Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning

Front Cell Infect Microbiol. 2024 Nov 4:14:1477638. doi: 10.3389/fcimb.2024.1477638. eCollection 2024.

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.

Keywords: diagnostic model; oral-gut microbiome; prediabetes mellitus; tongue diagnosis; type 2 diabetes mellitus.

MeSH terms

  • Adult
  • Diabetes Mellitus, Type 2* / microbiology
  • Feces / microbiology
  • Female
  • Gastrointestinal Microbiome*
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Machine Learning*
  • Male
  • Microbiota
  • Middle Aged
  • Prediabetic State* / microbiology
  • RNA, Ribosomal, 16S / genetics
  • Support Vector Machine
  • Tongue* / microbiology

Substances

  • RNA, Ribosomal, 16S

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The research, authorship, and publication of this article were financially supported by the National Natural Science Foundation of China (reference number: 82104738), the General Project of China Postdoctoral Science Foundation (reference number: 2023M732337), the High-level Key Discipline Construction Project of Traditional Chinese Medicine by the National Administration of Traditional Chinese Medicine (reference number: ZYYZDXK-2023069), and the Shanghai Super Postdoctoral Incentive Program (reference number: 2022509). The funding bodies had no involvement in the study’s design, execution, data collection, management, analysis, interpretation, or manuscript writing.