The IGF-1 as a biomarker did not show differences between GHDon and healthy control (Table 1); however, the metabolomics/lipidomics could predict significantly altered sets of small molecules. Consequently, the use of IGF-1 as a biomarker is again considered reductionist and not very sensitive during adulthood. The low specificity of biomarkers for GHD and the evident change in lipid metabolism led to the application of non-targeted metabolomics/lipidomics in the search for new sources of biomarkers linked to the phenotypic state of GHD (i.e., downstream readouts of cellular signaling, transcriptomic and proteomic changes).
Metabolomics and lipidomics analysis by DIMS showed good coverage of the GHD metabolome; notwithstanding, it is important to highlight those similar results can be achieved computational algorithms in treating DIMS data, then such platforms require previous knowledge in computing programming. For this reason, we developed MassAligner software to be user-friendly, regarding previous programming knowledge. DIMS may be a more practical alternative in large-scale metabolite studies due to lower time consumption in data acquisition, and MassAligner Software guaranteed the accessible Metaboanalyst® platform.
The PLS-DA model proved to be efficient in the data drive prediction of GHD through the metric values of the cross-validation; prediction coefficients (Q2) are in agreement with those suggested in metabolomic studies (i.e., Q2> 0.6) (Figure 2) [26]. Permutation test and ROC curve corroborate the observed statistical difference between the sample means and predictive performance of PLS-DA models [27].
Other studies have already demonstrated the potential of metabolomics to elucidate biochemical pathways and unknown metabolites [28,29]. Among the Omics sciences applied in GHD, metabolomics is scarcer than the large-scale molecular study techniques used in molecular biology (i.e., transcriptomics) [17,18]. A recent study using single-cell transcriptomics has provided insights into the transcriptional landscape of human pituitary development, which will allow data integration for the development of multi-omics GHD studies [18].
4.1. Metabolomics approach
Although the metabolomics sample preparation protocol favors polar metabolites, the glycerophospholipid class produced significant features that classify GHD (Figure 4A and 4B). It is interesting to highlight that the participation of glycerophospholipids can be established from the structural composition of cells, mediation of cellular responses, and precursors of biological effectors [30–32]. Thus, the glycerophospholipid alterations in the GHD population were considered: 1) subsequent action of the change in fatty acids; 2) high oxidative stress coupled with the imbalance of reactive oxygen species can destabilize membranes glycerophospholipids; 3) sources of building blocks for biological effectors.
Amino acids also stood out as significant metabolites to differentiate GHD individuals from healthy controls, which directly reflects the ability of metabolomics and its branches to create panoramas of the studied phenotype. The pathways analysis indicated a significant change in the metabolism of the following amino acids: glutamine/glutamate, taurine/hypotaurine, cysteine/methionine, arginine/proline, alanine/serine/glutamate (Figure 4B). Alteration of amino acids metabolism are reflective of the metabolic actions of homeostasis during the lack of GH as a biological effector so that it can lead to hypoglycemia (i.e., GH acting as an insulin counter-controller); consequently, these proteinogenic and glycogenic amino acids are redirected to catabolism (e.g., glutamine, arginine, proline, alanine). Amino acids can be obtained from proteolysis of muscle tissues and follow as an energy substrate in GHD [33]; hence, glycolysis/gluconeogenesis and the TCA cycle are intermediary pathways that suffer from substrate alternation.
The change in the pathways of sulfuric amino acids (e.g., taurine/hypotaurine, cysteine /methionine) makes up the need to use the thiol group available in these amino acids for various molecules with biological actions (e.g., Glutathione) [34]. From an endocrinological point of view, this condition may be related to the fact that several biomolecules require the help of sulfotransferase enzymes to perform their metabolic functions effectively (e.g., steroids, bile salts, peptides) [35]. With proteomic analysis and omics data integration, it will be possible to determine which enzymes are involved with oxidative stress in GHD conditions.
Protein synthesis uses arginine in the anabolic process, and glutamine is an essential precursor for arginine biosynthesis [36,37]. The annotation of glutamic acid, cysteinyl-proline peptide, arginine, capreomycidine directly impacted the arginine metabolism in GHDon and GHDoff conditions (Table 2). Oh, et al. (2017) demonstrated that L-arginine promotes cellular proliferation and increases phosphorylation of proteins linked to the MAPK/ERK signaling pathway. The role of L-arginine also includes enhancing the expression of genes responsible for GH and IGHF-1 regulation [36].
