Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Nitschke, Amanda
Master of Science - MSc
University of British Columbia
Background: Antibiotics are among the most used medications during pregnancy, and while their short-term benefits are clear, their potential long-term effects are underexplored. Antibiotic exposure induces changes in the maternal microbiome, which could in turn influence the development of the infant’s microbiome with implications for neurodevelopment. This thesis examined the associations of 1) intrapartum antibiotic exposure and 2) prenatal antibiotic exposure with autism spectrum disorder (ASD) in offspring. Methods: Using multiple linked population-level datasets, we examined everyone who delivered a live singleton term infant in British Columbia, Canada between April 1st 2000 and December 31st 2014. To examine the associations among pregnant individuals treated for the same indication, we studied sub-cohorts of those diagnosed 1) as group B streptococcus-positive, and 2) with urinary tract infections. Cox proportional hazards models were used to estimate unadjusted and adjusted hazard ratios. Sensitivity analyses were conducted to examine the impact of different classes of antibiotics, and to assess for dose-response and temporal relationships. Additionally, potential effect modification was examined based on sex and mode of delivery. To account for unmeasured confounding, we ran a conditional logistic regression of discordant sibling pairs. Results: We found no association between intrapartum antibiotic administration and ASD in offspring. In contrast, we observed a small statistically significant increase in risk of ASD associated with prenatal antibiotic exposure. In particular, highest risk was observed if exposure took place during the second trimester, to penicillins or other beta lactams, and for 15 days or longer. Among the cohort restricted to pregnant individuals diagnosed with UTIs, findings showed a positive association between prenatal antibiotics and ASD with an increased effect size; yet, this association was no longer statistically significant, possibly due to a reduced sample size. The relationship between prenatal antibiotic exposure and ASD was no longer significant in the conditional logistic regression. Conclusions: Overall, our findings suggest that the risk of ASD should not factor into clinical decisions on whether to administer antibiotics both prenatally and during labour and delivery. Results may suggest a hypothesized role of impaired fetal metabolic programming by the maternal microbiome and metabolome in ASD development.
ASSOCIATION OF PRENATAL AND INTRAPARTUM ANTIBIOTIC EXPOSURE WITH AUTISM SPECTRUM DISORDER IN OFFSPRING: A POPULATION-BASED STUDY FROM BRITISH COLUMBIA by Amanda Nitschke B.HSc, University of Ottawa, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2022 © Amanda Nitschke, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Association of prenatal and intrapartum antibiotic exposure with autism spectrum disorder in offspring: A population-based study from British Columbia submitted by Amanda Nitschke in partial fulfillment of the requirements for the degree of Master of Science in Population and Public Health Examining Committee: Dr. Tim F. Oberlander, Professor, Faculty of Medicine, Department of Pediatrics, UBC Co-supervisor Dr. Gillian E. Hanley, Associate Professor, Faculty of Medicine, Department of Obstetrics and Gynaecology, UBC Co-supervisor Dr. Bruce Vallance, Professor, Faculty of Medicine, Department of Pediatrics, UBC Supervisory Committee Member Dr. Sarka Lisonkova, Associate Professor, Faculty of Medicine, Department of Obstetrics and Gynaecology, UBC Additional Examiner iii Abstract Background Antibiotics are among the most used medications during pregnancy, and while their short-term benefits are clear, their potential long-term effects are underexplored. Antibiotic exposure induces changes in the maternal microbiome, which could in turn influence the development of the infant’s microbiome with implications for neurodevelopment. This thesis examined the associations of 1) intrapartum antibiotic exposure and 2) prenatal antibiotic exposure with autism spectrum disorder (ASD) in offspring. Methods Using multiple linked population-level datasets, we examined everyone who delivered a live singleton term infant in British Columbia, Canada between April 1st 2000 and December 31st 2014. To examine the associations among pregnant individuals treated for the same indication, we studied sub-cohorts of those diagnosed 1) as group B streptococcus-positive, and 2) with urinary tract infections. Cox proportional hazards models were used to estimate unadjusted and adjusted hazard ratios. Sensitivity analyses were conducted to examine the impact of different classes of antibiotics, and to assess for dose-response and temporal relationships. Additionally, potential effect modification was examined based on sex and mode of delivery. To account for unmeasured confounding, we ran a conditional logistic regression of discordant sibling pairs. Results We found no association between intrapartum antibiotic administration and ASD in offspring. In contrast, we observed a small statistically significant increase in risk of ASD associated with prenatal antibiotic exposure. In particular, highest risk was observed if exposure took place iv during the second trimester, to penicillins or other beta lactams, and for 15 days or longer. Among the cohort restricted to pregnant individuals diagnosed with UTIs, findings showed a positive association between prenatal antibiotics and ASD with an increased effect size; yet, this association was no longer statistically significant, possibly due to a reduced sample size. The relationship between prenatal antibiotic exposure and ASD was no longer significant in the conditional logistic regression. Conclusions Overall, our findings suggest that the risk of ASD should not factor into clinical decisions on whether to administer antibiotics both prenatally and during labour and delivery. Results may suggest a hypothesized role of impaired fetal metabolic programming by the maternal microbiome and metabolome in ASD development. v Lay Summary Antibiotics are taken in up to 30% of pregnancies and 40% of deliveries; and while they are beneficial to reduce short-term maternal and neonatal complications, knowledge of potential long-term outcomes in exposed children is limited. Antibiotic use induces changes to the maternal gut microbiota, and these alterations may impact fetal brain development through the microbiome-gut-brain axis. This thesis explored the relationship between antibiotic exposure during pregnancy, labour and delivery with the risk of ASD in children. Our findings suggest that while antibiotic exposure during labour and delivery is not associated with ASD, earlier exposure during pregnancy, specifically during the second trimester, may be associated with a small increase in risk. These results indicate that antibiotic use during pregnancy is not directly associated with ASD in a clinically meaningful way. However, results may suggest a potential role for altered fetal metabolic programming by the maternal microbiome and metabolome in ASD development. vi Preface This is original intellectual work by Amanda S. Nitschke. The two studies discussed in this thesis were approved by the University of British Columbia Children's and Women's Research Ethics Board (UBC C&W number: H21-01517). A version of Chapter 2 has been published in the American Academy of Pediatrics: Nitschke AS, Karim JL, Vallance BA, Bickford C, Ip A, Lanphear N, Lanphear B, Weikum W, Oberlander TF, Hanley GE, Autism risk and perinatal antibiotic use, Pediatrics published online: August 26th 2022 (doi: 10.1542/peds.2022-057346). Chapter 3 is based on research conducted by Amanda S. Nitschke (AN), Helena A. do Valle (HV), Bruce A. Vallance (BV), Celeste Bickford (CB), Angie Ip (AI), Nancy Lanphear (NL), Bruce Lanphear (BL), Whitney Weikum (WW), Tim F. Oberlander (TO), and Gillian E. Hanley (GH). A version of Chapter 3 has been submitted for publication and is currently under review. AN, GH, and TO conceptualized and designed the study, interpreted the data, and critically reviewed and revised the manuscripts. AN conducted the statistical analysis, created all tables and figures, and drafted the initial manuscripts. BV participated in the conceptualization of the study, interpretation of the data, and critically reviewed and revised the manuscripts. JK and HV participated in the statistical analysis and critically reviewed and revised the manuscripts. AI, NL, BL, CB, and WW provided administrative, technical, or material support, and critically reviewed and revised the manuscripts. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Abbreviations .................................................................................................................. xv Acknowledgements ................................................................................................................... xvii CHAPTER 1: Introduction .......................................................................................................... 1 1.1 Introduction to autism spectrum disorder......................................................................... 1 1.1.1 Definition and diagnosis ........................................................................................... 1 1.1.2 Epidemiology ............................................................................................................ 3 1.1.3 Sex-differences ......................................................................................................... 4 1.1.4 Pathophysiology ........................................................................................................ 7 1.1.5 Genetic factors .......................................................................................................... 9 1.1.6 Environmental factors ............................................................................................. 10 1.1.7 Common comorbidities ........................................................................................... 10 1.1.8 The association between gut dysfunction and ASD ............................................... 11 1.2 Connection between gut microbiota and central nervous system function .................... 12 viii 1.2.1 Microbiome-gut-brain axis ..................................................................................... 12 1.2.2 Microbiome and immune system ............................................................................ 12 1.2.2 Microbiome and endocrine function ....................................................................... 13 1.2.3 Microbiome and neural pathways ........................................................................... 13 1.3 Microbiome-gut-brain axis and ASD ............................................................................. 14 1.3.1 Neurodevelopment .................................................................................................. 14 1.3.2 A distinct ASD microbiome.................................................................................... 15 1.3.3 Therapeutic approaches .......................................................................................... 16 1.4 Microbiota colonization ................................................................................................. 17 1.4.1 Emerging theory: Intrauterine transfer .................................................................... 17 1.4.2 Birth events ............................................................................................................. 18 1.4.3 Infant-feeding .......................................................................................................... 18 1.5 The maternal microbiome during pregnancy and fetal development ............................. 19 1.5.1 Normal changes to the maternal microbiome ......................................................... 19 1.5.2 Maternal microbiome and fetal neurodevelopment ................................................ 20 1.6 Antibiotic use and the infant microbiome ...................................................................... 21 1.6.1 Prenatal antibiotic use ............................................................................................. 21 1.6.2 Intrapartum antibiotic use ....................................................................................... 22 1.7 Association between maternal infection during pregnancy and ASD ............................ 23 1.7.1 Epidemiology .......................................................................................................... 23 ix 1.7.2 Suggested mechanisms ........................................................................................... 24 1.7.3 Confounding by indication ..................................................................................... 25 1.8 Prenatal antibiotic exposure and ASD: Summary of existing evidence......................... 26 1.9 Research objectives ........................................................................................................ 29 1.10 Thesis Roadmap ............................................................................................................. 29 CHAPTER 2: Autism Risk and Perinatal Antibiotic Use ....................................................... 31 2.1 Introduction .................................................................................................................... 31 2.2 Methods .......................................................................................................................... 33 2.2.1 Study design and subjects ....................................................................................... 33 2.2.2 Study cohort ............................................................................................................ 33 2.2.3 Covariates ............................................................................................................... 34 2.2.4 Statistical analysis ................................................................................................... 35 2.2.5 Sensitivity analysis.................................................................................................. 36 2.3 Results ............................................................................................................................ 36 2.3.1 Main analysis .......................................................................................................... 36 3.3.2 Sensitivity analysis.................................................................................................. 46 3.3.3 GBS-positive cohort................................................................................................ 46 3.3.4 Stratified analyses ................................................................................................... 46 2.4 Discussion ...................................................................................................................... 47 2.4.1 Principal findings .................................................................................................... 47 x 2.4.2 Comparison with previous findings ........................................................................ 47 2.4.3 Clinical implications ............................................................................................... 48 2.4.4 Strengths and limitations......................................................................................... 48 2.5 Conclusion ...................................................................................................................... 50 CHAPTER 3: Association between prenatal antibiotic exposure and autism spectrum disorder: A population-based cohort study .............................................................................. 51 3.1 Introduction .................................................................................................................... 51 3.2 Methods .......................................................................................................................... 52 3.2.1 Study design and data sources ................................................................................ 52 3.2.2 Study cohort ............................................................................................................ 52 3.2.3 Covariates ............................................................................................................... 53 3.2.4 Statistical analysis ................................................................................................... 54 3.2.5 Sibling analysis ....................................................................................................... 55 3.3 Results ............................................................................................................................ 56 2.3.1 Main analysis .......................................................................................................... 56 3.3.2 Secondary analyses ................................................................................................. 62 2.3.3 Siblings analysis...................................................................................................... 64 3.4 Discussion ...................................................................................................................... 65 3.4.1 Principal findings .................................................................................................... 65 3.4.2 Comparison with previous findings ........................................................................ 65 xi 3.4.3 Implications............................................................................................................. 66 3.4.4 Strengths and limitations......................................................................................... 67 3.5 Conclusion ...................................................................................................................... 68 CHAPTER 4: Conclusions ......................................................................................................... 70 4.1 Summary of findings ...................................................................................................... 71 4.2 Strengths and limitations ................................................................................................ 73 4.2.1 Strengths ................................................................................................................. 73 4.2.2 Limitations .............................................................................................................. 74 4.3 Future directions ............................................................................................................. 76 4.3.1 Recommendations for future research .................................................................... 76 4.3.2 Implications............................................................................................................. 78 4.4 Conclusion ...................................................................................................................... 79 References .................................................................................................................................... 80 Appendices ................................................................................................................................... 97 Appendix A. Autism risk and perinatal antibiotic use for male neonates in the main cohort... 97 Appendix B. Autism risk and perinatal antibiotic use for male neonates in the group B strep-positive cohort ........................................................................................................................... 98 Appendix C. Autism risk and perinatal antibiotic use for female neonates in the main cohort 99 Appendix D. Autism risk and perinatal antibiotic use for female neonates in the group B strep-positive cohort ......................................................................................................................... 100 xii Appendix E. Autism risk and perinatal antibiotic use for the main cohort by mode of delivery ………………………………………………………………………………………...101 Appendix F. Autism risk and perinatal antibiotic use for the group B strep-positive cohort by mode of delivery...................................................................................................................... 102 Appendix G. Antibiotic classification according to American Hospital Formulary Service (AHFS) Pharmacologic-Therapeutic Classification System ................................................... 103 Appendix H. Select covariates from the BCPDR and reasons for inclusion in the analysis .. 104 Appendix I. Associations between prenatal antibiotic exposure and risk of ASD in female offspring of the main cohort .................................................................................................... 105 Appendix J. Associations between prenatal antibiotic exposure and risk of ASD in male offspring of the main cohort .................................................................................................... 107 xiii List of Tables Table 2.1A. Characteristics by Antibiotic Exposure for the Main Cohort………………………39 Table 2.1B. Characteristics by Antibiotic Exposure for the Group B Strep-positive Cohort…...41 Table 2.2A. Autism Risk and Perinatal Antibiotics in the Main Cohort………………………..43 Table 2.2B. Autism Risk and Perinatal Antibiotics in the Group B Strep-positive Cohort……..44 Table 3.1. Characteristics by antibiotic exposure for the full cohort……………………………58 Table 3.2. Associations between prenatal antibiotics exposure and risk of ASD in offspring of the main cohort…………………………………………………………………………………..60 Table 3.3. Associations between prenatal antibiotic exposure and risk of ASD in unmatched and matched sibling cohorts………………………………………………………………………….64 xiv List of Figures Figure 2.1. Flow diagram for a population-based study examining autism risk and perinatal antibiotic use……………………………………………………………………………………..37 Figure 2.2. Forest plot outlining the hazard ratios of autism spectrum disorder in the main cohort…………………………………………………………………………………………….45 Figure 2.3. Forest plot outlining the hazard ratios of autism spectrum disorder in the group B streptococcus-positive cohort……………………………………………………………….........45 Figure 3.1. Diagram for a population-based study examining the association of prenatal antibiotics and autism spectrum disorder in offspring…………………………………………...57 Figure 3.2. Forest plot outlining the hazard ratios of autism spectrum disorder in the main cohort…………………………………………………………………………………………….63 xv List of Abbreviations ADI-R Autism Diagnostic Interview-Revised ADOS Autism Diagnostic Observation Schedule aHR adjusted hazard ratio ASD autism spectrum disorder BBB blood-brain barrier BC British Columbia BCAAN British Columbia Autism Assessment Network BCPDR British Columbia Perinatal Data Registry BMI body mass index CNS central nervous system CNV copy number variants DALYs disability-adjusted life-years DOHaD developmental origins of health and disease DSM-5 Diagnostic and Statistical Manual of Mental Disorders 5th edition FMRI Fragile X Messenger Ribonucleoprotein 1 fMRI functional magnetic resonance imaging FMT fecal microbiota transplantation FPE female protective effect GABA gamma-aminobutyric acid GBS group B streptococcus GF germ-free GI gastrointestinal HPA hypothalamic-pituitary-adrenal HR hazard ratio IAP intrapartum antibiotic prophylaxis ICD-9 International Classification of Disease nineth revision LPS lipopolysaccharides xvi MECP2 methyl CpG binding protein 2 MIA maternal immune activation NICU neonatal intensive care unit OR odds ratio OTU operational taxonomic units PDD pervasive developmental disorder PDD-NOS pervasive development disorder not otherwise specified SCFA short-chain fatty acids SD standard deviation SNP single nucleotide polymorphisms SNV single nucleotide variants UTI urinary tract infection xvii Acknowledgements First, I would like to thank my supervisors Dr. Tim Oberlander and Dr. Gillian Hanley for providing me with this wonderful learning opportunity and for their continued support, encouragement and guidance throughout my studies and beyond. I am grateful to you both for your kindness and reassurance as I began this journey during unprecedented times. Tim, we connected over our mutual interest in the microbiome and I truly appreciate your support in helping me pursue that interest as my thesis. Gillian, thank you for your dedicated guidance every step of the way and for always being available to discuss any questions or concerns I had. Both the Hanley and Oberlander labs were such friendly and supportive environments to learn and grow as a researcher. I would like to give special thanks to my fellow lab member, Helena, for all the time she invested into helping me construct my datasets and for guiding me through coding in R. I would like to extend my gratitude to my advisory committee member, Dr. Bruce Vallance, for providing his valuable input and unique perspective, and to the co-authors on the publications resulting from this research, for their time and helpful feedback. I would also like to acknowledge that my work was generously supported by the University of British Columbia through the CIHR Frederick Banting and Charles Best Canada Graduate Scholarship and the Cordula and Gunter Paetzold Fellowship. Finally, I would like to thank my friends and family for being part of my amazing support system no matter what physical distance lies between us. To my parents, your unconditional love and encouragement allows me to pursue my passions. And to my partner, Vince. I am forever grateful for your unwavering love, devotion, and humour throughout the good and the challenging times. They ground me as I work my way forward. 