Integrative Exposome Modeling of Agrochemical Risks in U.S. Farmers

Abstract

Farmers in the United States experience sustained exposure to agrochemicals through occupational, environmental, residential, and dietary pathways. These exposures are typically low dose, cumulative, and heterogeneous, yet traditional risk assessment approaches continue to emphasize single chemicals and short-term exposure windows. Such paradigms are poorly suited to capture the complex, mixture-based exposure environments encountered in agriculture or to explain observed patterns of chronic disease, including cancer, neurodegenerative disorders, and cardiometabolic conditions, among farming populations. This review synthesizes current evidence on agrochemical-related chronic disease risk through the lens of the exposome, a framework that encompasses the totality of environmental exposures across the life course and their internal biological correlates. We examine major classes of agrochemicals used in U.S. farming systems, dominant exposure pathways, and disease outcomes characterized by long latency and multi-morbidity. Particular attention is given to integrative modeling approaches that combine spatial exposure estimation, biomonitoring, and mixture-aware statistical or computational methods to reconstruct cumulative exposure and internal dose. Our synthesis highlights that, while these integrative methods offer substantial promise for capturing real-world agricultural exposure complexity, their broader application remains constrained by persistent methodological challenges, including exposure misclassification, temporal mismatches between biomarkers and disease onset, confounding by co-exposures, and fragmented environmental and health data systems. Addressing these barriers is essential for translating exposome science into actionable public health evidence. By bridging environmental contamination with chronic disease risk through integrative exposome modeling, this review underscores the need to move beyond single-agent frameworks toward cumulative, life-course–informed risk assessment. Embedding these approaches into regulatory science and surveillance systems is critical for identifying exposure mixtures driving multi-morbidity patterns and for developing more equitable, preventive strategies to protect vulnerable agricultural communities.