Association of Autism Spectrum Disorder With Prenatal Exposure to Medication Affecting Neurotransmitter Systems

Gestational hypothyroxinemia induces ASD-like phenotypes in behavior, proinflammatory markers, and glutamatergic protein expression in mouse offspring of both sexes

Background

The prevalence of autism spectrum disorder (ASD) has significantly risen in the past three decades, prompting researchers to explore the potential contributions of environmental factors during pregnancy to ASD development. One such factor of interest is gestational hypothyroxinemia (HTX), a frequent condition in pregnancy associated with cognitive impairments in the offspring. While retrospective human studies have linked gestational HTX to autistic traits, the cellular and molecular mechanisms underlying the development of ASD-like phenotypes remain poorly understood. This study used a mouse model of gestational HTX to evaluate ASD-like phenotypes in the offspring.

Methods

To induce gestational HTX, pregnant mice were treated with 2-mercapto-1-methylimidazole (MMI), a thyroid hormones synthesis inhibitor, in the tap-drinking water from embryonic days (E) 10 to E14. A separate group received MMI along with a daily subcutaneous injection of T4, while the control group received regular tap water during the entire pregnancy. Female and male offspring underwent assessments for repetitive, anxious, and social behaviors from postnatal day (P) 55 to P64. On P65, mice were euthanized for the evaluation of ASD-related inflammatory markers in blood, spleen, and specific brain regions. Additionally, the expression of glutamatergic proteins (NLGN3 and HOMER1) was analyzed in the prefrontal cortex and hippocampus.

Results

The HTX-offspring exhibited anxious-like behavior, a subordinate state, and impaired social interactions. Subsequently, both female and male HTX-offspring displayed elevated proinflammatory cytokines in blood, including IL-1β, IL-6, IL-17A, and TNF-α, while only males showed reduced levels of IL-10. The spleen of HTX-offspring of both sexes showed increased Th17/Treg ratio and M1-like macrophages. In the prefrontal cortex and hippocampus of male HTX-offspring, elevated levels of IL-17A and reduced IL-10 were observed, accompanied by increased expression of hippocampal NLGN3 and HOMER1. All these observations were compared to those observed in the Control-offspring. Notably, the supplementation with T4 during the MMI treatment prevents the development of the observed phenotypes. Correlation analysis revealed an association between maternal T4 levels and specific ASD-like outcomes.

Discussion

This study validates human observations, demonstrating for the first time that gestational HTX induces ASD-like phenotypes in the offspring, highlighting the need of monitoring thyroid function during pregnancy.

Developmental dopaminergic signaling modulates neural circuit formation and contributes to autism spectrum disorder (ASD)-related phenotypes

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with a complex etiology. Recent evidence suggests that dopamine plays a crucial role in neural development. However, it remains unclear whether and how disrupted dopaminergic signaling during development contributes to ASD. In this study, human brain RNA-seq transcriptome analysis revealed a significant correlation between changes in dopaminergic signaling pathways and neural developmental signaling in ASD patients. In the zebrafish model, disrupted developmental dopaminergic signaling led to neural circuit abnormalities and behavior reminiscent of autism. Dopaminergic signaling may impact neuronal specification by potentially modulating integrins. These findings shed light on the mechanisms underlying the link between disrupted developmental dopamine signaling and ASD, and they point to the possibility of targeting dopaminergic signaling in early development for ASD treatment.

Prenatal SSRI Exposure Increases the Risk of Autism in Rodents via Aggravated Oxidative Stress and Neurochemical Changes in the Brain

The mechanisms underlying selective serotonin reuptake inhibitor (SSRI) use during pregnancy as a major autism risk factor are unclear. Here, brain neurochemical changes following fluoxetine exposure and in an autism model were compared to determine the effects on autism risk. The study was performed on neonatal male western albino rats which were divided into Groups one (control), two (propionic acid [PPA]-induced autism model), and three (prenatal SSRI-exposed newborn rats whose mothers were exposed to 5 mg/kg of fluoxetine over gestation days 10-20). SSRI (fluoxetine) induced significant neurochemical abnormalities in the rat brain by increasing lipid peroxide (MDA), Interferon-gamma (IFN-γ), and caspase-3 levels and by depleting Glutathione (GSH), Glutathione S-transferases (GST), Catalase, potassium (K+), and Creatine kinase (CK) levels, similarly to what has been discovered in the PPA model of autism when compared with control. Prenatal fluoxetine exposure plays a significant role in asset brain damage in newborns; further investigation of fluoxetine as an autism risk factor is thus warranted.

