescence, later, or not at all by “camouflaging” or compensating for their difficulties (14).
Thus, findings on the natural history of autistic symptoms are mixed, and they are not examined in nonclinical cohorts followed to adulthood. The aim of this study was to characterize the development and heterogeneity of autistic symptoms in a U.K. population-based cohort from childhood to age 25 using the same measure and rater across time. Typically, measures and raters change from adolescence to adult life, precluding the opportunity to reliably examine developmental trajectories. We used a latent variable approach to investigate autistic symptom developmental trajectories, and we tested the validity of these trajectories by examining associations with established measures of child and adult ASD, child IQ, communication problems, peer problems, and adult functioning.
Methods
Sample
We analyzed data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a well-established prospective longitudinal birth cohort study (15–17). The total possible sample comprises 14,901 children who were alive at 1 year of age. Ethical approval for the study was obtained from the ALSPAC Law and Ethics Committee and local research ethics committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants, following the recommendations of the ALSPAC Ethics and Law Committee at the time. Full details are provided in the online supplement .
As a demographic measure, parental income was assessed on a 10-point scale using parent-reported average weekly household income, including social benefits, when the child was approximately 11 years old.
Primary Measure of ASD Symptoms
Symptoms were primarily assessed using the parent-rated 12-item Social and Communication Disorders Checklist (SCDC; possible range, 0–24) (18) at approximately ages 7, 10, 13, 17, and 25 years. This screening questionnaire for autistic symptoms has been shown to have good discriminant validity in childhood and adolescence between participants with pervasive developmental disorder and control subjects (18, 19) using a cut-point of ≥9. Previous work in ALSPAC found the SCDC at age 7 to have excellent discriminant validity in identifying cases of ASD (area under the receiver operating characteristic curve=0.93) (20). The SCDC is yet to be validated in adulthood, although we have found that at age 25 it shows neurodevelopmental and genetic correlates similar to those observed in childhood (21). The SCDC also shows acceptable measurement invariance across age and sex in this sample (see the online supplement ).
Additional Measures of ASD
ASD diagnosis in childhood was defined in line with previous work (22) that reviewed clinical records of children with a suspected developmental disorder and the Pupil Level Annual Schools Census for England (2003) at child age 11.
High risk for childhood ASD diagnosis was measured by the mean of seven latent factors (23) derived from 93 measures of social, communication, and repetitive behaviors characterizing ASD from ages 6 months to 9 years: verbal ability, language acquisition, social understanding, semantic-pragmatic skills, repetitive-stereotyped behavior, articulation, and social inhibition. In line with previous research (24), we defined the high-risk group as those with the top 10% of scores.
High risk for adult ASD diagnosis was assessed using the 28-item version of the Autism Spectrum Quotient (AQ-28) (25, 26) (possible range, 28–112) that was completed by the young person (self) and a parent at age 25. The self-rated AQ-28 has been validated as a measure of clinical autism in adults, with a “stringent” cut-point of ≥70 (26); the same cutoff was used for the parent-rated AQ-28, for which there is currently no research to guide appropriate cut-points. Individuals who met this cut-point were categorized as being at high risk for adult ASD diagnosis for self- and parent-rated scores separately. The AQ-28 consists of two factors (26), “social behavior” (e.g., social skills, routine, switching, and imagination) and “attention to detail” (e.g., fascination with numbers/patterns), which were examined separately in sensitivity analyses.
Sensitivity analyses were conducted using age-13 Emotional Triangles Task data indexing theory of mind (27), one of the most widely reported cognitive deficits in ASD (28). Scores are based on participant ratings of the mental state of 16 animated triangles (possible range, 0–80, with higher scores reflecting better theory of mind). Scores were excluded where there was evidence that participants were not attending to the task, in line with previous work (29).
IQ and Childhood and Adult Communication Problems
Low IQ was defined as a score <80 on the Wechsler Intelligence Scale for Children (30) at age 8.
Child pragmatic language problems were measured using the parent-reported Children’s Communication Checklist pragmatic language subscale (31) at age 9 (derived from five subscales: inappropriate initiation, coherence, stereotyped conversation, use of conversational context, and conversational rapport: range, 86–162), with a recommended cut-point of ≤132 (31).
