Troll Martina, Brandmaier Stefan, Reitmeier Sandra, Adam Jonathan, Sharma Sapna, Sommer Alice, Bind Marie-Abèle, Neuhaus Klaus, Clavel Thomas, Adamski Jerzy, Haller Dirk, Peters Annette, Grallert Harald
Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
Institute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
Microorganisms. 2020 Apr 10;8(4):547. doi: 10.3390/microorganisms8040547.
The analysis of the gut microbiome with respect to health care prevention and diagnostic purposes is increasingly the focus of current research. We analyzed around 2000 stool samples from the KORA (Cooperative Health Research in the Region of Augsburg) cohort using high-throughput 16S rRNA gene amplicon sequencing representing a total microbial diversity of 2089 operational taxonomic units (OTUs). We evaluated the combination of three different components to assess the reflection of obesity related to microbiota profiles: (i) four prediction methods (i.e., partial least squares (PLS), support vector machine regression (SVMReg), random forest (RF), and M5Rules); (ii) five OTU data transformation approaches (i.e., no transformation, relative abundance without and with log-transformation, as well as centered and isometric log-ratio transformations); and (iii) predictions from nine measurements of obesity (i.e., body mass index, three measures of body shape, and five measures of body composition). Our results showed a substantial impact of all three components. The applications of SVMReg and PLS in combination with logarithmic data transformations resulted in considerably predictive models for waist circumference-related endpoints. These combinations were at best able to explain almost 40% of the variance in obesity measurements based on stool microbiota data (i.e., OTUs) only. A reduced loss in predictive performance was seen after sex-stratification in waist-height ratio compared to other waist-related measurements. Moreover, our analysis showed that the contribution of OTUs less prevalent and abundant is minor concerning the predictive power of our models.
关于医疗保健预防和诊断目的的肠道微生物群分析日益成为当前研究的重点。我们使用高通量16S rRNA基因扩增子测序分析了来自奥格斯堡地区合作健康研究（KORA）队列的约2000份粪便样本，该测序代表了2089个操作分类单元（OTU）的总微生物多样性。我们评估了三种不同成分的组合，以评估与微生物群谱相关的肥胖反映：（i）四种预测方法（即偏最小二乘法（PLS）、支持向量机回归（SVMReg）、随机森林（RF）和M5Rules）；（ii）五种OTU数据转换方法（即不转换、无对数转换和有对数转换的相对丰度，以及中心化和等距对数比转换）；（iii）来自九种肥胖测量的预测（即体重指数、三种身体形状测量和五种身体成分测量）。我们的结果显示了所有三种成分的重大影响。SVMReg和PLS与对数数据转换相结合的应用产生了与腰围相关终点的显著预测模型。这些组合最多只能仅基于粪便微生物群数据（即OTU）解释肥胖测量中近40%的方差。与其他腰围相关测量相比，在按性别分层的腰高比中，预测性能的损失有所减少。此外，我们的分析表明，对于我们模型的预测能力而言，不太普遍和丰富的OTU的贡献较小。