1 CHAPTER 1: Introduction 1.1 Introduction to autism spectrum disorder 1.1.1 Definition and diagnosis Autism spectrum disorder (ASD), as its name implies, constitutes a spectrum of heterogeneous neurodevelopmental disorders characterized by impairments in three domains: social interaction, communication, and repetitive or stereotyped patterns of behaviours and interests.1 Since no diagnostically informative biological tests or biomarkers for ASD exist, evaluation criteria are based on developmental history and behaviour.2 In Canada, the diagnosis is usually provided by physicians or psychologists based on ASD criteria from the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-5). When the DSM-5 was published in 2013, the concept of a ‘spectrum’ of disorders was introduced. As a result, ASD includes pervasive developmental disorder (PDD) diagnoses: autistic disorder, Asperger’s disorder, childhood disintegrative disorder, and pervasive developmental disorder not otherwise specified (PDD-NOS).2 This new description of ASD was developed to provide more accurate diagnoses and to facilitate diagnoses as early as 12 to 24 months of age; however, symptoms can be recognized before 12 months of age or after 24 months due to the varying severity of the condition. A recent systematic review and meta-analysis determined that the current global mean age at diagnosis is approximately 5 years.3 In British Columbia (BC) specifically, the majority of ASD cases are assessed and diagnosed by the BC Autism Assessment Network (BCAAN). BCAAN was first developed in 2004, and since then, all children have been evaluated for ASD in a consistent manner.4 Primary care practitioners and pediatricians conduct general developmental surveillance and field 2 concerns from parents. If there is reason to warrant an assessment for ASD, and the family opts to proceed with a publicly funded ASD assessment, the child is referred to the BCAAN for clinical evaluation made by pediatricians, psychiatrists or psychologists who have completed extensive structured training for the diagnosis of ASD.4 To reduce barriers to comprehensive diagnostic testing, BCAAN is distributed throughout regional health authorities and diagnostic assessment is provincially funded for all children under 19 years of age. All diagnoses include: 1) a psychological evaluation of cognitive functioning administered by an appropriate specialist physician; 2) an assessment by a speech-language-communication pathologist; and 3) a medical examination conducted by a pediatrician.4 The standardized assessment instruments utilized by BCAAN clinicians include the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R).5,6 The ADOS uses standardized activities and triggers to elicit communication, social interaction, imaginative use of play materials, and repetitive behaviors, to allow the examiner to observe the occurrence or non-occurrence of behaviors important to ASD diagnosis.5 The ADI-R is an investigator-based interview administered by a trained interviewer to the primary caregiver(s) of the child regarding ASD symptoms; this tool obtains information on social development, verbal and non-verbal communication skills, as well as repetitive, stereotyped interests and behaviours.6 Similar to the global mean age at diagnosis, the median age at assessment for children seen at BCAAN is 5.35 years. In addition, some children are diagnosed with ASD through private practitioners in BC; these private practitioners have also undergone extensive training in ASD assessment and are required to use the ADOS and ADI-R assessment tools. 3 1.1.2 Epidemiology As of 2018, ASD affects 1 in 66 Canadian children;7 and in the US, the prevalence of ASD has increased from 1 in 150 to 1 in 44 children between 2000 and 2018.8,9 Globally, there are an estimated 62 million cases resulting in approximately 9 million disability-adjusted life-years (DALYs).10,11 In terms of incidence, in 2019 the age-standardized incidence of ASD calculated across 204 countries was 9.32 per 100,000 people. However, in ASD epidemiology, incidence calculations are less informative for two main reasons: ASD diagnosis is distal to disease initiation, and the time between initiation and diagnosis can be influenced by various factors possibly unrelated to risk.12 For example, in BC, various socioeconomic factors may be influencing the 5.35 year median age at BCAAN assessment. An ASD assessment is required for diagnosis and access to support, yet, the wait time for a publicly funded assessment through BCAAN is approximately 84.7 weeks.13 Therefore, children may be exhibiting symptoms of ASD well before an official diagnosis is given. In order to avoid long wait times, some families proceed with private assessments; however, these assessments are not publicly-funded,14 and thus, are limited to more affluent families. Moreover, it has been reported that children assessed or identified with a special need and children assessed for ASD outside of BCAAN were more likely to speak English as their first language.15 This suggests that families who speak English as their first language are more successful at accessing diagnostic services and receiving funding and other forms of assistance.15 ASD occurs across all racial, ethnic, and socioeconomic groups; however, features of its diagnosis (i.e., age, comorbidities) vary between these groups.2 In the United States, for example, a higher risk of severe ASD phenotypes has been reported in children of foreign-born Black, Central/South American, Filipino, and Vietnamese mothers, as well as US-born African 4 Americans and Hispanics when compared to US-born White mothers.16 Similarly, another study observed that Black children with ASD were more likely to have intellectual disability and to be first assessed for ASD at older ages compared to White children.9,17 These differences may be due to stigma, poverty, lack of access to healthcare services, and having English as a second language.2,18 It also suggests that there are likely children with less severe forms of autism who never receive a diagnosis or support in these racial/ethnic groups. 1.1.3 Sex-differences It is widely reported that ASD is more common in males than females, with a prevalence ratio of 4:1.7,9,19,20 Presence of an intellectual disability appears to affect this ratio, as males disproportionally represent high-functioning ASD cases, while males and females are more evenly represented among cases with severe intellectual disability.19 Multiple mechanisms have been suggested to explain this sex bias. First, there are differing ASD phenotypes between sexes. Overt symptoms such as aggressive, stereotypic and repetitive behaviours are more commonly observed in males,19 while females are more likely to experience internalizing symptoms such as depression or anxiety, and are more likely to mask their social deficits through a process called “camouflaging”.19,21 Importantly, the overt symptoms associated with the male ASD phenotype are also hallmark diagnostic features; thus, raising questions around if the current diagnostic criteria accurately characterize females with ASD.22 This is partly a result of methodological limitations in ASD research, such as studying predominantly male samples as well as the development and use of clinical tools designed to primarily identify the male ASD phenotype.23 Together these factors may contribute to an underreporting or delayed diagnosis of ASD among females. 5 At the biological level, various sex-specific factors have been hypothesized to either protect females from ASD or increase males’ risk for ASD. The female protective effect (FPE) model theorises that females require a higher genetic burden to reach the diagnostic threshold for ASD, and thus, are on average more severely symptomatic compared to males once that threshold is reached.24 This model is supported by consistent evidence that compared to males, females with ASD carry a higher load of rare, uninherited, or de novo, variants in autism-linked genes compared to males with ASD; such variants include copy number variants (CNVs), single nucleotide variants (SNVs), microdeletions, and single nucleotide polymorphisms (SNPs).25–27 More specifically, the median number of genes per CNV identified in females with ASD is 10 to 15, in contrast to 2 to 3 genes per CNV among males with ASD.28,29 Thus, females seem to be ‘protected’ from the combined effects of autism-linked genes, as a larger number of genetic variants are required to manifest ASD characteristics. In addition, this model posits that relatives of females with ASD are more likely to be affected than relatives of males with ASD. In support, several studies have reported that siblings of females with ASD have higher autistic trait scores and higher recurrence risk compared to siblings of males with ASD.24,30,31 Two main biological mechanisms that are hypothesized to explain the female protection and male vulnerability to ASD focus on sex chromosomes and sex hormones.20 Sex chromosomes ultimately account for all sex differences between males and females. Importantly, since females have two copies of the X chromosome, one inherited by each parent, they are able to compensate if they carry a damaged copy of a gene.32 In contrast, males only carry one X chromosome, inherited by the mother, with an accompanying Y chromosome, inherited by the father, and thus, are unable to compensate. This difference supports the sex bias in fragile X syndrome, a rare ASD-linked condition.33 Fragile X syndrome occurs from mutations in the 6 Fragile X Messenger Ribonucleoprotein 1 (FMRI) X-chromosome gene and is commonly and more severely present among males.33 Similarly, Rett syndrome results from mutations in the X-chromosome gene, methyl CpG binding protein 2 (MECP2) and also frequently presents with ASD.32,34 However, since at least one functional copy of MECP2 is required for survival, Rett syndrome is observed predominantly in females.34 With respect to sex hormone differences, fetal testosterone levels have been suggested to promote male susceptibility to ASD, as male fetuses have considerably higher exposure levels compared with females. Fetal testosterone is involved in multiple areas of development and may interact with neurotransmitter, neuropeptide, or immune pathways to increase vulnerability. Additionally, animal studies have shown that fetal testosterone exposure impacts neurodevelopment both within and between sexes.35 Longitudinal studies of typically developing children report that testosterone levels within amniotic fluid samples were found to predict various ASD-characteristics.36–38 For example, elevated testosterone levels were associated with reduced eye contract at 12 months of age,36 reduced quality of social relationships and increased level of restricted interests at 4 years,38 and high scores on multiple questionnaires designed to measure ASD behaviours.37,39 In particular, testosterone levels were positively associated with scores on the Quantitative Checklist for Autism in Toddlers at 18 to 24 months37 and the Child Autism Spectrum Test and Autism-Spectrum Quotient-Children Version between 6 and 10 years of age.39 Other studies have found increased rates of testosterone-related disorders among females with ASD, such as Polycystic Ovarian Syndrome and hirsutism.40 Overall, it remains unclear which factors ultimately drive the male dominance in ASD diagnosis and whether these factors are primarily environmental, genetic or epigenetic in nature. 7 1.1.4 Pathophysiology The etiology of ASD remains unclear; however, it appears to involve a complex interplay of genetic and environmental factors affecting the developing brain.41 Multiple biological processes have been associated with ASD, including impaired neuronal connectivity,42 mitochondrial dysfunction,43 immune dysregulation and neuroinflammation,44 as well as microbiome abnormalities.45 Neuroimaging and post-mortem studies of individuals with ASD have provided evidence for disruptions in functional and structural connectivity in brain regions involved in social interaction and other core ASD-related behaviours.46 The literature on whole-brain connectivity remains mixed, with reports of both hypo- and hyperconnectivity in the brain of individuals with ASD; however, hypoconnectivity is predominantly reported.42 For example, trends from functional magnetic resonance imaging (fMRI) studies show decreased activity within frontal and temporal brain structures during social tasks, and overall reduced connectivity between these regions.47,48 Frontal and temporal brain structures are important for socio-emotional processing, and thus, alterations in these areas have been suggested to be involved in the social impairments observed with ASD.48 Mitochondrial dysfunction is one of the most prevalent metabolic disorders found in children with ASD.49 In addition, a meta-analysis found that mitochondrial disease is significantly more common among individuals with ASD compared to the general population, with a prevalence of 5% vs. 1%, respectively.43 This 5% prevalence is even considered to be an underestimate of the true burden of mitochondrial disease among individuals with ASD due to various methodological limitations of available studies.43 Mitochondria are cellular organelles responsible for producing the majority of the energy required to power a cell’s biochemical 8 reactions, and if dysfunctional, the affected areas of the body are unable to produce enough energy to function properly.43,50 In addition to energy supply, mitochondria are vital for successful apoptosis (i.e., programmed cell death), calcium homeostasis, synaptic plasticity, and neurotransmitter release.50 Therefore, mitochondrial dysfunction can result in widespread damage, especially in high energy demanding organ systems and processes, such as the gastrointestinal tract, the nervous system, and during neurodevelopment.43,51 Further, immune dysregulation and inflammation are commonly present in individuals with ASD, with many experiencing frequent infections, environmental and food allergies, asthma, and unexplained skin rashes.44 Interactions between several cell types and immune factors from both the innate and adaptive arms of the immune system can alter brain function and behaviour.52 Chronic and exaggerated microglial activation as well as elevated levels of pro-inflammatory molecules, called cytokines, are consistently found in post-mortem brain tissues of individuals who had ASD and are key indicators of excessive neuroinflammation.53,54 Microglia are the primary immune cells of the central nervous system (CNS) tasked to defend the brain by clearing cellular debris and dead neurons.44 However, when microglia activation is exaggerated, damage of healthy brain tissue can result, such as the loss of healthy synaptic connections between neurons.55 In addition, the cytokines observed at increased levels in the brain tissues of individuals with ASD are able to alter neuronal development, migration, differentiation and synapse formation; thus, an imbalance of cytokines may directly influence early brain development with lasting impact on behaviour.52 For example, one study found that elevated levels of plasma cytokines IL-1β, IL-6, IL-8 and IL-12p40 were associated with more impaired communication and aberrant ASD behaviours as measure using the ADI-R and ADOS clinical assessment tools.56 9 Finally, the role of microbiome abnormalities in the pathophysiology of ASD was first suggested in response to the frequent gastrointestinal (GI) symptoms observed in children with ASD.57 Further, children with frequent GI symptoms were reported to have worse ASD symptoms.58 Gut dysfunction associated with ASD as well as the proposed mechanisms through which abnormal microbiota composition may impact the brain and ASD etiology will be further discussed in subsequent sections. 1.1.5 Genetic factors While ASD is widely acknowledged to involve a significant genetic component, its genetic architecture is extremely heterogeneous.59 Approximately 10% of ASD cases can be linked to a genetic syndrome, such as Fragile X syndrome, tuberous sclerosis, Rett syndrome and neurofibromatosis.60 High numbers of low risk single nucleotide polymorphisms, as well as rare highly penetrant mutations or copy number variations, that are inherited or arise de novo in germ cells, also contribute to the risk of ASD.59 Strong evidence of a genetic component in ASD etiology is provided by family and twin studies. A higher relative recurrence risk of ASD is observed among full siblings compared to half-siblings, and among maternal half-siblings compared to paternal half-siblings, together highlighting a possible influence of the intrauterine environment.61 Further, higher proband concordance is found among monozygotic twins who share 100% of their genetic material compared to dizygotic twins who only share 50%.62 However, since monozygotic concordance is still less than 100% and symptoms and severity of ASD vary among concordant pairs, environmental factors are also understood to play an important role in ASD etiology, and potentially modulate genetic risk.2,12 10 1.1.6 Environmental factors Many prenatal and postnatal environmental factors have been explored in association with ASD.63,64 Prenatal factors such as maternal age, substance use, metabolic disorders (i.e., hypertension, diabetes, gestational weight gain), and diet, as well as exposure to air pollution, chemicals, maternal infection, and antibiotics have all been associated with ASD.65–68 Further, obstetric factors including abnormal presentation, fetal distress, birth trauma, multiple birth, uterine bleeding, and neonatal factors including low birthweight, preterm delivery and low Apgar scores are also consistently reported to be correlated with ASD.12,63,64 However, the biological mechanisms underlying these associations remain unclear.12,68 Overall, there is currently insufficient evidence to implicate any one environmental factor in autism etiology. There is some evidence, however, to suggest that cumulative exposure to a variety of factors may compromise infant health and increase the risk of ASD.63 1.1.7 Common comorbidities Children with ASD frequently experience multiple biological and behavioural comorbid conditions. With regard to other developmental disorders, intellectual disability is currently estimated to be present in approximately 30% of cases,69 as well as co-occurring attention deficit hyperactivity disorder in 30-80% of cases.70 Further, language disorders, sensory integration disorder, and developmental coordination disorder are common among children with ASD.71,72 Psychiatric and neurological conditions regularly associated with ASD include, but are not limited to, depression, anxiety, obsessive compulsive disorder, Tourette syndrome, and epilepsy.73 In addition, children with ASD often experience sleep disturbances, immune deficits, metabolic disorders and GI problems.71 11 1.1.8 The association between gut dysfunction and ASD Of particular note, GI disturbances, such as chronic constipation, periods of diarrhea, abdominal pain, and indications of intestinal microorganism overgrowth (i.e., bacteria, virus, fungi) are especially common comorbid conditions.74,75 The true prevalence of GI disorders in ASD cases is challenging to estimate due to methodologic variability between studies and potential lack of recognition in children.75 However, a recent meta-analysis reported that children with ASD are 4 times as likely to experience GI symptoms as compared to children without ASD.57 Most children with ASD who experience these GI dysfunctions, display malabsorption, maldigestion, as well as deficient integrity of the gut epithelium and increased intestinal permeability.74,75 Importantly, GI dysfunction can affect multiple aspects of a child’s quality of life.76 While some presentation of symptoms, such as diarrhea, can easily be recognized and understood to affect quality of life, nontypical and potentially more prevalent presentations of GI distress, such as increased anxiety behaviours, can also greatly influence a child’s wellbeing and may go unrecognized, since children with ASD have trouble communicating.76 It is suggested that sleep disturbances, aggressive behaviours, irritability, and self-injury may be expressions of symptoms for children with ASD who are experiencing untreated GI distress.57,76,77 Such GI distress appears to be partly due to dysbiotic gut microbiota and perhaps abberant levels of bacterial-derived metabolites that affect GI function.78 Moreover, research suggests that abnormal composition of gut microbiota could render a child with a genetic predisposition for ASD, more likely to express autistic characteristics and more likely to experience increased severity of behavioural problems.58,79 Therefore, the microbiome-gut-brain axis is of particular interest in recent autism research, as it has been hypothesized to play a possible role in the etiology of ASD.80 12 1.2 Connection between gut microbiota and central nervous system function 1.2.1 Microbiome-gut-brain axis The human microbiota consists of the trillions of microorganisms, including bacteria, fungi, and viruses, that live in and on the body. There is a distinct microbiome in almost every areas of the body; however, the largest proportion of the microbiome is located in the GI tract.81 The microbiome-gut-brain axis reflects the bidirectional signalling between the gut, the intestinal microbiota, and the brain via pathways such as the vagal nerve, neurotransmitters, and molecular signaling mechanisms, including short chain fatty acids (SCFAs), cortisol, and immune factors.82,83 Overall, this axis allows for communication between the central and the enteric nervous system, connecting emotional and cognitive centers of the brain with peripheral intestinal functions.84 Several mechanisms of actions have been suggested through which the intestinal microbiota influence the CNS, and research evidence has highlighted that gut-brain communication occurs mainly through three psychoneuroimmunologic pathways: immune, endocrine, and neural pathways.83 1.2.2 Microbiome and immune system The gut microbiome contributes to the balance of systemic inflammation in the host. This is done by low-level leakage of microbes, metabolites, such as SCFAs, and bacterial cell wall components, such as lipopolysaccharides (LPS), from the intestinal lumen into the peritoneal cavity.85 Once in the peritoneal cavity, pathological bacteria can directly interact with immune cells and trigger an inflammatory response via the release of pro-inflammatory molecules, called cytokines. Importantly, some gut microbes have anti-inflammatory properties, while others encourage a pro-inflammatory state. For example, Lactobacillus (which secretes lactic acid), 13 lowers gut pH to a range prohibitive to the colonization of more inflammatory, pathological microbes, such as Enterobacteriaceae and Staphylococcus.86 In infants, Lactobacillus colonization is negatively associated with pro-inflammatory cytokines such as interleukin-10 and -4. Therefore, early life dysbiosis of the microbiome could affect the developing immune system, promoting a pro-inflammatory state for the infant.87 Once triggered, inflammatory cytokines can exert effects on the CNS, influencing brain function, mood, stress response, and behaviour.87 Moreover, high levels of cytokines in the CNS is significant, as cumulating evidence, outlined in section 1.1.4, supports the involvement of neuroinflammation in the pathogenesis of ASD.88 1.2.2 Microbiome and endocrine function The hypothalamic-pituitary-adrenal (HPA) axis is a major neuroendocrine system regulating various body processes in response to psychological and physical stressors.89 It remains unclear if the microbiome can directly affect the HPA axis; however, by triggering the release of pro-inflammatory cytokines, microbiota can indirectly activate all levels of the HPA axis.87,90 Through this widespread HPA activation, microbiota have the potential to manipulate cortisol secretion as well as impact the developing stress response in infants. While chronically elevated cortisol levels negatively affect brain function,91,92 animal research has shown that elevated cortisol levels are also able to affect the composition of the gut microbiota as well as increase gastrointestinal permeability.93 This sets up a harmful positive feedback loop between chronic stress, microbiota, and systemic inflammation.87 1.2.3 Microbiome and neural pathways The vagus nerve is the major nerve of the parasympathetic division of the autonomic nervous system. Its main function is bringing in sensory information from inner organs, providing an important line of communication between the gut and the brain.94 In addition, the vagus nerve 14 maintains intestinal homeostasis, modulates inflammation, and regulates food intake and energy homeostasis.94 Direct communication between the gut and the brain occurs when bacteria interact with nerve cells in the intestinal walls, triggering the production of neuropeptides. These peptides stimulate afferent sensory endings of the vagus nerve, and the resulting signals are then transmitted to the CNS to impact behaviour and efferent neural signals.87 The vagus nerve may also influence CNS function and behaviour indirectly through feedback involving the HPA axis and various inflammatory mediators.87 Furthermore, gut microbiota can influence CNS function through the production of neurotransmitters, such as serotonin, gamma-aminobutyric acid (GABA), dopamine, and noradrenaline.95,96 In particular, an altered intestinal microbial colonisation early in life impacts the availability of tryptophan, the precursor of serotonin.97 In early-life, serotonin is considered as an essential signaling molecule that regulates the development of many systems throughout the body, including stress-regulatory mechanisms, mood and chronic pain.95,97 Thus, dysregulation of tryptophan metabolism and its impact on serotonergic signalling is involved in many brain and GI disorders.98 1.3 Microbiome-gut-brain axis and ASD 1.3.1 Neurodevelopment The assembly of a child’s gut microbiome typically occurs during the first 2 to 3 years of life and depends on genetic and perinatal factors. Colonisation by the microbiota occurs coincident with neurodevelopment, with both sharing similar critical developmental windows sensitive to damage.98 Thus, early life events during initial microbiota colonization and development can alter brain-gut signaling, increasing the risk of neurodevelopmental disorders, like ASD.98 15 Several animal studies have demonstrated that gut microbiota affects neurodevelopment. One study found that neurogenesis in the dorsal hippocampus of adult germ-free (GF) mice was higher than control mice with intact microbiomes.99 Similarly, another study reported that neurogenesis of the hippocampus and the number of monocytes (i.e., a type of immune cell) were reduced in mice treated with antibiotics over a prolonged period of time.100 Furthermore, the blood-brain barrier (BBB) was shown to be more permeable without the presence of gut microbiota, likely due to reduced expression of proteins that comprise the tight junctions of the brain endothelium.101 This increased permeability allows macromolecules to enter the brain and impact its function. Interestingly, after mice were recolonized with gut microbiota, reduced BBB permeability was then observed.101 In sum, this evidence suggests that gut bacteria may play a role in neurodevelopmental disorders. 