Xiaoguang Liu, Lin Zhu, Jingxin Liu, Zichen Nie, Wenjun Qiu
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Targeted metabolomics of plasma samples was performed by ultra-performance liquid chromatography coupled to the tandem mass spectrometry, and principal component analysis, variable importance in projection, and orthogonal partial least squares discriminant analysis were used to obtain the differentially expressed metabolites. Pathway analysis was conducted with the Homo sapiens (HSA) sets in the Kyoto Encyclopedia of Genes and Genomes. We used machine learning algorithms to obtain the potential biomarkers and Spearman correlation analysis to clarify the association between potential biomarkers and clinical parameters. We found that clinical parameters and metabolite clusters were significantly changed in obese children with IR before and after weight loss intervention. Mechanistically, weight loss intervention significantly changed 61 metabolites in obese children with IR. Furthermore, 12 pathways were significantly changed. Moreover, the machine learning algorithm found 6 important potential biomarkers. In addition, these potential biomarkers were strongly associated with major clinical parameters. These data indicate different metabolomic profiles in obese children with IR after weight loss intervention, providing insights into the clinical parameters and metabolite mechanisms involved in weight loss programs.</p></div>","PeriodicalId":7810,"journal":{"name":"Amino Acids","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364699/pdf/","citationCount":"0","resultStr":"{\"title\":\"Effect of weight loss interventions on metabolomic signatures in obese children with insulin resistance\",\"authors\":\"Xiaoguang Liu, Lin Zhu, Jingxin Liu, Zichen Nie, Wenjun Qiu\",\"doi\":\"10.1007/s00726-024-03409-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The obesity epidemic among children has become a major public health issue, and the presence of childhood insulin resistance (IR) has been demonstrated prior to the onset of type 2 diabetes mellitus. However, it is unclear whether the metabolomic signature is associated with weight loss interventions in obese children with IR. Thirty-six obese children with IR were selected from the weight loss camp (Shenzhen Sunshine Xing Yada health Technology Co., LTD). Clinical parameters were collected before and after weight loss intervention. Targeted metabolomics of plasma samples was performed by ultra-performance liquid chromatography coupled to the tandem mass spectrometry, and principal component analysis, variable importance in projection, and orthogonal partial least squares discriminant analysis were used to obtain the differentially expressed metabolites. Pathway analysis was conducted with the Homo sapiens (HSA) sets in the Kyoto Encyclopedia of Genes and Genomes. We used machine learning algorithms to obtain the potential biomarkers and Spearman correlation analysis to clarify the association between potential biomarkers and clinical parameters. We found that clinical parameters and metabolite clusters were significantly changed in obese children with IR before and after weight loss intervention. Mechanistically, weight loss intervention significantly changed 61 metabolites in obese children with IR. Furthermore, 12 pathways were significantly changed. Moreover, the machine learning algorithm found 6 important potential biomarkers. In addition, these potential biomarkers were strongly associated with major clinical parameters. These data indicate different metabolomic profiles in obese children with IR after weight loss intervention, providing insights into the clinical parameters and metabolite mechanisms involved in weight loss programs.</p></div>\",\"PeriodicalId\":7810,\"journal\":{\"name\":\"Amino Acids\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364699/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Amino Acids\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00726-024-03409-2\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Amino Acids","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s00726-024-03409-2","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
儿童肥胖症已成为一个重大的公共卫生问题,儿童胰岛素抵抗(IR)在 2 型糖尿病发病前就已被证实。然而,目前还不清楚代谢组特征是否与患有胰岛素抵抗的肥胖儿童的减肥干预措施有关。研究人员从减肥训练营(深圳市阳光兴亚达健康科技有限公司)中挑选了36名患有IR的肥胖儿童。收集了减肥干预前后的临床指标。采用超高效液相色谱-串联质谱法对血浆样本进行靶向代谢组学分析,并通过主成分分析、投影变量重要性分析和正交偏最小二乘判别分析获得差异表达的代谢物。我们利用京都基因和基因组百科全书中的智人(HSA)集进行了通路分析。我们使用机器学习算法来获得潜在的生物标志物,并使用斯皮尔曼相关性分析来阐明潜在生物标志物与临床参数之间的关联。我们发现,在减肥干预前后,患有IR的肥胖儿童的临床参数和代谢物群发生了显著变化。从机理上讲,减肥干预明显改变了患有 IR 的肥胖儿童的 61 种代谢物。此外,12 条途径也发生了明显变化。此外,机器学习算法还发现了 6 个重要的潜在生物标志物。此外,这些潜在生物标志物与主要临床参数密切相关。这些数据表明,患有IR的肥胖儿童在减肥干预后会出现不同的代谢组学特征,为减肥计划中涉及的临床参数和代谢物机制提供了见解。
Effect of weight loss interventions on metabolomic signatures in obese children with insulin resistance
The obesity epidemic among children has become a major public health issue, and the presence of childhood insulin resistance (IR) has been demonstrated prior to the onset of type 2 diabetes mellitus. However, it is unclear whether the metabolomic signature is associated with weight loss interventions in obese children with IR. Thirty-six obese children with IR were selected from the weight loss camp (Shenzhen Sunshine Xing Yada health Technology Co., LTD). Clinical parameters were collected before and after weight loss intervention. Targeted metabolomics of plasma samples was performed by ultra-performance liquid chromatography coupled to the tandem mass spectrometry, and principal component analysis, variable importance in projection, and orthogonal partial least squares discriminant analysis were used to obtain the differentially expressed metabolites. Pathway analysis was conducted with the Homo sapiens (HSA) sets in the Kyoto Encyclopedia of Genes and Genomes. We used machine learning algorithms to obtain the potential biomarkers and Spearman correlation analysis to clarify the association between potential biomarkers and clinical parameters. We found that clinical parameters and metabolite clusters were significantly changed in obese children with IR before and after weight loss intervention. Mechanistically, weight loss intervention significantly changed 61 metabolites in obese children with IR. Furthermore, 12 pathways were significantly changed. Moreover, the machine learning algorithm found 6 important potential biomarkers. In addition, these potential biomarkers were strongly associated with major clinical parameters. These data indicate different metabolomic profiles in obese children with IR after weight loss intervention, providing insights into the clinical parameters and metabolite mechanisms involved in weight loss programs.
期刊介绍:
Amino Acids publishes contributions from all fields of amino acid and protein research: analysis, separation, synthesis, biosynthesis, cross linking amino acids, racemization/enantiomers, modification of amino acids as phosphorylation, methylation, acetylation, glycosylation and nonenzymatic glycosylation, new roles for amino acids in physiology and pathophysiology, biology, amino acid analogues and derivatives, polyamines, radiated amino acids, peptides, stable isotopes and isotopes of amino acids. Applications in medicine, food chemistry, nutrition, gastroenterology, nephrology, neurochemistry, pharmacology, excitatory amino acids are just some of the topics covered. Fields of interest include: Biochemistry, food chemistry, nutrition, neurology, psychiatry, pharmacology, nephrology, gastroenterology, microbiology