Association of gut microbiota with dyslipidemia and chronic coronary syndrome patients undergoing coronary angiography

doi:10.21203/rs.3.rs-3231426/v1

Download PDF

Article

Association of gut microbiota with dyslipidemia and chronic coronary syndrome patients undergoing coronary angiography

https://doi.org/10.21203/rs.3.rs-3231426/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 03 Apr, 2024

Read the published version in Frontiers in Microbiology →

Version 1

posted

You are reading this latest preprint version

Chronic coronary syndrome (CCS) has a high mortality rate, and dyslipidemia is a major risk factor. Atherosclerosis, a cause of CCS, is influenced by gut microbiota dysbiosis and its metabolites. We aimed to study the diversity and composition of gut microbiota and related clinical parameters among CCS patients undergoing coronary angiography and dyslipidemia patients in comparison to healthy volunteers in Thailand. We reported that CCS patients had more risk factors and higher inflammatory marker, high-sensitivity C-reactive protein (hs-CRP) than others. We showed that the alpha diversity was lower in dyslipidemia and CCS patients than in the healthy group. A significant difference in the composition of gut microbiota was observed among the three groups. The relative abundance of Proteobacteria, Fusobacteria, Enterobacteriaceae, Prevotella and Streptococcus were significantly increased while Roseburia, Ruminococcus and Faecalibacterium were lower in CCS patients. In CCS patients, Lachnospiraceae, Peptostreptococcaceae and Pediococcus were positively correlated with hs-CRP. In dyslipidemia patients, Megasphaera was strongly positively correlated with triglyceride (TG) level and negatively correlated with high-density lipoprotein cholesterol (HDL-C). The modification of gut microbiota was associated with changes in clinical parameters involved in the development of coronary artery disease (CAD) in CCS patients.

Biological sciences/Microbiology

Health sciences/Cardiology

Cardiovascular disease (CVD) is the world's leading cause of mortality and one of Thailand's significant health issues. This disease has a number of risk factors, including dyslipidemia ¹, diabetes mellitus ², hypertension ³, obesity, insulin resistance, and metabolic syndrome ⁴. Currently, lifestyle changes like exercising, eating healthily, and consuming less salt to lower blood pressure can help to reduce the risk factors of CVD. These changes can be combined with blood-sugar-lowering drugs, statins, and antihypertensive drugs. However, morbidity and mortality rates, which are still very high all over the world, including Thailand, are impacted by CVD.

Despite the fact that there are other coronary artery disease (CAD) risk factors, dyslipidemia is a major one ⁵. Acute and chronic coronary syndromes are two subtypes of CAD that are distinguished by the timing of the onset of the signs and symptoms. A phrase used to describe CAD as a chronic, progressive condition that can be stabilized is chronic coronary syndrome (CCS) ⁶.

More than 1,100 different bacterial species have been linked to numerous symptoms and diseases, including cancer, diabetes mellitus, obesity, and CVD ⁷, according to earlier research. Since metabolic syndrome is a cardiovascular condition, eating the proper amount of healthful food can both prevent and treat it. The majority of gastrointestinal tract microorganisms cannot be cultured. The genes of the microorganism, also known as the microbiome, must therefore be examined in order to determine the type and quantity of these microorganisms.

Gut microbiota is a microorganism that lives in the human digestive tract. The quantity and type of microorganisms in the gastrointestinal tract are determined and influenced by a number of external factors such as geographic origin, age, genetics, diet, and use of prebiotics and antibiotics ^8,9. The gastrointestinal tract contains a variety of microorganisms such as Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, and Fusobacteria, in which their mechanisms of action and habitats are different. Crucial gastrointestinal bacteria that have been presented since birth include Lactobacillus and Bifidobacterium ¹⁰. Nowadays people pay attention to the diversity of gut microbiota, which affects the immune system, nutrient metabolism, and the stabilization of the lining of the gastrointestinal tract (gut epithelium). The gut microbiota is also responsible for digesting certain nutrients that cannot be digested in the stomach and also help to synthesize vitamins and hormones in the body. The body and the gut microbiome have a complicated interaction through metabolites, including short-chain fatty acids (SCFAs), bile acid (BA), and trimethylamine-N-oxide (TMAO). Several organs may be affected by these metabolites' effects. Atherosclerosis, a cause of CVD, is influenced by gut microbiota and metabolites ¹¹.

Recent studies have highlighted the role of gut dysbiosis in several diseases, including atherosclerosis and many CVD. These microorganisms are inherited from the mother at birth, as well as environment, food and medicine ¹². The changing intake of macronutrients led to metabolic syndrome, if there is an imbalance of microorganisms (dysbiosis) in the gastrointestinal tract. Alterations in gut microbiota will lead to an increase in various diseases and consuming a low fiber, high fat and high sugar diet affects the digestive system including the gut ecosystem. The increase of abundance of lipopolysaccharides (LPS)-producing gram-negative bacteria, such as Escherichia, Shigella, Veillonella, Haemophilus, and Klebsiella, increased with the severity of CAD in contrast to the bacteria that produce butyric acid, such as Ruminococcaceae and Lachnospiraceae, decreased the onset of CAD. Moreover, several types of gut microbiota, including those from Bacteroidetes phylum, Roseburia intestinalis, Faecalibacterium prausnitzii, and Eubacterium rectale, are also protective factors against CAD ^13–15.

Sequencing of the 16S rRNA gene is a clustering independent widely used technique in microbiome studies to analyze the composition and diversity of bacterial communities. This technique provides insights into the diversity, distribution, and relative abundance of bacteria, which are essential for understanding the role of microbiota in various environments, including the human body, soil, water, and other ecological systems ^16,17.

To the best of our knowledge, no any study has been conducted in Thailand to determine if the gut microbiome is associated with the parameters of dyslipidemia and CCS patients undergoing coronary angiography. The purpose of this exploratory study is to identify potential relationships among relative gut microbiota composition and related parameters in patients with dyslipidemia and CCS patients undergoing coronary angiography in Thailand.

Clinical characteristics

Ninety-one patients were included and divided into three groups: CCS patients undergoing coronary angiography, dyslipidemia patients, and healthy volunteers, with 30, 32, and 29 patients in each group, respectively. To be mentioned, 96 patients were recruited but the microbiota data of one dyslipidemia patient and four CCS patients were missed because stool samples could not be collected. So, we examined 91 samples of the gut microbiota. The patients were 43.96% female, with the mean age of 57.57 ± 8.35 years and 26.37% had hypertension. There was no statistically significant difference in age or gender among the groups. Baseline characteristics is shown in Table 1.