Adult communication problems were assessed using the parent-rated Communication Checklist–Adult (32) at age 25 (derived from three subscales: language structure, pragmatic skills, and social engagement: possible range, 0–210). Based on previous work, communication problems were defined as scoring ≥2 standard deviations from the mean on any subscale (32).
Peer Problems in Childhood, Adolescence, and Adulthood
Adult Functioning
“Not in education, employment, or training” status was derived from self-reports at age 25, in line with the U.K. Office for National Statistics definition (detailed in the online supplement ) (34).
Distress and impairment were measured by parent- and self-rated adult Strengths and Difficulties Questionnaire impact scores, which assess distress and impairment associated with mental health problems (e.g., emotional, concentration, behavior problems) (possible range, 0–10), using the recommended cut-points of ≥2 (33). There is currently no research to suggest alternative cut-points in adulthood.
Statistical Analysis
Developmental trajectories of social communication problems were derived using growth mixture modeling (GMM) to identify developmental trajectories of ASD symptoms from ages 7 to 25 in Mplus (35). GMM aims to group individuals into categories (trajectories) based on patterns of change across multiple time points, with individuals within each category assumed to have the same growth curve (36). Variation in ASD symptom levels is therefore captured using a data-driven approach (i.e., based on observed differences rather than a specified cut-point). Starting with a single k-class solution, k+1 solutions were fitted until the optimum solution was reached. Given the large gap between the last two time points, models were fitted for a piecewise growth model with a single intercept and two linear slope factors, one for ages 7–17 and one for ages 17 and 25: the second slope variance was fixed to zero to avoid nonidentification, as only two time points were included in this growth factor. The GMM therefore included an intercept, one slope for ages 7–17, and a second slope for ages 17–25. Models were run using a robust maximum likelihood parameter estimator (35). Class sizes are reported based on the estimated model, with Ns rounded to the nearest integer. As our GMM was run on parent-rated data, sensitivity analyses were conducted limiting the sample to participants who had regular parent-offspring contact at age 25. Sex-specific developmental trajectories were then derived by running GMM for males and females separately. ASD trajectory associations with other measures were investigated in Mplus using a bias-free three-step approach that accounts for measurement error in class assignment (R3STEP for multinomial regression, DU3STEP for prevalence rates, and BCH for sensitivity analyses with continuous measures) (37). Additional sensitivity checks were undertaken on the age-13 task data and age-25 AQ-28 subscales.
Missing Data
The primary sample included individuals for whom SCDC data were available from at least two time points (N=8,094). GMM was conducted using full information maximum likelihood estimation (35), and associations with other measures (“covariates”) were conducted where data were available. Analyses examining potential bias arising from missing data were conducted using a range of approaches, including complete case analyses, inverse probability weighting, and multiple imputation (38–40) (more information is provided in the online supplement ).
Results
Descriptive Data
Mean SCDC scores by age and sex, sample size, and prevalence of those scoring above the cut-point are shown in Figure 1. Correlations between SCDC scores at each age and individual item frequencies are listed in Tables S1 and S2 in the online supplement . Mean SCDC scores for the whole cohort decreased across childhood and increased into late adolescence (13), and, based on new adult data, then declined by age 25 (Figure 1).
FIGURE 1. Mean Social and Communication Disorders Checklist (SCDC) score, by age, in a study of autism symptom trajectoriesa
a The graph includes the sample of participants with at least two time points of SCDC data; maximum N=8,094. The prevalence of meeting the cut-point (whole sample) is in parentheses. Error bars indicate 95% confidence intervals.
