| Purpose:Using 16 S rDNA high-throughput sequencing technology and non-targeted metabolomics technology,the differences between patients with stable angina and sputum stasis of coronary heart disease and healthy people were discussed from metabolomics and intestinal flora,and the metabolites and intestinal flora characteristics of patients with stable angina and sputum stasis of coronary heart disease were clarified,potential biomarkers were found,the occurrence and damage mechanism of stable angina and sputum and stasis syndrome of coronary heart disease was explored,a disease diagnosis model was established,and the individualized and precise diagnosis and treatment of coronary heart disease was promoted,so as to "treat sputum and stasis" in traditional Chinese medicine Coronary heart disease provides scientific evidence.The correlation between "risk factors for coronary heart disease-intestinal flora-serum metabolites" was further analyzed,and the possible mechanism of risk factors interfering with the homeostasis of intestinal flora and participating in coronary heart disease sputum and stasis through metabolite pathways was explored.Material and method: A total of 60 patients with stable angina pectoris and sputum stasis with coronary heart disease admitted to the Affiliated Hospital of Liaoning University of Chinese Medicine were included as the observation group(GXBA group)and 60 healthy people as the control group(ZC group).Using 16 S r DNA high-throughput sequencing and non-targeted metabolomics technology,healthy volunteers were used as normal controls to observe and compare the characteristics of intestinal flora and serum metabolites in patients with stable angina pectoris and sputum stasis intersection,and to look for potential biomarkers.Spearman correlation analysis was used to analyze the correlation between coronary heart disease risk factors,gut microorganisms,and metabolites.Based on the key differential bacteria and key differential metabolites,the random forest algorithm was used to construct a diagnosis model for coronary heart disease sputum and stasis and healthy people,and the working characteristic(ROC)curve was drawn to evaluate the prediction effect of the model.Results:1.Intestinal flora characteristics:1.1 Analysis of intestinal flora diversity: There was no significant difference in the diversityαof intestinal flora between patients with stable angina pectoris and sputum stasis in coronary heart disease and healthy controls,and β diversity analysis showed that the two groups of samples had certain aggregation and some samples were scattered,indicating that the intestinal flora structure of the two groups of samples had both certain similarities and certain differences.1.2Species analysis of flora: the dominant phylums of the two groups were Firmicutes,Bacteroides,Proteobacteria,and Actinomycetes.The dominant genera of the two groups of patients were Faecalibacter,Bacteroides,and Escherichia-Shigella.1.3Analysis of microbiota differences: Compared with the control group,the abundance of verrucous micromycetes increased in the phylum observation group,and the abundance of unknown phylum and Bacteroides decreased significantly,and the difference was statistically significant(P<0.05).Compared with the control group,the abundance of Ackermanella,CAG-352,Limosilactobacillus,Mitsuokella,Ligilactobacillus,Enterococcus,Corynebacterium,Acetivibrio,and Lacticaseibacillus increased in the genus observation group(p < 0.05).Compared with the control group,the abundance of Bacteroides,Megasphaera,Lachnospira,Parabacteroides,Lachnoclostridium,Eubacterium]＿eligens＿group,Paraprevotella,Lachnospirac eae＿UCG-010,Sutterella,Butyricicoccus,Barnesiella,Lachnospiraceae＿ND3007＿group,Lachn ospiraceae＿UCG-004,Colidextribacter,Clostridia＿unclassified,Butyricimonas,Odoribacter,Co probacter,Family＿XIII＿UCG-001 decreased(p<0.05).2.Serum metabolite characteristics:2.1 Differential metabolite analysis: Univariate analysis and multivariate analysis showed that there was a good aggregation trend in the observation group and the control group,and there was a clear separation trend between the groups,indicating that there were obvious differences in serum metabolites between the two groups.2.2Differential metabolite screening and identification: There were differences in serum metabolites between the observation group and the control group,and a total of 26 differential metabolites were identified,of which 15 were upregulated and 11 were down-regulated in the observation group.The contents of alpha-chaconine,solanine,8-amino-7-oxononanoate,s-sulfo-L-cysteine,taurocholate,n-methyl-2-pyridone-5-carboxyamide,l-gamma-glutamyl-l-hy poglycin,octanoylglucuronide,l-tyrosine,dibutyl phthalate,oleamide,cyclohexanecarboxylic acid,soyasaponin ii,stearamide and d-(+)-maltose were higher than those in the control group(p<0.01).The contents of 5-(2-hydroxyethyl)-4-methylthiazole,phenylacetaldehyde,(r)-acetoin,piperine,S-glutathionyl-l-cysteine,2-dehydro-d-gluconate,guanosine,hydantoin-5-propionate,d-(-)-mannitol,guanine,and soyasaponin iii were lower than those in the control group(p < 0.01).Differential metabolites are classified according to HMDB database,which belong to eight categories: lipids and lipid molecules,oxygenated organic compounds,organic acids and their derivatives,organic heterocyclic compounds,benzene ring compounds,nucleotides and analogues,phenylpropriates and polyketones,alkaloids and their derivatives.2.3Differential metabolite pathway analysis: differential metabolite is enriched to 29 metabolic pathways,belonging to amino acids,carbohydrates,lipids,purines,and vitamin metabolism.The six