Table 1

Clinical characteristics of the patients (N = 91) in this study. Categorical variables are expressed as frequency and percentage. Continuous variables are expressed as mean ± standard deviation. ^a One-way ANOVA, post hoc test analysis using Scheffe, significant when p-value < 0.05, ^b Kruskal-Wallis test, post hoc test analysis using Mann-Whitney U test, significant when p-value < 0.017 (0.05/3), ^c Chi square test, significant when p-value < 0.05, ^d Fisher’s exact test, significant when p-value < 0.05, ⁺Obesity, BMI ≥ 25 kg/m², ^*Paunchy = waist circumference > 90 cm for male, waist circumference > 80 cm for female, ^**Hypertriglyceridemia = triglyceride ≥ 150 mg/dl, ^#Low HDL-C = HDL-C < 40 mg/dL for male, HDL-C < 50 mg/dL for female, ^@Impaired fasting glucose = FBS ≥ 100 mg/dL, ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CAD, coronary artery disease; CCS, chronic coronary syndrome patients; DLP, dyslipidemia patients; FBS, fasting blood sugar; HbA1C, hemoglobin A1C; healthy, healthy volunteers; hs-CRP, high-sensitivity C-reactive protein; HDL-C, high-density lipoprotein cholesterol; IU/L, international units per liter; LDL-C, low-density lipoprotein cholesterol; mmHg, millimeters of mercury; mg/dL, milligrams per deciliter, ng/L, nanogram per liter; PAD, peripheral artery disease; SBP, systolic blood pressure; DBP, diastolic blood pressure.
Parameters	Total (n = 91)	CCS (n = 30)	DLP (n = 32)	Healthy (n = 29)	p-value
Age (years)	57.57 ± 8.35	59.60 ± 9.04	57.44 ± 8.88	55.62 ± 6.60	0.066^b
Male (%)	51 (56.04)	20 (66.67)	16 (50.00)	15 (51.72)	0.356^c
BMI (kg/m²)	24.10 ± 4.07	24.63 ± 3.47	25.19 ± 5.45	22.37 ± 1.78	0.003^b
Waist circumference (cm)	82.32 ± 9.76	88.20 ± 8.92	81.25 ± 10.59	77.43 ± 6.04	< 0.001^b
History of CAD (%)	17 (18.68)	17 (56.67)	0 (0.00)	0 (0.00)	< 0.001^c
Medication
Antiplatelets	30 (32.97)	30 (100.00)	0 (0.00)	0 (0.00)	< 0.001^c
Antihypertensive drugs	24 (26.37)	24 (80.00)	0 (0.00)	0 (0.00)	< 0.001^c
Oral antidiabetic drugs	7 (7.69)	7 (23.33)	0 (0.00)	0 (0.00)	< 0.001^d
Statins Statin intensity Low intensity Moderate intensity High intensity Duration ≥ 3 months	58 (63.74) 2 (3.45) 18 (31.03) 38 (65.52) 54 (93.10)	29 (96.67) 0 (0.00) 2 (6.90) 27 (93.10) 29 (100.00)	29 (90.63) 2 (6.90) 16 (55.17) 11 (37.93) 25 (86.21)	0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00)	< 0.001^d < 0.001^d 0.112^d
Obesity (%)	19 (21.11)	13 (43.33)	6 (19.35)	0 (0.00)	< 0.001^c
Paunchy (%)^*	21 (23.08)	13 (43.33)	5 (15.63)	3 (10.34)	0.005^c
Hypertriglyceridemia (%)^**	18 (19.78)	8 (26.67)	10 (31.25)	0 (0.00)	0.005^c
Low HDL-C (%)^#	18 (19.78)	13 (43.33)	5 (15.63)	0 (0.00)	< 0.001^c
Impaired fasting glucose (%)^@	28 (30.77)	21 (70.00)	3 (9.38)	4 (13.79)	< 0.001^c
Metabolic syndrome (%)	10 (10.99)	10 (33.33)	0 (0.00)	0 (0.00)	< 0.001^d
Hypertension (%)	24 (26.37)	24 (80.00)	0 (0.00)	0 (0.00)	< 0.001^c
Diabetes mellitus (%)	9 (9.89)	9 (30.00)	0 (0.00)	0 (0.00)	< 0.001^d
Dyslipidemia (%)	49 (53.85)	17 (56.67)	32 (100.00)	0 (0.00)	< 0.001^c
Heart failure (%)	1 (1.10)	1 (3.33)	0 (0.00)	0 (0.00)	0.648^d
Stroke (%)	0 (0.00)	0 (0.00)	0 (0.00)	0 (0.00)	1.000^d
PAD (%)	1 (1.10)	1 (3.33)	0 (0.00)	0 (0.00)	0.648^d
SBP (mmHg)	121.65 ± 11.88	124.23 ± 14.34	122.41 ± 10.12	118.14 ± 10.35	0.174^b
DBP (mmHg)	73.99 ± 9.64	72.27 ± 7.76	73.59 ± 10.57	76.21 ± 10.20	0.283^a
Laboratory data
FBS (mg/dl)	99.74 ± 18.81	114.31 ± 23.98	95.59 ± 11.00	89.76 ± 8.53	< 0.001^b
HbA1C (mg/dL)	5.69 ± 0.75	6.04 ± 0.97	5.74 ± 0.50	5.28 ± 0.47	< 0.001^b
Total Cholesterol (mg/dL)	175.76 ± 44.19	144.33 ± 41.24	202.93 ± 43.67	178.28 ± 22.28	< 0.001^b
Triglyceride (mg/dL)	119.26 ± 74.86	135.83 ± 92.69	136.44 ± 76.38	83.17 ± 25.98	0.003^b
LDL-C (mg/dL)	106.72 ± 39.97	77.11 ± 34.44	129.96 ± 40.78	111.70 ± 21.97	< 0.001^b
HDL-C (mg/dL)	56.31 ± 17.89	45.90 ± 15.08	59.84 ± 18.09	63.17 ± 15.88	< 0.001^b
Serum creatinine (mg/dL)	0.86 ± 0.22	0.98 ± 0.20	0.83 ± 0.21	0.77 ± 0.19	< 0.001^b
AST (IU/L)	22.90 ± 11.23	24.50 ± 11.96	25.19 ± 10.60	18.72 ± 10.29	< 0.001^b
ALT (IU/L)	23.22 ± 15.45	26.10 ± 12.79	25.59 ± 10.94	17.62 ± 20.43	< 0.001^b
hs-CRP (mg/dL)	2.36 ± 2.99	3.46 ± 4.26	2.03 ± 2.05	1.58 ± 1.80	0.043^b

The average age of the CCS patients was 59.60 ± 9.04 years and 56.67% of them had history of CAD. In comparison to other groups, more patients had obesity, low high-density lipoprotein cholesterol (HDL-C) level, metabolic syndrome, hypertension, and diabetes mellitus at 43.33%, 43.33%, 33.33%, 80% and 30% with statistically significant, respectively. Additionally, the level of fasting blood glucose (FBS), hemoglobin A1C (HbA1C) and high-sensitivity C-reactive protein (hs-CRP) were higher in this group than others with statistical significance.

In dyslipidemia patients, the mean age was 57.44 ± 8.88 years. No patients with hypertension and diabetes mellitus. There were 9.38% of the patients with impaired fasting glucose in this group however the average of FBS and HbA1C was in the normal range. Total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) were higher than others with statistical significance.