Data from the PLS-DA model of GHDon vs. healthy control and GHDoff vs. healthy control, Table 2, shows that regardless of GHRT, there are variables (features or putative metabolites) that remain unregulated in the patients. Upregulation and downregulation from these metabolites, such as tetracosapentaenoic acid (m/z 199.1297) (Table 2), show that GHRT influences the metabolism status of GHD patients in order to compensate for dysregulations caused by the disease. GHDon vs. GHDoff analysis corroborates the action of GHRT through the projection of unbalanced variables compared to healthy control. Table 02 contains significant annotated vital metabolites associated with amino acid and lipid metabolism, and a list of all noted analytes and their structural information and annotation are available in Table S1. Besides lipid and amino acid changes, bioenergetic metabolism appears to be subsequently affected due to the partial or total absence of GH in homeostatic control (e.g., alterations in TCA, β-oxidation, glycolysis/gluconeogenesis levels). Recently, Hoffman and Cols (2020) have used metabolomics and transcriptomics analyses in growth hormone-releasing hormone knockout (GHRH-KO) in mice, and the results showed increased transcript levels of mitochondrial amino acid genes; while metabolomics showed that mitochondrial metabolites are differentially regulated in GHRH-KO as well signal of genotype-by-sex interactions [38].
4.2. Lipidomics approach
As the metabolomics results suggested the importance of lipids in GHD patients, a lipidomics approach was carried out in the samples. The results from the new approach corroborated with the previous study, indicating a significant change in the glycerophospholipid class (Figure 3A and 4A). It was observed that the enrichment analysis revealed important species of lipids that act as biological effectors during the bioenergetic metabolism of GHD, such as acyl-carnitines, glycerolipids, unsaturated fatty acids, and n-acyl-amines. These classes of metabolites proved to be significant regardless of therapeutic approaches in individuals with GHD (Figure 4A). In parallel, metabolomics pointed to similar lipid species, although only the glycerophospholipids were significant in the metabolomics pathway analysis (Figure 4B).
Another metabolic difference in GHD patients revealed by the lipidomics approach was the change of fatty acyls metabolism, noted by the putative annotation of lipids such as Palmitic acid; Docosanoic acid; Myristic acid; 8-Hydroperoxylinoleic acid. It must be noted that Fatty Acyls correspond to a “super” lipid class formed by the loss of hydroxyl from the carboxy group of a fatty acid. The synthesis and oxidation of fatty acids are well defined by the enzymes ACC1 and ACC2 that regulate a set of reactions in the cytosol and mitochondria, respectively [39]. The condition of GHD prevents insulin counter-regulation; therefore, there will be greater intensity in fatty acid elongation, which explains one of the parameters that unbalance the metabolism of fatty acids [33,40].
Moreover, acyl-carnitines such as 3-hydroxyoctanoyl carnitine; Linoleoyl carnitine were up-regulated in the GHDoff individuals (Table 2). These unbalanced lipids are participants in biosynthesis and lipid oxidation metabolism, indicating that the absence of GHRT in adulthood affects bioenergetic metabolism [40].
Lipolysis is directly linked to GH stimuli; however, the absence of GH does not correctly stimulate the use of lipid reserves found in adipocytes to obtain energy and/or intermediate metabolites [41]. At the same time, the proteolysis of muscles becomes a source of energy, which is significant in GHD [42,43]. Treep and Cols (2008) showed that GH therapy stimulates oxidation of lipids originating from skeletal muscle in GHD patients [44]; therefore, GH is an important parameter that regulates the use of these lipid reserves efficiently, and metabolomic/lipidomic profile could guarantee a better understanding of GHRT.
The lipid changes noted between GHDon vs. healthy control are associated with bioenergetic metabolism (e.g., 2-hydroxydecanedioic acid), communication/signalization (e.g., 8-Hydroperoxylinoleic acid), and N-acyl-amines (e.g., 2,4-Undecadiene-8,10-isobutyl amide) that have a role important in lipid transport [45] (Table 2).
Phosphatidylcholine, phosphatidylserine, phosphatidylglycerol, and phosphatidic acids are up-regulated features in GHD individuals regardless of GHRT (Table 2). Differences between patients on GHRT and healthy controls also remained significant in the work of Abd Rahman (2013).
GHD condition has been shown to limit lipids as an energy source and could subsequently affect other biochemical pathways that use lipids, such as inflammation, cognition, metabolic homeostasis, and transport [45,46]. The imbalance in lipid diversity can affect essential unsaturated fatty acids that are precursors or intermediates of biological effectors (i.e., prostaglandins, glycerophospholipids, and post-translation modification (PTM) of proteins with acyl groups regulates various biological processes [47,48].