The association between congenital anomalies and autism spectrum disorders in a Finnish national birth cohort
LAURA TIMONEN-SOIVIO
RAIJA VANHALA
HELI MALM
SUSANNA LEIVONEN
ELINA JOKIRANTA
SUSANNA HINKKA-YLI-SALOMÄKI
MIKA GISSLER
ALAN S BROWN
ANDRE SOURANDER
Correspondence to Dr Laura Timonen-Soivio, Department of Child Psychiatry, University of Turku, Lemminkäisenkatu 3/Teutori (3rd floor), 20014 Turku,Finland.lamtim@utu.fi
Issue date 2015 Jan.
Abstract
Aim
The first aim of this study was to evaluate the association between different subgroups of autism spectrum disorders (ASDs) (childhood autism, Asperger syndrome, and pervasive developmental disorder/pervasive developmental disorder – not otherwise specified [PDD/PDD-NOS]) and congenital anomalies. Second, we assessed the association among intellectually disabled children with ASDs in the subgroups of childhood autism and PDD/PDD-NOS.
Method
Nationwide population-based register data for children with a diagnosis of ASD (n=4449; 3548 males, 901 females) were collected during years 1987–2000 from the Finnish Hospital Discharge Register. Data on congenital anomalies were derived from the National Register of Congenital Malformations. Conditional logistic regression models were used as a statistical method. The association between ASD subgroups and congenital anomalies was stratified by the presence or absence of intellectual disability.
Results
Congenital anomalies occurred more frequently in all subgroups of ASD than in comparison participants (adjusted odds ratio [OR] for major congenital anomalies 1.8, 95% confidence interval [CI] 1.5–2.2,p<0.001). The association between congenital anomalies and childhood autism (OR 2.4, 95% CI 1.6–3.6,p<0.001) and between congenital anomalies and PDD/PDD-NOS (OR 3.7, 95% CI 2.4–5.7,p<0.001) among children with an intellectual disability was strong but remained significant also without intellectual disability (childhood autism: OR 1.7, 95% CI 1.3–2.3,p<0.001; PDD/PDD-NOS: OR 2.3, 95% CI 1.9–2.8,p<0.001).
Interpretation
The results suggest a significant association between ASDs and congenital anomalies regardless of the ASD subgroup. The association between childhood autism and PDD/PDD-NOS and congenital anomalies is stronger among children with intellectual disability is stronger than among those without intellectual disability. These results may have relevance in examining early risk factors in autism during fetal neurodevelopment.
The autism spectrum disorders (ASDs) consist of impaired social interaction and communication, and stereotypical and restricted behavioural patterns. The main subgroups of ASDs (International Classification of Diseases –Tenth Revision, ICD-10) are childhood autism, Asperger syndrome, and pervasive developmental disorder/pervasive developmental disorder – not otherwise specified (PDD/PDD-NOS). Several studies suggest that genetic origins and environmental risk factors may play an important role in the aetiology of ASD.1,2 Congenital anomalies and ASD arise from genetic and/or environmental factors during fetal development.3
Several studies have indicated that ASDs are associated with a higher rate of congenital anomalies. Specifically, seven population-based register studies have examined the association between ASDs and congenital anomalies,4–10 and five of these used a case–control design.4,5,7–9 In all of these registry studies, congenital anomalies were more common among children with ASD than in comparison groups or the general population. There are, however, some methodological limitations in these studies, and only one previous population-based study examined the association between congenital anomalies and ASDs in subgroups of ASDs including childhood autism, Asperger syndrome, and PDD-NOS. Conducted in Australia,9 the study suggested that congenital anomalies/birth defects were significantly associated with PDD-NOS and childhood autism but not with Asperger syndrome. One limitation is that only a few population-based studies have included potential confounding factors.4,7,10 Second, only two studies have considered intellectual disability when examining congenital anomalies in children with ASD,8,10 suggesting that intellectual disability is strongly associated with ASD. Further, most studies had relatively modest sample sizes, the exception being one study from Taiwan.8
Our primary aim was to examine the association between congenital anomalies and ASDs in a large, Finnish, nationwide population-based sample including 4449 children diagnosed with ASDs. The second aim was to examine whether there is a difference in the association between congenital anomalies and different ASD subgroups based on previous findings.9 We hypothesized that the existence of congenital anomalies is associated with the severity of ASD, being strongest in childhood autism and weakest in Asperger syndrome. The third aim was to categorize congenital anomalies in ASD subgroups of PDD/PDD-NOS and childhood autism with and without intellectual disability. According to previous studies, the association of medical disorders and congenital anomalies in ASD is strongest among individuals with an ASD and intellectual disability.8,11 We therefore further hypothesized that congenital anomalies may be more prevalent among individuals with autism and intellectual disability, indicating a more severe and possibly aetiologically heterogeneous and different clinical condition from ASD without intellectual disability.