key pathways most correlated with metabolite differences are phosphotransferase system(PTS),amino acid metabolism,starch and sucrose metabolism,purine metabolism,carbohydrate absorption,fructose and mannitol metabolism.3.Multi-omics correlation analysis and random forest model construction:3.1Relationship between risk factors for coronary heart disease and intestinal flora: The age of risk factors for coronary heart disease was positively correlated with the potential pathogenic bacteria such as Verrucobacterium,Akkermansia and Klebsiella pneumoniae with increased abundance in the observation group,and negatively correlated with the beneficial bacteria Bacteroidota and Bacteroides with decreased abundance in the disease group(p<0.01 or p<0.05).The history of hypertension was positively correlated with the potential pathogenic bacterium Limosilactobacillus with increased abundance in the observation group(p<0.05)and with the beneficial bacteria Lachnospiraceae＿ND3007＿group,Oscillospira,and Lacticaseibacillus in the observation group(p<0.01 or p<0.05)3.2 Relationship of intestinal flora with metabolites:(1)Correlation of key differentiating phylums to metabolites:The upregulated differential metabolites S-sulfo-L-cysteine and taurocholate were positively correlated with Verrucomicrobia with increased abundance in the observation group(P<0.01,P<0.05,respectively).The differential metabolites 5-(2-hydroxyethyl)-4-methylthiazole and saponin III in the observation group were positively correlated with the Bacteroidota in the observation group(p<0.05 or p<0.01).The differential metabolites soybean saponin III,(r)-acetoin,and 2-dehydro-D-gluconate were negatively correlated with the upregulated abundance of the phylum of Verrucomicrobia in the observation group(p<0.05 or p<0.01)(2)Correlation of key differentiating genera with metabolites:The upregulated differential metabolites taurocholate in the observation group were positively correlated with the upregulated differential bacteria genites Limosilactobacillus and Akkermansia in the observation group,and negatively correlated with the downregulated Parabacteroides,Butyricimonas and Lachnospira in the observation group(p<0.05 or p<0.01).The up-regulated differential metabolite L-tyrosine in the observation group was negatively correlated with the differential bacteria genus Lachnospira and its related bacteria and Butyricimonas,which were negatively correlated(p<0.05 or p<0.01).Metabolites with potential cardioprotective effects,such as the differential metabolite(r)-acetoin,downregulated in the observation group,were negatively correlated with the potentially pathogenic bacteria Akkermansia in the observation group,and positively correlated with the abundances of Butyricimonas and Lachnospira(p<0.05 or p<0.01).The differential metabolite saponin III down-regulated in the observation group was negatively correlated with the potentially pathogenic bacterium Akkermansia in the observation group,and positively correlated with the downregulated abundance of Parabacteroides,Oscillospira,Lachnospira,Butyricimonas,Odoribacter,Butyric Acid,Odorobacteria,and Butyricicoccus(P<0.05 or P<0.01).(3)Correlation between key differential cultures and metabolites: The upregulated differential metabolites α-chaconine,solanine,maltose,L-tyrosine,d-(+)-maltose,l-tyrosine and S-sulfo-L-cysteine were positively correlated with the differentiating species Klebsiella pneumoniae in the observation group(p<0.05).The downregulated differential metabolites S-glutathionyl-L-Cysteine,soyasaponin iii,(r)-acetoin were negatively correlated with the differential species Klebsiella pneumoniae species with up-regulated abundance in the observation group(P<0.05).3.3 Construction of random forest model: A diagnostic model of sputum and stasis of coronary heart disease based on intestinal flora.The AUC value of Limosilactobacillus was0.746,the AUC value of Lachnospira was 0.716,and the AUC value of Enterococcus was0.06935.Diagnostic model of sputum and stasis of coronary heart disease based on serum metabolites.The AUC value of s-sulfo-l-cysteine was 0.9814,the AUC value of α-chaconine was 0.9208,the AUC value of taurocholate was 0.9089,the AUC value of(r)-acetoin was0.9036,the AUC value of solanine was 0.9008,the AUC value of l-tyrosine was 0.9006,the AUC value of soyasaponin iii was 0.8969,the AUC value of oleamide was 0.8233,the AUC value of d-(+)-maltose was 0.8083,The AUC value of hydantoin-5-propionate is 0.7558.Conclusion:1.Patients with mutual syndrome of phlegm and blood stasis of cronary artery disease stable angina have changes in the structure of intestinal flora compared with healthy people,which is mainly manifested as an increase in potential pathogenic bacteria and a decrease in beneficial bacteria.2.Patients with mutual syndrome of phlegm and blood stasis of cronary artery disease stable angina have changes in the features of serum metabolites compared with healthy people.Abnormal changes in these differential metabolites suggest that there are disorders in various metabolic pathways such as amino acids,carbohydrates,lipids,purines,and vitamins.3.Risk factors for coronary heart disease may participate in the process of sputum and stasis intermediation of coronary heart disease through changes in intestinal flora structure and serum metabolites.4.The random forest model demonstrated that the key differences in intestinal flora and serum metabolites as potential biomarkers have the potential ability to diagnose and differentiate between mutual syndrome of phlegm and blood stasis of cronary artery disease stable angina. |
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