Edited by: Rudi Crncec, South Western Sydney Local Health District, Australia
Reviewed by: Roger Blackmore, South Western Sydney Local Health District, Australia; Rebecca A. Harrington, Johns Hopkins University, USA
This article was submitted to the journal Frontiers in Human Neuroscience.
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Autism spectrum disorders (ASD) are currently diagnosed in the presence of impairments in social interaction and communication, and a restricted range of activities and interests. However, there is considerable variability in the behaviors of different individuals with an ASD diagnosis. The heterogeneity spans the entire range of IQ and language abilities, as well as other behavioral, communicative, and social functions. While any psychiatric condition is likely to incorporate a degree of heterogeneity, the variability in the nature and severity of behaviors observed in ASD is thought to exceed that of other disorders. The current paper aims to provide a model for future research into ASD subgroups. In doing so, we examined whether two proposed risk factors – low birth weight (LBW), and
Autism spectrum disorders (ASD) are currently diagnosed in the presence of impairments in social interaction and communication, and a restricted range of activities and interests. However, there is considerable variability in the behaviors of different individuals with an ASD diagnosis. Traditionally, researchers have conceptualized ASD as a unitary disorder with a large spectrum, and have sought to discover a single aetiological factor that leads to disorder. However, the behavioral heterogeneity has been mirrored at the genetic level, for instance, many susceptibility loci have been identified, yet each has been found to account for a small amount of variance only (1–2%) (Weiss et al.,
Research in this area has traditionally adopted a “top-down approach” by constraining behavioral phenotypes in the hope that this will facilitate the identification of biological subtypes. For example, Buxbaum et al. (
A “bottom-up” approach to identify biological subtypes of ASD has not received the same level of research attention. This methodology focuses on known aetiological risk factors, and whether individuals exposed to these risk factors have a more homogenous phenotype. In this paper, we report on this bottom-up approach, focusing on aspects of the phenotype that are not part of the core defining features of the disorder. We know that comorbid medical conditions are highly prevalent in ASD (Bauman,
Selective serotonin reuptake inhibitors use during pregnancy has gained considerable attention over the last 2 years and is thought to be implicated in an increased risk of ASD diagnosis (Croen et al.,
Using a similar population-based nested case-control design, Rai et al. (
Low birth weight (<2500 g) has also been considered an environmental risk factor implicated in a range of psychiatric disorders including ASD, anxiety disorder, and depression (Indredavik et al.,
This paper will adopt a “bottom-up” approach to parsing ASD heterogeneity by investigating the behavioral phenotype associated with two possible environmental risk factors. The first study compared the behavioral and developmental phenotype of children with ASD whose mothers used SSRIs during pregnancy with the phenotype for a tightly matched group of children with ASD whose mothers did not use SSRIs during pregnancy. It was hypothesized that those children with ASD whose mothers used SSRIs during pregnancy would display early feeding and sleep disturbances compared to the control group of children with ASD. We also examined whether these children showed a distinguishable behavioral phenotype. Study 2 compared the phenotype of children with ASD born with LBW with a matched group of children with ASD born with NBW. It was hypothesized that those LBW children with ASD would display greater sleep disturbances (e.g., sleep-disordered breathing), language difficulties, and socio-emotional problems compared to the NBW group. This “proof of principle” study seeks to examine two potential risk factors within the context of a “bottom-up” research design. If the hypotheses are supported this paper may provide a blueprint for using the “bottom-up” approach as a feasible method for creating homogenous groups compared with the more costly “top-down” approach which requires large sample sizes.
Participants were part of the Western Australian Autism Biological Registry (WAABR), which is an ongoing study of children with a clinical diagnosis of an ASD and their families taking place at the Telethon Institute for Child Health Research in Perth, Western Australia (see Taylor et al.,
Prior to attending a face-to-face assessment, families were mailed and asked to complete a comprehensive case-history questionnaire relating to the mother’s pregnancy and the ASD child’s development. Mothers were asked to provide details of any history of psychological disorder such as major depression or anxiety. They were also asked to provide the name of any prescription or non-prescription medications, the dosage, and the amount they used during pregnancy. A series of questionnaires were also included in this package, including the Social Responsiveness Scale (SRS; Constantino and Gruber,
The SRS is a 65-item questionnaire used to examine a range of social behaviors characteristic of ASD in children over the last 6 months. A total score can be calculated for the SRS as well as five subscale scores, namely, social communication, autism mannerisms, social motivation, social awareness, and social cognition. Parents respond using a four-point scale ranging from “not true” (1) to “almost always true” (4). A higher total score on this measure is indicative of greater social difficulties. The CSHQ is a 34-item parent-report instrument that was used to examine sleep behavior over a “typical week.” Parents were asked to rate how often their child showed behaviors such as “struggle at bedtime” and “show fear at sleeping alone” using a one to three point scale corresponding to “rarely,” “sometimes,” or “usually,” respectively. A total score and eight subscale scores (bedtime resistance, sleep onset delay, sleep duration, sleep anxiety, night wakings, parasomnias, sleep-disordered breathing, and daytime sleepiness) can be calculated for responses on the CSHQ. Higher total scores on the CSHQ indicate that the child has a greater number of sleep problems.
