Skip to main content Accessibility help
×
Home
Hostname: page-component-558cb97cc8-9njm9 Total loading time: 4.349 Render date: 2022-10-07T03:12:57.582Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": true, "useSa": true } hasContentIssue true

Early environmental risk factors for neurodevelopmental disorders – a systematic review of twin and sibling studies

Published online by Cambridge University Press:  24 July 2020

Torkel Carlsson*
Affiliation:
Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research; Department of Women's and Children's Health, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden PRIMA Child and Adult Psychiatry, Stockholm, Sweden
Felix Molander
Affiliation:
Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research; Department of Women's and Children's Health, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
Mark J. Taylor
Affiliation:
Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
Ulf Jonsson
Affiliation:
Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research; Department of Women's and Children's Health, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden Department of Neuroscience, Child and Adolescent Psychiatry, Uppsala University, Uppsala, Sweden
Sven Bölte
Affiliation:
Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research; Department of Women's and Children's Health, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden Curtin Autism Research Group, School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, Western Australia
*
Author for correspondence: Torkel Carlsson, KIND, Child and Adolescent Psychiatry Research Center, Gävlegatan 22, 11330Stockholm, Sweden; E-mail: torkel.carlsson@ki.se.
Rights & Permissions[Opens in a new window]

Abstract

While neurodevelopmental disorders (NDDs) are highly heritable, several environmental risk factors have also been suggested. However, the role of familial confounding is unclear. To shed more light on this, we reviewed the evidence from twin and sibling studies. A systematic review was performed on case control and cohort studies including a twin or sibling within-pair comparison of neurodevelopmental outcomes, with environmental exposures until the sixth birthday. From 7,315 screened abstracts, 140 eligible articles were identified. After adjustment for familial confounding advanced paternal age, low birth weight, birth defects, and perinatal hypoxia and respiratory stress were associated with autism spectrum disorder (ASD), and low birth weight, gestational age and family income were associated with attention-deficit/hyperactivity disorder (ADHD), categorically and dimensionally. Several previously suspected factors, including pregnancy-related factors, were deemed due to familial confounding. Most studies were conducted in North America and Scandinavia, pointing to a global research bias. Moreover, most studies focused on ASD and ADHD. This genetically informed review showed evidence for a range of environmental factors of potential casual significance in NDDs, but also points to a critical need of more genetically informed studies of good quality in the quest of the environmental causes of NDDs.

Type
Regular Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Introduction

Neurodevelopmental disorders (NDD) are characterized by alterations in the functioning, architecture, and maturation of the brain causing impairments in cognitive and adaptive functioning. NDDs comprise intellectual disability (ID), autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), communication disorders (CD), specific learning disorder (SLD), and motor disorders, including developmental coordination disorder (DCD), and tic disorders (TD) (APA, 2013). The prevalence of NDDs is 10–15% of all births in the United States (Aschner & Costa, Reference Aschner and Costa2015), and they are increasingly being diagnosed worldwide (Elsabbagh et al., Reference Elsabbagh, Divan, Koh, Kim, Kauchali, Marcin and Fombonne2012). ASD and ADHD are currently the most commonly diagnosed NDDs, with prevalence estimates ranging from 0.70% up to 2.64% for ASD (CDC, 2019; Elsabbagh et al., Reference Elsabbagh, Divan, Koh, Kim, Kauchali, Marcin and Fombonne2012) and 5–10% for ADHD (Hansen & Rogers, Reference Hansen and Rogers2013; Polanczyk, Willcutt, Salum, Kieling, & Rohde, Reference Polanczyk, Willcutt, Salum, Kieling and Rohde2014; Xu et al., Reference Xu, Strathearn, Liu, Yang and Bao2018). Males exhibit NDDs more often than females, although NDDs in females might be underdiagnosed (Bargiela, Steward, & Mandy, Reference Bargiela, Steward and Mandy2016; Lai et al., Reference Lai, Lombardo, Ruigrok, Chakrabarti, Auyeung, Szatmari and Baron-Cohen2017). NDD phenotypes are heterogeneous, and their complexity is compounded by high comorbidity rates with several conditions (i.e. other psychiatric disorders, neurological and immunological disorders, gastrointestinal disturbances, and congenital anomalies) (Muskens, Velders, & Staal, Reference Muskens, Velders and Staal2017; Simonoff et al., Reference Simonoff, Pickles, Charman, Chandler, Loucas and Baird2008). In face of the substantial individual burden and the societal costs these conditions incur on public health care, educational, and long-term support systems, it is of paramount importance to identify specific factors involved in the etiology of NDDs that might facilitate earlier detection and open up for earlier interventions (Bellinger, Reference Bellinger2012; Grandjean, Pichery, Bellanger, & Budtz-Jørgensen, Reference Grandjean, Pichery, Bellanger and Budtz-Jørgensen2012; Trasande & Liu, Reference Trasande and Liu2011).

The causes of NDDs are multiple, both genetic and environmental (Martin, Taylor, & Lichtenstein, Reference Martin, Taylor and Lichtenstein2018; Taylor et al., Reference Taylor, Martin, Lu, Brikell, Lundström, Larsson and Lichtenstein2019), but, the exact causes driving atypical neurodevelopment remain poorly understood. Based on findings from twin and family studies, NDDs are considered highly heritable (Polderman et al., Reference Polderman, Benyamin, de Leeuw, Sullivan, van Bochoven, Visscher and Posthuma2015; Posthuma & Polderman, Reference Posthuma and Polderman2013; Ronald & Hoekstra, Reference Ronald and Hoekstra2011), with both common and rare genetic variants contributing to the phenotypes (Hansen & Rogers, Reference Hansen and Rogers2013). While the research focus has until recently been mostly on genetic causes (Bauxbaum & Hof, Reference Buxbaum and Hof2011; Demontis et al., Reference Demontis, Walters, Martin, Mattheisen, Als, Agerbo and Baldursson2019; Landrigan, Lambertini, & Birnbaum, Reference Landrigan, Lambertini and Birnbaum2012; Szatmari et al., Reference Szatmari, Paterson, Zwaigenbaum, Roberts, Brian, Liu and Meyer2007), heritability estimates leave space for the potential significance of environmental factors (Herbert, Reference Herbert2010; Pessah, Cherednichenko, & Lein, Reference Pessah, Cherednichenko and Lein2010; Shelton, Hertz-Picciotto, & Pessah, Reference Shelton, Hertz-Picciotto and Pessah2012; Zuk et al., Reference Zuk, Hechter, Sunyaev and Lander2012). In addition, for several NDDs, such as ASD and ADHD, clinical phenotypes, broader phenotypes, and traits of the conditions are continuously distributed in the general population, with overlapping etiologies and sources of variation (Martin et al., Reference Martin, Taylor and Lichtenstein2018). Therefore, it is also important to look at outcomes of NDD as both categorical (diagnoses) and dimensional (traits and symptoms) for two reasons. First, as dimensional definitions in contrast to categorical ones may be more sensitive to subtle subclinical toxic effects, they may enable the development of more detailed exposure–response profiles and facilitate the testing of complex functional relationships between continuous behavior measures and biological outcomes like brain structure and behavior (Rauh & Margolis, Reference Rauh and Margolis2016). Second, because the etiology of clinical phenotypes overlaps with the etiology of subclinical phenotypes and condition traits, studying those traits might generate heuristic hypotheses to be tested in clinical samples.

Animal, human cell, and epidemiological studies suggest a wide range of environmental risks impact on neurodevelopment. Recently, prenatal maternal anemia has been associated with several NDDs, including ID, ASD, and ADHD (Wiegersma, Dalman, Lee, Karlsson, & Gardner, Reference Wiegersma, Dalman, Lee, Karlsson and Gardner2019). In ASD, associations with advanced parental age, maternal valproate intake during pregnancy, toxic chemical exposure, maternal diabetes, enhanced steroidogenic activity, immune activation, possibly altered zinc-copper cycles, and treatment with selective serotonin reuptake inhibitors (SSRI) during pregnancy have been reported (Bölte, Girdler, & Marschik, Reference Bölte, Girdler and Marschik2019). Environmental factors commonly linked to ADHD are food additives/diet, lead contamination, cigarette and alcohol exposure during pregnancy, and low birth weight (Banerjee, Middleton, & Faraone, Reference Banerjee, Middleton and Faraone2007). For reading disabilities, Mascheretti, Andreola, Scaini, and Sulpizio (Reference Mascheretti, Andreola, Scaini and Sulpizio2018) found evidence for gestational age and birth weight being the most important pre- and perinatal risk factors, while reporting inconclusive findings for maternal cigarette smoking, family history of psychiatric and medical diseases, and risk of miscarriage. Prenatal alcohol consumption, diabetes, treatment with antidepressants, being deficient in iodine or iron, and dietary fish, as well as postnatal depression, low birth weight, and neonatal problems have all been linked to motor difficulties in childhood (Golding, Emmett, Iles-Caven, Steer, & Lingam, Reference Golding, Emmett, Iles-Caven, Steer and Lingam2014). Pregnancy-related noxious exposures and lower birth weight may be more frequent in pregnancies of children who later develop Tourette's syndrome, particularly maternal smoking and prenatal life stressors, and psychosocial stress influences tic severity (Hoekstra, Dietrich, Edwards, Elamin, & Martino, Reference Hoekstra, Dietrich, Edwards, Elamin and Martino2013). With regards to developmental mechanisms, research from different disciplines found alterations of key biological systems in NDDs, such as catecholaminergic imbalances, glutamatergic synapse function, chromatin modelling, and ion channel pathways (Cristino et al., Reference Cristino, Williams, Hawi, An, Bellgrove, Schwartz and Claudianos2014; Geschwind & Levitt, Reference Geschwind and Levitt2007; Pinto et al., Reference Pinto, Delaby, Merico, Barbosa, Merikangas, Klei and Scherer2014). It is suggested that changes to immunological, endocrinological, and gut–brain axis processes are involved in causal pathways (Edmiston, Ashwood, & Van de Water, Reference Edmiston, Ashwood and Van de Water2017; Kelly, Minuto, Cryan, Clarke, & Dinan, Reference Kelly, Minuto, Cryan, Clarke and Dinan2017).

Familial confounding is a major limitation to much of the current literature on environmental risk factors. Familial confounders are shared factors within a family, including both unmeasured shared environmental and genetic factors, that increase similarity in siblings. Although many of the above environmental factors have been shown to be associated with NDDs, many of the exposures are in themselves, to a degree, heritable. Thus, it cannot be ruled out that they are driven by genetic links between exposure and outcome, and not by the environment itself. As discussed by van Dongen, Slagboom, Draisma, Martin, and Boomsma (Reference van Dongen, Slagboom, Draisma, Martin and Boomsma2012) and D'Onofrio, Lahey, Turkheimer, and Lichtenstein (Reference D'Onofrio, Lahey, Turkheimer and Lichtenstein2013b), twin, sibling, and family studies, as compared to conventional case control studies, have the potential to disentangle the effects of environment from genetic and unknown environmental factors. By comparing the risk of a given outcome in twins or siblings who are differentially exposed to a given factor—or conversely, comparing exposure across pairs who are discordant for the outcome—it is possible to adjust for many factors that are shared within the pairs of twins or siblings. This has often been neglected in previous research on environmental factors in NDD. Indeed, making causal inference with confidence requires far more prerequisites than just control for familial confounding (Hill, Reference Hill1965; Sjölander & Zetterqvist, Reference Sjölander and Zetterqvist2017). Still, this type of adjustment has proven highly useful in refuting proposed causal associations. For example, a meta-analysis by Mezzacappa et al. (Reference Mezzacappa, Lasica, Gianfagna, Cazas, Hardy, Falissard and Gressier2017) estimated the odds ratio [OR] for ASD to be 1.52 (95% CI, 1.09–2.12) for SSRI exposure during pregnancy. However, a later epidemiological study found that this association was to a large degree confounded by familial factors since it was attenuated in a sibling comparison analyses (Rai et al., Reference Rai, Lee, Dalman, Newschaffer, Lewis and Magnusson2017). Likewise, regarding the above listed potential environmental risk factors for ADHD, a more recent review by Sciberras, Mulraney, Silva, and Coghill (Reference Sciberras, Mulraney, Silva and Coghill2017) revealed a pattern indicating that the stronger the study design—especially regarding genetic and familial confounders—the less likely it was to support an association of SSRI use in pregnancy and the presence of ADHD in offspring. Similarly, the strong association between ADHD and smoking during pregnancy seem to be better accounted for by genetic and familial factors rather than a causal association between smoking during pregnancy and ADHD. This is key to understanding the rationale behind twin and sibling studies. Other analytical approaches assume that there are no concurrent explanations of the associations among initial risks, the mediating variables, and the outcome of interest (in this case smoking during pregnancy and ADHD), although it is clearly the case in reality. First, other environmental risks such as parental intellectual abilities, socioeconomic status (SES), and psychiatric problems also predict offspring ADHD; second, smoking during pregnancy is influenced by genetic factors (D'Onofrio et al., Reference D'Onofrio, Lahey, Turkheimer and Lichtenstein2013b). The same also holds true for many other environmental factors and makes studies controlling for familial confounding a crucial aspect when trying to establish causal inference.

When comparing sibling and twin studies, the within-pair comparisons among twins, in particular those in monozygotic (MZ) twins, hold the best premises for adjustment for familial confounding when studying environmental risks. Despite this, there are several valid reasons why sibling studies are occasionally preferred over twin studies. First, it is rarely possible to measure prenatal differences in twins sharing the same prenatal environment. In order to be able to perform an analysis of the within-pair association of a prenatal factor with a particular outcome, one would require individual and separate prenatal exposure information for each twin, an often-impossible demand. In some instances, as in the case of gestational age, there is no within-pair difference to measure. Therefore, we are left with studying siblings from different pregnancies, regarding prenatal exposures, when trying to adjust for familial confounding. Second, siblings are more common than twins, and therefore, sibling studies are easier to perform, and larger cohorts possible to collect. Third, replication of results in both twins and siblings ensures that the results obtained from twin studies generalize beyond twins.

This systematic review spans from pregnancy-related factors to the early childhood, inviting the investigation of the timing related to the effect of potential environmental factors. Interestingly, studies have shown that the heritability of fetal growth rate changes across trimesters (Workalemahu et al., Reference Workalemahu, Grantz, Grewal, Zhang, Louis and Tekola-Ayele2018), and that the heritability of autistic traits changes from childhood to early adulthood (Taylor, Gillberg, Lichtenstein, & Lundström, Reference Taylor, Gillberg, Lichtenstein and Lundström2017). These are examples pointing to the possibility that the controlling for familial confounding might be differentially important during different stages of development.

The aim of this systematic review was to summarize the evidence from twin and family studies about the role of environmental risk factors for NDDs, defined both dimensionally and categorically, controlling for familial confounding, in order to inform researchers and funding agencies in both preclinical and applied areas of NDDs, and guide clinical management. The potential costs of environmental factors being incorrectly connected to NDDs, owing to a lack of control of familial confounding in research, include waste of public resources, unnecessary worry, misleading advice, and eroded public trust. The broad and systematic approach of this review, incorporating all NDDs according to the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5) nomenclature, allowed us to map a wide range of environmental factors postulated to be involved in their etiology and identify factors that have not yet been sufficiently studied in relation to NDDs.

Method

This systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement (Moher, Liberati, Tetzlaff, & Altman, Reference Moher, Liberati, Tetzlaff and Altman2009). The protocol was registered in advance with PROSPERO (CRD42018079513).

Search strategy

A systematic literature search was performed by two librarians at Karolinska Institutet in October 2017 in the following databases: Medline (Ovid), PsycInfo (Ovid), Embase, Web of Science Core Collection and Cochrane Library. The search was updated in March 2019 for recently published articles. The complete search strategy for each database is available in Supplementary Appendix 1.

Eligibility

Study design: Case control and cohort studies including a twin or sibling comparison. Case control studies should include twins or siblings discordant for one or more NDDs, with the unaffected or less affected twin or sibling as the comparator. Cohort studies should include twins or siblings discordant for exposure and with one or more NDD as the outcome.

Exposure: Any specified environmental factor, with exposure time up to the age of 5 years. Only studies with a specified environmental factor were included.

Outcome: One or more of the NDDs included in DSM-5 (ASD, ADHD, ID, CD, SLD, DCD, and TD). The conditions could either be reported as categorical (diagnoses) or dimensional (symptom or traits severity). Categorical outcomes were defined according to DSM-III, DSM-IV, DSM-5, International Classification of Diseases (ICD-9), ICD-10, or earlier diagnostic practices, and based on clinical assessment, medical registries, or cut-offs for diagnosis on diagnostic instruments (APA, 1987, 2000, 2013; NCHS, 1990; WHO, Reference Zivetz1992). Dimensional outcomes were defined using disorder specific scales, or scales measuring constructs closely related to the respective conditions. Eligible studies should report the within-pair association of the exposure with one or more NDD, or with symptom or traits severity. Studies only reporting on the heritability in general terms were excluded.

Publication type: Peer-reviewed articles published in English.

Study selection and data extraction

After removal of duplicates the titles and abstracts of the studies retrieved from the search were screened using EndNote X8 and X9. The titles and abstracts of all references were screened independently by two reviewers. At this stage, a publication was excluded if the reviewers unanimously found that it was clear that it did not meet the given eligibility criteria. Publications found to be of potential relevance by at least one of the reviewers were obtained in full text and assessed for eligibility independently by two reviewers. Disagreement at this stage was solved by consensus. If necessary, a third reviewer was consulted.

The main study characteristics and results were extracted independently by two reviewers. A data extraction sheet was created, pilot tested, and modified based on the Cochrane EPOC Data Collection Checklist (Higgins et al., Reference Higgins, Altman, Gøtzsche, Jüni, Moher, Oxman and Sterne2011). Discrepancies were solved by consensus. Items extracted included: author; publication year; country; study design; study cohort; sample size; sex; age; sibling or twin control; disorder /-s studied; environmental factor /-s studied; study methodology; recruitment method; completion rates; missing data; outcomes and type of measures; and the main results.

Risk of bias assessment

The overall risk of bias of each study was rated according to the Newcastle–Ottawa Scale (NOS) for longitudinal case control and cohort studies (Wells et al., Reference Wells, Shea, O'Connell, Peterson, Welch, Losos and Tugwell2019). Three quality domains (selection, comparability, and exposure) and additional subdomains according to the NOS were assessed. Subdomains for case control studies were: adequacy of the case, representativeness of the cases, selection of controls, definition of controls, comparability of cases and controls on the basis of the design or analysis, ascertainment of exposure, same method of exposure ascertainment in cases and controls, and nonresponse rate. Subdomains for cohort studies were: representativeness of the exposed cohort, selection of the nonexposed cohort, ascertainment of exposure, demonstration that outcome of interest was not present at start of study, comparability of cohorts on the basis of the design or analysis, assessment of outcome, follow-up long enough for outcomes to occur, and adequacy of follow-up of cohorts. The NOS scores range from 0 to 9, with one point given for each subdomain reaching a predefined quality threshold (except for “comparability” where a maximum of 2 points could be allotted). It was modified to fit the bias assessment for twin and sibling studies, so that only twin studies could reach the maximum of 2 points for the comparability criterion. Consequently, sibling studies could not exceed 8 points. Studies with a score of 2 points or below were excluded. The quality of each study was assessed independently by two reviewers. If discrepancy could not be solved by consensus, a third reviewer was consulted.

