BIOREGULATORY MEDICINE

Abstracts & Links

Traditional Asian Medicine Tongue Diagnosis

Anastasi, J. K., Currie, L. M., & Kim, G. H. (2009). Understanding diagnostic reasoning in TCM practice: tongue diagnosis. Alternative therapies in health and medicine, 15(3), 18.

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Jiang, B., Liang, X., Chen, Y., Ma, T., Liu, L., Li, J., ... & Li, S. (2012). Integrating next-generation sequencing and traditional tongue diagnosis to determine tongue coating microbiome. Scientific reports, 2, 936.

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Lo, L. C., Chen, Y. F., Chen, W. J., Cheng, T. L., & Chiang, J. Y. (2012). The study on the agreement between automatic tongue diagnosis system and traditional chinese medicine practitioners. Evidence-Based Complementary and Alternative Medicine, 2012.

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Lo, L. C., Chen, C. H., Chiang, J. Y., Cheng, T. L., Lin, H. J., & Chang, H. H. (2013). Tongue diagnosis of traditional Chinese medicine for rheumatoid arthritis. African Journal of Traditional, Complementary and Alternative Medicines, 10(5), 360-369.

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Lo, L. C., Cheng, T. L., Chen, Y. J., Natsagdorj, S., & Chiang, J. Y. (2015). TCM tongue diagnosis index of early-stage breast cancer. Complementary therapies in medicine, 23(5), 705-713.

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TCM practitioners use systematic reasoning patterns to determine diagnoses associated with evaluation of tongues. These processes are congruent with those observed in Western medicine whereby clinician reasoning involves a combination of analytical reasoning of domain knowledge and the use of exemplar patterns. An explicit understanding of TCM reasoning processes can inform clinical practice and education and will facilitate the development of supporting technologies and identifi cation of best practices.

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Tongue diagnosis is a unique method in traditional Chinese medicine (TCM). This is the first investigation on the association between traditional tongue diagnosis and the tongue coating microbiome using next-generation sequencing. The study included 19 gastritis patients with a typical white-greasy or yellow-dense tongue coating corresponding to TCM Cold or Hot Syndrome respectively, as well as eight healthy volunteers. An Illumina paired-end, double-barcode 16S rRNA sequencing protocol was designed to profile the tongue-coating microbiome, from which approximately 3.7 million V6 tags for each sample were obtained. We identified 123 and 258 species-level OTUs that were enriched in patients with Cold/Hot Syndromes, respectively, representing "Cold Microbiota" and "Hot Microbiota". We further constructed the tongue microbiota-imbalanced networks associated with Cold/Hot Syndromes. The results reveal an important connection between the tongue-coating microbiome and traditional tongue diagnosis, and illustrate the potential of the tongue-coating microbiome as a novel holistic biomarker for characterizing patient subtypes.

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Tongue diagnosis is an important practice in traditional Chinese medicine (TCM) for diagnosing diseases before determining proper means of treatments. Traditionally, it depends solely on personal knowledge and experience of the practitioner, thereby being criticized as lacking of objectivity. Currently, no research regarding intra- and inter-agreements of automatic tongue diagnosis system (ATDS) and TCM doctors has been conducted. In this study, the ATDS is developed to extract a variety of tongue features and provide practitioners with objective information to assist diagnoses. To evaluate the ATDS clinical stability, 2 sets of tongue images taken 1 hour apart from 20 patients with possible variations in lighting and extruding tongue, are employed to investigate intra-agreement of the ATDS, intra-agreement of the TCM doctors, and the inter-agreement between the ATDS and TCM doctors. The ATDS is shown to be more consistent with significantly higher intra-agreement than the TCM doctors (kappa value: 0.93 ± 0.06 versus 0.64 ± 0.13) with P < 0.001 (Student’s t-test). Inter-agreements between the ATDS and TCM doctors, as well as among the TCM doctors are both moderate. The high agreement of the ATDS can provide objective and reliable tongue features to facilitate doctor in making effective observation and diagnosis of specific diseases.

