Using Metrics of a Mixture Effect and Nutrition from an Observational Study for Consideration towards Causal...

Area of Science:

• Environmental Health
• Nutritional Science
• Epidemiology

Background:

• Human health is impacted by environmental chemical exposures and nutritional status.
• In vivo and in vitro studies on single chemicals do not reflect real-world exposure to complex mixtures.
• Weighted Quantile Sum (WQS) regression provides a method to assess the joint effects of chemical mixtures.

Purpose of the Study:

• To demonstrate the utility of indices for causally linking human exposure to environmental mixtures and nutrition with health outcomes.
• To apply WQS regression for endocrine-disrupting chemicals (EDCs) and a My Nutrition Index (MNI) for dietary patterns.
• To utilize causal inference methods with observational data from the SELMA pregnancy cohort.

Main Methods:

• WQS regression was used to create an index for 26 EDCs.
• My Nutrition Index (MNI) was calculated based on dietary guideline values.
• G-computation with counterfactuals was employed for causal inference, assessing reduced EDC exposure and improved nutrition.

Main Results:

• A one standard deviation (SD) reduction in the WQS index for EDCs was associated with significant cognitive function improvement.
• A one SD improvement in the MNI was also significantly linked to enhanced cognitive function.
• Both strategies suggest a positive impact on cognitive development in the context of pregnancy.

Conclusions:

• Indices integrating complex exposures like EDCs and nutritional status can be valuable tools for causal inference in observational studies.
• Reducing prenatal EDC exposure and improving maternal nutrition are effective strategies for enhancing child cognitive function.
• Findings support evidence-based decision-making for environmental risk management and public health nutrition initiatives.