This study was approved by the Research Ethics Committee of Northwest Minzu University and the Gansu Provincial People’s Hospital (Approval No: XBMZ-YX-2021008), both in Lanzhou, China. All participants were informed of the study’s purpose and provided informed consent following the Declaration of Helsinki.

In the current study, 210 volunteers were enrolled, with 70 patients undergoing acute coronary syndrome and 70 patients with chronic coronary syndrome admitted to the Department of Cardiology, Gansu Public Provincial Hospital, and the physical examination center of Lanzhou University Second Hospital in Gansu Province, China, was used to select 70 healthy individuals. Each participant’s demographic, clinical, and biological information was documented. The following were the patient group’s inclusion criteria: (i) ECG requirements include ST-segment elevation or new left bundle-branch block in two or more standard leads, or at least 2 mm in two or more consecutive precordial leads; and (ii) angiographically verified coronary thrombi. Chronic viral infections (including hepatitis C, HIV, and herpes simplex type 2), chronic inflammatory bowel disease, renal failure, and pregnancy were also removed from both groups. Within 15 days after the acute ischemia event, the majority of ACS (50/70; 71%) and CCS (59/70; 84%) patients were included. Healthy people without a history of cardiovascular diseases or active infection symptoms were considered. All volunteers were ethnic Chinese.

Clinically certified team members drew blood samples in Vacutainer EDTA Blood Collection Tubes. Reagents and materials were disinfected and wore lab clothes, masks, and disposable gloves to avoid contamination of foreign DNA. For each volunteer 3 ml blood sample was drawn in the morning following overnight fasting conditions and stored at -80°C.

DNA extraction and implication of V3-V4 regions of 16Sr RNA gene were amplified by using 338F: 5’-ACTCCTACGGGAGGCAGCA-3’ and 806R: 5’-GGACTACHVGGGTWTCTAAT-3’ universal primer set by following our previous study protocols (Khan et al., 2022b). Followed by Amplicons sequencing using Illumina Novaseq 6000. Graph Pad Prism (V=8.0) was used to do all statistical analyses on obtained data. The mean and standard deviation were used to present all the data. One-way ANOVA and Tukey’s multiple comparisons post-hoc test were used for clinical parameters such as, High-density lipoprotein, Low-density lipoprotein, Tryglyceride, and Body mass index. A negative control sample of sterile water was also utilized to purify DNA libraries ( Supplementary Figure S1 ). The importance of several factors was determined using a p-value less than 0.05.

We recruited 70 ACS patients, 70 CCS patients, and 70 healthy subjects. ( Table 1 ) summarizes the clinical indexes such as age, gender, and body mass index that were compared between the three groups. The patients with acute and chronic coronary syndromes were older than healthy individuals (61.8 ± 12.1; 62.58 ± 11.16; 41.2 ± 10.1). Based on a one-way ANOVA test, we found that acute coronary syndrome, chronic coronary syndrome, and healthy groups differed significantly in age, height, body mass index, systolic blood pressure, diastolic blood pressure, high-density lipoprotein, fasting blood glucose, total cholesterol, hypertension, diabetes, and smoking. In contrast, there was no significant difference between the three groups in terms of gender, weight, triglycerides, and low-density lipoprotein ( Table 2 ).

After sequencing 210 blood samples, a total of 16,809,565 paired-end reads were generated. Upon that, 16,786,147 clean reads were obtained after paired-end reads quality control and assembly. Each sample generated a minimum of 79,267 clean readings and an average of 79,934 clean reads. Finally, a total of 1,608 OTUs were obtained among all three groups. According to the species accumulation curve, the predicted OTUs richness has already exceeded saturation at this sequencing depth, suggesting that most of the variety has been observed ( Figure 1A ). A Venn diagram revealed that 1,343 of the total 1,608 OTUs were shared throughout the three groups, with 6 OTUs being unique to acute coronary syndrome, 21 to chronic coronary syndrome, and 32 to healthy group ( Figure 1B ).