METHOD
Study design
The Finnish Prenatal Study of Autism is a case–control study based on linkages of nationwide registries. The sample is based on all children born in Finland between 1 January 1987 and 31 December 2000 (n=868 766). The children were followed for the diagnosis of ASD (including childhood autism, Asperger syndrome, and PDD/PDD-NOS) until 31 December 2007 (n=4449; 3548 males, 901 females). Overall, the ratio of control participants (n=17727) to cases matched from the nationwide Medical Birth Register was 4:1. The linkages between the registries were performed using personal identity codes assigned to each Finnish citizen at birth. The detailed description of data sources and study design has been published previously.12
The Finnish Prenatal Study of Autism has been authorized by the Ministry of Social Affairs and Health in Finland. The ethics committees of the hospital district of Southwest Finland, the Finnish National Institute for Health and Welfare (THL), and the Institutional Review Board of the New York State Psychiatric Institute have given approval for the study.
National registries
The data were collected from four nationwide registries as detailed below.
The Finnish Register of Congenital Malformations (FRCM)
The information on congenital anomalies was obtained from the FRCM. The register was established in 1963 and is maintained by THL. The register collects data from hospitals, health care professionals, and cytogenetic laboratories. It also draws data from other nationwide registries such as the Medical Birth Register. In 2005, the register started collecting data from outpatient services. The FRCM contains data on congenital chromosomal abnormalities and structural anomalies detected in stillborn and live-born infants and fetuses. The data are collected mainly during the first year after birth, but information is also encoded for anomalies detected later in childhood. The FRCM uses the coding system of the World Health Organization ICD and collects mainly data on major congenital anomalies (MCAs) according to the inclusion/exclusion criteria by the European Surveillance of Congenital Anomalies.13 The present study is based on the codes of the ICD, Ninth Revision (ICD-9), for congenital anomalies (740–759). MCAs are defined as being of serious cosmetic or functional significance to the individual’s health, while minor physical anomalies (MPAs) are considered to have limited social, medical, or cosmetic significance. The definition and reporting of MPAs varies and is not as systematic as MCAs. While MPAs do not greatly affect the child, they can be related to MCAs or indicate certain syndromes.3,13
The Finnish Hospital Discharge Register (FHDR)
The FHDR was used to identify the children diagnosed with ASDs or intellectual disability diagnoses, and also to obtain data on potential confounders. The FHDR is maintained by THL. The register includes inpatient diagnoses covering all hospitals in Finland since 1967 and outpatient diagnoses in public hospitals since 1998. It covers all medical diagnoses made in hospitals, health centres with wards, military wards, prisons, and private hospitals. The register includes the personal identity codes unique for every person, dates of admission and discharge, primary diagnosis at discharge, and three possible subsidiary diagnoses. The diagnostic classification is based on the ICD14 (ICD-10 since 1 January 1996).
The Finnish Medical Birth Register (FMBR)
The FMBR was used to identify the controls and mothers and to obtain the data on potential confounders for all participants. The register is maintained by THL and was established in 1987. It includes the personal identity codes for mothers and every live-born child, and data obtained during pregnancy, the prenatal period, and the neonatal period up to the age of 7 days for all births occurring in Finland. The data are entered into local databases and submitted to THL by the delivery hospitals and by the health care personnel in rare cases of home deliveries.
The Finnish Central Population Register (FCPR)
The FCPR contains the basic information about Finnish residents and foreign citizens living permanently in Finland. The data include name, address, personal identity code, citizenship, native language, family relations, and dates of birth and death. The register is maintained by the Finnish Population Register Centre. The FCPR was used to identify the fathers, including the father’s age, which was examined as a potential confounding factor. Paternity was based on an individual’s status as a husband of the mother at the time of the birth of the child. If the mother was unmarried, paternity was confirmed by the acknowledgement of the father or by using a DNA test if father agreed to such testing. Paternity was established in 98.3% of the children.
