Maternal mid-gestational and child cord blood immune signatures are strongly associated with offspring risk of ASD

Maternal immune activation and autism in the offspring-what is the evidence for causation?

The maternal immune activation hypothesis has gained attention over the past two decades as a potential contributor to the etiology of autism. This hypothesis posits that maternal conditions associated with inflammation during pregnancy may increase the risk of autism in offspring. Autism is highly heritable, and causal environmental contributors to autism largely remain elusive. We review studies on maternal conditions during pregnancy, all associated with some degree of systemic inflammation; namely, maternal infections, autoimmunity, and high BMI. We additionally review studies of inflammatory markers in biological samples collected from the mother during pregnancy or from the neonate and their relationship with autism assessed in children later in life. Recent reports indicate familial clustering of autism, autoimmunity and infections, as well as genetic correlations between autism and aspects of immune function. In light of this literature, there is an apparent risk of confounding of the reported associations between inflammatory exposures and autism by familial genetic factors in both clinical and epidemiological cohort studies. We highlight recent studies that have attempted to address potential confounding to assess evidence of causal effects of inflammation during early life in autism.

Autism likelihood in infants born to mothers with asthma is associated with blood inflammatory gene biomarkers in pregnancy

Mothers with asthma or atopy have a higher likelihood of having autistic children, with maternal immune activation in pregnancy implicated as a mechanism. This study aimed to determine, in a prospective cohort of mothers with asthma and their infants, whether inflammatory gene expression in pregnancy is associated with likelihood of future autism. Mothers with asthma were recruited to the Breathing for Life Trial. RNA was extracted from blood samples collected at mid-pregnancy. 300 ng total RNA was hybridized with the nCounter Human Inflammation gene expression panel (Nanostring Technologies, 249 inflammation-related genes). Parents completed the First Year Inventory (FYI) at 12-month follow-up, which assessed an infant's likelihood for autism across 2 behavioural domains: social communication and sensory regulation. A total score ≥19.2 indicated increased likelihood for future autism. Inflammatory gene expression was profiled from 24 mothers: four infants scored in the high autism likelihood range; 20 scored in the low autism likelihood range. Six inflammatory genes were differentially expressed and significantly up-regulated in the high autism likelihood group: CYSLTR2, NOX1, C1QA, CXCL10, C8A, IL23R. mRNA count significantly correlated with social communication FYI score for CYSLTR2 (Pearson r = 0.46, p = 0.024) and CXCL10 (r = 0.43, p = 0.036) and with sensory regulation score for ALOX5 (r = -0.43, p = 0.038) and MAFK (r = -0.46, p = 0.022). In this proof-of-concept study, inflammatory gene expression during pregnancy in mothers with asthma was associated with an infant's likelihood of future autism as well as scores relating to social communication and sensory regulation.

Arachidonic acid-derived dihydroxy fatty acids in neonatal cord blood relate symptoms of autism spectrum disorders and social adaptive functioning: Hamamatsu Birth Cohort for Mothers and Children (HBC Study)

AIM

Autism spectrum disorder (ASD) is associated with abnormal lipid metabolism, such as a high total ratio of omega-6 to omega-3 in polyunsaturated fatty acids (PUFAs). PUFAs are metabolized to epoxy fatty acids by cytochrome P450 (CYP); then, dihydroxy fatty acid is produced by soluble epoxide hydrolase. This study examined the association between PUFA metabolites in the cord blood and ASD symptoms and adaptive functioning in children.

METHODS

This prospective cohort study utilized cord blood to quantify PUFA metabolites of the CYP pathway. The Autism Diagnostic Observation Schedule (ADOS-2) and Vineland Adaptive Behaviors Scales, Second Edition (VABS-II) were used to assess subsequent ASD symptoms and adaptive functioning in children at 6 years. The analysis included 200 children and their mothers.

RESULTS

Arachidonic acid-derived diols, 11,12-diHETrE was found to impact ASD symptom severity on the ADOS-2-calibrated severity scores and impairment in the socialization domain as assessed by the VABS-II (P = 0.0003; P = 0.004, respectively). High levels of 11,12-diHETrE impact social affect in ASD symptoms (P = 0.002), while low levels of 8,9-diHETrE impact repetitive/restrictive behavior (P = 0.003). Notably, there was specificity in the association between diHETrE and ASD symptoms, especially in girls.

