Micronutrients as Mitigators of Endocrine Disrupting Chemical Health Effects: A Scoping Review and Framework for Epidemiologic Studies

微量营养素作为内分泌干扰化学物质健康效应的缓解剂:流行病学研究的范围界定综述和框架

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Abstract

PURPOSE OF REVIEW: Exposure to endocrine disrupting chemicals (EDC) is linked to numerous adverse health outcomes. However, limiting exposure to EDCs remains a significant challenge due to their widespread uses and persistence in the environment. Adequate micronutrient status supports optimal health and may offer actionable strategies for mitigating the adverse health effects of EDCs. This scoping review aimed to summarize the epidemiologic evidence on micronutrients as potential mitigators of EDC-related health outcomes, with the goal of guiding future research and methodologies. RECENT FINDINGS: We identified 71 epidemiologic studies assessing micronutrients as mitigators of EDC-outcome relations, focused primarily on exposures during pregnancy (n = 34). Most studies examined phthalates and/or environmental phenols (n = 25), per- and polyfluoroalkyl substances (n = 15), polycyclic aromatic hydrocarbons (n = 10), and self-reported pesticide exposure (n = 6). Most studies suggested higher levels of some micronutrients attenuated adverse associations of EDCs with some health outcomes, particularly iodine (thyroid hormones); folic acid (fertility, birth outcomes, neurodevelopment); vitamin D (lung function, neurodevelopment); and antioxidants (birth outcomes, aging, metabolic health). However, included studies assessed a wide range of micronutrients, EDCs, and outcomes, with limited overlap across studies. This scoping review identified few topics with substantial evidence to warrant focused systematic reviews, suggesting that additional prospective research is needed, especially in at-risk populations and sensitive periods outside of pregnancy. Future epidemiologic research should consider the co-occurrence of EDCs and micronutrients in foods and include multiple methods for assessing micronutrients. Finally, to strengthen causal inference, future research should thoughtfully model potential confounding, mediation, effect measure modification, and/or statistical interaction.

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