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

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.

Internet-Based Healthcare Knowledge Service for Improvement of Chinese Medicine Healthcare Service Quality

With the development of new-generation information technology and increasing health needs, the requirements for Chinese medicine (CM) services have shifted toward the 5P medical mode, which emphasizes preventive, predictive, personalized, participatory, and precision medicine. This implies that CM knowledge services need to be smarter and more sophisticated. This study adopted a bibliometric approach to investigate the current state of development of CM knowledge services, and points out that accurate knowledge service is an inevitable requirement for the modernization of CM. We summarized the concept of smart CM knowledge services and highlighted its main features, including medical homogeneity, knowledge service intelligence, integration of education and research, and precision medicine. Additionally, we explored the intelligent service method of traditional Chinese medicine under the 5P medical mode to support CM automatic knowledge organization and safe sharing, human-machine collaborative knowledge discovery and personalized dynamic knowledge recommendation. Finally, we summarized the innovative modes of CM knowledge services. Our research will guide the quality assurance and innovative development of the traditional Chinese medicine knowledge service model in the era of digital intelligence.

Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases

High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.

The Holistic Philosophy of Traditional Chinese Medicine and Conflicts With Modern Medicine

Traditional Chinese medicine (TCM) has sparked the public's attention for its potential in new drug development and its holistic view toward health, which is totally different from the reductionistic science of modern medicine. Although many scholars try to connect TCM with precision medicine or apply new methods and technology to integrate TCM with modern medicine, the misunderstandings and gap between TCM and modern medicine limit the development of evidence-based TCM. Traditional Chinese medicine is actually a medical science encompassing not only medicine but also philosophy and art in direct contrast to molecular-based modern medicine. As more and more multidisciplinary studies are being published, finding ways to integrate TCM with modern or precision medicine through artificial intelligence, new study design and technology may become a critical issue. This article aims to briefly review the unique philosophy of TCM and its conflicts with modern medicine, with a focus on the potential integration of TCM and modern medicine. We also provide insight for the key attributes of TCM and the associated investigation with Western research approaches.

Care patterns and Traditional Chinese Medicine constitution as factors of depression and anxiety in patients with systemic sclerosis: A cross-sectional study during the COVID-19 pandemic

Objective

Care patterns and Traditional Chinese Medicine (TCM) constitution affects the emotion and health of patients with systemic sclerosis (SSc) while the prevalence of COVID-19 may aggravate such patients' emotion and health. We investigated the depression and anxiety levels of patients with SSc during the pandemic to identify the correlation between care patterns, TCM constitution, and patients' emotion.

Materials and methods

This was a cross-sectional study. Patients with SSc and healthy individuals were surveyed using the patient health questionnaire-9, generalized anxiety disorder-7, and constitution in Chinese medicine questionnaire and a modified care pattern questionnaire. Factors correlated with depression and anxiety were screened using univariate and multivariate logistic regression analyses.

Results

A total of 273 patients with SSc and 111 healthy individuals were included in the analysis. The proportion of patients with SSc who were depressed was 74.36%, who had anxiety was 51.65%, and who experienced disease progression during the pandemic was 36.99%. The proportion of income reduction in the online group (56.19%) was higher than that in the hospital group (33.33%) (P = 0.001). Qi-deficiency [adjusted odds ratio (OR) = 2.250] and Qi-stagnation (adjusted OR = 3.824) constitutions were significantly associated with depression. Remote work during the outbreak (adjusted OR = 1.920), decrease in income (adjusted OR = 3.556), and disease progression (P = 0.030) were associated with the occurrence of depression.

Conclusion

Chinese patients with SSc have a high prevalence of depression and anxiety. The COVID-19 pandemic has changed the care patterns of Chinese patients with SSc, and work, income, disease progression, and change of medications were correlates of depression or anxiety in patients with SSc. Qi-stagnation and Qi-deficiency constitutions were associated with depression, and Qi-stagnation constitution was associated with anxiety in patients with SSc.

Trial registration

http://www.chictr.org.cn/showproj.aspx?proj=62301, identifier ChiCTR2000038796.

Reveal the Mechanisms of Yi-Fei-Jian-Pi-Tang on Covid-19 through Network Pharmacology Approach

Objectives

The Traditional Chinese Medicine (TCM) formula Yi-Fei-Jian-Pi-Tang (YFJPT) has been demonstrated effective against Corona Virus Disease 2019 (Covid-19). The aim of this article is to make a thorough inquiry about its active constituent as well as mechanisms against Covid-19 via TCM network pharmacology.

