In focus – Dr. Chris Gennings
Meet our EDC-MixRisk scientists working behind the scenes. Prof. Chris Gennings shares her insights into the project. She is Professor of Biostatistics at the Icahn School of Medicine at Mount Sinai, NY, USA and her research focuses particularly on the design and statistical analysis methodologies for studies of chemical mixtures.
Hi Chris – What are you researching in EDC-MixRisk?
We are using the SELMA pregnancy cohort data to identify mixtures of endocrine disrupting chemicals (EDCs) measured in early pregnancy that are associated with health effects in each of three health domains for the children: neurodevelopment, metabolism and growth, and sexual development. Our strategy includes evaluation of chemicals across a wide range of chemical classes. Once the sets of ‘bad actors’ are identified, we calculate a ‘typical mixture’ from the SELMA cohort data for each health domain using pharmacokinetic modeling. These mixtures are then constructed and experimentally evaluated across the consortium.
What are the key results so far?
Important results so far include the identification of bad actors that are detected in all or nearly all of the pregnant women in the SELMA cohort. Second, these identified chemicals come from multiple chemical classes, indicating that focusing on single chemical classes may underestimate risk. Further, we have detected health effects from each of the three health domains, indicating the potential comorbidity associated with exposures to EDCs.
What these findings could implicate?
The strategy we use for identifying bad actors is based on the simultaneous evaluation of mixtures of chemicals related to a health effect, i.e., the so-called ‘mixture effect’. An advantage of this strategy is the inference is focused towards detecting the mixture effect and thereby has more power to find it. Single chemical analyses may not indicate statistically significant associations; but combining across components we detect higher signal. The potential consequences are that we may identify that current regulatory guidelines, which generally focus on single chemicals or single chemical classes, are not adequately protective.
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