Synthesis

Identified environmental factors were sorted according to chronology (prenatal; perinatal/neonatal; and infancy/childhood) and grouped by category for readability. For studies with categorical NDD outcomes, the relevant estimated association(s) were extracted. The estimates presented in the studies were sorted based on the type of measure of association used (hazard ratio; odds ratios; relative risks; and other). When no estimated association was reported, available data were used to calculate such estimates if possible. Since studies with dimensional measures routinely reported several estimated associations, an evaluation of these studies was conducted to determine if the overall findings provided a signal of an association or not (yes; possibly; or no).

A narrative synthesis of the eligible studies for each NDD was performed, with separate presentations of studies with categorical and dimensional outcomes. For environmental factors represented in more than one study for a specific condition, a judgement was made on the importance of the exposure across studies. The judgement was based on the estimated associations and the risk of bias in the respective studies. When appropriate, meta-analyses of the results on specific environmental factors and conditions were conducted, unless prevented by heterogeneity of the included studies’ exposures, study characteristics, or data presentation (Higgins & Green, Reference Higgins and Green2011).

Results

Study selection

A total of 140 studies were identified for inclusion (Figure 1). The search provided 7,315 unique citations. Two additional studies were identified from reference lists in published articles. After reviewing the abstracts, 7,061 citations were discarded in the preliminary screening, mainly due to not being consistent with the defined study design. The remaining 254 citations were examined in full text; of these 114 did not meet the eligibility criteria and were excluded (see Supplementary Appendix 2 available online). All included studies reported on within-pair associations, referred to as “association” below.

Figure 1. The 2009 PRISMA Flow Diagram

ASD

Study characteristics

In total, 58 studies (22 cohort studies and 36 case control studies) on ASD were included (see Table 1 for full list of references). All studies used a categorical definition of ASD, except for one study that used a dimensional outcome (Ronald et al., Reference Ronald, Happé, Dworzynski, Bolton and Plomin2010) and one that used both (Willfors et al., Reference Willfors, Carlsson, Anderlid, Nordgren, Kostrzewa, Berggren and Bölte2017). The studies were published between 1971 and 2019, with a steady increase from the year 2000. The studies were predominantly conducted in Scandinavia (k = 25) and North America (k = 18). The majority were sibling studies (k = 51), and seven were twin studies. The number of cases in the case control studies ranged from 5 to 1,133, with a median of 72, while the number of analyzed siblings or twins in the cohort studies ranged from 68 to 2 665,666, with a median of 921. Prospectively collected data were used in all but two of the cohort studies, and in approximately half of the case control studies. Regarding age at diagnosis, all but three of the cohort studies lacked information for the sibling subsamples, as well as a majority of the case control studies. When considering the general methodology of the complete samples, the risk of misclassification bias due to age of diagnosis was deemed low. The sex distribution among differently exposed sibling or twins was not reported in the majority of the cohort studies, while this distribution was skewed towards male cases in the case control studies. The NOS score for study quality ranged from three to nine, with more consistently high scores for cohort studies. Typical reasons for downgrading the study quality were ascertainment of exposure and definition of controls. See Table 1.

Table 1. Study characteristics—autism spectrum disorder (ASD)

Dimensional outcomes in italics. “–” and “ ”  = not reported.

Abbreviations: N = number of subjects, M = mean, SD = standard, deviation, pop. based = population based, pro.= pro. exposure data, retro. = retrospective exposure data, quasi-exp. = quasi-experimental, AFN = artificial neural networks, ASD = autism spectrum disorder, ADHD = attention-deficit/hyperactivity disorder, ID = intellectual disability, CD = communication disorders, PDD-NOS = pervasive developmental disorder, not otherwise specified, Clinical. = clinical assessment, Quest. = questionnaire, MZ = monozygotic, DZ = dizygotic

a All births in Denmark between 1997 and 2013, in total differently exposed 8,156 siblings in analysis.

b All births in Sweden between 1973 and 2008.

c All births in Sweden between 1987 and 2007, with in total 368,549 third-born siblings.

d All births in Sweden between 1982 and 2010.

e Stockholm Youth Cohort born 1996 and 2007, in total 174,428 in the ASD sibling cohort.

f All births in Sweden between 1973 and 2008, in total 2,665,666 siblings in analyses.

g All births in Sweden between 1973 and 2001, including offspring from 1,408,669 distinct fathers and 1,404,484 distinct mothers.

h Stockholm Youth Cohort born 1984 and 2007, in total 4,775 siblings in the analysis with 605 differently exposed.

i All births identified in the Manitoba Health Insurance Registry between 1 April 1998 and 31 March 2016.

j All births in Sweden between 1983 and 1992 and followed up until 2002.

k All births in Sweden between 1992 and 2005, in total 694,612 in sibling analysis.

l All births in Denmark between 1980 and 2003, 16,588 children from 7,005 families in sibling cohort.

m All births in Sweden between 1973 and 1998.

n Stockholm Youth Cohort born all individuals aged 0–17 living in Stockholm County in 2001–11 (n = 735 096).

o All births in Denmark 1996 and 2006 (n = 668,468). For SSRI: 81 exposed and 6,036 unexposed.

p All births in Sweden between 1996 and 2012. For SSRI: 9,063 exposed and 15,906 unexposed.

q All births in Sweden between 1973 and 2012.

r All births in Denmark between 1998 and 2008.

s 17 MZ and 15 DZ twin pairs.

t From the California Autism Twin Study (CATS).

u All births in Sweden between 1992 and 2007.

v High-functioning autism.

w Ages at examination ranged from 8 to 29 years (M = 16.2, SD = 5.2). In total 70 MZ pairs and 49 DZ pairs.

Prenatal exposure

The included studies examined a total of 42 prenatal exposures, 18 of which were investigated in more than one study (Table 2). Three factors were identified with predominantly positive findings. Advanced paternal age was found to be associated with ASD in three large population-based sibling cohort studies with HR (95% CI) between 1.39 (1.01–1.90) and 3.45 (1.62–7.33) and F (3, 631) = 2.40, P = .049 (D'Onofrio et al., Reference D'Onofrio, Rickert, Frans, Kuja-Halkola, Almqvist, Sjölander and Lichtenstein2014b; Hultman et al., Reference Hultman, Sandin, Levine, Lichtenstein and Reichenberg2011; Parner et al., Reference Parner, Baron-Cohen, Lauritsen, Jorgensen, Schieve, Yeargin-Allsopp and Obel2012), while a small sibling case control study with a higher risk of bias failed to replicate this finding (Hadjkacem et al., Reference Hadjkacem, Ayadi, Turki, Yaich, Khemekhem, Walha and Ghribi2016). Similarly, two population-based twin cohort studies (Losh et al., Reference Losh, Esserman, Anckarsäter, Sullivan and Lichtenstein2012; Willfors et al., Reference Willfors, Carlsson, Anderlid, Nordgren, Kostrzewa, Berggren and Bölte2017) and two population-based sibling cohort studies (Class et al., Reference Class, Rickert, Larsson, Lichtenstein and D'Onofrio2014; Pettersson et al., Reference Pettersson, Larsson, D'Onofrio, Almqvist and Lichtenstein2019) found associations for low birth weight with HR 2.44 (95% CI, 1.99–2.97), OR (95% CI) between 3.25 (1.47–7.18) and 1.38 (1.31–1.44) and Z = − 2.20, p = .028, while three sibling case control studies with higher risk of bias did not (Chien et al., Reference Chien, Chou, Chou, Wu, Tsai, Chiu and Gau2018; Mason-Brothers et al., Reference Mason-Brothers, Ritvo, Pingree, Petersen, Jenson and McMahon1990; Oerlemans et al., Reference Oerlemans, Burmanje, Franke, Buitelaar, Hartman and Rommelse2016). A similar pattern was seen for birth defects were two large population-based studies, one cohort and one case control, found an association with HR 1.3 (95% CI, 1.0–1.7) and OR 1.5 (95% CI, 1.0–2.3) (Dawson et al., Reference Dawson, Glasson, Dixon and Bower2009; Tillman et al., Reference Tillman, Hakelius, Hoijer, Ramklint, Ekselius, Nowinski and Papadopoulos2018), while one sibling case control study with higher risk of bias did not (Mason-Brothers et al., Reference Mason-Brothers, Ritvo, Pingree, Petersen, Jenson and McMahon1990). Mixed findings were reported for antidepressive medication during pregnancy (k = 5 studies) (with only one reporting a positive association (Rai et al., Reference Rai, Lee, Dalman, Newschaffer, Lewis and Magnusson2017)), advanced maternal age (k = 4), rubella infection during pregnancy (k = 2), birth order (k = 2), gestational weight gain (k = 2), stress during pregnancy (k = 2), as well as for a composite scores of prenatal complications (k = 9). No statistically significant within-pair associations were reported regarding maternal uterine bleeding (k = 4), maternal infection during pregnancy (k = 3), season of birth (k = 3), preeclampsia (k = 3), prenatal testosterone level (k = 2), urinary tract infection (k = 2), gestational diabetes (k = 2), and pre-pregnancy body mass index (k = 2). All these studies reported low effect sizes, except for the smaller of the two case control studies on urinary tract infection that reported a medium effect size (Hadjkacem et al., Reference Hadjkacem, Ayadi, Turki, Yaich, Khemekhem, Walha and Ghribi2016). An additional 24 factors were investigated in single studies. These studies found associations of ASD with measles and mumps infections during pregnancy, an interpregnancy interval of more than year, metal uptake in uterus (lead and manganese), low serum level of vitamin D at birth, and a parity greater than two.

Table 2. Environmental factors, prenatal—autism spectrum disorder (ASD)

Abbreviations: N = number of exposed twins/sibling or number of twin/sibling cases, depending on cohort or case/control study, SSRI = selective serotonin reuptake inhibitor, PCB = polychlorinated biphenyl, PVC = polyvinyl chloride, GWG = gestational weight gain, ns = nonsignificant at p = .05 level.

a HR 0.81 (0.57 to 1.14) for SSRI.

b Transformed by us from logistic regression betas.

c Asthma without medications as reference.

d Multivitamin, iron, and folic acid.

e All births in Sweden between 1987 and 2007, with in total 368,549 third-born siblings.

f All births in Sweden between 1973 and 2001, including offspring from 1,408,669 distinct fathers and 1,404,484 distinct mothers.

g Together with maternal age<35.

h Affected versus unaffected status remained significant in the regression model containing birth order as a predictor (F1,164 = 12.2; p < .001).

i Piven et al., Reference Piven, Simon, Chase, Wzorek, Landa, Gayle and Folstein1993 reports without statistics that autistic subjects were more commonly first or fourth born.

j All births in Sweden between 1973 and 2008.

k All births in Denmark between 1997 and 2013, in total differently exposed 8,156 siblings in analysis.

l Subjects had lower vitamin-D level (mean = 24.0 nM, SD = 19.6, n = 58) than in their siblings (mean = 31.9 nM, SD = 27.7, n = 5).

m Optimal one or two as reference.

Perinatal and neonatal exposure

Out of 19 perinatal and neonatal exposures, 17 were investigated in more than one study (Table 3). Twelve case control studies investigated composite scores of complications occurring during the neonatal period or earlier, of which nine reported an association. Predominantly positive findings were found for hypoxia and respiratory stress. Hypoxia was measured in two case control studies, of twins (N = 274) and siblings (N = 941), respectively, both of which showed a significant association with ASD with an OR (95% CI) of 1.71 (1.08–2.71) and 1.81 (1.21–2.69) (Froehlich-Santino et al., Reference Froehlich-Santino, Londono Tobon, Cleveland, Torres, Phillips, Cohen and Hallmayer2014; Glasson et al., Reference Glasson, Bower, Petterson, de Klerk, Chaney and Hallmayer2004). Similarly, three out of four case control studies on respiratory distress found an association, both using twins (N = 274) (Froehlich-Santino et al., Reference Froehlich-Santino, Londono Tobon, Cleveland, Torres, Phillips, Cohen and Hallmayer2014) and using siblings (N = 1,125) (Glasson et al., Reference Glasson, Bower, Petterson, de Klerk, Chaney and Hallmayer2004; Hadjkacem et al., Reference Hadjkacem, Ayadi, Turki, Yaich, Khemekhem, Walha and Ghribi2016; Piven et al., Reference Piven, Simon, Chase, Wzorek, Landa, Gayle and Folstein1993). Small effect sizes with OR (95% CI) between 1.64 (1.15–2.34) and 2.11 (1.27–3.51) were reported in all studies but one, where the confidence interval was wide, (Hadjkacem et al., Reference Hadjkacem, Ayadi, Turki, Yaich, Khemekhem, Walha and Ghribi2016). Mixed findings were reported for preterm birth (k = 5 studies) with one large population-based sibling cohort study reporting an HR of 3.2 (95%CI, 2.6–4.0), labor induction (k = 4), jaundice (k = 4), and low Apgar scores (k = 3). No statistically significant within-pair associations were reported regarding elective (k = 6) and emergency (k = 3) cesarean section, general anesthesia during labor (k = 3), breech presentation (k = 3), gestation more than 42 weeks (k = 2), difficult labor (k = 2), umbilical cord around neck (k = 2), and resuscitation (k = 2). All these studies reported small effect sizes, except for one small sibling case control study on difficult labor reporting a medium effect size (Hadjkacem et al., Reference Hadjkacem, Ayadi, Turki, Yaich, Khemekhem, Walha and Ghribi2016). Single studies found associations of ASD with incubation and neonatal respiratory infection.

Table 3. Environmental factors, perinatal, neonatal, infancy, and childhood – autism spectrum disorder (ASD)

Significant associations in bold.

Abbreviation: N = number of exposed twins/sibling or number of twin/sibling cases, depending on cohort or case/control study, ns = nonsignificant at p = 0.05 level.

a All births in Sweden between 1973 and 2008, in total 2,665,666 siblings in analyses.

b All births in Sweden between 1992 and 2005, in total 694,612 in sibling analysis.

c Violating the assumption of independent data in analysis.

d Transformed by us from logistic regression betas.

e Mainly upper respiratory tract infections and otitis media.

Infancy and childhood exposure

Nine types of exposures in infancy and childhood were investigated (Table 3). Breastfeeding (k = 3 studies) and early exposure to antibiotics (k = 2) were the only factors included in more than one study. The studies on breastfeeding were of small sizes, assessed as having high risk of bias and showed mixed results with wide confidence intervals (Brown et al., Reference Brown, Austin and Busija2014; Burd et al., Reference Burd, Fisher, Kerbeshian, Vesely, Durgin and Reep1988; Manohar et al., Reference Manohar, Pravallika, Kandasamy, Chandrasekaran and Rajkumar2018). No statistically significant within-pair association was observed for early exposure to antibiotics with both sibling studies reporting small effect sizes (Grossi et al., Reference Grossi, Migliore and Muratori2018; Hamad et al., Reference Hamad, Alessi-Severini, Mahmud, Brownell and Kuo2018). Single studies reported significant associations with recurrent infections in childhood, dysregulation during first year of life, and medical events the first 5 years of childhood.

ADHD

Study characteristics

A total of 69 studies (53 cohort studies and 16 case control studies) on ADHD were included (see Tables 4 and 5 for full list of references). A categorical definition of ADHD was used in 30 studies, while 36 studies used dimensional outcomes and three studies applied both (Altink et al., Reference Altink, Arias-Vasquez, Franke, Slaats-Willemse, Buschgens, Rommelse and Buitelaar2008; Chatterji et al., Reference Chatterji, Lahiri and Kim2014; Eilertsen et al., Reference Eilertsen, Gjerde, Reichborn-Kjennerud, Orstavik, Knudsen, Stoltenberg and Ystrom2017). The studies were published between 1987 and 2019. Similar to ASD, the amount of publications increased considerably during the last decade. The studies originated mainly from Scandinavia (k = 36) and North America (k = 19). A twin design was used in 13 of the studies, while the remaining 57 studies used siblings as controls. The number of cases in the case control designs ranged from 16 to 3,447, with a median of 233.5. In the cohort studies, the number of analyzed siblings or twins ranged from 28 to 2 665,666, with a median of 12,674. Prospectively collected data were used in 46 cohort studies, and in four of the case control studies. Regarding age at diagnosis, all but two of the cohort studies on ADHD diagnosis lacked information for the sibling subsamples. As for the ASD studies, when considering the general methodology of the whole samples, the risk of misclassification bias due to age of diagnosis was deemed low. Of the 39 studies using a dimensional outcome, 11 were performed on participants aged five years or younger. Several case control studies included a larger proportion of males among the cases than among the controls, and the sex distribution was often insufficiently reported. The NOS quality scores of the studies ranged between 3 to 9, with low scores more frequently seen in the case control studies. Common reasons for reduced scores were ascertainment of exposure and definition of controls. See Tables 4 and 5.

Table 4. Study characteristics – attention-deficit/hyperactivity disorder (ADHD), categorical (diagnosis)

Dimensional outcomes in italic. “–” and “ ” = not reported.

Abbreviations: N = number of subjects, M = mean, SD = standard, deviation, pop. based = population based, pro. = pro. exposure data, retro. = retrospective exposure data, quasi-exp. = quasi-experimental, G×E = Gene×Environment interaction, ASD = autism spectrum disorder, ADHD = attention-deficit/hyperactivity disorder, ID = intellectual disability, CD = communication disorders, DCD = developmental coordination disorder, ADHD-sympt. = ADHD-symptoms, MZ = monozygotic, DZ = dizygotic, Clinical. = clinical assessment, Quest. = questionnaire.

a All births in Demark between 1997 and 2013, in total differently exposed 12,467 siblings in analysis.

b All births in Denmark between 1997 and 2010, in total 117,529 exposed to cesarean delivery and 483,546 exposed to antibiotic treatment, with 6,821 informative families in analyses.

c All births in Sweden between 1988 and 2003, in total 988,625 (48.7% ♀) in analyses.

d All births in Sweden between 1992 and 2000, in total 272,790 siblings with 91.0% families contributing with two siblings.

e All births between in Sweden 1973 to 2008.

f All births in Sweden between 1987 and 2007, with 368,549 third-born siblings.

g All births in Sweden between 1990 and 2008.

h All births between 1973 to 2008, in total 2,665,666 siblings in analyses.

i All births between 1973 and 2001, including offspring from 1,408,669 distinct fathers and 1,404,484 distinct mothers.

j From the Norwegian Mother and Child Birth Cohort Study between 1999 and 2008 with 34,283 siblings from 94,907 mothers, covering 41% of all pregnancies in Norway.

k All births in Sweden between 1992 and 2002.

l All births in Denmark between 1991 and 2005.

m All births in Sweden between 1992 and 2000, with 430,344 siblings within 202,408 families.

n All birth in Denmark between 1996 and 2009, 44,660 siblings were discordant for exposure, with 2,246 children contributing informative to the estimates.

o All birth in Denmark between 1996 and 2009.

p All births in Hong Kong public hospitals between 2001 and 2009, with 53,616 siblings in analysis.

q From a large regional health care system in the upper Midwest of the United States of America, in total 1,958 siblings in analysis.

r All births in Finland between 1987 and 2001.

s All births in Denmark between 1991 and 2006, in total 684,042 siblings in analysis.

t All births in Sweden between 1973 and 1998.

u All births in Sweden between 1992 and 2000.

v All births in Sweden between 1996 and 2012. For SSRI: 9,063 exposed and 15,906 unexposed.

w All births in Sweden between 1973 and 2012.

x All births in Sweden between 1992 and 2005.

y Belgium, Germany, Ireland, Spain, Switzerland, the Netherlands, the United Kingdom, and Israel.

z Ages at examination ranged from 8 to 29 years (M = 16.2, SD = 5.2) for the whole sample. In total 70 MZ pairs and 49 DZ pairs.

