Investigating the effects of Liushen Capsules on the metabolome of seasonal influenza: A randomized clinical trial

Abstract

Background: Traditional Chinese Medicines (TCMs) are effective strategies for preventing influenza infection. Liushen Capsules can inhibit influenza virus proliferation, significantly mitigate virus-induced inflammation and improve acute lung injury in vitro or in vivo. However, the efficacy and safety of LS in clinical trials, and the role of LS in regulating metabolites in patients are not well known. Materials and methods: A randomized, double-blind, placebo-controlled clinical trial was designed in this study. All participants were enrolled between December 2019 and November 2020. The efficacy and safety were assessed by primary efficacy endpoint ((area under the curve (AUC) analysis)) and secondary endpoint (individual scores for each symptom, remission of symptoms, and rates of inflammatory factors). The serum samples were collected from patients to detect the levels of inflammatory factors using RT-PCR and to identify metabolites using a non-targeted metabolomics ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS). Results: 81 participants from The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and the First Affiliated Hospital of Guangzhou Medical University were completed the full study. After 14 days of intervention, the area under the curve (AUC) of the total symptom scores in LS group was significantly smaller than that in Placebo group (p < 0.001). Alleviation of sore throat, cough and nasal congestion in the LS group was significantly better than that in the Placebo group. The time and number to alleviation of symptoms or complete alleviation of symptoms in LS group was significantly better than that in Placebo group. The adverse effects of clinical therapy were slightly higher in LS group than in Placebo group, but there was no statistical difference. After 14 days of LS intervention, the levels of IL-1ra, Eotaxin, IFN-γ, IL-6, IL-10, IL-13, SCF and TRAIL in serum of participants with influenza infection were significantly decreased compared with Placebo group. It was observed that there were significant differences in the serum metabolic profiles between start- and end- LS groups. Further correlation analysis showed a potential regulatory crosstalk between glycerophospholipids, sphingolipids fatty acyls and excessive inflammation and clinical symptoms. Importantly, it may be closely related to phospholipid, fatty acid, arachidonic acid and amyl-tRNA synthesis pathway metabolic pathways. Conclusion: The study showed there were no clinically significant adverse effects on LS, and a significant improvement in influenza-like symptomatology and inflammatory response in patients treated with LS. Further analysis showed that LS could significantly correct the metabolic disorders in the serum metabolite profile of the patients. This provided new insights into the potential mechanism of LS for the treatment of influenza.

Keywords: Chinese traditional medicines; clinical trial; influenza virus; liushen capsules; non-targeted metabolomics.

FIGURE 1

Schematic view of participant flow.

FIGURE 2

Inflammatory factors in the start- and end-LS groups.

FIGURE 3

Serum metabolomics was used to quantify metabolites in the start- and end- LS groups. (A) OPLS-DA plot showing the spatial division between start- and end- LS groups. (B) Alignment diagram of correlation coefficients of OPLS-DA model. (C) Volcano plot showing the metabolites that differed cumulatively and significantly changed in start- and end- LS groups. (D) Heat map of the association between 89 metabolites and start/end LS intervention.

FIGURE 4

Pathway analysis was used to enrich the metabolic pathways of differential metabolites start- and end- LS treatment. (A) KEGG pathway showing the differential metabolic pathways in start- and end- LS groups. (B) Correlation analysis of serum metabolites with significantly difference. Only metabolite correlations with |PCC| ≥ 0.9 was considered. Circle indicated metabolites and triangles indicated metabolic pathways, with darker colors indicating higher correlations.

FIGURE 5

Metabolite-clinical symptom correlation in serum samples. Only metabolite correlations with |PCC| ≥ 0.9 and p < 0.001 were considered.

FIGURE 6

Metabolite-cytokine correlation in serum samples. Only metabolite correlations with |PCC| ≥ 0.9 and p < 0.001 were considered.