Methods

All the ingredients of YFJPT are obtained from the pharmacology database of the TCM system. The genes which are associated with the targets are obtained by utilizing UniProt. The herb-target network is built up by utilizing Cytoscape. The target protein-protein interaction network is built by utilizing the STRING database and Cytoscape. The critical targets of YFJPT are explored by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Results

The outcomes show that YFJPT might has 33 therapeutic targets on Covid-19, namely, interleukin 2 (IL2), heme oxygenase 1 (HMOX1), interleukin 4 (IL4), interferon gamma (FNG), α nuclear factor of kappa light polypeptide gene enhancer in Bcells inhibitor, alpha (NFKBIA), nuclear factor-k-gene binding (NFKB), nitric oxide synthase 3 (NOS3), intercellular adhesion molecule 1 (ICAM1), hypoxia inducible factor 1 subunit alpha (HIF1A), mitogen-activated protein kinase 3 (MAPK3), epidermal growth factor receptor (EGFR), interleukin 10 (IL10), jun proto-oncogene (JUN), C-C motif chemokine ligand 2 (CCL2), C-X-C motif chemokine ligand 8 (CXCL8), tumor protein p53 (TP53), interleukin 1 beta (IL1B), AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor (TNF), interleukin 6 (IL6), erb-b2 receptor tyrosine kinase 2 (ERBB2), RELA proto-oncogene (RELA), NF-κB subunit, caspase 8 (CASP8), peroxisome proliferator activated receptor alpha (PPARA), TIMP metallopeptidase inhibitor 1 (TIMP1), transforming growth factor beta 1 (TGFB1), interleukin 1 alpha (IL1A), signal transducer and activator of transcription 1 (STAT1), mitogen-activated protein kinase 8 (MAPK8), myeloperoxidase (MPO), matrix metallopeptidase 3 (MMP3), matrix metallopeptidase 1 (MMP1), and NFE2 like bZIP transcription factor 2 (NFE2L2). The gene enrichment analysis prompts that YFJPT most likely contributes to patients related to Covid-19 by regulating the pathways of cancers.

Conclusions

That will lay a foundation for the clinical rational application and further experimental research of YFJPT.

Obese Individuals With and Without Phlegm-Dampness Constitution Show Different Gut Microbial Composition Associated With Risk of Metabolic Disorders

Background

Obesity is conventionally considered a risk factor for multiple metabolic diseases, such as dyslipidemia, type 2 diabetes, hypertension, and cardiovascular disease (CVD). However, not every obese patient will progress to metabolic disease. Phlegm-dampness constitution (PDC), one of the nine TCM constitutions, is considered a high-risk factor for obesity and its complications. Alterations in the gut microbiota have been shown to drive the development and progression of obesity and metabolic disease, however, key microbial changes in obese patients with PDC have a higher risk for metabolic disorders remain elusive.

Methods

We carried out fecal 16S rRNA gene sequencing in the present study, including 30 obese subjects with PDC (PDC), 30 individuals without PDC (non-PDC), and 30 healthy controls with balanced constitution (BC). Metagenomic functional prediction of bacterial taxa was achieved using PICRUSt.

Results

Obese individuals with PDC had higher BMI, waist circumference, hip circumference, and altered composition of their gut microbiota compared to non-PDC obese individuals. At the phylum level, the gut microbiota was characterized by increased abundance of Bacteroidetes and decreased levels of Firmicutes and Firmicutes/Bacteroidetes ratio. At the genus level, Faecalibacterium, producing short-chain fatty acid, achieving anti-inflammatory effects and strengthening intestinal barrier functions, was depleted in the PDC group, instead, Prevotella was enriched. Most PDC-associated bacteria had a stronger correlation with clinical indicators of metabolic disorders rather than more severe obesity. The PICRUSt analysis demonstrated 70 significantly different microbiome community functions between the two groups, which were mainly involved in carbohydrate and amino acid metabolism, such as promoting Arachidonic acid metabolism, mineral absorption, and Lipopolysaccharide biosynthesis, reducing Arginine and proline metabolism, flavone and flavonol biosynthesis, Glycolysis/Gluconeogenesis, and primary bile acid biosynthesis. Furthermore, a disease classifier based on microbiota was constructed to accurately discriminate PDC individuals from all obese people.

Conclusion

Our study shows that obese individuals with PDC can be distinguished from non-PDC obese individuals based on gut microbial characteristics. The composition of the gut microbiome altered in obese with PDC may be responsible for their high risk of metabolic diseases.

TRIM25 Rescues Against Doxorubicin-Induced Pyroptosis Through Promoting NLRP1 Ubiquitination

Doxorubicin (DOX) is an antineoplastic agent that is widely employed in carcinomas, but it can cause cardiotoxicity in clinic. TRIM25 has E3 ubiquitin ligase activities and can ubiquitinate its target proteins. The role of TRIM25 in DOX-induced cardiotoxicity remains unknown. In this study, our results showed that DOX induced pyroptosis of H9c2 cells by TUNEL staining and Western blot assay. Interestingly, TRIM25 was downregulated in DOX-treated H9c2 cells in a time- and dose-dependent manner. TRIM25 attenuated DOX-induced pyroptosis of H9c2 cells. Furthermore, in vitro ubiquitination assay proved that TRIM25 decreased the stability of NLRP1 via promoting the ubiquitination of NLRP1. The rescue experiments confirmed that TRIM25 inhibited DOX-induced H9c2 cells pyroptosis by regulating NLRP1 stability. Animal experiments demonstrated that overexpression of TRIM25 attenuated DOX-induced cardiomyocyte pyroptosis in rats. In summary, TRIM25 exerts its cardioprotective effects by promoting the ubiquitination of NLRP1 in DOX-induced cardiomyocyte pyroptosis, which provides a novel therapeutic strategy for DOX-induced cardiotoxicity.