Study population.
Comparison of general data between the HC and ACS group. There were no statistically significant differences in age, sex, previous history of basic diabetes between the HC and the ACS groups (P>0.05), and the data were comparable (Table 1). A total of 66 consecutive cardiology patients were enrolled according to the exclusion criteria, the remaining 66 were divided in three groups for analysis: an ACS group, including 31AMI patients, 29 UAP patients and 6AS patients; and a control group comprising 46 healthy volunteers, no evidence of enrollment bias was found.
Table 1
Comparisons of clinical characteristics between ACS patients and controls. BMI, Body mass index; AST, Aspartate aminotransferase; ALT, alanine aminotransferase; CHOL, cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; CRP, C reactive protein; Cre, creatinine; WBC, white blood cell; Glu, serum glucose. Data were shown as mean ± SD. Differences between two groups (AMI vs. HC, UAP vs. HC, AS vs. HC ) were compared using Fisher’s exact test for sex; two-sample t-test for numerical data with normal distribution; Mann-Whitney U test for numerical data without normal distribution. A value of P<0.05 was considered statistically significant.*P<0.05,**P<0.01,***P<0.001.
Variables
|
HC(n=46)
|
AMI(n=31)
|
UAP(n=29)
|
AS(n=6)
|
Age, y
|
35.3±11.1
|
55±11.6
|
58.1±4.8
|
56.8±12.4
|
Sex, male
|
27(46)
|
25(66)
|
22(66)
|
6(66)
|
BMI, kg/m2
|
21.43±2.5
|
25.2±2.6***
|
24.9±3.5***
|
25.7±2.9**
|
AST, U/L
|
18.64±4
|
116.2±105***
|
31.7±23.9
|
26.8±10.8
|
ALT, U/L
|
17.9±8.6
|
46.5±22.1***
|
45.4±33.7***
|
42.6±18.5**
|
CHOL, mg/dL
|
4.3±0.7
|
4.1±0.9
|
3.8±0.2
|
1±0.2
|
TG, mg/dL
|
1.2±0.6
|
1.8±1.1*
|
2.2±1.4***
|
1.7±1.2
|
HDL-C, mg/dL
|
1.4±0.3
|
0.9±0.2***
|
1.8±0.2***
|
1±0.2***
|
LDL-C, mg/dL
|
2.3±0.6
|
2.2±0.5
|
1.9±0.6*
|
2.3±0.5
|
CRP, mg/dL
|
0.9±0.8
|
5.3±3.2
|
1.5±1.3
|
2.1±1.9
|
Cre,μmol/L
|
54.4±7.3
|
64.5±15.7**
|
71±14***
|
72±14**
|
WBC,10^9/L
|
6.6±1.4
|
9.8±2.7***
|
7.1±2.4
|
6.6±1.5
|
Glu,mmol/L
|
4.7±0.4
|
5.8±1.4***
|
5.3±1.2*
|
4.9±0.5
|
Characteristics of study participants.
The baseline characteristics of UAP patients, AMI patients, AS patients and healthy controls were shown in Table 1. Compared to healthy controls, parameters in ACS including body weights (BWs), low-density lipoprotein (LDL)(Fig1), triglyceride (TG), total cholesterol (TC), C-reactive protein (CRP) and high homocysteine (HCY) were elevated; whereas high-density lipoprotein (HDL) were decreased.