Somatic comorbidities of mental disorders in pregnancy

BACKGROUND

Mental and physical health conditions are frequently comorbid. Despite the widespread physiological and behavioral changes during pregnancy, the pattern of comorbidities among women in pregnancy is not well studied. This study aimed to systematically examine the associations between mental and somatic disorders before and during pregnancy.

METHOD

The study used data from mothers of a nationally representative birth cohort of children born in Israel (1997-2008). We compared the risk of all major somatic disorders (International Classification of Diseases, Ninth Revision) in pregnant women with and without a mental disorder. All analyses were adjusted for maternal age, child's birth year, family socioeconomic status, and the total number of maternal encounters with health services around pregnancy period.

RESULTS

The analytical sample included 77,030 mother-child dyads, with 30,083 unique mothers. The mean age at child's birth was 29.8 years. Prevalence of diagnosis of mental disorder around pregnancy in our sample was 4.4%. Comorbidity between mental and somatic disorders was two times higher than the comorbidity between pairs of different somatic disorders. Of the 17 somatic disorder categories, seven were positively associated with mental health disorders. The highly prevalent comorbidities associated with mental disorders in pregnancy included e.g. musculoskeletal (OR = 1.30; 95% CI = 1.20-1.42) and digestive system diseases (OR = 1.23; 95% CI = 1.13-1.34).

CONCLUSIONS

We observed that associations between maternal diagnoses and mental health stand out from the general pattern of comorbidity between nonmental health diseases. The study results confirm the need for screening for mental disorders during pregnancy and for potential comorbid conditions associated with mental disorders.

Association of Maternal Use of Triptans During Pregnancy With Risk of Attention-Deficit/Hyperactivity Disorder in Offspring

Importance

Triptans are commonly used in the treatment of migraine. Prenatal exposure to triptans may be associated with adverse fetal neurodevelopment; however, there is limited information about the long-term safety of triptan use during pregnancy.

Objective

To examine the association between maternal use of triptans during pregnancy and diagnosis and symptoms of attention-deficit/hyperactivity disorder (ADHD) among offspring.

Design, Setting, and Participants

This study used data from the Norwegian Mother, Father and Child Cohort Study (recruitment 1999-2008), linked to national health registries. Live-born singleton children born to women with migraine before or during pregnancy were included. Two analytic samples were defined: one to assess ADHD diagnosis and one to assess ADHD symptoms. Data were analyzed from May 1 to November 30, 2021.

Exposure

Maternal self-report of triptan use during pregnancy. Exposed children were compared with 2 groups of unexposed children whose mothers reported migraine (1) during pregnancy and (2) before pregnancy only.

Main Outcomes and Measures

An ADHD diagnosis was defined as diagnosis of hyperkinetic disorder or receipt of dispensed ADHD medication. Symptoms of ADHD at 5 years were measured by the Conners' Parent Rating Scale, where a higher score indicates more symptoms of ADHD. Cox proportional hazards regression models and generalized linear models with inverse probability weights were used to estimate weighted hazard ratios (HRs) and standardized mean differences, respectively, with 95% CIs.

Results

The ADHD diagnosis sample comprised 10 167 children (mean [SD] maternal age, 30.2 [4.6] years; 5231 boys [51.5%]), and the ADHD symptoms sample comprised 4367 children (mean [SD] maternal age, 30.6 [4.4] years; 2191 boys [50.2%]). Children were followed up for a mean (SD) of 10.6 (2.2) years. Children with prenatal triptan exposure had no increased risk of ADHD diagnosis compared with unexposed children whose mothers had migraine during pregnancy (weighted HR, 1.16; 95% CI, 0.78-1.74) and compared with unexposed children whose mothers had migraine only before pregnancy (weighted HR, 1.28; 95% CI, 0.84-1.94). There were no differences in ADHD symptom scores between exposed and unexposed children.

Conclusions and Relevance

The findings of this study suggest that there is no increased risk of ADHD among offspring associated with prenatal exposure to triptans.

Familial coaggregation of major psychiatric disorders in first-degree relatives of individuals with autism spectrum disorder: a nationwide population-based study

BACKGROUND

Family coaggregation of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD) and schizophrenia have been presented in previous studies. The shared genetic and environmental factors among psychiatric disorders remain elusive.

METHODS

This nationwide population-based study examined familial coaggregation of major psychiatric disorders in first-degree relatives (FDRs) of individuals with ASD. Taiwan's National Health Insurance Research Database was used to identify 26 667 individuals with ASD and 67 998 FDRs of individuals with ASD. The cohort was matched in 1:4 ratio to 271 992 controls. The relative risks (RRs) and 95% confidence intervals (CI) of ADHD, ASD, BD, MDD and schizophrenia were assessed among FDRs of individuals with ASD and ASD with intellectual disability (ASD-ID).

