Identification of Traditional Chinese Medicine Constitutions and Physiological Indexes Risk Factors in Metabolic Syndrome: A Data Mining Approach

Integrative analysis of microbiota and metabolomics in individuals exhibiting different TCM constitutions utilizing 16S rDNA sequencing and LC/MS metabolomics

BACKGROUND

Traditional Chinese Medicine (TCM) theory posits a close relationship between an individual's constitutional types and the overall health. Variations in metabolic processes and microbial composition have been observed across different constitution types. This study aims to explore the relationship between TCM constitutions, intestinal flora, and metabolites to devise personalized TCM treatment strategies, enhancing evidence-based guidance for clinical practice.

METHODS

The research investigated differences in microbial diversity and composition among three TCM constitution types: yin-deficiency constitution (PA), balanced constitution (PH), and yang-deficiency constitution (PI). A significant elevation of the Chao1 metric was noted in the PH group compared to the PI group.

RESULTS

PCoA and CPCoA analyses demonstrated distinct group separation based on floral samples. Dominant phyla included Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria, with varying abundance at the genus level. Metabolic pathway analysis unveiled disparities in metabolites associated with different pathways among constitution groups. KEGG pathway enrichment analysis emphasized pathways such as steroid hormone biosynthesis, ovarian steroidogenesis, and tryptophan metabolism. Furthermore, correlation analysis revealed associations between specific bacterial taxa and metabolites.

CONCLUSION

This study delineated the variations in intestinal flora and metabolic profiles among individuals with PA, PH, and PI constitution types, providing valuable insights for the development of personalized TCM treatment approaches.

Metabolic syndrome prediction model using Bayesian optimization and XGBoost based on traditional Chinese medicine features

Metabolic syndrome (MetS) has a high prevalence and is prone to many complications. However, current MetS diagnostic methods require blood tests that are not conducive to self-testing, so a user-friendly and accurate method for predicting MetS is needed to facilitate early detection and treatment. In this study, a MetS prediction model based on a simple, small number of Traditional Chinese Medicine (TCM) clinical indicators and biological indicators combined with machine learning algorithms is investigated. Electronic medical record data from 2040 patients who visited outpatient clinics at Guangdong Chinese medicine hospitals from 2020 to 2021 were used to investigate the fusion of Bayesian optimization (BO) and eXtreme gradient boosting (XGBoost) in order to create a BO-XGBoost model for screening nineteen key features in three categories: individual bio-information, TCM indicators, and TCM habits that influence MetS prediction. Subsequently, the predictive diagnostic model for MetS was developed. The experimental results revealed that the model proposed in this paper achieved values of 93.35 %, 90.67 %, 80.40 %, and 0.920 for the F1, sensitivity, FRS, and AUC metrics, respectively. These values outperformed those of the seven other tested machine learning models. Finally, this study developed an intelligent prediction application for MetS based on the proposed model, which can be utilized by ordinary users to perform self-diagnosis through a web-based questionnaire, thereby accomplishing the objective of early detection and intervention for MetS.

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.

Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome

OBJECTIVE

The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models.

METHODS

The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sensitivity, specificity, Youden index, and AUROC value are used to evaluate the model classification performance; post-hoc model-agnostic interpretation methods (variable importance, LIME) are used to interpret the results of the predictive model.

RESULTS

Eighteen physical examination indicators are screened out by RFE, which can effectively solve the problem of physical examination data redundancy. Random forest and XGBoost models have higher accuracy, sensitivity, specificity, Youden index, and AUROC values compared with logistic regression. XGBoost models have higher sensitivity, Youden index, and AUROC values compared with random forest. The study uses variable importance, LIME and PDP for global and local interpretation of the optimal MetS risk prediction model (XGBoost), and different interpretation methods have different insights into the interpretation of model results, which are more flexible in model selection and can visualize the process and reasons for the model to make decisions. The interpretable risk prediction model in this study can help to identify risk factors associated with MetS, and the results showed that in addition to the traditional risk factors such as overweight and obesity, hyperglycemia, hypertension, and dyslipidemia, MetS was also associated with other factors, including age, creatinine, uric acid, and alkaline phosphatase.

CONCLUSION

The model interpretability methods are applied to the black box model, which can not only realize the flexibility of model application, but also make up for the uninterpretable defects of the model. Model interpretability methods can be used as a novel means of identifying variables that are more likely to be good predictors.

Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models

Background

We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS.

Methods

This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model.

Results

Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise.

Conclusions

Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.

Valid and Convenient Questionnaire Assessment of Chinese Body Constitution: Item Characteristics, Reliability, and Construct Validation

BACKGROUND

Body constitution is a fundamental concept in traditional Chinese medicine (TCM) for clinical diagnosis, treatment of illness, and community-based health promotion. Clinical assessment of patients' body constitutions, however, has never been easy and consistent, even by well-trained clinicians and TCM practitioners. Therefore, questionnaires such as the popular Constitution in Chinese Medicine Questionnaire (CCMQ) can be an appealing and convenient assessment alternative. The present research borrowed advanced methodologies for questionnaire development in psychology and other social sciences to examine the performance of the CCMQ in terms of (i) the strength of relations of each item with its designated constitution, (ii) the reliabilities of each constitution, and (iii) the overall 9-constitution structure. This research provided empirical evidence to support the use of the CCMQ and proposed directions for refinement in future revisions of the CCMQ or similar measures.

METHODS

A total of 1571 volunteers from three villages in southern China participated in the CCMQ survey. The item characteristics, reliabilities, interconstitution correlations, and confirmatory factor analysis of the 9-body-constitution structure were examined.

RESULTS

The results generally supported the appropriateness of the clinical observations (the questionnaire items) and the CCMQ 9-constitution classification structure. Nevertheless, some relatively weaker items, item pairs with similar meanings, and highly overlapping constitutions were identified for future CCMQ revisions.

CONCLUSION

The CCMQ measured the 9 constitutions efficiently and with reasonably good reliability and construct validity. Given the various challenges to assessing TCM body constitutions even by experienced clinicians, the CCMQ provides an appealing alternative to measure the Chinese body constitutions of healthy participants in large-scale research or community health promotion programs. The present study also demonstrated how advanced methodologies in social sciences can help validate and refine the CCMQ and similar complementary medicine measures.

Association of Traditional Chinese Medicine Body Constitution and Health-Related Quality of Life in Female Patients with Systemic Lupus Erythematosus: A Cross-Sectional Study

Background

Traditional Chinese medicine (TCM) body constitution has been studied in many diseases, but few have focused on systemic lupus erythematosus (SLE) and particularly their association with disease-specific quality of life (QoL). Therefore, the aim of this study was to investigate the association of TCM body constitution and QoL in female patients with SLE.

Methods

A cross-sectional study was conducted on adult female patients with a clinician-confirmed diagnosis of SLE in a regional hospital in Taiwan. TCM body constitution types were determined using the Constitution in Chinese Medicine Questionnaire (CCMQ). Disease-specific QoL of the participants was assessed using the LupusQoL. Multiple linear regression analyses were conducted to assess the associations between TCM body constitution types with the score of each of the eight domains of LupusQoL and between the numbers of multiple unbalanced body constitution types and score of each of the eight domains of LupusQoL.

Results

Of the 317 female patients with SLE, 22 (6.9%) were classified to have a gentleness balanced body constitution type. Among the remaining 295 patients with unbalanced body constitution types, Qi-deficiency was the most common (64.4%), followed by Yin-deficiency (57.6%). Multiple linear regression analyses showed that Qi-deficiency was significantly associated with the emotional, pain, and fatigue domains of the LupusQoL, whereas Yin-deficiency was significantly associated with the emotional and fatigue domains of the LupusQoL. In addition, all domains of the LupusQoL showed a general pattern of poorer QoL with increasing numbers of unbalanced body constitution types.

Conclusions

Different TCM body constitution types were significantly associated with various domains of the LupusQoL. A high prevalence of multiple body constitution types in patients with SLE was observed. A consistent pattern of poorer LupusQoL with increasing numbers of unbalanced body constitution types was evident.

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.

Enlightenment about using TCM constitutions for individualized medicine and construction of Chinese-style precision medicine: research progress with TCM constitutions

TCM constitution is a new branch of TCM. It provides enlightenment on individualized medicine, including the development of new models of individualized research based on nine constitutions, the acquisition of comprehensive health information for individuals, and establishment of a consistent individualized diagnosis and treatment system. Further, we propose a Chinese-style "precision medicine" based on individualization using the TCM constitutions.