Discrepant gut microbiota markers for the classification of obesity-related metabolic abnormalities

Abstract

The gut microbiota (GM) is related to obesity and other metabolic diseases. To detect GM markers for obesity in patients with different metabolic abnormalities and investigate their relationships with clinical indicators, 1,914 Chinese adults were enrolled for 16S rRNA gene sequencing in this retrospective study. Based on GM composition, Random forest classifiers were constructed to screen the obesity patients with (Group OA) or without metabolic diseases (Group O) from healthy individuals (Group H), and high accuracies were observed for the discrimination of Group O and Group OA (areas under the receiver operating curve (AUC) equal to 0.68 and 0.76, respectively). Furthermore, six GM markers were shared by obesity patients with various metabolic disorders ( Bacteroides, Parabacteroides, Blautia, Alistipes, Romboutsia and Roseburia). As for the discrimination with Group O, Group OA exhibited low accuracy (AUC = 0.57). Nonetheless, GM classifications to distinguish between Group O and the obese patients with specific metabolic abnormalities were not accurate (AUC values from 0.59 to 0.66). Common biomarkers were identified for the obesity patients with high uric acid, high serum lipids and high blood pressure, such as Clostridium XIVa, Bacteroides and Roseburia. A total of 20 genera were associated with multiple significant clinical indicators. For example, Blautia, Romboutsia, Ruminococcus2, Clostridium sensu stricto and Dorea were positively correlated with indicators of bodyweight (including waistline and body mass index) and serum lipids (including low density lipoprotein, triglyceride and total cholesterol). In contrast, the aforementioned clinical indicators were negatively associated with Bacteroides, Roseburia, Butyricicoccus, Alistipes, Parasutterella, Parabacteroides and Clostridium IV. Generally, these biomarkers hold the potential to predict obesity-related metabolic abnormalities, and interventions based on these biomarkers might be beneficial to weight loss and metabolic risk improvement.