RESULTS

FDRs of individuals with ASD have higher RRs of major psychiatric disorders compared with controls: ASD 17.46 (CI 15.50-19.67), ADHD 3.94 (CI 3.72-4.17), schizophrenia 3.05 (CI 2.74-3.40), BD 2.22 (CI 1.98-2.48) and MDD 1.88 (CI 1.76-2.00). Higher RRs of schizophrenia (4.47, CI 3.95-5.06) and ASD (18.54, CI 16.18-21.23) were observed in FDRs of individuals with both ASD-ID, compared with ASD only.

CONCLUSIONS

The risk for major psychiatric disorders was consistently elevated across all types of FDRs of individuals with ASD. FDRs of individuals with ASD-ID are at further higher risk for ASD and schizophrenia. Our results provide leads for future investigation of shared etiologic pathways of ASD, ID and major psychiatric disorders and highlight the importance of mental health care delivered to at-risk families for early diagnoses and interventions.

Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

Effects of the Selective Serotonin Reuptake Inhibitor Fluoxetine on Developing Neural Circuits in a Model of the Human Fetal Cortex

The developing prenatal brain is particularly susceptible to environmental disturbances. During prenatal brain development, synapses form between neurons, resulting in neural circuits that support complex cognitive functions. In utero exposure to environmental factors such as pharmaceuticals that alter the process of synapse formation increases the risk of neurodevelopmental abnormalities. However, there is a lack of research into how specific environmental factors directly impact the developing neural circuitry of the human brain. For example, selective serotonin reuptake inhibitors are commonly used throughout pregnancy to treat depression, yet their impact on the developing fetal brain remains unclear. Recently, human brain models have provided unprecedented access to the critical window of prenatal brain development. In the present study, we used human neurons and cortical spheroids to determine whether the selective serotonin reuptake inhibitor fluoxetine alters neurite and synapse formation and the development of spontaneous activity within neural circuits. We demonstrate that cortical spheroids express serotonin transporter, thus recapitulating the early developmental expression of serotonin transporter associated with cortical pyramidal neurons. Cortical spheroids also appropriately express serotonin receptors, such as synaptic 5-HT2A and glial 5-HT5A. To determine whether fluoxetine can affect developing neural circuits independent of serotonergic innervation from the dorsal and medial raphe nuclei, we treated cortical neurons and spheroids with fluoxetine. Fluoxetine alters neurite formation in a dose-dependent fashion. Intriguingly, in cortical spheroids, neither acute nor chronic fluoxetine significantly altered excitatory synapse formation. However, only acute, but not chronic fluoxetine exposure altered inhibitory synaptogenesis. Finally, fluoxetine reversibly suppresses neuronal activity in a dose-dependent manner. These results demonstrate that fluoxetine can acutely alter synaptic function in developing neural circuits, but the effects were not long-lasting. This work provides a foundation for future studies to combine serotonergic innervation with cortical spheroids and assess the contributions of fluoxetine-induced alterations in serotonin levels to brain development.

Studying Autism Using Untargeted Metabolomics in Newborn Screening Samples

Main risk factors of autism spectrum disorder (ASD) include both genetic and non-genetic factors, especially prenatal and perinatal events. Newborn screening dried blood spot (DBS) samples have great potential for the study of early biochemical markers of disease. To study DBS strengths and limitations in the context of ASD research, we analyzed the metabolomic profiles of newborns later diagnosed with ASD. We performed LC-MS/MS-based untargeted metabolomics on DBS from 37 case-control pairs randomly selected from the iPSYCH sample. After preprocessing using MZmine 2.41, metabolites were putatively annotated using mzCloud, GNPS feature-based molecular networking, and MolNetEnhancer. A total of 4360 mass spectral features were detected, of which 150 (113 unique) could be putatively annotated at a high confidence level. Chemical structure information at a broad level could be retrieved for 1009 metabolites, covering 31 chemical classes. Although no clear distinction between cases and controls was revealed, our method covered many metabolites previously associated with ASD, suggesting that biochemical markers of ASD are present at birth and may be monitored during newborn screening. Additionally, we observed that gestational age, age at sampling, and month of birth influence the metabolomic profiles of newborn DBS, which informs us on the important confounders to address in future studies.