Gut microbial profile in patients with ACS and HC
diversity index analysis
Shown in Figure2.A and B, with the increase of sample sequencing depth, the observed species index curve tended to be flat, indicating that the current sequencing depth is sufficient to detect predominant species contained in each sample. The abundance was reflected by the length of the curve on the horizontal axis and the evenness was reflected by the shape of the curve. After analyzing the rank-abundance curve about OTU of the samples, we found a smooth curve, indicting high evenness among samples. The gut microbiota of HCs and patients with ACS showed consistent values in the analysis of observed species and Shannon diversity index. Figure2C represented species diversity and indicated significant differences in the number of species among AS-HC (p=0.0000), HC-UAP (p=0.0000) and AMI-HC (p=0.0000). There was no significant difference in the number of species among AMI-AS (p=0.7204) AMI-UAP (p=0.792) and AS-UAP (p=0.8776). It was suggested that there were significant differences between the disease group (AMI, UAP, AS) and the healthy group. There was no difference in comparative analysis between disease groups. Figure.2D representing the Shannon index found the significant differences in HC-UAP (p=0.0000), AMI-HC (p=0.0001) and AS-HC (p=0.0291). There was no significant difference in Shannon index between AS-UAP (p=0.5457), AMI-AS (p=0.7100) and AMI-UAP (p=0.7232). Principle Coordinate Analysis (PCoA) (weighted UniFrac distance) between the ACS and control groups showed distinguished in total (Fig. 2C), top 40 abundant stool microbial taxa, and overlapping clustering (Fig.2D) was observed in AMI, UAP and AS groups.
β diversity analysis
Based on unweighted UniFrac distance and Bray-Curtis distance matrices of the 16S rRNA sequence, samples contribution rates of the first pCoA(PC1), second pCoA(PC2) were 21.45% and 9.25%, respectively, which highlighted a clear clustering of the microbial populations of the ACS patients away from that of the healthy controls.(A) NMDS(B) showed that the ACS and control groups were distinguished by total (Fig. 3A) or top 40 (Fig. 3B) abundant stool microbial taxa (stress=0.16, Stress<0.2), and overlapping clustering was observed in AMI, UAP and AS groups.
OUT analysis
A Venn diagram (Fig.4) showed that 789 OTUs were commonly detected between HC and AMI, common OTUs in the two groups of HC and UAP were 788, 632 OTUs were generally recognized between HC and AS, in addition, 596 OTUs were commonly detected in the four groups, while 450, 91, 144 and 20 OTUs were unique in the HC, AMI, UAP, or AS, respectively.
The ACS and HC samples were significantly different in multivariate analyses. At the phylum level (Fig. 5), compared to healthy controls, the dominant stool microbes in the ACS group were Firmicutes, Bacteroidetes, Proteobacteria. The relative abundances of Bacteroidetes, Proteobacteria, and Verrucomicrobia were increased, while Firmicutes was decreased in the ACS group compared to the HC group(p<0.05). At the genus level (Fig. 6), compared to HC group, the levels of Bacteroides, Parabacteroide, Unidentified Enterobacteriaceae, Subdoligranulum, Akkermansia, Alistipes, Streptococcus, Paraprevotella and Paraprevotella were significantly increased, whereas Subdoligranulum, Roseburia, Faecalibacterium, Blautia, Agathobacter, Bifidobacterium and Anaerostipes were significantly reduced.
Further analysis of the vegetation of the AMI, UAP, AS and HC healthy groups using the network diagram showed that there are significant differences in four groups (Fig.7). Compared with the entire HC group, the dominant ACS bacteria (AMI, UAP) in Bacteroides, Parabacteroide, Unidentified_Enterobacteriaceae and Subdoligranulum were increased significantly. Akkermansia, Anaerostipes Blautia, Agathbacter were decreased significantly. The abundances of Alistipes, Streptococcus and Paraprevotella were also higher in ACS than those in control samples.
Analysis of inflammatory factors
Compared with the HC healthy group (Fig. 8), the inflammatory factors including TNF-α (P=0.049), MCP-1 (P =0.046), IL-6 (P =0.047), IL-1β (P =0.035), IL-10 (P =0.049) and LPS (P =0.0001) in ACS group (AMI group and UAP group) were significantly increased, suggesting that there were significant changes in inflammatory factors in ACS.