Developmental Course of Social Communication Problems
We identified three distinct trajectory classes (see the online supplement for details on deriving the best-fitting model): a low trajectory class (88.5%, N=7,165), a declining trajectory class (5.0%, N=403), and a late-emerging trajectory class (6.5%, N=526), as shown in Figure 2. Male sex was associated with an increased likelihood of being in the declining trajectory class (72.7% male: odds ratio=2.84, 95% CI=2.19–3.69, p<0.001). Sex differences were not observed for the late-emerging (51.5% male: odds ratio=1.00, 95% CI=0.80–1.24, p=0.96) compared with the low trajectory class (48.9% male). Higher parental income was associated with a reduced likelihood of being in both the late-emerging (odds ratio=0.91, 95% CI=0.87–0.96, p<0.001) and declining (odds ratio=0.88, 95% CI=0.84–0.92, p<0.001) trajectory classes compared with the low trajectory class, with similar levels of association between the two (declining versus late-emerging, odds ratio=0.96, 95% CI=0.90–1.03, p=0.31). Sensitivity analyses limiting the sample to those with regular parent-offspring contact at age 25 showed a similar pattern of results (see the online supplement ).
FIGURE 2. Mean Social and Communication Disorders Checklist (SCDC) score, by autism symptom trajectory classa
a The mean trajectory is shown, with 95% confidence intervals.
Social Communication Trajectory: Associations With Established Measures of ASD, Neurodevelopmental Problems, and Functioning
The rates of child and adult ASD or high risk for ASD diagnosis, low IQ, communication problems, peer problems, and adult functioning difficulties, by trajectory class, are shown in Figure 3. As shown in Table 1, both the late-emerging and declining ASD trajectory classes showed higher rates in both childhood and adulthood compared with the low trajectory class.
FIGURE 3. Prevalence of associated features, including other measures of ASD, low IQ, communication problems, peer problems, and adult functioning, by autism symptom trajectory classa
a Error bars indicate 95% confidence intervals. ASD=autism spectrum disorder.
| Overall Test | Late-Emerging vs. Low Trajectory Class | Declining vs. Low Trajectory Class | Declining vs. Late-Emerging Class | |||||
|---|---|---|---|---|---|---|---|---|
| Measure | χ2 (df=3) | p | Odds Ratio | 95% CI | Odds Ratio | 95% CI | Odds Ratio | 95% CI |
| Measures of ASD | ||||||||
| Childhood ASD diagnosis | 35.41 | <0.001 | 24.39 | 6.45, 92.28 | 136.45 | 46.05, 404.32 | 5.59 | 2.24, 13.96 |
| High risk for childhood ASD | 233.06 | <0.001 | 6.01 | 4.52, 7.98 | 15.55 | 11.74, 20.59 | 2.59 | 1.77, 3.78 |
| High risk for adult ASD | ||||||||
| Parent-rated | 188.25 | <0.001 | 32.15 | 22.66, 45.61 | 6.71 | 4.02, 11.22 | 0.21 | 0.12, 0.37 |
| Self-rated | 43.25 | <0.001 | 3.33 | 2.27, 4.89 | 2.63 | 1.71, 4.04 | 0.79 | 0.43, 1.44 |
| IQ and communication problems | ||||||||
| Low childhood IQ | 56.33 | <0.001 | 4.09 | 2.88, 5.81 | 3.60 | 2.54, 5.12 | 0.88 | 0.55, 1.41 |
| Child pragmatic language problems | 106.38 | <0.001 | 11.42 | 6.21, 21.08 | 44.55 | 29.47, 67.33 | 3.89 | 2.02, 7.50 |
| Adult communication problems | 181.24 | <0.001 | 31.86 | 21.95, 46.23 | 5.91 | 3.69, 9.49 | 0.19 | 0.11, 0.32 |
| Peer problems | ||||||||
| Childhood peer problems | 107.32 | <0.001 | 5.21 | 3.71, 7.31 | 10.10 | 7.25, 14.08 | 1.94 | 1.26, 3.00 |
| Adolescent peer problems | 90.21 | <0.001 | 6.29 | 4.46, 8.88 | 6.65 | 3.40, 13.02 | 1.06 | 0.49, 2.27 |
| Adult peer problems | ||||||||
| Parent-rated | 202.92 | <0.001 | 23.41 | 17.02, 32.18 | 3.61 | 2.28, 5.71 | 0.15 | 0.09, 0.26 |
| Self-rated | 63.11 | <0.001 | 4.22 | 2.98, 5.99 | 2.54 | 1.72, 3.75 | 0.60 | 0.35, 1.03 |
| Adult functioning | ||||||||
| Not in education, employment, or training | 29.26 | <0.001 | 5.81 | 3.27, 10.29 | 3.81 | 2.14, 6.78 | 0.66 | 0.29, 1.49 |
| Distress and impairment | ||||||||
| Parent-rated | 183.61 | <0.001 | 56.61 | 39.13, 81.91 | 3.34 | 1.73, 6.45 | 0.06 | 0.03, 0.11 |
| Self-rated | 65.76 | <0.001 | 7.41 | 4.99, 11.01 | 2.67 | 1.68, 4.23 | 0.36 | 0.19, 0.67 |
a
See Figure 3 for rates. Odds ratios are based on trajectory class as the exposure, regardless of temporal precedence, for comparability.
