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K-means clustering combined with latent structure analysis categorizes depression patients into 9 subtypes with distinct medication patterns

This study analyzed 4434 TCM 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. Different subtypes showed distinct medication patterns, 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.

Last updated: Aug 26, 2026•0 RCTs•📖 Read as article →

Evidence Score

Evidence Score32/100
Human RCT☆☆☆☆☆
Meta-analysis☆☆☆☆☆
Mechanism★★★★★
Safety★★★★☆
Confidencelow

Study Evidence

Study 1. [Depression Syndrome Typing and Medication Pattern Analysis Through Unsupervised Clustering Combined With Latent Structure Dual Analysis].

observational

Zhu H, Yu C, Li X, Wang R, Chen Y, Wang T, Wu W, Yao L · Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition (2025)

Participants: N/A
Duration: N/A
Intervention: K-means clustering combined with latent structure analysis for objective classification of depression subtypes and analysis of TCM medication patterns
Outcome: Identification of 9 depression subtypes, their core TCM formulas, herb properties (4 natures, 5 flavors, meridian tropism), therapeutic efficacy categories, and herb co-occurrence patterns
Effect Size: N/A
Population: Depression patients from clinical literature on TCM formulas retrieved from CNKI, Wanfang Data, VIP Database, Sinomed, Web of Science, and PubMed

Result:

PubMed: 40964125DOI: 10.12182/20250460202

Mechanism Graph

Data collection of 3537 publications and 4434 prescriptions
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Standardization of patient symptoms and medication information
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Application of K-means clustering and latent structure analysis for objective classification
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Identification of 9 depression subtypes with distinct symptom profiles and core formulas
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Statistical analysis of herb properties, flavors, meridian tropism, and co-occurrence patterns

Limitations

  • ⚠The study is based on retrospective literature analysis rather than prospective clinical trials, which may introduce selection bias
  • ⚠The classification into 9 subtypes relies on unsupervised clustering algorithms, and the clinical validity of these subtypes has not been independently verified

Frequently Asked Questions

What are the most common symptoms of depression in this TCM study?▼

The most common symptoms among depression patients were insomnia and depressed mood.

Which TCM herbs were most frequently used for depression?▼

The most commonly 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 many depression subtypes were identified in this study?▼

The study identified 9 depression subtypes using K-means clustering and latent structure analysis, with Cluster 6 accounting for the largest proportion.

What were the core TCM formulas for the different depression subtypes?▼

The core formulas included Zishui Qinggan Decoction, Danzhi Xiaoyao Powder, Huanglian Wendan Tang, Chaihu Guizhi Tang, Modified Xiaoyao Powder, Qinggan Jieyu Tang, Xiaoyao Powder, Xuefu Zhuyu Decoction, and Bazhen Decoction.

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References

  1. 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
Disclaimer: This content is for educational purposes only and is not medical advice. Evidence scores reflect the quality and quantity of available research, not clinical recommendations. Always consult a healthcare professional before starting any supplement or intervention.