The alpha diversity of blood microbiota among acute coronary syndrome, chronic coronary syndrome, and healthy was measured using the Chao1, observed species, ACE, Coverage, Shannon, and Simpson indices ( Table 3 ). A significantly higher Alpha diversity Chao1 index ( Figure 1C ), and Shannon index ( Figure 1D ) were observed in acute coronary syndrome compared to chronic coronary syndrome, and healthy groups although, the chronic coronary syndrome group was significantly lower alpha diversity compared to acute coronary syndrome and healthy groups. A higher coverage indicates that a species in the sample is more likely to be detected. This value is used to evaluate whether the sequencing data is adequate to present the real situation of the microbial community in the sample.

We performed a beta diversity analysis to examine and contrast the differences and similarities in microbial population structure between the groups. ( Figure 2A ) shows that the three groups had different microbial compositions (PC1 = 35.58 percent and PC2 = 7.90 percent) using Bray-Curtis-based principal coordinate analysis (PCoA). PERMANOVA indicated that the blood bacterial communities among the three groups clustered significantly separated from each other (R² = 0.191, p = 0.001), as depicted in ( Figure 2B ). Finally, the investigations demonstrated that the microbial composition of the acute coronary syndrome and chronic coronary syndrome groups differed from that of the healthy group, implying that the microbial ecology of the patient’s blood is shifting.

At the phylum level, the blood microbiota of the acute coronary syndrome, chronic coronary syndrome, and healthy groups was dominated by Firmicutes (39%, 45%, 43%) Bacteroidetes (31%, 32%, 29%), and Proteobacteria (19%, 15%, 15%), ( Supplementary Table S1 ). The relative abundance of the top 10 blood bacterial phyla between the three groups, see ( Figure 3A ), accounted for up to 95% of the relative abundance on average. Bacterial genera Lachnospiraceae NK4A136 group (16%, 19%, 18%), Lactobacillus (16%, 24%, 13%), and Ligilactobacillus (10%, 15%, 9%) ( Supplementary Table S2 ), the high abundance genera were observed between three groups ( Figure 3B ). The phylum and genus level microbial composition of all blood samples is shown in ( Supplementary Figures S2 and S3 ).

First, we examined how microbial communities differed between diseased groups. Proteobacteria (19% vs 9%) were overrepresented, while Firmicutes and Bacteroidota (44% vs 38% and 32% vs 31%) were underrepresented in ACS compared to the CCS group. The significant abundance of phylum Acidobacteriota (0.026 ± 0.0194 vs 0.005 ± 0.009; p < 0.001), Followed by Actinobacteriota (0.025 ± 0.014 vs 0.021 ± 0.015; p > = 0.1), Cloacimonadota (0.0475 ± 0.0748 vs 0.0229 ± 0.0989; p > = 0.1), Fibrobacterota (0.028 ± 0.048 vs 0.0009 ± 0.0003; p < 0.01), Fusobacteriota (0.009 ± 0.007; 0.005 ± 0.005; p < 0.001), Latescibacterota (0.0005 ± 0.001 vs 0.00006 ± 0.00043; p < 0.01), Myxococcota (0.0016 ± 0.002 vs 0.0010 ± 0.0017; p < 0.1), Nitrospirota (0.0037 ± 0.0031 vs 0.0015 ± 0.0027; p < 0.001), Proteobacteria (0.1827 ± 0.0978 vs 0.1422 ± 0.0911; p < 0.05), Synergistota (0.0014 ± 0.0014 vs 0.0003 ± 0.0009; p < 0.001), and Thermotogota (0.0002 ± 0.0003 vs 0.00001 ± 0.00008; p < 0.1) were significantly increased, although Cyanobacteria (0.005 ± 0.007 vs 0.013 ± 0.013; p < 0.001), Deferribacterota (0.005 ± 0.005 vs 0.008 ± 0.005; p < 0.01), Desulfobacterota (0.014 ± 0.003 vs 0.014 ± 0.006; p > = 0.1), Firmicutes (0.375 ± 0.049 vs 0.435 ± 0.070; p < 0.001), Gemmatimonadota (0.0004 ± 0.0006 vs 0.0008 ± 0.0020), Patescibacteria (0.0028 ± 0.0024 vs 0.0042 ± 0.0034; p < 0.02), Verrucomicrobiota (0.005 ± 0.004 vs 0.007 ± 0.007; p < 0.05) were observed significantly decreased in acute coronary syndrome group compared to chronic coronary syndrome ( Figure 3C ).