CONCLUSION

These findings suggest that the dynamics of diHETrE during the fetal period is important in the developmental trajectory of children after birth. Given that the role of diol metabolites in neurodevelopment in vivo is completely uncharacterized, the results of this study provide important insight into the role of diHETrE and ASD pathophysiology.

Epigenetic scores derived in saliva are associated with gestational age at birth

BACKGROUND

Epigenetic scores (EpiScores), reflecting DNA methylation (DNAm)-based surrogates for complex traits, have been developed for multiple circulating proteins. EpiScores for pro-inflammatory proteins, such as C-reactive protein (DNAm CRP), are associated with brain health and cognition in adults and with inflammatory comorbidities of preterm birth in neonates. Social disadvantage can become embedded in child development through inflammation, and deprivation is overrepresented in preterm infants. We tested the hypotheses that preterm birth and socioeconomic status (SES) are associated with alterations in a set of EpiScores enriched for inflammation-associated proteins.

RESULTS

In total, 104 protein EpiScores were derived from saliva samples of 332 neonates born at gestational age (GA) 22.14 to 42.14 weeks. Saliva sampling was between 36.57 and 47.14 weeks. Forty-three (41%) EpiScores were associated with low GA at birth (standardised estimates |0.14 to 0.88|, Bonferroni-adjusted p-value < 8.3 × 10-3). These included EpiScores for chemokines, growth factors, proteins involved in neurogenesis and vascular development, cell membrane proteins and receptors, and other immune proteins. Three EpiScores were associated with SES, or the interaction between birth GA and SES: afamin, intercellular adhesion molecule 5, and hepatocyte growth factor-like protein (standardised estimates |0.06 to 0.13|, Bonferroni-adjusted p-value < 8.3 × 10-3). In a preterm subgroup (n = 217, median [range] GA 29.29 weeks [22.14 to 33.0 weeks]), SES-EpiScore associations did not remain statistically significant after adjustment for sepsis, bronchopulmonary dysplasia, necrotising enterocolitis, and histological chorioamnionitis.

CONCLUSIONS

Low birth GA is substantially associated with a set of EpiScores. The set was enriched for inflammatory proteins, providing new insights into immune dysregulation in preterm infants. SES had fewer associations with EpiScores; these tended to have small effect sizes and were not statistically significant after adjusting for inflammatory comorbidities. This suggests that inflammation is unlikely to be the primary axis through which SES becomes embedded in the development of preterm infants in the neonatal period.

Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.

Pre-autism: What a paediatrician should know about early diagnosis of autism‎

Autism, also known as an autism spectrum disorder, is a complex neurodevelopmental disorder usually diagnosed in the first three years of a child's life. A range of symptoms characterizes it and can be diagnosed at any age, including adolescence and adulthood. However, early diagnosis is crucial for effective management, prognosis, and care. Unfortunately, there are no established fetal, prenatal, or newborn screening programs for autism, making early detection difficult. This review aims to shed light on the early detection of autism prenatally, natally, and early in life, during a stage we call as "pre-autism" when typical symptoms are not yet apparent. Some fetal, neonatal, and infant biomarkers may predict an increased risk of autism in the coming baby. By developing a biomarker array, we can create an objective diagnostic tool to diagnose and rank the severity of autism for each patient. These biomarkers could be genetic, immunological, hormonal, metabolic, amino acids, acute phase reactants, neonatal brainstem function biophysical activity, behavioral profile, body measurements, or radiological markers. However, every biomarker has its accuracy and limitations. Several factors can make early detection of autism a real challenge. To improve early detection, we need to overcome various challenges, such as raising community awareness of early signs of autism, improving access to diagnostic tools, reducing the stigma attached to the diagnosis of autism, and addressing various culturally sensitive concepts related to the disorder.

