K-means clustering combined with latent structure analysis identifies 9 depression subtypes with distinct medication patterns in Traditional Chinese Medicine
This study analyzed 4434 prescriptions from 3537 publications and used K-means clustering with latent structure analysis to classify depression patients into 9 subtypes. The most common symptoms were insomnia and depressed mood, and the most frequently used herbs were Radix Bupleuri, Radix Paeoniae Alba, Poria, Rhizoma Chuanxiong, and Radix Curcumae. Medication patterns varied by subtype, with blood-activating herbs used for Clusters 1, 2, and 6; qi-regulating herbs for Clusters 3, 4, 5, 8, and 9; and qi-supplementing herbs for Cluster 7.
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K-means clustering combined with latent structure analysis identifies 9 depression subtypes with distinct medication patterns in Traditional Chinese Medicine The current body of evidence comprises 1 study. EvidenceHub rates the overall confidence at 32/100 (low).
The Claim
K-means clustering combined with latent structure analysis identifies 9 depression subtypes with distinct medication patterns in Traditional Chinese Medicine
This conclusion is most relevant to: Depression patients from clinical literature on TCM formulas, sourced from CNKI, Wanfang Data, VIP Database, Sinomed, Web of Science, and PubMed.
What the Research Shows
The conclusion draws on 1 linked study. Highlights from the cited literature:
- ▸[Depression Syndrome Typing and Medication Pattern Analysis Through Unsupervised Clustering Combined With Latent Structure Dual Analysis]. (Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2025) —
How It Works
The proposed biological pathway:
- ▸Symptom and medication data were standardized from 4434 prescriptions
- ▸K-means clustering combined with latent structure analysis classified patients into 9 subtypes
- ▸Core symptoms and formulas were identified for each subtype
- ▸Herb properties and co-occurrence patterns were analyzed to reveal medication patterns
Who Might Benefit
Evidence fit by population:
- ▸Depression patients from clinical literature on TCM formulas, sourced from CNKI, Wanfang Data, VIP Database, Sinomed, Web of Science, and PubMed
Recommended Dose
N/A
Limitations & Caveats
Important context when interpreting this evidence:
- ▸The study is based on literature review rather than primary clinical data, which may introduce publication bias
- ▸The subjective nature of TCM syndrome differentiation and lack of unified classification standards may affect reproducibility
Frequently Asked Questions
What are the 9 depression subtypes identified in this study?▼
The study identified 9 subtypes (Clusters 1-9) using K-means clustering, with Cluster 6 being the largest. Each subtype had distinct core TCM formulas such as Zishui Qinggan Decoction, Danzhi Xiaoyao Powder, and Xiaoyao Powder.
Which herbs were most commonly used for depression in this analysis?▼
The most frequently used herbs were Radix Bupleuri (Chai Hu), Radix Paeoniae Alba (Bai Shao), Poria (Fu Ling), Rhizoma Chuanxiong (Chuan Xiong), and Radix Curcumae (Yu Jin).
How were the depression subtypes differentiated in terms of medication?▼
Clusters 1, 2, and 6 were treated mainly with blood-activating and stasis-dissolving herbs; Clusters 3, 4, 5, 8, and 9 with qi-regulating herbs; and Cluster 7 with qi-supplementing herbs. Meridian tropism also varied, targeting spleen, heart, or liver meridians.
What is the significance of this study for clinical practice?▼
The study provides a machine learning-based objective classification system for depression subtypes, clarifying the rationale for syndrome-based treatment and offering theoretical support for clinicians in selecting appropriate TCM formulas.
References
- 1.Zhu H, Yu C, Li X, Wang R, Chen Y, Wang T, Wu W, Yao L. “[Depression Syndrome Typing and Medication Pattern Analysis Through Unsupervised Clustering Combined With Latent Structure Dual Analysis]..” Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2025. PMID: 40964125 DOI: 10.12182/20250460202