4.8 Article

Benchmark dose approach in investigating the relationship between blood metal levels and reproductive hormones: Data set from human study

Journal

ENVIRONMENT INTERNATIONAL
Volume 165, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.envint.2022.107313

Keywords

BMD approach; Dose-response relationship; Toxic metals; Reproductive toxicity; Endocrine disruption

Funding

  1. Science Fund of the Republic of Serbia
  2. PROMIS [6066532]

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The main objective of this research was to investigate the dose-response relationship between metal/metalloid blood concentration and serum hormone levels. The study included male participants from different cohorts and confirmed the dose-response relationships for all investigated metals/metalloid and hormones.
The main objective of this research was to conduct a dose-response modeling between the internal dose of measured blood Cd, As, Hg, Ni, and Cr and hormonal response of serum testosterone, luteinizing hormone (LH), and follicle-stimulating hormone (FSH). The study included 207 male participants from subjects of 5 different cohorts (patients with prostate, testicular, and pancreatic cancer, patients suffering from various thyroid and metabolic disorders, as well as healthy volunteers), enrolled from January 2019 to May 2021 at the Clinical Centre of Serbia in Belgrade, Serbia. Benchmark dose-response modeling analysis was performed with the PROAST software version 70.1, showing the hormone levels as quantal data. The averaging technique was applied to compute the Benchmark dose (BMD) interval (BMDI), with benchmark response set at 10%. Doseresponse relationships between metal/metalloid blood concentration and serum hormone levels were confirmed for all the investigated metals/metalloid and hormones. The narrowest BMDI was found for Cdtestosterone and Hg-LH pairs, indicative of high confidence in these estimates. Although further research is needed, the observed findings demonstrate that the BMD approach may prove to be significant in the dose-response modeling of human data.

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