Comparisons between the late-emerging and declining ASD trajectory classes are also shown in Table 1 and Figure 3. The declining trajectory class showed higher levels of childhood difficulties than the late-emerging class (childhood ASD diagnosis, high risk for ASD diagnosis, pragmatic language problems, peer problems) and the late-emerging trajectory class showed higher levels of adult difficulties than the declining trajectory class when reported by parents (high risk for adult ASD diagnosis, adult communication problems, peer problems, distress, and impairment). However, for self-reported measures at age 25, high risk for ASD diagnosis and peer problems were similar in the late-emerging and declining trajectory classes, although self-rated distress and impairment were higher in the late-emerging class. Both trajectory classes had similar levels of low IQ in childhood and having “not in education, employment, or training” status in adulthood relative to the low trajectory class.
Sensitivity Analyses: Task-Based Indicator of ASD in Early Adolescence and ASD and Communication Subscales in Adulthood
Sensitivity analyses examining emotional triangles test scores at age 13 and the AQ-28 factors and Communication Checklist–Adult subscales at age 25 are summarized in Table S3 in the online supplement . Theory of mind as indexed by the emotional triangles test at age 13 showed the lowest levels in the declining trajectory class and intermediate levels for the late-emerging class. Age-25 associations were consistent across AQ-28 subscales with the exception that while parent-rated scores related to social behavior/interaction were higher in the late-emerging compared with the declining trajectory class, those related to attention to detail were equally elevated in both classes relative to the low trajectory class.
Sex-Specific Developmental Trajectories
Male-specific analyses identified a similar three-class model: low (88.2%, N=3,585), declining (5.7%, N=233) and late-emerging (6.1%, N=249) trajectory classes. Female-specific analyses identified a two-class model that did not include a declining trajectory class: low (91.9%, N=3,702) and late-emerging (8.1%, N=325). Full details of the sex-specific models are provided in the online supplement .
Missing Data
Additional analyses revealed a similar pattern of results for both deriving trajectories based on varying levels of missingness and examining associations between social communication trajectories and other measures of ASD, IQ and communication problems, peer problems, and adult functioning using different approaches to handling missing data (see the online supplement ).
Discussion
Our aim in this study was to characterize the natural history and heterogeneity of autistic symptoms in a U.K. population-based cohort from childhood to age 25. Using repeated measures of parent-rated social communication problems, we identified three distinct trajectories spanning childhood, adolescence, and young adulthood. Most of the sample belonged to a persistently low symptom trajectory group, as would be expected in a population-based cohort. In line with much of the clinical literature, another group showed high autistic symptoms in childhood that declined over time. However, we also detected a third, “late-emerging” trajectory group who showed initially low ASD symptom levels in childhood that increased across adolescence and into young adulthood. Previous work in ALSPAC has found an increase in ASD symptoms across adolescence, particularly for females (13); our work differs in that we investigated distinct developmental trajectories of ASD symptoms into young adulthood, identifying both declining and late-emerging symptom trajectory groups. Furthermore, we examined associations with other measures in childhood and adulthood to investigate these different developmental patterns.
