Multi-omics driven paradigm for construction of traditional Chinese Medicine Zheng (syndrome) diagnosis and treatment model, taking Shi Zheng (syndrome of dampness) as an example.

Background: Shi Zheng (SZ, syndrome of dampness) is a major syndrome type in traditional Chinese Medicine (TCM), the ambiguity of its pathomechanism and the lack of blood diagnostic indicators have limited the understanding of the development of SZ.

Purpose: To explore the pathological mechanism of SZ and establish a symptom-centered diagnosis and treatment model.

Methods: We recruited 250 participants, including healthy individuals and patients diagnosed with SZ. Serum metabolomics and proteomics analyses were performed to screen common pathways. Along with the biological significance of common pathways, a common pathway-symptom correlation diagram was constructed to elucidate the pathological mechanism underlying the occurrence and development of SZ. The enrichment score and correlations with SZ main symptom was used to screen the key common pathways. The key common pathways related to differential metabolites and proteins were used to establish a multi-index diagnostic model and protein therapy target group.

Results: Joint metabolomics and proteomics analyses revealed 18 common pathways associated with symptoms. Six key pathways, such as pathogenic Escherichia coli infection, rheumatoid arthritis, PPAR signaling pathway, bile secretion, GnRH signaling pathway, and fat digestion and absorption were correlated with the main symptoms of SZ. These symptoms included greasy/thick/slippery tongue coating, heavy head, heavy body, heavy limbs, heavy joints, greasy hair, sticky mouth, sticky stool, and damp scrotum. Moreover, seven differential metabolites related to the key pathways were identified: LysoPA (20:3(5Z,8Z,11Z)/0:0), prostaglandin E2, leukotriene B4, lithocholate 3-O-glucuronide, 3-hydroxyquinine, lithocholic acid glycine conjugate, and PA(18:0/22:6(5Z,8E,10Z,13Z,15E,19Z)-2OH(7S, 17S)), and the combined diagnostic value of the seven indicators was the highest (discovery cohort: AUC = 0.90; validation cohort: AUC = 0.99). There were 23 differential proteins related to the key pathways, and six protein targets were identified, including RHOA, TNFSF13, PRKCD, APOA2, ATP1A1, and FABP1.

Conclusion: The combined analysis of metabolomics and proteomics established a symptom-centered diagnosis and treatment model of Shi Zheng.