1.3.2 A distinct ASD microbiome Consistent evidence shows differences in microbial composition in children with ASD compared to unaffected siblings and neurotypical controls.102 However, consistency is lacking in the type off bacterial species present and their relative abundances compared to controls. Children with ASD are reported to have significantly higher proportions of Sutterella and Streptococcus, and significantly lower abundances of the genera Prevotella, Coprococcus, and unclassified Veillonellaceae.103 Another study, using typically developing siblings as controls reported a lower Bacteroidetes-to-Firmicutes ratio, and a higher abundance of Clostridium and Desulfovibrio bacterial species.104 This is significant as Clostridium is a neurotoxin-producing pathogen which inhibits neurotransmitter release by nerve cells and is linked to GI dysfunction and ASD disease severity, as measured using the Childhood Autism Rating score.75 Furthermore, 16 Desulfovibrio and a lower Bacteroidetes-to-Firmicutes ratio have been linked with more severe ASD neurobehavioral symptoms.104 1.3.3 Therapeutic approaches Interventions that alter microbiota composition have been employed to modulate brain activity and influence mood and behaviour. One preliminary study randomized 75 infants to either receive a probiotic (Lactobacillus rhamnosus) or a placebo during the first 6 months of life.105 At the 13-year follow-up, they found a lower risk of ADHD and Asperger’s syndrome in infants who received probiotics; however, no specific change in microbiota composition was found at 13 years of age. Only a lower abundance of Bifidobacterium was observed at 6 months of age in children later diagnosed with ADHD or Asperger’s syndrome.105 In addition, one study examined the therapeutic effect of a seven-to-eight-week fecal microbiota transplantation (FMT) protocol on 18 children with ASD.106 Children were given vancomycin for two weeks as well as bowel cleansing prior to transplantation. As rated by the Gastrointestinal Symptom Rating Scale, GI symptoms, such as constipation, diarrhea, and abdominal pain, were approximately 80% reduced at the end of treatment protocol. ASD symptoms, measured by Parental Global Impression III, were also improved and maintained for at least 8 weeks after discontinuation of treatment. Bacterial changes observed after FMT included increased diversity and altered abundance of three genera: Bifidobacterium, Prevotella, and Desulfovibrio.106 Further, the same research group published a follow-up study on the same 18 FMT patients, reporting maintained improvements in GI and ASD symptoms that lasted up to 2 years post-treatment.107 Important changes to the gut microbiota persisted, including significant increased in bacterial diversity and relative abundance of Bifidobacteria and Prevotella.107 Overall, these findings provide some evidence of gut microbiota involvement in the etiology of ASD. 17 1.4 Microbiota colonization 1.4.1 Emerging theory: Intrauterine transfer Accumulating evidence is challenging the long-standing hypothesis that the intrauterine environment and fetus are sterile until delivery, except in cases of pathological infections usually associated with adverse pregnancy outcomes.108 Due to advances in DNA sequencing technology, studies have improved the ability to detect bacteria in maternal tissues outside of what was achievable using only culture-based methods.109 In turn, research has demonstrated bacterial presence in the placenta, amniotic fluid, and the infants’ meconium (i.e., a newborn’s first stool after birth) in healthy pregnancies.108,110–112 From these findings, it has been suggested that maternal microbiota transfer between mother and fetus may be occurring before birth. For example, one study demonstrated that placentas from healthy pregnancies consistently hold their own microbiome, characterized by non-pathogenic commensal bacteria from the phyla Firmicutes, Tenericutes, Proteobacteria, Bacteroidetes, and Fusobacteria.108 This placental microbiome was unique from other areas of the body, such as the urogenital tract, yet appeared to be most similar to the oral cavity microbiome.108 Another study on 23 healthy infants found that all samples of meconium collected were not sterile and instead contained diversified microbiota, specifically, an enrichment of Proteobacteria and reduction of Bacteroidetes.113 Interestingly, the bacterial composition of meconium did not differ based on vaginal or cesarean mode of delivery. Since mode of delivery is understood to exert influence over the infant microbiome at birth, these results suggest that colonization of the fetal gut may begin in utero.113 However, the potential mechanisms by which bacteria pass from the mother to the fetus are still unconfirmed and the supporting evidence remains under debate.114 18 1.4.2 Birth events Regardless of the prenatal fetal transfer, birth remains the most critical moment when the infant microbiome is first assembled, and mode of delivery is still considered a major source of the diversity of bacteria transmitted to the infant gut.115 In vaginal delivery, as the neonate descends through the birth canal, it becomes inoculated with maternal vaginal microbes. These microbes deoxygenate the gut and prime the neonate for correct growth and development.115 More specifically, microbes acquired via vaginal delivery are generally facultative anaerobic bacteria which thrive on residual oxygen in the infant gut. The resulting lowered redox potential allows obligate anaerobic bacteria to proliferate, such as Bifidobacterium and Bacteroides, which become the predominant genera present in early life.116 Consistent evidence shows that vaginally delivered newborns have a higher composition of commensal microbes, such as Lactobacillus and Bifidobacterium, while cesarean delivered newborns have higher compositions of pathogenic microbes including the genus Clostridium and family Enterobacteriaceae.115,117 1.4.3 Infant-feeding The next important factor in shaping the infant intestinal microbiome is mode of feeding: breastmilk vs. formula. Human breastmilk provides complete nutrition for infant development. Its key contents include bioactive factors, immunoglobulins, cytokines and chemokines, growth factors, hormones, and antibodies.115,118 In addition, breastmilk transfers specific peptides, lipids, and bacteria to the infant that are vital for healthy immune development and homeostasis as well as gut function.115,118 Differences in gut microbiota composition and activity exist between exclusively breastfed and formula-fed infants.119–121 If breastfed, the infant gut microbiota is higher in proportion of Lactobacillus and Bifidobacterium species, and has an overall reduced microbial diversity.118 In contrast, formula-fed infant microbiota are dominant in Clostridium 19 and Bacteroidetes and E. coli, and have a higher microbial diversity which appears to increased the risk of gut dysbiosis and metabolic and immune diseases. 118,122 1.5 The maternal microbiome during pregnancy and fetal development Apart from the maternal microbiota’s effect on initial infant microbial colonization, mounting evidence describes the impact maternal microbiota may have on other aspects of fetal development. Prompt remodeling of the maternal microbiome occurs throughout normal pregnancy with immunologic, hormonal and metabolic changes; and simultaneously, the maternal microbiota influences the development of the immune system, metabolism and brain in offspring. 1.5.1 Normal changes to the maternal microbiome The compositions and abundance of the maternal intestinal microbiota significantly change throughout the pregnancy period. These changes manifest as an increase in Actinobacteria and Proteobacteria, with an accompanying decrease in microbial diversity and Faecalibacterium species.123 Faecalibacterium is a butyrate-producing bacteria with anti-inflammatory effects, and its reduction helps facilitate a proinflammatory state. Levels of inflammation vary during pregnancy, however pro-inflammatory states occur during early and late pregnancy as the accompanying cytokines, chemokines, and growth factors are critical for successful implantation, placentation, and delivery.124,125 Despite rises in inflammation, the fetus is protected from rejection by anti-inflammatory properties at the placental bed. However, when maternal inflammatory conditions, such as obesity, gestational diabetes, or gut dysbiosis and increased intestinal permeability, push the level of inflammation from physiologic to excessive, vascular dysfunction of the placenta may occur with negative consequences for fetal health (i.e., 20 growth restriction and preeclampsia).126 Furthermore, the vaginal microbiome also undergoes changes during pregnancy, with an overall decrease in diversity and increased stability and enrichment of Lactobacillus species.127 Elevated levels of estrogens and progesterone are key contributors to the increased dominance of lactobacillus species as pregnancy advances.128 In addition, this dominance of Lactobacillus species may provide some protection against infection and negative pregnancy outcomes.129 As a main regulator of immune function and metabolism, the maternal intestinal microbiota is able to influence every aspect of metabolism and nutritional availability throughout pregnancy.130 During the first two trimesters of pregnancy, fetal growth is limited, and accordingly, the mother is in an anabolic state characterized by increasing fat storage. During the third trimester, fetal growth is rapid, and as a result, the maternal metabolism shifts into a catabolic state that is characterized by increased basal metabolic rate, enhanced transfer of nutrients, glucose production and breaking down fat stores.86 Gut microbiota play an essential role in isolating these nutrients and energy substrates from food, as they digest and ferment complex carbohydrates to produce SCFAs, amino acids, and various vitamins, such as vitamin K and B-group vitamins.132 1.5.2 Maternal microbiome and fetal neurodevelopment Adaptations to the maternal microbiome occurs to meet the energetic demands of successful fetal neurodevelopment.130 During the early anabolic phase of pregnancy, the fetal CNS undergoes neural migration and synaptogenesis, while processes such as myelination and formation of synapses take place during the latter catabolic phase. Any sudden changes to the maternal microbiome could impact these critical processes for brain development and function.131 For example, SCFAs (e.g. butyrate), which are maternal microbiota-derived metabolites, fuel 21 mitochondrial energy production.130 Other biological mechanisms dependant on microbiota-derived metabolites include chromatin remodeling and signaling through G protein-coupled receptors, among other mechanisms still to be characterized.130 Furthermore, recent animal research has also shown that maternal microbiota regulate metabolites found in the fetus, and specifically, in the fetal brain.133 Findings suggest that metabolites such as trimethylamine oxide, trimethyl-5-aminovalerate, imidazole propionate, 3-indole sulfate, and Hippurate, may affect neurodevelopment in offspring by influencing axon outgrowth and axonogenesis, altering the amount of white matter, and disrupting somatosensory cortical architecture.133 1.6 Antibiotic use and the infant microbiome 1.6.1 Prenatal antibiotic use Antibiotics are among the most used medication during pregnancy,134 with roughly 30% of pregnant individuals receiving treatment for common indications such as urinary tract infections, other genitourinary infections, and respiratory infections.66,135 Antibiotics that are generally considered safe for use during pregnancy include: penicillins (i.e., amoxicillin and ampicillin) and other beta lactams, such as cephalosporins, macrolides (i.e., erythromycin and azithromycin), and clindamycin.134 Penicillins, however, are the most frequently prescribed antibiotics during pregnancy.134 The short-term benefits of antibiotic use are clear, as untreated infections pose significant risk to the fetus (i.e., spontaneous abortion or preterm delivery); however, the potential long-term effects of antibiotic exposure during critical periods of fetal development have not been well studied. 22 It is widely recognized that gut microbiota play an essential role in health and disease,136 and previous research has shown that prenatal exposure to antibiotics induces changes in the maternal microbiome.137 These changes can then impact the colonisation of the infant microbiome, or, as discussed in section 1.5, may impact fetal development before birth.133,138,139 With regard to microbial colonization, one study found that prenatal exposure to antibiotics is associated with differences in diversity and composition of infant meconium.138 Upon conducting 16S ribosomal RNA gene sequencing on bacterial DNA of meconium samples, results showed that newborns who were exposed to prenatal antibiotics and were vaginally delivered, had 75 fewer operational taxonomic units (OTU; i.e., categorized groups of bacteria based on sequence similarity) as compared to unexposed vaginally delivered newborns. Further, pathogenic OTUs from the genus Clostridium and the family Enterobacteriaceae were enriched in meconium samples from exposed infants.138 In sum, prenatal antibiotic use appears to have the potential to alter the first fetal-microbial interactions, and could influence the developmental trajectory of the infant microbiome. 1.6.2 Intrapartum antibiotic use During labour and delivery, over 40% of individuals receive antibiotic treatment in contexts of intrapartum fever, prolonged or premature rupture of membranes, or as prophylaxis for cesarean sections and group B streptococcus infections.135,140 Several studies have shown that intrapartum antibiotic prophylaxis (IAP) influences the infant’s gut microbiota colonisation with an overall reduced abundance of beneficial microorganisms.141–143 More specifically, IAP exposure was associated with a decreased transmission rate of vaginal Lactobacillus species to the neonate and a significantly lower development of Bacteroides and Bifidobacterium over time.141,142 Further, studies reported that infants who were exposed to antibiotics during the intrapartum or 23 peripartum period had significant changes in both the quantities and diversity of microbial composition that continued until at least one year of age.144,145 1.7 Association between maternal infection during pregnancy and ASD 1.7.1 Epidemiology Reports suggest that between 50% and 64% of individuals experience at least one infection during pregnancy.146,147 Infection during pregnancy is a plausible risk factor for ASD, given the known teratogenic effects of prenatal infections such as rubella or Toxoplasma gondii on the CNS.148,149 In fact, following a rubella epidemic in 1964, children with congenital rubella syndrome were found to have an increased rate of ASD.150 As a result, various epidemiological studies have explored the association between prenatal infection and ASD, expanding the infectious exposure of interest to a wide variety of viruses and other pathogens including bacteria; however, many studies have reported inconsistent results.146,151–154 For example, one study using medical information obtained in a large Danish database, found that pregnant individuals hospitalized for viral infection in the first trimester and bacterial infections in the second trimester had an increased risk of ASD in offspring.151 This suggests that viral and bacterial infection may impact the risk of ASD differently depending on gestational age at time of exposure.151 Another study reported that maternal infections diagnosed during a hospital admission, particularly bacterial infections during any trimester, were at increased risk of delivering a child who was diagnosed with ASD. However, when this same study included pregnant individuals diagnosed with an infection either during hospitalization or in an outpatient clinic visit, they found no association between maternal infection and ASD in offspring.146 Since infections that are diagnosed in an inpatient setting are likely more severe than those diagnosed 24 in the outpatient setting, these results indicate that more severe infections during pregnancy may increase the risk of ASD.146 Similarly, another study found that maternal infection anytime during pregnancy was not associated with ASD, however they did observe that an infection during the second trimester accompanied by a fever increased the risk for ASD by approximately twofold,154 again suggesting that more severe infections may be increasing the risk of ASD. Overall, a systematic review and meta-analysis of the current literature on the association between prenatal infection and ASD reported a 12% increase in odds of ASD based on the pooled estimates of multiple studies.67 Further, this increase in odds was more pronounced for maternal infections requiring hospitalization. Subgroup analyses were conducted regarding type of infectious agent, timing of infection, and location of infection. Results revealed that viral infections were not associated with ASD, while bacterial infections were associated with an increased risk of ASD if exposed during the second and third trimesters. Finally, genitourinary and skin infections were the only sites of infection found to be associated with an increased risk of ASD.67 1.7.2 Suggested mechanisms The exact biological mechanism underlying the relationship between maternal infection during pregnancy and fetal neurodevelopment is not clear;67 and in response, various animals models have been developed in order to further our understanding. In rodent models, maternal injection of the synthetic double-stranded RNA poly(I:C) is used to evoke an antiviral inflammatory response, while maternal injection of LPS is used to induce an antibacterial inflammatory response.155 Both of these approaches used to provoke maternal immune activation (MIA) produce similar behavioural abnormalities and neuropathologies in offspring.156 Several core symptoms of ASD are observed in offspring exposed to maternal immune activation, including 25 impaired communication, deficits in social interaction, and elevated anxiety behaviours.157 Elevated levels of pro-inflammatory cytokines as a results of MIA appear to be responsible for these altered behaviours, specifically, the cytokine IL-6. Research shows that injecting pregnant dams with IL-6 alone is sufficient to produce these ASD-like behaviours.158 Additionally, blocking the effects of IL-6 during MIA with an anti-IL-6 antibody prevents the development of these behaviours.158 In contrast, increased levels of the anti-inflammatory cytokine IL-10 protects against altered behaviour in the MIA model.159 Together, these results suggest the importance of a balance between pro- and anti-inflammatory processes during fetal brain development.159 Another possible mechanism is that the maternal immune system could alter the immune status of the fetal brain. Animal experiments have shown that MIA during pregnancy can affect the fetal hematopoietic stem cells in the placenta leading to permanent alterations to the peripheral immune system of offspring.156,160 This is significant as many studies have reported both systemic and cellular irregularities in the immune systems of children with ASD, as discussed in section 1.1.4. Finally, pregnancy and delivery complications resulting from the maternal infection could increase the risk of ASD.67 For example, multiple infections have been linked to complications such as preterm delivery and intrauterine growth restriction,161,162 both of which are additional risk factors for ASD. 1.7.3 Confounding by indication Taking into consideration the available epidemiological evidence and animal research on possible biological mechanisms, infection during pregnancy has been identified as an additional risk factor for ASD development.67 Therefore, confounding by indication is a concern when observing associations between antibiotic medication use and risk of ASD, since there is a possibility that the indication (i.e., infection) for this medication use could be increasing the risk 26 for ASD rather than the medication itself.163 Moreover, various factors associated with maternal infections during pregnancy, such as severity of infection (i.e., fever, hospitalization), type of infectious agent (i.e., viral, bacterial), exposure time, and site of infection (i.e., genitourinary, respiratory, skin), seem to differentially influence the risk of ASD in offspring.67 Thus, these factors should be taken into consideration when assessing the role of confounding by indication in the relationship between prenatal antibiotic use and risk of ASD. 1.8 Prenatal antibiotic exposure and ASD: Summary of existing evidence To date, four population-based cohort studies have explored the association of prenatal antibiotic exposure with risk of ASD and have reported mixed results.66,164–166 Atladóttir et al164 conducted an exploratory population-based cohort study in Denmark investigating the association between self-reported maternal infections, febrile episodes, and prenatal antibiotic use with the risk of ASD and infantile autism. They reported no association found between maternal infection or febrile episodes and ASD; however, they did observe an increased risk of ASD following prenatal antibiotic exposure (adjusted hazard ratio [aHR] 1.20 [95% CI 1.00, 1.40]). Further, they examined exposure based on type of antibiotic and trimester of exposure and observed a stronger association between prenatal antibiotic exposure and ASD for the use of sulfonamides anytime during pregnancy (aHR 1.5 [95% CI 1.0, 2.2]) and use of penicillins during the second (aHR 1.4 [95% CI 1.1, 1.8]) and third (aHR 1.4 [95% CI 1.0, 1.8]) trimester. This study was strengthened by its prospective data collection, large sample size (n = 96 736 children), reliable ASD case diagnosis, and the many covariates considered in the analysis. However, a key limitation of this study is the potential for nondifferential exposure misclassification since 27 antibiotic use was self-reported via telephone interviews. Additionally, this study likely suffers from important residual confounding by genetic, environmental, and other unmeasured factors. Another Danish population-based study explored otitis media, antibiotics and risk of ASD.165 To note, this study had an overlapping population with Atladóttir et al164 for six of the 11 years of the study period. They found an association between ASD and diagnosis of otitis media (aHR 1.83 [95% CI 1,71, 1.95]) and with childhood broad-spectrum antibiotic dispensation (aHR 1.29 [95% CI 1.17, 1.43]). However, combining exposure to a broad-spectrum antibiotic and children diagnosed with otitis media did not significantly influence the risk estimate (aHR 1.77 [95% CI 1.36, 2.30]), thus, this could suggest that the observed risk with antibiotic exposure is confounded by indication. With regard to prenatal antibiotic exposure, they observed a statistically significant positive association between exposure to antibiotics anytime during pregnancy and subsequent risk of ASD (aHR 1.18 [95% CI 1.13, 1.23]).165 However, assessment of prenatal antibiotic exposure was not a primary focus of the study, and was only included as a secondary analysis in the supplementary materials. Further, different antibiotic classes or timings of exposure were not examined, genetics and shared environmental factors were not accounted for, and analyses were only controlled for sex and parental age. Hamad at al66 observed a statistically significant association between prenatal antibiotic exposure and ASD (aHR 1.10 [95% CI 1.01, 1.19]). The association was strongest for exposure in the second and third trimesters (aHR 1.11 [95% CI 1.01, 1.23] and 1.17 [95% CI 1.06, 1.30], respectively) or when exposed to penicillins or other beta-lactams (aHR 1.13 [95% CI 1.04, 1.24] and 1.18 [95% CI 1.03, 1.37]), respectively). Upon conducting the analysis on a cohort of children who had at least one exposure-discordant sibling, the association remained stable however no longer statistically significant (aHR 1.08 [95% CI 0.90, 1.30]). This study was 28 strengthened by use of several linked population-level administrative databases to identify exposure, outcome, and a vast number of potential covariates, eliminating the potential for recall or selection bias. Moreover, this study was able to assess for temporal and dose-response relationships, as well as relationships based on antibiotic class. A couple of limitations are important to mention. First, they used diagnostic codes from administrative datasets for outcome ascertainment and employed an ASD identification algorithm that had not been previously independently validated. Further, recent research has found that administrative data is not the most reliable method to identify ASD cases, and while it may produce a good positive predictive value, the negative predictive value is of concern.167 In addition, they were unable to assess the indications for antibiotic use and thus acknowledged that confounding by indication is a concern that may have influenced their findings. Fan et al166 conducted a population-based cohort study to assess the association between macrolide antibiotic exposure during pregnancy and the risk of adverse child outcome. To minimize the effects of confounding by indication, they compared cohorts of pregnant individuals who received macrolide treatment and pregnant individuals who received penicillin treatment. No statistically significant association was observed for risk of ASD in offspring; however, macrolide prescription at any point during pregnancy was associated with an increased risk of genital malformations (aHR 1.58 [95% CI 1.14, 2.19]) and macrolide prescription during the first trimester was associated with an increased risk of major malformations (aHR 1.55 [95% CI 1.19, 2.03]).166 While this study aimed to reduce the effects of confounding by indication, residual confounding could be an issue if macrolides were prescribed to treat certain indications more often than penicillins, or if potential risk factors for ASD differed between the two treatment cohorts. 29 Only one study to date looked specifically at antibiotic exposure during the peripartum period and the risk of ASD in offspring. This population-based case-control study found no increase in odds of ASD associated with peripartum antibiotic use among vaginal and cesarean deliveries (odds ratio [OR] 1.00 [95% CI 0.55,1.79] and 1.25 [95% CI 0.81, 1.92], respectively).168 However, those study results were weakened by a small sample size, wide confidence intervals and imprecise estimates.168 1.9 Research objectives From a microbiome-gut-brain axis perspective, the aim of this thesis was to investigate the association of prenatal and intrapartum antibiotic exposure with ASD in offspring, building upon the limitations of previous literature. All children born in British Columbia, Canada between the study period of 2000 to 2016 were assessed using individual-level clinical diagnostic data from the BC Autism Assessment Network linked with multiple population based administrative datasets. The following specific objectives are addressed in subsequent chapters: 1) Examine the association of intrapartum antibiotic exposure with risk of ASD in offspring. 