Neonatal immune signatures differ by sex regardless of neurodevelopmental disorder status: Macrophage migration inhibitory factor (MIF) alone reveals a sex by diagnosis interaction effect

Immune dysregulation, including aberrant peripheral cytokine/chemokine levels, is implicated in neurodevelopmental disorders (NDD) such as autism spectrum disorder (ASD). While the diagnosis of ASD is more common in males compared to females, sex effects in immune dysregulation related to neurodevelopment remain understudied. The aim of this exploratory study was to determine whether there are sex-specific effects in neonatal immune dysregulation with respect to an ASD or delayed development (DD) diagnosis. We utilized the data from the Early Markers for Autism study, a population based case-control study of prenatal and neonatal biomarkers of ASD. The immune profile of newborns later diagnosed with ASD (n = 482, 91 females), DD (n = 140, 61 females) and sex-matched general population controls (GP; n = 378, 67 females) were analyzed using neonatal bloodspots (NBS) via 42-plex multiplex assay. Multiple linear regression analysis was performed to identify whether sex was associated with differences in cytokine/chemokine levels of children with ASD, DD, and GP. A sex by diagnosis interaction effect was observed only for the chemokine macrophage migration inhibitory factor (MIF), with males displaying higher levels of NBS MIF than females in the GP control group (p = 0.02), but not in ASD (p = 0.52) or DD (p = 0.29) groups. We found that regardless of child diagnosis, newborn bloodspot eluates from females had a significantly higher concentration than males with the same diagnosis of the chemokines granulocyte chemotactic protein 2 (GCP-2; p < 0.0001), macrophage inflammatory protein 2-alpha (GROβ; p = 0.002), interferon-inducible t-cell alpha chemoattractant (I-TAC; p < 0.0001), stromal cell-derived factor 1 alpha and beta (SDF-1α-β; p = 0.03), innate inflammatory chemokines interferon-gamma induced protein 10 (IP-10; p = 0.02), macrophage inflammatory protein 1-alpha (MIP-1α; p = 0.02), and Th1-related pro-inflammatory cytokine interleukin-12 active heterodimer (IL-12p70; p = 0.002). In contrast, males had a higher concentration than females of secondary lymphoid-tissue chemokine (6CKINE; p = 0.02), monocyte chemotactic protein 1 (MCP-1; p = 0.005) and myeloid progenitor inhibitory factor 1 (MPIF-1; p = 0.03). Results were similar when analyses were restricted to NBS from DD and ASD further classified as ASD with intellectual disability (ID), ASD without ID, and DD (GCP-2, p = 0.007; I-TAC, p = 0.001; IP-10, p = 0.005; IL-12p70, p = 0.03 higher in females; MPIF-1, p = 0.03 higher in male). This study is the first to examine sex differences in neonatal cytokine/chemokine concentrations, and whether these differences are associated with neurodevelopmental outcomes. Results highlight the importance of considering sex as a critical factor in understanding the immune system as it relates to child development.

Metabolomic analysis of maternal mid-gestation plasma and cord blood in autism spectrum disorders

The discovery of prenatal and neonatal molecular biomarkers has the potential to yield insights into autism spectrum disorder (ASD) and facilitate early diagnosis. We characterized metabolomic profiles in ASD using plasma samples collected in the Norwegian Autism Birth Cohort from mothers at weeks 17-21 gestation (maternal mid-gestation, MMG, n = 408) and from children on the day of birth (cord blood, CB, n = 418). We analyzed associations using sex-stratified adjusted logistic regression models with Bayesian analyses. Chemical enrichment analyses (ChemRICH) were performed to determine altered chemical clusters. We also employed machine learning algorithms to assess the utility of metabolomics as ASD biomarkers. We identified ASD associations with a variety of chemical compounds including arachidonic acid, glutamate, and glutamine, and metabolite clusters including hydroxy eicospentaenoic acids, phosphatidylcholines, and ceramides in MMG and CB plasma that are consistent with inflammation, disruption of membrane integrity, and impaired neurotransmission and neurotoxicity. Girls with ASD have disruption of ether/non-ether phospholipid balance in the MMG plasma that is similar to that found in other neurodevelopmental disorders. ASD boys in the CB analyses had the highest number of dysregulated chemical clusters. Machine learning classifiers distinguished ASD cases from controls with area under the receiver operating characteristic (AUROC) values ranging from 0.710 to 0.853. Predictive performance was better in CB analyses than in MMG. These findings may provide new insights into the sex-specific differences in ASD and have implications for discovery of biomarkers that may enable early detection and intervention.