Table 5. Study characteristics—attention-deficit/hyperactivity disorder (ADHD), dimensional (traits or symptoms)

Dimensional outcomes in italic. “–” and “ “ = not reported.

Abbreviations: N = number of subjects, M = mean, SD = standard, deviation, pop. based = population based, pro. = pro. exposure data, retro. = retrospective exposure data, quasi-exp. = quasi-experimental, G×E = Gene×Environment interaction, ASD = autism spectrum disorder, ADHD = attention-deficit/hyperactivity disorder, ID = intellectual disability, CD = communication disorders, DCD = developmental coordination disorder, ADHD-sympt. = ADHD-symptoms, Hyper. = hyperactivity traits, Int. = Intellectual, Lang. = language, IQ = Intelligent quotient, Att. prob. = Attention problems, MZ = monozygotic, DZ = dizygotic, UZ = unknown zygosity, Clinical. = Clinical assessment, Quest. = questionnaire.

a From the Twins Early Development Study (TEDS).

b Same-sex siblings.

c From the Norwegian Mother and Child Birth Cohort Study between 1999 and 2008, covering approximately 40% of all pregnancies in Norway.

d Externalizing behavior as proxy for ADHD-symptoms.

e All sibling pairs were same-sex.

f A total of 13,191 siblings.

g A total of 34,283 siblings (48% female).

h A total of 17,830 siblings in analyses.

i From the Norwegian Mother and Child Birth Cohort Study between 1999 and 2009, covering 41% of all pregnancies in Norway.

j Same-sex DZ twin pairs.

k 633 siblings in analysis.

l From the Missouri Mothers and Their Children study (MO-MATCH), birthyears 1998–2005.

m The Twins Early Development Study (TEDS), with twins born in England and Wales between 1994 and 1996.

n Parents of all Swedish 9- and 12-year-old twins born between 1992 and 2000 were interviewed. A total of 21,775 twins.

o A total of 17,910 siblings in analysis.

p Belgium, Germany, Ireland, Spain, Switzerland, the Netherlands, the United Kingdom and Israel.

q A total of 3,447 siblings from 1,258 mothers in analyses.

r From the National Longitudinal Survey of Youth 1979 and their children.

s A total of 1,752 siblings from 704 mothers in analyses.

t A total of 1,684 siblings in analyses.

u From a research project on developmental dyslexia with 238 probands and 230 siblings.

Prenatal exposure

The studies included 19 prenatal exposures, of which 10 were investigated in more than one study (Tables 6 and 7). Predominantly positive associations were observed for fetal growth/birth weight. For a categorical outcome, two large population-based sibling studies (Class et al., Reference Class, Rickert, Larsson, Lichtenstein and D'Onofrio2014; Pettersson et al., Reference Pettersson, Larsson, D'Onofrio, Almqvist and Lichtenstein2019) and a small twin study (N = 38) (Lehn et al., Reference Lehn, Derks, Hudziak, Heutink, van Beijsterveldt and Boomsma2007) showed associations including a reported HR of 2.44 (95% CI, 1.99–2.97), an OR of 2.36 (95% CI, 2.27–2.43) and t (18) = −1.99, p (one-tailed) = .031, while a study with higher risk of bias reported no association (N = 1,464) (Chatterji et al., Reference Chatterji, Lahiri and Kim2014). The same pattern was seen for a dimensional outcome, with two large population-based sibling studies (Jackson & Beaver, Reference Jackson and Beaver2015; Lim et al., Reference Lim, Liu, Schoeler, Cecil, Barker, Viding and Pingault2018) and two twin studies (N = 8,594) (Groen-Blokhuis et al., Reference Groen-Blokhuis, Middeldorp, van Beijsterveldt and Boomsma2011; Hultman et al., Reference Hultman, Torrang, Tuvblad, Cnattingius, Larsson and Lichtenstein2007; Pettersson et al., Reference Pettersson, Sjölander, Almqvist, Anckarsäter, D'Onofrio, Lichtenstein and Larsson2015; Tore et al., Reference Tore, Antoniou, Reed, Southwood, Smits, McCleery and Zeegers2018) reporting associations, while studies with higher risk of bias reported no associations or mixed results (N = 2,581) (Asbury et al., Reference Asbury, Dunn and Plomin2006; Mascheretti et al., Reference Mascheretti, Trezzi, Giorda, Boivin, Plourde, Vitaro and Marino2017). Mixed results were seen for smoking (k = 11 studies) and alcohol use (k = 3) during pregnancy, parental age (k = 6) and maternal depression (k = 2). Smoking was frequently studied, and, interestingly, somewhat different patterns emerged depending of types of outcome. Predominantly, no statistically significant within-pair associations were seen regarding a diagnosis of ADHD, with three large population-based sibling cohort studies (Obel et al., Reference Obel, Olsen, Henriksen, Rodriguez, Jarvelin, Moilanen and Gissler2011, Reference Obel, Zhu, Olsen, Breining, Li, Gronborg and Rutter2016; Skoglund et al., Reference Skoglund, Chen, D'Onofrio, Lichtenstein and Larsson2014) and one sibling case control study (N = 476) (Oerlemans et al., Reference Oerlemans, Burmanje, Franke, Buitelaar, Hartman and Rommelse2016) showing no association, and with one sibling case control study of higher risk of bias reporting an association (N = 906) (Altink et al., Reference Altink, Arias-Vasquez, Franke, Slaats-Willemse, Buschgens, Rommelse and Buitelaar2008). For dimensional measures of ADHD-symptomatology the results were less clear, with one large population-based sibling cohort study (Gustavson et al., Reference Gustavson, Ystrom, Stoltenberg, Susser, Surén, Magnus and Reichborn-Kjennerud2017) and two sibling case control studies (D'Onofrio et al., Reference D'Onofrio, Van Hulle, Waldman, Rodgers, Harden, Rathouz and Lahey2008; Ellingson et al., Reference Ellingson, Goodnight, Van Hulle, Waldman and D'Onofrio2014), showing no association, with two studies of higher risk of bias reporting an association (Altink et al., Reference Altink, Arias-Vasquez, Franke, Slaats-Willemse, Buschgens, Rommelse and Buitelaar2008; Mascheretti et al., Reference Mascheretti, Trezzi, Giorda, Boivin, Plourde, Vitaro and Marino2017) and with two of a lower risk of bias reporting mixed results (Knopik et al., Reference Knopik, Marceau, Bidwell, Palmer, Smith, Todorov and Heath2016; Marceau et al., Reference Marceau, Cinnamon Bidwell, Karoly, Evans, Todorov, Palmer and Knopik2017). A population-based cohort study on siblings found an association between alcohol use during pregnancy and a dimensional but not categorical ADHD outcome (Eilertsen et al., Reference Eilertsen, Gjerde, Reichborn-Kjennerud, Orstavik, Knudsen, Stoltenberg and Ystrom2017), while one population-based case control study (D'Onofrio et al., Reference D'Onofrio, Van Hulle, Waldman, Rodgers, Rathouz and Lahey2007) and one cohort study (Ichikawa et al., Reference Ichikawa, Fujiwara and Kawachi2018), both on siblings, found no association using dimensional measures. For parental age, one sibling case control study found no association of parental age with a categorical outcome of ADHD (N = 476) (Oerlemans et al., Reference Oerlemans, Burmanje, Franke, Buitelaar, Hartman and Rommelse2016), one large population-based sibling cohort study found teenage birth to be protective, HR 0.81 (95%CI, 0.71–0.94), one did not, HR 1.28 (95%CI, 0.94–1.73) (Chang et al., Reference Chang, Lichtenstein, D'Onofrio, Almqvist, Kuja-Halkola, Sjölander and Larsson2014; Hvolgaard Mikkelsen, Olsen, Bech, & Obel, Reference Hvolgaard Mikkelsen, Olsen, Bech and Obel2017), and one sibling case control study with higher risk of bias found an association with a small effect size between advanced maternal age and ADHD (N = 108) (Mimouni-Bloch et al., Reference Mimouni-Bloch, Kachevanskaya, Mimouni, Shuper, Raveh and Linder2013). Furthermore, one population-based sibling cohort study found a strong association between advanced paternal age and ADHD diagnosis, HR 13.13 (95%CI, 6.85–25.16) (D'Onofrio et al., Reference D'Onofrio, Rickert, Frans, Kuja-Halkola, Almqvist, Sjölander and Lichtenstein2014b). Two sibling cohort studies with moderate risk of bias, including age of participants five years or younger, showed conflicting results regarding associations between maternal depression and dimensional outcomes of ADHD (Gjerde et al., Reference Gjerde, Eilertsen, Reichborn-Kjennerud, McAdams, Zachrisson, Zambrana and Ystrom2017; Nulman et al., Reference Nulman, Koren, Rovet, Barrera, Streiner and Feldman2015). No statistically significant within-pair associations were reported for antidepressive medication during pregnancy (k = 3 categorical and 2 dimensional studies), maternal infection (k = 2 categorical and 1 dimensional study), stress or adverse family life events during pregnancy (k = 2 categorical and 1 dimensional studies), maternal weight (k = 2 categorical studies), and birth order (k = 2 categorical studies). All these studies reported small effect sizes, except for one small sibling case control study on stress during pregnancy reporting a medium effect size, with wide confidence interval, on ADHD diagnosis (Grizenko et al., Reference Grizenko, Fortier, Zadorozny, Thakur, Schmitz, Duval and Joober2012). An additional 10 environmental factors were investigated in single studies. These studies suggested associations of head circumference at birth and orofacial clefts with ADHD diagnosis, and paracetamol exposure and possibly history of miscarriage with ADHD-symptoms.

Table 6. Environmental factors—categorical attention-deficit/hyperactivity disorder (ADHD)-diagnosis

Significant associations in bold.

Abbreviations: N = number of exposed twins/sibling or number of twin/sibling cases, depending on cohort or case/control study, VD = vaginal delivery, CS = cesarean section, ns = nonsignificant at p = .05 level.

a HR = 0.94 (0.73–1.22) for SSRI.

b All births in Hong Kong public hospitals between 2001 and 2009, with 53,616 siblings in analysis.

c All birth in Denmark between 1996 and 2009, 44,660 siblings were discordant for exposure, with 2,246 children contributing informative to the estimates.

d This association remained significant after stratifying by 7-repeat allele carriership.

e All births in Finland between 1987 and 2001.

f All births in Denmark between 1991 and 2006, in total 684,042 siblings in analysis.

g From the Norwegian Mother and Child Birth Cohort Study between 1999 and 2008 with 34,283 siblings from 94,907 mothers, covering 41% of all pregnancies in Norway.

h All births between 1973 and 2001, including offspring from 1,408,669 distinct fathers and 1,404,484 distinct mothers.

i All births in Sweden between 1988 and 2003, in total 988,625 (48.7% ♀) in analyses.

j All births between in Sweden 1973 to 2008.

k All births in Demark between 1997 and 2013, in total differently exposed 12,467 siblings in analysis.

l Transformed by us from logistic regression betas.

m All births in Sweden between 1992 and 2000, in total 272,790 siblings with 91.0% families contributing with two siblings.

n Obesity HR: 1.15 (0.85 to 1.56).

o From a large regional health care system in the upper Midwest of the United States of America, in total 1,958 siblings in analysis.

p Maternal infection requiring hospitalization during pregnancy.

q All births between 1973 to 2008, in total 2,665,666 siblings in analyses.

r All births in Denmark between 1997 and 2010, in total 117,529 exposed to cesarean delivery and 483,546 exposed to antibiotic treatment, with 6,821 informative families in analyses.

s All births in Sweden between 1992 and 2000, with 430,344 siblings within 202,408 families.

Table 7. Environmental factors—dimensional attention-deficit/hyperactivity disorder (ADHD) traits or symptoms.

Abbreviations: N = number of exposed twins/sibling or number of twin/sibling cases, depending on cohort or case/control study, SD = standard deviation, 95%CI = 95% confidence interval, MZ = monozygotic.

a A total of 13,191 siblings.

b From a research project on developmental dyslexia with 238 probands and 230 siblings.

c A total of 1,752 siblings from 704 mothers in analyses.

d A total of 1,684 siblings in analyses.

e A total of 34,283 siblings (48% female).

f A total of 3,447 siblings from 1,258 mothers in analyses.

g Parents of all Swedish 9- and 12-year-old twins born between 1992 and 2000 were interviewed. A total of 21,775 twins.

h A total of 633 siblings in analysis.

i A total of 17,830 siblings in analyses.

j A total of 17,910 siblings in analysis.

Perinatal and neonatal exposure

Out of 11 perinatal and neonatal exposures, the only factors included in more than one study were mode of delivery and gestational age (Tables 6 and 7). Regarding gestational age, two large sibling cohort studies found associations; one with a categorical, HR 2.3 (95% CI, 2.0–2.8), and one with a dimensional outcome (Ask et al., Reference Ask, Gustavson, Ystrom, Havdahl, Tesli, Askeland and Reichborn-Kjennerud2018; D'Onofrio et al., Reference D'Onofrio, Class, Rickert, Larsson, Langstrom and Lichtenstein2013a). For mode of delivery all studies used a categorical outcome of ADHD. The results were mixed depending on the specific mode. One large population-based sibling cohort study showed an association, HR 1.13 (95% CI, 1.01–1.26), between emergency cesarean section, but not for elective cesarean or assisted vaginal delivery (Curran et al., Reference Curran, Khashan, Dalman, Kenny, Cryan, Dinan and Kearney2016), while another large population-based sibling cohort study found no statistically significant within-pair association with either form of cesarean section (Axelsson et al., Reference Axelsson, Clausen, Petersen, Hageman, Pinborg, Kessing and Lokkegaard2019). A small twin study with higher risk of bias also found no association (N = 32) (Pearsall-Jones et al., Reference Pearsall-Jones, Pieka, Martin, Rigoli, Levy and Hay2008). One single sibling study found associations of ADHD diagnosis with a composite score of pre-, peri-, and neonatal complications (Ben Amor et al., Reference Ben Amor, Grizenko, Schwartz, Lageix, Baron, Ter-Stepanian and Joober2005). Regarding dimensional outcomes, three single studies found an association of attention problems with heart surgery, hypothyroidism, and neuroblastoma, respectively, and one study found an association between higher levels of phenylalanine exposure and executive functions.

Infancy and childhood exposure

Twelve different exposures in infancy and early childhood were investigated (Tables 6 and 7). Breastfeeding (k = 2 studies), low income or transient income decline (k = 2), meningitis (k = 2), and parenting (k = 2, based on the same cohort) were examined in more than one study. Positive associations were found for low income or transient income decline with one large cohort study regarding ADHD diagnosis, HR 1.37 (95% CI, 1.07–1.75) (Larsson et al., Reference Larsson, Sariaslan, Långström, D'Onofrio and Lichtenstein2014), and one large cohort study regarding dimensionally assessed externalizing behaviors (Ramanathan et al., Reference Ramanathan, Balasubramanian and Faraone2017). Regarding breastfeeding, one sibling case control study with high risk of bias reported an association between lack of breastfeeding at 3 months and a categorical outcome of ADHD (N = 108) (Mimouni-Bloch et al., Reference Mimouni-Bloch, Kachevanskaya, Mimouni, Shuper, Raveh and Linder2013), while another sibling case control study showed no association to ADHD-symptoms (Mascheretti et al., Reference Mascheretti, Trezzi, Giorda, Boivin, Plourde, Vitaro and Marino2017). Mixed results were found for meningitis and parenting, with all studies using dimensional outcomes. Single studies reported associations of ADHD diagnosis with parental divorce and maternal depression.

Intellectual disability

Study characteristics

A total of 26 studies (21 cohort studies and five case control studies) on ID or a dimensional measure of IQ were identified (see Tables 8 and 9 for full list of references). A categorical definition was used in six studies (Chatterji et al., Reference Chatterji, Lahiri and Kim2014; Heuvelman et al., Reference Heuvelman, Abel, Wicks, Gardner, Johnstone, Lee and Rai2018; Monset-Couchard et al., Reference Monset-Couchard, de Bethmann and Relier2004; Steingass et al., Reference Steingass, Taylor, Wilson-Costello, Minich and Hack2013; Sussmann et al., Reference Sussmann, McIntosh, Lawrie and Johnstone2009; Tillman et al., Reference Tillman, Hakelius, Hoijer, Ramklint, Ekselius, Nowinski and Papadopoulos2018), while 19 studies included analyses based on IQ scores, and one study used both (Petik et al., Reference Petik, Czeizel, Banhidy and Czeizel2012). The studies were published between 1965 and 2019, and were predominantly from North America (k = 16). A twin design was used in eight of the studies. The number of cases in the case control studies ranged from 49 to 3,296. In the cohort studies, the number of analyzed siblings or twins ranged from 24 to 20,471, with a median of 73. The data were prospectively collected in all of the studies. Age at assessment were reported in all but six of the studies. When reported, the sex distribution was less skewed than in the ASD and ADHD studies. The NOS scores ranged from 5 to 9, with the most common reason for downgrading being representativeness of the exposed cohort. See Tables 8 and 9.

Table 8. Study characteristics—categorical (diagnosis) intellectual disability, communication disorders, developmental coordination disorder and TIC disorder

Dimensional outcomes in italic. “–” and “ ”  = not reported.

Abbreviations: N = number of subjects, M = mean, SD = standard, deviation, pop. based = population based, pro. = pro. exposure data, retro. = retrospective exposure data, quasi-exp. = quasi-experimental, G×E = Gene×Environment interaction, ASD = autism spectrum disorder, ADHD =  attention-deficit/hyperactivity disorder, ID = intellectual disability, CD = communication disorders, DCD = developmental coordination disorder, ADHD-sympt. = ADHD-symptoms, MZ = monozygotic, DZ = dizygotic, Clinical. = Clinical assessment, Quest. = questionnaire.

a All births in Sweden between 1973 and 2003, including 947,942 families with at least two differently exposed children, and 3,563 families including siblings discordant for tic disorders.

b All births in Sweden between 1973 and 2012.

c The Stockholm Youth Cohort of all individuals under 18 years of age who lived in Stockholm County for at least 1 year between 2001 and 2011.

Table 9. Study characteristics—Dimensional (traits/symptoms) intellectual disability, communication disorders, developmental coordination disorder and TIC disorder

Dimensional outcomes in italic. “–” and “ ”  = not reported.