2) Examine the association of prenatal antibiotic exposure with risk of ASD in offspring and assess whether class of antibiotic, trimester of exposure, or cumulative duration of exposure influences this risk. 1.10 Thesis Roadmap Including the introduction, this thesis consists of four chapters. Chapter 2 investigates the association between intrapartum antibiotic exposure and the risk of ASD in offspring, while 30 accounting for the possibility of confounding by indication. Chapter 3 explores the association between prenatal antibiotic exposure and the risk of ASD in offspring with a focus on timing of exposure, duration of exposure, and type of antibiotic. Finally, Chapter 4 synthesizes the findings presented across the two studies, highlights implications, discusses the strengths and weaknesses of our approach, and provides some future recommendations for further research. 31 CHAPTER 2: Autism Risk and Perinatal Antibiotic Use 2.1 Introduction Antibiotics are administered during labour and delivery to over 40% of individuals for treatment of intrapartum fever, prolonged or premature rupture of membranes, or as prophylaxis for cesarean sections and group B streptococcus infections.135,140 While antibiotics are beneficial to reduce short-term maternal and neonatal complications, they can also induce lasting changes in the maternal microbiome.135 An altered maternal microbiome can in turn influence the colonization and development of the infant’s intestinal microbiome, and the long-term effects of this are not well understood.169 Children with ASD are commonly found to carry an abnormal composition of gut microbiota as well as display overt gut dysfunction.170,57 Substantial evidence supports a distinct microbiome of children with ASD compared to children without ASD, yet the types of bacterial species and their relative abundances remain inconsistent.170 These inconsistences could possibly be a result of differences between studies with regard to techniques for DNA sequencing, sample sizes, selection of controls (i.e., siblings vs non-siblings), as well as geographic location and dietary habits.170 As such, recent autism research focuses on the proposed mechanisms of the microbiome-gut-brain axis and suggests that an altered intestinal microbiome in childhood could be a contributing factor in ASD development.82,171 Colonization of the microbiota occurs during early periods of neurodevelopment, with both sharing similar critical developmental windows that are sensitive to damage.98 Therefore, early life exposures during an important period of infant microbial colonization could alter brain-gut signaling and increase the risk of neurodevelopmental disorders, like ASD.98 Birth is a critical moment in 32 assembling an infant’s microbiome,172 thus, antibiotic use during labour and delivery is an important exposure to investigate further. However, this observational research is inherently confounded by the indication for the antibiotic use, because infection during pregnancy, and the resulting maternal immune activation and inflammatory responses, have also been associated with increased risk of ASD.67,160,158 Thus, examining relationships between antibiotic use in a group of individuals with the same underlying indication for use could remove that confounding. Group B streptococcus (GBS) colonizes the gastrointestinal and genitourinary tracts in up to 30% of pregnant individuals and, if the vagina is colonized during labour, there is a 36% chance of transmitting GBS to the neonate.173,174 While most GBS colonized neonates are asymptomatic, up to 1% develop invasive early-onset GBS disease, which may result in death from bacterial sepsis or meningitis.175 Due to this severe (albeit rare) outcome, antibiotic prophylaxis is recommended. However, many GBS-positive individuals do not receive intrapartum antibiotics (e.g., declined treatment, laboured too quickly, etc.). The purpose of this population-based study from British Columbia (BC), Canada was to examine the potential association between antibiotic use during labour and delivery with ASD in offspring using detailed antenatal care data linked with high quality data on ASD diagnoses. As an approach to address confounding by indication, we examined the relationship between antibiotic use and ASD risk in the entire population delivering in BC and in the subgroup of pregnant individuals who tested positive for GBS. 33 2.2 Methods 2.2.1 Study design and subjects We conducted a retrospective cohort study of everyone who delivered a live singleton infant in the province of BC, Canada (population of approximately 5 million) between April 1st 2000 and December 31st 2014. Children were followed until a clinical diagnosis of ASD, death, the study end date of December 31st 2016 or until they moved out of the province and were lost to follow up. Population Data BC linked data from the BC Perinatal Data Registry176, the BC Consolidation file177, Vital statistics178, the BC Autism Assessment Network179, and the BC Ministry of Education180. Ethics approval was obtained from the UBC Children’s and Women’s Research ethics board. Approval by the Ethics Board and the BC Data stewards for use of deidentified administrative data files includes a waiver of informed consent from participants. 2.2.2 Study cohort The BC Perinatal Data Registry (BCPDR) is a province-wide registry that includes information on antenatal, intrapartum, and postpartum maternal and infant care and outcomes for nearly 100% of deliveries in British Columbia, including home births. These data were used to build our cohorts. We examined the relationship between antibiotic administration during labour and delivery and ASD among all singleton, term deliveries, regardless of mode of delivery. We excluded children who did not have two full years of follow-up and children who were assessed for ASD before the age of two, since assessments are more reliable in children aged two years and above.181 To examine the relationship between administration of antibiotics during labour and delivery among a group of pregnant individuals being treated for the same underlying indication, we restricted our main cohort to a sub-cohort of only those who tested positive for GBS. In BC, 34 GBS is diagnosed by bacterial culture and is considered a definite infection. Moreover, since intrapartum antibiotic prophylaxis for GBS infection is not recommended for planned cesarean sections in the absence of labour and rupture of membranes, cesarean deliveries were only included in the GBS-positive cohort if they were coupled with a spontaneous or induced labour code. Exposure. The BCPDR includes data fields that are specific to antibiotic administration during the labour and delivery period. All systemic antibiotics were considered. Outcome. In BC, ASD assessment is publicly funded through the BC Autism Assessment Network (BCAAN), and conducted by pediatricians, psychiatrists or psychologists who have completed intensive training in diagnosing ASD.179 Since 2004, all children have been evaluated in a standardized manner at BCAAN using the following two assessment instruments: ADOS or ADOS-2 and the ADI-R.5,6 Private practitioners who have undergone specialty training in ASD assessment also diagnose some children in BC, and are required to use the ADOS or ADOS-2 and ADI-R as well. We identified these cases through linkage to the data held by the Ministry of Education. While our cohort includes children born on or after January 1st, 2000, very few children would have been diagnosed prior to standardization in 2004. This is because the median age of ASD assessment at BCAAN is 5.35 years; and because we excluded children in our cohort that were diagnosed under the age of two. The few children diagnosed between 2002 and 2004 were captured in our linked Ministry of Education data. 2.2.3 Covariates We adjusted for sociodemographic variables including maternal age at delivery, neighbourhood income quintile, location of residence (urban, rural, or semi-rural residence), and co-parent age. Additionally, we adjusted for pregnancy conditions and risk factors that could have influenced 35 fetal health, including parity, smoking during pregnancy, pre-existing diabetes, gestational diabetes, pregnancy-induced hypertension and other types of hypertensions (which include pre-existing hypertension, hypertensive renal disease, high blood pressure (distinct from pre-existing hypertension), pre-pregnancy body mass index (BMI), and prenatal antibiotic exposure. Further, we controlled for labour, delivery, and antenatal care characteristics such as year of delivery, and mode of delivery. Finally, we examined neonatal characteristics including sex, birth weight, neonatal antibiotics administered during hospital admission, gestational age, and whether the infant was small or large for gestational age. Infants were considered small for gestational age if they were below the 10th percentile and large for gestational age if they were above the 90th percentile of birth weight for their gestational age and sex.182 For BMI, due to a high degree of missing data, multiple imputation was used. For all other variables, multiple imputation was not used, and data were assumed missing at random. 2.2.4 Statistical analysis Covariates were compared according to antibiotic use, using standardized mean differences, first in the main cohort and then within the sub-cohort of pregnant individuals who tested positive for GBS. We considered a covariate to have a clinically meaningful difference between the two exposure groups if the standardized mean difference was greater than 0.1.183 Using Cox proportional hazards models, we derived crude and adjusted hazards ratios (HR) and their 95% confidence intervals. We censored children who died or who were no longer registered in the province’s universal health insurance, as this likely reflects a move out of the province. Clustering between multiple deliveries to the same mother over our study period was accounted for using robust standard error estimates. These HRs can be interpreted as the relative risk of ASD, since ASD is considered a rare disorder with a prevalence of less than 10%. 36 2.2.5 Sensitivity analysis As a proxy for dose, since intrapartum antibiotic prophylaxis for GBS infection is given at the onset of labour and again every 4 hours until delivery, we studied sub-cohorts of pregnant individuals who had their first stage of labour less than 4 hours, between 4 and 7 hours, between 8 and 11 hours, and 12 hours or longer, to assess for a potential dose-response relationship between antibiotic exposure and the risk of ASD. In addition, we examined a sub-cohort of pregnant individuals who were exposed to antibiotics both prenatally and during labour and delivery, and another sub-cohort of children exposed to labour and delivery antibiotics as well as neonatal antibiotics. Finally, all analyses were stratified by sex and mode of delivery to examine potential effect modification. 2.3 Results 2.3.1 Main analysis We identified 599, 280 singleton births among 386, 066 mothers between April 1st 2000 and December 31st 2014 (Figure 2.1). After the exclusion criteria were applied, the main cohort included 569, 953 deliveries to 366, 152 mothers. Of these deliveries, 231, 629 infants (40.6%) were exposed to antibiotics during labour and delivery. Children were followed up for a mean of 8.7 years (standard deviation [SD], 4.3 years). 37 Figure 2.1. Flow Diagram for a Population-based Study Examining Autism Risk and Perinatal Antibiotic Use 599, 280 Singleton deliveries in British Columbia, Canada, between April 1, 2000 and December 31, 2014 (386, 066 mothers) 569, 953 Singleton deliveries in the main cohort 231, 629 Exposed to antibiotics during labour and delivery (183, 191 mothers) 338, 324 Unexposed to antibiotics during labour and delivery (244, 223 mothers) 79, 260 Singleton deliveries included in group B streptococcus-positive cohort 69, 019 Exposed to antibiotics during labour and delivery (60, 480 mothers) 10, 241 Unexposed to antibiotics during labour and delivery (9, 846 mothers) 29, 327 Excluded 29, 257 Did not complete 2 yrs. of follow-up (19, 895 mothers) 70 Assessed for ASD before the age of 2 yrs. (19 mothers) 490, 693 Excluded 251, 234 Group B strep (GBS) test was negative (182, 312 mothers) 164, 397 Did not have a GBS test (115, 134 mothers) 75, 062 Did not have a spontaneous or induced labour (59, 642 mothers) 141, 731 Included in sensitivity analysis because first stage of labour was < 4 hours 139, 881 Included in sensitivity analysis because first stage of labour was 4-7 hours 67, 370 Included in sensitivity analysis because first stage of labour was 8-11 hours 57, 274 Included in sensitivity analysis because first stage of labour was 12+ hours 23, 766 Included in sensitivity analysis because first stage of labour was < 4 hours 79, 260 Included in sensitivity analysis because first stage of labour was 4-7 hours 10, 591 Included in sensitivity analysis because first stage of labour was 8-11 hours 8, 553 Included in sensitivity analysis because first stage of labour was 12+ hours 38 There were important differences between deliveries according to antibiotic exposure (Table 2.1A). Mothers exposed to antibiotics during labour and delivery were older, more likely to live in urban settings, had a higher prevalence of hypertension, and were more commonly multiparous, diabetic, and higher in pre-pregnancy BMI. Further, exposed deliveries had a higher proportion of cesarean sections, epidural use, and labour induction, and a longer first stage of labour. Infants who were exposed during labour and delivery had higher birth weights, older gestational ages, higher likelihood of being admitted to the neonatal intensive care unit (NICU) and receiving neonatal antibiotics, and shorter study follow-up times. Among the GBS-positive cohort, those exposed to antibiotics were more likely to have been delivered by cesarean section, given an epidural, had their labour induced or augmented, had a longer first stage of labour, and were more likely exposed to neonatal antibiotics (Table 2.1B). By the end of follow-up on December 31, 2016, there were 8729 children (1.53%) who had been diagnosed with ASD. Of note, 72% of these diagnoses were made by clinicians in the British Columbia Autism Assessment Network, the remaining 28% were diagnosed by private practitioners in the province. Of the children who were exposed to antibiotics during labour and delivery, 1.66% received an ASD diagnosis. In the group of children who were unexposed during labour and delivery, 1.44% received an ASD diagnosis. The unadjusted risk difference was 0.22%. The association between ASD and exposure to antibiotics during labour and delivery had an unadjusted HR of 1.29 (95% CI 1.24, 1.35; Table 2.2A; Figure 2.2). After adjusting for maternal sociodemographic characteristics, pregnancy conditions and risk factors, labour, delivery and antenatal care characteristics, and neonatal characteristics, the association attenuated to the null with a HR of 0.99 (95% CI 0.94-1.04). 39 Table 2.1A. Characteristics by Antibiotic Exposure for the Main Cohort Antibiotics during labour and delivery Standardized mean difference Not Exposed (N = 338,324) Exposed (N = 231,629) Maternal Sociodemographic Characteristics Age, mean years (SD) 30.25 (5.52) 30.94 (5.58) 0.126 Age categories, No. (%) 0.113 <20 11, 580 (3.4) 6441 (2.8) 20-29 145, 973 (43.1) 90, 172 (38.9) 30-39 169, 129 (50.0) 123, 817 (53.5) 40+ 11, 642 (3.4) 11, 199 (4.8) Income quintile, No. (%) 0.035 1 78, 716 (23.9) 56, 315 (25.2) 2 68, 432 (20.8) 46, 845 (20.9) 3 67, 132 (20.4) 43, 764 (19.5) 4 63, 608 (19.3) 41, 830 (18.7) 5 51, 317 (15.6) 35, 160 (15.7) Location of residence, No. (%) 0.162 Urban 221, 288 (65.6) 168, 594 (73.0) Semi-urban 71, 654 (21.1) 39,739 (17.2) Rural 44, 339 (13.1) 22, 620 (9.8) Coparents No. of coparents 320, 662 219, 622 Age, mean (SD) 32.79 (6.33) 33.29 (6.42) 0.078 Pregnancy Conditions and Risk Factors Parity, No. (%) 0.192 Nulliparous 196, 826 (58.2) 112, 694 (48.7) Multiparous 141, 487 (41.8) 118, 932 (51.3) Smoked during pregnancy, No. (%) 33, 171 (9.8) 21, 978 (9.5) 0.011 Diabetes, No. (%)a 23, 403 (6.9) 24, 553 (10.6) 0.131 Hypertension, No. (%) Pregnancy-induced 13, 333 (3.9) 13, 839 (6.0) 0.094 Otherb 8, 332 (2.5) 10, 140 (4.4) 0.106 Pre-pregnancy body mass index, No. (%) 0.121 < 18.5 18, 375 (5.4) 11, 102 (4.8) 18.5 – 24.9 231, 814 (68.5) 148, 857 (64.3) 25.0 – 29.9 56, 713 (16.8) 42, 687 (18.4) 40 >= 30.0 31, 422 (9.3) 28, 983 (12.5) Prenatal antibiotic use 97, 851 (28.9) 72, 071 (31.1) 0.048 Labour, Delivery, and Antenatal Care Characteristics Year of delivery, No. (%) 0.247 2000-2004 117, 007 (34.6) 56, 488 (24.4) 2005-2009 115, 348 (34.1) 80, 723 (34.9) 2010-2014 105, 969 (31.3) 94, 418 (40.8) Mode of delivery, No. (%) 0.919 Cesarean section 44, 992 (13.3) 121, 726 (52.6) Vaginal delivery 293, 332 (86.7) 109, 903 (47.4) Epidural use 82, 352 (24.3) 85, 122 (36.7) 0.272 Induction of labour, No. (%) 63, 065 (18.6) 55, 812 (24.1) 0.133 Augmentation of labour, No. (%) 136, 409 (40.3) 83, 536 (36.1) 0.088 Length of first stage of labour, No (%)c 0.241 < 4 hours 106, 654 (37.2) 35, 077 (29.3) 4-7 hours 100, 340 (35.0) 39, 541 (33.0) 8-11 hours 45, 250 (15.8) 22, 120 (18.5) 12+ hours 34, 332 (12.0) 22, 942 (19.2) Neonatal Characteristics Sex, No. (%) 0.034 Male 171, 326 (50.6) 121, 237 (52.3) Female 166, 991 (49.4) 110, 388 (47.7) Birth weight, mean g (SD) 3463.6 (491.4) 3393.1 (593.8) 0.129 Gestational age, mean weeks (SD) 38.98 (1.50) 38.46 (2.11) 0.286 Apgar score At 5 min, mean (SD) 9.09 (0.71) 9.03 (0.80) 0.083 <7 at 5 min, No. (%) 4, 121 (1.2) 4, 320 (1.9) 0.052 Congenital malformation, No (%) 16, 784 (5.0) 14, 007 (6.0) 0.048 Size at birth, No. (%)d Small-for-gestational age 22, 089 (6.5) 14, 480 (6.3) 0.011 Large-for-gestational age 13, 255 (3.9) 11, 534 (5.0) 0.052 Admission to NICU, No. (%) 10, 403 (3.1) 18, 165 (7.8) 0.211 Neonatal antibiotic exposure 4154 (1.2) 8767 (3.8) 0.164 Length of follow-up, mean years (SD) 9.11 (4.32) 8.14 (4.10) 0.231 a Consists of pre-existing diabetes and gestational diabetes b Consists of pre-existing hypertension, hypertensive kidney disease, high blood pressure, and proteinuria c From rupture of membranes to full cervical dilation, in hours d Based on population-based charts of Kierans et al.17 which were created in British Columbia 41 Table 2.1B. Characteristics by Antibiotic Exposure for the Group B Strep-positive Cohort Antibiotics during labour and delivery Standardized mean difference Not Exposed (N = 10, 241) Exposed (N = 69, 019) Maternal Sociodemographic Characteristics Age, mean years (SD) 31.01 (5.40) 30.52 (5.48) 0.090 Age categories, No. (%) 0.076 <20 254 (2.5) 2088 (3.0) 20-29 3922 (38.3) 28, 574 (41.4) 30-39 5650 (55.2) 35, 786 (51.8) 40+ 415 (4.1) 2571 (3.7) Income quintile, No. (%) 0.040 1 2155 (21.7) 15, 460 (23.3) 2 2066 (20.8) 13, 867 (20.9) 3 2022 (20.4) 13, 410 (20.2) 4 2027 (20.4) 12, 985 (19.5) 5 1651 (16.6) 10, 767 (16.2) Location of residence, No. (%) 0.076 Urban 6894 (67.5) 48, 456 (70.4) Semi-urban 2024 (19.8) 13, 181 (19.2) Rural 1293 (12.7) 7182 (10.4) Coparents No. of coparents 9802 65, 760 Age, mean (SD) 33.48 (6. 83) 32.93 (6.35) 0.087 Pregnancy Conditions and Risk Factors Parity, No. (%) 0.333 Nulliparous 6624 (64.7) 33, 401 (48.4) Multiparous 3617 (35.3) 35, 618 (51.6) Smoked during pregnancy, No. (%) 816 (8.0) 6412 (9.3) 0.047 Diabetes, No. (%)a 708 (6.9) 6129 (8.9) 0.073 Hypertension, No. (%) Pregnancy-induced 380 (3.7) 3675 (5.3) 0.078 Otherb 235 (2.3) 2361 (3.4) 0.068 Pre-pregnancy body mass index, No. (%) 0.073 < 18.5 481 (4.7) 3302 (4.8) 18.5 – 24.9 6916 (67.5) 44, 371 (64.3) 25.0 – 29.9 1748 (17.1) 12, 805 (18.6) 42 >= 30.0 1096 (10.7) 8541 (12.4) Prenatal antibiotic use 2982 (29.1) 21, 832 (31.6) 0.055 Labour, Delivery, and Antenatal Care Characteristics Year of delivery, No. (%) 0.091 2000-2004 862 (8.4) 4269 (6.2) 2005-2009 4742 (46.3) 31, 720 (46.0) 2010-2014 4637 (45.3) 33, 030 (47.9) Mode of delivery, No. (%) 0.348 Cesarean section 823 (8.0) 13, 744 (19.9) Vaginal delivery 9418 (92) 55, 275 (80.1) Epidural use 1882 (18.4) 27650 (40.1) 0.491 Induction of labour, No. (%) 1732 (16.9) 19, 876 (28.8) 0.286 Augmentation of labour, No. (%) 2985 (29.1) 32, 445 (47.0) 0.374 Length of first stage of labour, No (%)c 0.402 < 4 hours 4708 (52.3) 19, 058 (33.8) 4-7 hours 2626 (29.2) 19, 762 (35.1) 8-11 hours 961 (10.7) 9630 (17.1) 12+ hours 699 (7.8) 7854 (13.9) Neonatal Characteristics Sex, No. (%) 0.029 Male 5122 (50.0) 35, 529 (51.5) Female 5117 (50.0) 33, 489 (48.5) Birth weight, mean g (SD) 3468.8 (489.5) 3471.7 (495.6) 0.006 Gestational age, mean weeks (SD) 39.02 (1.50) 38.96 (1.55) 0.042 Apgar score At 5 min, mean (SD) 9.08 (0.74) 9.04 (0.76 0.061 <7 at 5 min, No. (%) 158 (1.6) 1146 (1.7) 0.008 Congenital malformation, No (%) 460 (4.5) 3435 (5.0) 0.023 Size at birth, No. (%)d Small-for-gestational age 698 (6.8) 4324 (6.3) 0.022 Large-for-gestational age 404 (3.9) 2902 (4.2) 0.013 Admission to NICU, No. (%) 481 (4.7) 3801 (5.5) 0.037 Neonatal antibiotic exposure 328 (3.17) 2374 (3.4) 0.013 Length of follow-up, mean years (SD) 7.17 (3.17) 6.99 (3.06) 0.059 a Consists of pre-existing diabetes and gestational diabetes b Consists of pre-existing hypertension, hypertensive kidney disease, high blood pressure, and proteinuria c From rupture of membranes to full cervical dilation, in hours d Based on population-based charts of Kierans et al.17 which were created in British Columbia 43 Table 2.2A. Autism Risk and Perinatal Antibiotics in the Main Cohort Antibiotics during labour and delivery, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Fully adjusted hazard ratio (95% CI)a Unexposed Exposed No. of children 338, 324 231, 629 569, 953 569, 953 523, 658 ASD cases 4875 (1.44) 3854 (1.66) 0.0022 1.29 (1.24, 1.35) 0.99 (0.94, 1.04) Length of first stage of labour < 4 hours No. of children 106, 654 35, 077 141, 731 141, 731 130, 855 ASD cases 1329 (1.25) 525 (1.5) 0.0025 1.24 (1.12, 1.38) 1.06 (0.95, 1.19) 4-7 hours No. of children 100, 340 39, 541 139, 881 139, 881 128, 593 ASD cases 1342 (1.34) 576 (1.46) 0.0088 1.13 (1.03, 1.25) 0.95 (0.85, 1.06) 8-11 hours No. of children 45, 250 22, 120 67, 370 67, 370 61, 608 ASD cases 667 (1.47) 390 (1.76) 0.0029 1.24 (1.10, 1.41) 1.03 (0.90, 1.18) 12+ hours No. of children 34, 332 22, 942 57, 274 57, 274 52, 163 ASD cases 535 (1.56) 417 (1.82) 0.0026 1.17 (1.03, 1.33) 1.02 (0.88, 1.17) Exposed to both prenatal and labour & delivery antibiotics No. of children 497, 882 72, 071 569, 953 569, 953 523, 658 ASD cases 7485 (1.50) 1244 (1.73) 0.0022 1.21 (1.14, 1.29) 1.02 (0.96, 1.09) Exposed to both labour & delivery and neonatal antibiotics No. of children 561, 186 8767 569, 953 569, 953 523, 658 ASD cases 8559 (1.53) 170 (1.94) 0.0041 2.31 (1.98, 2.69) 1.14 (0.96, 1.34) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, prenatal antibiotic exposure, year of delivery, mode of delivery, baby’s sex, birth weight, neonatal antibiotic exposure, gestational age, size-for-gestational age b From rupture of membranes to full cervical dilation, in hours 44 Table 2.2B. Autism Risk and Perinatal Antibiotics in the Group B Strep-positive Cohort Antibiotics during labour and delivery, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Fully adjusted hazard ratios (95% CI)a Unexposed Exposed No. of children 10, 241 69, 019 79, 260 79, 260 72, 760 ASD cases 144 (1.41) 1011 (1.46) 0.0006 1.07 (0.90, 1.27) 0.88 (0.74, 1.05) Length of first stage of labour < 4 hours No. of children 4708 19, 058 23, 766 23, 766 22, 023 ASD cases 55 (1.17) 238 (1.25) 0.0008 1.09 (0.82, 1.46) 0.98 (0.72, 1.32) 4-7 hours No. of children 10, 241 69, 019 79, 260 79, 260 72, 760 ASD cases 144 (1.41) 1011 (1.46) 0.0006 1.07 (0.90, 1.27) 0.88 (0.74, 1.05) 8-11 hours No. of children 961 9630 10, 591 10, 591 9718 ASD cases 19 (1.98) 141 (1.46) -0.0051 0.76 (0.47, 1.23) 0.64 (0.39, 1.04) 12+ hours No. of children 699 7854 8553 8553 7769 ASD cases 9 (1.29) 143 (1.82) 0.0053 1.41 (0.72, 2.76) 1.25 (0.61, 2.57) Exposed to both prenatal and labour & delivery antibiotics No. of children 57, 428 21, 832 79, 260 79, 260 72, 760 ASD cases 835 (1.45) 320 (1.47) 0.0001 1.00 (0.88, 1.14) 1.00 (0.87, 1.16) Exposed to both labour & delivery and neonatal antibiotics No. of children 76, 886 2374 79, 260 79, 260 72, 760 ASD cases 1128 (1.47) 27 (1.14) -0.0033 1.18 (0.80, 1.72) 0.78 (0.51, 1.19) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, prenatal antibiotic exposure, year of delivery, mode of delivery, baby’s sex, birth weight, neonatal antibiotic exposure, gestational age, size-for-gestational age b From rupture of membranes to full cervical dilation, in hours 45 Figure 2.2. Forest plot outlining the adjusted hazard ratios of autism spectrum disorder in the main cohort Figure 2.3. Forest plot outlining the adjusted hazard ratios of autism spectrum disorder in the group B streptococcus-positive cohort 46 3.3.2 Sensitivity analysis Upon categorizing deliveries based on length of first stage of labour, prenatal with labour and delivery antibiotic status, and labour and delivery with neonatal antibiotic status, results from the main cohort remained consistent in terms of direction and effect size (Table 2.2A; Figure 2.2). For all lengths of labour and antibiotic exposure sub-cohorts that we analyzed, the unadjusted hazard ratio suggested an increased risk associated with antibiotic exposure, but there was no statistically significant association between antibiotic exposure and ASD in the fully adjusted models. 3.3.3 GBS-positive cohort Of children who were exposed to antibiotic prophylaxis for GBS during labour and delivery, 1.41% received an ASD diagnosis, while 1.46% of unexposed children received an ASD diagnosis (Table 2.2B; Figure 2.3). The unadjusted risk difference was 0.06%. Results differed from the main cohort in terms of effect size and direction. Every model, even unadjusted, showed no statistically significant association between antibiotic exposure and ASD (HR 1.07 [95% CI 0.90-1.27]). Sensitivity analyses based on length of first stage of labour, as well as exposure to both prenatal and labour and delivery antibiotics, and both labour and delivery and neonatal antibiotic were also conducted for this GBS-positive cohort. Results showed no substantial difference in the observed associations (Table 2.2B; Figure 2.3). 3.3.4 Stratified analyses After stratifying the analysis by sex, the male cohort included 292, 563 children, while the female cohort consisted of 277, 379 children. Among males, 2.64% of children exposed to antibiotics during labour and delivery, and 2.32% of unexposed children received an ASD diagnosis (unadjusted risk difference of 0.32%). Among females, 0.59% of exposed children and 47 0.54% of unexposed children received an ASD diagnosis (unadjusted risk difference of 0.06%). Results were consistent with the main cohort regarding direction of association and effect size (Appendices A, B, C and D). The analyses were additionally stratified by mode of delivery with no substantial variation in the associations observed after adjusting for covariates in the different models (Appendices E and F). 