The declining symptom trajectory class showed associations with various features that typify ASD diagnosis, including male sex, low IQ, and communication and peer problems. Sensitivity analyses using a more detailed autism measure at age 25 suggested that while social interaction/behaviors somewhat improved into adulthood for this group, attention to detail remained. Also, despite the attenuation of social communication problems in the declining trajectory class from childhood to adulthood, this group still showed elevated levels of distress and impairment in adulthood and were more likely not to be in education, employment, or training at age 25 compared with the low-symptom group. This is consistent with previous longitudinal research on clinical cohorts, which has shown that ASD symptoms tend to decline with age but that outcomes vary, and impairment often persists (9–11).
The late-emerging ASD symptom trajectory class is unexpected given that ASD is defined as a childhood-onset neurodevelopmental disorder. This late-emerging group showed (elevated) levels of adult impairment similar to those of the declining ASD class in terms of not being in education, employment, or training and reported distress and impairment, as rated by both parents and the individuals themselves. However, they did not show the male preponderance that is typical of ASD. Interestingly, late-onset symptoms have been a growing controversy in relation to another childhood neurodevelopmental disorder, attention deficit hyperactivity disorder (ADHD) (41). However, unlike ADHD, where later onset has not been found to be associated with childhood neurodevelopmental problems, the late-emerging ASD group, at least in this cohort, do not appear to have entirely newly emerging neurodevelopmental difficulties. In childhood, the late-emerging ASD group displayed some neurodevelopmental impairment, including an elevated level of a broader range of ASD traits, pragmatic language problems, and peer problems, compared with the low-symptom group. It is also noteworthy that while ASD symptoms were relatively low in childhood, they were somewhat elevated compared with the low trajectory class (see Figure 2). Thus, it may be that ASD symptoms were “camouflaged” in childhood for this group, perhaps as a result of accommodating environments, scaffolding by families, or individual characteristics that enabled them to compensate during this developmental period, but that with increasing demands on social skills with age, social difficulties became more apparent (14). Interestingly, while some work has suggested that compensation or camouflaging may be particularly apparent in females (14), we did not observe a female preponderance in this group (although we also did not observe the “typical” ASD male preponderance).
The timing of the emergence of ASD symptoms in adolescence is also supported by sensitivity analyses using a task-based index of ASD measuring theory of mind at age 13, which suggested that the late-emerging group had intermediate scores between the low and declining groups at this age. Previous explanations as to why ASD is detected later (in childhood) despite earlier assessments include early symptoms being missed or overshadowed by other difficulties, “overdiagnosis” of later symptoms, or a genuinely later symptom onset (42). Our use of a population-based cohort makes misdiagnosis and overdiagnosis unlikely. However, we cannot rule out the possibility that ASD symptoms had previously been overshadowed or that late reported symptoms actually index another form of psychopathology. For example, post hoc analyses found the late-emerging trajectory group to have elevated emotional problems in young adulthood (results available on request), suggesting that the emerging symptoms identified in this group may reflect internalizing problems. Alternative study designs are needed to infer whether these adult emotional problems are a secondary consequence of the late ASD symptoms or whether some of the late-reported ASD symptoms are indexing emotional problems. We also cannot rule out the contribution of measurement error. Alternatively, it is possible that ASD symptoms genuinely show a much more variable age at first manifestation, at least in the general population, than previously realized.
While many of our measures were parent-rated, enabling consistency of rater and of measures across development, the inclusion of adult self-reports provided additional insights. By age 25, although parents reported that ASD-related difficulties and peer problems were higher in the late-emerging than the declining trajectory class (defined using parent reports), self-rated ASD-related problems and impairments were similar for each of these groups; thus, the observed symptom decline for those with high childhood symptom levels could be influenced by rater effects. One explanation is that by adulthood, individuals have better insight than their parents into some of their own social behavior and interaction–related ASD symptoms. Another possibility is that parents endorse autistic symptoms in their adult offspring more readily when impairment is present: we observed that although the rate of self-rated high risk for ASD was similar in the late-emerging and declining trajectory groups, self-reported distress and impairment was higher in the late-emerging class. Regardless, it seems that later-emerging ASD is problematic in adulthood across a variety of measures and raters.
