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

Abstract

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.

Investigating the Biomarkers of the Sasang Constitution via Network Pharmacology Approach

“…One major advantage of the drug-centric approach is that the SC types cannot be falsely labeled. In the previous studies [8][9][10][11][12], the SC types were classified by the SCM doctors, after considering the patient's morphological and psychological traits. However, its diagnostic procedure can be biased by the doctor's subjective clinical experience, which means that the relative importance of traits contributing to SC-type diagnosis can vary from doctor to doctor.…”

“…A prerequisite step for securing scientific evidence for SCM and expanding its application in personalized medicine is to identify biomarkers of each SC type. Previous studies, employing genome-wide association study (GWAS) and metabolomics study, have revealed genetic variations and metabolite-level biomarkers in healthy people [8][9][10][11][12]. However, the diagnosis of an SC type in these studies ultimately relied on the SCM doctors or questionnaires and was thus subject to bias due to the doctor's subjective clinical experience or low sensitivity of the questionnaire [13].…”

Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach

“…Despite the importance of personalized medicine, however, it is only possible to assume the classification principle of SCM because the classification criteria for the herbs into four Sasang type groups or the meaning of each herb belonging to each Sasang type are largely unknown. Thus far, although various studies have been conducted to find the criteria and the meaning of herbal classification [ [2] , [3] , [4] ], most studies were conducted by applying the theoretical concepts of herbs used in conventional traditional medicine or by narratively reviewing the results for each Sasang type [ 3 , 5 ]. Recently, chemical property-based various machine learning approaches have been applied to investigate natural products including herbal medicines [ 6 , 7 ].…”

Ultra‐high‐performance liquid chromatography‐tandem mass spectrometry‐based metabolomics unveils the metabolic alterations in colon cancer mice during CT‐guided radiofrequency ablation

“…Recently, metabolomics has become a powerful method to research cancer metabolism (Grønningsaeter et al, 2019). Metabolomics can be used to assess the validity of therapies by identifying endogenous small-molecule metabolites systematically and analyzing the concentration changes caused by external interference (Kim et al, 2019). Owing to the characteristics of convenient collection, feces and serum are the common samples for evaluating the effect of therapies.…”