儿童早期基因型-微生物组-代谢组的关联及其与BMI和儿童肥胖的关系

Andrea Aparicio, Zheng Sun, Diane R Gold, Augusto A. Litonjua, Scott T. Weiss, Kathleen Lee-Sarwar, Yang-Yu Liu
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引用次数: 0

摘要

基因型对确定人类肠道微生物组的影响已被广泛研究,但尚未发现明确的结论。为了填补这一知识空白,我们利用了参加维生素D产前哮喘减少试验(VDAART)的6个月至8岁儿童的数据。我们专注于先前在独立研究中发现的与肠道微生物组相关的12个基因池,建立了p值的Bonferroni校正显著性水平<2.29 x10 ^(6)。我们发现了FHIT基因(已知与肥胖和2型糖尿病相关)和肥胖相关微生物组特征的snp与儿童童年时期的BMI之间的显著关联。基于这些关联,我们定义了一组感兴趣的snp和一组感兴趣的分类群。采用多组学方法,我们将血浆代谢组数据整合到我们的分析中,发现儿童BMI、感兴趣的snp和感兴趣的分类群之间存在同步关联,涉及氨基酸、脂质、核苷酸和异种生物。利用我们的关联结果,我们构建了一个四分图,其中每个不相交的节点集代表FHIT基因、微生物分类群、血浆代谢物或BMI测量中的snp。网络分析发现了一些模式,这些模式确定了几种遗传变异、微生物分类群和代谢物,作为肥胖、2型糖尿病或胰岛素抵抗风险的新的潜在标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genotype-microbiome-metabolome associations in early childhood, and their link to BMI and childhood obesity
The influence of genotype on defining the human gut microbiome has been extensively studied, but definite conclusions have not yet been found. To fill this knowledge gap, we leverage data from children enrolled in the Vitamin D Antenatal Asthma Reduction Trial (VDAART) from 6 months to 8 years old. We focus on a pool of 12 genes previously found to be associated with the gut microbiome in independent studies, establishing a Bonferroni corrected significance level of p-value < 2.29x10^(-6). We identified significant associations between SNPs in the FHIT gene (known to be associated with obesity and type 2 diabetes) and obesity-related microbiome features, and the children's BMI through their childhood. Based on these associations, we defined a set of SNPs of interest and a set of taxa of interest. Taking a multi-omics approach, we integrated plasma metabolome data into our analysis and found simultaneous associations among children's BMI, the SNPs of interest, and the taxa of interest, involving amino acids, lipids, nucleotides, and xenobiotics. Using our association results, we constructed a quadripartite graph where each disjoint node set represents SNPs in the FHIT gene, microbial taxa, plasma metabolites, or BMI measurements. Network analysis led to the discovery of patterns that identify several genetic variants, microbial taxa and metabolites as new potential markers for obesity, type 2 diabetes, or insulin resistance risk.
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