Resting Metabolic Rate for Diagnosing Tae-Eum Sasang Type and Unraveling the Mechanism of Type-Specific Obesity

Increased resting metabolic rate (RMR), representing augmented energy expenditure, is a preferred physical characteristic; however, the Tae-Eum Sasang type, with a high incidence of obesity and metabolic diseases, has a higher RMR. This study scrutinized the physical characteristics of Sasang typology, a traditional Korean personalized medicine, to resolve this discrepancy, which can unravel the mechanism of Tae-Eum-type-specific obesity and improve the Tae-Eum Sasang-type diagnosis. A total of 395 healthy participants provided Sasang-type diagnosis using Sasang Constitutional Analysis Tool and physical features, including skeletal muscle mass, body fat mass, and RMR, along with those standardized using body weight. The Tae-Eum-type group showed significantly higher body weight, body mass index, body fat mass, and unstandardized RMR (kcal/day) than others, while their standardized measures of RMR per weigh (RMRw, kcal/day/kg) and percent skeletal muscle (PSM, %) were significantly lower. The logistic regression model revealed that the RMRw is pivotal for discriminating Tae-Eum type from others and explaining the developmental mechanism of Tae-Eum-type obesity. The aforementioned might provide a theoretical framework for Sasang-type diagnosis and Sasang-type-specific health promotion using bodily exercise and medical herbs.

Individual Differences in the Association of Lifestyle with Cardiometabolic Risk in Middle-Aged Koreans Based on Traditional Korean Medicine

Cardiometabolic (CM) risk differs in morbidity and disease progression depending on lifestyle and individual characteristics. This cross-sectional study aimed to analyze the association between CM risk and lifestyle and examine whether this association varies based on Sasang constitution (SC), a Korean medicine classification. Data were analyzed from 1996 individuals participating in the Korean Medicine Daejeon Citizen Cohort study. Individuals with CM risk had two to five CM risk factors. Lifestyle factors included physical activity, sleep duration, and eating index. SC types were Taeeum-in (TE) and non-TE. We estimated the odds ratio (OR) and 95% confidence interval for CM risk based on SC and lifestyle factors. Among the participants, 33.9% had a CM risk, and the TE and non-TE groups were 26.2% and 7.7%, respectively. In the TE group, CM risk was associated with low physical activity (OR, 1.63) and moderate eating index (OR, 1.41). In the non-TE group, CM risk was associated with ≥8 h of sleep (OR, 1.87) and marginally associated with ≤6 h of sleep. In the TE group alone, CM risk was significantly associated with lifestyle patterns that combined the three lifestyle factors and was approximately two-fold higher in patterns involving less physical activity. The effects of lifestyle on CM risk differed across SC types. To decrease the burden of cardiovascular diseases in middle-aged adults, we recommend an individualized management strategy of healthy lifestyle interventions for cardiovascular risks.

Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and management of hypertension. The implementation of such intervention requires an effective and easy-to-implement hypertension risk prediction model. This study evaluated and compared the performance of four machine learning algorithms on predicting the risk of hypertension based on easy-to-collect risk factors. A dataset of 29,700 samples collected through a physical examination was used for model training and testing. Firstly, we identified easy-to-collect risk factors of hypertension, through univariate logistic regression analysis. Then, based on the selected features, 10-fold cross-validation was utilized to optimize four models, random forest (RF), CatBoost, MLP neural network and logistic regression (LR), to find the best hyper-parameters on the training set. Finally, the performance of models was evaluated by AUC, accuracy, sensitivity and specificity on the test set. The experimental results showed that the RF model outperformed the other three models, and achieved an AUC of 0.92, an accuracy of 0.82, a sensitivity of 0.83 and a specificity of 0.81. In addition, Body Mass Index (BMI), age, family history and waist circumference (WC) are the four primary risk factors of hypertension. These findings reveal that it is feasible to use machine learning algorithms, especially RF, to predict hypertension risk without clinical or genetic data. The technique can provide a non-invasive and economical way for the prevention and management of hypertension in a large population.

