Login / Signup

Occupational Exposure to Mycotoxins in Swine Production: Environmental and Biological Monitoring Approaches.

Susana ViegasRicardo Manuel AssunçãoCarla MartinsCarla NunesBernd OstereschMagdalena TwarużekRobert KosickiJan GrajewskiEdna RibeiroCarla Viegas
Published in: Toxins (2019)
Swine production workers are exposed simultaneously to multiple contaminants. Occupational exposure to aflatoxin B₁ (AFB₁) in Portuguese swine production farms has already been reported. However, besides AFB₁, data regarding fungal contamination showed that exposure to other mycotoxins could be expected in this setting. The present study aimed to characterize the occupational exposure to multiple mycotoxins of swine production workers. To provide a broad view on the burden of contamination by mycotoxins and the workers' exposure, biological (urine) samples from workers (n = 25) and 38 environmental samples (air samples, n = 23; litter samples, n = 5; feed samples, n = 10) were collected. The mycotoxins biomarkers detected in the urine samples of the workers group were the deoxynivalenol-glucuronic acid conjugate (60%), aflatoxin M₁ (16%), enniatin B (4%), citrinin (8%), dihydrocitrinone (12%) and ochratoxin A (80%). Results of the control group followed the same pattern, but in general with a lower number of quantifiable results (<LOQ). Besides air samples, all the other environmental samples collected presented high and diverse contamination, and deoxynivalenol (DON), like in the biomonitoring results, was the most prominent mycotoxin. The results demonstrate that the occupational environment is adding and contributing to the workers' total exposure to mycotoxins, particularly in the case of DON. This was confirmed by the biomonitoring data and the high contamination found in feed and litter samples. Furthermore, he followed multi-biomarker approach allowed to conclude that workers and general population are exposed to several mycotoxins simultaneously. Moreover, occupational exposure is probably described as being intermittent and with very high concentrations for short durations. This should be reflected in the risk assessment process.
Keyphrases
  • risk assessment
  • human health
  • drinking water
  • health risk
  • electronic health record
  • machine learning
  • big data
  • climate change
  • deep learning
  • artificial intelligence