Abbreviations: N = number of subjects, M = mean, SD = standard, deviation, pop. based = population based, pro. = pro. exposure data, retro. = retrospective exposure data, quasi-exp. = quasi-experimental, G×E = Gene×Environment interaction, ASD = autism spectrum disorder, ADHD =  attention-deficit/hyperactivity disorder, ID = intellectual disability, CD = communication disorders, DCD = developmental coordination disorder, IQ = Intelligent quotient, ADHD-sympt. = ADHD symptoms, Int. = Intellectual, Lang. = language, MZ = monozygotic, DZ = dizygotic, Quest. = questionnaire.

a From the Norwegian Mother and Child Birth Cohort Study between 1999 and 2008, covering approximately 40% of all pregnancies in Norway.

b There were 49.4% females in total sibling sample.

c From all draft board examinations in Denmark between 1995 and 2015, 3.4% female in total sample.

d There were 22 clinically identical sets of twins.

Prenatal exposure

Seven prenatal exposures were identified (Tables 10 and 11). Fetal growth was investigated in six studies, with consistent results that differed between studies of ID and studies of IQ. Two twin studies of high quality (N = 248) and a case control study (N = 1,464) with higher risk of bias showed no statistical within-pair association with a diagnosis of ID (Chatterji et al., Reference Chatterji, Lahiri and Kim2014; Monset-Couchard et al., Reference Monset-Couchard, de Bethmann and Relier2004; Steingass et al., Reference Steingass, Taylor, Wilson-Costello, Minich and Hack2013). For IQ, on the other hand, two twin studies (N = 144) and one sibling cohort study (N = 50) found associations (Bellido-González et al., Reference Bellido-González, Defior-Citoler and Díaz-López2007; Churchill, Reference Churchill1965; Kilbride et al., Reference Kilbride, Thorstad and Daily2004). Suicide attempts with Tardyl during pregnancy was associated with ID and IQ in one sibling study, one large population-based sibling case control study suggested an increased risk for ID linked to nonoptimal gestational duration, and one large population-based sibling cohort study reported increased risk for ID linked to orofacial clefts.

Table 10. Environmental factors—categorical (diagnosis) for: intellectual disability, communication disorders, developmental coordination disorder and TIC disorder

Significant associations in bold.

Abbreviations: N = number of exposed twins/sibling or number of twin/sibling cases, depending on cohort or case/control study, NEC = necrotizing enterocolitis, ns = nonsignificant at p = .05 level.

a Gestational age 37–41 weeks as reference.

b All births in Sweden between 1973 and 2003, including 947,942 families with at least two differently exposed children, and 3,563 families including siblings discordant for tic disorders.

c No statistics reported, though Tourette's syndrome occurred in the lighter twin in all of the seven discordant twin pairs, with nine concordant pairs.

Table 11. Environmental factors—dimensional (symptoms and traits) for: intellectual disability, communication disorders, developmental coordination disorder and TIC disorder

Abbreviations: N = number of exposed twins/sibling or number of twin/sibling cases, depending on cohort or case/control study, SSRI = selective serotonin uptake inhibitor, ALL = acute lymphatic leukemia, SGA = small for gestational age, SD = standard deviation, 95%CI = 95% confidence interval, MZ = monozygotic.

a Coxsackievirus, echovirus, or poliovirus.

Perinatal, neonatal, infancy, and childhood

Seven perinatal and neonatal exposures were investigated in single studies, of which not being breastfed on discharge from a special care unit was associated with ID (Tables 10 and 11).

Seven different exposures in infancy and childhood were investigated, of which three were investigated in more than one study, all with dimensional outcome of IQ. Two small sibling cohort studies (N = 292) found an association between congenital hypothyroidism and IQ (Oerbeck et al., Reference Oerbeck, Sundet, Kase and Heyerdahl2003; Rovet, Reference Rovet1986), while three cohort studies showed mixed results regarding malnourishment (Beardslee et al., Reference Beardslee, Wolff, Hurwitz, Parikh and Shwachman1982; Klein et al., Reference Klein, Forbes and Nader1975; Lloyd-Still et al., Reference Lloyd-Still, Hurwitz, Wolff and Shwachman1974), and two cohort studies found mixed results for congenital heart disease surgery (Ellerbeck et al., Reference Ellerbeck, Smith, Holden, McMenamin, Badawi, Brenner and Hyman1998; Schultz et al., Reference Schultz, Ittenbach, Gerdes, Jarvik, Wernovsky, Bernbaum and Gaynor2017).

Developmental coordination disorder

Study characteristics

A total of 13 relevant studies (12 cohort studies and one case control study) were found for DCD (see Tables 8 and 9 for full list of references). A categorical definition was used in the case control study (Pearsall-Jones et al., Reference Pearsall-Jones, Pieka, Martin, Rigoli, Levy and Hay2008), while the cohort studies used dimensional outcomes of motor skills. The studies were published between 1974 and 2017, and were predominantly from North America (k = 8). A twin design was used in six of the studies. The case control study included 16 cases. In the cohort studies, the number of analyzed siblings or twins ranged from 28 to 3,590, with a median of 67. The data were prospectively collected in all the studies but one (Pearsall-Jones et al., Reference Pearsall-Jones, Pieka, Martin, Rigoli, Levy and Hay2008). Age at assessment was reported in all but two of the studies. When reported, no pattern of skewness in the sex distribution could be seen. The NOS scores ranged from 4 to 9, with the most common reason for downgrading being representativeness of the exposed cohort and adequacy of follow up of cohorts. See Tables 8 and 9.

Exposures

Twelve different exposures were identified for DCD or motor skills (see Tables 10 and 11 for full list of references). They were too few in order to be sorted according to chronology. Fetal growth was found associated with motor skills in two twin cohort studies (N = 116) and one sibling cohort study (N = 50) (Kilbride et al., Reference Kilbride, Thorstad and Daily2004; Monset-Couchard et al., Reference Monset-Couchard, de Bethmann and Relier2004; Ylitalo et al., Reference Ylitalo, Kero and Erkkola1988). Two small sibling cohort studies (N = 292) found an association between congenital hypothyroidism and motor skills (Oerbeck et al., Reference Oerbeck, Sundet, Kase and Heyerdahl2003; Rovet, Reference Rovet1986). Perinatal hypoxic risk was not significantly associated to motor skills in two studies from the same twin cohort (N = 56) (Raz et al., Reference Raz, Shah and Sander1996, Reference Raz, Glogowski-Kawamoto, Yu, Kronenberg, Hopkins, Lauterbach and Sander1998). One single population-based cohort showed an association with maternal paracetamol use during pregnancy (Brandlistuen et al., Reference Brandlistuen, Ystrom, Nulman, Koren and Nordeng2013), while one twin cohort study found an association with congenital heart disease surgery (Schultz et al., Reference Schultz, Ittenbach, Gerdes, Jarvik, Wernovsky, Bernbaum and Gaynor2017), both using dimensional outcomes for motor skills. No other statistically significant within-pair associations were found.

Communication disorder

Study characteristics

Eight eligible studies (seven cohort studies and one case control study) were identified for CD (see Tables 8 and 9 for full list of references). A categorical definition of communication disorder was used in one study (Tillman et al., Reference Tillman, Hakelius, Hoijer, Ramklint, Ekselius, Nowinski and Papadopoulos2018), while the remaining studies used dimensional outcome of language development. The studies were published between 1986 and 2018, and were predominantly from North America (k = 5). A twin design was used in two of the studies (Bishop, Reference Bishop1997; Schultz et al., Reference Schultz, Ittenbach, Gerdes, Jarvik, Wernovsky, Bernbaum and Gaynor2017). There were 19 cases in the case control study (Bishop, Reference Bishop1997). In the cohort studies, the number of analyzed siblings or twins ranged from 28 to 16,275, with a median of 90. The data were prospectively collected in all the studies. Age at assessment was reported in all but two of the studies. When reported, no pattern of skewness in the sex distribution could be seen. The NOS scores ranged from five to eight, with the most common reason for downgrading being representativeness of the exposed cohort. See Tables 8 and 9.

Exposures

Seven different exposures were identified for CD or language skills (Tables 10 and 11). They were too few in order to be sorted according to chronology. Congenital hypothyroidism was linked to lower language skills in one sibling cohort study (N = 202) (Rovet, Reference Rovet1986), while another sibling cohort study showed an inconsistent and weak association (N = 90) (Oerbeck et al., Reference Oerbeck, Sundet, Kase and Heyerdahl2003). One single large population-based cohort study suggested that orofacial clefts were associated with a categorical outcome of CD (Tillman et al., Reference Tillman, Hakelius, Hoijer, Ramklint, Ekselius, Nowinski and Papadopoulos2018). A possible association was also observed for fetal growth in preterm infants (Kilbride et al., Reference Kilbride, Thorstad and Daily2004). No other statistically significant within-pair associations were found.

Tic disorder

Study characteristics

Two studies were identified for TD (Tables 8 and 9). One of these was a large-scale Swedish cohort study based on registry data on siblings from the general population (Brander et al., Reference Brander, Rydell, Kuja-Halkola, Fernandez de la Cruz, Lichtenstein, Serlachius and Mataix-Cols2017). The other was a retrospective case control study from the USA based on 16 MZ twins pairs (Hyde et al., Reference Hyde, Aaronson, Randolph, Rickler and Weinberger1992). Both studies had a NOS quality score of 7.

Exposures

Seven different exposures were identified for TD (Tables 10 and 11). They were too few in order to be sorted according to chronology. Birth weight, the only exposure studied in both studies, was associated with both a diagnosis of tic disorder, HR 1.46 (95% CI, 1.06–2.01), and with symptom severity. No other statistically significant within-pair associations were found.

Specific learning disorder

No relevant studies were identified.

Discussion

Twin and sibling studies can help disentangle genetic and environmental contributions to the pathways underlying NDDs. In the current systematic review, we found evidence, beyond familial confounding, that advanced paternal age, low birth weight, birth defects, and perinatal hypoxia and respiratory stress are consistently associated with a diagnosis of ASD. We also found evidence that low birth weight, gestational age and low family income or transient income decline during childhood are associated with ADHD, both categorically and dimensionally. There was some evidence for congenital hypothyroidism being associated with lower IQ, low motor skills, and possibly low language skills, but our confidence in these results is limited due to a higher risk of bias. While some studies suggested low birth weight to be associated with TD, tic symptom severity and lower IQ, there was no association with a diagnosis of ID.

Furthermore, we found no evidence that maternal uterine bleeding, maternal infection during pregnancy, season of birth, preeclampsia, prenatal testosterone level, urinary tract infection during pregnancy, gestational diabetes, prepregnancy body mass index, elective and emergency cesarean section, general anesthesia during labor, breech presentation, gestation longer than 42 weeks, difficult labor, umbilical cord around neck, resuscitation, and early exposure to antibiotics in childhood is associated with a diagnosis of ASD when familial confounding is taken into account; no evidence that antidepressive medication, maternal infection, and stress or adverse family life events during pregnancy are is associated with ADHD defined both categorically and dimensionally, or that maternal weight, smoking during pregnancy, and birth order are associated with a diagnosis of ADHD; and no evidence that perinatal hypoxic risk is associated with low motor skills, when controlled for familial confounding. It is important to keep in mind that, in general, absence of evidence is not the same thing as evidence that no association exists. This is especially true when the empirical evidence is scarce, due to too few studies and/or small sample sizes. Regarding the associations of maternal uterine bleeding, preeclampsia, gestational diabetes, pre-pregnancy body mass index, and elective and emergency cesarean section, with ASD, our results point in the direction of evidence of no association beyond familial confounding. The same is the case for the associations of antidepressive medication, maternal infection, maternal weight, and maternal smoking during pregnancy, with a diagnosis of ADHD. For the rest, the conclusion to be drawn is that no clear statement can be made.

The most extensively studied factors with conflicting findings are the associations between ASD and antidepressive medication during pregnancy, advanced maternal age, preterm birth, labor induction, and neonatal jaundice; and the associations between ADHD, both categorically and dimensionally, and alcohol use during pregnancy, and parental age. We found categorically cross-disorder associations of low birth weight (ASD, ADHD, and TD) and cross-dimensional associations for congenital hypothyroidism (lower IQ, low motor skills, and possibly low language skills).

With familial confounding being controlled for, the findings of the current review may point to several possible mechanisms underlying the associations between NDDs and environmental factors. For ASD, it has been shown that the father's age at conception correlates to the number of de novo mutations in their children (Kong et al., Reference Kong, Frigge, Masson, Besenbacher, Sulem, Magnusson and Stefansson2012). De novo mutations are in turn, among others, linked to ASD, thereby suggesting a possible genetic pathway (Neale et al., Reference Neale, Kou, Liu, Ma'ayan, Samocha, Sabo and Daly2012; O'Roak et al., Reference O'Roak, Vives, Girirajan, Karakoc, Krumm, Coe and Eichler2012; Sanders et al., Reference Sanders, Murtha, Gupta, Murdoch, Raubeson, Willsey and State2012). For ADHD, a pathway has been hypothesized to explain the association between low family income or family income decline during early childhood and ADHD in offspring. These include evidence of a strong association between low SES and the prefrontal working memory system (Hackman, Farah, & Meaney, Reference Hackman, Farah and Meaney2010), in turn described as a neuropsychological ADHD endophenotype (Castellanos & Tannock, Reference Castellanos and Tannock2002). As for the pathways underlying the association of restricted fetal growth with ASD, ADHD, and TD our cross-disorder finding is in line with a body of evidence linking fetal growth to these and several other psychiatric disorders. It has even been modelled that a general factor of psychopathology is linked to restricted fetal growth (Pettersson et al., Reference Pettersson, Larsson, D'Onofrio, Almqvist and Lichtenstein2019). Furthermore, birth weight differences have previously been linked to altered brain development (Walhovd et al., Reference Walhovd, Fjell, Brown, Kuperman, Chung, Hagler and Dale2012), although with unknown mediating mechanisms. As for the link between smoking during pregnancy and ASD, Hultman, Sparén, and Cnattingius (Reference Hultman, Sparén and Cnattingius2002) reported an OR of 1.4 (95% CI 1.1–1.8), but as shown in the most recent study by Kalkbrenner et al. (Reference Kalkbrenner, Meier, Madley-Dowd, Ladd-Acosta, Fallin, Parner and Schendel2020) this link is better explained by familial confounding, with the exposure of maternal smoking being associated with numerous social and social-class related factors and the possibility of genes affecting both exposure and outcome. This leads to the conclusion that factors considered to be environmental might actually not be strictly environmental. Therefore, it is a problem with referring to them as being ‘nongenetic’. This has been pointed out before (Plomin, DeFries, Knopik, & Neiderhiser, Reference Plomin, DeFries, Knopik and Neiderhiser2016), and we suggest for future studies to more comprehensively consider the genetic basis of ‘environmental’ factors in order to help us understand the etiology of NDDs.

As noted above, some apparent discrepancies were observed when comparing categorical or dimensional outcomes. First, contrary to the associations with ASD, ADHD, and TD, fetal growth did not show an association with ID diagnosis, but to the level of IQ. This points to the possibility of a different mechanism for a clinical diagnosis of ID compared to IQ level in the rest of the distribution. This is in line with the findings of Reichenberg et al. (Reference Reichenberg, Cederlöf, McMillan, Trzaskowski, Kapra, Fruchter and Lichtenstein2016), which suggested that the profound ID is a distinct entity from milder ID, with different genetic and environmental influences to milder ID. Second, regarding ADHD it is interesting to note that for smoking during pregnancy, despite no evidence of it being associated to an ADHD diagnosis aside from familial confounding, three of the four studies with a positive association looking at dimensional outcomes noted a link to hyperactivity/impulsivity, but not to inattentiveness (Table 7). This suggests that these traits might have different underlying mechanisms. Although these dimensions are differentially implicated in neuropsychological impairment (Willcutt et al., Reference Willcutt, Nigg, Pennington, Solanto, Rohde, Tannock and Lahey2012), the underlying mechanisms are still unclear.

The latter shows that using dimensional outcomes compared to categorical ones differentiates different symptom dimensions within the same condition. It has previously been shown that social and nonsocial traits in ASD are genetically dissociable (Happé & Ronald, Reference Happé and Ronald2008), and that hyperactivity/impulsiveness and inattentiveness in ADHD have distinguishable underlying pathways (Castellanos, Sonuga-Barke, Milham, & Tannock, Reference Castellanos, Sonuga-Barke, Milham and Tannock2006; Kuntsi et al., Reference Kuntsi, Pinto, Price, van der Meere, Frazier-Wood and Asherson2014; Luo, Weibman, Halperin, & Li, Reference Luo, Weibman, Halperin and Li2019; Sonuga-Barke, Reference Sonuga-Barke2005). This review cannot answer whether this holds true also for different environmental factors and ASD, since first, only two of the included studies on ASD used a dimensional measure, and second, those two only used a combined measure of total ASD severity, not separated on social and nonsocial traits. So, it remains unclear if social and nonsocial traits in ASD are environmentally dissociable. Although the value of a dimensional approach in NDD research is now undisputed, it is also important to keep in mind that dimensional data do not necessarily have clinical relevance, and there might be a qualitative shift in mechanisms along the symptomatic continuum.

Strikingly, while there is a wealth of studies on exposures in ASD and ADHD, and to some extent low IQ/ID, there is little research on other NDDs, including few to no studies on CD, except for specific learning disorders, despite these being common in the general population (Aschner & Costa, Reference Aschner and Costa2015; Bishop, Reference Bishop2010). This systematic review also points to the lack of geographic dispersion with most twin and sibling studies being conducted in North America and Scandinavia, highly developed areas of the world both with regards of environmental regulations and health care. It may, for example, not be possible to generalize our findings on obstetrical complications not being associated with ASD, to areas of the world with less developed obstetrical and neonatal care. Additional factors, not yet identified, could potentially be of relevance for NDDs in other parts of the world. The limited geographical spread points to the existence of a global research bias and divide for NDDs. According to Zhang et al. (Reference Zhang, Chen, Gao, Yang, Zhen, Li and Zhao2017), only 1.13% of the research productivity worldwide in the field of psychiatry originates from low and lower-middle income countries.

In this review of genetically informed studies, we found evidence, albeit with modest effect sizes, for several environmental factors potentially on the casual pathways for different NDDs, particularly ASD and ADHD. Other previously discussed factors were questioned, such as season of birth and a series of obstetrical- and pregnancy-related factors. Interestingly, a recent meta-analysis on birth by cesarean delivery by Zhang et al. (Reference Zhang, Sidorchuk, Sevilla-Cermeño, Vilaplana-Pérez, Chang, Larsson and Fernández de la Cruz2019), came to a different result with an odds ratio [OR] of 1.33 (95% CI, 1.25–1.41) for ASD from 27 studies, and an OR of 1.17 (95% CI, 1.07–1.26) for ADHD from 13 studies. But, as the authors points out, the pattern of attenuation when performing sibling analyses suggested that the observed associations were likely due to familial confounding. Furthermore, our review found no evidence for antidepressive medication, maternal infection, and stress or adverse family life events during pregnancy being associated with ADHD, beyond familial confounding.