Maternal health around pregnancy and autism risk: a diagnosis-wide, population-based study

BACKGROUND

Many studies have reported an increased risk of autism spectrum disorder (ASD) associated with some maternal diagnoses in pregnancy. However, such associations have not been studied systematically, accounting for comorbidity between maternal disorders. Therefore our aim was to comprehensively test the associations between maternal diagnoses around pregnancy and ASD risk in offspring.

METHODS

This exploratory case-cohort study included children born in Israel from 1997 to 2008, and followed up until 2015. We used information on all ICD-9 codes received by their mothers during pregnancy and the preceding year. ASD risk associated with each of those conditions was calculated using Cox proportional hazards regression, adjusted for the confounders (birth year, maternal age, socioeconomic status and number of ICD-9 diagnoses during the exposure period).

RESULTS

The analytic sample consisted of 80 187 individuals (1132 cases, 79 055 controls), with 822 unique ICD-9 codes recorded in their mothers. After extensive quality control, 22 maternal diagnoses were nominally significantly associated with offspring ASD, with 16 of those surviving subsequent filtering steps (permutation testing, multiple testing correction, multiple regression). Among those, we recorded an increased risk of ASD associated with metabolic [e.g. hypertension; HR = 2.74 (1.92-3.90), p = 2.43 × 10-8], genitourinary [e.g. non-inflammatory disorders of cervix; HR = 1.88 (1.38-2.57), p = 7.06 × 10-5] and psychiatric [depressive disorder; HR = 2.11 (1.32-3.35), p = 1.70 × 10-3] diagnoses. Meanwhile, mothers of children with ASD were less likely to attend prenatal care appointment [HR = 0.62 (0.54-0.71), p = 1.80 × 10-11].

CONCLUSIONS

Sixteen maternal diagnoses were associated with ASD in the offspring, after rigorous filtering of potential false-positive associations. Replication in other cohorts and further research to understand the mechanisms underlying the observed associations with ASD are warranted.

Linking Autism Risk Genes to Disruption of Cortical Development

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder characterized by impairments in social communication and social interaction, and the presence of repetitive behaviors and/or restricted interests. In the past few years, large-scale whole-exome sequencing and genome-wide association studies have made enormous progress in our understanding of the genetic risk architecture of ASD. While showing a complex and heterogeneous landscape, these studies have led to the identification of genetic loci associated with ASD risk. The intersection of genetic and transcriptomic analyses have also begun to shed light on functional convergences between risk genes, with the mid-fetal development of the cerebral cortex emerging as a critical nexus for ASD. In this review, we provide a concise summary of the latest genetic discoveries on ASD. We then discuss the studies in postmortem tissues, stem cell models, and rodent models that implicate recently identified ASD risk genes in cortical development.

Maternal Influenza A(H1N1) Immunization During Pregnancy and Risk for Autism Spectrum Disorder in Offspring : A Cohort Study

BACKGROUND

There are concerns that influenza vaccine exposure during pregnancy may be associated with increased risk for autism spectrum disorder (ASD).

OBJECTIVE

To examine the risk for ASD in offspring of mothers who were vaccinated against influenza A(H1N1)pdm09 ("swine flu") during pregnancy.

DESIGN

Population-based cohort study using nationwide registers.

SETTING

Seven health care regions in Sweden.

PARTICIPANTS

Live births between October 2009 and September 2010, with follow-up through December 2016. In total, 39 726 infants were prenatally exposed to H1N1 vaccine (13 845 during the first trimester) and 29 293 infants were unexposed.

MEASUREMENTS

Cox regression was used to estimate hazard ratios (HRs) for the primary outcome, ASD, before and after adjustment for potential confounders. The secondary outcome was autistic disorder (AD).

RESULTS

Mean follow-up was 6.7 years in both unexposed and exposed children. During follow-up, 394 (1.0%) vaccine-exposed and 330 (1.1%) unexposed children had a diagnosis of ASD. In adjusted analyses, prenatal exposure to H1N1 vaccination was not associated with a later diagnosis of ASD (adjusted HR [aHR], 0.95 [95% CI, 0.81 to 1.12]) or AD (aHR, 0.96 [CI, 0.80 to 1.16]). The 6-year standardized cumulative incidence difference between the unexposed and exposed children was 0.04% (CI, -0.09% to 0.17%) for ASD and 0.02% (CI, -0.09% to 0.14%) for AD. Restricting the analysis to vaccination in the first trimester of pregnancy did not influence risk estimates (aHR, 0.92 [CI, 0.74 to 1.16] for ASD and 0.91 [CI, 0.70 to 1.18] for AD).

LIMITATION

Data on H1N1 influenza infection are lacking.