Case definition
Cases with ASD were identified from FHDR using codes from ICD-10 (F84x) and ICD-9 (299x). The most recent diagnosis was used in the classification. The majority of cases were diagnosed by ICD-10 codes. Only 19 cases had a diagnosis based on ICD-9. The total number of children with ASD was 4449, comprising childhood autism (F84.0) (n=1012), Asperger syndrome (F84.5) (n=1778), and PDD/PDD-NOS (F84.8/F84.9) (n=1659). In Finland the diagnosis of ASD is based on the ICD classification and is made in specialized care units of child neurology, child or adolescent psychiatry, or paediatrics. The FHDR diagnosis of childhood autism has been validated.15
Control inclusion criteria
Control participants were identified through the FMBR and had no diagnoses of ASDs or intellectual disability according to the FHDR. Each case was individually matched to four controls based on the date of birth (±30d), sex, place of birth by birth hospital, and residence in Finland. If a birth hospital control could not be found, the case was matched with a control born in the same hospital district.
Covariates
Previous studies have shown that potential confounding factors associated with ASDs or congenital anomalies include birthweight, gestational age, weight for gestational age (WGA), mother’s socio-economic status, paternal age, maternal age, and parental psychiatric disorders.4,16–18 We tested for the association of each covariate with both ASDs and congenital anomaly (Table I) in order to determine the potential confounders to include in adjusted analyses.
Table I.
Potential confounders in relation to congenital anomalies and in relation to risk of autism spectrum disorders (ASDs)
| Covariate | No congenital anomalies,n (%) | Minor congenital anomalies,n (%) | Major congenital anomalies,n (%) | p-value for covariate and congenital anomalies | p-value for covariate and ASDs |
|---|---|---|---|---|---|
| Maternal age (median 29y) | |||||
| <29 y | 9281 (55.3) | 271 (54.3) | 248 (57.0 | 0.698 | 0.002 |
| 29y or less | 7512 (44.7) | 228 (45.7) | 187 (43.0) | ||
| Paternal age (median 32y) | |||||
| >32y | 8009 (47.7) | 237 (47.5) | 229 (52.6) | 0.123 | 0.072 |
| 32y or less | 8784 (52.3) | 262 (52.5) | 206 (47.4) | ||
| Maternal SESa | |||||
| Upper white collar | 2003 (16.0) | 66 (15.8) | 56 (15.6) | ||
| Lower white collar | 5736 (45.9) | 180 (43.0) | 154 (43.0) | ||
| Blue collar | 2587 (20.7) | 102 (24.4) | 83 (23.2) | ||
| Other | 2175 (17.4) | 70 (16.8) | 65 (18.2) | 0.524 | 0.008 |
| Preterm birth | |||||
| <37wk | 859 (5.1) | 30 (6.0) | 38 (8.7) | 0.003 | <0.001 |
| 37wk or more | 15 934 (94.9) | 469 (94.0) | 397 (91.3) | ||
| Birthweight | |||||
| <2500g | 488 (2.9) | 17 (3.4) | 32 (7.4) | <0.001 | <0.001 |
| 2500g or more | 16 305 (97.1) | 482 (96.6) | 403 (92.6) | ||
| WGAb | |||||
| Normal (−2SD to +2SD) | 15 823 (94.8) | 474 (95.4) | 407 (94.4) | 0.010 | <0.001 |
| SGA (< −2SD) | 289 (1.7) | 10 (2.0) | 16 (3.7) | ||
| LGA (> +2SD) | 572 (3.4) | 13 (2.6) | 8 (1.8) | ||
| Maternal psychiatric disorder | |||||
| Yes | 937 (5.6) | 27 (5.4) | 27 (6.2) | 0.841 | <0.001 |
| No | 15 856 (94.4) | 472 (94.6) | 408 (93.8) | ||
| Paternal psychiatric disorderc | |||||
| Yes | 1327 (8.0) | 31 (6.2) | 40 (9.4) | 0.194 | <0.001 |
| No | 15 270 (92.0) | 466 (93.8) | 384 (90.6) |
Frequency missing=1133 cases and 4450 controls.
