The association between metabolic syndrome and body constitution in traditional Chinese medicine

Development and validation of non-invasive prediction models for migraine in Chinese adults

BACKGROUND

Migraine is a common disabling neurological disorder with severe physical and psychological damage, but there is a lack of convenient and effective non-invasive early prediction methods. This study aimed to develop a new series of non-invasive prediction models for migraine with external validation.

METHODS

A total of 188 and 94 subjects were included in the training and validation sets, respectively. A standardized professional questionnaire was used to collect the subjects' 9-item traditional Chinese medicine constitution (TCMC) scores, Pittsburgh Sleep Quality Index (PSQI) score, Zung's Self-rating Anxiety Scale and Self-rating Depression Scale scores. Logistic regression was used to analyze the risk predictors of migraine, and a series of prediction models for migraine were developed. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the discrimination and calibration of the models. The predictive performance of the models were further validated using external datasets and subgroup analyses were conducted.

RESULTS

PSQI score and Qi-depression score were significantly and positively associated with the risk of migraine, with the area of the ROC curves (AUCs) predicting migraine of 0.83 (95% CI:0.77-0.89) and 0.76 (95% CI:0.68-0.84), respectively. Eight non-invasive predictive models for migraine containing one to eight variables were developed using logistic regression, with AUCs ranging from 0.83 (95% CI: 0.77-0.89) to 0.92 (95% CI: 0.89-0.96) for the training set and from 0.76 (95% CI: 0.66-0.85) to 0.83 (95% CI: 0.75-0.91) for the validation set. Subgroup analyses showed that the AUCs of the eight prediction models for predicting migraine in the training and validation sets of different gender and age subgroups ranged from 0.80 (95% CI: 0.63-0.97) to 0.95 (95% CI: 0.91-1.00) and 0.73 (95% CI: 0.64-0.84) to 0.93 (95% CI: 0.82-1.00), respectively.

CONCLUSIONS

This study developed and validated a series of convenient and novel non-invasive prediction models for migraine, which have good predictive ability for migraine in Chinese adults of different genders and ages. It is of great significance for the early prevention, screening, and diagnosis of migraine.

Application of machine learning models in predicting insomnia severity: an integrative approach with constitution of traditional Chinese medicine

Objective

This study sought to explore the utility of machine learning models in predicting insomnia severity based on Traditional Chinese Medicine (TCM) constitution classifications, with an aim to discuss the potential applications of such models in the treatment and prevention of insomnia.

Methods

We analyzed a dataset of 165 insomnia patients from the Shanghai Minhang District Integrated Traditional Chinese and Western Medicine Hospital. TCM constitution was assessed using a standardized Constitution in Chinese Medicine (CCM) scale. Sleep quality, or insomnia severity, was evaluated using the Spiegel Sleep Questionnaire (SSQ). Machine learning models, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were utilized. These models were optimized using Grid Search algorithm and were trained and tested on stratified patient data, with the TCM constitution classifications serving as primary predictors.

Results

The RFC outperformed others, achieving a weighted average accuracy, precision, recall, and F1-score of 0.91, 0.94, 0.92, and 0.92 respectively, it also effectively classified the severity of insomnia with high area under receiver operating characteristic curve (AUC-ROC) values. Feature importance analysis demonstrated the Damp-heat constitution as the most influential predictor, followed by Yang-deficiency, Qi-depression, Qi-deficiency, and Blood-stasis constitutions.

Conclusion

The results demonstrate the potent utility of machine learning, specifically RFC, coupled with TCM constitution classifications in predicting insomnia severity. Notably, the constitution classifications such as Damp-heat and Yang-deficiency emerged as crucial determinants, emphasizing its potential in guiding targeted insomnia treatments. This approach enables the development of more personalized and efficient interventions, thereby enhancing patient outcomes.

Epigenetic Studies of Chinese Herbal Medicine: Pleiotropic Role of DNA Methylation

Accumulating knowledge has been achieved on DNA methylation participating in numerous cellular processes and multiple human diseases; however, few studies have addressed the pleiotropic role of DNA methylation in Chinese herbal medicine (CHM). CHM has been used worldwide for the prevention and treatment of multiple diseases. Newly developed epigenetic techniques have brought great opportunities for the development of CHM. In this review, we summarize the DNA methylation studies and portray the pleiotropic role of DNA methylation in CHM. DNA methylation serves as a mediator participating in plant responses to environmental factors, and thus affecting CHM medicinal plants growth and bioactive compound biosynthesis which are vital for therapeutic effects. Furthermore, DNA methylation helps to uncover the pharmaceutical mechanisms of CHM formulae, herbs, and herbal-derived compounds. It also provides scientific validation for constitution theory and other essential issues of CHM. This newly developed field of DNA methylation is up-and-coming to address many complicated scientific questions of CHM; it thus not only promotes disease treatment but also facilitates health maintenance.

High fructose diet-induced metabolic syndrome: Pathophysiological mechanism and treatment by traditional Chinese medicine

Fructose is a natural monosaccharide broadly used in modern society. Over the past few decades, epidemiological studies have demonstrated that high fructose intake is an etiological factor of metabolic syndrome (MetS). This review highlights research advances on fructose-induced MetS, especially the underlying pathophysiological mechanism as well as pharmacotherapy by traditional Chinese medicine (TCM), using the PubMed, Web of science, China National Knowledge Infrastructure, China Science and Technology Journal and Wanfang Data. This review focuses on de novo lipogenesis (DNL) and uric acid (UA) production, two unique features of fructolysis different from glucose glycolysis. High level of DNL and UA production can result in insulin resistance, the key pathological event in developing MetS, mostly through oxidative stress and inflammation. Some other pathologies like the disturbance in brain and gut microbiota in the development of fructose-induced MetS in the past years, are also discussed. In management of MetS, TCM is an excellent representative in alternative and complementary medicine with a complete theory system and substantial herbal remedies. TCMs against MetS or MetS components, including Chinese patent medicines, TCM compound formulas, single TCM herbs and active compounds of TCM herbs, are reviewed on their effects and molecular mechanisms. TCMs with hypouricemic activity, which specially target fructose-induced MetS, are highlighted. And new technologies and strategies (such as high-throughput assay and systems biology) in this field are further discussed. In summary, fructose-induced MetS is a multifactorial disorder with the underlying complex mechanisms. Current clinical and pre-clinical evidence supports the potential of TCMs in management of MetS. Additionally, TCMs may show some advantages against complex MetS as their holistic feature through multiple target actions. However, further work is needed to confirm the effectivity and safety of TCMs by high-standard clinical trials, clarify the molecular mechanisms, and develop new anti-MetS drugs by development and application of optimized and feasible strategies and methods.