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Maternal nutrition during gestation has been investigated for its role in child neurodevelopment. However, little is known about the potential impact of gestational caffeine exposure on child autistic behaviors. Here, we assess the relation between maternal caffeine intake during pregnancy and children’s behavioral traits related to Autism Spectrum Disorder (ASD). We harmonized data from two pregnancy cohorts, Early Autism Risk Longitudinal Investigation (EARLI) (n = 120), an enriched-risk cohort of mothers who previously had a child with ASD, from Pennsylvania, Maryland, and Northern California (2009–2012), and the Health Outcomes and Measures of the Environment (HOME) Study (n = 269), a general population cohort from Cincinnati, Ohio (2003–2006). Mothers self-reported caffeine intake twice during pregnancy. Caregivers reported child behavioral traits related to ASD using the Social Responsiveness Scale (SRS) when children were aged 3–8 years. Higher scores indicate more ASD-related behaviors. We estimated covariate-adjusted differences in continuous SRS T-scores per interquartile range increase in caffeine intake. Self-reported caffeine intake during pregnancy was positively associated with SRS T-scores among children in EARLI (β: 2.0; 95% CI -0.1, 4.0), but to a lesser extent in HOME (β: 0.6; 95% CI -0.5, 1.6). In HOME, pre-pregnancy body mass index (BMI) modified the association between caffeine intake and SRS T-scores, where more positive associations were observed among women with higher BMIs. Our findings suggest gestational caffeine intake may represent a marker of vulnerability to childhood ASD-related behaviors. Additional studies are warranted to extend these findings.

Semen analysis is one of the standard diagnostic tools currently used to assess male infertility and reproductive toxicity. However, semen analysis has a limited ability to separate fertile from infertile populations. Additional methods to detect impaired fertility are needed. The purpose of the present study was to evaluate how spermatozoal RNA content varies with sociodemographic and behavior/lifestyle factors, and to determine if spermatozoal large and small RNAs discriminate normal from abnormal spermatozoa. Semen specimens were collected from 133 men aged between 18 to 55 years undergoing semen analysis as part of couple infertility evaluation while 10 proven fertile donors were recruited as control group. Semen samples were classified as normal or abnormal according to World Health Organization (WHO) 2010 criteria. Sperm RNAs were extracted after somatic cells were lysed, and the association of large or small RNA content with semen quality and sociodemographic and behavioral/lifestyle factors was evaluated using a generalized additive model and one-way ANOVA. Inverse relationship was observed between large RNA content and sperm parameters such as sperm count, density and motility. Large RNA content per sperm was significantly increased in semen samples showing abnormal number of round cells. Furthermore, sperm motility was inversely associated with spermatozoal small RNA contents. Grouping donors by the number of semen abnormalities, we observed significant increased spermatozoal large and small RNA content in men with more than two semen abnormalities. Alcohol consumption was strongly associated with increased large RNA per sperm concentration after adjustment for age and BMI. Our study demonstrates a strong relationship between spermatozoal large RNA content and poor semen characteristics that may lead to a role in the assessment of male fertility, and may be used as an endpoint for reproductive toxicology risk assessment.

Epidemiological studies play an important role in quantifying how early life environmental chemical exposures influence the risk of childhood diseases. These studies face at least four major challenges that can produce noise when trying to identify signals of associations between chemical exposure and childhood health. Challenges include accurately estimating chemical exposure, confounding from causes of both exposure and disease, identifying periods of heightened vulnerability to chemical exposures, and determining the effects of chemical mixtures. We provide recommendations that will aid in identifying these signals with more precision.