Associations of prenatal exposure to mixtures of organochlorine pesticides and smoking and drinking behaviors in adolescence

Mixtures of serum concentrations of organochlorine pesticides and breastfeeding duration

The relationship between breastfeeding duration and maternal mixtures of organochlorine pesticides (OCP) biological concentrations has not been documented. For that reason, our objective was to evaluate the association between lactation duration and mixtures of OCP serum concentrations and their principal metabolites, as well as to identify the primary contributors within these mixtures. Consequently, we conducted a secondary analysis of 878 women over 18 years old who had at least one living child and served as controls in a population-based study from 2007 to 2011 in Northern Mexico. Through direct interviews, we collected data on breastfeeding duration, sociodemographic characteristics, and medical history. We determined serum concentrations of 24 OCP, including some metabolites, using gas chromatography with an electron microcapture detector. We applied Weighted Quantile Sum (WQS) regression models with binomial family specification to assess the relationship between breastfeeding duration (both for the first child and all children) and mixtures of OCP serum concentrations and their metabolites of interest. We identified a mixture of OCP negatively associated with breastfeeding the first child (OR = 0.63, 95% CI 0.52-0.77) and all children (OR = 0.59, 95% CI 0.46-0.75). The significant OCP or metabolites in both mixtures included p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE), trans-nonachlor, β-hexachlorocyclohexane (β-HCH), p,p'-dichlorodiphenyldichloroethane (p,p'-DDD), heptachlor, and hexachlorobenzene (HCB). Our results suggest serum concentrations of OCP mixtures in women who breastfed for at least 12 months are lower than those who breastfed for less than that time. Future studies are needed to evaluate the risk-benefit of multiple OCP in breast milk for maternal and child health.

Bayesian Kernel Machine Regression for Social Epidemiologic Research

BACKGROUND

Little attention has been devoted to framing multiple continuous social variables as a "mixture" for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects.

METHODS

Using data from the 2023 Survey of Racism and Public Health, we conducted a Bayesian kernel machine regression analysis to study several individual, social, and structural factors as an exposure mixture and their relationships with psychological distress among individuals with at least one police arrest. Factors included racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use. We complemented this analysis with a series of unadjusted and adjusted models for each exposure mixture variable.

RESULTS

We found that more self-reported discrimination experiences in the past year (posterior inclusion probability = 1.00) and greater substance use (posterior inclusion probability = 1.00) correlated with higher psychological distress. These associations were consistent with the findings from the unadjusted and adjusted linear regression analyses: past year perceived discrimination (unadjusted b = 2.58, 95% confidence interval [CI]: 1.86, 3.30; adjusted b = 2.20, 95% CI: 1.45, 2.94) and substance use (unadjusted b = 2.92, 95% CI: 2.21, 3.62; adjusted b = 2.59, 95% CI: 1.87, 3.31).

CONCLUSION

With the rise of big data and the expansion of variables in long-standing cohort and census studies, novel applications of methods from adjacent disciplines are a step forward in identifying exposure mixture associations in social epidemiology and addressing the health needs of socially vulnerable populations.

Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression

Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research.Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes.Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect.Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains.Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.

Epigenetics in the Anthropocene: an interview with Oskar Karlsson

In this interview, Oskar Karlsson speaks with Storm Johnson, commissioning editor for Epigenomics, on his work to date in the field of toxicological origins of disease and gene-environment interactions. Oskar Karlsson, is an associate professor at the Science for Life Laboratory (SciLifeLab), Department of Environmental Science, Stockholm University, Sweden. Dr. Karlsson earned a PhD in toxicology at the Department of Pharmaceutical Bioscience, Uppsala University, and has also worked at Centre of Molecular Medicine, Karolinska Institute, as well as Harvard University School of Public Health. His research combines experimental model systems, computational and omics tools, and epidemiological studies to investigate the influence of environmental exposures on wildlife and human health, and underlying molecular mechanisms. In particular, his research focuses on developmental origins of health and disease with an emphasis on environmental exposures and epigenetic mechanisms. The projects concern the effects of exposures such as endocrine disrupting chemicals, flame retardants, pesticides, metals and particulate air pollution, as well as drugs, psycho-social stressors and ethnical disparities. Ongoing efforts include studies of paternal epigenetic inheritance in the ERC-funded project PATER.