Title of project

Modeling pesticide exposure and its risk for endocrine, glycemic, and kidney dysfunctions

Abstract

Pesticides are widely used in agriculture and are important for food production, but increasing evidence suggests that some may disrupt endocrine function, impair kidney health, cause metabolic changes, and raise the risk of diabetes. Currently, knowledge about the potential links between pesticide exposure and these health issues is limited, mainly due to methodological weaknesses like cross-sectional study designs. To address this gap, this PhD project will utilize a machine learning approach to investigate potential associations between repeated measures of exposure to specific pesticide classes, primarily organophosphates and other insecticides, and outcomes such as thyroid function, kidney health, and glycemic regulation. The study will leverage data from the three-stage follow-up epidemiological design of the Pesticides, Asthma, and Diabetes (PEXADU) study, including blood biomarkers, urinary metabolites, acetylcholinesterase activity, silicone wristbands, and detailed exposure questionnaires.

The PEXADU study involved a population-based survey of 364 Ugandan smallholder farmers conducted in 2018-2019. Participants were surveyed three times—before, during, and after the main spraying season—and at each visit to the study’s research centre, blood samples were collected for metabolic analysis, including thyroid and kidney function tests, as well as measurements of their long-term blood sugar levels (HbA1c). Data on pesticide exposure and known risk factors, such as diet and physical activity levels, were also gathered through questionnaires. At each visit, participants’ blood was tested for the enzyme acetylcholinesterase, which indicates exposure to insecticides from the organophosphate and carbamate classes. Additionally, 126 randomly selected participants provided urine samples at each visit, which were analysed for degradation products of various pesticides. The same subset of participants also wore silicone bracelets to measure their pesticide exposure through skin contact.

The PEXADU study’s strong epidemiological design, featuring repeated measures, comprehensive biomarker data, and thorough confounder control, will enable more accurate assessments of exposure and health outcomes while reducing attenuation bias common in environmental health research. Additionally, structural equation modelling will be used to evaluate thyroid function as a mediator between pesticide exposure and glycemic dysregulation.

If findings from this study demonstrate a link between pesticide exposure and thyroid dysfunction, kidney function impairment, or glycemic dysregulation, it could reveal how pesticides affect key hormone systems, especially those related to the thyroid. These insights could lead to better ways to prevent endocrine and metabolic problems. The findings could also guide global pesticide regulations, support safer agricultural practices, and help shape more innovative policies on pesticide use in low- and middle-income countries.