Frequency missing=38 cases and 115 controls.
Fequency missing= 98cases and 209 controls. ASD, autistic spectrum disorder; LGA, large for gestational age; SES, socioeconomic status; SGA, small for gestational age; WGA, weight for gestational age.
Intellectual disability
The association between congenital anomalies and ASDs was stratified by the presence or absence of intellectual disability. Children diagnosed as having intellectual disability were identified by the FHDR using the following ICD-10/9 codes: F70/317 (IQ 50–70), F71/318.0 (IQ 35–49), F72/318.1 (IQ 20–34), F73/318.2 (IQ<20), F78 (no ICD-9 code, IQ unspecified), F79/319 (IQ unspecified).
Statistical analysis
The association between congenital anomalies and the three subgroups of ASDs (childhood autism, Asperger syndrome, and PDD/PDD-NOS) was examined. The analysis was based on a nested case–control design, in which the matched control participants for each case were drawn from the population at risk. The point and interval estimates of odds ratios (ORs) were obtained by fitting conditional logistic regression models for matched sets. Unadjusted ORs and 95% confidence intervals were calculated separately for all ASDs and the three subgroups. The adjusted model included the WGA as the covariate. Statistical significance was judged atp-value <0.05, two-sided. The statistical analyses were performed with SAS statistical software (SAS 9.4; SAS Institute, Cary, NC, USA).
RESULTS
Low birthweight (<2500g), preterm birth (<37wk), and WGA (–2SD) were associated with both ASDs and congenital anomaly atp<0.05 (Table I). WGA was included as a covariate in the adjusted analyses, since, after replacing WGA as a covariate with gestational age and birthweight, the results remained similar (data not shown).
In unadjusted analyses, both major and minor congenital anomalies were associated with total ASD, childhood autism, Asperger syndrome, and PDD/PDD-NOS (Table II). The OR of MCAs ranged from 1.7 (Asperger syndrome) to 3.0 (PDD/PDD-NOS). The OR of minor congenital anomalies were of similar magnitudes between ASD subgroups ranging from 1.5 (childhood autism) to 2.0 (PDD/PDD-NOS). After adjustment for a covariate, the findings remained similar; only the association between childhood autism and minor congenital anomalies was no longer significant.
Table II.
Association between autism spectrum disorder subgroups and congenital anomalies
| Congenital anomaly | Case,n (%) | Control,n (%) | OR (1) | CI (95%),p | OR (2) | CI (95%),p | |
|---|---|---|---|---|---|---|---|
| Autism spectrum disorder, total (n=4449) | No | 4010 (90.1) | 16 793 (94.7) | ||||
| Minor | 200 (4.5) | 499 (2.8) | 1.7 | 1.4–2.0, <0.001 | 1.6 | 1.4–2.0, <0.001 | |
| Major | 239 (5.4) | 435 (2.5) | 2.3 | 2.0–2.7, <0.001 | 1.8 | 1.5–2.2, <0.001 | |
| Childhood autism (n=1012) | No | 910 (89.9) | 3816 (94.5) | ||||
| Minor | 44 (4.3) | 119 (2.9) | 1.5 | 1.1–2.2, 0.012 | 1.4 | 0.9–2.1, 0.152 | |
| Major | 58 (5.7) | 103 (2.6) | 2.3 | 1.7–3.3, <0.001 | 1.8 | 1.2–2.7, 0.006 | |
| Asperger syndrome (n=1778) | No | 1633 (91.8) | 6701 (94.6) | ||||
| Minor | 73 (4.1) | 204 (2.9) | 1.5 | 1.1–2.0, 0.005 | 1.5 | 1.1–2.0, 0.005 | |
| Major | 72 (4.0) | 181 (2.6) | 1.7 | 1.3–2.2, 0.004 | 1.6 | 1.2–2.2, 0.001 | |
| Pervasive developmental disorder (n=1659) | No | 1467 (88.4) | 6276 (95.0) | ||||
| Minor | 83 (5.0) | 176 (2.7) | 2.0 | 1.6–2.7, <.001 | 2.0 | 1.5–2.7, <.001 | |
| Major | 109 (6.6) | 151 (2.3) | 3.0 | 2.4–3.9, <0.001 | 2.3 | 1.7–3.0, <0.001 |
Frequencies and odds ratios (ORs) with 95% confidence intervals (CIs) when unadjusted (1) and adjusted (2) for weight for gestational age.
