Exploring Heart Rate Variability as a Biomedical Diagnostic Tool for the Disympathetic Dimension of Eight-Constitution Medicine

Background. Eight-Constitution Medicine (ECM), an extension of Traditional Korean Medicine, divides the population into eight groups based on their physiological characteristics. ECM divides these eight groups into two larger groups based on autonomic reactivity: the Sympathicotonic group and the Vagotonic group (herein referred to as the Disympathetic Dimension). Heart Rate Variability (HRV) is a widely used biomedical tool to assess cardiac autonomic function. This raises the question of the utility of using HRV to correctly diagnose ECM constitutions. Methods. A systematic literature review was conducted to evaluate the correlation between HRV and constitutions in Korean Constitutional Medicine, including Eight-Constitution Medicine (ECM) and Sasang Constitution Medicine (SCM). The articles were obtained from both English (Scopus, PubMed, EMBASE, ProQuest, and Medline) and Korean databases (NDSL and RISS), in addition to Google Scholar, without date restriction. 20 studies met the inclusion criteria, and data were extracted against three aspects: (1) correlation between HRV and constitution, (2) HRV reporting and interpretation, and (3) extraneous factors that were controlled in the studies. Results. 386 articles were initially identified, which was reduced to n = 20 studies which met the inclusion criteria. Of these, 19 were SCM studies and 1 was an ECM study. Sample sizes varied from 10 to 8498 men and women, with an age range of 10–80 years. SCM studies explored HRV differences by constitution, measuring HRV at resting, with controlled breathing, before and after acupuncture stimulation, and by other interventions. SCM studies reported either no significant differences (HRV at resting or with controlled breathing studies) or conflicting data (HRV with acupuncture stimulation studies). The single ECM study measured HRV at resting and after acupuncture stimulation but reported no significant differences between the two groups of Sympathicotonia and Vagotonia. Conclusions. Due to inconsistencies in study design, study population, and measures of HRV, there was no consistency in the data to support the use of HRV as a biomedical determinant of ECM constitutions.

Investigating the Biomarkers of the Sasang Constitution via Network Pharmacology Approach

Sasang constitutional (SC) medicine classifies people into Soeum (SE), Soyang (SY), Taeeum (TE), and Taeyang (TY) types based on psychological and physical traits. However, biomarkers of these types are still unclear. We aimed to identify biomarkers among the SC types using network pharmacology methods. Target genes associated with the SC types were identified by grouping herb targets that preserve and strengthen the requisite energy (Bomyeongjiju). The herb targets were obtained by constructing an herb-compound-target network. We identified 371, 185, 146, and 89 target genes and their unique biological processes related to SE, SY, TE, and TY types, respectively. While the targets of SE and SY types were the most similar among the target pairs of the SC types, those of TY type overlapped with only a few other SC-type targets. Moreover, SE, SY, TE, and TY were related to “diseases of the digestive system,” “diseases of the nervous system,” “endocrine, nutritional, and metabolic diseases,” and “congenital malformations, deformations, and chromosomal abnormalities,” respectively. We successfully identified the target genes, biological processes, and diseases related to each SC type. We also demonstrated that a drug-centric approach using network pharmacology analysis provides a deeper understanding of the concept of Sasang constitutional medicine at a phenotypic level.

Metabolic Syndrome Prediction Models Using Machine Learning and Sasang Constitution Type

Background. Machine learning may be a useful tool for predicting metabolic syndrome (MetS), and previous studies also suggest that the risk of MetS differs according to Sasang constitution type. The present study investigated the development of MetS prediction models utilizing machine learning methods and whether the incorporation of Sasang constitution type could improve the performance of those prediction models. Methods. Participants visiting a medical center for a health check-up were recruited in 2005 and 2006. Six kinds of machine learning were utilized (K-nearest neighbor, naive Bayes, random forest, decision tree, multilayer perceptron, and support vector machine), as was conventional logistic regression. Machine learning-derived MetS prediction models with and without the incorporation of Sasang constitution type were compared to investigate whether the former would predict MetS with higher sensitivity. Age, sex, education level, marital status, body mass index, stress, physical activity, alcohol consumption, and smoking were included as potentially predictive factors. Results. A total of 750/2,871 participants had MetS. Among the six types of machine learning methods investigated, multiplayer perceptron and support vector machine exhibited the same performance as the conventional regression method, based on the areas under the receiver operating characteristic curves. The naive-Bayes method exhibited the highest sensitivity (0.49), which was higher than that of the conventional regression method (0.39). The incorporation of Sasang constitution type improved the sensitivity of all of the machine learning methods investigated except for the K-nearest neighbor method. Conclusion. Machine learning-derived models may be useful for MetS prediction, and the incorporation of Sasang constitution type may increase the sensitivity of such models.

Effect of Lifestyle Factors on Hypertension by Constitution Type: A Large Community-Based Study

Background. The risk of hypertension differs according to lifestyle factors and individual constitution types. The aim of this study was to investigate the effect of lifestyle factors on hypertension and to assess whether those effects differ according to the constitution types. Methods. A total of 5,793 men and women were recruited between 2012 and 2014. Odds ratios for hypertension associated with constitution types and lifestyle factors were estimated. Lifestyle factors included smoking status, body mass index, alcohol consumption, physical activity, and sleep quality. Constitution types were estimated based on the Sasang constitutional medicine as the TE type, SE type, and SY type. Results. The risk of hypertension was significantly higher for SY (odds ratio 1.25 (95% confidence interval 1.03 to 1.52) and TE types (1.38 (1.10 to 1.74)) than the SE type even with adjustment of health behaviors. Compared with individuals who had an unhealthy lifestyle, those with healthy lifestyle scores showed significantly lower risk of hypertension in only SY (odds ratio 0.62 (95% confidence interval 0.48 to 0.81)) and TE types (0.69 (0.58 to 0.81)). The difference in risk for hypertension among constitution types was decreased with a healthy lifestyle (1.34 in SY and 2.35 in TE types, as compared with the SE type) versus an unhealthy lifestyle (2.21 in SY and 3.64 in TE types, as compared with the SE type). Conclusion. The risk of hypertension was different by Sasang constitution types. The impact of lifestyle factors differed according to Sasang constitution types, and the difference in risk of hypertension among constitution types was decreased with a healthy lifestyle.