Dr. Chris Gennings is professor and Director of the Biostatistics Division within the Department of Environmental Medicine and Public Health at the Icahn School of Medicine at Mount Sinai. She is the Director of the Statistical Services and Analysis Resource in the Human Health Exposure Assessment Resource (HHEAR) Data Center. Her research interests focus on design and analysis methodologies for studies of complex mixtures, with recent focus on Weighted Quantile Sum (WQS) regression; methods for risk assessment of environmental mixtures including development of tests for sufficient similarity for a similar mixtures approach (SMACH), a novel approach that complements current cumulative risk assessment methods and does not require the default assumption of additivity; extensions to the distributed lag model that accommodates mixtures, called lagged WQS; a new class of nonlinear statistical models, called Acceptable Concentration Range (ACR) models, that incorporate and evaluate regulatory guideline values in analyses of health effects associated with exposure to chemical mixtures; development of a nutrition index called My Nutrition Index with a web-based nutrition app; and using g computation methods with indices such as the My Nutrition Index and a WQS index of environmental exposures to address the counterfactual question of what if exposures were reduced and dietary nutrition was improved.
Combined exposure to multiple chemicals: assessing risks across regulatory silosSee more
22/06 - 14:00Visit the agenda
Title of talk
Novel strategies to assess risks associated with human relevant mixtures
22/06 - 14:05
Abstract of talk
Growing evidence suggests exposure to mixtures of environmental chemicals are associated with important adverse health and developmental effects. Biomonitoring data indicate humans are exposed to broad classes of chemicals over their lifespan, including prenatally. Current risk assessment strategies generally focus on single compounds or convenient groups of compounds assumed to have similar modes of action. We have developed a mixture-centred risk assessment strategy that integrates epidemiological and experimental evidence using a Similar Mixture Approach (SMACH) in the EDC Mix Risk consortium. The strategy will be described using epidemiological data from a pregnancy cohort with prenatal endocrine-disrupting chemical (EDC) exposures measured with childhood neurodevelopmental outcomes. Weighted quantile sum (WQS) regression was used to characterise combinations of EDCs associated with an adverse outcome. A human-relevant typical mixture (relative proportions and total concentrations) was identified and synthesised for experimental testing of a reference mixture. Experimental evidence identified molecular pathways and dose-responses with points of departure in OECD validated in vivo models. For subjects with exposures determined to be sufficiently similar to the reference mixture, exposures were compared to the mixture point of departure using a similar mixture risk index (SMRI) where SMRI>1 indicates exposure ranges of concern. Integration of human observational data and experimental evidence provide additional evidence for assessing risk beyond regulatory silos. Final comments will focus on improving communication around the identification of adverse exposures in human cohort studies to stakeholders. These include interpretation of counterfactuals for demonstrating the impact of reducing exposures on important health effects compared to current exposures. In addition, the resilience effect of improved dietary nutrition to counterbalance the adverse effect of environmental exposures will be discussed, focusing on a personalised metric of the nutritional value of one’s diet.