Unexpectedly, TC and LDL-C were found to be lowest in CCS patients compared to patients with dyslipidemia and healthy volunteers at 144.33 ± 41.24 and 77.11 ± 34.44 vs 202.93 ± 43.67 and 129.96 ± 40.78 vs 178.28 ± 22.28 and 111.70 ± 21.97, respectively. This study found that CCS patients used statins at a rate of 96.67% with high-intensity statin of 93.10% and every patient took statins for at least three months, while patients with dyslipidemia used statins at a rate of 90.63% with only high-intensity statin of 37.93% with statistical significance.

Diversity of the gut microbiota

The alpha diversity analysis was used to measure the Chao1 index and Shannon diversity index. (see Fig. 1a, and 1b). From both Chao1 and Shannon indexes, there was a trend that showed lower diversity in dyslipidemia patients and CCS patients than in healthy volunteers.

The beta diversity was used to determine the similarities and differences in the composition structure of microbial communities using weighted and un-weighted UniFac distance. Principal coordinates analysis illustrated statistically significant difference (PERMANOVA, p < 0.01) in clustering of fecal samples between gut microbiome of the dyslipidemia patients, CCS patients, and healthy volunteers (see Fig. 1c and 1d).

Assessment of bacterial taxonomic composition of CCS and dyslipidemia patients and healthy volunteers

At the phylum level, among all the three groups, the most common bacterial phyla were Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria. The relative abundance of Proteobacteria was increased in CCS patients’ group (the percentage of Proteobacteria in CCS patients, dyslipidemia patients and healthy volunteers: 8.22, 4.79 and 4.16, respectively). Moreover, the relative abundance of Fusobacteria was also increased in CCS patients’ group (the percentage of Fusobacteria in CCS patients, dyslipidemia patients and healthy volunteers: 1.48, 1.07 and 0.89, respectively). On the other hand, in CCS patients, Actinobacteria and Verrucomicrobia had the lowest relative abundances proportion among the three groups. In terms of other phyla, there was no difference. The proportion of Firmicutes and Bacteroidetes was higher in the group of dyslipidemia patients than in the other groups (see Fig. 2a).

At the family level, when the gut microbiota of the three groups was analyzed at this level, Lachnospiraceae and the Bacteroidaceae families were shown to be the two most common families. Compared to other groups, Prevotellaceae, Enterobacteriaceae and Streptococcaceae families were more prevalent proportionately in CCS patients (see Fig. 2b).

At the genus level, the highest abundance in each group belonged to genus Bacteroides (the percentage of Bacteroides in CCS patients, dyslipidemia patients and healthy volunteers: 19.02, 19.15 and 21.50, respectively). In the group of CCS patients, Prevotella and Streptococcus genera were more prevalent abundance than other groups, with statistical significance following from Bacteroides genus (see Fig. 2c). Moreover, the relative abundance of Roseburia (the percentage of Roseburia in CCS patients, dyslipidemia patients and healthy volunteers: 3.17, 5.67 and 4.71, respectively), Ruminococcus (the percentage of Ruminococcus in CCS patients, dyslipidemia patients and healthy volunteers: 2.40, 4.54 and 3.79, respectively) and Faecalibacterium genera (the percentage of Faecalibacterium in CCS patients, dyslipidemia patients and healthy volunteers: 6.20, 6.98 and 7.08, respectively) were the lowest in CSS patients’ group than in the others.

At the specie level, from the pairwise phylogenetic heat tree, the abundance of Streptococcus, Veillonella dispar and Prevotella copri were higher in CCS patients than healthy volunteers with statistically significant. On the other side, fewer Roseburia faecis, Subdoligranulum variabile, Bifidobacterium and Anaerostipes were detected in this group (see Fig. 2d).

The characteristic of bacterial microbiota of CCS patients undergoing coronary angiography

In CCS patients’ group, the relative abundance of Prevotellaceae, Enterobacteriaceae, Streptococcaceae, Clostridiaceae and Paraprevotellaceae families had significantly risen at the family level compared with healthy volunteers, according to the linear discriminant analysis effect size (LEfSe). At the genus level, the LEfSe revealed that Prevotella, Streptococcus, Phascolarctobacterium, Dorea, Paraprevotella and Veillonella genera had significantly higher relative abundances in CCS patients compare to healthy volunteers (see Fig. 3a).

When compared CCS patients with dyslipidemia patients, The result showed that the relative abundance of Prevotellaceae, Enterobacteriaceae, Streptococcaceae, Fusobacteriaceae, Clostridiaceae, Veillonellaceae and Porphyromonadaceae families were higher in CCS patients (see Fig. 3c).

The characteristic of bacterial microbiota of dyslipidemia patients

In dyslipidemia patients’ group, the relative abundance of Prevotellaceae, Enterobacteriaceae and Fusobacteriaceae families had significantly risen at the family level compared with healthy volunteers’ group, according to the LEfSe. At the genus level, Prevotella, Clostridium and Dorea genera had significantly higher relative abundances in dyslipidemia patients compare to healthy volunteers (see Fig. 3b).

When we compared dyslipidemia patients with CCS patients, we found that the relative abundance of Lachnospiraceae family was higher in dyslipidemia patients (see Fig. 3c).

Associations of risk factors with gut microbiome composition in CCS patients undergoing coronary angiography

We calculated the Spearman correlation coefficient between a range of clinical indicators that may be associated with the risk factors of CCS patients (Table 1) and gut microbiota at genus level (see Fig. 4a). We found that Lachnospiraceae, Peptostreptococcaceae families and Pediococcus genus was positively correlated with hs-CRP. Weissella genus was positively correlated with LDL-C while Sutterella, Roseburia genera were negatively correlated with LDL-C.

Associations of risk factors with gut microbiome composition in dyslipidemia patients

In dyslipidemia patients, Pseudobutyrivibrio, Catenibacterium, Weissella, Prevotella and Anaerostipes genera were positively correlated with body mass index (BMI) while Bacteroides and Bifidobacterium genera were negatively correlated with BMI. For this group’s lipid profile, genus Subdoligranulum was positively correlated with TC level; genus Megasphaera was strongly positively correlated and genera Collinsella and Catenibacterium were also positively correlated with triglyceride (TG) level; genus Megasphaera was negatively correlated with HDL-C; genus Subdoligranulum was positively correlated with LDL-C level and genera Lactobacillus and Butyricimonas were negatively correlated with LDL-C level (see Fig. 4b).

This is the first investigation on gut microbiota and associated factors in CCS patients undergoing coronary angiography in Thailand. In this study, the gut microbiome differed among the three groups. There was a reduction in diversity in dyslipidemia patients’ group and CCS patients’ group, which correlated with other studies ^18–20. This finding is in consistent with other studies that patients suffering with varies diseases have also reduced bacterial diversity, for instance, hypertension ²¹, Crohn’s disease ²², psoriatic arthritis ²³, metabolic syndrome²⁴, diabetes mellitus and obesity ²⁵.