The CCC-2 is a parent-report questionnaire designed to assess the communication skills of children aged 4–16 years. The purposes of the CCC-2 are the identification of pragmatic language impairment, screening of receptive and expressive language skills, and assistance in screening for ASD. The CCC-2 consists of 70 items that are divided into 10 scales, each with 7 items. The first four scales focus on specific aspects of language and communications skills (content and form). The next four scales assess the pragmatic aspects of communication. The last two scales measure behaviors that are usually impaired in children with ASDs. The parent rates the frequency of the communication behavior described in each item from 0 (less than once a week or never) to 3 (several times a day or always). Interpretation is based on a General Communication Composite (GCC), with lower scores indicative of greater language and communication difficulties.
Parents also completed a brief questionnaire related to their child’s history of gastrointestinal problems. This questionnaire was developed specifically for the WAABR case-history questionnaire based on the list of complaints in Ibrahim et al. (
Families were then invited to the Telethon Institute for Child Health Research for a face-to-face behavioral assessment. Clinical diagnoses of ASD were confirmed using the Autism Diagnostic Observation Schedule-Generic (ADOS-G; Lord et al.,
Between-group differences in the quantitative scores of the SRS, CCC-2, CSHQ, and ADOS severity scale were investigated with independent-samples
The SSRI case (
Maternal |
Offspring |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|
SSRI taken during pregnancy | Period of pregnancy SSRI taken | Psychiatric diagnosis | Gestational age at birth (weeks) | Age at assessment | ADOS module administered | ADOS severity score | CSHQ score | SRS score | Number of gut problems | |
Case 1 | Lexapro | Daily | Major depression | 41 | 5, 6 | 2 | 1 | 42 | 146 | 2 |
Case 2 | Lexapro | Daily | Major depression | 40 | 4, 6 | 2 | 6 | 62 | 157 | 1 |
Case 3 | Lovan | 3 months | Major depression | 36 | 5, 2 | 2 | 8 | 54 | 158 | 4 |
Case 4 | Effexor | Daily | Major depression | 38 | 10, 2 | 3 | 3 | 46 | 172 | 2 |
Case 5 | Not specified | – | Major depression | 38 | 4, 3 | 2 | 7 | 59 | 166 | 0 |
Case 6 | Escitalopram | Daily | – | 38 | 2, 9 | 1 | 4 | 77 | 207 | 1 |
Case 7 | Fluoxetine | Daily | Anxiety disorder | 40 | 8, 5 | 3 | 3 | 63 | 145 | 1 |
Case 8 | Aropax | 1 month | Anxiety disorder | 39 | 3, 1 | 1 | 6 | 54 | 120 | 2 |
Case 9 | Zoloft | Daily | Depression | 38 | 3, 5 | 1 | 6 | 41 | 90 | 1 |
Independent-samples
SSRI cases |
Controls |
Statistic | ||||
---|---|---|---|---|---|---|
SD | SD | |||||
CSHQ | 55.56 | 11.36 | 51.58 | 11.96 | 0.40 | |
Bedtime resistance | 9.56 | 2.79 | 9.83 | 3.25 | ||
Sleep onset delay | 1.89 | 0.60 | 1.83 | 0.87 | ||
Sleep duration | 5.11 | 2.42 | 4.96 | 2.39 | ||
Sleep anxiety | 4.89 | 1.45 | 4.79 | 1.82 | ||
Night wakings | 6.00 | 1.50 | 5.04 | 2.03 | ||
Parasomnias | 11.33 | 2.78 | 9.58 | 3.36 | ||
Sleep dis-breathing | 4.11 | 1.83 | 4.08 | 1.56 | ||
Daytime sleepiness | 12.67 | 3.16 | 11.46 | 2.95 | ||
SRS | 151.22 | 32.84 | 145.11 | 25.86 | 0.57 | |
Social awareness | 16.56 | 1.81 | 16.44 | 2.94 | ||
Social cognition | 28.78 | 8.23 | 26.56 | 6.24 | ||
Social communication | 50.33 | 12.58 | 47.74 | 8.88 | ||
Social motivation | 25.56 | 5.36 | 24.93 | 5.95 | ||
Autistic mannerisms | 30.00 | 7.78 | 29.44 | 7.54 | ||
ADOS severity | 4.89 | 2.26 | 5.93 | 1.96 | 0.19 | |
CCC-2 | ||||||
GCC | 30.75 | 6.99 | 36.15 | 16.53 | 0.54 |
Gut complaint | SSRI cases |
Control |
|
---|---|---|---|
Constipation | 4 (44.4) | 4 (14.8) | 0.09 |
Diarrhea | 2 (22.2) | 3 (11.1) | 0.58 |
Gastro reflux | 2 (22.2) | 3 (11.1) | 0.58 |
Abdominal | 1 (11.1) | 2 (7.4) | 1.00 |
Feeding | 5 (55.6) | 8 (29.6) | 0.24 |
One or more complaints | 8 (88.9) | 13 (48.1) | 0.05 |
