Eftychia A Aggelaki, Aristeidis Giannakopoulos, Panagiota D Georgiopoulou, Styliani A Chasapi, Alexandra Efthymiadou, Dimitra Kritikou, Dionisios Chrysis, Georgios A Spyroulias
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引用次数: 0
Abstract
Introduction: The diagnosis of Growth Hormone Deficiency (GHD) during childhood has been the subject of much controversy over the last few years. Aiming to accurate medical treatment, there is a need for biomarker discovery.
Objective: To characterize the metabolic profile of GHD children, examine the effect of GH administration on the metabolic signature, and investigate the correlations between metabolites and IGF-1.
Methods: Nuclear Magnetic Resonance (NMR)-based untargeted and targeted metabolomic approach applied to study the metabolic profiles of children with GHD. Plasma, serum, and urine samples were collected from twenty-two children diagnosed with GHD and forty-eight age matched controls from the Pediatric Endocrinology Unit of the University Hospital of Patras. Experimental data were examined by both multivariate and univariate statistical analysis.
Results: The results of this pilot study revealed a different metabolic fingerprint of children with GHD in comparison to age-matched healthy individuals. However, the detected alterations in the metabolite patterns before and after GH treatment were subtle and of minor discriminative statistical power.
Conclusions: This study provides evidence that metabolome plays a pivotal role in GHD, but large-scale multicenter studies are warranted to validate the results.
期刊介绍:
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.