Frontiers in endocrinology · 2026
Correlation analysis between tongue hyperspectral imaging and tongue coating microbiota and feasibility study for early screening of type 2 diabetes mellitus status.
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Liu Zhao-Wei, Zhang Yan, Fu Lei, Xie Jing-Ya, Zhang Xin-Yu, Liu Ming, Zhao Jing
Аннотация
OBJECTIVE: To investigate the feasibility of applying hyperspectral imaging technology combined with machine learning algorithms for early non-invasive screening of type 2 diabetes mellitus (T2DM).
METHODS: In this study, tongue images of 153 subjects were acquired using a hyperspectral imaging system. Concurrently, 53 tongue coating samples were collected for 16S rRNA sequencing. Spearman correlation analysis was performed between tongue hyperspectral data and tongue coating microbiota. Finally, machine learning was integrated to construct a T2DM disease status identification model based on tongue hyperspectral data.
RESULTS: The experimental results revealed that 10 genera exhibited significant differences among the three groups (P < 0.05). Among these, the relative abundance of unclassified_p_Bacillota, Pseudoleptotrichia, Xylanibacter, Gemella, norank_f_Mitochondria, and Enhydrobacter showed significant correlations with hyperspectral tongue images (P < 0.05). The T2DM disease status identification models constructed based on tongue hyperspectral data achieved favorable modeling results, with the optimal model demonstrating ACC = 0.8966 and AUC = 0.8029.
CONCLUSION: The results of this study indicate that certain correlations exist between tongue hyperspectral features and tongue coating microbial genera in T2DM patients at different disease stages. The T2DM disease status identification model constructed based on tongue hyperspectral imaging and machine learning algorithms can be used for the identification of HC, IGR, and T2DM, and this method may provide a noninvasive and rapid screening approach for early identification of T2DM.
METHODS: In this study, tongue images of 153 subjects were acquired using a hyperspectral imaging system. Concurrently, 53 tongue coating samples were collected for 16S rRNA sequencing. Spearman correlation analysis was performed between tongue hyperspectral data and tongue coating microbiota. Finally, machine learning was integrated to construct a T2DM disease status identification model based on tongue hyperspectral data.
RESULTS: The experimental results revealed that 10 genera exhibited significant differences among the three groups (P < 0.05). Among these, the relative abundance of unclassified_p_Bacillota, Pseudoleptotrichia, Xylanibacter, Gemella, norank_f_Mitochondria, and Enhydrobacter showed significant correlations with hyperspectral tongue images (P < 0.05). The T2DM disease status identification models constructed based on tongue hyperspectral data achieved favorable modeling results, with the optimal model demonstrating ACC = 0.8966 and AUC = 0.8029.
CONCLUSION: The results of this study indicate that certain correlations exist between tongue hyperspectral features and tongue coating microbial genera in T2DM patients at different disease stages. The T2DM disease status identification model constructed based on tongue hyperspectral imaging and machine learning algorithms can be used for the identification of HC, IGR, and T2DM, and this method may provide a noninvasive and rapid screening approach for early identification of T2DM.