An important consideration in interpreting the results is whether the meaning of autistic items captured by the same measure (in this study, the SCDC) changes with age. There are many challenges to adopting a developmental perspective in research, one of which is that measures and informants typically change from childhood to adulthood (43). However, identical questions (e.g., “does not appear to understand how to behave when out”) may capture different impairments at different ages. The SCDC is also yet to be validated in adulthood, although our analyses suggested acceptable measurement invariance across the ages we assessed, and we previously reported that the adult and child SCDC show similar patterns of association with genetic risk scores (21).
Our study must be considered in light of its limitations. Like many longitudinal samples, ALSPAC suffers from nonrandom attrition, whereby individuals at elevated risk of psychopathology are more likely to drop out of the study (44)—approximately 54% of the original ALSPAC birth cohort were included in our age-25 analyses—which may have led to an underestimation of the number of individuals in high symptom trajectories. However, we used a range of statistical methods to assess the effect of missingness and found a similar pattern of results. Our trajectories were also based on a measure of social communication and did not include the repetitive behaviors and restricted interests domains of autism; although this measure has previously been validated against childhood ASD diagnosis in ALSPAC (20), these domains may show a different natural history (45). Also, we could not examine trajectories of self-rated symptoms, as these were only available in adult life. The use of a population sample (and the size of the sample) is also likely to have affected the trajectory classes that we detected. In particular, our model did not include a class with high childhood symptoms that persisted into adulthood, which would be expected in clinical samples (9). In model-fitting, a four-class solution did include a high-persistent trajectory, but this was a small class (1.8%), the inclusion of which did not improve model fit. Post hoc analyses comparing this model to our three-class solution found that the majority of those who would have been included in the high-persistent class were included in our declining class (approximately 72%, with the remainder in the late-emerging class); thus, our declining class likely includes a small proportion of individuals for whom symptoms persist into adulthood (reflected in the large confidence intervals for this group). This small “fourth” class shows a prevalence rate similar to the reported population prevalence of 1% for adult ASD (46, 47). Studies using high-risk, clinical, or larger general-population samples would be better placed to characterize differences between ASD symptoms that persist compared with desisting or increasing across development in individuals with a diagnosis. However, such samples may not be the ideal ones for detecting later-emerging problems.
In summary, we observed heterogeneity in the natural history of autistic symptoms in the general population. We found that for individuals with elevated symptoms in childhood, symptom levels tended to decline into young adulthood. Intriguingly, we also identified a group for whom autistic symptoms emerged later, across adolescence and adulthood, but who showed evidence of earlier neurodevelopmental impairment, including low IQ and language problems in childhood. Both groups showed elevated levels of distress and impairment in young adulthood. These findings support the continued monitoring of ASD symptoms and associated impairment across development. They also challenge our current understanding that ASD symptoms invariably manifest early in development. This requires further investigation, as the age at ASD symptom manifestation may be much more variable than previously realized.
Footnotes
Supported by the Wellcome Trust (204895/Z/16/Z). The Avon Longitudinal Study of Parents and Children (ALSPAC) receives its core support from the UK Medical Research Council, Wellcome Trust (grant ref: 102215/2/13/2), and the University of Bristol. A comprehensive list of grant funding is available on the ALSPAC website (www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). The primary outcome measures used in this study were specifically funded by the Wellcome Trust (204895/Z/16/Z). Dr. Wootton, Dr. Davey Smith, Dr. Stergiakouli, and Dr. Tilling work in a unit that receives funding from the University of Bristol and the UK Medical Research Council (MC_UU_00011/1 and MC_UU_00011/3).
The authors thank the families who took part in the study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.
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References
1.
Le Couteur A, Szatmari P: Autism spectrum disorder, in Rutter’s Child and Adolescent Psychiatry, 6th ed. Edited by Thapar A, Pine DS, Leckman JF, et al. Oxford, UK, Wiley Press, 2015, pp 661–682
2.
Mandy W, Lai MC: Annual Research Review: The role of the environment in the developmental psychopathology of autism spectrum condition. J Child Psychol Psychiatry 2016; 57:271–292
3.
Robinson EB, St Pourcain B, Anttila V, et al: Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat Genet 2016; 48:552–555
4.