In this study, the composition pattern of the bacterial microbiota differed significantly between the group of CCS patients and healthy volunteers. The most prevalent bacterial phyla were Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria, which correlated with other studies ²⁶. Alterations in the ratio of the major phyla Firmicutes to Bacteroidetes have been proposed as a potential CAD risk factor, suggesting that changes in the microbiome's composition may also contribute to the onset and progression of atherosclerosis and CAD ^27–29. Furthermore, it was established that a greater Firmicutes/Bacteroidetes ratio (F/B ratio) plays a significant impact in CAD patients ²⁹. F/B ratio is used to diagnose gut dysbiosis and the relationship between this ratio and numerous well-known cardiovascular risk factors, including age, sex, food, and BMI, has been demonstrated ^28,30–32. However, in this study, dyslipidemia patients’ group had the greatest F/B ratio whereas the CCS patients’ group did not.

Numerous pathogenic genera, including Escherichia, Salmonella, Vibrio, Yersinia, and Legionella, are members of the phylum Proteobacteria. In our study, the relative abundance proportion of Proteobacteria was the highest in CCS patients’ group. According to Shin et al. study, a higher relative abundance of Proteobacteria is associated with gut dysbiosis and many diseases (obesity, T2DM, and cancers) in human ³³. Mainly Proteobacteria, particularly by Enterobacteriaceae and some Firmicutes is the abundance of bacteria producing TMAO precursor ³⁴.

Fusobacteria proportion was increased in the group of CCS patients, however, Actinobacteria and Verrucomicrobia exhibited the lowest relative abundances proportion in CCS patients in comparison with others. This finding is consistent with the study by Zhang et al ³⁵ and Cui et al ²⁹. Fusobacterium nucleatum may initially cause periodontal disease, which then cause CAD due to atherosclerosis via inflammation and lipid metabolism ³⁶. However, no research on the association between Fusobacterium and CAD has been published.

In addition, Streptococcus, Veillonella and Prevotella genera were more common abundance than other groups in the group of CCS patients, with statistical significance following from the Bacteroides genus. Previous studies have reported that Prevotella and Streptococcus genera had a close relationship with metabolic syndrome, atherosclerosis and CAD ^37–40. It suggested that the changes in the abundance of Streptococcus, Veillonella, Prevotella was the characteristics of the bacterial microbiota of the CCS patients. The greatest associations were found for Streptococcus anginosus and Streptococcus oralis, according to Sayols-Baixeras et al.'s study of the correlation between Streptococcus spp. and subclinical coronary atherosclerosis ⁴¹. The prevalence of various LPS-producing gram-negative bacteria, including Escherichia, Shigella, Veillonella, Klebsiella and Haemophilus increased with the severity of the CAD, according to a study by Liu H. et al ¹⁴. High blood LPS level were associated with a threefold increased risk of incident atherosclerosis ⁴². Veillonella could be found in atherosclerosis plaque and associated with cholesterol levels ⁴³. In addition, Veillonella is high relative abundance in CAD patients ^35,44. These findings suggested that the differences in bacterial abundance between the three groups may be linked to the progression of CAD and lipid metabolism in dyslipidemia patients.

The role of Prevotella in human health is controversial. Prevotella is a beneficial microbe, however it is associated to chronic inflammation. Prevotella is linked to high in complex carbohydrates diets from plants, fruits and vegetables, whereas Bacteroides is connected to fat and protein diets ⁴⁵. Prevotella can be found in healthy human and is considered as commensal bacteria. According to De Filippis et al.'s research, vegetarians had the Mediterranean diet at a high level, that was connected with this bacteria strains and higher levels of SCFAs ⁴⁶. According to Emoto et, Lactobacillus, Streptococcus, and Enterococcus increased in the gut microbiota of CAD patients whereas Bacteroides and Prevotella was decreased ²⁸.

Prevotella strains' genetic diversity may help to explain the variations in how it reacts to dietary and health conditions in different patients ⁴⁷. For example, in the study of Italian people’s gut microbiome showed that varying dietary choices could be responsible for P. copri strains with different functions, which resulted in different way for human health ⁴⁸. P. copri increases the prevalence in non-Westernized people. They typically consume diets rich in fresh vegetables and fruits ⁴⁹.

However, recent studies have connected increasing Prevotella abundance and certain strains to metabolic syndrome, obesity, hypertension, insulin resistance, non-alcoholic fatty liver disease (NAFLD) and low-grade systemic inflammation ^37,50,51 due to augmentation mucosal helper T-cell (Th17) immune responses and stimulation epithelial cells to produce interleukin-1 (IL-1), IL-8, IL-6 and IL-23 ⁵².

P. copri was higher in CCS patients than healthy volunteers in our study, which is correlated with many studies. Numerous diseases are linked with this microorganism to chronic inflammatory process; for example, rheumatoid arthritis, periodontitis, HIV infection, metabolic syndrome, inflammatory bowel disease, CAD and cardiac valve calcification. This bacterium has a role in the development of rheumatoid arthritis (RA) and is immune-relevant ⁵³. In RA patients, gut dysbiosis may have a role in the early stage of RA as shown by the enrichment of P. copri ⁵⁴. Moreover, P. copri showed a high degree of genetic and functional diversity depending on the lifestyle of the patients and associated with worse arthritis in these patients ⁵⁵. In CVD patients, P. copri may be a major risk factor, particularly in cardiac valve calcification patients. This microorganism and LDL-C have a positive correlation, which likely supports its pro-inflammatory effects. It is a potential key pathogen implicated in CVDs because of its roles in immunity and inflammation ⁵⁶. Moreover, P. intermedia, P. nigrescens were periodontopathic bacteria in atherosclerotic plaques ⁵⁷.

In this investigation, Faecalibacterium genera were lower in CSS patients’ group than in the others. This finding is correlated with Zhu et al, Faecalibacterium, Subdoligranulum, Roseburia, and Eubacterium rectale were decreased ⁵⁸. There is a significant anti-inflammatory property of Faecalibacterium ⁵⁹.

The Bacteroides genus had the highest relative abundance when we compared the healthy volunteers' group to the other groups, and the relative abundance of the Roseburia genus was higher in the healthy volunteers' group than in the other groups in our study. Due to their ability to produce SCFAs, prior research indicated that Bacteroides and Bifidobacterium have a certain protective effect on metabolism and are primarily protective bacteria for CCS ^60–62. In addition, Roseburia has been linked to improved glucose intolerance and weight loss in mice and in patients with atherosclerosis comparatively high amounts of Collinsella, whereas the normal control group has a substantially larger abundance of Roseburia and Eubacterium ^14,63. A study by Liu H. et al. revealed that the prevalence of bacteria that produced butyric acid, such as Lachnospiraceae and Ruminococcaceae, decreased with the progression of CAD ¹⁴, this findings is correlated with our study that Lachnospiraceae and Ruminococcacea families were less proportion in CCS patients when compare to others.