Table III shows the results of subgroup analyses in which the sample was stratified by the presence or absence of comorbid intellectual disability. As shown, children in the ASD subgroups with intellectual disability have more congenital anomalies than those in the ASD subgroups without intellectual disability. In the ASD subgroups with intellectual disability, the OR ranged from 2.4 (childhood autism) to 3.7 (PDD/PDD-NOS). When the association of congenital anomalies and ASDs was examined separately for participants without intellectual disability, the OR ranged in the subgroups from 1.7 (childhood autism) to 2.3 (PDD/PDD-NOS).
Table III.
Association between childhood autism, pervasive developmental disorder, and congenital anomalies with and without intellectual disability when adjusted for WGA
| With intellectual disability | Without intellectual disability | |||||||
|---|---|---|---|---|---|---|---|---|
| n | No congenital anomaly,n (%) | Yes, congenital anomaly,n (%) | OR (95% CI),p | n | No congenital anomaly,n (%) | Yes, congenital anomaly,n (%) | OR (95% CI),p | |
| Childhood autism | 299 | 261 (87.3) | 38 (12.7) | 2.4 (1.6–3.6), <0.001 | 713 | 649 (91.0) | 64 (9.0) | 1.7 (1.3–2.3), 0.006 |
| Pervasive developmental disorder | 221 | 178 (80.5) | 43 (19.5) | 3.7 (2.4–5.7), <0.001 | 1438 | 1289 (89.6) | 149 (10.4) | 2.3 (1.9–2.8), <0.001 |
CI, confidence interval; OR, odds ratio; WGA, weight for gestational age.
DISCUSSION
First, the results show that congenital anomalies are associated with ASDs after adjusting for the effect of confounding factors. The results are in line with previous population-based studies4–10 showing that congenital anomalies are more prevalent among individuals with ASDs than in the general population. In our study, the prevalence of congenital anomalies was nearly twofold higher in participants with ASDs than in control subjects, in line with two earlier studies with smaller sample sizes.7,9
Second, the results indicate that congenital anomalies are associated with all selected ASD subgroups. The only previous study examining the association of congenital anomalies/birth defects with ASD subgroups, comprising childhood autism, Asperger syndrome, and PDD-NOS,9 suggested that congenital anomalies are significantly more common among children with childhood autism and PDD-NOS but not among children with Asperger syndrome. However, the sample size (n=465) of that study was considerably smaller and the covariates, such as birthweight and gestational age, were not included.
Third, the results suggest that ASDs and congenital anomalies comorbidity is more common in children with intellectual disability than in children without intellectual disability within the ASD subgroups. Our results are in line with those of a Taiwanese study8 indicating that congenital anomalies are strongly associated with ASD accompanied by intellectual disability. In the study by Miles et al.,19 children with ASDs with dysmorphic features were twice as likely to have intellectual disability and abnormal brain structure as non-dysmorphic children with ASDs. The present results support these findings and suggest that the group of children with ASDs and comorbid intellectual disability may differ with regard to their aetiology and brain dysfunction from those with ASDs but no intellectual disability.
Disturbances of early fetal development may explain the association of congenital anomalies and ASDs. The developing brain and the basic structures of organ systems are highly sensitive to environmental factors.3 Neuropathological and imaging studies have described several structural and functional changes in the brains of children with autism, indicating variability across the spectrum of disorders in children with ASDs.20 Craniofacial and palate anomalies among children with ASDs4 have been associated with structural anomalies of the brain, including anomalies of the corpus callosum, septum pellucidum, and the hippocampus and amygdala.21 These findings have been associated with both autism and schizophrenia and indicate that neurodevelopmental insult most likely occurs between the 6th and 15th gestational weeks.21 Exposure to thalidomide, valproate, of misoprostol during early fetal development is a good example of an exogenous factor affecting brain development and associated with higher rates of autism and identified physical anomalies.22,23 Evidence of an increased prevalence of exposure, during organogenesis, to other risk factors associated with congenital anomalies, such as maternal rubella infection and ethanol consumption, among children with ASDs with congenital anomalies is also indicative of the early timing of neurodevelopmental injury.24,25 These results support our study, suggesting that there may be common, developmental exogenous or endogenous injury in the early stages of fetal development. This affects neurodevelopment and causes physical anomalies in children with ASDs.