Correlations of changes in clinical indexes, inflammatory factors with alterations in intestinal flora
According to the correlation between clinical acute coronary syndrome and clinical correlation index and flora inflammation, we further analyzed the correlation between clinical related indexes such as age (Age), body mass index (BMI), alanine aminotransferase (ALT), hyperhomocysteine (HCY) and differential bacteria (Fig. 9A). The analysis of the relationship between age and differential bacteria found that age was negatively correlated with Firmicutes, but positively correlated with Bacteroidetes, Proteobacteria and Verrucomicrobia, suggesting that under disease conditions, the increase of age had obvious influence on the disorder of microflora. BMI was negatively correlated with Firmicutes and positively correlated with Bacteroidetes. In the disease condition, changes in body weight affected microbial changes. ALT was negatively correlated with Firmicutes and positively correlated with Bacteroidetes, suggesting that liver function damage was closely related to the bacterial community. HCY was positively correlated with Proteobacteria and Bacteroidetes, and negatively correlated with Firmicutes. There was a correlation between HDL-C and different bacteria. HDL-C is positively correlated with Firmicutes, negatively correlated with Bacteroidetes, and positively correlated with F/B. There was no statistical difference in the correlation analysis between LDL-C and bacteria. It may be caused by the small number of cases, drug intervention and other related interfering factors. TG was negatively correlated with Firmicutes and positively correlated with Bacteroidetes. It was suggested that age, weight, liver function damage and HCY sepsis in AMI and UAP patients can affect the distribution of microflora.
The relative abundance of Firmicutes was positively correlated with plasma inflammatory factors IL-1β, TNF-α, MCP-1 and LPS (Fig. 9B). The abundance of Bacteroides was negatively correlated with IL-1β, TNF-α, MCP-1 and LPS; Akkermansia was positively correlated with IL-10, and there was no significant difference in the correlation between IL-6 and Firmicutes and Streptococcus, suggesting that intestinal flora and inflammatory indicators interfered with each other and were closely related in ACS.
Awareness increases markedly regarding the involvement of gut microbes in the development of numerous cardiometabolic disease[12-14]. Growing evidences have shown that intestinal microflora plays an important role in the development of coronary heart disease.[15] The intestinal flora of healthy people was dominated by beneficial bacteria, with fewer harmful bacteria ,these two kinds of bacteria were dynamically balanced to maintain the health of the host. However, in abnormal conditions, the intestinal microbiota could be significantly changed by reducing beneficial bacteria and increasing harmful flora. [16] Unbalanced intestinal microflora could further worsen the disease, leading to a vicious circle. Studies have shown that intestinal microflora imbalance was closely related to infectious diseases, inflammation and metabolic diseases. The imbalance of intestinal microflora can lead to the disorder of bacterial structure and destroy the basic metabolic process of the host, which may be closely related to the occurrence of cardiovascular diseases such as coronary heart disease, hypertension and heart failure[17-20].
A large number of studies have shown that intestinal flora was involved in the process of atherosclerosis, but the specific regulatory role was unknown. Recent macroeconomic studies displayed the expression profile of intestinal microorganisms in patients with coronary heart disease, but the efficacy of specific intestinal flora in the prevention and treatment of coronary heart disease were still unclear [21].The purpose of this study was to explore the changes of bacterial diversity among ACS(AMI, UAP), AS patients and healthy controls. The results showed that compared with healthy group, there were significant flora changes in acute myocardial infarction and unstable angina pectoris, and there were also flora changes between AS and HC healthy controls, which were consistent with previous studies[22]. It was also supported that there were significant differences in intestinal flora in patients with atherosclerosis. At the same time, there was no difference between AMI and UAP, AMI and AS, UAP and AS, which may be related to clinical complexity, such as multiple unknown risk factors, drugs and so on.
In this study, dilution curve, rank-abundance, species richness, shannon index, PCoA and NMDS were separately used to analyze the diversity of different samples and species. The results showed that there were obvious changes in AS vs. HC, HC vs. UAP and AMI vs. HC flora, supporting that the flora distribution of coronary atherosclerotic heart disease and AS patients was significantly different from that of healthy controls[23].
In this study, the common and unique OUTs among diverse groups were obtained from the Wayne diagram (VennGraph). There were 789 species of common bacteria in AMI and HC healthy groups, 368 species of endemic bacteria in AMI, 788 species of endemic bacteria in UAP and HC healthy groups, 414 species of endemic bacteria in UAP, 632 species in healthy groups of AS and HC, and 182 species of endemic bacteria in AS. There were 596 species of healthy bacteria in AMI, UAP, AS and HC. It was further suggested that there were significant differences in flora between the disease group and the healthy group.
