1. Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.
- Author
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Deng J, Dai S, Liu S, Tu L, Cui J, Hu X, Qiu X, Jiang T, and Xu J
- Subjects
- Humans, Male, Female, Middle Aged, RNA, Ribosomal, 16S genetics, Adult, Support Vector Machine, Feces microbiology, Image Processing, Computer-Assisted methods, Microbiota, Tongue microbiology, Diabetes Mellitus, Type 2 microbiology, Machine Learning, Gastrointestinal Microbiome, Prediabetic State microbiology
- Abstract
Background: This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in diabetes progression., Methods: Participants included 30 Pre-DM patients, 37 individuals with T2DM, and 28 healthy controls. Tongue images and oral/fecal samples were analyzed using image processing and 16S rRNA sequencing. Machine learning techniques, including support vector machine (SVM), random forest, gradient boosting, adaptive boosting, and K-nearest neighbors, were applied to integrate tongue image data with microbiota profiles to construct predictive models for Pre-DM and T2DM classification., Results: Significant shifts in tongue characteristics were identified during the progression from Pre-DM to T2DM. Elevated Firmicutes levels along the oral-gut axis were associated with white greasy fur, indicative of underlying metabolic changes. An SVM-based predictive model demonstrated an accuracy of 78.9%, with an AUC of 86.9%. Notably, tongue image parameters (TB-a, perALL) and specific microbiota ( Escherichia , Porphyromonas-A ) emerged as prominent diagnostic markers for Pre-DM and T2DM., Conclusion: The integration of tongue diagnosis with microbiome analysis reveals distinct tongue features and microbial markers. This approach significantly improves the diagnostic capability for Pre-DM and T2DM., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Deng, Dai, Liu, Tu, Cui, Hu, Qiu, Jiang and Xu.)
- Published
- 2024
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