| ObjectiveWith the changes of life environment, children’s abnormal sexual development,especially early puberty onset rate increased. It has become one of the commonpediatric endocrine diseases at present. Precocious puberty causes great adverse effecton children’s physical and mental development and is very easy to cause children’spsychological disorder, even leads to fall into a wrong path, as well as social problems.Therefore, early diagnosis and treatment of precocious puberty is very important.However, the clinical diagnosis and detection methods at present can not effectivelyachieve the goal of early diagnosis. In recent years, the development of metabolomicsprovides a new method for the diagnosis of diseases. Metabolomics aims to usespectroscopy and pattern recognition methods with high-throughput, high sensitivityand high precision to observe the dynamic changes of endogenous metabolites of theorganism. So it can directly reflect the response to the external pathophysiologicalstimuli and intervention from an overall system perspective. In this study, highperformance liquid chromatography coupled with quadrupole time-of-flight massspectrometry (HPLC-QTOF/MS) was used to profile the metabolites of serum samplesfrom central precocious puberty (CPP) children and healthy controls. Multivariatestatistical analysis was applied to discriminate the CPP children with healthy childrenand to explore the metabolic changes in children with precocious puberty so as toprovide experimental basis for metabolomics and effective data for epidemiologicalinvestigation of precocious puberty. Thereby it will lay the foundation for theestablishment of a metabolomics-based diagnosis and treatment methods for precociouspuberty early.MethodFirstly, the proteins in the clinical serum samples were participated with serum-acetonitrile (1:3). After being centrifuged, the supernatant were filtered throughcaptiva-lipid column to further remove proteins and large molecules substances. Thenthe supernatant were detected by using high performance liquid chromatographycoupled with quadrupole time-of-flight mass spectrometry (HPLC-QTOF/MS) assay,and the metabolic patterns and related data of the pathological group and healthy groupwere obtained. The chromatographic conditions were as follows: Agilent Poroshell120Bonus-RP (2.1x100mm,2.7μm) chromatographic column; the mobile phase contains0.1%aqueous formic acid (A)-acetonitrile (B). The gradient elution for positive ionmode is as follows:0~1min,10%B;1~11min, B increases to60%;11to29min, Bincreases to95%;29to34min, B95%. The gradient elution for negative ion mode is asfollows:0~1min,10%B;1~6min, B increased to60%;6to16min, B increases to75%;16~30min, B increases to95%,30~35min, B95%. The flow rate was0.25mL/min; the column temperature is40°C. The injection volume was5μL. The massspectrometry conditions were as follows: using electrospray ionization in the positiveion mode (ESI+) and negative ion mode (ESI-) to analyze the samples; data acquisitionrange is m/z100~1700; the capillary voltage is4000V, the nozzle voltage is1000V; thesheath gas temperature is250°C; nebulizer pressure is45psi; the drying gas flow rate is10L/min; the temperature of drying gas is350°C; the fragmentation voltage is140V.Principal component analysis (PCA) and partial least squares-discriminant analysis(PLS-DA) were performed to analyze the experimental data and investigate thedifferences between children with precocious puberty and healthy children. Then judgethe efficacy of the biomarkers to divide the serum samples, moreover, inspect thesecompounds on the prediction ability of diagnosis.ResultThrough optimizing the experimental conditions, HPLC-QTOF/MS method wasestablished for determination of metabolites in the blood. Then metabolites in the bloodof23children with precocious puberty and43health children were relatively quantified.After statistical analysis, the PCA scores plot showed that the metabolic features of CPPand normal children blood were significantly different. According to the load graph andVIP value of the PLS analysis,24metabolic markers, whose ions have significantinfluence on data classification were identified. The metabolic pathways involved areanalyzed and the prediction ability of biological markers was verified by PLS-DA. ConclusionLC-MS is a common analytical technique to be used in metabolomics studies. Inthis study, it is applied to investigate the metabolic profiles of precocious pubertychildren, and to discover metabolic markers for further understanding the pathogenesis.Our results show that the24metabolic markers have good prediction ability forprecocious puberty. |
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