Untargeted UHPLC-Q-Orbitrap Metabolomics Reveals Hormonal Suppression and Candidate Serum Markers as Evidence of Testosterone Misuse in Cattle

Abstract

The illicit use of androgenic anabolic steroids, such as 17β-testosterone, in food-producing animals poses significant risks to animal welfare and consumer safety. Detecting exogenous administration of endogenous hormones like testosterone is particularly challenging, as the administered compound is chemically identical to naturally occurring hormones. In this study, we developed a metabolomics-based workflow using ultra-high-performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry to enhance detection of testosterone misuse in cattle. Serum samples from treated steers were analyzed using an untargeted metabolomics workflow combined with multivariate supervised modeling (OPLS-DA). Data processing with an optimized IPO-XCMS pipeline provided peak picking and alignment. OPLS-DA modeling provided robust class separation, correctly predicting the hold-out samples. Cross-validation and permutation testing further confirmed the model’s stability and predictive reliability. Untargeted analysis identified three molecular features with high discriminatory power and positive correlation with the treatment, and a significant suppression of endogenous hormones (androstenedione, corticosterone, and progesterone) as part of a negative feedback response. Notably, these suppression effects persisted beyond the period of elevated testosterone responses. The proposed workflow offers a sensitive tool to strengthen regulatory surveillance by identifying both novel candidate markers and endocrine disruptions in suspected samples.

Supplementary files

Article information

Article type
Paper
Submitted
23 Oct 2025
Accepted
19 Dec 2025
First published
26 Dec 2025

Anal. Methods, 2026, Accepted Manuscript

Untargeted UHPLC-Q-Orbitrap Metabolomics Reveals Hormonal Suppression and Candidate Serum Markers as Evidence of Testosterone Misuse in Cattle

D. G. Rocha, M. A. Lana, D. C. De Assis and A. Faria, Anal. Methods, 2026, Accepted Manuscript , DOI: 10.1039/D5AY01774B

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