This systematic review integrates a number of methodological strengths. First, the most prominent strength is its size with 140 included articles. Second, it is the first systematic review in this growing field of research trying to rule out familial confounding in the search for causal environmental factors for NDDs. Third, the broad approach on NDD, rather than a single diagnosis only, of this review allowed to follow threads otherwise hard to follow regarding diagnostic specificity of particular findings. Fourth, we have included studies of both dimensional and categorical outcomes, addressing the possibility of different pathways for symptom/traits and diagnosis. Fifth, the diversity of the exposures covered reaching from pregnancy to early childhood, has allowed us to relate our findings to the timing of the exposure.

A potential limitation of the present review is the inclusion of early studies on environmental factors dating back decades. With recent study designs and statistical methods, potential environmental factors for ASD such as rubella infection during pregnancy and labor induction have been found to be confounded by familial factors, compared to results from earlier studies with higher risk of bias (Tables 2 and 3). This shows that with incautiously applied family designs we risk deeming risk factors as being free from familial confounding, when in fact, the full information that twins and siblings provide is not utilized to fully account for the familial confounding. This points to the need to utilize state-of-the-art methods for twin and family data. Therefore, it is time to reevaluate potential environmental factors from the past decades with a contemporary statistical approach. Another potential weakness of this review is that there are other ways to control for familial confounding than twin and sibling studies. Particularly, multi-generational population-based cohorts, not only including siblings, but also half-siblings and cousins, sometimes in a quasi-experimental design. Other ways to deal with familial confounding are, as previously discussed, based upon adoptions or in vitro fertilization (IVF) designs (D'Onofrio, Reference D'Onofrio, Class, Lahey and Larsson2014a). As explained by Harold et al. (Reference Harold, Leve, Barrett, Elam, Neiderhiser, Natsuaki and Thapar2013), compared to family studies, these designs carry the advantage that further examination of associations between patterns of family interaction and child development is possible, as they also allow control for passive gene-environment interaction. As Loehlin (Reference Loehlin2016) highlights, the strength of adoption studies to estimate the effects of the prenatal and the postnatal environment, makes them well suited to investigate how familial confounding differentially applies to prenatal versus postnatal environmental risks. Furthermore, there is little control of comorbidity in the included studies. This limitation could not be addressed by this review, owing to a lack of reporting comorbidity in the primary studies examined. Future studies should be careful and comprehensive in mapping somatic and psychiatric comorbidity, which are frequent in NDD (Pan, Tammimies, & Bölte, Reference Pan, Tammimies and Bölte2019; Plana-Ripoll et al., Reference Plana-Ripoll, Pedersen, Holtz, Benros, Dalsgaard, de Jonge and McGrath2019) and may have a significant impact on developmental mechanisms. Another potential limitation is discrepancy in age of diagnosis. Regarding ASD, most of the included cohort studies lacked specific information regarding the sibling subsamples. Despite this, the overall assessment of the included studies’ methodologies gives little room for a misclassification bias being present. Regarding studies on ADHD, it is important to bear in mind that some of the results rely on dimensional outcomes at a young age thereby introducing a risk of misclassification bias. Finally, while the results indicate that some previously suspected environmental factors are due to familial confounding, we once again caution against general conclusions that absence of evidence of an association equals evidence of absence.

Conclusions and future directions

NDDs are common conditions, and although NDDs are highly heritable, environmental factors do contribute to their causal pathways and associated impairment. Studies on suspected environmental factors often suffer from the bias of familial confounding where exposures are in themselves heritable, with the risk of incorrectly connecting them to NDDs, possibly leading to waste of public resources, unnecessary worry, misleading advice, and eroded public trust.

The conclusions from this comprehensive systematic review of twin and sibling studies are as follows. First, we found evidence, beyond familial confounding, that:

  • advanced paternal age, low birth weight, birth defects, and perinatal hypoxia and respiratory stress are consistently associated with a diagnosis of ASD, and;

  • low birth weight, gestational age, and low family income or transient income decline during childhood are associated with ADHD, both categorically and dimensionally.

Second, our result points in the direction of evidence of no association beyond familial confounding regarding the associations of:

  • maternal uterine bleeding, preeclampsia, gestational diabetes, pre-pregnancy body mass index, and elective and emergency cesarean section, with ASD, and;

  • antidepressive medication, maternal infection, maternal weight and maternal smoking during pregnancy, with a diagnosis of ADHD.

Third, we found a substantial body of studies with conflicting findings regarding the associations of:

  • antidepressive medication during pregnancy, advanced maternal age, preterm birth, labor induction, and neonatal jaundice with ASD, and;

  • alcohol use during pregnancy, and parental age with ADHD, both categorically and dimensionally.

Fourth, there is a lack of geographic dispersion, with most twin and sibling studies being conducted in North America and Scandinavia. Additional factors, not yet identified, could potentially be of relevance for NDDs in other parts of the world. Finally, and perhaps most importantly, too few reliable conclusions can be drawn for conditions other than ASD and ADHD. This is unfortunate, given the considerable frequency of other NDDs, and points to a critical need of more genetically informed studies of good quality in the quest of the environmental causes of NDDs.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0954579420000620

Acknowledgments

We acknowledge the support by Klas Moberg & Carl Gornitzki, librarians at the Karolinska Institutet University Library for executing the literature search of this study. We also acknowledge the contribution of Philippe Wallner for his part in the abstract screening. This work has been funded by PRIMA child and adult psychiatry, The Sven Jerring Foundation, The Faculty of Medicine at Uppsala University Foundation for Psychiatric and Neurologic Research.

Funding Statement

This work was supported by the PRIMA Child and Adult Psychiatry AB Research grant, the Faculty of Medicine at Uppsala University Foundation for Psychiatric and Neurologic Research and the Sven Jerring Foundation.