2.4 Discussion 2.4.1 Principal findings In this population-based study in British Columbia, we observed no significant association between antibiotic use during labour and delivery and ASD after controlling for maternal sociodemographic and pregnancy related conditions and risk factors, as well as labour, delivery and neonatal characteristics. We conducted sensitivity analyses using length of first stage of labour as a proxy measure for dose to assess for a dose-response relationship. Additionally, we studied sub-cohorts of children who were exposed to both prenatal and labour and delivery antibiotics, and children who were exposed to both labour and delivery and neonatal antibiotics. Our results showed no substantial difference in the association between antibiotic exposure and ASD, depending on length of the first stage of labour or combined antibiotic statuses. 2.4.2 Comparison with previous findings Our findings are consistent with a previous population-based case-control study which found no significant association between peripartum antibiotic exposure and ASD in the offspring.184 However, those study results were limited by a small sample size, wide confidence intervals and imprecise estimates. Our null findings could be due to the magnitude of microbial change being too small to impact neurodevelopment, or that the duration of antibiotic exposure was too short 48 to produce an impactful change in the vaginal microbiome. Further research is needed to investigate longer durations of antibiotic use throughout pregnancy and its association with ASD. 2.4.3 Clinical implications Recent research has suggested a role of the childhood microbiome-gut-brain axis in the pathogenesis of ASD; thus, highlighting exposures that alter the microbiome as risk factors of ASD development.82,171 Several studies have shown that intrapartum antibiotic prophylaxis (IAP) reduces the microbial diversity of infants.141,142,143 Further, studies reported that infants who were exposed to antibiotics during the intrapartum or peripartum period had significant changes in both the quantities and diversity of microbial composition that continued until at least one year of age.144,145 Despite these findings, research following IAP exposed infants through to examine clinical neurodevelopmental outcomes is limited.185 Our study helped to address this knowledge gap by assessing the association between antibiotic use during labour and delivery with the risk of ASD in offspring. Our findings provide reassurance that this common obstetrical practice does not increase the risk of ASD. 2.4.4 Strengths and limitations This study has multiple strengths. First, the study population included all deliveries that occurred in BC during the designated study period, thus eliminating selection bias. Second, we were able to assess the association between antibiotic use and ASD in a group of deliveries that were given antibiotics for the same indication, maternal GBS. This provided a unique way to address confounding by indication, which has been a key limitation of previous research exploring associations with antibiotic exposure.166,66 Further, through linkage with the British Columbia Autism Assessment Network, we gained prospectively collected clinical gold standard diagnostic data for our ASD outcome. Many epidemiological studies have used diagnostic codes from 49 administrative datasets for outcome ascertainment. Recent research has found that this is not the most reliable method to identify ASD cases, and while it may produce a good positive predictive value, the negative predictive value is of concern.167 Finally, antibiotic exposure data came from the British Columbia Perinatal Data Registry. A validation study of the registry was previously conducted, and determined that its clinical variables on interventions during labour and delivery have overall excellent validity.186 This study has several limitations. Apart from GBS infection, we were unable to identify other indications for antibiotic use during labour and delivery in the main cohort. Stratifying by multiple indications for antibiotic use, especially by those of differing severity, would provide a deeper understanding into the potential role of confounding by indication. We were also unable to observe the exact number of doses and specific durations of intrapartum antibiotic exposure. It is likely that a higher number of doses and a longer cumulative duration of exposure would have a more significant impact on the vaginal microbiome and the initial colonization of the infant’s microbiome; thus, future research with data on the number of doses and durations of antibiotic exposure is needed for a strong assessment of a dose-response relationship between perinatal antibiotic exposure and ASD. In addition, we were unable to identify the specific classes of antibiotics administered or the ethnicities of the mothers included in our cohort. Our GBS-positive cohort had a smaller sample size, which widened confidence intervals and resulted in less precise risk estimates. Moreover, since this study has an observational design, some residual confounding likely remains. This is apparent when observing important differences between characteristics of deliveries in which antibiotics were administered and not administered. These differences were particularly evident among variables that suggest a worse prognosis for 50 mothers, such as diabetes, larger pre-pregnancy BMIs, higher prevalence of labour induction and augmentation, increased epidural usage, and greater NICU admissions. 2.5 Conclusion In this population-based study, antibiotic use during labour and delivery was not statistically significantly associated with an increased risk of autism spectrum disorder in the offspring. Our findings suggest that concern for ASD should not factor into the clinical decision on whether to administer antibiotics during labour and delivery for any indication. Future research should investigate the impact of longer periods of antibiotic exposure during pregnancy, and exposure by antibiotic class, on the risk of ASD while continuing to address confounding by indication. 51 CHAPTER 3: Association between prenatal antibiotic exposure and autism spectrum disorder: A population-based cohort study 3.1 Introduction Autism spectrum disorder (ASD) is a set of complex neurodevelopmental disorders that impair social interactions and communication, and lead to restricted and repetitive patterns of behaviour.1 As of 2018, ASD affects 1 in 44 children in the US;9 and despite striking and persistent increases in prevalence, the etiology remains unclear. ASD appears to involve a complex interplay of genetic and environmental factors.41 Abnormal composition of gut microbiota and overt gut dysfunction are commonly present in children with ASD.80 Thus, through the proposed mechanisms of the microbiome-gut-brain axis, microbial disruptions during fetal development and in childhood have been suggested as a contributing factor in ASD development.82,171 Previous research has shown that prenatal exposure to antibiotics induces changes in the maternal vaginal microbiome, and in turn, this can impact the colonization of the infant microbiome at childbirth.137 Moreover, recent research suggests that maternal microbiota may be influencing fetal development before birth.138,139,133 Since roughly 38% of pregnant individuals are treated with antibiotics,66 commonly for urinary tract infections (UTIs), other genitourinary infections, and respiratory infections,135 prenatal antibiotic use is an important exposure to investigate further. The purpose of this population-based study from BC, Canada was to examine the potential association between prenatal antibiotic use with ASD in offspring using detailed antenatal care data linked with clinical gold standard data on ASD diagnoses. We examined the 52 relationship between antibiotic use and ASD risk in the entire population delivering in BC as well in a subgroup of pregnancies with a diagnostic code indicating they had a UTI (to assess confounding by indication). 3.2 Methods 3.2.1 Study design and data sources We conducted a retrospective cohort study of everyone who delivered a live singleton infant in the province of BC, Canada (population approximately 5 million) between April 1st 2000 and December 31st 2014. Children were followed until a clinical diagnosis of ASD, their death, the study end date of December 31st 2016, or until they moved out of the province and were lost to follow up. Population Data BC linked data from the BC PharmaNet187, the BC Perinatal Data Registry176, BC Medical Services Plan Payment Information file188, the BC Consolidation file177, Vital statistics178, the BC Autism Assessment Network179, and the BC Ministry of Education180. Ethics approval was obtained from the UBC Children’s and Women’s Research ethics board. Approval by the Ethics Board and the BC Data stewards for use of deidentified administrative data files includes a waiver of informed consent from participants. 3.2.2 Study cohort The BCPDR is a province-wide registry that includes information on antenatal, intrapartum, and postpartum maternal and infant care and outcomes for nearly 100% of deliveries in British Columbia, including home births. These data were used to build our cohorts. We examined the relationship between prenatal antibiotic exposure and ASD among all singleton, term deliveries, regardless of mode of delivery. We excluded children who did not have two full years of follow-53 up and children who were assessed for ASD before the age of two, since assessments are more reliable in children aged two years and above.181 Exposure. We defined exposure as having filled one or more antibiotic prescriptions during pregnancy (antibiotics filled in our cohort are listed in Appendix G). Exposure status was identified using the BC PharmaNet, which contains all prescriptions dispensed in an outpatient setting in BC.187 Thus, these data completely capture all antibiotics filled in an outpatient setting among those in our cohort. Outcome. In BC, ASD assessment is publicly funded through the BCAAN, and conducted by pediatricians, psychiatrists or psychologists who have completed intensive training in diagnosing ASD.179 Since 2004, all children have been evaluated in a standardized manner at BCAAN and are informed by two assessment instruments: ADOS or ADOS-2 and the ADI-R.5,6 Private practitioners who have undergone specialty training in ASD assessment also diagnose some children in BC, and are required to use the ADOS or ADOS-2 and ADI-R as well. We identified cases diagnosed by private practitioners through linkage to the data held by the Ministry of Education. 3.2.3 Covariates We adjusted for sociodemographic variables including maternal age at delivery, neighbourhood income quintile, location of residence (urban, rural, or semi-rural residence), and co-parent age. Additionally, we adjusted for pregnancy conditions and risk factors that could have influenced fetal development and health, including parity, smoking during pregnancy, pre-existing diabetes, gestational diabetes, pregnancy-induced hypertension and other types of hypertensions (which include pre-existing hypertension, hypertensive renal disease, high blood pressure (distinct from pre-existing hypertension), and proteinuria), and pre-pregnancy BMI. Further, we controlled for 54 labour, delivery, and antenatal care characteristics such as year of delivery, mode of delivery, and intrapartum antibiotic exposure. Finally, we examined neonatal characteristics including sex, birthweight, gestational age, and whether the infant was small or large for gestational age. Infants were considered small for gestational age if they were below the 10th percentile and large for gestational age if they were above the 90th percentile of birthweight for their gestational age and sex.182 For BMI, due to a high degree of missing data, multiple imputation was used. For all other variables, multiple imputation was not used, and data were assumed missing at random. Appendix H in the Appendix explains why select covariates were included in the analyses. 3.2.4 Statistical analysis Covariates were compared according to antibiotic use, using standardized mean differences. We considered a covariate to have a clinically meaningful difference between the two exposure groups if the standardized mean difference was greater than 0.1.183 Using Cox proportional hazards models, we derived crude and adjusted hazards ratios (HR) and their 95% confidence intervals. We censored children who died or who were no longer registered in the province’s universal health insurance, as this likely reflects a move out of the province. Clustering between multiple deliveries to the same mother over our study period was accounted for using robust standard error estimates. These HRs will approximate the relative risk of ASD, given the prevalence of ASD in our cohort of 1.5%. We stratified our analysis by the pregnancy trimester of antibiotic exposure, cumulative duration of exposure (in days of medication dispensed) and class of antibiotic taken (penicillins, other beta lactams, macrolides, and other), to assess for a potential temporal relationship, dose-response relationship, and whether the risk of ASD varied depending on antibiotic class. In addition, we examined a sub-cohort of pregnant individuals who were exposed to antibiotics both prenatally and during labour 55 and delivery. Further, to examine potential effect modification, we stratified our analysis by sex and mode of delivery. Since this type of observational research is limited by confounding by indication, we examined the relationship between prenatal antibiotic exposure and ASD among a group of pregnant individuals who received a urinary tract infection diagnostic code, which was the most common indication for antibiotic treatment in our data. The nearest International Classification of Disease nineth revision (ICD-9) code prior to an antibiotic dispensation was identified in the BC Medical Services Plan Payment Information data. ICD-9 codes 595.X, 599.X, and 788.X were included in this cohort. 3.2.5 Sibling analysis Further analyses were conducted in a sibling cohort to account for unmeasured environmental and genetic confounding. Unconditional logistic regression models were run comparing deliveries among the cohort of mothers with two or more deliveries during the study period using robust standard errors. This was done to ensure the direction and effect size of the association between prenatal antibiotic exposure and ASD were similar in the sibling cohort to the total cohort. Next, we used conditional logistic regression comparing deliveries by the same mother. These models only make use of siblings that were discordant in both antibiotic exposure and ASD status born to the same mother. This approach controlled for characteristics that do not change or remain highly stable over time within a mother, such as genetics. 56 3.3 Results We identified 599, 280 singleton births among 386, 066 mothers between April 1st 2000 and December 31st 2014 (Figure 3.1). After exclusion criteria were applied, the main cohort included 569, 953 deliveries to 366, 152 mothers. Of these deliveries, 169, 922 (29.8%) children were exposed to antibiotics prenatally. Children were followed up for a mean of 8.7 years (SD, 4.3 years). We found important differences between deliveries according to prenatal antibiotic exposure (Table 3.1). Mothers exposed to prenatal antibiotics were younger, had lower income, were less likely to live in urban settings, were more likely to have smoked during pregnancy, and were higher in pre-pregnancy BMI. 2.3.1 Main analysis By the end of follow-up on December 31, 2016, 8729 children (1.53%) had been diagnosed with ASD. Of note, 72% of these diagnoses were made by clinicians in the British Columbia Autism Assessment Network; the additional 28% were diagnosed by private practitioners in the province. Of children who were exposed to prenatal antibiotics, 1.64% received an ASD diagnosis. In the group of children who were unexposed, 1.49% received an ASD diagnosis. The unadjusted risk difference was 0.15%. The association between ASD and exposure to prenatal antibiotics had an unadjusted hazard ratio (HR) of 1.07 (95% CI 1.02, 1.12; Table 3.2). After adjusting for covariates, including maternal sociodemographic characteristics, pregnancy conditions and risk factors, labour, delivery and antenatal care characteristics, and neonatal characteristics (Table 3.2), the statistically significant association remained unchanged with an aHR of 1.08 (95% CI 1.02, 1.13; Figure 3.2). 57 Figure 3.1. Diagram for a population-based study examining the association of prenatal antibiotics and autism spectrum disorder in offspring. 599, 280 Singleton deliveries in British Columbia, Canada, between April 1, 2000 and December 31, 2014 (386, 066 mothers) Main cohort 569, 953 Singleton deliveries in the main cohort 169, 922 Exposed to prenatal antibiotics (138, 761 mothers) 400, 031 Unexposed to prenatal antibiotics (289, 514 mothers) Unmatched sibling cohort 366, 810 Singleton deliveries who had at least one sibling born to the same mother 111, 810 Exposed to prenatal antibiotics (80, 649 mothers) 255, 000 Unexposed to prenatal antibiotics (144, 483 mothers) 29, 327 Excluded 29, 257 Did not complete 2 yrs. of follow-up (19, 895 mothers) 70 Assessed for ASD before the age of 2 yrs. (19 mothers) 203, 143 Excluded due to only one delivery during study period. Matched discordant sibling cohort 10, 994 Singleton deliveries sharing same mother and discordant in both antibiotic exposure and ASD status 3521 Exposed to prenatal antibiotics (2446 mothers) 7473 Unexposed to prenatal antibiotics (4137 mothers) 355, 816 Excluded since siblings were not discordant in both antibiotic and ASD status. 58 Table 3.1. Characteristics by antibiotic exposure for the full cohort Prenatal Antibiotics Standardized mean difference Not Exposed (N = 400,031) Exposed (N = 169,922) Maternal Sociodemographic Characteristics Age, mean years (SD) 31.00 (5.36) 29.43 (5.84) 0.281 Age categories, No. (%) 0.264 <20 8905 (2.2) 9116 (5.4) 20-29 155, 545 (38.9) 80, 600 (47.4) 30-39 218, 401 (54.6) 74, 545 (43.9) 40+ 17, 180 (4.3) 5661 (3.3) Income quintile, No. (%) 0.151 1 87, 971 (22.7) 47, 060 (28.5) 2 80, 307 (20.7) 34, 970 (21.2) 3 78, 745 (20.3) 32, 151 (19.4) 4 76, 428 (19.7) 29, 010 (17.5) 5 64, 326 (16.6) 22, 151 (13.4) Location of residence, No. (%) 0.203 Urban 284, 800 (71.5) 105, 082 (61.9) Semi-urban 70, 885 (17.8) 40, 508 (23.9) Rural 42, 910 (10.8) 24, 049 (14.2) Coparents No. of coparents 383, 144 157, 140 Age, mean (SD) 33.37 (6.22) 32. 07 (6.65) 0.202 Pregnancy Conditions and Risk Factors Parity, No. (%) 0.091 Nulliparous 211, 878 (53.0) 97, 642 (57.5) Multiparous 188, 146 (47.0) 72, 273 (42.5) Smoked during pregnancy, No. (%) 30, 363 (7.6) 24, 786 (14.6) 0.224 Diabetes, No. (%)a 33, 397 (8.3) 14, 559 (8.6) 0.008 Hypertension, No. (%) Pregnancy-induced 18, 213 (4.6) 8959 (5.3) 0.033 Otherb 12, 407 (3.1) 6065 (3.6) 0.026 Pre-pregnancy body mass index, No. (%) 0.147 < 18.5 21, 269 (5.3) 8208 (4.8) 18.5 – 24.9 273, 462 (68.4) 107, 209 (63.1) 25.0 – 29.9 67, 889 (17.0) 31, 511 (18.5) 59 >= 30.0 37, 411 (9.4) 22, 994 (13.5) Labour, Delivery, and Antenatal Care Characteristics Year of delivery, No. (%) 0.050 2000-2004 120, 538 (30.1) 52, 957 (31.2) 2005-2009 136, 006 (34.0) 60, 065 (35.3) 2011-2014 143, 487 (35.9) 56, 900 (33.5) Mode of delivery, No. (%) 0.021 Cesarian section 115, 894 (29.0) 50, 824 (29.9) Vaginal delivery 284, 137 (71.0) 119, 098 (70.1) Epidural use 117, 908 (29.5) 49, 566 (29.2) 0.007 Induction of labour, No. (%) 80, 907 (20.2) 37, 970 (22.3) 0.052 Augmentation of labour, No. (%) 153, 849 (38.5) 66, 096 (38.9) 0.009 Antibiotics administered during labour and delivery 159, 558 (39.9) 72, 071 (42.4) 0.051 Neonatal Characteristics Sex, No. (%) 0.002 Male 205, 193 (51.3) 87, 370 (51.4) Female 194, 830 (48.7) 82, 549 (48.6) Birthweight, mean g (SD) 3434.58 (529.37) 3435.71 (552.81) 0.002 Gestational age, mean weeks (SD) 38. 81 (1.75) 38.67 (1.89) 0.079 Apgar score At 5 min, mean (SD) 9.07 (0.73) 9.06 (0.79) 0.004 <7 at 5 min, No. (%) 5565 (1.4) 2876 (1.7) 0.025 Congenital malformation, No (%) 21, 210 (5.3) 9581 (5.6) 0.015 Size at birth, No. (%)c Small-for-gestational age 25, 990 (6.5) 10, 579 (6.2) 0.011 Large-for-gestational age 16, 361 (4.1) 8428 (5.0) 0.042 Admission to neonatal intensive care unit (NICU), No. (%) 18, 993 (4.7) 9575 (5.6) 0.040 Length of follow-up, mean years (SD) 8.65 (4.27) 8.86 (4.23) 0.049 a Consists of pre-existing diabetes and gestational diabetes b Consists of pre-existing hypertension, hypertensive kidney disease, high blood pressure, and proteinuria c Based on population-based charts of Kierans et al.17 which were created in British Columbia 60 Table 3.2. Associations between prenatal antibiotics exposure and risk of ASD in offspring of the main cohort Prenatal Antibiotics, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed No. of children 400, 031 169, 922 569, 953 569, 953 523, 658 ASD cases 5947 (1.49) 2782 (1.64) 0.0015 1.07 (1.02, 1.12) 1.08 (1.02, 1.13) Exposure by trimesterb 1st trimester No. of children 496, 852 73, 101 569, 953 569, 953 523, 658 ASD cases 7506 (1.51) 1223 (1.67) 0.0012 1.10 (1.03, 1.17) 1.06 (0.99, 1.13) 2nd trimester No. of children 486, 255 83, 698 569, 953 569, 953 523, 658 ASD cases 7337 (1.51) 1392 (1.66) 0.0015 1.08 (1.02, 1.15) 1.08 (1.02, 1.15) 3rd trimester No. of children 503, 280 66, 673 569, 953 569, 953 523, 658 ASD cases 7673 (1.52) 1056 (1.58) 0.0006 0.99 (0.93, 1.05) 1.03 (0.96, 1.11) Exposure by antibiotic class Penicillins No. of children 464, 054 105, 899 569, 953 569, 953 523, 658 ASD cases 7021 (1.51) 1708 (1.61) 0.0010 1.01 (0.96, 1.06) 1.06 (1.00, 1.12) Other beta lactams No. of children 537, 469 32, 484 569, 953 569, 953 523, 658 ASD cases 8181 (1.52) 548 (1.69) 0.0016 1.12 (1.03, 1.23) 1.10 (1.00, 1.20) Macrolides No. of children 547, 174 22, 779 569, 953 569, 953 523, 658 ASD cases 8335 (1.52) 394 (1.73) 0.0021 1.05 (0.95, 1.16) 1.06 (0.95, 1.19) Other No. of children 518, 471 51, 482 569, 953 569, 953 523, 658 ASD cases 7863 (1.51) 866 (1.68) 0.0017 1.17 (1.09, 1.25) 1.06 (0.98, 1.15) Cumulative duration of exposure 1 – 7 days No. of children 489, 750 80, 203 569, 953 569, 953 523, 658 ASD cases 7438 (1.52) 1291 (1.61) 0.0009 1.08 (1.02, 1.15) 1.04 (0.98, 1.11) 8 – 14 days No. of children 516, 794 53, 159 569, 953 569, 953 523, 658 61 ASD cases 7872 (1.52) 857 (1.61) 0.0009 1.01 (0.94, 1.08) 1.06 (0.98, 1.14) 15+ days No. of children 533, 393 36, 560 596, 953 569, 953 523, 658 ASD cases 8095 (1.52) 634 (1.73) 0.0022 1.07 (0.99, 1.16) 1.10 (1.00, 1.20) Stratified by sex Males No. of children 205, 193 87, 370 292, 563 292, 563 268, 905 ASD cases 4904 (2.39) 2273 (2.6) 0.0021 1.06 (1.01, 1.11) 1.06 (1.01, 1.12) Females No. of children 194, 830 82, 549 277, 379 277, 379 254, 753 ASD cases 1043 (0.54) 508 (0.62) 0.0008 1.12 (1.01, 1.24) 1.14 (1.02, 1.28) Stratified by mode of delivery Vaginal No. of children 284, 137 119, 098 403, 235 403, 235 370, 020 ASD cases 3878 (1.36) 1779 (1.49) 0.0013 1.06 (1.00, 1.13) 1.08 (1.01, 1.14) Cesarean No. of children 115, 894 50, 824 166, 718 166, 718 153, 638 ASD cases 2069 (1.79) 1003 (1.97) 0.0019 1.07 (1.00, 1.16) 1.08 (0.99, 1.16) Urinary tract infection subgroup No. of children 30, 203 53, 790 83, 993 83, 993 76, 186 ASD cases 478 (1.58) 904 (1.68) 0.0010 1.06 (0.95, 1.19) 1.10 (0.98, 1.25) Exposed to both prenatal and labour & delivery antibiotics subgroup No. of children 159, 558 72, 071 231, 629 231, 629 212, 055 ASD cases 2610 (1.64) 1244 (1.73) 0.0009 1.02 (0.96, 1.10) 1.03 (0.96, 1.11) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, year of delivery, mode of delivery, baby’s sex, birthweight, gestational age, size-for-gestational age. b First trimester is from date of conception to 12 weeks of pregnancy; second trimester is 13 to 27 weeks; third trimester is 28 weeks until date of birth. 62 3.3.2 Secondary analyses We observed statistically significant associations for exposure during the second trimester (aHR 1.08 [95% CI 1.02, 1.15]), as well as exposure to penicillins and other beta lactams (aHRs 1.06 [95% CI 1.00, 1.12] and 1.10 [95% CI 1.00, 1.20], respectively). Analysis based on cumulative duration of antibiotic exposure showed a dose response relationship, with the highest risk observed when exposure lasted for 15 days or longer (aHR 1.10 [95% CI 1.00, 1.20]; Table 3.2; Figure 3.2). Moreover, upon stratifying by sex, the association between prenatal antibiotics and ASD was stronger for female offspring (aHR 1.14 [95% CI 1.02, 1.28]) compared to male offspring (aHR 1.06 [95% CI 1.01, 1.12]; Table 3.2; Figure 3.2). Among females, the association was especially pronounced when exposed to other beta lactams (aHR 1.27 [95% CI 1.03, 1.56]; Appendix I), while among males, the association was strongest when exposed to penicillins (aHR 1.06 [95% CI 1.00, 1.23]; Appendix J). When stratifying by mode of delivery, the association between prenatal antibiotics and ASD in offspring was found to be the same for both vaginal and cesarean deliveries; however, the HR for cesarean deliveries was not statistically significant (aHRs 1.08 [95% CI 1.01, 1.14] and 1.08 [95% CI 0.99, 1.16], respectively; Table 3.2; Figure 3.2). Among pregnant individuals exposed to both prenatal and labour and delivery antibiotics, the association decreased to aHR 1.03 (95% CI 0.96, 1.11). Finally, in the subgroup of pregnancies treated for UTIs, the effect size increased compared to the main analysis, however the association was no longer statistically significant (aHR 1.10 [95% CI 0.98, 1.24]; Table 3.2; Figure 3.2). 63 Figure 3.2. Forest plot outlining the adjusted hazard ratios of autism spectrum disorder in the main cohort 64 2.3.3 Siblings analysis The results from the unmatched sibling cohort were similar to the main analysis in terms of direction and effect size (unadjusted HR, 1.05 [95% CI 1.00, 1.12] and adjusted HR 1.06 [95% CI 0.99, 1.13]; Table 3.3). The matched analysis of siblings sharing the same mother, and who were discordant for both antibiotic exposure and ASD status, resulted in unadjusted HR of 1.02 (95% CI 0.93, 1.12) and a fully adjusted HR of 1.05 (95% CI 0.93, 1.18). Table 3.3. Associations between prenatal antibiotic exposure and risk of ASD in unmatched and matched sibling cohorts Prenatal antibiotics, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed Unmatched sibling-pair analysisb No. of children 255, 000 111, 810 366, 810 366, 810 341, 383 ASD cases 3722 (1.46) 1720 (1.54) 0.0008 1.05 (1.00, 1.12) 1.06 (1.00, 1.13) Matched discordant sibling-pair analysisc No. of children 7473 3521 10, 994 10, 994 9982 ASD cases 3294 (44.1) 1529 (43.4) -0.0065 1.02 (0.93, 1.12) 1.05 (0.93, 1.18) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, year of delivery, mode of delivery, baby’s sex, birthweight, gestational age, size-for-gestational age. b Logistic regression models were run comparing deliveries discordant in exposure status among the cohort of mothers that had two or more deliveries during the study period. c Conditional logistic regression models were run matching and comparing deliveries of the same mother. These models only make use of offspring that were discordant in both exposure and ASD status born to the same mother. 