Family history of psychiatric conditions and development of siblings of children with autism.

Younger siblings (SIBS) of children with autism exhibit a wide range of clinical and subclinical symptoms including social, cognitive, language, and adaptive functioning delays. Identifying factors linked with this phenotypic heterogeneity is essential for improving understanding of the underlying biology of the heterogenous outcomes and for early identification of the most vulnerable SIBS. Prevalence of neurodevelopmental (NDD) and neuropsychiatric disorders (NPD) is significantly elevated in families of children with autism. It remains unknown, however, if the family history associates with the developmental outcomes among the SIBS. We quantified history of the NDDs and NPDs commonly reported in families of children with autism using a parent interview and assessed autism symptoms, verbal, nonverbal, and adaptive skills in a sample of 229 SIBS. Multiple regression analyses were used to examine links between family history and phenotypic outcomes, whereas controlling for birth year, age, sex, demographics, and parental education. Results suggest that family history of schizophrenia, depression, anxiety, bipolar disorder, and intellectual disability associate robustly with dimensional measures of social affect, verbal and nonverbal IQ, and adaptive functioning in the SIBS. Considering family history of these disorders may improve efforts to predict long-term outcomes in younger siblings of children with autism and inform about familial factors contributing to high phenotypic heterogenetity in this cohort.

A scoping review of multigenerational impacts of grandparental exposures on mental health in grandchildren.

PURPOSE OF REVIEW: The multigenerational effects of grandparental exposures on their grandchildren's mental health and neurodevelopment are gaining research attention. We conducted a scoping review to summarize the current epidemiological studies investigating pregnancy-related and environmental factors that affected grandparental pregnancies and mental health outcomes in their grandchildren. We also identified methodological challenges that affect these multigenerational health studies and discuss opportunities for future research. RECENT FINDINGS: We performed a literature search using PubMed and Embase and included 18 articles for this review. The most investigated grandparental pregnancy-related factors were the grandparental age of pregnancy (N = 6), smoking during pregnancy (N = 4), and medication intake (N = 3). The most frequently examined grandchild outcomes were autism spectrum disorder (N = 6) and attention-deficit/hyperactivity disorder (N = 4). Among these studies, grandparental smoking and the use of diethylstilbestrol were more consistently reported to be associated with neurodevelopmental disorders, while the findings for grandparental age vary across the maternal or paternal line. Grandmaternal weight, adverse delivery outcomes, and other spatial-temporal markers of physical and social environmental stressors require further scrutiny. The current body of literature has suggested that mental and neurodevelopmental disorders may be outcomes of unfavorable exposures originating from the grandparental generation during their pregnancies. To advance the field, we recommend research efforts into setting up multigenerational studies with prospectively collected data that span through at least three generations, incorporating spatial, environmental, and biological markers for exposure assessment, expanding the outcome phenotypes evaluated, and developing a causal analytical framework including mediation analyses specific for multigenerational research.

Per- and polyfluoroalkyl substances and sleep health in U.S. adults, NHANES 2005-2014.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals that induce oxidative inflammatory responses and disrupt the endocrine and central nervous systems, all of which can influence sleep. OBJECTIVE: To investigate the association between PFAS exposure and sleep health measures in U.S. adults. METHODS: We analyzed serum concentration data of four PFAS [perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonic acid (PFHxS), and perfluorononanoic acid (PFNA)] reported for 8913 adults in NHANES 2005-2014. Sleep outcomes, including trouble sleeping, having a diagnosis of sleep disorder, and recent daily sleep duration classified as insufficient or excessive sleep (<6 or >9 h/day) were examined. Weighted logistic regression was used to estimate the association between the sleep outcomes and each PFAS modeled continuously (log2) or in exposure tertiles. We applied quantile g-computation to estimate the effect of the four PFAS as a mixture on the sleep outcomes. We conducted a quantitative bias analysis to assess the potential influence of self-selection and uncontrolled confounding. RESULTS: We observed some inverse associations between serum PFAS and trouble sleeping or sleep disorder, which were more consistent for PFOS (e.g., per log2-PFOS (ng/ml) and trouble sleeping OR = 0.93, 95%CI: 0.89, 0.98; sleep disorder OR = 0.89, 95%CI: 0.83, 0.95). Per quartile increase of the PFAS mixture was inversely associated with trouble sleeping and sleep disorder. No consistent associations were found for sleep duration across analyses. Our bias analysis suggests that the finding on sleep disorder could be explained by a moderate level of self-selection and negative confounding effects. CONCLUSIONS: We found no evidence to suggest exposure to four legacy PFAS worsened self-reported sleep health among U.S. adults. While some inverse associations between specific PFAS and sleep disorder were observed, self-selection and uncontrolled confounding biases may play a role in these findings.

Use of clinical chemistry health outcomes and PFAS chain length to predict 28-day rodent oral toxicity.

Current literature suggests PFAS carbon chain length may be a predictive variable of toxicity. If so, statistical modeling may be used to help predict toxicity, thus improving the efficiency of PFAS regulation development. Data were analyzed using one-way ANOVAs, Tukey's HSD post hoc tests, and simple linear regressions. A dataset was predicted using modeling from this data. Analysis indicated that 11 of 15 health outcomes showed significant differences in mean values. Two of 15 health outcomes were analyzed using simple linear regressions, with statistically significant results. After predictive modeling generated a theoretical dataset, unpaired t-tests comparing the results of an actual dataset indicated no significant differences among the mean values of the two health outcomes. Therefore, predictive statistical modeling may be used to predict health outcomes for PFAS exposure.