Conflict of Interest

None

References

World Health Organization, santé, O. m. d. l., Staff, W., Health, W. H. O. D. o. M., AC01696920], A., WHO, & Zivetz, L. (1992). The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines: World Health Organization.Google Scholar
Aagaard, K., Bach, C. C., Henriksen, T. B., Larsen, R. T., & Matthiesen, N. B. (2018). Head circumference at birth and childhood developmental disorders in a nationwide cohort in Denmark. Paediatric and Perinatal Epidemiology, 32, 458466. doi:10.1111/ppe.12479CrossRefGoogle Scholar
Abd Elhameed, M. A., Abd Elbaky, A. E. O., & Kamel, E. A. (2011). A controlled study of the risk factors and clinical picture of children with Autism in an Egyptian sample. Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 48, 271276.Google Scholar
Altink, M. E., Arias-Vasquez, A., Franke, B., Slaats-Willemse, D. I., Buschgens, C. J., Rommelse, N. N., … Buitelaar, J. K. (2008). The dopamine receptor D4 7-repeat allele and prenatal smoking in ADHD-affected children and their unaffected siblings: No gene-environment interaction. Journal of Child Psychology and Psychiatry and Allied Disciplines, 49, 10531060. doi:10.1111/j.1469-7610.2008.01998.xCrossRefGoogle ScholarPubMed
American Psychiatric Association (1987). Diagnostic and statistical manual of mental disorders: DSM-III-R. Washington DC: American Psychiatric Publishing.Google Scholar
American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (4th ed.Text Revision (DSM-IV-TR))). Washington DC: American Psychiatric Association.Google Scholar
American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (DSM-5). Washington DC: American Psychiatric Publishing.Google Scholar
Antshel, K. M., & Waisbren, S. E. (2003). Timing is everything: Executive functions in children exposed to elevated levels of phenylalanine. Neuropsychology, 17, 458468. doi:10.1037/0894-4105.17.3.458CrossRefGoogle ScholarPubMed
Arora, M., Reichenberg, A., Willfors, C., Austin, C., Gennings, C., Berggren, S., … Bölte, S. (2017). Fetal and postnatal metal dysregulation in autism. Nature Communications, 8, 15493. doi:10.1038/ncomms15493CrossRefGoogle ScholarPubMed
Asbury, K., Dunn, J. F., Pike, A., & Plomin, R. (2003). Nonshared environmental influences on individual differences in early behavioral development: A monozygotic twin differences study. Child Development, 74, 933943. doi:10.1111/1467-8624.00577CrossRefGoogle ScholarPubMed
Asbury, K., Dunn, J. F., & Plomin, R. (2006). Birthweight-discordance and differences in early parenting relate to monozygotic twin differences in behaviour problems and academic achievement at age 7. Developmental Science, 9, F22F31. doi:10.1111/j.1467-7687.2006.00469.xCrossRefGoogle ScholarPubMed
Aschner, M., & Costa, L. G. (2015). Environmental factors in neurodevelopmental and neurodegenerative disorders. Cambridge: Elsevier Science.Google Scholar
Ask, H., Gustavson, K., Ystrom, E., Havdahl, K. A., Tesli, M., Askeland, R. B., & Reichborn-Kjennerud, T. (2018). Association of gestational age at birth with symptoms of attention-deficit/hyperactivity disorder in children. JAMA Pediatrics, 172, 749756. doi:10.1001/jamapediatrics.2018.1315CrossRefGoogle ScholarPubMed
Axelsson, P. B., Clausen, T. D., Petersen, A. H., Hageman, I., Pinborg, A., Kessing, L. V., … Lokkegaard, E. C. L. (2019). Investigating the effects of cesarean delivery and antibiotic use in early childhood on risk of later attention deficit hyperactivity disorder. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 60, 151159. doi:10.1111/jcpp.12961CrossRefGoogle ScholarPubMed
Banerjee, T. D., Middleton, F., & Faraone, S. V. (2007). Environmental risk factors for attention-deficit hyperactivity disorder. Acta Paediatrica, 96, 12691274. doi:10.1111/j.1651-2227.2007.00430.xCrossRefGoogle ScholarPubMed
Bargiela, S., Steward, R., & Mandy, W. (2016). The experiences of late-diagnosed women with autism spectrum conditions: An investigation of the female autism phenotype. Journal of Autism and Developmental Disorders, 46, 32813294. doi:10.1007/s10803-016-2872-8CrossRefGoogle ScholarPubMed
Beardslee, W. R., Wolff, P. H., Hurwitz, I., Parikh, B., & Shwachman, H. (1982). The effects of infantile malnutrition on behavioral development: A follow-up study. American Journal of Clinical Nutrition, 35, 14371441.CrossRefGoogle ScholarPubMed
Bellido-González, M., Defior-Citoler, S., & Díaz-López, M. A. (2007). Cognitive and verbal development of discordant twins without neurological morbidity. Journal of Reproductive and Infant Psychology, 25, 161168. doi:10.1080/02646830701292324CrossRefGoogle Scholar
Bellinger, D. C. (2012). A strategy for comparing the contributions of environmental chemicals and other risk factors to neurodevelopment of children. Environmental Health Perspectives, 120, 501. doi:10.1289/ehp.1104170CrossRefGoogle ScholarPubMed
Ben Amor, L., Grizenko, N., Schwartz, G., Lageix, P., Baron, C., Ter-Stepanian, M., … Joober, R. (2005). Perinatal complications in children with attention-deficit hyperactivity disorder and their unaffected siblings. Journal of Psychiatry & Neuroscience, 30, 120126.Google ScholarPubMed
Berg, S., Trollfors, B., Hugosson, S., Fernell, E., & Svensson, E. (2002). Long-term follow-up of children with bacterial meningitis with emphasis on behavioural characteristics. European Journal of Pediatrics, 161, 330336. doi:10.1007/s00431-002-0957-1CrossRefGoogle ScholarPubMed
Bergman, I., Painter, M. J., Wald, E. R., Chiponis, D., Holland, A. L., & Taylor, H. G. (1987). Outcome in children with enteroviral meningitis during the first year of life. Journal of Pediatrics, 110, 705709. doi:10.1016/S0022-3476(87)80006-9CrossRefGoogle ScholarPubMed
Bilder, D. A., Bakian, A. V., Viskochil, J., Clark, E. A., Botts, E. L., Smith, K. R., … Coon, H. (2013). Maternal prenatal weight gain and autism spectrum disorders. Pediatrics, 132, e12761283. doi:10.1542/peds.2013-1188CrossRefGoogle ScholarPubMed
Bilenberg, N., Hougaard, D., Norgaard-Pedersen, B., Nordenbæk, C., & Olsen, J. (2011). Twin study on transplacental-acquired antibodies and attention deficit/hyperactivity disorder–a pilot study. Journal of Neuroimmunology, 236, 7275. doi:10.1016/j.jneuroim.2011.04.012CrossRefGoogle ScholarPubMed
Bishop, D. V. M. (1997). Pre- and perinatal hazards and family background in children with specific language impairments: A study of twins. Brain & Language, 56, 126. doi:10.1006/brln.1997.1729CrossRefGoogle Scholar
Bishop, D. V. M. (2010). Which neurodevelopmental disorders get researched and why? PloS one, 5, e15112. doi:10.1371/journal.pone.0015112CrossRefGoogle Scholar
Bölte, S., Girdler, S., & Marschik, P. (2019). The contribution of environmental exposure to the etiology of autism spectrum disorder. Cellular and Molecular Life Sciences, 76, 12751297. doi:10.1007/s00018-018-2988-4CrossRefGoogle ScholarPubMed
Bolton, P., Pickles, A., Harrington, R., Macdonald, H., & Rutter, M. (1992). Season of birth: Issues, approaches and findings for autism. Journal of Child Psychology and Psychiatry and Allied Disciplines, 33, 509530. doi:10.1111/j.1469-7610.1992.tb00888.xCrossRefGoogle ScholarPubMed
Brander, G., Rydell, M., Kuja-Halkola, R., Fernandez de la Cruz, L., Lichtenstein, P., Serlachius, E., … Mataix-Cols, D. (2017). Perinatal risk factors in Tourette's and chronic tic disorders: A total population sibling comparison study. Molecular Psychiatry, 28, 28. doi:10.1038/mp.2017.31Google Scholar
Brandlistuen, R. E., Ystrom, E., Eberhard-Gran, M., Nulman, I., Koren, G., & Nordeng, H. (2015). Behavioural effects of fetal antidepressant exposure in a Norwegian cohort of discordant siblings. International Journal of Epidemiology, 44, 13971407. doi:10.1093/ije/dyv030CrossRefGoogle Scholar
Brandlistuen, R. E., Ystrom, E., Hernandez-Diaz, S., Skurtveit, S., Selmer, R., Handal, M., & Nordeng, H. (2017). Association of prenatal exposure to benzodiazepines and child internalizing problems: A sibling-controlled cohort study. PloS one, 12, e0181042. doi:10.1371/journal.pone.0181042CrossRefGoogle ScholarPubMed
Brandlistuen, R. E., Ystrom, E., Nulman, I., Koren, G., & Nordeng, H. (2013). Prenatal paracetamol exposure and child neurodevelopment: A sibling-controlled cohort study. International Journal of Epidemiology, 42, 17021713. doi:10.1093/ije/dyt183CrossRefGoogle ScholarPubMed
Brown, C. M., Austin, D. W., & Busija, L. (2014). Observable essential fatty acid deficiency markers and autism spectrum disorder. Breastfeeding Review, 22, 2126.Google ScholarPubMed
Brown, H. K., Ray, J. G., Wilton, A. S., Lunsky, Y., Gomes, T., & Vigod, S. N. (2017). Association between serotonergic antidepressant use during pregnancy and autism spectrum disorder in children. JAMA, 317, 15441552. doi:10.1001/jama.2017.3415CrossRefGoogle ScholarPubMed
Bryson, S. E., Smith, I. M., & Eastwood, D. (1988). Obstetrical suboptimality in autistic children. Journal of the American Academy of Child & Adolescent Psychiatry, 27, 418422. doi:10.1097/00004583-198807000-00006CrossRefGoogle ScholarPubMed
Burd, L., Fisher, W., Kerbeshian, J., Vesely, B., Durgin, B., & Reep, P. (1988). A comparison of breastfeeding rates among children with pervasive developmental disorder, and controls. Journal of Developmental & Behavioral Pediatrics, 9, 247251. doi:10.1097/00004703-198810000-00001CrossRefGoogle ScholarPubMed
Buxbaum, J. D., & Hof, P. R. (2011). The emerging neuroscience of autism spectrum disorders. Brain Research, 1380, 12. doi:10.1016/j.brainres.2011.02.030CrossRefGoogle ScholarPubMed
Castellanos, F. X., Sonuga-Barke, E. J., Milham, M. P., & Tannock, R. (2006). Characterizing cognition in ADHD: Beyond executive dysfunction. Trends in Cognitive Sciences, 10, 117123. doi:10.1016/j.tics.2006.01.011CrossRefGoogle ScholarPubMed
Castellanos, F. X., & Tannock, R. (2002). Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes. Nature Reviews Neuroscience, 3, 617628. doi:10.1038/nrn896CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention (2019). Summary of Autism Spectrum Disorder (ASD) Prevalence Studies. Retrieved 2019 September 3 from https://www.cdc.gov/ncbddd/autism/documents/ASDPrevalenceDataTable2016-508.pdfGoogle Scholar
Chang, Z., Lichtenstein, P., D'Onofrio, B. M., Almqvist, C., Kuja-Halkola, R., Sjölander, A., & Larsson, H. (2014). Maternal age at childbirth and risk for ADHD in offspring: A population-based cohort study. International Journal of Epidemiology, 43, 18151824. doi:10.1093/ije/dyu204CrossRefGoogle ScholarPubMed
Chatterji, P., Lahiri, K., & Kim, D. (2014). Fetal growth and neurobehavioral outcomes in childhood. Economics & Human Biology, 15, 187200. doi:10.1016/j.ehb.2014.09.002CrossRefGoogle ScholarPubMed
Chen, Q., Sjölander, A., Langstrom, N., Rodriguez, A., Serlachius, E., D'Onofrio, B. M., … Larsson, H. (2014). Maternal pre-pregnancy body mass index and offspring attention deficit hyperactivity disorder: A population-based cohort study using a sibling-comparison design. International Journal of Epidemiology, 43, 8390. doi:10.1093/ije/dyt152CrossRefGoogle ScholarPubMed
Chien, Y. L., Chou, M. C., Chou, W. J., Wu, Y. Y., Tsai, W. C., Chiu, Y. N., & Gau, S. S. F. (2018). Prenatal and perinatal risk factors and the clinical implications on autism spectrum disorder. Autism, 23, 783791. doi:10.1177/1362361318772813CrossRefGoogle ScholarPubMed
Churchill, J. A. (1965). The relationship between intelligence and birth weight in twins. Neurology, 15, 341347. doi:10.1212/WNL.15.4.341CrossRefGoogle ScholarPubMed
Class, Q. A., Rickert, M. E., Larsson, H., Lichtenstein, P., & D'Onofrio, B. M. (2014). Fetal growth and psychiatric and socioeconomic problems: Population-based sibling comparison. British Journal of Psychiatry, 205, 355361. doi:10.1192/bjp.bp.113.143693CrossRefGoogle ScholarPubMed
Class, Q. A., Rickert, M. E., Larsson, H., Oberg, A. S., Sujan, A. C., Almqvist, C., … D'Onofrio, B. M. (2018). Outcome-dependent associations between short interpregnancy interval and offspring psychological and educational problems: A population-based quasi-experimental study. International Journal of Epidemiology, 47, 11591168. doi:10.1093/ije/dyy042CrossRefGoogle ScholarPubMed
Creagh, O., Torres, H., Rivera, K., Morales-Franqui, M., Altieri-Acevedo, G., & Warner, D. (2015). Previous exposure to anesthesia and autism spectrum disorder (ASD): A Puerto Rican population-based sibling cohort study. Boletin Asociacion Medica de Puerto Rico, 107, 2937.Google ScholarPubMed
Cristino, A. S., Williams, S. M., Hawi, Z., An, J. Y., Bellgrove, M. A., Schwartz, C. E., … Claudianos, C. (2014). Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system. Molecular Psychiatry, 19, 294301. doi:10.1038/mp.2013.16CrossRefGoogle ScholarPubMed
Curran, E. A., Dalman, C., Kearney, P. M., Kenny, L. C., Cryan, J. F., Dinan, T. G., & Khashan, A. S. (2015). Association between obstetric mode of delivery and autism spectrum disorder: A population-based sibling design study. JAMA Psychiatry, 72, 935942. doi:10.1001/jamapsychiatry.2015.0846CrossRefGoogle ScholarPubMed
Curran, E. A., Khashan, A. S., Dalman, C., Kenny, L. C., Cryan, J. F., Dinan, T. G., & Kearney, P. M. (2016). Obstetric mode of delivery and attention-deficit/hyperactivity disorder: A sibling-matched study. International Journal of Epidemiology, 45, 532542. doi:10.1093/ije/dyw001CrossRefGoogle ScholarPubMed
Dawson, S., Glasson, E. J., Dixon, G., & Bower, C. (2009). Birth defects in children with autism spectrum disorders: A population-based, nested case-control study. American Journal of Epidemiology, 169, 12961303. doi:10.1093/aje/kwp059CrossRefGoogle ScholarPubMed
Deb, S., Prasad, K. B., Seth, H., & Eagles, J. M. (1997). A comparison of obstetric and neonatal complications between children with autistic disorder and their siblings. Journal of Intellectual Disability Research, 41, 8186. doi:10.1111/j.1365-2788.1997.tb00680.xCrossRefGoogle ScholarPubMed
Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., & Baldursson, G. (2019). Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nature Genetics, 51, 63. doi:10.1038/s41588-018-0269-7CrossRefGoogle ScholarPubMed
DeVilbiss, E. A., Magnusson, C., Gardner, R. M., Rai, D., Newschaffer, C. J., Lyall, K., … Lee, B. K. (2017). Antenatal nutritional supplementation and autism spectrum disorders in the Stockholm youth cohort: Population based cohort study. BMJ: British Medical Journal, 359), doi:doi.org/10.1136/bmj.j4273Google ScholarPubMed
Deykin, E. Y., & MacMahon, B. (1979). Original contributions. Viral exposure and autism. American Journal of Epidemiology, 109, 628638. doi:10.1093/oxfordjournals.aje.a112726CrossRefGoogle Scholar
Deykin, E. Y., & MacMahon, B. (1980). Pregnancy, delivery, and neonatal complications among autistic children. American Journal of Diseases of Children, 134, 860864. doi:10.1001/archpedi.1980.02130210044012Google ScholarPubMed
D'Onofrio, B. M., Class, Q. A., Lahey, B. B., & Larsson, H. (2014a). Testing the developmental origins of health and disease hypothesis for psychopathology using family-based quasi-experimental designs. Child Development Perspectives, 8, 151157. doi:10.1111/cdep.12078CrossRefGoogle Scholar
D'Onofrio, B. M., Class, Q. A., Rickert, M. E., Larsson, H., Langstrom, N., & Lichtenstein, P. (2013a). Preterm birth and mortality and morbidity: A population-based quasi-experimental study. JAMA Psychiatry, 70, 12311240. doi:10.1001/jamapsychiatry.2013.2107CrossRefGoogle Scholar
D'Onofrio, B. M., Lahey, B. B., Turkheimer, E., & Lichtenstein, P. (2013b). Critical need for family-based, quasi-experimental designs in integrating genetic and social science research. American Journal of Public Health, 103 (Suppl 1), S4655. doi:10.2105/ajph.2013.301252CrossRefGoogle Scholar
D'Onofrio, B. M., Rickert, M. E., Frans, E., Kuja-Halkola, R., Almqvist, C., Sjölander, A., … Lichtenstein, P. (2014b). Paternal age at childbearing and offspring psychiatric and academic morbidity. JAMA Psychiatry, 71, 432438. doi:10.1001/jamapsychiatry.2013.4525CrossRefGoogle Scholar
D'Onofrio, B. M., Van Hulle, C. A., Waldman, I. D., Rodgers, J. L., Harden, K. P., Rathouz, P. J., & Lahey, B. B. (2008). Smoking during pregnancy and offspring externalizing problems: An exploration of genetic and environmental confounds. Development & Psychopathology, 20, 139164. doi:10.1017/S0954579408000072CrossRefGoogle ScholarPubMed
D'Onofrio, B. M., Van Hulle, C. A., Waldman, I. D., Rodgers, J. L., Rathouz, P. J., & Lahey, B. B. (2007). Causal inferences regarding prenatal alcohol exposure and childhood externalizing problems. Archives of General Psychiatry, 64, 12961304. doi:10.1001/archpsyc.64.11.1296CrossRefGoogle ScholarPubMed
Edmiston, E., Ashwood, P., & Van de Water, J. (2017). Autoimmunity, autoantibodies, and autism spectrum disorder. Biological Psychiatry, 81, 383390. doi:10.1016/j.biopsych.2016.08.031CrossRefGoogle ScholarPubMed
Eilertsen, E. M., Gjerde, L. C., Reichborn-Kjennerud, T., Orstavik, R. E., Knudsen, G. P., Stoltenberg, C., … Ystrom, E. (2017). Maternal alcohol use during pregnancy and offspring attention-deficit hyperactivity disorder (ADHD): A prospective sibling control study. International Journal of Epidemiology, 24, 24. doi:10.1093/ije/dyx067Google Scholar
Ellerbeck, K. A., Smith, M. L., Holden, E. W., McMenamin, S. C., Badawi, M. A., Brenner, J. I., … Hyman, S. L. (1998). Neurodevelopmental outcomes in children surviving d-transposition of the great arteries. Journal of Developmental and Behavioral Pediatrics JDBP, 19, 335341. doi:10.1097/00004703-199810000-00003CrossRefGoogle ScholarPubMed
Ellingson, J. M., Goodnight, J. A., Van Hulle, C. A., Waldman, I. D., & D'Onofrio, B. M. (2014). A sibling-comparison study of smoking during pregnancy and childhood psychological traits. Behavior Genetics, 44, 2535. doi:10.1007/s10519-013-9618-6CrossRefGoogle ScholarPubMed
Elsabbagh, M., Divan, G., Koh, Y. J., Kim, Y. S., Kauchali, S., Marcin, C., … Fombonne, E. (2012). Global prevalence of autism and other pervasive developmental disorders. Autism Research, 5, 160179. doi:10.1002/aur.239CrossRefGoogle ScholarPubMed
Fernell, E., Bejerot, S., Westerlund, J., Miniscalco, C., Simila, H., Eyles, D., … Humble, M. B. (2015). Autism spectrum disorder and low vitamin D at birth: A sibling control study. Molecular Autism, 6, 3. doi:10.1186/2040-2392-6-3CrossRefGoogle ScholarPubMed
Finegan, J.-A., & Quarrington, B. (1979). Pre-, peri-, and neonatal factors and infantile autism. Child Psychology and Psychiatry and Allied Disciplines, 20, 119128. doi:10.1111/j.1469-7610.1979.tb00492.xCrossRefGoogle ScholarPubMed
Froehlich-Santino, W., Londono Tobon, A., Cleveland, S., Torres, A., Phillips, J., Cohen, B., … Hallmayer, J. (2014). Prenatal and perinatal risk factors in a twin study of autism spectrum disorders. Journal of Psychiatric Research, 54, 100108. doi:10.1016/j.jpsychires.2014.03.019CrossRefGoogle Scholar
Gardner, R. M., Lee, B. K., Magnusson, C., Rai, D., Frisell, T., Karlsson, H., … Dalman, C. (2015). Maternal body mass index during early pregnancy, gestational weight gain, and risk of autism spectrum disorders: Results from a Swedish total population and discordant sibling study. International Journal of Epidemiology, 44, 870883. doi:10.1093/ije/dyv081CrossRefGoogle ScholarPubMed
Geschwind, D. H., & Levitt, P. (2007). Autism spectrum disorders: Developmental disconnection syndromes. Current Opinion in Neurobiology, 17, 103111. doi:10.1016/j.conb.2007.01.009CrossRefGoogle ScholarPubMed
Gilman, S. E., Gardener, H., & Buka, S. L. (2008). Maternal smoking during pregnancy and children's cognitive and physical development: A causal risk factor? American Journal of Epidemiology, 168, 522531. doi:10.1093/aje/kwn175CrossRefGoogle ScholarPubMed
Ginsberg, Y., D'Onofrio, B. M., Rickert, M. E., Class, Q. A., Rosenqvist, M. A., Almqvist, C., … Larsson, H. (2019). Maternal infection requiring hospitalization during pregnancy and attention-deficit hyperactivity disorder in offspring: A quasi-experimental family-based study. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 60, 160168. doi:10.1111/jcpp.12959CrossRefGoogle ScholarPubMed
Gjerde, L. C., Eilertsen, E. M., Reichborn-Kjennerud, T., McAdams, T. A., Zachrisson, H. D., Zambrana, I. M., … Ystrom, E. (2017). Maternal perinatal and concurrent depressive symptoms and child behavior problems: A sibling comparison study. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 58, 779786. doi:10.1111/jcpp.12704CrossRefGoogle ScholarPubMed
Glasson, E. J., Bower, C., Petterson, B., de Klerk, N., Chaney, G., & Hallmayer, J. F. (2004). Perinatal factors and the development of autism: A population study. Archives of General Psychiatry, 61, 618627. doi:10.1001/archpsyc.61.6.618CrossRefGoogle ScholarPubMed
Golding, J., Emmett, P., Iles-Caven, Y., Steer, C., & Lingam, R. (2014). A review of environmental contributions to childhood motor skills. Journal of Child Neurology, 29, 15311547. doi:10.1177/0883073813507483CrossRefGoogle ScholarPubMed
Gong, T., Lundholm, C., Rejno, G., Bölte, S., Larsson, H., D'Onofrio, B. M., … Almqvist, C. (2019). Parental asthma and risk of autism spectrum disorder in offspring: A population and family-based case-control study. Clinical and Experimental Allergy: Journal of the British Society for Allergy and Clinical Immunology, 49, 883891. doi:10.1111/cea.13353CrossRefGoogle ScholarPubMed
Grandjean, P., Pichery, C., Bellanger, M., & Budtz-Jørgensen, E. (2012). Calculation of mercury's effects on neurodevelopment. Environmental Health Perspectives, 120, A452. doi:10.1289/ehp.1206033CrossRefGoogle ScholarPubMed
Grizenko, N., Fortier, M. E., Zadorozny, C., Thakur, G., Schmitz, N., Duval, R., & Joober, R. (2012). Maternal stress during pregnancy, ADHD symptomatology in children and genotype: Gene-environment interaction. Journal of the Canadian Academy of Child and Adolescent Psychiatry (Journal de l'Academie canadienne de psychiatrie de l'enfant et de l'adolescent), 21, 915.Google ScholarPubMed
Groen-Blokhuis, M. M., Middeldorp, C. M., van Beijsterveldt, C. E., & Boomsma, D. I. (2011). Evidence for a causal association of low birth weight and attention problems. Journal of the American Academy of Child & Adolescent Psychiatry, 50, 12471254.e1242. doi:10.1016/j.jaac.2011.09.007CrossRefGoogle ScholarPubMed
Grossi, E., Migliore, L., & Muratori, F. (2018). Pregnancy risk factors related to autism: An Italian case-control study in mothers of children with autism spectrum disorders (ASD), their siblings and of typically developing children. Journal of Developmental Origins of Health and Disease, 9, 442449. doi:10.1017/S2040174418000211CrossRefGoogle Scholar
Gustavson, K., Ystrom, E., Stoltenberg, C., Susser, E., Surén, P., Magnus, P., … Reichborn-Kjennerud, T. (2017). Smoking in pregnancy and child ADHD. Pediatrics, 139, 2. doi:10.1542/peds.2016-2509CrossRefGoogle ScholarPubMed
Hackman, D. A., Farah, M. J., & Meaney, M. J. (2010). Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews Neuroscience, 11, 651659. doi:10.1038/nrn2897CrossRefGoogle ScholarPubMed
Hadjkacem, I., Ayadi, H., Turki, M., Yaich, S., Khemekhem, K., Walha, A., … Ghribi, F. (2016). Prenatal, perinatal and postnatal factors associated with autism spectrum disorder. Jornal de Pediatria, 92, 595601. doi:10.1016/j.jped.2016.01.012CrossRefGoogle ScholarPubMed
Hagberg, K. W., Robijn, A. L., & Jick, S. (2018). Maternal depression and antidepressant use during pregnancy and the risk of autism spectrum disorder in offspring. Clinical Epidemiology, 10, 15991612. doi:10.2147/CLEP.S180618CrossRefGoogle ScholarPubMed
Hamad, A. F., Alessi-Severini, S., Mahmud, S. M., Brownell, M., & Kuo, I. F. (2018). Early childhood antibiotics use and autism spectrum disorders: A population-based cohort study. International Journal of Epidemiology, 47, 14971506. doi:10.1093/ije/dyy162CrossRefGoogle ScholarPubMed
Hansen, R. L., & Rogers, S. J. (2013). Autism and other neurodevelopmental disorders. Washington, DC: American Psychiatric Association Publishing.Google Scholar
Happé, F., & Ronald, A. (2008). The ‘fractionable autism triad': A review of evidence from behavioural, genetic, cognitive and neural research. Neuropsychology Review, 18, 287304. doi:10.1007/s11065-008-9076-8CrossRefGoogle ScholarPubMed
Harold, G. T., Leve, L. D., Barrett, D., Elam, K., Neiderhiser, J. M., Natsuaki, M. N., … Thapar, A. (2013). Biological and rearing mother influences on child ADHD symptoms: Revisiting the developmental interface between nature and nurture. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54, 10381046. doi:10.1111/jcpp.12100CrossRefGoogle ScholarPubMed
Herbert, R. M. (2010). Contributions of the environment and environmentally vulnerable physiology to autism spectrum disorders. Current Opinion in Neurology, 23, 103110. doi:10.1097/WCO.0b013e328336a01fCrossRefGoogle ScholarPubMed
Heuvelman, H., Abel, K., Wicks, S., Gardner, R., Johnstone, E., Lee, B., … Rai, D. (2018). Gestational age at birth and risk of intellectual disability without a common genetic cause. European Journal of Epidemiology, 33, 667678. doi:10.1007/s10654-017-0340-1CrossRefGoogle ScholarPubMed
Higgins, J. P. T., Altman, D. G., Gøtzsche, P. C., Jüni, P., Moher, D., Oxman, A. D., … Sterne, J. A. C. (2011). The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ: British Medical Journal, 343, d5928. doi:10.1136/bmj.d5928CrossRefGoogle ScholarPubMed
Higgins, J. P. T., & Green, S. (2011). Cochrane handbook for systematic reviews of interventions. Chichester (UK): Wiley.Google Scholar
Hill, A. B. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58, 295. doi:10.1177/003591576505800503CrossRefGoogle ScholarPubMed
Hoekstra, P. J., Dietrich, A., Edwards, M. J., Elamin, I., & Martino, D. (2013). Environmental factors in Tourette syndrome. Neuroscience and Biobehavioral Reviews, 37, 10401049. doi:10.1016/j.neubiorev.2012.10.010CrossRefGoogle ScholarPubMed
Hultman, C. M., Sandin, S., Levine, S. Z., Lichtenstein, P., & Reichenberg, A. (2011). Advancing paternal age and risk of autism: New evidence from a population-based study and a meta-analysis of epidemiological studies. Molecular Psychiatry, 16, 12031212. doi:10.1038/mp.2010.121CrossRefGoogle Scholar
Hultman, M. C., Sparén, P., & Cnattingius, S. (2002). Perinatal risk factors for infantile autism. Epidemiology, 13, 417423. doi:10.1097/00001648-200207000-00009CrossRefGoogle ScholarPubMed
Hultman, C. M., Torrang, A., Tuvblad, C., Cnattingius, S., Larsson, J. O., & Lichtenstein, P. (2007). Birth weight and attention-deficit/hyperactivity symptoms in childhood and early adolescence: A prospective Swedish twin study. Journal of the American Academy of Child & Adolescent Psychiatry, 46, 370377. doi:10.1097/01.chi.0000246059.62706.22CrossRefGoogle ScholarPubMed
Hvolgaard Mikkelsen, S., Olsen, J., Bech, B. H., & Obel, C. (2017). Parental age and attention-deficit/hyperactivity disorder (ADHD). International Journal of Epidemiology, 46, 409420. doi:10.1093/ije/dyw073Google Scholar
Hyde, T. M., Aaronson, B. A., Randolph, C., Rickler, K. C., & Weinberger, D. R. (1992). Relationship of birth weight to the phenotypic expression of Gilles de la Tourette's syndrome in monozygotic twins. Neurology, 42, 652658. doi:10.1212/WNL.42.3.652CrossRefGoogle ScholarPubMed
Ichikawa, K., Fujiwara, T., & Kawachi, I. (2018). Prenatal alcohol exposure and child psychosocial behavior: A sibling fixed-effects analysis. Frontiers in Psychiatry, 9, 570. doi:10.3389/fpsyt.2018.00570CrossRefGoogle ScholarPubMed
Isaksson, J., Pettersson, E., Kostrzewa, E., Diaz Heijtz, R., & Bölte, S. (2017). Brief report: Association between autism spectrum disorder, gastrointestinal problems and perinatal risk factors within sibling pairs. Journal of Autism & Developmental Disorders, 47, 26212627. doi:10.1007/s10803-017-3169-2CrossRefGoogle ScholarPubMed
Jackson, D. B., & Beaver, K. M. (2015). Sibling differences in low birth weight, dopaminergic polymorphisms, and ADHD symptomatology: Evidence of GxE. Psychiatry Research, 226, 467473. doi:10.1016/j.psychres.2015.01.025CrossRefGoogle ScholarPubMed
Jannoun, L. (1983). Are cognitive and educational development affected by age at which prophylactic therapy is given in acute lymphoblastic leukaemia? Archives of Disease in Childhood, 58, 953958. doi:10.1136/adc.58.12.953CrossRefGoogle ScholarPubMed
Kalkbrenner, A. E., Meier, S. M., Madley-Dowd, P., Ladd-Acosta, C., Fallin, M. D., Parner, E., & Schendel, D. (2020). Familial confounding of the association between maternal smoking in pregnancy and autism spectrum disorder in offspring. Autism Research, 13, 134144. doi:10.1002/aur.2196CrossRefGoogle ScholarPubMed
Kelly, J. R., Minuto, C., Cryan, J. F., Clarke, G., & Dinan, T. G. (2017). Cross talk: The microbiota and neurodevelopmental disorders. Frontiers in Neuroscience, 11, 490. doi:10.3389/fnins.2017.00490CrossRefGoogle ScholarPubMed
Kilbride, H. W., Thorstad, K., & Daily, D. K. (2004). Preschool outcome of less than 801-gram preterm infants compared with full-term siblings. Pediatrics, 113, 742747. doi:10.1542/peds.113.4.742CrossRefGoogle ScholarPubMed
Klein, P. S., Forbes, G. B., & Nader, P. R. (1975). Effects of starvation in infancy (pyloric stenosis) on subsequent learning abilities. Journal of Pediatrics, 87, 815. doi:10.1016/S0022-3476(75)80060-6CrossRefGoogle ScholarPubMed
Knopik, V. S., Marceau, K., Bidwell, L. C., Palmer, R. H., Smith, T. F., Todorov, A., … Heath, A. C. (2016). Smoking during pregnancy and ADHD risk: A genetically informed, multiple-rater approach. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics: the Official Publication of the International Society of Psychiatric Genetics, 171, 971981. doi:10.1002/ajmg.b.32421CrossRefGoogle ScholarPubMed
Kong, A., Frigge, M. L., Masson, G., Besenbacher, S., Sulem, P., Magnusson, G., … Stefansson, K. (2012). Rate of de novo mutations and the importance of father's age to disease risk. Nature, 488, 471475. doi:10.1038/nature11396CrossRefGoogle ScholarPubMed
Kung, K. T. F., Spencer, D., Pasterski, V., Neufeld, S. A. S., Hindmarsh, P. C., Hughes, I. A., … Hines, M. (2018). Emotional and behavioral adjustment in 4 to 11-year-old boys and girls with classic congenital adrenal hyperplasia and unaffected siblings. Psychoneuroendocrinology, 97, 104110. doi:10.1016/j.psyneuen.2018.07.004CrossRefGoogle ScholarPubMed