At the genus level, we observed that Lachnospiraceae_NK4A136_group (15% vs19%), and Lactobacillus (15% vs 23%) were found to decrease in the acute coronary syndrome group than chronic coronary syndrome. In addition, the considerable increase proportion of Brevibacillus (0.0010 ± 0.0010 vs 0.0000 ± 0.0003; p < 0.001), followed by Brevibacterium (0.0036 ± 0.0025 vs 0.0007 ± 0.0016; p < 0.001), Candidatus_Solibacter (0.0031 ± 0.0031 vs 0.0004 ± 0.0015; p < 0.001), Chujaibacter (0.0017 ± 0.0015 vs 0.0003 ± 0.0012; p < 0.001), Clostridium_sensu_stricto_1 (0.0036 ± 0.0057 vs 0.0019 ± 0.0026; p > = 0.1), Kosakonia (0.0071 ± 0.0048 vs 0.0023 ± 0.0036; p < 0.001), Paenibacillus (0.000086 ± 0.000358 vs 0.000008 ± 0.00007; p > = 0.1) Pantoea (0.0061 ± 0.0040 vs 0.0016 ± 0.0026; p < 0.001), Parvimonas (0.0010 ± 0.0009 vs 0.0000 ± 0.0003; p < 0.001), Phenylobacterium (0.0012 ± 0.0015 vs 0.0001 ± 0.0005; p < 0.001), Staphylococcus (0.0217 ± 0.014 vs 0.0136 ± 0.013; p > = 0.1), while, Lachnospira (0.0008 ± 0.0017 vs 0.0030 ± 0.0045; p < 0.001) were observed decreased in acute coronary syndrome group than chronic coronary syndrome ( Figure 3D ).

Bacteroidota and Proteobacteria (31.2% vs 28% and 19% vs 15%) were the most predominant phylum in the acute coronary syndrome group, while Firmicutes (43% vs 38%) were observed enriched in healthy. Further, The proportion of Acidobacteriota was significantly greater (0.0264 ± 0.0194 vs 0.0100 ± 0.0165; p < 0.001), Cloacimonadota (0.000475 ± 0.000748 vs 0.000081 ± 0.000247; p > 0.01), Desulfobacterota (0.0144 ± 0.0036 vs 0.0119 ± 0.0053; p < 0.02), Fibrobacterota (0.0002 ± 0.0004 vs 0.000088 ± 0.000286; p < 0.01), Fusobacteriota (0.0098 ± 0.0070 vs 0.0083 ± 0.0082; p > = 0.1), Latescibacterota (0.0005 ± 0.0011 vs 0.0002 ± 0.0008; p < 0.1), Myxococcota (0.0016 ± 0.002 vs 0.0005 ± 0.0011; p < 0.001), Nitrospirota (0.0037 ± 0.0031 vs 0.0024 ± 0.0046; p < 0.1), Patescibacteria (0.0028 ± 0.0025 vs 0.0025 ± 0.0024; p > = 0.1), Proteobacteria (0.1827 ± 0.0978 vs 0.1519 ± 0.0855; p > = 0.1), and Synergistota (0.0014 ± 0.0014 vs 0.0006 ± 0.0017; p < 0.01), although, Actinobacteriota (0.0258 ± 0.0147 vs 0.0571 ± 0.0634; p < 0.001), Cyanobacteria (0.0053 ± 0.0070 vs 0.0059 ± 0.0066; p > = 0.1), Deferribacterota (0.0056 ± 0.0054 vs 0.0066 ± 0.0057; p > = 0.1), Firmicutes (0.3752 ± 0.0491 vs 0.4265 ± 0.0689; p < 0.001), Gemmatimonadota (0.0004 ± 0.0006 vs 0.0005 ± 0.0013), Thermotogota (0.0002 ± 0.000319 vs 0.0003 ± 0.0008; p > = 0.1), Verrucomicrobiota (0.0052 ± 0.0042 vs 0.0053 ± 0.0045; p > = 0.1) phyla were lower in acute coronary syndrome group than healthy ( Figure 3C ).