Further analysis on the flora composition at the phylum and genus level, compared with the healthy group, at the phylum level: Firmicutes, Bacteroidetes and Proteobacteria were the main dominant flora, the relative abundance of Firmicutes was decreased significantly, the relative abundance of Bacteroidetes was increased significantly, the F/B ratio was decreased, and the relative abundance of Verrucomicrobia was increased significantly. At the genus level, there were also flora differences among the four groups. Compared with the HC healthy group, ACS (AMI, UAP) analysis showed that Bacteroides, Unidentified_Enterobacteriaceae, Subdoligranulum Alistipes, Streptococcus, Akkermansia and Parabacteroide were increased significantly, Subdoligranulum, Roseburia, Faecalibacterium, Blautia, Agathobacter, Anaerostipes, Bifidobacterium were decreased significantly. Butyrate-producing bacteria played a key role in human health, and these bacteria, including Roseburia, Subdoligranulum and Faecalibacterium , were relatively depleted in atherosclerotic cardiovascular disease (ACVD) and type 2 diabetes (T2D) samples[24]. The butyrate-producing bacterium Roseburia was inversely correlated with atherosclerotic lesion development in mice, and the addition of Roseburia in combination with a high-fiber diet reduced the sizes of atherosclerotic plaques in the aorta. Faecalibacterium was an anti-inflammation-associated bacterium that produced butyrate. Treatment with atorvastatin increased the abundance of Faecalibacterium in 27 hypercholesterolemic patients compared with that found in 15 untreated hypercholesterolemic patients[25] .The results of this study were consistent with the results of Emoto et al.[26] using terminal restriction fragment length polymorphism (T-RFLP) and 16s rRNA to study the differences of intestinal microorganisms between patients with coronary heart disease and healthy volunteers in 2016. The results showed that the number of mature Lactobacilli increased significantly, but Bacteroides (Bifidobacterium and Proteus) were notably decreased in patients with coronary heart disease. In addition, the ratio of Firmicutes to Bacteroides was increased significantly. The results were consistent with the results reported by Karlssion et al in 2012 [21]using genome-wide sequencing to determine the possible link between changes in intestinal microflora and atherosclerotic heart disease. Compared with healthy people, the number of Escherichia coli was increased, while numbers of Rosella and Eubacterium were decreased.
Inflammation was an important process of myocardial infarction, which was caused by the release of cytokines and activation of the immune system in the injured myocardium[27-29]. In the first 24-72 hours after myocardial infarction, cardiomyocyte injury triggered the activation of macrophages mediated by damage-associated molecular patterns (DAMPs), which secreted pro-inflammatory cytokines including IL-1β, IL-6 and TNF-α. The cascade release of inflammatory factors aggravated fibrosis, microvascular and myocardial dysfunction. In this study, when intestinal flora disturbance occurred in ACS patients, the intestinal mucosa would be damaged to lead to excessive inflammatory response with elevated serum levels of CRP, IL-6, MCP-1 and TNF-α, which may contribute to the pathogenesis of atherosclerotic diseases. At the same time, we analyzed the presence of intestinal flora disorder in ACS. At present, a large number of studies have shown that inflammatory factors were closely related to intestinal flora disorder. Therefore, this study analyzed the relationship between inflammatory factors and intestinal flora, especially the correlation between differential bacteria and inflammatory factors including IL-1β, IL-6, TNF- α, MCP-1, IL-10, LPS. The results showed that the relative abundance of Firmicutes was positively correlated with plasma IL-1β, TNF- α, IL-6, MCP-1 and LPS, while the relative abundance of Bacteroidetes was negatively correlated with inflammatory factors IL-1β and TNF-α. The abundance of Bacteroides was negatively correlated with IL-1β, TNF- α, MCP-1 and LPS, while Akkermansia was positively correlated with IL-10, revealing that the disturbance of intestinal flora and inflammatory indexes were closely correlated with each other in myocardial infarction. Interestingly, increases in both pro-inflammatory and anti-inflammatory factors suggested that there were dynamic changes of inflammation in AMI and UAP.