Kuntsi, J., Pinto, R., Price, T. S., van der Meere, J. J., Frazier-Wood, A. C., & Asherson, P. (2014). The separation of ADHD inattention and hyperactivity-impulsivity symptoms: Pathways from genetic effects to cognitive impairments and symptoms. Journal of Abnormal Child Psychology, 42, 127136. doi:10.1007/s10802-013-9771-7CrossRefGoogle ScholarPubMed
Lai, M.-C., Lombardo, M. V., Ruigrok, A. N., Chakrabarti, B., Auyeung, B., Szatmari, P., … Baron-Cohen, S. (2017). Quantifying and exploring camouflaging in men and women with autism. Autism, 21, 690702. doi:10.1177/1362361316671012CrossRefGoogle ScholarPubMed
Landrigan, P. J., Lambertini, L., & Birnbaum, L. S. (2012). A research strategy to discover the environmental causes of autism and neurodevelopmental disabilities. Environmental Health Perspectives, 120, a258a260. doi:10.1289/ehp.1104285CrossRefGoogle ScholarPubMed
Larsson, H., Sariaslan, A., Långström, N., D'Onofrio, B., & Lichtenstein, P. (2014). Family income in early childhood and subsequent attention deficit/hyperactivity disorder: A quasi-experimental study. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 55, 428435. doi:10.1111/jcpp.12140CrossRefGoogle ScholarPubMed
Laugesen, K., Byrjalsen, A., Froslev, T., Olsen, M. S., & Sørensen, H. T. (2017). Use of glucocorticoids during pregnancy and risk of attention-deficit/hyperactivity disorder in offspring: A nationwide Danish cohort study. BMJ Open, 7, e016825. doi:10.1136/bmjopen-2017-016825CrossRefGoogle ScholarPubMed
Laugesen, K., Olsen, M. S., Telen Andersen, A. B., Froslev, T., & Sørensen, H. T. (2013). In utero exposure to antidepressant drugs and risk of attention deficit hyperactivity disorder: A nationwide Danish cohort study. BMJ Open, 3, e003507. doi:10.1136/bmjopen-2013-003507CrossRefGoogle ScholarPubMed
Lehn, H., Derks, E. M., Hudziak, J. J., Heutink, P., van Beijsterveldt, T. C., & Boomsma, D. I. (2007). Attention problems and attention-deficit/hyperactivity disorder in discordant and concordant monozygotic twins: Evidence of environmental mediators. Journal of the American Academy of Child & Adolescent Psychiatry, 46, 8391. doi:10.1097/01.chi.0000242244.00174.d9CrossRefGoogle ScholarPubMed
Lim, K. X., Liu, C.-Y., Schoeler, T., Cecil, C. A. M., Barker, E. D., Viding, E., … Pingault, J.-B. (2018). The role of birth weight on the causal pathway to child and adolescent ADHD symptomatology: A population-based twin differences longitudinal design. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 59, 10361043. doi:10.1111/jcpp.12949CrossRefGoogle ScholarPubMed
Lloyd-Still, J. D., Hurwitz, I., Wolff, P. H., & Shwachman, H. (1974). Intellectual development after severe malnutrition in infancy. Pediatrics, 54, 306.Google ScholarPubMed
Loehlin, J. C. (2016). What can an adoption study tell us about the effect of prenatal environment on a trait? Behavior Genetics, 46, 329333. doi:10.1007/s10519-015-9730-xCrossRefGoogle ScholarPubMed
Lord, C., Mulloy, C., Wendelboe, M., & Schopler, E. (1991). Pre- and perinatal factors in high-functioning females and males with autism. Journal of Autism & Developmental Disorders, 21, 197209. doi:10.1007/BF02284760CrossRefGoogle ScholarPubMed
Losh, M., Esserman, D., Anckarsäter, H., Sullivan, P. F., & Lichtenstein, P. (2012). Lower birth weight indicates higher risk of autistic traits in discordant twin pairs. Psychological Medicine, 42, 10911102. doi:10.1017/S0033291711002339CrossRefGoogle ScholarPubMed
Luo, Y., Weibman, D., Halperin, J. M., & Li, X. (2019). A review of heterogeneity in attention deficit/hyperactivity disorder (ADHD). Frontiers in Human Neuroscience, 13, 42. doi:10.3389/fnhum.2019.00042CrossRefGoogle Scholar
Man, K. K. C., Chan, E. W., Ip, P., Coghill, D., Simonoff, E., Chan, P. K. L., … Wong, I. C. K. (2017). Prenatal antidepressant use and risk of attention-deficit/hyperactivity disorder in offspring: Population based cohort study. BMJ: British Medical Journal, 357, j2350. doi:10.1136/bmj.j2350CrossRefGoogle ScholarPubMed
Manning, J. T., Baron-Cohen, S., Wheelwright, S., & Sanders, G. (2001). The 2nd to 4th digit ratio and autism. Developmental Medicine & Child Neurology, 43, 160164. doi:10.1111/j.1469-8749.2001.tb00181.xCrossRefGoogle ScholarPubMed
Manohar, H., Pravallika, M., Kandasamy, P., Chandrasekaran, V., & Rajkumar, R. P. (2018). Role of exclusive breastfeeding in conferring protection in children at-risk for autism spectrum disorder: results from a sibling case-control study. Journal of Neurosciences in Rural Practice, 9, 132136. doi:10.4103/jnrp.jnrp_331_17Google ScholarPubMed
Marceau, K., Cinnamon Bidwell, L., Karoly, H. C., Evans, A. S., Todorov, A. A., Palmer, R. H., … Knopik, V. S. (2017). Within-family effects of smoking during pregnancy on ADHD: The importance of phenotype. Journal of Abnormal Child Psychology, 30, 30. doi:10.1007/s10802-017-0320-7Google Scholar
Martin, J., Taylor, M. J., & Lichtenstein, P. (2018). Assessing the evidence for shared genetic risks across psychiatric disorders and traits. Psychological Medicine, 48, 17591774. doi:10.1017/S0033291717003440CrossRefGoogle ScholarPubMed
Mascheretti, S., Andreola, C., Scaini, S., & Sulpizio, S. (2018). Beyond genes: A systematic review of environmental risk factors in specific reading disorder. Research in Developmental Disabilities, 82, 147152. doi:10.1016/j.ridd.2018.03.005CrossRefGoogle ScholarPubMed
Mascheretti, S., Trezzi, V., Giorda, R., Boivin, M., Plourde, V., Vitaro, F., … Marino, C. (2017). Complex effects of dyslexia risk factors account for ADHD traits: Evidence from two independent samples. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 58, 7582. doi:10.1111/jcpp.12612CrossRefGoogle ScholarPubMed
Mason-Brothers, A., Ritvo, E. R., Freeman, B., Jorde, L. B., Pingree, C. C., McMahon, W. M., … Mo, A. (1993). The UCLA-University of Utah epidemiologic survey of autism: Recurrent infections. European Child & Adolescent Psychiatry, 2, 7990. doi:10.1007/BF02098863CrossRefGoogle ScholarPubMed
Mason-Brothers, A., Ritvo, E. R., Pingree, C., Petersen, P. B., Jenson, W. R., McMahon, W. M., et al. (1990). The UCLA-University of Utah epidemiologic survey of autism: Prenatal, perinatal, and postnatal factors. Pediatrics, 86, 514519.Google ScholarPubMed
McCusker, C. G., Armstrong, M. P., Mullen, M., Doherty, N. N., & Casey, F. A. (2013). A sibling-controlled, prospective study of outcomes at home and school in children with severe congenital heart disease. Cardiology in the Young, 23, 507516. doi:10.1017/S1047951112001667CrossRefGoogle ScholarPubMed
Mezzacappa, A., Lasica, P. A., Gianfagna, F., Cazas, O., Hardy, P., Falissard, B., … Gressier, F. (2017). Risk for autism spectrum disorders according to period of prenatal antidepressant exposure: A systematic review and meta-analysis. JAMA Pediatrics, 171, 555563. doi:10.1001/jamapediatrics.2017.0124CrossRefGoogle ScholarPubMed
Mimouni-Bloch, A., Kachevanskaya, A., Mimouni, F. B., Shuper, A., Raveh, E., & Linder, N. (2013). Breastfeeding may protect from developing attention-deficit/hyperactivity disorder. Breastfeeding Medicine, 8, 363367. doi:10.1089/bfm.2012.0145CrossRefGoogle ScholarPubMed
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Journal of Clinical Epidemiology, 62, 10061012. doi:10.1016/j.jclinepi.2009.06.005CrossRefGoogle ScholarPubMed
Monset-Couchard, M., de Bethmann, O., & Relier, J. P. (2004). Long term outcome of small versus appropriate size for gestational age co-twins/triplets. Archives of Disease in Childhood Fetal & Neonatal Edition, 89, F310314. doi:10.1136/adc.2002.021626CrossRefGoogle ScholarPubMed
Muskens, J., Velders, F., & Staal, W. (2017). Medical comorbidities in children and adolescents with autism spectrum disorders and attention deficit hyperactivity disorders: A systematic review. European Child & Adolescent Psychiatry, 26, 10931103. doi:10.1007/s00787-017-1020-0CrossRefGoogle ScholarPubMed
Musser, E. D., Willoughby, M. T., Wright, S., Sullivan, E. L., Stadler, D. D., Olson, B. F., … Nigg, J. T. (2017). Maternal prepregnancy body mass index and offspring attention-deficit/hyperactivity disorder: A quasi-experimental sibling-comparison, population-based design. Journal of Child Psychology and Psychiatry, 58, 240247. doi:10.1111/jcpp.12662CrossRefGoogle ScholarPubMed
Myers, L., Van't Westeinde, A., Kuja-Halkola, R., Tammimies, K., & Bölte, S. (2018). 2D:4D Ratio in neurodevelopmental disorders: A twin study. Journal of Autism and Developmental Disorders, 48, 32443252. doi:10.1007/s10803-018-3588-8CrossRefGoogle ScholarPubMed
National Center for Health Statistics (1990). ICD-9-CM Addendum: International Classification of Diseases, Ninth Revision, Clinical Modification, Update: Official Authorized Addendum, Effective Date, October 1. Hyattsville: Department of Health and Human Services, Public Health Service, National Center for Health Statistics.Google Scholar
Neale, B. M., Kou, Y., Liu, L., Ma'ayan, A., Samocha, K. E., Sabo, A., … Daly, M. J. (2012). Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature, 485, 242245. doi:10.1038/nature11011CrossRefGoogle ScholarPubMed
Nulman, I., Koren, G., Rovet, J., Barrera, M., Streiner, D. L., & Feldman, B. M. (2015). Neurodevelopment of children prenatally exposed to selective reuptake inhibitor antidepressants: Toronto sibling study. Journal of Clinical Psychiatry, 76, e842847. doi:10.4088/JCP.14m09240CrossRefGoogle ScholarPubMed
Obel, C., Olsen, J., Henriksen, T. B., Rodriguez, A., Jarvelin, M. R., Moilanen, I., … Gissler, M. (2011). Is maternal smoking during pregnancy a risk factor for hyperkinetic disorder?–Findings from a sibling design. International Journal of Epidemiology, 40, 338345. doi:10.1093/ije/dyq185CrossRefGoogle ScholarPubMed
Obel, C., Zhu, J. L., Olsen, J., Breining, S., Li, J., Gronborg, T. K., … Rutter, M. (2016). The risk of attention deficit hyperactivity disorder in children exposed to maternal smoking during pregnancy - a re-examination using a sibling design. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 57, 532537. doi:10.1111/jcpp.12478CrossRefGoogle ScholarPubMed
Oberg, A. S., D'Onofrio, B. M., Rickert, M. E., Hernandez-Diaz, S., Ecker, J. L., Almqvist, C., … Bateman, B. T. (2016). Association of labor induction with offspring risk of autism spectrum disorders. JAMA Pediatrics, 170, e160965. doi:10.1001/jamapediatrics.2016.0965CrossRefGoogle ScholarPubMed
Oerbeck, B., Sundet, K., Kase, B. F., & Heyerdahl, S. (2003). Congenital hypothyroidism: Influence of disease severity and L-thyroxine treatment on intellectual, motor, and school-associated outcomes in young adults. Pediatrics, 112, 923930. doi:10.1542/peds.112.4.923CrossRefGoogle ScholarPubMed
Oerbeck, B., Sundet, K., Kase, B. F., & Heyerdahl, S. (2005). Congenital hypothyroidism: No adverse effects of high dose thyroxine treatment on adult memory, attention, and behaviour. Archives of Disease in Childhood, 90, 132137. doi:10.1136/adc.2003.043935CrossRefGoogle ScholarPubMed
Oerlemans, A. M., Burmanje, M. J., Franke, B., Buitelaar, J. K., Hartman, C. A., & Rommelse, N. N. (2016). Identifying unique versus shared pre- and perinatal risk factors for ASD and ADHD using a simplex-multiplex stratification. Journal of Abnormal Child Psychology, 44, 923935. doi:10.1007/s10802-015-0081-0CrossRefGoogle ScholarPubMed
O'Roak, B. J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B. P., … Eichler, E. E. (2012). Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature, 485, 246250. doi:10.1038/nature10989CrossRefGoogle ScholarPubMed
Otake, T., Yoshinaga, J., Seki, Y., Matsumura, T., Watanabe, K., Ishijima, M., & Kato, N. (2006). Retrospective in utero exposure assessment of PCBs using preserved umbilical cords and its application to case-control comparison. Environmental Health & Preventive Medicine, 11, 6568. doi:10.1007/BF02898144CrossRefGoogle ScholarPubMed
Pan, P. Y., Tammimies, K., & Bölte, S. (2019). The association between somatic health, autism spectrum disorder, and autistic traits. Behavior Genetics, doi:10.1007/s10519-019-09986-3Google ScholarPubMed
Parner, E. T., Baron-Cohen, S., Lauritsen, M. B., Jorgensen, M., Schieve, L. A., Yeargin-Allsopp, M., & Obel, C. (2012). Parental age and autism spectrum disorders. Annals of Epidemiology, 22, 143150. doi:10.1016/j.annepidem.2011.12.006CrossRefGoogle ScholarPubMed
Pearsall-Jones, J. G., Pieka, J. P., Martin, N. C., Rigoli, D., Levy, F., & Hay, D. A. (2008). A monozygotic twin design to investigate etiological factors for DCD and ADHD. Journal of Pediatric Neurology, 6, 209219. doi:10.1055/s-0035-1557464Google Scholar
Pessah, I. N., Cherednichenko, G., & Lein, P. J. (2010). Minding the calcium store: Ryanodine receptor activation as a convergent mechanism of PCB toxicity. Pharmacology and Therapeutics, 125, 260285. doi:10.1016/j.pharmthera.2009.10.009CrossRefGoogle ScholarPubMed
Petik, D., Czeizel, B., Banhidy, F., & Czeizel, A. E. (2012). A study of the risk of mental retardation among children of pregnant women who have attempted suicide by means of a drug overdose. Journal of Injury & Violence Research, 4, 1019. doi:10.5249/jivr.v4i1.85CrossRefGoogle ScholarPubMed
Pettersson, E., Larsson, H., D'Onofrio, B., Almqvist, C., & Lichtenstein, P. (2019). Association of fetal growth with general and specific mental health conditions. JAMA Psychiatry, doi:10.1001/jamapsychiatry.2018.4342CrossRefGoogle ScholarPubMed
Pettersson, E., Sjölander, A., Almqvist, C., Anckarsäter, H., D'Onofrio, B. M., Lichtenstein, P., & Larsson, H. (2015). Birth weight as an independent predictor of ADHD symptoms: A within-twin pair analysis. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 56, 453459. doi:10.1111/jcpp.12299CrossRefGoogle ScholarPubMed
Pinto, D., Delaby, E., Merico, D., Barbosa, M., Merikangas, A., Klei, L., … Scherer, S. W. (2014). Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. American Journal of Human Genetics, 94, 677694. doi:10.1016/j.ajhg.2014.03.018CrossRefGoogle ScholarPubMed
Piven, J., Simon, J., Chase, G. A., Wzorek, M., Landa, R., Gayle, J., & Folstein, S. (1993). The etiology of autism: Pre-, peri- and neonatal factors. Journal of the American Academy of Child & Adolescent Psychiatry, 32, 12561263. doi:10.1097/00004583-199311000-00021CrossRefGoogle ScholarPubMed
Plana-Ripoll, O., Pedersen, C. B., Holtz, Y., Benros, M. E., Dalsgaard, S., de Jonge, P., … McGrath, J. J. (2019). Exploring comorbidity within mental disorders among a Danish national population. JAMA psychiatry, 76, 259270. doi:10.1001/jamapsychiatry.2018.3658CrossRefGoogle ScholarPubMed
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016). Top 10 replicated findings from behavioral genetics. Perspectives on Psychological Science, 11, 323. doi:10.1177/1745691615617439CrossRefGoogle ScholarPubMed
Polanczyk, G. V., Willcutt, E. G., Salum, G. A., Kieling, C., & Rohde, L. A. (2014). ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43, 434442. doi:10.1093/ije/dyt261CrossRefGoogle ScholarPubMed
Polderman, T. J., Benyamin, B., de Leeuw, C. A., Sullivan, P. F., van Bochoven, A., Visscher, P. M., & Posthuma, D. (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics, 47, 702709. doi:10.1038/ng.3285CrossRefGoogle ScholarPubMed
Posthuma, D., & Polderman, T. J. (2013). What have we learned from recent twin studies about the etiology of neurodevelopmental disorders? Current Opinion in Neurology, 26, 111121. doi:10.1097/WCO.0b013e32835f19c3CrossRefGoogle ScholarPubMed
Rai, D., Lee, B. K., Dalman, C., Newschaffer, C., Lewis, G., & Magnusson, C. (2017). Antidepressants during pregnancy and autism in offspring: Population based cohort study. BMJ: British Medical Journal, 358, j2811. doi:10.1136/bmj.j2811CrossRefGoogle ScholarPubMed
Ramanathan, S., Balasubramanian, N., & Faraone, S. V. (2017). Familial transient financial difficulties during infancy and long-term developmental concerns. Psychological Medicine, 47, 21972204. doi:10.1017/S0033291717000666CrossRefGoogle ScholarPubMed
Rauh, V. A., & Margolis, A. E. (2016). Research review: Environmental exposures, neurodevelopment, and child mental health - new paradigms for the study of brain and behavioral effects. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 57, 775793. doi:10.1111/jcpp.12537CrossRefGoogle Scholar
Raz, S., Glogowski-Kawamoto, B., Yu, A. W., Kronenberg, M. E., Hopkins, T. L., Lauterbach, M. D., … Sander, C. J. (1998). The effects of perinatal hypoxic risk on developmental outcome in early and middle childhood: A twin study. Neuropsychology, 12, 459467. doi:10.1037/0894-4105.12.3.459CrossRefGoogle ScholarPubMed
Raz, S., Shah, F., & Sander, C. J. (1996). Differential effects of perinatal hypoxic risk on early developmental outcome: A twin study. Neuropsychology, 10, 429436. doi:10.1037/0894-4105.10.3.429CrossRefGoogle Scholar
Reichenberg, A., Cederlöf, M., McMillan, A., Trzaskowski, M., Kapra, O., Fruchter, E., … Lichtenstein, P. (2016). Discontinuity in the genetic and environmental causes of the intellectual disability spectrum. Proceedings of the National Academy of Sciences, 113, 10981103. doi:10.1073/pnas.1508093112CrossRefGoogle ScholarPubMed
Ronald, A., Happé, F., Dworzynski, K., Bolton, P., & Plomin, R. (2010). Exploring the relation between prenatal and neonatal complications and later autistic-like features in a representative community sample of twins. Child Development, 81, 166182. doi:10.1111/j.1467-8624.2009.01387.xCrossRefGoogle Scholar
Ronald, A., & Hoekstra, R. A. (2011). Autism spectrum disorders and autistic traits: A decade of new twin studies. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 156b, 255274. doi:10.1002/ajmg.b.31159CrossRefGoogle ScholarPubMed
Rosenqvist, M. A., Sjölander, A., Ystrom, E., Larsson, H., & Reichborn-Kjennerud, T. (2018). Adverse family life events during pregnancy and ADHD symptoms in five-year-old offspring. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 60, 665675. doi:10.1111/jcpp.12990CrossRefGoogle ScholarPubMed
Rovet, J. F. (1986). A prospective investigation of children with congenital hypothyroidism identified by neonatal thyroid screening in Ontario. Canadian Journal of Public Health, 77(Suppl. 1), 164173.Google ScholarPubMed
Rutt, C. N., & Offord, D. R. (1971). Prenatal and perinatal complications in childhood schizophrenics and their siblings. Journal of Nervous & Mental Disease, 152, 324331. doi:10.1097/00005053-197105000-00003CrossRefGoogle ScholarPubMed
Sanders, S. J., Murtha, M. T., Gupta, A. R., Murdoch, J. D., Raubeson, M. J., Willsey, A. J., … State, M. W. (2012). De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature, 485, 237241. doi:10.1038/nature10945CrossRefGoogle ScholarPubMed
Schultz, A. H., Ittenbach, R. F., Gerdes, M., Jarvik, G. P., Wernovsky, G., Bernbaum, J., … Gaynor, J. W. (2017). Effect of congenital heart disease on 4-year neurodevelopment within multiple-gestation births. Journal of Thoracic & Cardiovascular Surgery, 154, 273281.e272. doi:10.1016/j.jtcvs.2017.02.022CrossRefGoogle ScholarPubMed
Schultz, A. H., Jarvik, G. P., Wernovsky, G., Bernbaum, J., Clancy, R. R., D'Agostino, J. A., … Gaynor, J. W. (2005). Effect of congenital heart disease on neurodevelopmental outcomes within multiple-gestation births. Journal of Thoracic and Cardiovascular Surgery, 130, 15111516. doi:10.1016/j.jtcvs.2005.07.040CrossRefGoogle ScholarPubMed
Sciberras, E., Mulraney, M., Silva, D., & Coghill, D. (2017). Prenatal risk factors and the etiology of ADHD—review of existing evidence. Current Psychiatry Reports, 19, 1. doi:10.1007/s11920-017-0753-2CrossRefGoogle ScholarPubMed
Shelton, J. F., Hertz-Picciotto, I., & Pessah, I. N. (2012). Tipping the balance of autism risk: Potential mechanisms linking pesticides and autism. Environmental Health Perspectives, 120, 944. doi:10.1289/ehp.1104553CrossRefGoogle ScholarPubMed
Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child & Adolescent Psychiatry, 47, 921929. doi:10.1097/CHI.0b013e318179964fCrossRefGoogle Scholar
Sjölander, A., & Zetterqvist, J. (2017). Confounders, mediators, or colliders. Epidemiology, 28, 540547. doi:10.1097/EDE.0000000000000649CrossRefGoogle ScholarPubMed
Skoglund, C., Chen, Q., D'Onofrio, B. M., Lichtenstein, P., & Larsson, H. (2014). Familial confounding of the association between maternal smoking during pregnancy and ADHD in offspring. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 55, 6168. doi:10.1111/jcpp.12124CrossRefGoogle ScholarPubMed
Sonuga-Barke, E. J. (2005). Causal models of attention-deficit/hyperactivity disorder: From common simple deficits to multiple developmental pathways. Biological Psychiatry, 57, 12311238. doi:10.1016/j.biopsych.2004.09.008CrossRefGoogle ScholarPubMed
Sørensen, M. J., Grønborg, T. K., Christensen, J., Parner, E. T., Vestergaard, M., Schendel, D., & Pedersen, L. H. (2013). Antidepressant exposure in pregnancy and risk of autism spectrum disorders. Clinical Epidemiology, 5, 449459. doi:10.2147/CLEP.S53009CrossRefGoogle ScholarPubMed
Steffenburg, S., Gillberg, C., Hellgren, L., Andersson, L., Gillberg, I. C., Jakobsson, G., & Bohman, M. (1989). A twin study of autism in Denmark, Finland, Iceland, Norway and Sweden. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 30, 405416. doi:10.1111/j.1469-7610.1989.tb00254.xCrossRefGoogle ScholarPubMed
Steingass, K. J., Taylor, H. G., Wilson-Costello, D., Minich, N., & Hack, M. (2013). Discordance in neonatal risk factors and early childhood outcomes of very low birth weight (&lt;1.5 kg) twins. Journal of Perinatology, 33, 388393. doi:10.1038/jp.2012.121CrossRefGoogle Scholar
Stevens, M. C., Fein, D. H., & Waterhouse, L. H. (2000). Season of birth effects in autism. Journal of Clinical & Experimental Neuropsychology: Official Journal of the International Neuropsychological Society, 22, 399407. doi:10.1076/1380-3395(200006)22:3;1-V;FT399CrossRefGoogle ScholarPubMed
Stokholm, L., Talge, N. M., Christensen, G. T., Juhl, M., Mortensen, L. H., & Strandberg-Larsen, K. (2018). Labor augmentation during birth and later cognitive ability in young adulthood. Clinical Epidemiology, 10, 17651772. doi:10.2147/CLEP.S181012CrossRefGoogle ScholarPubMed
Sujan, A. C., Rickert, M. E., Oberg, A. S., Quinn, P. D., Hernandez-Diaz, S., Almqvist, C., … D'Onofrio, B. M. (2017). Associations of maternal antidepressant use during the first trimester of pregnancy with preterm birth, small for gestational age, autism spectrum disorder, and attention-deficit/hyperactivity disorder in offspring. JAMA, 317, 15531562. doi:10.1001/jama.2017.3413CrossRefGoogle ScholarPubMed
Sun, L. S., Li, G., Miller, T. L. K., Salorio, C., Byrne, M. W., Bellinger, D. C., … McGowan, F. X. (2016). Association between a single general anesthesia exposure before age 36 months and neurocognitive outcomes in later childhood. JAMA, 315, 23122320. doi:10.1001/jama.2016.6967CrossRefGoogle ScholarPubMed
Sussmann, J. E., McIntosh, A. M., Lawrie, S. M., & Johnstone, E. C. (2009). Obstetric complications and mild to moderate intellectual disability. British Journal of Psychiatry, 194, 224228. doi:10.1192/bjp.bp.106.033134CrossRefGoogle ScholarPubMed
Szatmari, P., Paterson, A. D., Zwaigenbaum, L., Roberts, W., Brian, J., Liu, X.-Q., … Meyer, K. J. (2007). Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nature Genetics, 39, 319. doi:10.1038/ng1985Google ScholarPubMed
Taylor, M. J., Gillberg, C., Lichtenstein, P., & Lundström, S. (2017). Etiological influences on the stability of autistic traits from childhood to early adulthood: Evidence from a twin study. Molecular Autism, 8, 55. doi:10.1186/s13229-017-0120-5CrossRefGoogle ScholarPubMed
Taylor, M. J., Martin, J., Lu, Y., Brikell, I., Lundström, S., Larsson, H., & Lichtenstein, P. (2019). Association of genetic risk factors for psychiatric disorders and traits of these disorders in a Swedish population twin sample. JAMA Psychiatry, 76, 280289. doi:10.1001/jamapsychiatry.2018.3652CrossRefGoogle Scholar
Tillman, K. K., Hakelius, M., Hoijer, J., Ramklint, M., Ekselius, L., Nowinski, D., & Papadopoulos, F. C. (2018). Increased risk for neurodevelopmental disorders in children with orofacial clefts. Journal of the American Academy of Child & Adolescent Psychiatry, 57, 876883. doi:10.1016/j.jaac.2018.06.024CrossRefGoogle ScholarPubMed
Tore, E. C., Antoniou, E. E., Reed, K., Southwood, T. R., Smits, L., McCleery, J. P., & Zeegers, M. P. (2018). The association of intrapair birth-weight differences with internalizing and externalizing behavior problems. Twin Research and Human Genetics: the Official Journal of the International Society for Twin Studies, 21, 253262. doi:10.1017/thg.2018.13CrossRefGoogle ScholarPubMed
Trasande, L., & Liu, Y. (2011). Reducing the staggering costs of environmental disease in children, estimated at $76.6 billion In 2008. Health Affairs, 30, 863870. doi:10.1377/hlthaff.2010.1239CrossRefGoogle ScholarPubMed
van Dongen, J., Slagboom, P. E., Draisma, H. H. M., Martin, N. G., & Boomsma, D. I. (2012). The continuing value of twin studies in the omics era. Nature Reviews Genetics, 13, 640653. doi:10.1038/nrg3243CrossRefGoogle ScholarPubMed
Walhovd, K. B., Fjell, A. M., Brown, T. T., Kuperman, J. M., Chung, Y., Hagler, D. J., … Dale, A. M. (2012). Long-term influence of normal variation in neonatal characteristics on human brain development. Proceedings of the National Academy of Sciences U S A, 109, 2008920094. doi:10.1073/pnas.1208180109CrossRefGoogle ScholarPubMed
Wells, G. A., Shea, B., O'Connell, D., Peterson, J., Welch, V., Losos, M., & Tugwell, P. (2019). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Retrieved 2019 December 27 from http://www.ohri.ca/programs/clinical_epidemiology/oxford.aspGoogle Scholar
Wiegersma, A. M., Dalman, C., Lee, B. K., Karlsson, H., & Gardner, R. M. (2019). Association of prenatal maternal anemia with neurodevelopmental disorders. JAMA Psychiatry, 76, 12941304. doi:10.1001/jamapsychiatry.2019.2309CrossRefGoogle ScholarPubMed
Wiggs, K. K., Rickert, M. E., Hernandez-Diaz, S., Bateman, B. T., Almqvist, C., Larsson, H., … D'Onofrio, B. M. (2017). A family-based study of the association between labor induction and offspring attention-deficit hyperactivity disorder and low academic achievement. Behavior Genetics, 47, 383393. doi:10.1007/s10519-017-9852-4CrossRefGoogle ScholarPubMed
Willcutt, E. G., Nigg, J. T., Pennington, B. F., Solanto, M. V., Rohde, L. A., Tannock, R., … Lahey, B. B. (2012). Validity of DSM-IV attention deficit/hyperactivity disorder symptom dimensions and subtypes. Journal of Abnormal Psychology, 121, 9911010. doi:10.1037/a0027347CrossRefGoogle ScholarPubMed
Willfors, C., Carlsson, T., Anderlid, B. M., Nordgren, A., Kostrzewa, E., Berggren, S., … Bölte, S. (2017). Medical history of discordant twins and environmental etiologies of autism. Translational Psychiatry, 7, e1014. doi:10.1038/tp.2016.269CrossRefGoogle ScholarPubMed
Williams, P., Hersh, J. H., Allard, A., & Sears, L. L. (2008). A controlled study of mercury levels in hair samples of children with autism as compared to their typically developing siblings. Research in Autism Spectrum Disorders, 2, 170175. doi:10.1016/j.rasd.2007.05.001CrossRefGoogle Scholar
Workalemahu, T., Grantz, K. L., Grewal, J., Zhang, C., Louis, G. M. B., & Tekola-Ayele, F. (2018). Genetic and environmental influences on fetal growth vary during sensitive periods in pregnancy. Scientific Reports, 8, 7274. doi:10.1038/s41598-018-25706-zCrossRefGoogle ScholarPubMed
Xu, G., Strathearn, L., Liu, B., Yang, B., & Bao, W. (2018). Twenty-year trends in diagnosed attention-deficit/hyperactivity disorder among US children and adolescents, 1997-2016. JAMA Network Open, 1, e181471. doi:10.1001/jamanetworkopen.2018.1471CrossRefGoogle Scholar
Yang, F., Chen, J., Miao, M.-H., Yuan, W., Li, L., Liang, H., … Li, J. (2017). Risk of autism spectrum disorder in offspring following paternal use of selective serotonin reuptake inhibitors before conception: A population-based cohort study. BMJ Open, 7, e016368. doi:10.1136/bmjopen-2017-016368CrossRefGoogle ScholarPubMed
Ylitalo, V., Kero, P., & Erkkola, R. (1988). Neurological outcome of twins dissimilar in size at birth. Early Human Development, 17, 245255. doi:10.1016/S0378-3782(88)80011-2CrossRefGoogle ScholarPubMed
Zachrisson, H. D., Dearing, E., Lekhal, R., & Toppelberg, C. O. (2013). Little evidence that time in child care causes externalizing problems during early childhood in Norway. Child Development, 84, 11521170. doi:10.1111/cdev.12040CrossRefGoogle ScholarPubMed
Zhang, J., Chen, X., Gao, X., Yang, H., Zhen, Z., Li, Q., … Zhao, X. (2017). Worldwide research productivity in the field of psychiatry. International Journal of Mental Health Systems, 11, 20. doi:10.1186/s13033-017-0127-5CrossRefGoogle ScholarPubMed
Zhang, T., Sidorchuk, A., Sevilla-Cermeño, L., Vilaplana-Pérez, A., Chang, Z., Larsson, H., … Fernández de la Cruz, L. (2019). Association of cesarean delivery with risk of neurodevelopmental and psychiatric disorders in the offspring: A systematic review and meta-analysis. JAMA Network Open, 2, e1910236e1910236. doi:10.1001/jamanetworkopen.2019.10236CrossRefGoogle ScholarPubMed
Zheng, D. J., Krull, K. R., Chen, Y., Diller, L., Yasui, Y., Leisenring, W., … Kadan-Lottick, N. S. (2018). Long-term psychological and educational outcomes for survivors of neuroblastoma: A report from the Childhood Cancer Survivor Study. Cancer, 124, 32203230. doi:10.1002/cncr.31379CrossRefGoogle ScholarPubMed
Zuk, O., Hechter, E., Sunyaev, S. R., & Lander, E. S. (2012). The mystery of missing heritability: Genetic interactions create phantom heritability. Proceedings of the National Academy of Sciences, 109(4), 11931198. http://dx.doi.org/10.1073/pnas.1119675109CrossRefGoogle ScholarPubMed
Zwaigenbaum, L., Szatmari, P., Jones, M. B., Bryson, S. E., MacLean, J. E., Mahoney, W. J., … Tuff, L. (2002). Pregnancy and birth complications in autism and liability to the broader autism phenotype. Journal of the American Academy of Child & Adolescent Psychiatry, 41, 572579. doi:10.1097/00004583-200205000-00015CrossRefGoogle ScholarPubMed
Figure 0

