Occupational exposures and genetic susceptibility to urinary tract cancers: a systematic review and meta-analysis

The importance of understanding the distribution of GSTM1 and GSTT1 genotypes and haplotypes in a region with intense agriculture activity

Brazil is one of the largest pesticide consumers in the world, mainly due to its intense agricultural activity. The State of Goias, situated in Central Brazil, is a region recognized as an essential producer of soy, corn, beans, sorghum, sugar cane, and cotton. In this study, we evaluated 602 unrelated individuals, distributed in central and southern regions in Goias, presenting combined frequencies (haplotypes) of the GSTT1 and GSTM1 genes. In all municipalities, the frequency of the GSTT1 null genotype was 38.2% and of the GSTM1 null genotype was 50.3%. Goiania, the capital of Goias, presented the highest frequencies of GSTT1 and GSTM1 null genotypes, probably due to a founder effect of non-representative colonizing ancestors. So, the ancestral population adapted to the environment, with the frequencies observed in Goiania. However, nowadays, as there is excessive use of pesticides, the community becomes susceptible to the harmful effects of xenobiotics exposure, mainly due to the high frequency of GSTT1 and GSTM1 null genotypes. As in Goias, the consumption of pesticides has shown considerable growth, haplotypes with null alleles are of high risk for the population. Our results indicated that it is essential to understand the frequencies of the GSTT1 and GSTM1 genes for the monitoring of risk groups, like farmers, who have contact with pesticides, directly or indirectly, as well as assisting in the development of preventive medicine practices.

Evaluation of genotoxic effects in Brazilian agricultural workers exposed to pesticides and cigarette smoke using machine-learning algorithms

Monitoring exposure to xenobiotics by biomarker analyses, such as a micronucleus assay, is extremely important for the precocious detection and prevention of diseases, such as oral cancer. The aim of this study was to evaluate genotoxic effects in rural workers who were exposed to cigarette smoke and/or pesticides and to identify possible classification patterns in the exposure groups. The sample included 120 participants of both sexes aged between 18 and 39, who were divided into the following four groups: control group (CG), smoking group (SG), pesticide group (PG), and smoking + pesticide group (SPG). Their oral mucosa cells were stained with Giemsa for cytogenetic analysis. The total numbers of nuclear abnormalities (CG = 27.16 ± 14.32, SG = 118.23 ± 74.78, PG = 184.23 ± 52.31, and SPG = 191.53 ± 66.94) and micronuclei (CG = 1.46 ± 1.40, SG = 12.20 ± 10.79, PG = 21.60 ± 8.24, and SPG = 20.26 ± 12.76) were higher (p < 0.05) in the three exposed groups compared to the GC. In this study, we considered several different classification algorithms (the artificial neural network, K-nearest neighbors, support vector machine, and optimum path forest). All of the algorithms displayed good classification (accuracy > 80%) when using dataset2 (without the redundant exposure type SPG). It is clear that the data form a robust pattern and that classifiers could be successfully trained on small datasets from the exposure groups. In conclusion, exposing agricultural workers to pesticides and/or tobacco had genotoxic potential, but concomitant exposure to xenobiotics did not lead to additive or potentiating effects.