Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling

Author:

Boyang Hu12345,Yanjun Yao12345,Jing Zhuang12345,Chenxin Yan6,Ying Mei12345,Shuwen Han12345,Qiang Yan12345

Affiliation:

1. Department of General Surgery, Huzhou Central Hospital, Affiliated Huzhou Hospital, Zhejiang University School of Medicine , No.1558, Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province 313000 , China

2. Department of Hepatobiliary and Pancreatic Surgery, Huzhou Central Hospital , No.1558, Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province 313000 , China

3. Department of General Surgery, Huzhou Central Hospital, Affiliated Central Hospital, Huzhou University , No.1558, Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province 313000 , China

4. Department of General Surgery, Huzhou Central Hospital, Affiliated Huzhou Central Hospital, The Fifth School of Clinical Medicine, Zhejiang Chinese Medical University , No.1558, Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province 313000 , China

5. Huzhou Key Laboratory of Intelligent and Digital Precision Surgery , No.1558, Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province 313000 , China

6. Shulan International Medical school, Zhejiang Shuren University , No.848 Dongxin Road, Gongshu District, Hangzhou City, Zhejiang Province 310000 , China

Abstract

Abstract Background Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction. Objective To reveal the correlation between cholelithiasis and gut bacteria. Methods Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis. Results There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962). Conclusion The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis.

Funder

Medical and Health Research Project of Zhejiang Province

Publisher

Oxford University Press (OUP)

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