KJCKorea
Journal Central

Objectives Metabolic syndrome (MetS) increases the risk of cardiovascular disease and type 2 diabetes. Sleep is a key lifestyle factor influencing metabolic health. This study examined the association between sleep patterns and MetS according to Sasang constitution (SC) types among Korean adults. Methods This study analyzed data from 1,967 participants in the follow-up phase of the Korean Medicine Daejeon Citizen Cohort (KDCC). Participants were classified into two SC types: Taeeumin (TE) and non-Taeeumin (non-TE). Sleep pattern was defined using five components: sleep duration, insomnia, snoring, daytime sleepiness, and chronotype. A sleep score (0–5) was calculated and categorized as poor sleep pattern (PSP: 0–2 points) or good sleep pattern (GSP: 3–5 points). Multivariable logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the association between sleep patterns and MetS by SC type. Results Poor sleep patterns were more common among individuals with MetS in both SC types. In the TE group, PSP was significantly associated with MetS (OR = 1.827, 95% CI = 1.354–2.466). Insomnia (OR = 1.763, 95% CI = 1.012– 3.071) and snoring (OR = 2.378, 95% CI = 1.729–3.270) were linked to MetS in the TE type. In the non-TE group, short or long sleep duration (OR = 1.904, 95% CI = 1.022–3.547) and snoring (OR = 2.143, 95% CI = 1.169–3.927) were significant factors. Conclusions Poor sleep patterns were associated with higher MetS risk, especially in the TE type. SC-specific sleep interventions may be effective in preventing MetS.

Objectives Efforts to develop a Sasang type diagnostic method with high validity and clinical utility have continued, with growing interest in large-scale data and machine learning (ML). However, ML often suffers from reliance on input data, overfitting, and limited theoretical interpretability, reducing its clinical applicability. This study aimed to develop a diagnostic model with improved interpretability, predictive performance, and accessibility by systematically selecting clinical features and using an intuitive ML interface. Methods Psychological, physical, and hematological features were selected stepwise through literature review, ANCOVA, and correlation analysis. Data from 2,407 participants were split into training (70%) and test(30%) sets. Feature contribution was evaluated by information gain (IG), and the diagnostic performance of random forest (RF), naïve Bayes (NB), neural network (NN), and stochastic gradient descent (SGD) was assessed using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), ROC curve and AUC. Analyses were performed using Orange, an open-source Python-based Graphic User Interface. Results IG analysis showed notable contributions from BMI (0.531), RMRw (0.292), RMR (0.207), three SPQ subscales (0.329). Including BMI yielded high accuracy (0.817), but after its removal to balance features, accuracy remained at 0.755. Algorithm efficiency depended on interaction with input data. Conclusions Balanced integration of psychological, physical, and hematological features improved consistency with Sasang typology theory, clinical utility, and model generalizability. Appropriate feature selection had a greater impact on performance than algorithm choice. The proposed procedure and Orange workflow offer clinicians and researchers a practical foundation for ML-based analysis, contributing to the globalization of Sasang typology.

Objectives The aim of this study was to evaluate alterations in pulse wave characteristics in patients with cancer compared with healthy subjects, and to analyze differences according to Sasang constitution. Furthermore, this study aimed to investigate the potential applicability of pulse wave analysis as an objective indicator for assessing the health status of cancer patients and for developing Sasang constitution-based management. Methods This study was designed as a retrospective comparative study involving 139 participants, including 76 healthy subjects and 63 cancer patients. Sasang constitutional diagnosis and pulse wave measurements were performed for all participants. Pulse wave measurements were obtained using the automated robotic pulse analyzer equipped with five pressure sensors. Results In the comparison of pulse wave variables between cancer patients and healthy subjects, cancer patients showed higher contact pressure, variance of total time, and ratio of systolic period area, but lower ratio of diastolic period area, and tended to exhibit a relatively sinking pulse (沈脈) in terms of pulse depth. In the comparison of cancer patients' pulse wave variables according to Sasang constitution, the time to preincisura (t2) was longest in the Taeumin and shortest in the Soyangin, while the ratios of time to preincisura to total time (t2/t) and time to tidal wave to total time (t3/t) were highest in the Taeumin and lowest in the Soyangin. Conclusions Based on the above results, monitoring pulse wave variables may be useful for evaluating the health status of cancer patients and supporting Sasang constitutional diagnosis.