SCFAs are metabolites of the fermentation of complex carbohydrates. Members of phylum Bacteroidetes can produce butyrate and acetate, whereas phylum Firmicutes can produce butyrate. SCFAs have a favorable correlation with Roseburia, Bacteroides spp., and Eubacterium rectale. They have been proposed as a protective effect of CCS patients and SCFAs producers have decreased in some CCS patients. Additionally, they improve health by boosting the immunological response of the host, preserving the integrity of the intestinal barrier by controlling the expression of tight junction proteins, and reducing blood lipid levels by preventing the production of cholesterol ^64–66. SCFAs plays a protective role in atherosclerosis, while LPS stimulates body inflammation and accelerates the formation of atherosclerosis ⁶⁷.

When butyrate-producing bacteria disappear, the gut barrier may become damaged, making it easier for microbial toxins like LPS to leak and cause inflammation by binding to Toll-like receptors. Patients with CAD have been found to have a higher LPS biosynthesis in the microbiome, which has connected to insulin resistance and abdominal obesity ^68,69.

The synthesis of TMAO, a strong risk factor for the development of CAD, involves dietary choline, betaine, phosphatidylcholine, L-carnitine and lecithin which are obtained from a variety of sources, including egg, fish, red meat, soybean, peanuts ^58,70. By producing choline and the intermediary molecule trimethy-lamine (TMA), the gut bacteria also contribute to the synthesis of TMAO. The capacity of the gut microbiota to synthesise choline via the phospholipase D (PLD) enzyme has just recently been discovered. The flavin-containing monooxygenase (FMO) enzyme metabolizes the microbiome-derived TMA molecule into TMAO in hepatocytes ⁷¹. Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes are the TMA producers. The production of foam cells is stimulated by the impairment of cholesterol metabolism in macrophages caused by TMAO-dependent activation of macrophage scavenger receptors and CD36 expression. The higher TMAO production, the higher CAD risk ⁷². In the context of increased intestinal permeability, TMAO is also linked to C-reactive protein (CRP), endothelial dysfunction, and elevated blood level of the LPS endotoxin. It can also cause platelet hyperreactivity, which have an impact on CAD progression ⁷³.

The LDL-C level was lowest in CCS patients. The reasons for the differences in LDL-C levels was that the majority of patients (96.67%) were treated with statins. The treatment with statins is associated with lower prevalence of gut microbiota dysbiosis ⁷⁴. In mice study, statins could moderate gut microbiota by increasing the abundance of Bacteroides, Butyricimonas and Mucispirillum ⁷⁵. Moreover, the clinical response to statin therapy in individuals with CAD is associated with the gut microbiota. For instance, poor statin response is associated with a significant reduction in the number of beneficial bacteria (Akkermansia muciniphila and Lactobacillus) and an increase in the number of bacteria (Holdemanella and Facecallibacterium) ⁷⁶. Statins have also been linked to anti-inflammatory and immunomodulatory properties ⁷⁷.

The genus Megasphaera was strongly positively correlated with TG level and negatively correlated with HDL-C. Lopez-Montoya et al. investigation confirmed our results, showing Megasphaera and Escherichia-Shigella were highly associated with atherogenic dyslipidemia patients, defined as both hypertriglyceridemia and low HDL-C ⁷⁸. Moreover, one study has shown that patients with symptomatic stroke had an altered gut microbiota and defined Megasphaera as opportunistic pathogens ⁷⁹. In obesity and overweight patients, Megasphaera was significantly increased relative abundance and correlated with low physical activity ⁸⁰. Megasphaera is involved in a mechanism that generates ammonia, which has negative consequences ⁸¹.

Hs-CRP can help in identifying chronic inflammation. Periodontal disease was linked to CAD and low grade inflammation, increased CRP and fibrinogen, according to de Oliveira et al.’s research ⁸². There is correlation between hs-CRP and CAD severity in CCS patients ⁸³. Patients with myocardial infarction with elevated hs-CRP level (≥ 2 mg/L) were at higher risk of major adverse cardiovascular events and death ⁸⁴. In CCS patients of our study, the Lachnospiraceae, Peptostreptococcaceae families and Pediococcus genus was positively correlated with hs-CRP, a finding that has not been reported before. There are some limitations in our study, the specific function and the metabolites of the gut microbiota were not examined.

In conclusion, this study demonstrated a significant difference in the composition of gut microbiota between CCS patients, dyslipidemia patients and healthy volunteers. The alpha diversity was lower in CCS and dyslipidemia patients than in healthy volunteers. The relative abundance of Proteobacteria, Fusobacteria, Prevotella and Streptococcus were significantly increased while Roseburia, Ruminococcus and Faecalibacterium were lower in CCS patients. In dyslipidemia patients, Megasphaera was strongly positively correlated with TG level and negatively correlated with HDL-C. Modification of gut microbiota is associated with changes in clinical parameters involved in development of CAD in CCS patients.

Subject enrollment

From 25 February 2023 to 25 June 2023, patients between the ages of 35 and 70 were admitted to Chulabhorn Hospital's cardiovascular center were recruited. In patients with CCS undergoing coronary angiography group, they must meet the following criteria: (1) patients with stable anginal symptoms (2) patients < 1 year after an acute coronary syndrome (ACS) with stabilized symptoms after revascularization (3) CCS patients > 1 year after initial diagnosis or revascularization (4) asymptomatic patients in whom CAD is found after screening ⁶. Coronary angiography was used to confirm the diagnosis of CCS in those with ≥ 70% stenosis in coronary arteries larger than 2.5 mm in one view; ≥ 50% stenosis in coronary arteries in two views; and ≥ 50% stenosis in the left main coronary artery ⁸⁵; and they must assent by signing the consent forms. In the group of dyslipidemia patients, they must only have one risk factor for CVD, dyslipidemia (TC ≥ 200 mg/dL or TG ≥ 150 mg/dL or LDL-C ≥ 160 mg/dL or patients on treatment) ⁸⁶, without type 2 diabetes mellitus (T2DM) (FBS ≥ 126 mg/dL for 2 times separately or patients on treatment) ⁸⁷, hypertension (systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg at least 2 different days or patients on treatment) ⁸⁸, obesity (body mass index ≥ 30 kg/m2 ) ⁸⁹ and metabolic syndrome (at least 3 of the following risk factors: waist circumference ≥ 90 cm for men and ≥ 80 cm for women, blood pressure ≥ 130/85 mmHg or on antihypertensive drugs, TG ≥ 150 mg/dL or on medication, HDL-C ≤ 40 mg/dL for men and ≤ 50 mg/dL for women and FBS ≥ 100 mg/dL or on medication) ⁹⁰.

Additionally, none of the aforementioned diseases must exist among the group of healthy volunteers. Patients who met the following criteria were excluded: (1) Kidney disease (estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2 for more than 3 months), liver disease (Aspartate aminotransferase (AST), Alanine aminotransferase (ALT) > 5 times normal or jaundice), cancer, intestinal diseases such as inflammatory bowel disease, thyroid dysfunction, immunodeficiency such as human immunodeficiency virus (HIV); (2) use antibiotics, immunosuppressants, probiotics supplement, synbiotics, herbal supplements, antacids or laxatives within 4 weeks before participating in the study; (3) History of gastrointestinal disease and other infections within 4 weeks; (4) Smoking; (5) Alcoholism; (6) Pregnant or lactating; (7) Covid-19 infection within 4 weeks.

