Purpose: The discovery of biomarkers to detect growth hormone deficiency (GHD) and monitor growth hormone replacement therapy (GHRT) remains challenging. Among “omics” technologies used for screening biomarkers, metabolomics stands out as a powerful tool for large-scale identification and quantification of small molecules present in biological matrixes. Metabolomic profiles allow us to infer the phenotypic state; therefore, metabolomics is a great ally in investigating biomarkers and understanding biological processes. Methods: In this study, global metabolomics (N = 39; range scan 50-600 m/z) and lipidomics (N = 36; range scan 50-1500 m/z) approaches were performed by high-resolution direct infusion-mass spectrometry. Partial least-square discriminant analysis (PLS-DA) models were used for data-driven diagnosis of GHD and evaluation of GHRT (VIP score >1.5). Cross-validation, permutation test, and ROC curves confirmed the predictive accuracy of PLS-DA models. The features were annotated using accurate mass measurements matched against the metabolomics database. Results: Data analysis revealed changes in the class of proteinogenic/glycogenic acids, carnitines, n-acyl-amines, unsaturated fatty acids, and sulfur amino acids, and pathway analyzes revealed changes in glycerophospholipid metabolism. Regardless of GH therapy, GHD individuals remain with changes in lipids and amino acids compared to healthy control. Conclusion: GHRT influences the metabolism status of GHD patients in order to compensate for dysregulations caused by GHD. The data has corroborated the action of GHRT and indicated new potential biomarkers for treatment follow-up.

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