How Can Science Explain the Healing Power of Traditional Chinese Medicine
Traditional Chinese Medicine (TCM) represents thousands of years of accumulated wisdom, evolving from early practices that helped people combat diseases to become a cornerstone of global medical advancements. Today, TCM remains a valuable treasure of Chinese culture.
The history of TCM dates back to primitive societies when humans fought against natural disasters, wild animals, and illnesses to survive. They gradually learned to distinguish the medicinal properties of various plants. According to legend, the Divine Farmer, Shennong, tasted hundreds of herbs, documenting their properties and therapeutic effects in the Shennong Bencao Jing (Shennong's Classic of Materia Medica), laying the foundation for TCM.
Modern TCM has also made remarkable contributions. In 2015, Chinese scientist Tu Youyou received the Nobel Prize in Medicine for discovering artemisinin, an effective treatment for malaria. During the COVID-19 pandemic, TCM played a crucial role in global prevention and control strategies, showcasing its unique value.
However, despite its demonstrated efficacy, TCM's theories have often been questioned due to a lack of scientific explanation. The classical principles of TCM lack experimental validation, the composition of herbal medicines is complex, and traditional methods of drug screening are difficult to apply. Can TCM's principles of treatment be explained using modern scientific language?
Bridging TCM and Modern Science
For a long time, TCM has accumulated extensive clinical experience, but its theoretical framework often seems shrouded in mystery when viewed from a modern scientific perspective. Concepts like "Yin-Yang Balance" and "Five Elements' Inter-Generation and Inter-Restriction" describe disease mechanisms and treatment methods in TCM, but these abstract ideas are challenging to validate experimentally.
With advances in technology, scientists are increasingly applying modern biology and systems science to uncover scientific evidence supporting TCM's therapeutic principles.
Challenges in Interpreting TCM with Modern Science
1. Abstract Theories
TCM emphasizes holistic and individualized treatment, focusing on symptom identification and regulation, while modern medicine prioritizes pathological mechanisms. This makes it challenging to directly translate TCM's theoretical framework into modern scientific language.
2. Complexity of Herbal Medicines
TCM often uses multi-herb formulations with numerous active components that interact with each other. Traditional pharmacological methods struggle to identify the mechanisms of all these components.
A breakthrough came when a collaborative research team from China and the United States published a study in *Science Advances*, introducing a framework called network medicine to explain the systemic principles of TCM treatment. This study, supported by real-world clinical data, marked the first time a scientific theory had been established to explain TCM's therapeutic mechanisms.
https://www.science.org/doi/10.1126/sciadv.adh0215
Network Medicine: A Bridge Between TCM and Modern Science
Network medicine is an emerging field based on protein-protein interaction (PPI) networks. By analyzing the relationships between diseases, symptoms, and drugs within protein networks, it reveals the mechanisms of disease occurrence and drug action.
In simple terms, PPI networks are "relationship maps" composed of proteins, where each protein represents a node, and their interactions form a complex network structure. Research shows that proteins associated with a particular disease often cluster together in the network, forming what is known as a "disease module."
TCM treatments do not directly target diseases; instead, they tailor treatments to patients' symptoms. For example, two patients with fever might receive different treatments based on factors like "cold-heat imbalance" or "deficiency-excess patterns." Network medicine has found that TCM's "symptom-driven" approach is analogous to the "symptom modules" in PPI networks—proteins associated with related symptoms tend to cluster together. This provides important evidence for explaining TCM's therapeutic principles.
Mechanisms of TCM Treatment
1. Constructing Symptom-Gene Networks
Researchers linked TCM symptom phenotypes with modern genomic data to identify genes associated with each symptom. These genes were then mapped to the PPI network, connecting them to their corresponding protein nodes.
2. Analyzing Symptom Module Distances
By examining the distances between symptom modules in the PPI network, researchers found that symptoms with closer module distances are more likely to co-occur in diseases. For example, "fever" and "diarrhea" have close network distances, explaining their frequent co-occurrence in inflammatory bowel disease or COVID-19. Conversely, "eye pain" and "anorexia" have more distant modules, making them less likely to co-occur.
3. Incorporating Herbal Medicine Targets
The chemical components of herbal medicines were also mapped to protein nodes in the network. By analyzing the distances between herbal targets and symptom modules, researchers could predict which herbs would be effective for specific symptoms. For instance, Yinchaihu (Radix Stellariae) has a close network distance to the fever module, indicating its significant efficacy in treating fever. In contrast, with a greater distance, Chuanwu (Radix Aconitum) is less suitable for fever treatment but more effective for abdominal pain.
Scientific Validation: Empirical Evidence for TCM
1. Patient Case Analysis
Researchers analyzed 1,936 electronic medical records of cirrhosis patients from Hubei Provincial Hospital of TCM, documenting 114 symptoms and the use of 218 herbal medicines. Their analysis revealed that herbs prescribed in clinical practice had significantly shorter network distances to symptom modules compared to herbs not used, aligning with predictions from network medicine.
2. Symptom Recovery Rates
Another study compared patients treated with herbal medicines to those without treatment, showing that herbs with higher recovery rates corresponded to shorter network distances. For example, Baizhu (Atractylodis macrocephalae rhizoma), used for anorexia, had a shorter network distance and higher clinical efficacy compared to other herbs.
3. Propensity Score Matching (PSM) Validation
Using statistical methods to analyze Baizhu treatment cases, researchers found that patients treated with Baizhu for anorexia had a recovery rate of 79.53%, significantly higher than the untreated group's 72.51%. This further validated the predictive power of network distances.
The Future of TCM Research
Through the network medicine framework, scientists have taken the first step in explaining TCM's therapeutic principles from the perspective of complex systems. This milestone not only modernizes TCM but also offers new ideas for drug discovery.
In the future, with broader data support and continued research, TCM may transcend traditional theoretical limitations, using scientific language to showcase its unique charm and benefit more people worldwide.
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