By using statistical matching techniques with age and sex, participants were separated into three groups: CCS patients undergoing coronary angiography, patients with cardiovascular risk factors, dyslipidemia and healthy volunteers. The study received approval from Chulabhorn Hospital and Srinakharinwirot University research ethics committees (IEC No: 174/2564, dated: 22/07/2022 and IEC No: SWUEC/E/M-100/2565E, dated: 22/02/2023, respectively), and the study was conducted in accession with the Good Practices for Clinical Research in Thailand. The study was approved by Thai Clinical Trails Registry (TCTR) (TCTR20230428002). Written informed consent was obtained from all study participants.

The sample size calculation is based on “Hypothesis testing and power calculations for taxonomic-based human microbiome data, in this research, the value of Alpha (α) = 5%, power = 80% and function of the number of sequence reads is equal to 50,000 with three groups. A total of 25 volunteers were obtained and it was determined that the dropout rate of 20%, resulting in a total of 30 volunteers per group, or 90 volunteers in total ⁹¹.

Sampling and sequencing

Each patient’s blood was collected to evaluate FBS, HbA1C, TC, TG, HDL-C, LDL-C, AST, ALT, creatinine and hs-CRP. Fresh stool was collected in DNA/RNA shield fecal collection tubes (Zymo Research, CA, USA) 1 day before an appointment and immediately frozen at -20°C for 48 hours before further analysis. Using the QIAamp Stool Mini kit (Qiagen, USA), DNA was extracted from stool samples. Nanodrop and electrophoresis were used to evaluate the quantity and quality of DNA. Using 515 F and 806R primers and 2X KAPA hot-start ready mix, the V4 hypervariable region of the 16S rRNA gene was amplified by PCR. The PCR conditions included an initial denaturation at 94°C for 3 min, followed by 25 cycles of 98°C for 20 s., 55°C for 30 s., 72°C for 30 s., and a final extension step at 72°C for 5 min. The 16S amplicons were purified using AMPure XP beads and indexed using Nextera XT index kit, followed by 8 cycles of the forementioned PCR condition. Finally, the PCR products were cleaned and pooled for cluster generation and 250-bp paired-end read sequencing on the Illumina® MiSeq™.

Sequencing data analysis

QIIME 2 2019.10 was used for microbiome bioinformatics ⁹². The raw sequence data was demultiplexed using the q2-demux plugin, and reads with expected errors (maxEE) higher than 3.0 was discarded by denoising software, DADA2 (via q2-dada2). A phylogeny was constructed using the SEPP q2-plugin, placing short sequences into sepp-refs-gg-13-8.qza reference phylogenetic tree. Alpha‐diversity metric, beta-diversity metric, and Principle Coordinate Analysis (PCoA) were estimated using q2- diversity after samples were rarefied (subsampled without replacement) to a minimum read. Taxonomy was assigned to ASVs using the classify-sklearn naïve Bayes taxonomy classifier against the Greengenes 13_8 99% operational taxonomic units (OTUs) reference sequences. Statistical tests of alpha and beta diversity were performed using Kruskal-Wallis and permutational multivariate analysis of variance (PERMANOVA) (number of permutations = 999), respectively. Moreover, the significantly differential abundance analysis of microbiota was conducted using LEfSe ⁹³ via the algorithm module on the Galaxy platform at http://huttenhower.sph.harvard.edu/galaxy. First, nonparametric factorial Kruskal-Wallissum-rank tests was applied to choose features differentially distributed among classes (p < 0.05). The linear discriminant analysis (LDA) model was used to estimate their effect sizes and supported by 30-fold bootstrapping (cutoff = logarithmic LDA score of ≥ 2.0). In addition, significant p-values associated with the microbial significantly differential features by LEfSe were corrected for multiple hypothesis testing using the Benjamini and Hochberg false discovery rate correction.

Statistical analysis and visualization

Stata/SE 16.1 software (StataCorp LP, College Station, TX, USA) was used to analyze the statistical data. Statistics were considered significant when p-value < 0.05. All study variables were subjected to descriptive statistics analysis, which was provided as frequency (%) for categorical data and mean ± standard deviation (SD) or median for nonnormal quantitative data. One-way analysis of variance (ANOVA) statistic and post hoc analysis using the Scheffe test with p-value < 0.05 were utilized if the distribution of the quantitative data, such as age and laboratory results, was normal. The Kruskal-Wallis test and post hoc Mann-Whitney U test with p-value < 0.01 were used if the data distribution was not normal.

The following Spearman’s correlation coefficient was analyzed; (1) anthropometric measurements, including BMI and waist circumference (2) physical examination; blood pressure and (3) blood tests; FBS, HbA1C, TC, TG, LDL-C, HDL-C, AST, ALT, and hs-CRP. Correlation heat map visualization was performed using the ggplot2 R package. A p-value < 0.05 was considered statistically significant and was labeled in the figure. In addition, the phylogenetic heat tree was visualized using the metacoder R package

Data availability

The raw sequence data are available under BioProject PRJNA1000984, and Biosample SAMN36786011.

Acknowledgements

This study was supported by the Faculty of Medicine, Srinakharinwirot University (grant number 505/2565) and Center of Excellent in Probiotics Srinakharinwirot University (grant number 324/2565).

Author contributions

W.L., P.P., and M.T. performed the statistical analysis and wrote the main manuscript. W.L., P.P., and M.T. performed gut microbiota statistical analysis. W.L., M.T., V.M., and D.S. provided clinical data and samples. P.P., M.T., and E.E. provided expert review and editing. All authors reviewed the manuscript.

Competing interests

The authors declare no competing interests.

