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Untargeted LC/MS-Based Metabolic Phenotyping of Hypopituitarism in Young Males.

ABSTRACT: Objective: Hypopituitarism (Hypo-Pit) is partial or complete insufficiency of anterior pituitary hormones. Besides hormone metabolism, the global metabolomics in Hypo-Pit are largely unknown. We aimed to explore potential biomarkers to aid in diagnosis and personalized treatment. Methods: Using both univariate and multivariate statistical methods, we identified 72 differentially abundant features through liquid chromatography coupled to high-resolution mass spectrometry, obtained in 134 males with Hypo-Pit and 90 age matched healthy controls. Results: Hypopituitarism exhibits an increased abundance of metabolites involved in amino acid degradation and glycerophospholipid synthesis, but decreased content of metabolites in steroid hormone synthesis and fatty acid beta-oxidation. Significantly changed metabolites included creatine, creatinine, L-alanine, phosphocholines, androstenedione, hydroprenenolone, and acylcarnitines. In Hypo-Pit patients, the increased ratio of creatine/creatinine suggested reduced creatine uptake and impaired creatine utilization, whereas the decreased level of beta-hydroxybutyrate, acetylcarnitine (C2) and a significantly decreased ratio of decanoylcarnitine (C10) to free carnitine suggested an impaired beta-oxidation. Furthermore, the creatine/creatinine and decanoylcarnitine/carnitine ratio were identified as diagnostic biomarkers for Hypo-Pit with AUCs of 0.976 and 0.988, respectively. Finally, we found that the creatinine and decanoylcarnitine/carnitine ratio could distinguish cases that were sensitive vs. resistant to human chorionic gonadotropin therapy. Conclusion: We provided a global picture of altered metabolic pathways in Hypo-Pit, and the identified biomarkers in creatine metabolism and beta-oxidation might be useful for the preliminary screening and diagnosis of Hypo-Pit.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC8295757 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNonpuerperal mastitis (NPM) is a disease that presents with redness, swelling, heat, and pain during nonlactation and can often be confused with breast cancer. The etiology of NPM remains elusive; however, emerging clinical evidence suggests a potential involvement of lipid metabolism.MethodLiquid chromatography‒mass spectrometry (LC/MS)-based untargeted lipidomics analysis combined with multivariate statistics was performed to investigate the NPM lipid change in breast tissue. Twenty patients with NPM and 10 controls were enrolled in this study.ResultsThe results revealed significant differences in lipidomics profiles, and a total of 16 subclasses with 14,012 different lipids were identified in positive and negative ion modes. Among these lipids, triglycerides (TGs), phosphatidylethanolamines (PEs) and cardiolipins (CLs) were the top three lipid components between the NPM and control groups. Subsequently, a total of 35 lipids were subjected to screening as potential biomarkers, and the chosen lipid biomarkers exhibited enhanced discriminatory capability between the two groups. Furthermore, pathway analysis elucidated that the aforementioned alterations in lipids were primarily associated with the arachidonic acid metabolic pathway. The correlation between distinct lipid populations and clinical phenotypes was assessed through weighted gene coexpression network analysis (WGCNA).ConclusionsThis study demonstrates that untargeted lipidomics assays conducted on breast tissue samples from patients with NPM exhibit noteworthy alterations in lipidomes. The findings of this study highlight the substantial involvement of arachidonic acid metabolism in lipid metabolism within the context of NPM. Consequently, this study offers valuable insights that can contribute to a more comprehensive comprehension of NPM in subsequent investigations.Trial registrationShuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (Number: 2019-702-57; Date: July 2019).

Project description:BackgroundLiquid chromatography-mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.ResultsTo comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC-MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 CONCLUSIONS: In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC-MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

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Untargeted LC/MS-Based Metabolic Phenotyping of Hypopituitarism in Young Males.

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