Serum Levels of Stress Hormones and Oxidative Stress Biomarkers Differ according to Sasang Constitutional Type

Comparative gut microbiome analysis of the Prakriti and Sasang systems reveals functional level similarities in constitutionally similar classes

The traditional medicinal systems (TMS) of India (Prakriti) and Korea (Sasang) classify human individuals based on their constitution determined by the physiological and psychological traits of individuals. Similarities in the constitutions are already found between the classes of Prakriti (Vata, Pitta, and Kapha) and Sasang (TE: Taeeumin, SE: Soeumin, and SY: Soyangin) systems. Gut health is an important aspect of this constitution based classification in TMS. To determine the role of gut microbes in such classifications, we have analyzed the gut microbiome (taxa and imputed functions) in the constitutionally similar Prakriti and Sasang classes. An enrichment of Bacteroides and Prevotella enterotypes is observed in the Sasang and Prakriti samples, respectively. The impact of the constitution is found to be more prominent with respect to the taxa and predicted-functions within the Prakriti classes. Gut microbiome functional-level similarities are found to correlate well with the host phenotypes of the constitutionally similar Prakriti and Sasang classes. An enrichment of carbohydrate and amino-acid metabolism is observed in the Vata and SE classes which may be responsible for meeting with their high energy demands and lean phenotype. The Pitta and SY classes exhibit the high capacity to metabolize toxins. An enrichment of functions responsible for predisposition to obesity and high drug metabolism is observed in the Kapha and TE classes. The contribution of gut adaptive functions is found to correlate with the constitution-based classification in both Prakriti and Sasang systems. The TE class harboured the highest number of biofilm-forming and stress-tolerant microbes thus exhibiting the maximum tolerance of environmental stress. Similarities in the gut microbiota and the resulting disease predisposition patterns are found to exist between the constitutionally matching Prakriti and Sasang classes.

Metabolite Markers for Characterizing Sasang Constitution Type through GC-MS and 1H NMR-Based Metabolomics Study

Sasang constitutional medicine classifies human beings into four types based on their physical and psychological characteristics. Despite its potential value in achieving personalized medicine, the diagnosis of sasang constitution (SC) type is complex and subjective. In this study, gas chromatography–mass spectrometry and ¹H nuclear magnetic resonance–based metabolic analyses were conducted to find maker metabolites in serum and urine according to different SC types. Although some samples were overlapped on orthogonal projection to latent structure discriminant analysis score plots, serum samples showed separation between different SC types. Levels of lactate, glutamate, triglyceride, and fatty acids in serum and glycolic acid in urine of Tae-Eum type were higher than those of So-Eum and So-Yang type. Fatty acids, triglyceride, and lactate levels were found to be metabolites related to body mass index, indicating that marker metabolites for the diagnosis of SC type could be associated with obese. However, Tae-Eum type showed higher lactate levels in serum than So-Yang type for both normal weight and overweight groups, suggesting that the contents of serum lactate might be dependent on the SC type regardless of body weight. These results suggest that metabolomics analysis could be used to determine SC type.

Modification of the Integrated Sasang Constitutional Diagnostic Model

In 2012, the Korea Institute of Oriental Medicine proposed an objective and comprehensive physical diagnostic model to address quantification problems in the existing Sasang constitutional diagnostic method. However, certain issues have been raised regarding a revision of the proposed diagnostic model. In this paper, we propose various methodological approaches to address the problems of the previous diagnostic model. Firstly, more useful variables are selected in each component. Secondly, the least absolute shrinkage and selection operator is used to reduce multicollinearity without the modification of explanatory variables. Thirdly, proportions of SC types and age are considered to construct individual diagnostic models and classify the training set and the test set for reflecting the characteristics of the entire dataset. Finally, an integrated model is constructed with explanatory variables of individual diagnosis models. The proposed integrated diagnostic model significantly improves the sensitivities for both the male SY type (36.4% → 62.0%) and the female SE type (43.7% → 64.5%), which were areas of limitation of the previous integrated diagnostic model. The ideas of these new algorithms are expected to contribute not only to the scientific development of Sasang constitutional medicine in Korea but also to that of other diagnostic methods for traditional medicine.