Hedayatnia, M. et al. Dyslipidemia and cardiovascular disease risk among the MASHAD study population. Lipids Health Dis. 19, 42 (2020).
Abdul-Ghani, M. et al. Cardiovascular disease and type 2 diabetes: Has the dawn of a new era arrived? Diabetes Care 40, 813–820 (2017).
Dustan, H. P. Hypertension. J. Am. Coll. Cardiol. 35, 10B-12B (2000).
Tune, J. D., Goodwill, A. G., Sassoon, D. J. & Mather, K. J. Cardiovascular consequences of metabolic syndrome. Transl. Res. 183, 57–70 (2017).
Yusuf, S. et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. The Lancet 364, 937–952 (2004).
Knuuti, J. et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur. Heart J. 41, 407–477 (2020).
Afzaal, M. et al. Human gut microbiota in health and disease: Unveiling the relationship. Front. Microbiol. 13, 1–14 (2022).
Thursby, E. & Juge, N. Introduction to the human gut microbiota. Biochem J 474, 1823–1836 (2017).
Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).
Yang, B. et al. Bifidobacterium and Lactobacillus composition at species level and gut microbiota diversity in Infants before 6 weeks. Int. J. Mol. Sci. 20, 3306 (2019).
Pieczynska, M. D. et al. Gut microbiota and its metabolites in atherosclerosis development. Molecules 25, 594 (2020).
Santacroce, L., Man, A., Alexandros Charitos, I., Haxhirexha, K. & Topi, S. Current knowledge about the connection between health status and gut microbiota from birth to elderly. Landmark 26, 135 (2021).
Liu, H. et al. The role of the gut microbiota in coronary heart disease. Curr. Atheroscler. Rep. 22, 77 (2020).
Liu, H. et al. Alterations in the gut microbiome and metabolism with coronary artery disease severity. Microbiome 7, 68 (2019).
Jonsson, A. L. & Bäckhed, F. Role of gut microbiota in atherosclerosis. Nat. Rev. Cardiol. 14, 79–87 (2017).
Lane, D. J. et al. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc. Natl. Acad. Sci. U.S.A. 82, 6955–6959 (1985).
Wensel, C. R., Pluznick, J. L., Salzberg, S. L. & Sears, C. L. Next-generation sequencing: insights to advance clinical investigations of the microbiome. J. Clin. Investig. 132, e154944 (2022).
Sawicka-Smiarowska, E. et al. Gut microbiome in chronic coronary syndrome patients. J. Clin. Med. 10, 5074 (2021).
Fu, J. et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Cir. Res. 117, 817–824 (2015).
Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
Yang, T. et al. Gut dysbiosis is linked to hypertension. Hypertension 65, 1331–1340 (2015).
Hansen, R. et al. Microbiota of de-novo pediatric IBD: Increased Faecalibacterium Prausnitzii and reduced bacterial diversity in Crohn’s but not in ulcerative colitis. Am. J. Gastroenterol. 107, 1913–1922 (2012).
Scher, J. U. et al. Decreased bacterial diversity characterizes the altered gut microbiota in patients with psoriatic arthritis, resembling dysbiosis in inflammatory bowel disease: Gut microbiota in PsA. Arthritis Rheumatol. 67, 128–139 (2015).
Arora, T. & Bäckhed, F. The gut microbiota and metabolic disease: current understanding and future perspectives. J. Intern. Med. 280, 339–349 (2016).
Pascale, A., Marchesi, N., Govoni, S., Coppola, A. & Gazzaruso, C. The role of gut microbiota in obesity, diabetes mellitus, and effect of metformin: new insights into old diseases. Curr. Opin. Pharmacol. 49, 1–5 (2019).
Zheng, Y.-Y. et al. Gut microbiome-based diagnostic model to predict coronary artery disease. J. Agric. Food Chem. 68, 3548–3557 (2020).
Emoto, T. et al. Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease. Heart Vessels. 32, 39–46 (2017).
Emoto, T. et al. Analysis of gut microbiota in coronary artery disease patients: a possible link between gut microbiota and coronary artery disease. J. Atheroscler. Thromb. 23, 908–921 (2016).
Cui, L., Zhao, T., Hu, H., Zhang, W. & Hua, X. Association study of gut flora in coronary heart disease through high-throughput sequencing. Biomed Res. Int. 2017, 1–10 (2017).
Canfora, E. E. et al. Supplementation of diet with galacto-oligosaccharides increases Bifidobacteria, but not insulin sensitivity, in obese prediabetic individuals. Gastroenterology. 153, 87-97.e3 (2017).
Mariat, D. et al. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol 9, 123 (2009).
Holscher, H. D. et al. Fiber supplementation influences phylogenetic structure and functional capacity of the human intestinal microbiome: follow-up of a randomized controlled trial. Am. J. Clin. Nutr. 101, 55–64 (2015).
Shin, N.-R., Whon, T. W. & Bae, J.-W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 33, 496–503 (2015).
Dalla Via, A. et al. Urinary TMAO levels are associated with the taxonomic composition of the gut microbiota and with the choline tma-lyase gene (cutC) harbored by Enterobacteriaceae. Nutrients 12, 62 (2019).
Zhang, Y., Xu, J., Wang, X., Ren, X. & Liu, Y. Changes of intestinal bacterial microbiota in coronary heart disease complicated with nonalcoholic fatty liver disease. BMC Genom. 20, 862 (2019).
Zhou, J., Liu, L., Wu, P., Zhao, L. & Wu, Y. Fusobacterium nucleatum accelerates atherosclerosis via macrophage-driven aberrant proinflammatory response and lipid metabolism. Front. Microbiol. 13, 798685 (2022).
Madhogaria, B., Bhowmik, P. & Kundu, A. Correlation between human gut microbiome and diseases. Infectious Medicine 1, 180–191 (2022).
Yakob, M. et al. Prevotella nigrescens and Porphyromonas gingivalis are associated with signs of carotid atherosclerosis in subjects with and without periodontitis: Periodontal microorganisms and atherosclerosis. J. Periodontal Res. 46, 749–755 (2011).
Abranches, J. et al. Invasion of human coronary artery endothelial cells by Streptococcus mutans OMZ175: S. mutans endothelial cell invasion. Oral Microbiol. Immunol. 24, 141–145 (2009).
Bazaz, R. et al. Transient increase in atherosclerotic plaque macrophage content following Streptococcus pneumoniae pneumonia in ApoE-deficient mice. Front. Cell. Infect. Microbiol. 13, 1090550 (2023).
Sayols-Baixeras, S. et al. Streptococcus species abundance in the gut is linked to subclinical coronary atherosclerosis in 8973 participants from the SCAPIS cohort. Circulation 1–14 (2023) doi:10.1161/CIRCULATIONAHA.123.063914.
Mayr, M. et al. Oxidized low-density lipoprotein autoantibodies, chronic Infections, and carotid atherosclerosis in a population-based study. J. Am. Coll. Cardiol. 47, 2436–2443 (2006).
Koren, O. et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl. Acad. Sci. U.S.A. 108, 4592–4598 (2011).
Bouzid, F. et al. A potential oral microbiome signature associated with coronary artery disease in Tunisia. Biosci. Rep. 42, BSR20220583 (2022).
Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).
De Filippis, F. et al. High-level adherence to a Mediterranean diet beneficially impacts the gut microbiota and associated metabolome. Gut 65, 1812–1821 (2016).
Ley, R. E. Prevotella in the gut: choose carefully. Nat. Rev. Gastroenterol. Hepatol. 13, 69–70 (2016).
De Filippis, F. et al. Distinct genetic and functional traits of human intestinal Prevotella copri strains are associated with different habitual diets. Cell Host Microbe. 25, 444-453.e3 (2019).
Tett, A. et al. The Prevotella copri complex comprises four distinct clades underrepresented in Westernized populations. Cell Host Microbe. 26, 666-679.e7 (2019).
Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).