Compared to the healthy group, Lactobacillus was observed higher (16% vs 13%) while Bacteroides (15.1% vs 21%) and Lachnospiraceae_NK4A136_group (16% vs 18%) were decreased in the ACS group compared to healthy. We further observed a significant higher proportion of Brevibacillus (0.0010 ± 0.0010 vs 0.000319 ± 0.000871; p < 0.001), Brevibacterium (0.0036 ± 0.0025 vs 0.001624 ± 0.0024; p < 0.001), Candidatus_Solibacter (0.0031 ± 0.0031 vs 0.0009 ± 0.0019; p < 0.001), Chujaibacter (0.0017 ± 0.0015 vs 0.000453 ± 0.000917; p < 0.001), Kosakonia (0.0071 ± 0.0048 vs 0.0032 ± 0.004401; p < 0.001), Lachnospira (0.0008 ± 0.0017 vs 0.000406 ± 0.000875; p > = 0.1), Pantoea (0.0061 ± 0.0040 vs 0.0037 ± 0.0043; p < 0.001), Parvimonas (0.0010 ± 0.0009 vs 0.00033 ± 0.00078; p < 0.001), Phenylobacterium (0.0012 ± 0.0015 vs 0.0004 ± 0.0011; p < 0.001) while, Clostridium_sensu_stricto_1 (0.0036 ± 0.0057 vs 0.009147 ± 0.010172; p < 0.001), Paenibacillus (0.000086 ± 0.000358 vs 0.001591 ± 0.002693; p < 0.001), Staphylococcus (0.0217 ± 0.014 vs 0.0602 ± 0.0608; p < 0.001), were found decreased in acute coronary syndrome than healthy group ( Figure 3D ).

Firmicutes and Bacteroidota (44% vs 43% and 15% vs 14%) were observed slightly increased while Proteobacteria (14% vs 15%) was found to decrease in the chronic coronary syndrome group than the healthy. Furthermore, the significant increased level of Cyanobacteria (0.0134 ± 0.0136 vs 0.0059 ± 0.0066; p < 0.001), Cloacimonadota (0.0002 ± 0.0009 vs 0.0000 ± 0.0002; p > = 0.1), Deferribacterota (0.008 ± 0.005 vs 0.006 ± 0.005; p < 0.1), Fusobacteriota (0.005 ± 0.005 vs 0.008 ± 0.008; p < 0.05), Desulfobacterota (0.014 ± 0.006 vs 0.0119 ± 0.0053; p < 0.01), Firmicutes (0.435 ± 0.070 vs 0.4265 ± 0.0689; p > = 0.1), Gemmatimonadota (0.0008 ± 0.0020 vs 0.0005 ± 0.0013), Myxococcota (0.0010 ± 0.0017 vs 0.0005 ± 0.0011; p > = 0.1), Patescibacteria (0.0042 ± 0.0034 vs 0.0025 ± 0.0024; p > = 0.1), Verrucomicrobiota (0.0075 ± 0.0071 vs 0.0053 ± 0.0045; p < 0.05) whereas, Acidobacteriota (0.005 ± 0.009 vs 0.0100 ± 0.0165; p > = 0.1), Actinobacteriota (0.021 ± 0.015 vs 0.0571 ± 0.063; p < 0.001), Latescibacterota (0.00006 ± 0.00043 vs 0.0002 ± 0.0008; p > = 0.1), Fusobacteriota (0.005 ± 0.005 vs 0.008 ± 0.008; p < 0.05), Nitrospirota (0.0015 ± 0.0027 vs 0.0024 ± 0.0046; p > = 0.1), Proteobacteria (0.1422 ± 0.0911 vs 0.1519 ± 0.0855; p > = 0.1), Synergistota (0.0003 ± 0.0009 vs 0.0006 ± 0.0017; p > = 0.1), and Thermotogota (0.00001 ± 0.00008 vs 0.0003 ± 0.0008; p < 0.01) were found decreased in chronic coronary syndrome group compare to healthy ( Figure 3C ).