Thapar A, Cooper M, Rutter M: Neurodevelopmental disorders. Lancet Psychiatry 2017; 4:339–346
5.
Szatmari P: Complexity and parsimony in natural history studies of children with autism spectrum disorder. J Am Acad Child Adolesc Psychiatry 2017; 56:636–638
6.
Visser JC, Rommelse NNJ, Lappenschaar M, et al: Variation in the early trajectories of autism symptoms is related to the development of language, cognition, and behavior problems. J Am Acad Child Adolesc Psychiatry 2017; 56:659–668
7.
Szatmari P, Chawarska K, Dawson G, et al: Prospective longitudinal studies of infant siblings of children with autism: lessons learned and future directions. J Am Acad Child Adolesc Psychiatry 2016; 55:179–187
8.
Tomaszewski B, Smith DaWalt L, Odom SL: Growth mixture models of adaptive behavior in adolescents with autism spectrum disorder. Autism 2019; 23:1472–1484
9.
Howlin P, Magiati I: Autism spectrum disorder: outcomes in adulthood. Curr Opin Psychiatry 2017; 30:69–76
10.
Simonoff E, Kent R, Stringer D, et al: Trajectories in symptoms of autism and cognitive ability in autism from childhood to adult life: findings from a longitudinal epidemiological cohort. J Am Acad Child Adolesc Psychiatry 2020; 59:1342–1352
11.
Steinhausen HC, Mohr Jensen C, Lauritsen MB: A systematic review and meta-analysis of the long-term overall outcome of autism spectrum disorders in adolescence and adulthood. Acta Psychiatr Scand 2016; 133:445–452
12.
Pickles A, McCauley JB, Pepa LA, et al: The adult outcome of children referred for autism: typology and prediction from childhood. J Child Psychol Psychiatry 2020; 61:760–767
13.
Mandy W, Pellicano L, St Pourcain B, et al: The development of autistic social traits across childhood and adolescence in males and females. J Child Psychol Psychiatry 2018; 59:1143–1151
14.
Livingston LA, Happé F: Conceptualising compensation in neurodevelopmental disorders: reflections from autism spectrum disorder. Neurosci Biobehav Rev 2017; 80:729–742
15.
Boyd A, Golding J, Macleod J, et al: Cohort profile: the “children of the 90s”: the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol 2013; 42:111–127
16.
Fraser A, Macdonald-Wallis C, Tilling K, et al: Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol 2013; 42:97–110
17.
Northstone K, Lewcock M, Groom A, et al: The Avon Longitudinal Study of Parents and Children (ALSPAC): an update on the enrolled sample of index children in 2019. Wellcome Open Res 2019; 4:51
18.
Skuse DH, Mandy WP, Scourfield J: Measuring autistic traits: heritability, reliability, and validity of the Social and Communication Disorders Checklist. Br J Psychiatry 2005; 187:568–572
19.
Bölte S, Westerwald E, Holtmann M, et al: Autistic traits and autism spectrum disorders: the clinical validity of two measures presuming a continuum of social communication skills. J Autism Dev Disord 2011; 41:66–72
20.
Skuse DH, Mandy W, Steer C, et al: Social communication competence and functional adaptation in a general population of children: preliminary evidence for sex-by-verbal IQ differential risk. J Am Acad Child Adolesc Psychiatry 2009; 48:128–137
21.
Riglin L, Leppert B, Langley K, et al: Investigating attention-deficit hyperactivity disorder and autism spectrum disorder traits in the general population: what happens in adult life? J Child Psychol Psychiatry (Online ahead of print, July 14, 2020)
22.
Williams E, Thomas K, Sidebotham H, et al: Prevalence and characteristics of autistic spectrum disorders in the ALSPAC cohort. Dev Med Child Neurol 2008; 50:672–677
23.
Steer CD, Golding J, Bolton PF: Traits contributing to the autistic spectrum. PLoS One 2010; 5:e12633
24.
Rai D, Culpin I, Heuvelman H, et al: Association of autistic traits with depression from childhood to age 18 years. JAMA Psychiatry 2018; 75:835–843
25.
