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The study aims to identify specific acupoint prescription patterns for treating primary dysmenorrhea through data mining and network analysis.

March 18, 2026Journal of Pain ResearchOpen Access

Core Acupoint Prescription Patterns for Primary Dysmenorrhea: Data Mining and Network-Based Analysis

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The study aims to identify specific acupoint prescription patterns for treating primary dysmenorrhea through data mining and network analysis.
Conducted a literature search across nine databases for clinical studies on acupuncture for primary dysmenorrhea.
Extracted and standardized acupoint prescriptions from identified studies.
Performed frequency analysis and association rule mining to determine commonly used acupoints.
Used a network pharmacology framework to explore molecular targets and bioactive compounds linked to acupoint combinations.
Constructed a protein-protein interaction (PPI) network and conducted GO/KEGG enrichment analyses.
Identified 177 relevant articles leading to 291 prescriptions with 66 unique acupoints analyzed.
SP6, CV4, and SP8 were the most frequently used acupoints, with SP6–CV4–SP8 forming a core combination.
Network analysis connected the core combination to 29 active compounds and 640 potential targets, including 94 related to primary dysmenorrhea.
Identified 10 core targets through PPI analysis, with significant pathways involving inflammation and hormonal regulation.

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Data mining discovers acupoint patterns for primary dysmenorrhea, suggesting new therapies.

Yang et al. (2026) studied this question.

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