Figure 1. The 2009 PRISMA Flow Diagram

Figure 1

Table 1. Study characteristics—autism spectrum disorder (ASD)

Figure 2

Table 2. Environmental factors, prenatal—autism spectrum disorder (ASD)

Figure 3

Table 3. Environmental factors, perinatal, neonatal, infancy, and childhood – autism spectrum disorder (ASD)

Figure 4

Table 4. Study characteristics – attention-deficit/hyperactivity disorder (ADHD), categorical (diagnosis)

Figure 5

Table 5. Study characteristics—attention-deficit/hyperactivity disorder (ADHD), dimensional (traits or symptoms)

Figure 6

Table 6. Environmental factors—categorical attention-deficit/hyperactivity disorder (ADHD)-diagnosis

Figure 7

Table 7. Environmental factors—dimensional attention-deficit/hyperactivity disorder (ADHD) traits or symptoms.

Figure 8

Table 8. Study characteristics—categorical (diagnosis) intellectual disability, communication disorders, developmental coordination disorder and TIC disorder

Figure 9

Table 9. Study characteristics—Dimensional (traits/symptoms) intellectual disability, communication disorders, developmental coordination disorder and TIC disorder

Figure 10

Table 10. Environmental factors—categorical (diagnosis) for: intellectual disability, communication disorders, developmental coordination disorder and TIC disorder

Figure 11

Table 11. Environmental factors—dimensional (symptoms and traits) for: intellectual disability, communication disorders, developmental coordination disorder and TIC disorder

Supplementary material: File

Carlsson et al. supplementary material

Carlsson et al. supplementary material 1

Download Carlsson et al. supplementary material(File)
File 27 KB
Supplementary material: File

Carlsson et al. supplementary material

Carlsson et al. supplementary material 2

Download Carlsson et al. supplementary material(File)
File 50 KB
Supplementary material: File

Carlsson et al. supplementary material

Carlsson et al. supplementary material 3

Download Carlsson et al. supplementary material(File)
File 83 KB
You have Access Open access
27
Cited by