Table_1_Assessing the Relationship Between Gut Microbiota and Bone Mineral Density.docx

BackgroundRecent study demonstrates the comprehensive effects of gut microbiota on complex diseases or traits. However, limited effort has been conducted to explore the potential relationships between gut microbiota and BMD.
MethodsWe performed a polygenetic risk scoring (PRS) analysis to systematically explore the relationships between gut microbiota and body BMD. Significant SNP sets associated with gut microbiota were derived from previous genome-wide association study (GWAS). In total, 2,294 to 5,065 individuals with BMD values of different sites and their genotype data were obtained from UK Biobank cohort. The gut microbiota PRS of each individual was computed from the SNP genotype data for each study subject of UK Biobank by PLINK software. Using computed PRS as the instrumental variables of gut microbiota, Pearson correlation analysis of individual PRS values and BMD values was finally conducted to test the potential association between gut microbiota and target trait.
ResultsIn total, 31 BMD traits were selected as outcome to assess their relationships with gut microbiota. After adjusted for age, sex, body mass index, and the first 5 principal components (PCs) as the covariates using linear regression model, pelvis BMD (P = 0.0437) showed suggestive association signal with gut microbiota after multiple testing correction.
ConclusionOur study findings support the weak relevance of gut microbiota with the development of BMD.

## 研究背景
近期研究表明，肠道菌群（gut microbiota）对复杂疾病或表型具有广泛调控作用。然而，目前针对肠道菌群与骨密度（BMD）之间潜在关联的探索仍较为有限。

## 研究方法
本研究采用多基因风险评分（PRS）分析，系统探究肠道菌群与全身骨密度的关联。与肠道菌群相关的显著单核苷酸多态性（SNP）集取自既往全基因组关联研究（GWAS）。研究从英国生物库（UK Biobank）队列中获取了2294至5065名受试者的不同部位骨密度数据及基因型数据。借助PLINK软件，基于每位受试者的SNP基因型数据计算其肠道菌群多基因风险评分。以计算得到的PRS作为肠道菌群的工具变量，最终通过Pearson相关分析个体PRS值与骨密度值的相关性，以检验肠道菌群与目标表型的潜在关联。

## 研究结果
本研究共选取31项骨密度表型作为结局指标，以评估其与肠道菌群的关联。经线性回归模型校正年龄、性别、体质量指数及前5个主成分（PCs）作为混杂因素后，经多重检验校正，骨盆骨密度（P = 0.0437）与肠道菌群呈现提示性关联信号。

## 研究结论
本研究结果显示，肠道菌群与骨密度的发生发展仅存在较弱的相关性。