65 3.4 Discussion 3.4.1 Principal findings In this population-based study in British Columbia, we observed a small statistically significant increase in risk of ASD associated with prenatal antibiotic exposure after controlling for maternal sociodemographics and pregnancy related conditions and risk factors, as well as labour, delivery and neonatal characteristics. The increased risk was shown for those exposed to antibiotics during the second trimester, if exposure was to penicillins and other beta lactams, or if exposure lasted 15 days or longer. The highest risk was observed for female offspring exposed to other beta lactams. To further adjust for possibly residual confounding, we conducted a discordant sibling-pair analysis. These results attenuated the association, but the association remained positive between prenatal antibiotics and ASD, although it was no longer statistically significant. This suggests that our main results do likely suffer from some residual confounding. 3.4.2 Comparison with previous findings Our findings are consistent with previous population-based studies by Atladóttir et al164, which found a significant increase in risk of ASD associated with prenatal penicillin exposure, and by Hamad et al66, which found an increase in risk of ASD associated with prenatal penicillin and other beta lactam exposure. Additionally, similar to Hamad et al66, we observed a dose-response effect with the greatest ASD risk associated with exposure lasting longer than 14 days. In contrast, while these studies observed an increase in risk of ASD when exposure took place during the second and third trimester, we only observed a significant increase in ASD risk with second trimester exposure. Moreover, unlike previous studies, we observed a greater risk among females compared to male offspring. 66 While the study by Atladóttir et al164 did not find an association between prenatal infection and ASD risk, other research has reported a positive association.151 Therefore, confounding by indication for antibiotic use is of concern with this type of observational research. To assess confounding by indication in this study, we explored the association among a subgroup of pregnancies treated for the most common prenatal infection observed in our data, UTIs. Results continued to show a positive association between prenatal antibiotics and ASD; however, the relationship was no longer statistically significant, which could be a result of the substantially smaller sample size. 3.4.3 Implications Interestingly, we observed the same ASD risk associated with both vaginal and cesarean deliveries. The only difference was that the HR for cesarian deliveries fell just below the cut-off for statistical significance, however, this model had a much smaller sample size. Consistent evidence shows differences between microbial profiles of infants by mode of delivery, with infants born via vaginal delivery being colonised with more commensal microbes and infant born via cesarean delivery being colonized with more pathogenic microbes172. Therefore, our finding suggests that altered microbial colonization at childbirth, and its potential impact on infant neurodevelopment, is not likely the biological mechanism behind the observed association between prenatal antibiotic exposure and ASD. In this study we observed the largest increase in ASD risk with second trimester antibiotic exposure and when female offspring were exposed. This finding is similar to previous research which found a significant association between second trimester antibiotic exposure and childhood obesity,189,190,191 and specifically in female offspring192. The second trimester of pregnancy is a period of rapid development of the fetus and fetal gut, and thus, may be a 67 particularly sensitive time for metabolic programming by the maternal microbiome.190,193 Animal research has also suggested that metabolites produced by the maternal microbiome, such as trimethylamine oxide, trimethyl-5-aminovalerate, imidazole propionate, 3-indole sulfate, and Hippurate, may affect brain neurodevelopment in offspring.133 In addition, gut metabolites, especially short chain fatty acids, can also influence mitochondrial function.49 Interestingly, mitochondrial dysfunction is one of the most prevalent metabolic disorders found in children with ASD,49 and is even more prevalent in children with comorbid GI disorders.194 Environmental exposures, including antibiotic exposure, can cause both mitochondrial and gut dysfunction;194 and as such, it has been suggested that mitochondria could be part of the biological link between prenatal exposures and neurometabolic abnormalities seen with ASD.194,49 Antibiotic-induced changes to the maternal microbiota and their metabolites may underlie trimester-specific associations with ASD risk, however, future mechanistic studies are required to support this hypothesis.190 3.4.4 Strengths and limitations This study has multiple strengths. First, the study population included all deliveries that occurred in BC during the designated study period. Second, we were able to assess the association between antibiotic use and ASD in a group of pregnant individuals that were treated with antibiotics for the same indication (i.e., UTIs). This provided a way to assess confounding by indication, which has been a key limitation of previous research exploring associations with antibiotic exposure.66,166 Further, through linkage with the BCAAN, we gained prospectively collected clinical gold standard diagnostic data for our ASD outcome. Many studies have used diagnostic codes from administrative datasets for outcome ascertainment. Recent research has found that this is not the most reliable method to identify ASD cases, and while it may produce a 68 good positive predictive value, the negative predictive value is of concern.167 Finally, covariate data came from the BCPDR. A validation study of the registry was previously conducted, and determined that its clinical variables on interventions during labour and delivery have overall excellent validity.186 This study has some limitations. There is potential for exposure misclassification, as antibiotic dispensation does not necessarily confirm antibiotic use. Further, we did not include inpatient antibiotic use in the analysis. Still, previous research did not find that including inpatient antibiotic data altered the association between prenatal antibiotic exposure and ASD.66 Moreover, since we were unable to reliably identify the biological fathers of this cohort of children, the sibling pair analysis only makes use of maternal siblings; which is an imperfect way to completely account for genetic confounding factors. Additionally, since this study has an observational design, some residual confounding likely remains. Lastly, given that various factors are associated with maternal infections during pregnancy, including site of infection, infectious agent, and severity, certain types of infections may differentially influence the potential risk of ASD.67 While we were able to assess confounding by indication for UTIs, further research should stratify by various features of prenatal infections to provide a deeper understanding into the potential role of confounding by indication. 3.5 Conclusion In this population-based study, prenatal antibiotic exposure was associated with a small increase in risk of ASD in offspring. Our findings do provide some insight into potential biological processes involved in ASD. However, due to the possibility of residual confounding by indication and unmeasured familial factors, these findings are not considered clinically 69 meaningful. Further research is needed to assess the role of confounding by indication in greater detail. 70 CHAPTER 4: Conclusions Children with ASD frequently experience significant GI dysfunction, with symptoms ranging from abdominal pain and maldigestion to deficient integrity of the gut epithelium and increased intestinal permeability. Moreover, children with ASD are commonly found to carry an abnormal composition of gut microbiota, and research suggests that this dysbiotic microbiome could not only drive the observed GI dysfunction, but may also increase a child’s likelihood to express autistic characteristics and experience increased severity of symptoms. As such, the proposed mechanisms of the microbiome-gut-brain axis have been hypothesized to play a possible role in the etiology of ASD. Since development and colonization of the infant intestinal microbiome occurs during critical periods of neurodevelopment, early life exposures – perhaps even before birth – that alter initial microbial colonization have been identified as potential risk factors for ASD. This thesis investigated the associations between intrapartum and prenatal antibiotic exposure with the risk of ASD in offspring using multiple linked population-level datasets. Antibiotics have been shown to have a significant and lasting effect on the host microbiome; thus, we used maternal antibiotic exposure as a proxy measure for an altered microbiome in the mother, before and during childbirth, and a resulting altered initial microbial colonization in the infant. Since this observational research in inherently confounded by indication for the antibiotic use, we employed various methods to reduce this confounding and examine its potential role in the relationship between antibiotics and ASD. In addition, we explored the potential impact timing of antibiotic exposure, duration of exposure, and class of antibiotic used might have on the risk of ASD. 71 4.1 Summary of findings In Chapter 2, “Autism risk and perinatal antibiotic use”, we observed no association between antibiotic use during labour and delivery and ASD in offspring of the main cohort of deliveries, which were treated for any infection indication. As a method to control for confounding by indication, we ran our analysis on a sub-cohort restricted to pregnant individuals with the same indication for antibiotic use, a positive group B streptococcus test. Results from this cohort revealed no significant association, however, when compared to the main cohort results, these findings showed a small non-significant reduced risk of ASD associated with antibiotic use to treat GBS. Further, we found no dose-response relationship between length of first stage of labour and risk of ASD. Children exposed to both prenatal and labour and delivery antibiotics as well as children exposed to both labour and delivery and neonatal antibiotics had no substantial difference in associated ASD risk. In order to examine if longer durations of antibiotic exposure, and if timing of exposure with respect to fetal development could impact the risk of ASD, we investigated antibiotic use throughout pregnancy in the subsequent chapter. In Chapter 3, “Association between prenatal antibiotic exposure and autism spectrum disorder: A population-based cohort study”, we reported a small statistically significant association between prenatal antibiotic exposure and ASD in offspring, particularly for exposure that took place during the second trimester of pregnancy. An increase in ASD risk was also associated with exposure to penicillin or other beta lactam antibiotics at any point during pregnancy and when the cumulative duration of antibiotic exposure lasted more than two weeks. We did observe a sex difference in the relationship between prenatal antibiotic use and ASD. There was a non-significant higher risk of ASD among females compared to males, especially when females were exposed to other beta lactam antibiotics. Interestingly, we also observed that 72 mode of delivery: vaginal vs cesarean, did not modify the association between prenatal antibiotics and risk of ASD. Among the cohort restricted to pregnant individuals diagnosed with UTIs, findings continued to show a positive association between prenatal antibiotics and ASD, suggesting that the biological processes occurring in the mother as a result of the UTI may not be responsible for the observed association with ASD risk. Further, results from our discordant sibling pair analysis were not statistically significant, but a positive association remained between prenatal antibiotic exposure and ASD in offspring. In sum, these results indicate that an altered microbial colonization at childbirth does not appear to increase the risk of ASD in childhood. However, we did observe that antibiotic exposure during an earlier period of fetal development, specifically the second trimester, was associated with a small increase in risk of ASD. One possible and tentative explanation for this could be that antibiotic-induced alterations to the maternal microbiome and metabolome during pregnancy impact fetal development, contributing to ASD development. The second trimester of pregnancy is a time for rapid development of the fetus and may be a critical window sensitive to environmental insults that alter the normal metabolic programming by the maternal microbiome. In addition to affecting the microbiome and metabolome, antibiotic exposure may also impact mitochondrial function.49 Considering that mitochondrial dysfunction is one of the most prevalent metabolic disorders found in children with ASD, mitochondria could be part of the biological link between prenatal exposures and some ASD cases. However, considering the association between prenatal antibiotics and ASD attenuated upon conducting our discordant sibling-pair analysis, residual confounding likely remains, and thus, results must be interpreted with caution. 73 4.2 Strengths and limitations 4.2.1 Strengths This research aimed to examine the association between prenatal and perinatal antibiotic exposure with risk of ASD in offspring while addressing important methodological limitations of previous research on this topic. Through the use of multiple linked population-level databases, we were able to identify exposure, outcome, and many potential confounders without having to rely on participant self-reporting and the resulting threat of recall bias. In addition, since our cohorts consisted of all live births that occurred in BC during our designated study period, we eliminated the possibility of selection bias while constructing study cohorts that had larger sample sizes compared to previous studies investigating this association.66,164,166,168 Through linkage with the British Columbia Autism Assessment Network, we gained prospectively collected clinical gold standard diagnostic data for our ASD outcome. Access to BCAAN data is unique as many epidemiological studies exploring associations with ASD use diagnostic codes from administrative datasets for outcome ascertainment.66,165,166 Previous research has shown that this is not the most reliable method to identify ASD cases, as it delivers a concerning negative predictive value.167 Further in regard to the quality of our studies’ datasets, intrapartum antibiotic exposure as well as the majority of our covariate information came from the BC Perinatal Data Registry. A validation study of the registry was previously conducted, and determined that its clinical variables on interventions during labour and delivery have overall excellent validity.186 Finally, multiple methods were employed to help reduce the impact of residual confounding. For our sibling analysis in Chapter 3, we identified maternal siblings who were discordant in both ASD and antibiotic exposure status. This approach reduced the confounding 74 effects of environmental and social factors shared between siblings, and controlled for characteristics that remain highly stable over time within a mother, such as genetics. In addition, we were able to study sub-cohorts of pregnant individuals diagnosed with the same infection. In Chapter 2 we examined a cohort restricted to group B streptococcus positive deliveries and in Chapter 3 we examined a cohort restricted to pregnant individuals diagnosed with UTIs. These sub-cohorts provided a way to begin to assess the role of confounding by indication, which has been a key limitation of previous research exploring associations with antibiotic exposure.66,165,168 4.2.2 Limitations Despite our strengths, this research faces various limitations inherent to its observational research design and the use of administrative data. In Chapter 2, our intrapartum antibiotic exposure information came from the BCPDR. The BCPDR provided us with information on whether antibiotics were administered during labour and delivery, however it did not include information on the number of doses and specific durations of antibiotic exposure. This is valuable information, since a higher number of doses and a longer cumulative duration of exposure would likely have a more profound impact on the maternal microbiome. Further, we also lacked information on the specific classes of antibiotics administered during labour and delivery, and thus, could not assess whether some classes of antibiotics differentially influenced the association with ASD. We were able to address many of these limitations in Chapter 3, since the BC PharmaNet database included information on number of prenatal antibiotic prescriptions, duration of each prescription, and type of antibiotics prescribed. However, since antibiotic prescription and dispensation are not definite indications of antibiotic use, the possibility of misclassification of the exposure remained. This misclassification would be non-differential, 75 steering results towards the null. Furthermore, since we were unable to reliably identify the biological fathers in our administrative data, our discordant sibling pair analysis only made use of maternal siblings. This means that our sibling cohort included both full and half siblings, which is not ideal to completely account for genetic confounding factors. We also lacked reliable data on breastfeeding initiation, duration, and exclusivity, which are relevant to the development and health of a child’s microbiome and immune system.115 As discussed earlier, breastfed infants were found to have significantly different compositions of gut microbiota compared to formula-fed infants, with higher proportions of beneficial bacteria.119 In addition, it has been reported that a longer duration of breastfeeding is associated with improved cognitive function and reduced autistic traits in children.195 Therefore, evaluating the potential protective effect of breastfeeding would have strengthened our research. Moreover, since our studies had observational designs, some residual confounding likely remains. This was particularly apparent in Chapter 2, when observing important differences between deliveries in which intrapartum antibiotics were administered and not administered. Differences between these two groups were evident among variables that suggest an overall worse prognosis for mothers, such as diabetes, larger pre-pregnancy BMIs, higher prevalence of labour induction and augmentation, increased epidural usage, and greater NICU admissions. In Chapter 3, residual confounding was likely at play since the association observed from the discordant sibling pair analysis was attenuated compared to the association observed in the main cohort. Further, both studies in Chapter 2 and Chapter 3 only had a designated follow-up period of 2 years. Since the global mean age at ASD diagnosis is approximately 5 years3, children born closer to the end of our study period had less than 5 years of follow-up. This means that 76 nondifferential misclassification bias of ASD outcome may have occurred, with some children being classified as non-ASD in our studies, yet later received an ASD diagnosis at an older age. Finally, despite our efforts to address confounding by indication, it still remains a key limitation of our research. Apart from GBS infection, we were unable to identify and control for other indications for intrapartum antibiotic use. Further, we only attempted to control for one indication for prenatal antibiotic use, urinary tract infections. While GBS and UTIs are some of the most common indications for intrapartum and prenatal antibiotic treatment, respectively, they may also differ from other types of infections in important ways. For example, previous research has found that various characteristics of maternal infections during pregnancy, including site of infection, infectious agent, and severity of infections may differentially influence the potential risk of ASD.67 Therefore, stratifying our analyses by various features of infections would provide a deeper understanding into the role of confounding by indication in the associations of intrapartum and prenatal antibiotic exposure with ASD in offspring. 4.3 Future directions 4.3.1 Recommendations for future research Future research should further examine the role of confounding by indication in the relationship between antibiotic use and ASD. In Chapter 3, we found that upon controlling for UTIs during pregnancy, the observed association between prenatal antibiotics and ASD increased slightly in effect size. This makes us question if different types of infections modify the relationship between prenatal antibiotic exposure and ASD in offspring. Ideally, future studies will test this association in sub-cohorts restricted to individuals diagnosed with infections sharing various key features. For instance, common sites of prenatal infections to be explored include the 77 genitourinary tract, upper respiratory tract, and skin. Both viral and bacterial infections should be compared, especially since immune responses to these infectious agents differs.67 Finally, different levels of infection severity should be assessed based on presence of a fever or if the individual was hospitalized to receive treatment. In addition, future ASD epidemiological research would benefit from consistent reporting of sex-specific comparisons. As discussed in Chapter 1, the male sex-biased prevalence is one of the most consistent features of ASD.20 In addition, sex-differences in ASD phenotype exist, with females displaying greater socio-communicative severity, lower full-scale IQ and adaptive functioning, but less of the core diagnostic symptoms of ASD, such as stereotyped and repetitive behaviours.196 Despite these differences, many studies exploring associations with ASD continue to adjust for sex during statistical analysis, however do not assess for effect modification.164–166 Moving forward, ASD studies in general should report results stratified by sex to improve our understanding of the sex differences in ASD etiology. Moreover, our studies required children to have at least 2 years of follow-up to ensure children in our cohort met the minimum age at which ASD can be reliably diagnosed, as outlined by the DSM-5 and previous research.1,181 Although this 2 year minimum follow-up time was longer than what previous studies have designated, future research should aim for longer minimum follow-up periods. This recommendation is made based on the 5-year global mean age at ASD diagnosis;3 a trend similarly observed at BCAAN. ASD diagnoses occurring at earlier ages usually indicate a more severe manifestation of the disorder. Thus, shorter follow-up periods likely exclude children with less severe ASD, and this could skew research findings. Finally, more studies are recommended to investigate the involvement of maternal gut microbiota and their metabolites in ASD etiology at the biological level; and in particular, how 78 antibiotic-induced microbial dysbiosis could be underlying trimester-specific associations with ASD risk. Additionally, future studies are warranted to examine other factors affecting microbiome composition in association with risk of ASD, such as breastfeeding. 4.3.2 Implications Despite consistent increases in prevalence, the etiology of ASD remains unclear; however, it likely involves an interplay between genetic susceptibility and environmental exposures.41 The theory of developmental origins of health and disease (DOHaD) is based in the concept that environmental stressors during the early stages of life can alter the developmental trajectory and future health of an individual. Additionally, growing evidence supports the role of the microbiome as a fundamental part of human physiology in health and disease; and thus, is suggested to be important to our understanding of DOHaD.197 With this in mind, exposures during the prenatal and intrapartum period, particularly those that impact the microbiome, have been highlighted as potential risk factors for ASD development. Previous studies have explored associations of prenatal and intrapartum antibiotic use with ASD, however, these studies reported mixed findings and were weakened by various methodological limitations.66,164–166,168 This thesis aimed to improve this literature by addressing previous limitations, and as a result, shed further light on these associations. Overall, results indicate that the risk of ASD should not factor into clinical decisions on whether to administer antibiotics both prenatally and during labour and delivery. Our results do, however, point to possible new avenues of research for ASD, specifically with regard to a potential role of the maternal microbiome and metabolome in fetal metabolic programming and neurodevelopment. Uncovering abnormal biological processes associated with ASD provides an opportunity for biomarker identification.198 If biomarkers can be developed to measure these biological abnormalities accurately, there would be implications 79 for identifying those at risk for ASD, facilitating earlier diagnoses, classifying disease severity, monitoring response to treatment interventions, and ideally, informing disease prevention.198,199 4.4 Conclusion Through a microbiome-gut-brain axis perspective, this thesis aimed to explored the associations between prenatal and intrapartum antibiotic use with autism spectrum disorder by utilizing multiple linked population-level datasets. Our findings suggest that while intrapartum antibiotic exposure is not associated with ASD in offspring, earlier exposure during fetal development, specifically during the second trimester, may be associated with a small increase in risk. Overall, results indicate that the risk of ASD should not factor into clinical decisions on whether to administer antibiotics both prenatally and during labour and delivery. However, these research findings do highlight a possible role of impaired fetal metabolic programming by the maternal microbiome and metabolome in ASD development; further research investigating this connection is warranted. 