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Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease with unknown aetiology that causes the immune system to attack the joints (synoviums), leading to chronic inflammation. According to the traditional Chinese medicine (TCM), RA falls into the category of Impediment disease (“Bi” syndrome), that is, poor circulation of qi and blood (stasis). Tongue diagnosis is an important method of TCM to detect blood stasis. In this study, 74 RA patients, meeting the pre-set criteria, were recruited via rheumatology outpatient clinic and examined by experienced rheumatology physicians. Two images－one of the tongue and the other, sublingual vessels－of the same patient were taken by a Canon digital camera in a darkroom with uniform lighting conditions. Relevant features of the tongue were extracted by utilising image processing techniques. Every tongue was classified into corresponding patterns based on the features identified. The subjects included 62 females and 12 males with an average age of 49.86±13.81 years old, an average morbidity period of 4.56±3.92 years, an average rheumatoid factor (RF) of 225.3±373.8 IU/mL and an average erythrocyte sedimentation rate of (ESR) 40.9±31.9m/hr. According to our study, 86% of the patients with RA have tongues with sublingual vessels with a width of more than 2.7mm, a length of more than 3/5 from tongue tipto sublingual caruncle, or a count of sublingual vessels more than 2. Moreover, since RA index is highly correlated with blood stasis in TCM, a logistic regression is conducted to predict the probability of presence of RA using RF and ESR as explanatory variables. Also, the logistic regression analysis of RA with respect to the conventional tongue diagnosis criteria was performed. Based on the aforementioned studies, we concluded that tongue diagnosis is helpful in detecting blood stasis of RA.

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Objectives: This paper investigates discriminating tongue features to distinguish between early stage breast cancer (BC) patients and non-breast cancer individuals through non-invasive traditional Chinese medicine (TCM) tongue diagnosis. Design: The tongue features for 67 patients with 0 and 1 stages of BC, and 70 non-breast cancer individuals are extracted by the automatic tongue diagnosis system (ATDS). A total of nine tongue features, namely, tongue color, tongue quality, tongue fissure, tongue fur, red dot, ecchymosis, tooth mark, saliva, and tongue shape are identified for each tongue. Features extracted are further sub-divided according to the areas located, i.e., spleen–stomach, liver–gall-left, liver–gall-right, kidney, and heart–lung areas. This study focuses on deriving significant tongue features (p < 0.05) to discriminate early-stage BC patients from non-breast cancer individuals. Results: The Mann–Whitney test shows that the amount of tongue fur (p = 0.024), maximum covering area of tongue fur (p = 0.009), thin tongue fur (p = 0.009), the average area of red dot (p = 0.049), the maximum area of red dot (p = 0.009), red dot in the spleen–stomach area (p = 0.000), and red dot in the heart–lung area (p = 0.000) demonstrate significant differences. The data collected are further classified into two groups. The training group consists of 57 early-stage BC patients and 60 non-breast cancer individuals, while the testing group is composed of 10 early-stage BC patients and 10 non-breast cancer individuals. The logistic regression by utilizing these 7 tongue features with significant differences in Mann–Whitney test as factors is performed. In order to reduce the number of tongue features employed in prediction, tongue features with the least amount of significant difference, namely, maximum area of red dot and average area of red dot, are removed progressively. The tongue features of the testing group are employed in the aforementioned three models to test the power of significant tongue features identified in predicting early-stage BC. An accuracy of 80%, 80% and 90% is reached on non-breast cancer individuals by applying the 7, 6 and 5 significant tongue features obtained through Mann–Whitney test, respectively, while 60%, 60% and 50% is reached on the corresponding early-stage BC patients. Conclusion: The TCM tongue diagnosis can serve as a preliminary screening procedure in the early detection of BC in light of its simple and non-invasive nature, followed by other more accurate testing process. To the best of our knowledge, this is the first attempt in applying non-invasive TCM tongue diagnosis to the discrimination of early-stage BC patients and non-breast cancer individuals.