This is the first study to examine the relationship between SSRI exposure and ASD phenotype. There were no differences between the cases and individually matched control participants in scores on the SRS, CCC-2, CSHQ, or ADOS-G severity. However, children with ASD whose mothers took SSRIs during pregnancy were significantly more likely to experience gastrointestinal complaints during childhood. Further examination of the relationship between gastrointestinal complaints and
The current study was limited by the absence of a control group of children whose mothers had affective disorders but who did not take SSRIs during pregnancy, and therefore we are unable to parse out whether the differences in the frequency of gut problems is related to mood disturbances or SSRI use. Rai et al. (
The study involved using data for 16 participants from WAABR whose birth weight was ≤2500 g (LBW). Each of these participants was individually matched on gender and chronological age at assessment (within 18 months) with two further control children with ASD (
Within the case-history questionnaire, mothers were asked to report their child’s birth weight. For the purposes of Study 2, data collected for each child using the SRS, CSHQ, ADOS severity, CCC-2, and gastrointestinal complaints questionnaire were analyzed.
Between-group differences in the quantitative scores of the SRS, the CSHQ, CCC-2, and ADOS severity scale were investigated with independent-samples
The LBW (
Birth weight | Gestational age at birth | Age at assessment | ADOS module | ADOS severity score | CSHQ score | SRS score | GCC | Number of gut problems | |
---|---|---|---|---|---|---|---|---|---|
Case 1 | 600 | 24 | 11; 1 | 3 | 4 | 45 | 152 | 42 | 1 |
Case 2 | 895 | 27 | 7; 4 | 2 | 6 | 57 | 166 | 32 | 3 |
Case 3 | 985 | 29 | 5; 6 | 1 | 3 | 39 | 133 | 38 | 1 |
Case 4 | 1565 | 30 | 4; 7 | 1 | 6 | 42 | 172 | – | 0 |
Case 5 | 1640 | 37 | 5; 2 | 1 | 6 | 65 | 169 | 40 | 2 |
Case 6 | 1665 | 37 | 5; 2 | 2 | 4 | 56 | 143 | 49 | 1 |
Case 7 | 1725 | 35 | 14; 4 | 3 | 4 | 63 | 171 | 28 | 3 |
Case 8 | 1765 | 34 | 2; 8 | 1 | 7 | 47 | 107 | – | 1 |
Case 9 | 2097 | 40 | 13; 1 | 1 | 6 | 54 | 183 | – | 2 |
Case 10 | 2285 | 36 | 5; 2 | 2 | 8 | 54 | 158 | – | 4 |
Case 11 | 2300 | 37 | 5; 11 | 1 | 7 | 51 | 163 | – | 0 |
Case 12 | 2426 | 37 | 9; 7 | 1 | 8 | 69 | 200 | - | 1 |
Case 13 | 2450 | 32 | 4; 7 | 2 | 6 | 52 | 159 | 56 | 1 |
Case 14 | 2500 | 38 | 4; 3 | 2 | 7 | 59 | 166 | 39 | 0 |
Case 15 | 2125 | 37 | 11; 3 | 3 | 6 | 66 | 191 | 7 | 1 |
Case 16 | 2480 | 38 | 4; 6 | 2 | 5 | 66 | 156 | – | 2 |
LBW |
NBW |
Statistics | ||||
---|---|---|---|---|---|---|
SD | SD | |||||
CSHQ | 55.31 | 9.08 | 47.84 | 8.84 | 0.01 | |
Bedtime resistance | 9.81 | 2.76 | 8.13 | 2.38 | ||
Sleep onset delay | 1.88 | 0.72 | 1.52 | 0.68 | ||
Sleep duration | 5.69 | 2.39 | 4.55 | 1.93 | ||
Sleep anxiety | 4.63 | 1.63 | 4.74 | 1.86 | ||
Night wakings | 5.19 | 1.87 | 4.19 | 1.66 | ||
Parasomnias | 10.44 | 2.71 | 9.97 | 2.56 | ||
Sleep dis-breathing | 4.31 | 1.49 | 3.29 | 0.82 | 0.00 | |
Daytime sleepiness | 13.38 | 3.36 | 11.19 | 2.56 | 0.02 | |
SRS | 0.46 | |||||
Social awareness | 17.31 | 2.73 | 17.97 | 2.33 | ||
Social cognition | 30.25 | 3.64 | 28.77 | 5.81 | ||
Social communication | 54.00 | 8.25 | 51.13 | 8.58 | ||
Social motivation | 27.69 | 4.70 | 26.68 | 4.88 | ||
Autistic mannerisms | 32.56 | 7.16 | 31.68 | 7.36 | ||
ADOS severity | 5.81 | 1.47 | 6.56 | 1.98 | 0.19 | |
CCC-2 | ||||||
GCC | 36.78 | 13.92 | 28.88 | 13.67 | 0.15 |
Similarly, children with LBW (
Gut complaint | LBW |
NBW |
|
---|---|---|---|
Constipation | 7 (43.8) | 9 (28.1) | 0.22 |
Diarrhea | 2 (12.5) | 3 (9.4) | 0.55 |
Gastro reflux | 5 (31.3) | 8 (25) | 0.45 |
Abdominal | 0 (0) | 4 (12.5) | 0.19 |
Feeding | 9 (56.3) | 13 (40.6) | 0.24 |
One or more complaints | 8 (88.9) | 13 (48.1) | 0.05 |
The second study examined the phenotype of children with ASD born with LBW relative to a group of children with ASD born with NBW. This study did not find any significant differences between the groups on the gastrointestinal complaints questionnaire, SRS, ADOS-G severity, or CCC-2. This is inconsistent with findings of greater socio-emotional issues and reduced language ability in LBW children compared to NBW children in the absence of an ASD diagnosis (Barre et al.,