Low nutritional status links to the prevalence of pre-metabolic syndrome and its cluster in metabolically high-risk Korean adults: A cross-sectional study

Diet plays a crucial role as a modifiable risk factor related to the development of metabolic syndrome (MetS) and its cluster. Constitution type of traditional Korean medicine has shown accuracy to predict the risk for MetS. We attempted to examine the association between nutritional status, pre-MetS, and its cluster in Korean adults by their constitution type.Participants aged 30 to 55 years who had no cancer or cardiovascular diseases (CVDs) were assigned to join in the present study. Pre-MetS was defined as ≥2 of the following factors: abdominal obesity; elevated triglycerides (TG); reduced high-density lipoprotein cholesterol (HDL-C); elevated blood pressure (BP); and elevated fasting plasma glucose (FPG). Constitution type was categorized into Tae-Eumin (TE) or non-TE. Dietary assessment of the subjects were surveyed using a short-form of the food frequency questionnaire (FFQ) and the nutrition quotient (NQ), which uses 4 factors, namely, balance, diversity, moderation, and dietary behavior.A total of 986 subjects were evaluated by constitution type with MetS status. Of these subjects, 48.6% had pre-MetS, 89.5% were obese and had the highest waist circumference (WC) in Pre-MetS TE. BP, FPG, TG were higher, while HDL-C was lower, than normal TE or non-TE both in Pre-MetS TE and non-TE. The prevalence of pre-MetS was positively associated with lower status of dietary behavior (odds ratio [ORs]: 2.153, 95% confidence interval [CI]: 1.179-3.931) while negatively related to higher vegetables and fruits intakes (ORs: 0.594, 95% CI: 0.359-0.983) in TE. Lower status of NQ had about 2 times higher risk of Pre-MetS (ORs: 1.855, 95% CI: 1.018-3.380) and abdominal obesity (ORs: 2.035, 95% CI: 1.097-3.775) in TE compared with higher status of NQ after controlling for covariates.Poor diet was a key contributor to the development of Pre-MetS and abdominal obesity in Korean adults with TE. Customized nutrition care and integrated medicinal approaches are strongly suggested to conduct optimal preventive care for people who are vulnerable to health risk.

Relationship between Low Vegetable Consumption, Increased High-Sensitive C-Reactive Protein Level, and Cardiometabolic Risk in Korean Adults with Tae-Eumin: A Cross-Sectional Study

An anti-inflammatory diet has many beneficial effects on cardiometabolic diseases. Constitution type of traditional Korean medicine can predict cardiometabolic risk factors. We examined the relationship between vegetable consumption and the high-sensitive C-reactive protein (hs-CRP) level on cardiometabolic risk factors in Korean adults by constitution types. Data from 1,983 eligible participants (mean age, 44.3 years) were included in the present cross-sectional study. The inflammatory status of the participants was categorized into low- (<3.0 mg/L) or high-risk (≥3.0 mg/L) groups based on their constitution types. Cardiometabolic risk factors (abdominal obesity, elevated triglycerides, reduced high-density lipoprotein-cholesterol, elevated blood pressure, elevated fasting plasma glucose, and ≥2 concurrent cardiovascular diseases (CVDs) risk factors) and dietary assessment of the participants were assessed. A total of 11.1% of Tae-eumin (TE) and 4.9% of non-TE groups had a higher hs-CRP level (TE: 6.6 ± 0.2, non-TE: 8.4 ± 0.3) than a low hs-CRP level TE and non-TE (TE: 0.9 ± 0.1, non-TE: 0.6 ± 0.1). Vegetable consumption of <91.5 g/day was highly associated with a high-risk hs-CRP level (adjusted odds ratio (ORs): second tertile (T2): 2.290, (95% confidence interval (CI): 1.285-4.082); first tertile (T1): 2.474 (95% CI: 1.368-4.475), P=0.003) compared with that of the highest (T3) in TE. Low (T1 and T2) vegetable consumption was associated with a 54-63% increased prevalence of more than two concurrent CVDs risk factors compared with that of the highest in the TE group (P=0.012). Higher vegetable consumption greatly decreased the prevalence of CVDs risk factors by 63-86% in the low-risk and high-risk hs-CRP TE groups. Our results highlight the cardioprotective effects of higher consumption of vegetables in Korean adults with TE. Evidence-based clinical risk factor management and multifaceted approaches at the community and population levels targeting prevention in high-burden groups are recommended to reduce the premature mortality attributed to CVD.

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.