Agelink van Rentergem JA, Lever AG, Geurts HM: Negatively phrased items of the Autism Spectrum Quotient function differently for groups with and without autism. Autism 2019; 23:1752–1764
26.
Hoekstra RA, Vinkhuyzen AA, Wheelwright S, et al: The construction and validation of an abridged version of the Autism-Spectrum Quotient (AQ-Short). J Autism Dev Disord 2011; 41:589–596
27.
Boraston Z, Blakemore S-J, Chilvers R, et al: Impaired sadness recognition is linked to social interaction deficit in autism. Neuropsychologia 2007; 45:1501–1510
28.
Happé F: Autism as a neurodevelopmental disorder of mind-reading. J Br Acad 2015; 3:197–209
29.
Warrier V, Baron-Cohen S: Genetic contribution to “theory of mind” in adolescence. Sci Rep 2018; 8:3465
30.
Wechsler D, Golombok S, Rust J: WISC-III UK Wechsler Intelligence Scale for Children: UK Manual. Sidcup, UK, Psychological Corp, 1992
31.
Bishop DV: Development of the Children’s Communication Checklist (CCC): a method for assessing qualitative aspects of communicative impairment in children. J Child Psychol Psychiatry 1998; 39:879–891
32.
Whitehouse AJ, Coon H, Miller J, et al: Narrowing the broader autism phenotype: a study using the Communication Checklist–Adult Version (CC-A). Autism 2010; 14:559–574
33.
Goodman R: The Strengths and Difficulties Questionnaire: a research note. J Child Psychol Psychiatry 1997; 38:581–586
34.
Statistical Bulletin: Young people not in education, employment, or training (NEET), UK: Feb 2020: estimates of young people (aged 16 to 24 years) who are not in education, employment, or training, by age and sex. London, Office for National Statistics, 2020. https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/bulletins/youngpeoplenotineducationemploymentortrainingneet/february2020
35.
Muthén LK, Muthén BO: Mplus User’s Guide, 7th ed. Los Angeles, Muthén & Muthén, 1998–2012
36.
Muthén B, Muthén LK: Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res 2000; 24:882–891
37.
Asparouhov T Muthén B : Auxiliary variables in mixture modeling: 3-step approaches using Mplus. Mplus Web Notes 2013, no 15. https://www.statmodel.com/examples/webnotes/webnote15.pdf
38.
Seaman SR, White IR: Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res 2013; 22:278–295
39.
Seaman SR, White IR, Copas AJ, et al: Combining multiple imputation and inverse-probability weighting. Biometrics 2012; 68:129–137
40.
White IR, Royston P, Wood AM: Multiple imputation using chained equations: issues and guidance for practice. Stat Med 2011; 30:377–399
41.
Asherson P, Agnew-Blais J: Annual Research Review: Does late-onset attention-deficit/hyperactivity disorder exist? J Child Psychol Psychiatry 2019; 60:333–352
42.
Davidovitch M, Levit-Binnun N, Golan D, et al: Late diagnosis of autism spectrum disorder after initial negative assessment by a multidisciplinary team. J Dev Behav Pediatr 2015; 36:227–234
43.
Thapar A, Riglin L: The importance of a developmental perspective in psychiatry: what do recent genetic-epidemiological findings show? Mol Psychiatry 2020; 25:1631–1639
44.
Taylor AE, Jones HJ, Sallis H, et al: Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children. Int J Epidemiol 2018; 47:1207–1216
45.
Happé F, Ronald A, Plomin R: Time to give up on a single explanation for autism. Nat Neurosci 2006; 9:1218–1220
46.
Brugha T, Cooper SA, McManus S, et al: Estimating the Prevalence of Autism Spectrum Conditions in Adults: Extending the 2007 Adult Psychiatric Morbidity Survey. NHS Information Centre for Health and Social care, 2012
47.
Brugha TS, McManus S, Bankart J, et al: Epidemiology of autism spectrum disorders in adults in the community in England. Arch Gen Psychiatry 2011; 68:459–465
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Received: 28 July 2020
Revision received: 12 October 2020
Revision received: 7 December 2020
Accepted: 16 December 2020
Published online: 26 April 2021
Published in print: August 01, 2021
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