CONCLUSION

This large cohort study found no association between maternal H1N1 vaccination during pregnancy and risk for ASD in the offspring.

PRIMARY FUNDING SOURCE

Swedish Research Council.

Possible therapeutic applications of cannabis in the neuropsychopharmacology field

Cannabis use induces a plethora of actions on the CNS via its active chemical ingredients, the so-called phytocannabinoids. These compounds have been frequently associated with the intoxicating properties of cannabis preparations. However, not all phytocannabinoids are psychotropic, and, irrespective of whether they are psychotropic or not, they have also shown numerous therapeutic properties. These properties are mostly associated with their ability to modulate the activity of an intercellular communication system, the so-called endocannabinoid system, which is highly active in the CNS and has been found altered in many neurological disorders. Specifically, this includes the neuropsychopharmacology field, with diseases such as schizophrenia and related psychoses, anxiety-related disorders, mood disorders, addiction, sleep disorders, post-traumatic stress disorder, anorexia nervosa and other feeding-related disorders, dementia, epileptic syndromes, as well as autism, fragile X syndrome and other neurodevelopment-related disorders. Here, we gather, from a pharmacological and biochemical standpoint, the recent advances in the study of the therapeutic relevance of the endocannabinoid system in the CNS, with especial emphasis on the neuropsychopharmacology field. We also illustrate the efforts that are currently being made to investigate at the clinical level the potential therapeutic benefits derived from elevating or inhibiting endocannabinoid signaling in animal models of neuropsychiatric disorders.

Identification of newborns at risk for autism using electronic medical records and machine learning

BACKGROUND

Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample.

METHODS

We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]).

RESULTS

All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset.

CONCLUSIONS

We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.

Probing disrupted neurodevelopment in autism using human stem cell-derived neurons and organoids: An outlook into future diagnostics and drug development

Autism spectrum disorders (ASDs) represent a spectrum of neurodevelopmental disorders characterized by impaired social interaction, repetitive or restrictive behaviors, and problems with speech. According to a recent report by the Centers for Disease Control and Prevention, one in 68 children in the US is diagnosed with ASDs. Although ASD-related diagnostics and the knowledge of ASD-associated genetic abnormalities have improved in recent years, our understanding of the cellular and molecular pathways disrupted in ASD remains very limited. As a result, no specific therapies or medications are available for individuals with ASDs. In this review, we describe the neurodevelopmental processes that are likely affected in the brains of individuals with ASDs and discuss how patient-specific stem cell-derived neurons and organoids can be used for investigating these processes at the cellular and molecular levels. Finally, we propose a discovery pipeline to be used in the future for identifying the cellular and molecular deficits and developing novel personalized therapies for individuals with idiopathic ASDs.

Use and validity of child neurodevelopment outcome measures in studies on prenatal exposure to psychotropic and analgesic medications - A systematic review

In recent years there has been increased attention to child neurodevelopment in studies on medication safety in pregnancy. Neurodevelopment is a multifactorial outcome that can be assessed by various assessors, using different measures. This has given rise to a debate on the validity of various measures of neurodevelopment. The aim of this review was twofold. Firstly we aimed to give an overview of studies on child neurodevelopment after prenatal exposure to central nervous system acting medications using psychotropics and analgesics as examples, giving special focus on the use and validity of outcome measures. Secondly, we aimed to give guidance on how to conduct and interpret medication safety studies with neurodevelopment outcomes. We conducted a systematic review in the MEDLINE, Embase, PsycINFO, Web of Science, Scopus, and Cochrane databases from inception to April 2019, including controlled studies on prenatal exposure to psychotropics or analgesics and child neurodevelopment, measured with standardised psychometric instruments or by diagnosis of neurodevelopmental disorder. The review management tool Covidence was used for data-extraction. Outcomes were grouped as motor skills, cognition, behaviour, emotionality, or "other". We identified 110 eligible papers (psychotropics, 82 papers, analgesics, 29 papers). A variety of neurodevelopmental outcome measures were used, including 27 different psychometric instruments administered by health care professionals, 15 different instruments completed by parents, and 13 different diagnostic categories. In 23 papers, no comments were made on the validity of the outcome measure. In conclusion, establishing neurodevelopmental safety includes assessing a wide variety of outcomes important for the child's daily functioning including motor skills, cognition, behaviour, and emotionality, with valid and reliable measures from infancy through to adolescence. Consensus is needed in the scientific community on how neurodevelopment should be assessed in medication safety in pregnancy studies. Review registration number: CRD42018086101 in the PROSPERO database.