Xue-Feng Gao, Bin-Bin Wu, Yong-Long Pan, Shao-Ming Zhou, Ming Zhang, Yue-Hua You, Yun-Peng Cai, Yan Liang
{"title":"青少年肥胖的肠道微生物组生物标志物:一项区域研究。","authors":"Xue-Feng Gao, Bin-Bin Wu, Yong-Long Pan, Shao-Ming Zhou, Ming Zhang, Yue-Hua You, Yun-Peng Cai, Yan Liang","doi":"10.1007/s13755-023-00236-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to characterize the gut microbiota in obese adolescents from Shenzhen (China), and evaluate influence of gender on BMI-related differences in the gut microbiome.</p><p><strong>Methods: </strong>Evaluation of physical examination, blood pressure measurement, serological assay and body composition were conducted in 205 adolescent subjects at Shenzhen. Fecal microbiome composition was profiled via high-throughput sequencing of the V3-V4 regions of the 16S rRNA gene. A Random Forest (RF) classifier model was built to distinguish the BMI categories based on the gut bacterial composition.</p><p><strong>Results: </strong>Fifty-six taxa consisting mainly of Firmicutes were identified that having significant associations with BMI; 2 OTUs belonging to Ruminococcaceae and 1 belonging to Lachnospiraceae had relatively strong positive correlations with body fate rate, waistline and most of serum biochemical properties. Based on the 56 BMI-associated OTUs, the RF model showed a robust classification accuracy (AUC 0.96) for predicting the obese phenotype. Gender-specific differences in the gut microbiome composition was obtained, and a lower relative abundance of <i>Odoribacter</i> genus was particularly found in obese boys. Functional analysis revealed a deficiency in bacterial gene contents related to peroxisome and PPAR signaling pathway in the obese subjects for both genders.</p><p><strong>Conclusions: </strong>This study reveals unique features of gut microbiome in terms of microbial composition and metabolic functions in obese adolescents, and provides a baseline for reference and comparison studies.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-023-00236-9.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"11 1","pages":"37"},"PeriodicalIF":4.7000,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435439/pdf/","citationCount":"0","resultStr":"{\"title\":\"Gut microbiome biomarkers in adolescent obesity: a regional study.\",\"authors\":\"Xue-Feng Gao, Bin-Bin Wu, Yong-Long Pan, Shao-Ming Zhou, Ming Zhang, Yue-Hua You, Yun-Peng Cai, Yan Liang\",\"doi\":\"10.1007/s13755-023-00236-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>This study aimed to characterize the gut microbiota in obese adolescents from Shenzhen (China), and evaluate influence of gender on BMI-related differences in the gut microbiome.</p><p><strong>Methods: </strong>Evaluation of physical examination, blood pressure measurement, serological assay and body composition were conducted in 205 adolescent subjects at Shenzhen. Fecal microbiome composition was profiled via high-throughput sequencing of the V3-V4 regions of the 16S rRNA gene. A Random Forest (RF) classifier model was built to distinguish the BMI categories based on the gut bacterial composition.</p><p><strong>Results: </strong>Fifty-six taxa consisting mainly of Firmicutes were identified that having significant associations with BMI; 2 OTUs belonging to Ruminococcaceae and 1 belonging to Lachnospiraceae had relatively strong positive correlations with body fate rate, waistline and most of serum biochemical properties. Based on the 56 BMI-associated OTUs, the RF model showed a robust classification accuracy (AUC 0.96) for predicting the obese phenotype. Gender-specific differences in the gut microbiome composition was obtained, and a lower relative abundance of <i>Odoribacter</i> genus was particularly found in obese boys. Functional analysis revealed a deficiency in bacterial gene contents related to peroxisome and PPAR signaling pathway in the obese subjects for both genders.</p><p><strong>Conclusions: </strong>This study reveals unique features of gut microbiome in terms of microbial composition and metabolic functions in obese adolescents, and provides a baseline for reference and comparison studies.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13755-023-00236-9.</p>\",\"PeriodicalId\":46312,\"journal\":{\"name\":\"Health Information Science and Systems\",\"volume\":\"11 1\",\"pages\":\"37\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2023-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435439/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Information Science and Systems\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s13755-023-00236-9\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Information Science and Systems","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s13755-023-00236-9","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Gut microbiome biomarkers in adolescent obesity: a regional study.
Purpose: This study aimed to characterize the gut microbiota in obese adolescents from Shenzhen (China), and evaluate influence of gender on BMI-related differences in the gut microbiome.
Methods: Evaluation of physical examination, blood pressure measurement, serological assay and body composition were conducted in 205 adolescent subjects at Shenzhen. Fecal microbiome composition was profiled via high-throughput sequencing of the V3-V4 regions of the 16S rRNA gene. A Random Forest (RF) classifier model was built to distinguish the BMI categories based on the gut bacterial composition.
Results: Fifty-six taxa consisting mainly of Firmicutes were identified that having significant associations with BMI; 2 OTUs belonging to Ruminococcaceae and 1 belonging to Lachnospiraceae had relatively strong positive correlations with body fate rate, waistline and most of serum biochemical properties. Based on the 56 BMI-associated OTUs, the RF model showed a robust classification accuracy (AUC 0.96) for predicting the obese phenotype. Gender-specific differences in the gut microbiome composition was obtained, and a lower relative abundance of Odoribacter genus was particularly found in obese boys. Functional analysis revealed a deficiency in bacterial gene contents related to peroxisome and PPAR signaling pathway in the obese subjects for both genders.
Conclusions: This study reveals unique features of gut microbiome in terms of microbial composition and metabolic functions in obese adolescents, and provides a baseline for reference and comparison studies.
Supplementary information: The online version contains supplementary material available at 10.1007/s13755-023-00236-9.
期刊介绍:
Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.