Zhu, L. et al. Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: A connection between endogenous alcohol and NASH. Hepatol. 57, 601–609 (2013).
Larsen, J. M. The immune response to Prevotella bacteria in chronic inflammatory disease. Immunol. 151, 363–374 (2017).
Pianta, A. et al. Evidence of the immune relevance of Prevotella copri, a gut microbe, in patients with rheumatoid arthritis. Arthritis Rheumatol. 69, 964–975 (2017).
Alpizar-Rodriguez, D. et al. Prevotella copri in individuals at risk for rheumatoid arthritis. Ann. Rheum. Dis. 78, 590–593 (2019).
Nii, T. et al. Genomic repertoires linked with pathogenic potency of arthritogenic Prevotella copri isolated from the gut of patients with rheumatoid arthritis. Ann. Rheum. Dis. 82, 621–629 (2023).
Liu, Z. et al. The intestinal microbiota associated with cardiac valve calcification differs from that of coronary artery disease. Atherosclerosis 284, 121–128 (2019).
Gaetti-Jardim, E., Marcelino, S. L., Feitosa, A. C. R., Romito, G. A. & Avila-Campos, M. J. Quantitative detection of periodontopathic bacteria in atherosclerotic plaques from coronary arteries. J. Med. Microbiol. 58, 1568–1575 (2009).
Zhu, W. et al. Gut microbial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk. Cell 165, 111–124 (2016).
Machiels, K. et al. A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Gut 63, 1275–1283 (2014).
Hoving, L. R. et al. Dietary mannan oligosaccharides modulate gut microbiota, increase fecal bile acid excretion, and decrease plasma cholesterol and atherosclerosis development. Mol. Nutr. Food Res. 62, 1700942 (2018).
Kazemian, N., Mahmoudi, M., Halperin, F., Wu, J. C. & Pakpour, S. Gut microbiota and cardiovascular disease: opportunities and challenges. Microbiome 8, 36 (2020).
Abdi, M., Esmaeili GG, H. & Ranjbar, R. Lactobacilli and Bifidobacterium as anti-atherosclerotic agents. Iran. J. Basic. Med. Sci. 25, 934–46 (2022).
Zhu, Q. et al. Dysbiosis signatures of gut microbiota in coronary artery disease. Physiol. Genomics 50, 893–903 (2018).
Chambers, E. S., Preston, T., Frost, G. & Morrison, D. J. Role of gut microbiota-generated short-chain fatty acids in metabolic and cardiovascular health. Curr. Nutr. Rep. 7, 198–206 (2018).
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
Krishnan, S., Alden, N. & Lee, K. Pathways and functions of gut microbiota metabolism impacting host physiology. Curr. Opin. Biotechnol. 36, 137–145 (2015).
Vinolo, M. A. R. et al. Suppressive effect of short-chain fatty acids on production of proinflammatory mediators by neutrophils. J. Nutr. Biochem. 22, 849–855 (2011).
Trøseid, M. et al. Plasma lipopolysaccharide is closely associated with glycemic control and abdominal obesity. Diabetes Care 36, 3627–3632 (2013).
Brandsma, E. et al. A proinflammatory gut microbiota increases systemic inflammation and accelerates atherosclerosis. Circ. Res. 124, 94–100 (2019).
Tang, W. H. W. et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 368, 1575–1584 (2013).
Phillips, I. R. et al. The molecular biology of the flavin-containing monooxygenases of man. Chem. Biol. Interact. 96, 17–32 (1995).
Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).
Al-Obaide, M. et al. Gut microbiota-dependent trimethylamine-N-oxide and serum biomarkers in patients with T2DM and advanced CKD. J. Clin. Med. 6, 86 (2017).
Vieira-Silva, S. et al. Statin therapy is associated with lower prevalence of gut microbiota dysbiosis. Nature 581, 310–315 (2020).
Kim, J. et al. Alterations in gut microbiota by statin therapy and possible intermediate effects on hyperglycemia and hyperlipidemia. Front. Microbiol. 10, 1947 (2019).
Wang, L. et al. The gut microbiota is associated with clinical response to statin treatment in patients with coronary artery disease. Atherosclerosis 325, 16–23 (2021).
Lee, S. et al. Atorvastatin and rosuvastatin improve physiological parameters and alleviate immune dysfunction in metabolic disorders. Biochem. Biophys. Res. Commun. 478, 1242–1247 (2016).
López-Montoya, P. et al. Association of gut microbiota with atherogenic dyslipidemia, and its impact on serum lipid levels after bariatric surgery. Nutrients 14, 3545 (2022).
Zeng, X. et al. Higher risk of stroke is correlated with increased opportunistic pathogen load and reduced levels of butyrate-producing bacteria in the gut. Front. Cell. Infect. Microbiol. 9, 4 (2019).
Palmas, V. et al. Gut microbiota markers associated with obesity and overweight in Italian adults. Sci. Rep. 11, 5532 (2021).
Anand, S., Kaur, H. & Mande, S. S. Comparative in silico analysis of butyrate production pathways in gut commensals and pathogens. Front. Microbiol. 7, (2016).
De Oliveira, C., Watt, R. & Hamer, M. Toothbrushing, inflammation, and risk of cardiovascular disease: results from Scottish health survey. Br. Med. J. 340, c2451–c2451 (2010).
Habib, S. S. & Al Masri, A. A. Relationship of high sensitivity C-reactive protein with presence and severity of coronary artery disease. Pak. J. Med. Sci. 29, (2013).
Carrero, J. J., Andersson Franko, M., Obergfell, A., Gabrielsen, A. & Jernberg, T. Hs-CRP level and the risk of death or recurrent cardiovascular events in patients with myocardial infarction: a healthcare‐based study. J. Am. Heart Assoc. 8, e012638 (2019).
Jiangping, S. et al. Assessment of coronary artery stenosis by coronary angiography: A head-to-head comparison with pathological coronary artery anatomy. Circ. Cardiovasc. Interv. 6, 262–268 (2013).
Lin, C.-F., Chang, Y.-H., Chien, S.-C., Lin, Y.-H. & Yeh, H.-Y. Epidemiology of Dyslipidemia in the Asia Pacific Region. Int. J. Gerontol. 12, 2–6 (2018).
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 37 Suppl 1, S81-90 (2014).
Williams, B. et al. 2018 ESC/ESH guidelines for the management of arterial hypertension. Eur. Heart J. 77, 71–159 (2019).
Samadoulougou, S. et al. Validated methods for identifying individuals with obesity in health care administrative databases: A systematic review. Obes. Sci. Pract. 6, 677–693 (2020).
Huang, P. L. A comprehensive definition for metabolic syndrome. Dis. Model Mech. 2, 231–237 (2009).
La Rosa, P. S. et al. Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS One. 7, e52078 (2012).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

No competing interests reported.

Download PDF

Journal Publication

published 03 Apr, 2024

Read the published version in Frontiers in Microbiology →

Version 1

posted

You are reading this latest preprint version

Association of gut microbiota with dyslipidemia and chronic coronary syndrome patients undergoing coronary angiography

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

Clinical characteristics

Diversity of the gut microbiota

Assessment of bacterial taxonomic composition of CCS and dyslipidemia patients and healthy volunteers

The characteristic of bacterial microbiota of CCS patients undergoing coronary angiography

The characteristic of bacterial microbiota of dyslipidemia patients

Associations of risk factors with gut microbiome composition in CCS patients undergoing coronary angiography

Associations of risk factors with gut microbiome composition in dyslipidemia patients

Discussion

Methods

Subject enrollment

Sampling and sequencing

Sequencing data analysis

Statistical analysis and visualization

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1