Christian H. Lindh, Jorge Peñaloza-Castañeda, Ana María Mora, Jane A. Hoppin, Karin Bröberg

Abstract

Objective: Children from agricultural families are often para-occupationally exposed to pesticides. We associated urinary biomarkers of pesticide exposure with relative telomere length (rTL) and mtDNA copy number (mtDNA-cn) in 270 5-year-old children from agricultural families who participated in the Infants’ Environmental (ISA) birth cohort.

Methods: We repeatedly measured biomarkers of pesticide exposure in maternal urine during pregnancy and in children’s urine at 5-years. rTL and mtDNA-cn were determined in children’s saliva, relative to a single-copy hemoglobin beta gene, using real-time PCR. We associated log10 transformed mean urinary biomarker concentrations with log10 transformed rTL and mtDNA-cn, using separate multivariate linear regression and generalized additive models, and adjusted for possible confounders.

Results: Exposure to fungicides and 2,4-D was associated with changes in rTL and mtDNA-cn. Higher prenatal hydroxy-thiabendazole was associated with shorter rTL, while current 2,4-D was associated with longer rTL: %-change-in-β (95%CI) = -0.19 (-0.36, -0.02) and = 0.38 (0.01, 0.75) per tenfold increase in exposure, respectively. In addition, prenatal hydroxy-pyrimethanil was slightly associated with mtDNA-cn: β =0.33 (-0.05, 0.70) and 2,4-D was non-linearly associated with mtDNA-cn (estimated degrees of freedom of smoothed beta = 2.78, p=0.04), while current urinary ethylene thiourea (biomarker of mancozeb) and hydroxy-tebuconazole were associated with decreased and increased mtDNA-cn: %-change-in-β (95%CI) = -0.84 (1.71, 0.05) and 0.71 (0.01, 1.40), respectively. Biomarkers of pyrethroid and chlorpyrifos exposure were not associated with rTL or mtDNA-cn.

Conclusions: Prenatal and childhood exposure to fungicides and 2,4-D may alter rTL and mtDNA-cn, which is of concern as shorter TL may reflect an increased risk of age-related diseases. Longer TL might reflect changes in cellular aging, including increased cellular mutation risks. Finally, increased mtDNA-cn indicates compensation for oxidative stress, while decreased levels reflect oxidative stress.

Shuai Xie, Nicole C Deziel, Pabitra R Josse, Jessica M Madrigal, Christine Parks, Dale P Sandler, Gabriella Andreotti, Mary H Ward, Jonathan N Hofmann, Laura E Beane Freeman

Abstract

Introduction: An algorithm to estimate non-occupational pesticide exposure from take-home, agricultural drift, and residential use pathways was previously developed for spouses of farmers who applied pesticides in the Agricultural Health Study (AHS). The algorithm combined participant- and pesticide active ingredient-specific information with pathway weights derived from meta-analyses of published data through 2015. We updated and evaluated the algorithm by expanding our literature review and analysis to include studies published in the last 10 years.

Methods: We conducted an updated literature review through 2024 and abstracted published measurements. For each pathway, we examined the relative differences (RD) in indoor dust pesticide concentrations using mixed-effect meta-regression models, which we used to update the algorithm.

Results: We added exposure measurements from 9 publications to the original 10 publications. In total, this provided 58, 68, and 93 summary statistics for the take-home, agricultural drift, and residential use pathways, respectively. The algorithm’s assumption that non-occupational exposure increases with increasing number of days of an active ingredient’s agricultural use was confirmed by AHS-specific dust measurements. For the take-home pathway, weights increased from 2.3 (95%CI: 1.5-3.3) to 3.7 (95%CI: 1.9, 7.4) in the updated algorithm based on measurements in homes with known use of the active ingredient. By contrast, the updates for the agricultural drift pathway lowered the weights for all ‘home-to-field’ distance categories; for example, the RD dropped from 3.0 (95%CI: 1.7-5.4) to 2.0 (95%CI: 1.3-3.1) for homes located <100 yards from field. We found minimal changes to the residential use pathway weights. We also updated the algorithm to apply the adjustment factor for ‘time at home’ to all pathways, rather than just the take-home pathway.

Discussion: Our update confirmed several algorithm assumptions and provided updated pathway weights. The refined algorithm will better characterize non-occupational pesticide exposure contrast among the AHS spouses for future etiologic analyses.

Julie Elbæk Pedersen

Abstract

Objective: Agricultural work may involve exposure to various agents that potentially may contribute to hematopoietic cancers, including pesticides and viruses. Exposure levels may vary among different groups of workers and over calendar periods. However, epidemiologic studies examining different types of agricultural sectors across historical periods are lacking.

Material and methods: The Danish Cancer Registry was used to identify men born 1946+ with specific hematopoietic cancers (Hodgkin lymphoma, non-Hodgkin lymphoma, multiple myeloma, lymphatic leukemia, and myeloid leukemia). A total of 9,021 cases were included, and 451,050 (1:50) controls were individually matched by year of birth and free of cancer at the time of diagnosis of the corresponding case. Cases and controls were born in Denmark and have an employment history (1964+), obtained from the Supplementary Pension Fund register with compulsory membership. Odds ratios (OR) with 95% confidence intervals (CI) were estimated by logistic regression, adjusted for physical activity and socioeconomic status.

Results: Ever employment in agriculture was associated with an increased OR of Hodgkin’s lymphoma (OR=1.39, CI: 1.15-1.68) and myeloid leukemia (OR=1.42, CI: 1.16-1.74). The highest OR for Hodgkin’s lymphoma was observed for employment before 1975 (OR=1.68, CI: 0.91-3.11). When focusing on ever employment in different sub-groups, noteworthy increased risk estimates were observed for Hodgkin’s lymphoma and “unspecified agriculture” (OR=1.90, CI: 1.39-2.60) and “livestock” (OR=1.51, CI: 1.12-2.05); non-Hodgkin’s lymphoma and “horticulture” (OR=1.33, CI: 1.01-1.74); and myeloid leukemia and “agricultural services” (OR=1.87, CI: 1.17-2.98).

Conclusion: Our results show elevated risk of Hodgkin’s lymphoma and myeloid leukemia among men employed in agriculture. Additionally, certain agricultural sectors and periods of employment were associated with a higher risk of specific hematopoietic cancers. Future studies should differentiate between agricultural sectors and periods and include detailed exposure data to improve understanding of the disease patterns among agricultural workers.