80 References 1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (5th Ed.). American Psychiatric Association 2. Hodges H, Fealko C, Soares N. Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation. Transl Pediatr. 2020;9(Suppl 1):S55-S65. doi:10.21037/tp.2019.09.09 3. van ’t Hof M, Tisseur C, van Berckelear-Onnes I, et al. Age at autism spectrum disorder diagnosis: A systematic review and meta-analysis from 2012 to 2019. Autism. 2021;25(4):862-873. doi:10.1177/1362361320971107 4. Dua V. Standards and Guidelines for the Assessment and Diagnosis of Young Children with Autism Spectrum Disorder in British Columbia: An Evidence-Based Report prepared for The British Columbia Ministry of Health Planning. British Columbia Ministry of Health Planning. Published online 2003. 5. Lord C, Risi S, Lambrecht L, et al. The Autism Diagnostic Observation Schedule—Generic: A Standard Measure of Social and Communication Deficits Associated with the Spectrum of Autism. J Autism Dev Disord. 2000;30(3):205-223. doi:10.1023/A:1005592401947 6. Lord C, Rutter M, Le Couteur A. Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24(5):659-685. doi:10.1007/BF02172145 7. Canada PHA of. Autism Spectrum Disorder among Children and Youth in Canada 2018. Published March 29, 2018. Accessed January 11, 2022. https://www.canada.ca/en/public-health/services/publications/diseases-conditions/autism-spectrum-disorder-children-youth-canada-2018.html 8. Autism and Developmental Disabilities Monitoring Network Surveillance Year 2000 Principal Investigators, Centers for Disease Control and Prevention. Prevalence of autism spectrum disorders--autism and developmental disabilities monitoring network, six sites, United States, 2000. MMWR Surveill Summ. 2007;56(1):1-11. 9. Maenner MJ, Shaw KA, Baio J, et al. Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016. MMWR Surveill Summ. 2020;69(4):1-12. doi:10.15585/mmwr.ss6904a1 10. Vos T, Abajobir AA, Abate KH, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 2017;390(10100):1211-1259. doi:10.1016/S0140-6736(17)32154-2 81 11. Hay SI, Abajobir AA, Abate KH, et al. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 2017;390(10100):1260-1344. doi:10.1016/S0140-6736(17)32130-X 12. Newschaffer CJ, Croen LA, Daniels J, et al. The Epidemiology of Autism Spectrum Disorders. Annu Rev Public Health. 2007;28(1):235-258. doi:10.1146/annurev.publhealth.28.021406.144007 13. BC Autism Assessment Network. Accessed August 13, 2022. http://www.phsa.ca/our-services/programs-services/bc-autism-assessment-network#2368 14. Development M of C and F. How autism is diagnosed - Province of British Columbia. Accessed August 13, 2022. https://www2.gov.bc.ca/gov/content/health/managing-your-health/child-behaviour-development/support-needs/autism-spectrum-disorder/diagnosis/how-autism-is-diagnosed 15. Ip A, Poon BT, Hanley G, Guhn M, Oberlander TF. Developmental profiles of children at risk for autism spectrum disorder at school entry. Autism Research. 2022;15(7):1301-1310. doi:10.1002/aur.2742 16. Becerra TA, von Ehrenstein OS, Heck JE, et al. Autism Spectrum Disorders and Race, Ethnicity, and Nativity: A Population-Based Study. Pediatrics. 2014;134(1):e63-e71. doi:10.1542/peds.2013-3928 17. Angell AM, Empey A, Zuckerman KE. Chapter Four - A Review of Diagnosis and Service Disparities Among Children With Autism From Racial and Ethnic Minority Groups in the United States. In: Hodapp RM, Fidler DJ, eds. International Review of Research in Developmental Disabilities. Vol 55. International Review of Research in Developmental Disabilities. Academic Press; 2018:145-180. doi:10.1016/bs.irrdd.2018.08.003 18. COUNCIL ON COMMUNITY PEDIATRICS, Gitterman BA, Flanagan PJ, et al. Poverty and Child Health in the United States. Pediatrics. 2016;137(4):e20160339. doi:10.1542/peds.2016-0339 19. Werling DM, Geschwind DH. Sex differences in autism spectrum disorders. Curr Opin Neurol. 2013;26(2):146-153. doi:10.1097/WCO.0b013e32835ee548 20. Werling DM. The role of sex-differential biology in risk for autism spectrum disorder. Biol Sex Differ. 2016;7(1):58. doi:10.1186/s13293-016-0112-8 21. Tubío-Fungueiriño M, Cruz S, Sampaio A, Carracedo A, Fernández-Prieto M. Social Camouflaging in Females with Autism Spectrum Disorder: A Systematic Review. J Autism Dev Disord. 2021;51(7):2190-2199. doi:10.1007/s10803-020-04695-x 82 22. Lockwood Estrin G, Milner V, Spain D, Happé F, Colvert E. Barriers to Autism Spectrum Disorder Diagnosis for Young Women and Girls: a Systematic Review. Rev J Autism Dev Disord. 2021;8(4):454-470. doi:10.1007/s40489-020-00225-8 23. Young H, Oreve MJ, Speranza M. Clinical characteristics and problems diagnosing autism spectrum disorder in girls. Archives de Pédiatrie. 2018;25(6):399-403. doi:10.1016/j.arcped.2018.06.008 24. Werling DM, Geschwind DH. Understanding sex bias in autism spectrum disorder. Proceedings of the National Academy of Sciences. 2013;110(13):4868-4869. doi:10.1073/pnas.1301602110 25. Sanders SJ, Ercan-Sencicek AG, Hus V, et al. Multiple Recurrent De Novo CNVs, Including Duplications of the 7q11.23 Williams Syndrome Region, Are Strongly Associated with Autism. Neuron. 2011;70(5):863-885. doi:10.1016/j.neuron.2011.05.002 26. Sanders SJ, He X, Willsey AJ, et al. Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron. 2015;87(6):1215-1233. doi:10.1016/j.neuron.2015.09.016 27. Jacquemont S, Coe BP, Hersch M, et al. A Higher Mutational Burden in Females Supports a “Female Protective Model” in Neurodevelopmental Disorders. The American Journal of Human Genetics. 2014;94(3):415-425. doi:10.1016/j.ajhg.2014.02.001 28. Gilman SR, Iossifov I, Levy D, Ronemus M, Wigler M, Vitkup D. Rare De Novo Variants Associated with Autism Implicate a Large Functional Network of Genes Involved in Formation and Function of Synapses. Neuron. 2011;70(5):898-907. doi:10.1016/j.neuron.2011.05.021 29. Levy D, Ronemus M, Yamrom B, et al. Rare De Novo and Transmitted Copy-Number Variation in Autistic Spectrum Disorders. Neuron. 2011;70(5):886-897. doi:10.1016/j.neuron.2011.05.015 30. Palmer N, Beam A, Agniel D, et al. Association of Sex With Recurrence of Autism Spectrum Disorder Among Siblings. JAMA Pediatrics. 2017;171(11):1107-1112. doi:10.1001/jamapediatrics.2017.2832 31. Robinson EB, Lichtenstein P, Anckarsäter H, Happé F, Ronald A. Examining and interpreting the female protective effect against autistic behavior. Proceedings of the National Academy of Sciences. 2013;110(13):5258-5262. doi:10.1073/pnas.1211070110 32. Ferri SL, Abel T, Brodkin ES. Sex Differences in Autism Spectrum Disorder: a Review. Curr Psychiatry Rep. 2018;20(2):9. doi:10.1007/s11920-018-0874-2 33. Belmonte MK, Bourgeron T. Fragile X syndrome and autism at the intersection of genetic and neural networks. Nat Neurosci. 2006;9(10):1221-1225. doi:10.1038/nn1765 83 34. Lombardi LM, Baker SA, Zoghbi HY. MECP2 disorders: from the clinic to mice and back. doi:10.1172/JCI78167 35. Cohen-Bendahan CCC, van de Beek C, Berenbaum SA. Prenatal sex hormone effects on child and adult sex-typed behavior: methods and findings. Neuroscience & Biobehavioral Reviews. 2005;29(2):353-384. doi:10.1016/j.neubiorev.2004.11.004 36. Lutchmaya S, Baron-Cohen S, Raggatt P. Foetal testosterone and eye contact in 12-month-old human infants. Infant Behavior and Development. 2002;25(3):327-335. doi:10.1016/S0163-6383(02)00094-2 37. Auyeung B, Taylor K, Hackett G, Baron-Cohen S. Foetal testosterone and autistic traits in 18 to 24-month-old children. Mol Autism. 2010;1:11. doi:10.1186/2040-2392-1-11 38. Knickmeyer R, Baron-Cohen S, Raggatt P, Taylor K. Foetal testosterone, social relationships, and restricted interests in children. Journal of Child Psychology and Psychiatry. 2005;46(2):198-210. doi:10.1111/j.1469-7610.2004.00349.x 39. Auyeung B, Baron-Cohen S, Ashwin E, Knickmeyer R, Taylor K, Hackett G. Fetal testosterone and autistic traits. British Journal of Psychology. 2009;100(1):1-22. doi:10.1348/000712608X311731 40. Ingudomnukul E, Baron-Cohen S, Wheelwright S, Knickmeyer R. Elevated rates of testosterone-related disorders in women with autism spectrum conditions. Hormones and Behavior. 2007;51(5):597-604. doi:10.1016/j.yhbeh.2007.02.001 41. Watts TJ. The Pathogenesis of Autism. Clinical medicine Pathology. 2008;1:CPath.S1143. doi:10.4137/CPath.S1143 42. Di Martino A, Yan CG, Li Q, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014;19(6):659-667. doi:10.1038/mp.2013.78 43. Rossignol DA, Frye RE. Mitochondrial dysfunction in autism spectrum disorders: a systematic review and meta-analysis. Mol Psychiatry. 2012;17(3):290-314. doi:10.1038/mp.2010.136 44. Bjorklund G, Saad K, Chirumbolo S, et al. Immune dysfunction and neuroinflammation in autism spectrum disorder. Acta Neurobiol Exp (Wars). 2016;76(4):257-268. doi:10.21307/ane-2017-025 45. Vuong HE, Hsiao EY. Emerging Roles for the Gut Microbiome in Autism Spectrum Disorder. Biological Psychiatry. 2017;81(5):411-423. doi:10.1016/j.biopsych.2016.08.024 46. Hernandez LM, Rudie JD, Green SA, Bookheimer S, Dapretto M. Neural Signatures of Autism Spectrum Disorders: Insights into Brain Network Dynamics. Neuropsychopharmacol. 2015;40(1):171-189. doi:10.1038/npp.2014.172 84 47. Di Martino A, Ross K, Uddin LQ, Sklar AB, Castellanos FX, Milham MP. Functional Brain Correlates of Social and Nonsocial Processes in Autism Spectrum Disorders: An Activation Likelihood Estimation Meta-Analysis. Biological Psychiatry. 2009;65(1):63-74. doi:10.1016/j.biopsych.2008.09.022 48. Ameis SH, Catani M. Altered white matter connectivity as a neural substrate for social impairment in Autism Spectrum Disorder. Cortex. 2015;62:158-181. doi:10.1016/j.cortex.2014.10.014 49. Frye RE, Cakir J, Rose S, et al. Mitochondria May Mediate Prenatal Environmental Influences in Autism Spectrum Disorder. Journal of Personalized Medicine. 2021;11(3):218. doi:10.3390/jpm11030218 50. Brand MD, Orr AL, Perevoshchikova IV, Quinlan CL. The role of mitochondrial function and cellular bioenergetics in ageing and disease. Br J Dermatol. 2013;169(0 2):1-8. doi:10.1111/bjd.12208 51. Rossignol DA, Frye RE. A review of research trends in physiological abnormalities in autism spectrum disorders: immune dysregulation, inflammation, oxidative stress, mitochondrial dysfunction and environmental toxicant exposures. Mol Psychiatry. 2012;17(4):389-401. doi:10.1038/mp.2011.165 52. Onore C, Careaga M, Ashwood P. The role of immune dysfunction in the pathophysiology of autism. Brain, Behavior, and Immunity. 2012;26(3):383-392. doi:10.1016/j.bbi.2011.08.007 53. Vargas DL, Nascimbene C, Krishnan C, Zimmerman AW, Pardo CA. Neuroglial activation and neuroinflammation in the brain of patients with autism. Annals of Neurology. 2005;57(1):67-81. doi:10.1002/ana.20315 54. Morgan JT, Chana G, Pardo CA, et al. Microglial Activation and Increased Microglial Density Observed in the Dorsolateral Prefrontal Cortex in Autism. Biological Psychiatry. 2010;68(4):368-376. doi:10.1016/j.biopsych.2010.05.024 55. Rodriguez JI, Kern JK. Evidence of microglial activation in autism and its possible role in brain underconnectivity. Neuron Glia Biology. 2011;7(2-4):205-213. doi:10.1017/S1740925X12000142 56. Ashwood P, Krakowiak P, Hertz-Picciotto I, Hansen R, Pessah I, Van de Water J. Elevated plasma cytokines in autism spectrum disorders provide evidence of immune dysfunction and are associated with impaired behavioral outcome. Brain, Behavior, and Immunity. 2011;25(1):40-45. doi:10.1016/j.bbi.2010.08.003 57. McElhanon BO, McCracken C, Karpen S, Sharp WG. Gastrointestinal Symptoms in Autism Spectrum Disorder: A Meta-analysis. Pediatrics. 2014;133(5):872-883. doi:10.1542/peds.2013-3995 85 58. Adams JB, Johansen LJ, Powell LD, Quig D, Rubin RA. Gastrointestinal flora and gastrointestinal status in children with autism – comparisons to typical children and correlation with autism severity. BMC Gastroenterology. 2011;11(1):22. doi:10.1186/1471-230X-11-22 59. Cheroni C, Caporale N, Testa G. Autism spectrum disorder at the crossroad between genes and environment: contributions, convergences, and interactions in ASD developmental pathophysiology. Molecular Autism. 2020;11(1):69. doi:10.1186/s13229-020-00370-1 60. Devlin B, Scherer SW. Genetic architecture in autism spectrum disorder. Current Opinion in Genetics & Development. 2012;22(3):229-237. doi:10.1016/j.gde.2012.03.002 61. Grønborg TK, Schendel DE, Parner ET. Recurrence of Autism Spectrum Disorders in Full- and Half-Siblings and Trends Over Time: A Population-Based Cohort Study. JAMA Pediatrics. 2013;167(10):947-953. doi:10.1001/jamapediatrics.2013.2259 62. Hallmayer J, Cleveland S, Torres A, et al. Genetic Heritability and Shared Environmental Factors Among Twin Pairs With Autism. Archives of General Psychiatry. 2011;68(11):1095-1102. doi:10.1001/archgenpsychiatry.2011.76 63. Gardener H, Spiegelman D, Buka SL. Perinatal and Neonatal Risk Factors for Autism: A Comprehensive Meta-analysis. Pediatrics. 2011;128(2):344-355. doi:10.1542/peds.2010-1036 64. Wang C, Geng H, Liu W, Zhang G. Prenatal, perinatal, and postnatal factors associated with autism. Medicine (Baltimore). 2017;96(18):e6696. doi:10.1097/MD.0000000000006696 65. Krakowiak P, Walker CK, Bremer AA, et al. Maternal Metabolic Conditions and Risk for Autism and Other Neurodevelopmental Disorders. Pediatrics. 2012;129(5):e1121-e1128. doi:10.1542/peds.2011-2583 66. Hamad AF, Alessi-Severini S, Mahmud SM, Brownell M, Kuo IF. Prenatal antibiotics exposure and the risk of autism spectrum disorders: A population-based cohort study. PLoS One. 2019;14(8):e0221921. doi:10.1371/journal.pone.0221921 67. Jiang H yin, Xu L lian, Shao L, et al. Maternal infection during pregnancy and risk of autism spectrum disorders: A systematic review and meta-analysis. Brain, Behavior, and Immunity. 2016;58:165-172. doi:10.1016/j.bbi.2016.06.005 68. Lyall K, Croen L, Daniels J, et al. The Changing Epidemiology of Autism Spectrum Disorders. Annu Rev Public Health. 2017;38:81-102. doi:10.1146/annurev-publhealth-031816-044318 69. Christensen DL, Braun KVN, Baio J, et al. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. MMWR Surveill Summ. 2018;65(13):1-23. doi:10.15585/mmwr.ss6513a1 86 70. Rommelse NNJ, Franke B, Geurts HM, Hartman CA, Buitelaar JK. Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder. Eur Child Adolesc Psychiatry. 2010;19(3):281-295. doi:10.1007/s00787-010-0092-x 71. Levy SE, Giarelli E, Lee LC, et al. Autism Spectrum Disorder and Co-occurring Developmental, Psychiatric, and Medical Conditions Among Children in Multiple Populations of the United States. Journal of Developmental & Behavioral Pediatrics. 2010;31(4):267-275. doi:10.1097/DBP.0b013e3181d5d03b 72. Miller HL, Sherrod GM, Mauk JE, Fears NE, Hynan LS, Tamplain PM. Shared Features or Co-occurrence? Evaluating Symptoms of Developmental Coordination Disorder in Children and Adolescents with Autism Spectrum Disorder. J Autism Dev Disord. 2021;51(10):3443-3455. doi:10.1007/s10803-020-04766-z 73. Croen LA, Zerbo O, Qian Y, et al. The health status of adults on the autism spectrum. Autism. 2015;19(7):814-823. doi:10.1177/1362361315577517 74. Emanuele E, Orsi P, Boso M, et al. Low-grade endotoxemia in patients with severe autism. Neuroscience Letters. 2010;471(3):162-165. doi:10.1016/j.neulet.2010.01.033 75. Cristiano C, Lama A, Lembo F, Mollica MP, Calignano A, Mattace Raso G. Interplay Between Peripheral and Central Inflammation in Autism Spectrum Disorders: Possible Nutritional and Therapeutic Strategies. Front Physiol. 2018;9:184. doi:10.3389/fphys.2018.00184 76. Patusco R, Ziegler J. Role of Probiotics in Managing Gastrointestinal Dysfunction in Children with Autism Spectrum Disorder: An Update for Practitioners. Advances in Nutrition. 2018;9(5):637-650. doi:10.1093/advances/nmy031 77. Kang V, Wagner GC, Ming X. Gastrointestinal Dysfunction in Children With Autism Spectrum Disorders. Autism Research. 2014;7(4):501-506. doi:10.1002/aur.1386 78. Krajmalnik-Brown R, Lozupone C, Kang DW, Adams JB. Gut bacteria in children with autism spectrum disorders: challenges and promise of studying how a complex community influences a complex disease. Microbial Ecology in Health & Disease. 2015;26(0). doi:10.3402/mehd.v26.26914 79. de Theije CGM, Wu J, da Silva SL, et al. Pathways underlying the gut-to-brain connection in autism spectrum disorders as future targets for disease management. European Journal of Pharmacology. 2011;668:S70-S80. doi:10.1016/j.ejphar.2011.07.013 80. Luna RA, Savidge TC, Williams KC. The Brain-Gut-Microbiome Axis: What Role Does It Play in Autism Spectrum Disorder? Curr Dev Disord Rep. 2016;3(1):75-81. doi:10.1007/s40474-016-0077-7 81. The Microbiota-Gut-Brain Axis | Physiological Reviews. Accessed July 30, 2022. https://journals.physiology.org/doi/full/10.1152/physrev.00018.2018?rfr_dat=cr_pub 87 82. El Aidy S, Stilling R, Dinan TG, Cryan JF. Microbiome to Brain: Unravelling the Multidirectional Axes of Communication. In: Lyte M, ed. Microbial Endocrinology: Interkingdom Signaling in Infectious Disease and Health. Advances in Experimental Medicine and Biology. Springer International Publishing; 2016:301-336. doi:10.1007/978-3-319-20215-0_15 83. Grenham S, Clarke G, Cryan J, Dinan T. Brain–Gut–Microbe Communication in Health and Disease. Frontiers in Physiology. 2011;2. Accessed January 20, 2022. https://www.frontiersin.org/article/10.3389/fphys.2011.00094 84. Carabotti M, Scirocco A, Maselli MA, Severi C. The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. Ann Gastroenterol. 2015;28(2):203-209. 85. Duerkop BA, Vaishnava S, Hooper LV. Immune Responses to the Microbiota at the Intestinal Mucosal Surface. Immunity. 2009;31(3):368-376. doi:10.1016/j.immuni.2009.08.009 86. Johansson MA, Saghafian-Hedengren S, Haileselassie Y, et al. Early-Life Gut Bacteria Associate with IL-4−, IL-10− and IFN-γ Production at Two Years of Age. PLOS ONE. 2012;7(11):e49315. doi:10.1371/journal.pone.0049315 87. Yang I, Corwin EJ, Brennan PA, Jordan S, Murphy JR, Dunlop A. The Infant Microbiome: Implications for Infant Health and Neurocognitive Development. Nurs Res. 2016;65(1):76-88. doi:10.1097/NNR.0000000000000133 88. Theoharides TC, Asadi S, Patel AB. Focal brain inflammation and autism. J Neuroinflammation. 2013;10(1):815. doi:10.1186/1742-2094-10-46 89. Smith SM, Vale WW. The role of the hypothalamic-pituitary-adrenal axis in neuroendocrine responses to stress. Dialogues Clin Neurosci. 2006;8(4):383-395. 90. Dinan TG, Cryan JF. Regulation of the stress response by the gut microbiota: Implications for psychoneuroendocrinology. Psychoneuroendocrinology. 2012;37(9):1369-1378. doi:10.1016/j.psyneuen.2012.03.007 91. Liu B, Liu J, Wang M, Zhang Y, Li L. From Serotonin to Neuroplasticity: Evolvement of Theories for Major Depressive Disorder. Frontiers in Cellular Neuroscience. 2017;11. Accessed January 20, 2022. https://www.frontiersin.org/article/10.3389/fncel.2017.00305 92. Farzi A, Fröhlich EE, Holzer P. Gut Microbiota and the Neuroendocrine System. Neurotherapeutics. 2018;15(1):5-22. doi:10.1007/s13311-017-0600-5 93. de Punder K, Pruimboom L. Stress Induces Endotoxemia and Low-Grade Inflammation by Increasing Barrier Permeability. Frontiers in Immunology. 2015;6. Accessed January 20, 2022. https://www.frontiersin.org/article/10.3389/fimmu.2015.00223 88 94. Breit S, Kupferberg A, Rogler G, Hasler G. Vagus Nerve as Modulator of the Brain–Gut Axis in Psychiatric and Inflammatory Disorders. Frontiers in Psychiatry. 2018;9. doi:10.3389/fpsyt.2018.00044 95. O’Mahony SM, Clarke G, Dinan TG, Cryan JF. Early-life adversity and brain development: Is the microbiome a missing piece of the puzzle? Neuroscience. 2017;342:37-54. doi:10.1016/j.neuroscience.2015.09.068 96. Strandwitz P. Neurotransmitter modulation by the gut microbiota. Brain Research. 2018;1693:128-133. doi:10.1016/j.brainres.2018.03.015 97. O’Mahony SM, Clarke G, Borre YE, Dinan TG, Cryan JF. Serotonin, tryptophan metabolism and the brain-gut-microbiome axis. Behavioural Brain Research. 2015;277:32-48. doi:10.1016/j.bbr.2014.07.027 98. Borre YE, O’Keeffe GW, Clarke G, Stanton C, Dinan TG, Cryan JF. Microbiota and neurodevelopmental windows: implications for brain disorders. Trends in Molecular Medicine. 2014;20(9):509-518. doi:10.1016/j.molmed.2014.05.002 99. Ogbonnaya ES, Clarke G, Shanahan F, Dinan TG, Cryan JF, O’Leary OF. Adult Hippocampal Neurogenesis Is Regulated by the Microbiome. Biological Psychiatry. 2015;78(4):e7-e9. doi:10.1016/j.biopsych.2014.12.023 100. Möhle L, Mattei D, Heimesaat MM, et al. Ly6Chi Monocytes Provide a Link between Antibiotic-Induced Changes in Gut Microbiota and Adult Hippocampal Neurogenesis. Cell Reports. 2016;15(9):1945-1956. doi:10.1016/j.celrep.2016.04.074 101. Braniste V, Al-Asmakh M, Kowal C, et al. The gut microbiota influences blood-brain barrier permeability in mice. Science Translational Medicine. 2014;6(263):263ra158-263ra158. doi:10.1126/scitranslmed.3009759 102. Bezawada N, Phang TH, Hold GL, Hansen R. Autism Spectrum Disorder and the Gut Microbiota in Children: A Systematic Review. Ann Nutr Metab. 2020;76(1):16-29. doi:10.1159/000505363 103. Kang DW, Park JG, Ilhan ZE, et al. Reduced Incidence of Prevotella and Other Fermenters in Intestinal Microflora of Autistic Children. PLOS ONE. 2013;8(7):e68322. doi:10.1371/journal.pone.0068322 104. Tomova A, Husarova V, Lakatosova S, et al. Gastrointestinal microbiota in children with autism in Slovakia. Physiology & Behavior. 2015;138:179-187. doi:10.1016/j.physbeh.2014.10.033 105. Pärtty A, Kalliomäki M, Wacklin P, Salminen S, Isolauri E. A possible link between early probiotic intervention and the risk of neuropsychiatric disorders later in childhood: a randomized trial. Pediatr Res. 2015;77(6):823-828. doi:10.1038/pr.2015.51 89 106. Kang DW, Adams JB, Gregory AC, et al. Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study. Microbiome. 2017;5(1):10. doi:10.1186/s40168-016-0225-7 107. Kang DW, Adams JB, Coleman DM, et al. Long-term benefit of Microbiota Transfer Therapy on autism symptoms and gut microbiota. Sci Rep. 2019;9(1):5821. doi:10.1038/s41598-019-42183-0 108. Aagaard K, Ma J, Antony KM, Ganu R, Petrosino J, Versalovic J. The Placenta Harbors a Unique Microbiome. Science Translational Medicine. 2014;6(237):237ra65-237ra65. doi:10.1126/scitranslmed.3008599 109. Walker RW, Clemente JC, Peter I, Loos RJ. The prenatal gut microbiome: Are we colonized with bacteria in utero? Pediatr Obes. 2017;12(Suppl 1):3-17. doi:10.1111/ijpo.12217 110. Jiménez E, Marín ML, Martín R, et al. Is meconium from healthy newborns actually sterile? Research in Microbiology. 2008;159(3):187-193. doi:10.1016/j.resmic.2007.12.007 111. Steel JH, Malatos S, Kennea N, et al. Bacteria and Inflammatory Cells in Fetal Membranes Do Not Always Cause Preterm Labor. Pediatr Res. 2005;57(3):404-411. doi:10.1203/01.PDR.0000153869.96337.90 112. Stout MJ, Conlon B, Landeau M, et al. Identification of intracellular bacteria in the basal plate of the human placenta in term and preterm gestations. American Journal of Obstetrics and Gynecology. 2013;208(3):226.e1-226.e7. doi:10.1016/j.ajog.2013.01.018 113. Hu J, Nomura Y, Bashir A, et al. Diversified Microbiota of Meconium Is Affected by Maternal Diabetes Status. PLOS ONE. 2013;8(11):e78257. doi:10.1371/journal.pone.0078257 114. Perez-Muñoz ME, Arrieta MC, Ramer-Tait AE, Walter J. A critical assessment of the “sterile womb” and “in utero colonization” hypotheses: implications for research on the pioneer infant microbiome. Microbiome. 2017;5(1):48. doi:10.1186/s40168-017-0268-4 115. Moore RE, Townsend SD. Temporal development of the infant gut microbiome. Open Biology. 9(9):190128. doi:10.1098/rsob.190128 116. Henderickx JGE, Zwittink RD, van Lingen RA, Knol J, Belzer C. The Preterm Gut Microbiota: An Inconspicuous Challenge in Nutritional Neonatal Care. Frontiers in Cellular and Infection Microbiology. 2019;9. Accessed September 6, 2022. https://www.frontiersin.org/articles/10.3389/fcimb.2019.00085 117. Dominguez-Bello MG, Costello EK, Contreras M, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proceedings of the National Academy of Sciences. 2010;107(26):11971-11975. doi:10.1073/pnas.1002601107 90 118. Gomez-Gallego C, Garcia-Mantrana I, Salminen S, Collado MC. The human milk microbiome and factors influencing its composition and activity. Seminars in Fetal and Neonatal Medicine. 2016;21(6):400-405. doi:10.1016/j.siny.2016.05.003 119. Azad MB, Konya T, Maughan H, et al. Gut microbiota of healthy Canadian infants: profiles by mode of delivery and infant diet at 4 months. CMAJ. 2013;185(5):385-394. doi:10.1503/cmaj.121189 120. Madan JC, Hoen AG, Lundgren SN, et al. Association of Cesarean Delivery and Formula Supplementation With the Intestinal Microbiome of 6-Week-Old Infants. JAMA Pediatrics. 2016;170(3):212-219. doi:10.1001/jamapediatrics.2015.3732 121. O’Sullivan A, Farver M, Smilowitz JT. Article Commentary: The Influence of Early Infant-Feeding Practices on the Intestinal Microbiome and Body Composition in Infants. Nutr Metab Insights. 2015;8s1:NMI.S29530. doi:10.4137/NMI.S29530 122. Mueller NT, Bakacs E, Combellick J, Grigoryan Z, Dominguez-Bello MG. The infant microbiome development: mom matters. Trends in Molecular Medicine. 2015;21(2):109-117. doi:10.1016/j.molmed.2014.12.002 123. Koren O, Goodrich JK, Cullender TC, et al. Host Remodeling of the Gut Microbiome and Metabolic Changes during Pregnancy. Cell. 2012;150(3):470-480. doi:10.1016/j.cell.2012.07.008 124. Bränn E, Edvinsson Å, Rostedt Punga A, Sundström-Poromaa I, Skalkidou A. Inflammatory and anti-inflammatory markers in plasma: from late pregnancy to early postpartum. Sci Rep. 2019;9(1):1863. doi:10.1038/s41598-018-38304-w 125. Mor G, Aldo P, Alvero AB. The unique immunological and microbial aspects of pregnancy. Nat Rev Immunol. 2017;17(8):469-482. doi:10.1038/nri.2017.64 126. Edwards SM, Cunningham SA, Dunlop AL, Corwin EJ. The Maternal Gut Microbiome during Pregnancy. MCN Am J Matern Child Nurs. 2017;42(6):310-317. doi:10.1097/NMC.0000000000000372 127. Aagaard K, Riehle K, Ma J, et al. A Metagenomic Approach to Characterization of the Vaginal Microbiome Signature in Pregnancy. PLOS ONE. 2012;7(6):e36466. doi:10.1371/journal.pone.0036466 128. Anahtar MN, Gootenberg DB, Mitchell CM, Kwon DS. Cervicovaginal Microbiota and Reproductive Health: The Virtue of Simplicity. Cell Host & Microbe. 2018;23(2):159-168. doi:10.1016/j.chom.2018.01.013 129. Elovitz MA, Gajer P, Riis V, et al. Cervicovaginal microbiota and local immune response modulate the risk of spontaneous preterm delivery. Nat Commun. 2019;10(1):1305. doi:10.1038/s41467-019-09285-9 91 130. Jašarević E, Bale TL. Prenatal and postnatal contributions of the maternal microbiome on offspring programming. Frontiers in Neuroendocrinology. 2019;55:100797. doi:10.1016/j.yfrne.2019.100797 131. Lain KY, Catalano PM. Metabolic Changes in Pregnancy. Clinical Obstetrics and Gynecology. 