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Lead is a known neurotoxicant with many detrimental health effects, including neurocognitive deficits and cardiovascular and metabolic disorders. However, few studies have tested the association between lead exposure and the physiological stress response, which in and of itself may act as a precursor to and/or underlying mechanism of detrimental health outcomes. The purpose of this study was to examine the influence of early childhood and early adolescent low-level lead exposure on early adolescent heart rate variability, a widely-used measure of physiological stress. Participants were 408 children from Jintan, China for whom blood lead levels were measured between 3 and 5 years (early childhood) and again at 12 years (early adolescence). Heart rate variability was assessed at 12 years while participants underwent an induced stress task utilizing the ratio of low to high frequency (LF/HF) ECG measures. Mean blood lead levels in the cohort were 6.63 mcg/dl and 3.10 mcg/dl at 3–5 years and 12 years, respectively. Blood lead levels at 3–5 years of age (β 0.06, p = 0.027), but not at age 12 (β −0.05, p = 0.465), were significantly associated with LF/HF measures while controlling for multiple sociodemographic variables, potentially reflecting a dysregulated stress response with a shift towards sympathetic dominance. These findings suggest that early childhood lead exposure may have a detrimental influence on early adolescent autonomic responses to acute stress, which holds implications for cardiovascular health and overall growth and development. [ABSTRACT FROM AUTHOR]

Background: This study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally., Methods: Data from the China Jintan Child Cohort study (CJCC; n=848) for model development and the US Healthy Brain and Behavior Study (HBBS; n=454) for external validation were employed. Maternal pregnancy histories, obstetric data, and adolescent sleep problems were collected. Several machine learning techniques were employed, including least absolute shrinkage and selection operator, logistic regression, random forest, naïve bayes, extreme gradient boosting, decision tree, and neural network. The area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and root mean square of residuals were used to evaluate model performance., Results: Key predictors for CJCC adolescents' sleep problems include gestational age, birthweight, duration of delivery, and maternal happiness during pregnancy. In HBBS adolescents, the duration of postnatal depressive emotions was the primary perinatal predictor. The prediction models developed in the CJCC had good-to-excellent internal validation performance but poor performance in predicting the sleep problems in HBBS adolescents., Conclusion: The identification of specific perinatal risk factors associated with adolescent sleep problems can inform targeted interventions during and after pregnancy to mitigate these risks. Health providers should consider integrating these predictive factors into routine pre- and postnatal assessments to identify at-risk populations. The variability in model performance across different cohorts highlights the need for context-specific models and the cautious application of predictive analytics across diverse populations. Future research should focus on refining predictive models to account for such variations, potentially through the incorporation of additional socio-cultural factors and genetic markers. This study emphasizes the importance of personalized and culturally sensitive approaches in the prediction and management of adolescent sleep problems, leveraging advanced computational methods to enhance maternal and child health outcomes., Competing Interests: Declaration of competing interest This work has not been published previously, and it is not under consideration for publication elsewhere. This submission is approved by all authors, and if accepted, it will not be published elsewhere in the same form in English or in any other language including electronically without the written consent of the copyright-holder., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Lead exposure has been shown to dysregulate physiological stress responses. However, few studies have investigated the effect of lead exposure on later heart rate variability (HRV), an indicator of a stress response, in large samples of children. Furthermore, the interaction between social environmental factors and lead exposure in childhood, which commonly co-occur, remains understudied. This study examined relationships between childhood lead exposure and early adolescent physiological stress responses at different levels of parental education. Participants were 406 children from Jintan, China. Blood lead levels (BLLs) and parental education data were collected at 3-5 years of age, and HRV outcomes assessed at 12 years via frequency domain measures (LF/HF ratio) collected during an induced stress test. Results show a significant interaction between parental education and BLLs at 3-5 years. This relationship was found to be most consistent for the interaction between BLLs and mother's years of education for both the planning ( β  = 0.12, p  = 0.046) and speaking ( β  = 0.11, p  = 0.043) phase of the stress task, suggesting that increasing years of mother's education may enhance the deleterious influence of lead exposure on the HRV frequency measure, LF/HF ratio. This research highlights the complexity in lead exposure induced outcomes.