Food consumption data as a tool to estimate exposure to mycoestrogens

Carla Martins, Duarte Torres, Carla Lopes, Daniela Correia, Ana Goios, Ricardo Assunção, Paula Alvito, Arnau Vidal, Marthe de Boevre, Sarah de Saeger, Carla Nunes

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
8 Downloads (Pure)


Zearalenone and alternariol are mycotoxins produced by Fusarium and Alternaria species, respectively, that present estrogenic activity and consequently are classified as endocrine disruptors. To estimate the exposure of the Portuguese population to these two mycotoxins at a national level, a modelling approach, based on data from 94 Portuguese volunteers, was developed considering as inputs: i) the food consumption data generated within the National Food and Physical Activity Survey; and ii) the human biomonitoring data used to assess the exposure to the referred mycotoxins. Six models of association between mycoestrogens urinary levels (zearalenone, total zearalenone and alternariol) and food items (meat, cheese, and fresh-cheese, breakfast cereals, sweets) were established. Applying the obtained models to the consumption data (n = 5811) of the general population, the median estimates of the probable daily intake revealed that a fraction of the Portuguese population might exceed the tolerable daily intake defined for zearalenone. A reference intake value for alternariol is still lacking, thus the characterization of risk due to the exposure to this mycotoxin was not possible to perform. Although the unavoidable uncertainties, these results are important contributions to understand the exposure to endocrine disruptors in Portugal and the potential Public Health consequences.

Original languageEnglish
Article number118
Issue number2
Publication statusPublished - 1 Jan 2020


  • Food consumption
  • Modelling
  • Mycotoxins
  • Public health
  • Urinary biomarkers


Dive into the research topics of 'Food consumption data as a tool to estimate exposure to mycoestrogens'. Together they form a unique fingerprint.

Cite this