2007;50(4):938-948. doi:10.1097/GRF.0b013e31815a5494 132. LeBlanc JG, Milani C, de Giori GS, Sesma F, van Sinderen D, Ventura M. Bacteria as vitamin suppliers to their host: a gut microbiota perspective. Current Opinion in Biotechnology. 2013;24(2):160-168. doi:10.1016/j.copbio.2012.08.005 133. Vuong HE, Pronovost GN, Williams DW, et al. The maternal microbiome modulates fetal neurodevelopment in mice. Nature. 2020;586(7828):281-286. doi:10.1038/s41586-020-2745-3 134. Bookstaver PB, Bland CM, Griffin B, Stover KR, Eiland LS, McLaughlin M. A Review of Antibiotic Use in Pregnancy. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2015;35(11):1052-1062. doi:10.1002/phar.1649 135. Tejada D, Martinez B. Antibiotic Use and Misuse during Pregnancy and Delivery: Benefits and Risks. International Journal of Environmental Research and Public Health. 2014;11(8):7993-8009. doi:10.3390/ijerph110807993 136. Sekirov I, Russell SL, Antunes LCM, Finlay BB. Gut Microbiota in Health and Disease. Physiological Reviews. 2010;90(3):859-904. doi:10.1152/physrev.00045.2009 137. Stokholm J, Schjørring S, Eskildsen CE, et al. Antibiotic use during pregnancy alters the commensal vaginal microbiota. Clinical Microbiology and Infection. 2014;20(7):629-635. doi:10.1111/1469-0691.12411 138. Wong WSW, Sabu P, Deopujari V, et al. Prenatal and Peripartum Exposure to Antibiotics and Cesarean Section Delivery Are Associated with Differences in Diversity and Composition of the Infant Meconium Microbiome. Microorganisms. 2020;8(2):179. doi:10.3390/microorganisms8020179 139. Nyangahu DD, Jaspan HB. Influence of maternal microbiota during pregnancy on infant immunity. Clinical and Experimental Immunology. 2019;198(1):47-56. doi:10.1111/cei.13331 140. Ledger W, Blaser M. Are we using too many antibiotics during pregnancy? BJOG: An International Journal of Obstetrics & Gynaecology. 2013;120(12):1450-1452. doi:10.1111/1471-0528.12371 141. Keski-Nisula L, Kyynäräinen HR, Kärkkäinen U, Karhukorpi J, Heinonen S, Pekkanen J. Maternal intrapartum antibiotics and decreased vertical transmission of Lactobacillus to neonates during birth. Acta Paediatrica. 2013;102(5):480-485. doi:10.1111/apa.12186 92 142. Coker M, Hoen A, Dade E, et al. Specific class of intrapartum antibiotics relates to maturation of the infant gut microbiota: a prospective cohort study. BJOG: An International Journal of Obstetrics & Gynaecology. 2020;127(2):217-227. doi:10.1111/1471-0528.15799 143. Aloisio I, Quagliariello A, De Fanti S, et al. Evaluation of the effects of intrapartum antibiotic prophylaxis on newborn intestinal microbiota using a sequencing approach targeted to multi hypervariable 16S rDNA regions. Appl Microbiol Biotechnol. 2016;100(12):5537-5546. doi:10.1007/s00253-016-7410-2 144. Ainonen S, Tejesvi MV, Mahmud MR, et al. Antibiotics at birth and later antibiotic courses: effects on gut microbiota. Pediatr Res. 2022;91(1):154-162. doi:10.1038/s41390-021-01494-7 145. Persaud R, Azad MB, Konya T, et al. Impact of perinatal antibiotic exposure on the infant gut microbiota at one year of age. Allergy, Asthma & Clinical Immunology. 2014;10(1):A31. doi:10.1186/1710-1492-10-S1-A31 146. Zerbo O, Qian Y, Yoshida C, Grether JK, Van de Water J, Croen LA. Maternal Infection During Pregnancy and Autism Spectrum Disorders. J Autism Dev Disord. 2015;45(12):4015-4025. doi:10.1007/s10803-013-2016-3 147. Collier SA, Rasmussen SA, Feldkamp ML, Honein MA. Prevalence of self-reported infection during pregnancy among control mothers in the National Birth Defects Prevention Study. Birth Defects Research Part A: Clinical and Molecular Teratology. 2009;85(3):193-201. doi:10.1002/bdra.20540 148. Chess S, Korn SJ, Fernandez PB. Psychiatric Disorders of Children with Congenital Rubella. Brunner/Mazel; 1971:xi, 178. 149. Brown AS, Schaefer CA, Quesenberry CP, Liu L, Babulas VP, Susser ES. Maternal Exposure to Toxoplasmosis and Risk of Schizophrenia in Adult Offspring. AJP. 2005;162(4):767-773. doi:10.1176/appi.ajp.162.4.767 150. Chess S. Autism in children with congenital rubella. J Autism Dev Disord. 1971;1(1):33-47. doi:10.1007/BF01537741 151. Atladóttir HÓ, Thorsen P, Østergaard L, et al. Maternal Infection Requiring Hospitalization During Pregnancy and Autism Spectrum Disorders. J Autism Dev Disord. 2010;40(12):1423-1430. doi:10.1007/s10803-010-1006-y 152. Fang SY, Wang S, Huang N, Yeh HH, Chen CY. Prenatal Infection and Autism Spectrum Disorders in Childhood: A Population-Based Case–Control Study in Taiwan. Paediatric and Perinatal Epidemiology. 2015;29(4):307-316. doi:10.1111/ppe.12194 153. Lee BK, Magnusson C, Gardner RM, et al. Maternal hospitalization with infection during pregnancy and risk of autism spectrum disorders. Brain, Behavior, and Immunity. 2015;44:100-105. doi:10.1016/j.bbi.2014.09.001 93 154. Croen LA, Qian Y, Ashwood P, et al. Infection and Fever in Pregnancy and Autism Spectrum Disorders: Findings from the Study to Explore Early Development. Autism Research. 2019;12(10):1551-1561. doi:10.1002/aur.2175 155. Patterson PH. Maternal infection and immune involvement in autism. Trends in Molecular Medicine. 2011;17(7):389-394. doi:10.1016/j.molmed.2011.03.001 156. Patterson PH. Immune involvement in schizophrenia and autism: Etiology, pathology and animal models. Behavioural Brain Research. 2009;204(2):313-321. doi:10.1016/j.bbr.2008.12.016 157. Malkova NV, Yu CZ, Hsiao EY, Moore MJ, Patterson PH. Maternal immune activation yields offspring displaying mouse versions of the three core symptoms of autism. Brain Behav Immun. 2012;26(4):607-616. doi:10.1016/j.bbi.2012.01.011 158. Smith SEP, Li J, Garbett K, Mirnics K, Patterson PH. Maternal Immune Activation Alters Fetal Brain Development through Interleukin-6. J Neurosci. 2007;27(40):10695-10702. doi:10.1523/JNEUROSCI.2178-07.2007 159. Meyer U, Murray PJ, Urwyler A, Yee BK, Schedlowski M, Feldon J. Adult behavioral and pharmacological dysfunctions following disruption of the fetal brain balance between pro-inflammatory and IL-10-mediated anti-inflammatory signaling. Mol Psychiatry. 2008;13(2):208-221. doi:10.1038/sj.mp.4002042 160. Hsiao EY, Patterson PH. Placental regulation of maternal-fetal interactions and brain development. Developmental Neurobiology. 2012;72(10):1317-1326. doi:10.1002/dneu.22045 161. Harris RE, Thomas VL, Shelokov A. Asymptomatic bacteriuria in pregnancy: Antibody-coated bacteria, renal function, and intrauterine growth retardation. American Journal of Obstetrics and Gynecology. 1976;126(1):20-25. doi:10.1016/0002-9378(76)90458-0 162. Pinto-Martin JA, Levy SE, Feldman JF, Lorenz JM, Paneth N, Whitaker AH. Prevalence of Autism Spectrum Disorder in Adolescents Born Weighing <2000 Grams. Pediatrics. 2011;128(5):883-891. doi:10.1542/peds.2010-2846 163. Källén B. The Problem of Confounding in Studies of the Effect of Maternal Drug Use on Pregnancy Outcome. Obstetrics and Gynecology International. 2011;2012:e148616. doi:10.1155/2012/148616 164. Atladóttir HÓ, Henriksen TB, Schendel DE, Parner ET. Autism after infection, febrile episodes, and antibiotic use during pregnancy: an exploratory study. Pediatrics. 2012;130(6):e1447-1454. doi:10.1542/peds.2012-1107 165. Wimberley T, Agerbo E, Pedersen CB, et al. Otitis media, antibiotics, and risk of autism spectrum disorder. Autism Res. 2018;11(10):1432-1440. doi:10.1002/aur.2015 94 166. Fan H, Gilbert R, O’Callaghan F, Li L. Associations between macrolide antibiotics prescribing during pregnancy and adverse child outcomes in the UK: population based cohort study. BMJ. 2020;368:m331. doi:10.1136/bmj.m331 167. Bickford CD, Oberlander TF, Lanphear NE, et al. Identification of Pediatric Autism Spectrum Disorder Cases Using Health Administrative Data. Autism Research. 2020;13(3):456-463. doi:10.1002/aur.2252 168. Almasri J, Barazi A, King KS, et al. Peripartum Antibiotics Exposure and the Risk of Autoimmune and Autism Disorders in the Offspring. Avicenna J Med. 2021;11(3):118-125. doi:10.1055/s-0041-1732485 169. Dunn AB, Jordan S, Baker BJ, Carlson NS. The Maternal Infant Microbiome: Considerations for Labor and Birth. MCN Am J Matern Child Nurs. 2017;42(6):318-325. doi:10.1097/NMC.0000000000000373 170. Xu M, Xu X, Li J, Li F. Association Between Gut Microbiota and Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. Frontiers in Psychiatry. 2019;10:473. doi:10.3389/fpsyt.2019.00473 171. Srikantha P, Mohajeri MH. The Possible Role of the Microbiota-Gut-Brain-Axis in Autism Spectrum Disorder. International Journal of Molecular Sciences. 2019;20(9):2115. doi:10.3390/ijms20092115 172. Langdon A, Crook N, Dantas G. The effects of antibiotics on the microbiome throughout development and alternative approaches for therapeutic modulation. Genome Medicine. 2016;8(1):39. doi:10.1186/s13073-016-0294-z 173. Barcaite E, Bartusevicius A, Tameliene R, Kliucinskas M, Maleckiene L, Nadisauskiene R. Prevalence of maternal group B streptococcal colonisation in European countries. Acta Obstetricia et Gynecologica Scandinavica. 2008;87(3):260-271. doi:10.1080/00016340801908759 174. Colbourn T, Gilbert R. An overview of the natural history of early onset group B streptococcal disease in the UK. Early Human Development. 2007;83(3):149-156. doi:10.1016/j.earlhumdev.2007.01.004 175. Le Doare K, Heath PT. An overview of global GBS epidemiology. Vaccine. 2013;31:D7-D12. doi:10.1016/j.vaccine.2013.01.009 176. Perinatal Services BC (2021). British Columbia Perinatal Data Registry. Population Data BC. Data Extract. PSBC (2021). http://www.perinatalservicesbc.ca/health-professionals/data-surveillance/perinatal-data-registry 177. British Columbia Ministry of Health (2021): Consolidation File (MSP Registration & Premium Billing). Population Data BC. Data Extract. MOH (2021). https://www.popdata.bc.ca/data 95 178. British Columbia Ministry of Health (2021): Vital Events Deaths. Population Data BC. Data Extract. MOH (2021). http://www.popdata.bc.ca/data 179. British Columbia Ministry of Health (2021): BC Autism Assessment Network data. Population Data BC. Data Extract. MOH (2021). https://www.popdata.bc.ca/data 180. British Columbia Ministy of Health (2021): BC Ministry of Education data. Population Data BC. Data Extract. MOH (2021). https://www.popdata.bc.ca/data 181. Lord C, Risi S, DiLavore PS, Shulman C, Thurm A, Pickles A. Autism From 2 to 9 Years of Age. Archives of General Psychiatry. 2006;63(6):694-701. doi:10.1001/archpsyc.63.6.694 182. Kierans WJ, Kendall PRW, Foster LT, Liston RM, Tuk T. New birth weight and gestational age charts for the British Columbia population. :5. 183. Stuart EA, Lee BK, Leacy FP. Prognostic score–based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research. Journal of Clinical Epidemiology. 2013;66(8, Supplement):S84-S90.e1. doi:10.1016/j.jclinepi.2013.01.013 184. Almasri J. The Association between Antibiotics Exposure during the Peripartum Period and Delivery Mode and the Risk of Developing Autoimmune and Autistic Spectrum Disorders. M.S. College of Medicine - Mayo Clinic. Accessed November 21, 2021. https://www.proquest.com/docview/2217194359/abstract/AC9117FA9FA24A10PQ/1 185. Seedat F, Stinton C, Patterson J, et al. Adverse events in women and children who have received intrapartum antibiotic prophylaxis treatment: a systematic review. BMC Pregnancy and Childbirth. 2017;17(1):247. doi:10.1186/s12884-017-1432-3 186. Frosst G, Hutcheon J, Joseph K, Kinniburgh B, Johnson C, Lee L. Validating the British Columbia Perinatal Data Registry: a chart re-abstraction study. BMC Pregnancy and Childbirth. 2015;15(1):123. doi:10.1186/s12884-015-0563-7 187. BC Ministry of Health [creator] (2022): PharmaNet. V2. BC Ministry of Health [publisher]. Data Extract. Data Stewardship Committee (2022). http://www.popdata.bc.ca/data 188. Medical Services Plan (MSP) Payment Information File (DI Program) | Population Data BC. Accessed March 17, 2022. https://www.popdata.bc.ca/node/678 189. Wang B, Liu J, Zhang Y, et al. Prenatal Exposure to Antibiotics and Risk of Childhood Obesity in a Multicenter Cohort Study. American Journal of Epidemiology. 2018;187(10):2159-2167. doi:10.1093/aje/kwy122 190. Zhang M, Differding MK, Benjamin-Neelon SE, Østbye T, Hoyo C, Mueller NT. Association of prenatal antibiotics with measures of infant adiposity and the gut microbiome. Ann Clin Microbiol Antimicrob. 2019;18(1):18. doi:10.1186/s12941-019-0318-9 96 191. Cassidy-Bushrow AE, Burmeister C, Havstad S, et al. Prenatal antimicrobial use and early-childhood body mass index. Int J Obes. 2018;42(1):1-7. doi:10.1038/ijo.2017.205 192. Lin M, Song H, Liu W, et al. Sex-specific associations between prenatal antibiotics exposure and offspring’s body mass index. Int J Obes. 2020;44(10):2044-2051. doi:10.1038/s41366-020-0629-0 193. Malas MA, Aslankoç R, Üngör B, Sulak O, Candir Ö. The development of large intestine during the fetal period. Early Human Development. 2004;78(1):1-13. doi:10.1016/j.earlhumdev.2004.03.001 194. Frye RE, Rose S, Slattery J, MacFabe DF. Gastrointestinal dysfunction in autism spectrum disorder: the role of the mitochondria and the enteric microbiome. Microbial Ecology in Health and Disease. 2015;26(s1):27458. doi:10.3402/mehd.v26.27458 195. Boucher O, Julvez J, Guxens M, et al. Association between breastfeeding duration and cognitive development, autistic traits and ADHD symptoms: a multicenter study in Spain. Pediatr Res. 2017;81(3):434-442. doi:10.1038/pr.2016.238 196. Frazier TW, Georgiades S, Bishop SL, Hardan AY. Behavioral and Cognitive Characteristics of Females and Males With Autism in the Simons Simplex Collection. Journal of the American Academy of Child & Adolescent Psychiatry. 2014;53(3):329-340.e3. doi:10.1016/j.jaac.2013.12.004 197. Stiemsma LT, Michels KB. The Role of the Microbiome in the Developmental Origins of Health and Disease. Pediatrics. 2018;141(4):e20172437. doi:10.1542/peds.2017-2437 198. Frye RE, Vassall S, Kaur G, Lewis C, Karim M, Rossignol D. Emerging biomarkers in autism spectrum disorder: a systematic review. Ann Transl Med. 2019;7(23):792. doi:10.21037/atm.2019.11.53 199. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework - - 2001 - Clinical Pharmacology & Therapeutics - Wiley Online Library. Accessed August 22, 2022. https://ascpt.onlinelibrary.wiley.com/doi/abs/10.1067/mcp.2001.113989 97 Appendices Appendix A. Autism risk and perinatal antibiotic use for male neonates in the main cohort Antibiotics during labour and delivery, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed No. of children 171, 326 121, 237 292, 563 292, 563 268, 905 ASD cases 3978 (2.32) 3199 (2.64) 0.0032 1.27 (1.21, 1.33) 0.99 (0.93, 1.04) Length of first stage of labourb < 4 hours No. of children 52, 956 17, 795 70, 751 70, 751 65, 245 ASD cases 1059 (2) 420 (2.36) 0.0036 1.22 (1.09, 1.37) 1.06 (0.94, 1.21) 4-7 hours No. of children 51, 067 20, 398 71, 465 71, 465 65, 821 ASD cases 1097 (2.15) 483 (2.37) 0.0022 1.15 (1.03, 1.27) 0.96 (0.85, 1.08) 8-11 hours No. of children 23, 087 11, 567 34, 654 34, 654 31, 681 ASD cases 551 (2.39) 320 (2.77) 0.0038 1.20 (1.04, 1.38) 0.99 (0.85 1.15) 12+ hours No. children 17, 389 12, 126 29, 515 29, 515 26, 891 ASD cases 429 (2.47) 361 (2.98) 0.0051 1.21 (1.05, 1.39) 1.06 (0.91, 1.24) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, prenatal antibiotic exposure, year of delivery, mode of delivery, baby’s sex, birth weight, neonatal antibiotic exposure, gestational age, size-for-gestational age b From rupture of membranes to full cervical dilation, in hours 98 Appendix B. Autism risk and perinatal antibiotic use for male neonates in the group B strep-positive cohort Antibiotics during labour and delivery, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed No. of children 5122 35, 529 40, 651 40, 651 37, 368 ASD cases 114 (2.23) 841 (2.37) 0.0014 1.09 (0.90, 1.33) 0.89 (0.73, 1.09) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, prenatal antibiotic exposure, year of delivery, mode of delivery, baby’s sex, birth weight, neonatal antibiotic exposure, gestational age, size-for-gestational age Note: length of first stage of labour sensitivity analysis was removed due to privacy requirements regarding low sample sizes 99 Appendix C. Autism risk and perinatal antibiotic use for female neonates in the main cohort Antibiotics during labour and delivery, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed No. of children 166, 991 110, 388 277, 379 277, 379 254, 753 ASD cases 896 (0.54) 655 (0.59) 0.0006 1.24 (1.12, 1.38) 0.97 (0.87, 1.09) Length of first stage of labourb < 4 hours No. of children 53, 697 17, 281 70, 978 70, 978 65, 610 ASD cases 269 (0.5) 105 (0.61) 0.0011 1.26 (1.00, 1.58) 1.07 (0.83, 1.36) 4-7 hours No. of children 49, 268 19, 142 68, 410 68, 410 62, 772 ASD cases 245 (0.5) 93 (0.49) -0.0001 1.02 (0.80, 1.29) 0.80 (0.62, 1.05) 8-11 hours No. of children 22, 163 10, 553 32, 716 32, 716 29, 927 ASD cases 116 (0.52) 70 (0.66) 0.0014 1.34 (0.99, 1.80) 1.12 (0.81, 1.55) 12+ hours No. of children 16, 943 10, 816 27, 759 27, 759 25, 272 ASD cases 106 (0.63) 56 (0.52) -0.0011 0.83 (0.60, 1.15) 0.70 (0.49, 1.01) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, year of delivery, prenatal antibiotic exposure, mode of delivery, baby’s sex, birth weight, neonatal antibiotic exposure, gestational age, size-for-gestational age b From rupture of membranes to full cervical dilation, in hours 100 Appendix D. Autism risk and perinatal antibiotic use for female neonates in the group B strep-positive cohort Antibiotics during labour and delivery, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed No. of children 5117 33, 489 38, 606 38, 606 35, 392 ASD cases 29 (0.57) 170 (0.51) -0.0007 0.92 (0.62, 1.36) 0.74 (0.50, 1.11) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, prenatal antibiotic exposure, year of delivery, mode of delivery, baby’s sex, birth weight, neonatal antibiotic exposure, gestational age, size-for-gestational age Note: length of first stage of labour sensitivity analysis was removed due to privacy requirements regarding low sample sizes 101 Appendix E. Autism risk and perinatal antibiotic use for the main cohort by mode of delivery Antibiotics during labour and delivery, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed Vaginal Deliveries No. of children 293, 332 109, 903 403, 235 403, 235 370, 020 ASD cases 3937 (1.34) 1720 (1.57) 0.0022 1.18 (1.11, 1.25) 1.00 (0.94, 1.07) Cesarean Deliveries No. of children 44, 992 121, 726 166, 718 166, 718 153, 638 ASD cases 938 (2.08) 2134 (1.75) -0.0033 1.20 (1.11, 1.29) 0.94 (0.86, 1.02) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, prenatal antibiotic exposure, year of delivery, mode of delivery, baby’s sex, birth weight, neonatal antibiotic exposure, gestational age, small-for-gestational age, and large-for-gestational age 102 Appendix F. Autism risk and perinatal antibiotic use for the group B strep-positive cohort by mode of delivery Antibiotics during labour and delivery, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed Vaginal Deliveries No. of children 9418 55, 275 64, 693 64, 693 59, 477 ASD cases 125 (1.33) 746 (1.35) 0.0002 1.02 (0.85, 1.24) 0.85 (0.70. 1.03) Cesarean Deliveriesb No. of children 2156 19, 103 21, 259 21, 259 19, 488 ASD cases 44 (2.04) 335 (1.75) -0.0029 1.13 (0.83, 1.55) 0.93 (0.66, 1.29) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, prenatal antibiotic exposure, year of delivery, mode of delivery, baby’s sex, birth weight, neonatal antibiotic exposure, gestational age, size-for-gestational age b Since intrapartum antibiotic prophylaxis is not recommended for planned cesarean sections in the absence of labour and rupture of membranes, cesarean deliveries were only included in the group B streptococcus-positive analysis if they were coupled with a spontaneous or induced labour code 103 Appendix G. Antibiotic classification according to American Hospital Formulary Service (AHFS) Pharmacologic-Therapeutic Classification System Antibiotic class Generic names AHFS code Penicillins Amoxicillin; ampicillin; bacampicillin; cloxacillin; floxacillin; penicillin; piperacillin; tazobactam; pivampicillin; pivmecillinam; ticarcillin 8:12.16 Other beta lactams Aztreonam lysine; cefaclor; cefadroxil; cefazolin; cefepime; cefixime; cefotaxime; cefotetan; cefoxitin; cefprozil; ceftazidime; ceftizoxime; ceftriaxone; cefuroxime; cephalexin; ertapenem; imipenem; meropenem 8:12.06, 8:12.07 Macrolides Azithromycin; clarithromycin; erythromycin; spiramycin; telithromycin 8:12.12 Other Amikacin sulfate; chloramphenicol; ciprofloxacin; clindamycin; colistin; daptomycin; demeclocycline; doxycycline; Fosfomycin; fusidate; gatifloxacin; gemifloxacin; gentamicin; grepafloxacin; levofloxacin; linezolid; methenamine; metronidazole; minocycline; moxifloxacin; nalidixic acid; neomycin; nitrofurantoin; norfloxacin; ofloxacin; streptomycin; sulfadiazine; sulfamethoxazole; sulphapyridine; sulfisoxazole; tetracycline; tigecycline; tobramycin; tri-sulfa/phenylpropanolamine/pyrilamine/pheniramine; trimethoprim; trovafloxacin; vancomycin 8:12.02, 8:12.08, 8:12.18, 8:12.20, 8:12.24, 8:12.28 104 Appendix H. Select covariates from the BCPDR and reasons for inclusion in the analysis BCPDR Variable Reason for Inclusion in Analysis Maternal sociodemographic characteristics and pregnancy related risk factors Age Exposure groups had a standardized mean difference greater than 0.1, indicating a meaningful difference.24 Income quintile Location of residence Coparent age Smoking during pregnancy Pre-pregnancy BMI Neonatal characteristics Birthweight We included these covariates in our model even though they occurred well downstream from the antibiotic exposure. This was done to account for the possibility that the infection and resulting antibiotic exposure may have altered the course of the pregnancy setting the course for pregnancy-related conditions, or altered fetal size, for example. Gestational age Small for gestational age Large for gestational age Abbreviations: BCPDR, British Columbia Perinatal Database Registry; BMI, body mass index. 105 Appendix I. Associations between prenatal antibiotic exposure and risk of ASD in female offspring of the main cohort Prenatal Antibiotics, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratios (95% CI)a Unexposed Exposed No. of children 194, 830 82, 549 277, 379 277, 379 254, 753 ASD cases 1043 (0.54) 508 (0.62) 0.0008 1.12 (1.01, 1.24) 1.14 (1.02, 1.28) Exposure by trimesterb 1st trimester No. of children 241, 800 35, 579 277, 379 277, 379 254, 753 ASD cases 1324 (0.55) 227 (0.64) 0.0009 1.16 (1.01, 1.33) 1.08 (0.93, 1.26) 2nd trimester No. of children 236, 876 40, 503 277, 379 277, 379 254, 753 ASD cases 1303 (0.55) 248 (0.61) 0.0006 1.09 (0.95, 1.25) 1.14 (0.99, 1.32) 3rd trimester No. of children 244, 800 32, 579 277, 379 277, 379 254, 753 ASD cases 1364 (0.56) 187 (0.57) 0.0002 0.98 (0.84, 1.14) 1.06 (0.90, 1.24) Exposure by antibiotic class Penicillins No. of children 225, 730 51, 649 277, 379 277, 379 254, 753 ASD cases 1251 (0.55) 300 (0.58) 0.0003 0.99 (0.87, 1.12) 1.05 (0.92, 1.21) Other beta lactams No. of children 261, 664 15, 715 277, 379 277, 379 254, 753 ASD cases 1449 (0.55) 102 (0.65) 0.0010 1.19 (0.98, 1.46) 1.27 (1.03, 1.57) Macrolides No. of children 266, 342 11, 037 277, 379 277, 379 254, 753 ASD cases 1478 (0.55) 73 (0.66) 0.0011 1.10 (0.87, 1.39) 1.12 (0.87, 1.45) Other No. of children 252, 419 24, 960 277, 379 277, 379 254, 753 ASD cases 1394 (0.55) 157 (0.63) 0.0008 1.20 (1.02, 1.42) 1.09 (0.90, 1.31) Cumulative Duration of exposure 1 – 7 days No. of children 238, 490 38, 889 277, 379 277, 379 254, 753 ASD cases 1319 (0.55) 232 (0.60) 0.0004 1.10 (0.96, 1.27) 1.09 (0.94, 1.26) 8 – 14 days 106 No. of children 251, 531 25, 848 277, 379 277, 379 254, 753 ASD cases 1388 (0.55) 163 (0.63) 0.0008 1.09 (0.93, 1.28) 1.12 (0.94, 1.33) 15+ days No. of children 259, 567 17, 812 277, 379 277, 379 254, 753 ASD cases 1438 (0.55) 113 (0.63) 0.0008 1.07 (0.89, 1.12) 1.12 (0.91, 1.38) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, year of delivery, mode of delivery, baby’s sex, birthweight, gestational age, size-for-gestational age. b First trimester is from date of conception to 12 weeks of pregnancy; second trimester is 13 to 27 weeks; third trimester is 28 weeks until date of birth. 107 Appendix J. Associations between prenatal antibiotic exposure and risk of ASD in male offspring of the main cohort Prenatal Antibiotics, No. (%) Unadjusted risk difference Unadjusted hazard ratio (95% CI) Adjusted hazard ratio (95% CI)a Unexposed Exposed No. of children 205, 193 87, 370 292, 563 292, 563 268, 905 ASD cases 4904 (2.39) 2273 (2.6) 0.0021 1.06 (1.01, 1.11) 1.06 (1.01, 1.12) Exposure by trimesterb 1st trimester No. of children 255, 043 37, 520 292, 563 292, 563 268, 905 ASD cases 6182 (2.42) 995 (2.65) 0.0023 1.08 (1.01, 1.16) 1.05 (0.98, 1.13) 2nd trimester No. of children 249, 370 43, 193 292, 563 292, 563 268, 905 ASD cases 6034 (2.42) 1143 (2.65) 0.0023 1.07 (1.01, 1.14) 1.07 (1.00. 1.14) 3rd trimester No. of children 258, 470 34, 093 292, 563 292, 563 268, 905 ASD cases 6308 (2.44) 869 (2.55) 0.0011 0.99 (0.93, 1.07) 1.03 (0.95, 1.11) Exposure by antibiotic class Penicillins No. of children 238, 316 54, 247 292, 563 292, 563 268, 905 ASD cases 5770 (2.42) 1407 (2.59) 0.0017 1.01 (0.95, 1.07) 1.06 (1.00, 1.13) Other beta lactams No. of children 275, 796 16, 767 292, 563 292, 563 268, 905 ASD cases 6732 (2.44) 445 (2.65) 0.0021 1.10 (1.00, 1.21) 1.06 (0.96, 1.18) Macrolides No. of children 280, 821 11, 742 292, 563 292, 563 268, 905 ASD cases 6856 (2.44) 321 (2.73) 0.0029 1.04 (0.93, 1.16) 1.05 (0.93, 1.18) Other No. of children 266, 043 26, 520 292, 563 292, 563 268, 905 ASD cases 6469 (2.43) 708 (2.67) 0.0024 1.16 (1.07, 1.25) 1.05 (0.97, 1.15) Cumulative duration of exposure 1 – 7 days No. of children 251, 249 41, 314 292, 563 292, 563 268, 905 ASD cases 6118 (2.44) 1059 (2.56) 0.0013 1.07 (1.00, 1.14) 1.03 (0.96, 1.10) 8 – 14 days 108 No. of children 265, 253 27, 310 292, 563 292, 563 268, 905 ASD cases 6483 (2.44) 694 (2.54) 0.0010 0.99 (0.92, 1.07) 1.04 (0.96, 1.13) 15+ days No. of children 273, 817 18, 746 292, 563 292, 563 268, 905 ASD cases 6657 (2.43) 520 (2.77) 0.0034 1.07 (0.98, 1.17) 1.09 (0.99, 1.20) a Adjusted for maternal age, income quintile, location of residence (urban, semi-urban, or rural), and coparent age, nulliparity, smoking during pregnancy, gestational or pre-existing diabetes, pregnancy-induced hypertension, other hypertension, and pre-pregnancy BMI, year of delivery, mode of delivery, baby’s sex, birthweight, gestational age, small-for-gestational age, and large-for-gestational age. b First trimester is from date of conception to 12 weeks of pregnancy; second trimester is 13 to 27 weeks; third trimester is 28 weeks until date of birth.
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Oberlander, Tim F.
Hanley, Gillian E.
Association of prenatal and intrapartum antibiotic exposure with autism spectrum disorder in offspring : a population-based study from British Columbia
Text