Association between history of miscarriage and autism spectrum disorder

This case-control study was designed to examine the association between different types of miscarriage history and autism spectrum disorder (ASD), and determine whether the number of miscarriage history affects the risk of ASD. All of 2274 children with ASD and 1086 healthy controls were recruited. Sociodemographic and prenatal, perinatal, and neonatal characteristics were compared between the two groups. Multivariable logistic regression analyses were applied to investigate association between miscarriage history and ASD. Stratified analyses based on sex and types of miscarriages were similarly performed. History of miscarriage was potential risk factors for ASD ([aOR] = 2.919; 95% [CI] = 2.327-3.517). Stratified analyses revealed that induced ([aOR] = 2.763, 95% [CI] = 2.259-3.379) and spontaneous miscarriage history ([aOR] = 3.341, 95% [CI] = 1.939-4.820) were associated with high risk of ASD, respectively. A sex-biased ratio in the risk of ASD was observed between females ([aOR] = 3.049, 95% [CI] = 2.153-4.137) and males ([aOR] = 2.538, 95% [CI] = 1.978-3.251). Stratified analysis of induced miscarriage history revealed that only iatrogenic miscarriage history was associated with an increased risk ASD ([aOR] = 2.843, 95% [CI] = 1.534-4.268). Also, multiple spontaneous miscarriage histories ([aOR] = 1.836, 95% [CI] = 1.252-2.693) were associated with higher autism risk than one spontaneous miscarriages history ([aOR] = 3.016, 95% [CI] = 1.894-4.174). In conclusion, miscarriage history is related to an increased risk for ASD in offspring, which is affected by the types of miscarriage and sex of the fetus.

Cohort-guided insights into gene-environment interactions in autism spectrum disorders

Prospective birth cohorts offer unprecedented opportunities to investigate the pathogenesis of complex disorders such as autism, in which gene-environment interactions must be appreciated in a temporal context. This Perspective article considers the history of autism research, including missteps that reflected an incomplete understanding of the epidemiology of autistic spectrum disorders, the effects of advocacy and philanthropy on the trajectory of scientific inquiry, and the current and future roles of prospective birth cohort research in illuminating the pathology of these and other complex disorders wherein exposures during gestation might not manifest until later in life.

Autism spectrum disorder diagnosis using a new panel of immune- and inflammatory-related serum biomarkers: A case-control multicenter study

Background and objectives

Children with autism spectrum disorder (ASD) present with distinctive clinical features. No objective laboratory assay has been developed to establish a diagnosis of ASD. Considering the known immunological associations with ASD, immunological biomarkers might enable ASD diagnosis and intervention at an early age when the immature brain has the highest degree of plasticity. This work aimed to identify diagnostic biomarkers discriminating between children with ASD and typically developing (TD) children.

Methods

A multicenter, diagnostic case-control study trial was conducted in Israel and Canada between 2014 and 2021. In this trial, a single blood sample was collected from 102 children with ASD as defined in Diagnostic Statistical Manual of Mental Disorders [DSM)-IV (299.00) or DSM-V (299.00)], and from 97 typically developing control children aged 3-12 years. Samples were analyzed using a high-throughput, multiplexed ELISA array which quantifies 1,000 human immune/inflammatory-related proteins. Multiple logistic regression analysis was used to obtain a predictor from these results using 10-fold cross validation.

Results

Twelve biomarkers were identified that provided an overall accuracy of 0.82 ± 0.09 (sensitivity: 0.87 ± 0.08; specificity: 0.77 ± 0.14) in diagnosing ASD with a threshold of 0.5. The resulting model had an area under the curve of 0.86 ± 0.06 (95% CI: 0.811-0.889). Of the 102 ASD children included in the study, 13% were negative for this signature. Most of the markers included in all models have been reported to be associated with ASD and/or autoimmune diseases.

Conclusion

The identified biomarkers may serve as the basis of an objective assay for early and accurate diagnosis of ASD. In addition, the markers may shed light on ASD etiology and pathogenesis. It should be noted that this was only a pilot, case-control diagnostic study, with a high risk of bias. The findings should be validated in larger prospective cohorts of consecutive children suspected of ASD.

Inductively coupled plasma mass spectrometry based urine metallome to construct clinical decision models for autism spectrum disorder

BACKGROUND

The global prevalence of autism spectrum disorder (ASD) is on the rise, and high levels of exposure to toxic heavy metals may be associated with this increase. Urine analysis is a noninvasive method for investigating the accumulation and excretion of heavy metals. The aim of this study was to identify ASD-associated urinary metal markers.