A higher proportion of genus Lachnospiraceae_NK4A136_group (19.3% vs 17.9%) and Lactobacillus 24% vs 13%). Additionally, the significantly higher abundance of Chujaibacter (0.0003 ± 0.0012 vs 0.000453 ± 0.000917; p > = 0.1), Lachnospira (0.0030 ± 0.0045 vs 0.000406 ± 0.000875; p < 0.001), although a lower proportion of Brevibacillus (0.0000 ± 0.0003 vs 0.000319 ± 0.000871; p > = 0.1), Brevibacterium (0.0007 ± 0.0016 vs 0.001624 ± 0.0024; p < 0.05), Candidatus_Solibacter (0.0004 ± 0.0015 vs 0.000963 ± 0.001935 vs; p > = 0.1), Clostridium_sensu_stricto_1 (0.0019 ± 0.0026; p < 0.001 vs 0.009147 ± 0.010172), Kosakonia (0.0023 ± 0.0036 vs 0.0032 ± 0.004401; p > = 0.1), Paenibacillus (0.000086 ± 0.000358 vs 0.001591 ± 0.002693; p > = < 0.001), Pantoea (0.0016 ± 0.0026 vs 0.0037 ± 0.0043; p < 0.01), Parvimonas (0.0010 ± 0.0009 vs 0.00033 ± 0.00078; p < 0.1), Phenylobacterium (0.0001 ± 0.0005 vs 0.0004 ± 0.0011; p > = 0.1), and Staphylococcus (0.0136 ± 0.013 vs 0.0602 ± 0.0608; p < 0.01) were observed in chronic coronary syndrome group compared to healthy ( Figure 3D ).

As illustrated in ( Figure 4 ), LEfSe modeling was used to confirm both the statistical and taxonomic differences between the blood microbiota of patients with acute coronary syndrome or chronic coronary syndrome and those of healthy, using a logarithmic LDA score cutoff of 3. LEfSe revealed substantial variations at several taxonomic levels across the three groups with a threshold score of LDA >3 after analyzing each participant’s taxonomic profile. Meanwhile, using linear discriminant analysis, the seventeen taxa were found significantly distinct among the three groups. Of them, phylum_ Actinobacteria, class_ Actinobacteria, order_ Staphylococcales, and Micrococcales, family_ Staphylococcaceae and Micrococcaceae, genus_ Staphylococcus, and Species_ Unclassified Staphylococcus in healthy group; phylum_ Firmicutes, class_ Clostridia, order_ Lactobacillales, family_ Lactobacillaceae, genus_ Lactobacillus, and Species_ Unclassified Lactobacillus in chronic coronary syndrome group, and phyla_ Proteobacteria and Acidobacteriota, class_ Alphaproteobacteria in acute coronary syndrome group were significantly different among three groups ( Figure 4A ). Additionally, ( Figure 4B ) shows the microbiome differences between the three groups at various phylogenic levels using a cladogram.

The functional analysis through COG analysis among three groups showed that significantly up-regulated pathways such as energy production and conversion, intracellular trafficking, secretion, and vesicular transport, coenzymes transport and metabolism, lipid transport and metabolism, cell motility, secondary metabolites biosynthesis, transport and catabolism, posttranslational modification. Protein turnover, chaperones, chromatin structure and dynamics, amino acid transport, and metabolism were observed in the acute coronary syndrome group compared to the chronic coronary syndrome group. Although nucleotide transport and metabolism, replication, recombination and repair, cell cycle control, cell division, chromosome partitioning, translation, ribosomal structure and biogenesis, carbohydrates transport and metabolism, and transcription were found significantly up-regulated in chronic coronary syndrome patients than acute coronary syndrome group ( Supplementary Figure S4 ).

In addition, a comparison between the acute coronary syndrome group with Healthy showed intracellular trafficking, secretion, and vesicular transport, cell motility, cell wall, membrane, envelope biogenesis, extracellular structures, posttranslational modification. Protein turnover, chaperones, cytoskeleton, and signal transduction mechanisms pathways were considerably up-regulated in the acute coronary syndrome group than healthy, while carbohydrates transport and metabolism, inorganic ion transport and metabolism, and transcription pathways were significantly up-regulated in the healthy group compared to acute coronary syndrome ( Supplementary Figure S5 ).

Furthermore, the chronic coronary syndrome and healthy group comparison showed that the COG functional pathways such as, Cell cycle control, cell division, chromosome partitioning, Nucleotide transport, and metabolism, replication, recombination, and repair, translation, ribosomal structure and biogenesis, cell wall, membrane, envelope biogenesis, intracellular trafficking, secretion vesicular transport, and cytoskeleton were significantly up-regulated in chronic coronary syndrome group than healthy, whereas, energy production and conversion, coenzymes transport and metabolism, amino acid transport and metabolism, inorganic ion transport and metabolism, general function prediction only, lipid transport and metabolism, RNA processing and modification functional pathways were significantly up-regulated in the healthy group compared to chronic coronary syndrome. The significance was judged by a p-value less than p < 0.05 ( Supplementary Figure S6 ).

