Epidemiological studies and disease burden assessment of emerging contaminants

Emerging contaminants (ECs) are synthetic or naturally occurring chemical substances that are increasingly detected in the environment, posing potential health risks to humans. Due to their environmental persistence, bioaccumulation, and widespread exposure, ECs have become a pressing global public health threat. Specifically, these substances span diverse categories, including persistent organic pollutants (POPs), endocrine-disrupting chemicals (EDCs), antibiotics, and microplastics. Biomonitoring studies have shown that ECs are commonly detected in human biological matrices such as blood, urine, and adipose tissue, confirming ubiquitous internal exposure and underscoring the urgency for comprehensive risk assessment.A substantial body of epidemiological studies has identified associations between exposure to ECs and diverse adverse health outcomes. It has been shown that endocrine-disrupting chemicals, including per- and polyfluoroalkyl substances (PFAS), bisphenol A (BPA), and phthalates (PAEs), are closely linked to reproductive health issues. Such outcomes include reduced male semen quality, increased risks of female reproductive disorders such as polycystic ovary syndrome and endometriosis, and adverse pregnancy outcomes like preeclampsia and impaired fetal development. Furthermore, ECs such as PFAS, PAEs, and antibiotics may disrupt the body’s metabolic balance through endocrine disruption, lipid metabolism alterations, and other mechanisms, contributing to the rising prevalence of obesity, type 2 diabetes, and thyroid dysfunction. Additional evidence links various contaminants to thyroid, breast, and other cancers. The integrity of the cardiovascular, neurological, and respiratory systems is also at risk. Studies have linked ECs exposure to neurodevelopmental disorders, while the detection of micro- and nanoplastics in atherosclerotic plaques suggests their potential mechanistic involvement in cardiovascular disease. Moreover, numerous studies have focused on vulnerable populations such as infants, young children, and pregnant women, as exposure to environmental toxicants during critical developmental or physiological stages may lead to significant and lasting health effects.Despite the evidence, current research on the disease burden of ECs remains limited and faces significant methodological hurdles. Although ECs are confirmed to pose serious health threats, existing evidence and subsequent disease burden estimations, primarily based on biomarker-derived exposure levels, suffer from wide confidence intervals in effect estimates and, more critically, uncertainty in exposure-disease causality. Such uncertainty often stems from reliance on cross-sectional or case-control study designs that are inadequate to establish temporality. The assessment is further complicated by the unique toxicological properties of ECs, including non-monotonic dose-response relationships and low-dose effects, as well as the challenge of evaluating the synergistic effects of complex chemical mixtures. A critical limitation stems from regional disparities in data, highlighting the need for more comprehensive, multidimensional exposure assessments and cross-population validation. Findings from Western cohorts cannot be directly extrapolated to regions such as China, which has distinct exposure patterns and population susceptibilities.To address these profound gaps, this study proposes a comprehensive “mechanism analysis - assessment optimization- closed-loop governance” paradigm. This paradigm works by integrating insights from multidisciplinary approaches to build an innovative research framework. Mechanism analysis emphasizes the elucidation of complex biological pathways and the identification of early biomarkers, advocating for the deeper integration of multi-omics technologies to move beyond single-endpoint toxicology. Assessment optimization calls for the innovation of disease burden assessment methods, particularly through the adoption of advanced tools such as artificial intelligence to model high-dimensional data, non-linearities, and complex mixture effects. Furthermore, the paradigm promotes the translation of scientific evidence on ECs into policy through a closed-loop governance system incorporating national dynamic monitoring, the generation of high-quality local evidence, cost-benefit analysis of interventions, and a multi-stakeholder framework, thereby enhancing risk management and protecting public health in a targeted and effective manner.