Currently, there are no norms to describe performance of typically developing LBW children on the CSHQ. It would be interesting to compare sleep disturbance between LBW typically developing children and LBW children with ASD. Thus it may be useful to conduct a more comprehensive study of LBW and NBW children with and without ASD to look more closely at the significance of the present findings. Unsurprisingly, the LBW children had a significantly lower gestational age at birth than the NBW children, which raises the possibility that gestational age may be driving the findings and not birth weight. However, it is important to note that the study by Lampi et al. (
This present study used a “bottom-up” approach to seek understanding of the heterogeneity of ASD by investigating the behavioral phenotype associated with two suspected environmental risk factors, namely,
The numbers of children with ASD in the “aetiological risk” subgroups are small, and therefore we urge caution in drawing conclusions from these data. Rather, we seek to highlight a different method for understanding the heterogeneity in the ASD phenotype. We believe that the preliminary findings of increased levels of non-core symptoms of ASD among certain “aetiological risk” subgroups, provides evidence that this “bottom-up” methodology may assist ASD research. Studies including larger samples of children with ASD will build on the research presented here, and provide the opportunity to validate our preliminary findings.
Whilst the present study did not find any differences in core ASD symptoms between LBW and SSRI-exposed children with their respective control groups, we know that each child who is given an ASD diagnosis presents with the triad of core symptoms irrespective of their severity. It is unlikely that a single environmental factor could be attributed to “causing” one of these core impairments. Rather we may expect that the interplay between the environment and a child’s genetic profile contributes to the variable expression of autistic-related traits (Ratajczak,
Recently, Whitehouse and Stanley (
A key question facing the field is whether the long-held view that autism is a unitary disorder with a single causal pathway is correct, or whether autism may best be conceptualized as an umbrella term for a collection of behavioral disorders resulting from a range of causal pathways, analogous to cerebral palsy. Current evidence suggests that the latter may be a more accurate representation. Heterogeneity in the distal causes of autism is now well-established. It is estimated that between 10 and 15% of individuals with autism have a known genetic aetiology, but the loci and nature of these lesions vary, from known syndromes to observable cytogenetic lesions and rare
Given that diagnosis is currently based on behavior, the question of whether autism is one or multiple disorders is ultimately a query over the proximal causes of these behaviors, and one perhaps best addressed in neuroscience. Neuroscientific studies may help determine whether (a) distal risk factors “fan in” on a common neurobiological substrate that has the capability of underpinning the considerable behavioral heterogeneity in autism (one disorder), or (b) the exact combination of distal risk factors determines the brain regions and functions that are affected, which in turn prescribe the behavioral profile of each individual (multiple disorders). A key research aim will be to investigate the correspondence (if any) between known distal (genetic and environmental) and proximal (neurobiological) risk factors for autistic behaviors, using increasingly sophisticated environmental monitoring, genetic sequencing, and neuroimaging techniques.
Using preliminary data in this study we have demonstrated how a “bottom-up” approach can be applied to current aetiological research. Grouping individuals using this method may facilitate the identification of subtypes of people with ASD. Elucidating the underlying nature of the disorder(s) is a crucial step toward achieving perhaps the “holy grail” of autism research: tailoring intervention to the biological and cognitive makeup of each individual (Whitehouse and Stanley,
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.