KEGG-based functional annotations between acute coronary syndrome and chronic coronary syndrome analysis showed that the majority of pathways were significantly up-regulated in the acute coronary syndrome group than chronic coronary syndrome such as Amino acid metabolism, substance dependence, energy metabolism, global and overview maps, lipids metabolism, metabolism of cofactors and vitamins, circulatory system, sensory system, cell growth and death, aging, xenobiotic degradation and metabolism, neurodegenerative diseases, cancers; specific types, and drug resistance: antineoplastics. Although infectious diseases: bacterial, immune diseases, nucleotide metabolism, transcription, carbohydrate metabolism, replication and repair, membrane transport, drug resistance: antimicrobial, translation, signaling molecules and interaction, folding, sorting and degradation, and nervous system pathways were considerably upregulated in chronic coronary syndrome group compared to acute coronary syndrome ( Supplementary Figure S7 ).

Additionally, acute coronary syndrome and healthy groups revealed that the majority of gene pathways were significantly up-regulated in the acute coronary syndrome group compared to the healthy such as cell growth and death, circulatory system, digestive system, cardiovascular diseases, drug resistance: antineoplastics, cancers; specific types, endocrine and metabolic diseases, sensory system, infectious diseases; viral, cell motility, signal transduction, drug resistance: antimicrobial, and cancer: overview genes, while global and overview maps, immune diseases, membrane transport, carbohydrate metabolism pathways were up-regulated in the healthy group compared to acute coronary syndrome ( Supplementary Figure S8 ).

Moreover, comparing chronic coronary syndrome and healthy groups showed that the KEGG functional genes pathways such as infectious diseases: bacterial, drug resistance: antimicrobial, transcription, nucleotide metabolism, endocrine, and metabolic diseases, translation, signaling molecule and interaction, cell growth and death, replication, and repair, folding, sorting and degradation, cancer: overview, digestive system, biosynthesis of other secondary metabolites, cellular community-prokaryotes, and glycan biosynthesis and metabolism were significantly up-regulated in chronic coronary syndrome group compare to healthy, although global and overview maps, amino acid metabolism, metabolism of cofactor and vitamins, aging, lipid metabolism, substance dependence, transport, and catabolism pathways were observe significantly up-regulated in the healthy group than chronic coronary syndrome. The significance was judged by a p-value less than 0.05 ( Supplementary Figure S9 ).

To demonstrate pairwise comparisons of clinical variables, a color gradient denoting Spearman’s correlation coefficient was used ( Figure 5 ). Clinical considerations were primarily focused on acute coronary syndrome and chronic coronary syndrome prognostic risk factors. Phylum-level correlations between clinical parameters and healthy, acute coronary syndrome, and chronic coronary syndrome taxa were used to investigate the effect of clinical parameters on circulating microbial composition, as depicted in ( Figures 5A–C ). Herein, the current findings revealed that there was no significant influence of clinical parameters on bacterial diversity. Hence, it was hypothesized that acute coronary syndrome and chronic coronary syndrome may have a major impact on the bacterial composition of the blood.

Considering multiple recent studies that showed changes in blood microbiota composition and diversity are linked to non-communicable diseases, by examining changes in blood microbial signature among acute coronary syndrome, chronic coronary syndrome, and Healthy group, we hope to determine the role of circulating microbiota on atherosclerosis. In addition, our results revealed that the patients with acute coronary syndrome and chronic coronary syndrome were significantly older than healthy individuals, these findings are similar to our previous results (Khan et al., 2022b), this suggests that another important factor influencing atherosclerosis is age, a detailed study worth needed to investigate the blood microbiota profile between young and aged individuals. Improving the accuracy of risk prediction is very important because it helps target those subjects who are most likely to benefit from preventive treatment.

Although the current study’s findings were substantial, the study’s main limitation is the small sample size, which may impair the accuracy of the findings due to rarity. A larger study cohort was needed to investigate the effects of blood microbiota signature. Meanwhile, more modern techniques such as metagenomics and metabonomics must be used to investigate the potential health effects of specific circulating microbiota and their metabolites.