Abstract
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.
Highlights
As a consequence of modern lifestyles, excessive body weight has become an important health burden, with a prevalence that has greatly surpassed underweight on a global scale [1]
We evaluated the suitability of different methods to capture the complexity of a recently generated operational taxonomic units (OTUs) data set in approx. 2000 individuals from the cross-sectional KORA (Cooperative Health Research in the Region of Augsburg) FF4 study (2013/14)
Study participants were older than the German median age [34] as well as overweight according to the World Health Organization (WHO) guidelines [35] (Table 1)
Summary
As a consequence of modern lifestyles, excessive body weight has become an important health burden, with a prevalence that has greatly surpassed underweight on a global scale [1]. One of the biological factors influencing obesity is the gut microbiome [2], which has been linked to comorbidities, such as diabetes [3], and to even further pathophysiological processes, such as neurological and mental disorders [4]. The gut microbiota has its role in the mediation and conversion of external input from the environment, including medication [6,7], food, and energy metabolism [5,8,9] These findings introduced the gut microbiome as a key player for the comprehension of and possibly novel intervention strategies for the obese phenotype. For improved understanding of the interactions and derivation of correct conclusions, it is important to dissect data processing strategies and their impact on study outcomes [12,13]
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