The present study has some limitations that must be considered. First, children with ASDs who have not received medical or psychiatric attention are not found in the registries. However, in the Finnish health care system all children systematically visit the child health clinics regularly before school, and beginning at the age of 7 years, once a year after starting school. Therefore, the coverage of the registries of Finnish children is high. As practically all Finnish children receive medical attention regularly, having a congenital anomaly would not lead to earlier diagnosis of ASDs. In addition, the validity and reliability of register data have been shown to be good.15 Second, the children born near the end of the study period may have been too young or the diagnoses may change in the future. However, because the median age at diagnosis of ASDs in children is about 7 years, the youngest children would have been born at the end of the year 2000 and were followed until the year 2007. The longer follow-up time increases confidence in the diagnosis. Third, the data on the anomalies, especially the MPAs, depend on the reporting activity and diagnostic accuracy of professionals in the hospitals and other medical units. However, as the MCAs often require specialized care and hospital treatment, there is a high probability that will be noticed after birth and reported to the FRCM, and therefore the coverage of the MCAs is expected to be good. The reporting of MPAs is not systematic, which should be taken into account when interpreting the results. In conclusion, our study provides a large population-based sample, a case–control study design, and a long follow-up period. It also considers the different ASD subgroups, potential confounding factors, and the effect of intellectual disability when interpreting the results. In conclusion, the association of congenital anomalies is higher in ASD subgroups than in control participants after adjusting for the effect of potential confounding factors. In addition, comorbidity of ASD and congenital anomalies is more frequent in children with intellectual disability than in children without intellectual disability. The increased prevalence of congenital anomalies among children with ASD reflects a disturbance of early neurodevelopment. In addition, the timing of neurodevelopmental injury in ASDs may be similar regardless of the subgroup. Furthermore, children with ASD with intellectual disability may have a different, more severe, clinical condition children than ASD without intellectual disability. ASDs could be also associated with several aetiologically different clinical conditions with intellectual disability. This study was not designed to consider genetic aetiology. Information regarding the more specific timing of the neurodevelopmental injury could provide possibilities to explore the environmental, genetic, and/or epigenetic factors associated with ASDs and periods of susceptibility during gestation. Future studies examining the specific congenital anomalies or congenital anomalies in organ systems in children with ASDs could provide more information regarding the embryological origins. Further studies could also examine the interaction of genetic risk and environmental factors and the association of specific teratogens in relation to the congenital anomalies in children with ASD and lead to a better understanding of the complex aetiology of ASDs.
What this paper adds.
Congenital anomalies are strongly associated with ASD even after adjusting for the effect of potential confounding factors.
ASD and congenital anomalies comorbidity is more common in children with intellectual disability than in those without intellectual disability.
Acknowledgments
This study was supported by grants from the Sigrid Juselius Foundation, Finland (A. Sourander), the Jane and Aatos Erkko Foundation (A. Sourander, E. Jokiranta), the National Institute of Environmental Health Sciences, USA (RO1 ES 019004 to A.S. Brown), the National Institute of Mental Health, USA (KO2 MH 65422 to A.S. Brown), the Finnish Brain Foundation/Child Psychiatry, Finland (L. Timonen-Soivio), and the Arvo Ylppö Foundation, Finland (L. Timonen-Soivio). None of the funding sources played a role in the conduct of the research. We would like to thank the members of the Department of Child Psychiatry at the University of Turku and Annukka Ritvanen from THL for assistance and performing the data collection process.
ABBREVIATIONS
- ASD
Autism spectrum disorder
- FHDR
Finnish Hospital Discharge Register
- FRCM
Finnish Register of Congenital Malformations
- MCA
Major congenital anomaly
- MPA
Minor physical anomaly
- PDD
Pervasive developmental disorder
- PDD-NOS
Pervasive developmental disorder – not otherwise specified
- THL
Finnish National Institute for Health and Welfare
- WGA
Weight for gestational age
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