METHODS

Overall, 70 children with ASD and 71 children with typical development (TD) were enrolled in this retrospective case-control study. In this metallomics investigation, inductively coupled plasma mass spectrometry was performed to obtain the urine profile of 27 metals.

RESULTS

Children with ASD could be distinguished from children with TD based on the urine metal profile, with ASD children showing an increased urine metal Shannon diversity. A metallome-wide association analysis was used to identify seven ASD-related metals in urine, with cobalt, aluminum, selenium, and lithium significantly higher, and manganese, mercury, and titanium significantly lower in the urine of children with ASD than in children with TD. The least absolute shrinkage and selection operator (LASSO) machine learning method was used to rank the seven urine metals in terms of their effect on ASD. On the basis of these seven urine metals, we constructed a LASSO regression model for ASD classification and found an area under the receiver operating characteristic curve of 0.913. We also constructed a clinical prediction model for ASD based on the seven metals that were different in the urine of children with ASD and found that the model would be useful for the clinical prediction of ASD risk.

CONCLUSIONS

The study findings suggest that altered urine metal concentrations may be an important risk factor for ASD, and we recommend further exploration of the mechanisms and clinical treatment measures for such alterations.

Maternal blood inflammatory marker levels increased in fetuses with ventriculomegaly

Background

Fetal ventriculomegaly (VM) is one of the most common abnormalities of the central nervous system (CNS), which can be significantly identified by brain anomalies prenatally by magnetic resonance imaging (MRI). Aberrant white blood cells (WBCs) levels indicate that the maternal is suffering from the infection. Previous studies have confirmed that prenatal infection can affect fetal brain structure, but there is no research revealed the association between maternal blood parameters with fetal VM until now.

Methods

We measured the width of the lateral ventricle of 142 fetuses, which were divided into the fetal VM group (n = 70) and the normal lateral ventricle group (n = 72). We compared maternal blood cell levels between the two groups and investigate potential biomarkers of fetal VM.

Result

High levels of maternal WBC and neutrophil (NE#) levels were observed in fetuses with VM (p < 0.001), while lymphocyte percentage, monocytes (MO#), neutrophil/lymphocyte ratio (NLR), and platelet were also increased in the fetal VM group (p = 0.033, 0.027, 0.034, and 0.025, respectively). receiver-operator curve (ROC) analysis suggested that WBC and NE# counts might be useful to distinguish fetuses with enlarged lateral ventricles (AUC = 0.688, 0.678, respectively).

Conclusion

The current study emphasizes the importance of maternal infection for fetal brain growth, which could provide important information for prenatal diagnosis of CNS anomalies. Future research needs longitudinal analysis and exploration of the influence of maternal blood inflammatory marker levels on fetal brain development.

Differential Expression of Endogenous Retroviruses and Inflammatory Mediators in Female and Male Offspring in a Mouse Model of Maternal Immune Activation

Maternal infections during pregnancy and the consequent maternal immune activation (MIA) are the major risk factors for autism spectrum disorder (ASD). Epidemiological evidence is corroborated by the preclinical models in which MIA leads to ASD-like behavioral abnormalities and altered neuroinflammatory profiles, with an increase in pro-inflammatory cytokines and microglial markers. In addition to neuroinflammatory response, an abnormal expression of endogenous retroviruses (ERVs) has been identified in neurodevelopmental disorders and have been found to correlate with disease severity. Our aim was to evaluate the transcriptional profile of several ERV families, ERV-related genes, and inflammatory mediators (by RT real-time PCR) in mouse offspring of both sexes, prenatally exposed to polyinosinic:polycytidylic acid (Poly I:C), a synthetic double-stranded RNA molecule targeting TLR-3 that mimics viral maternal infection during pregnancy. We found that prenatal exposure to Poly I:C deregulated the expression of some ERVs and ERV-related genes both in the prefrontal cortex (PFC) and hippocampus, while no changes were detected in the blood. Interestingly, sex-related differences in the expression levels of some ERVs, ERV-related genes, and inflammatory mediators that were higher in females than in males emerged only in PFC. Our findings support the tissue specificity of ERV and ERV-related transcriptional profiles in MIA mice.