Causal relationship between gut microbiota and cardiovascular diseases: a bidirectional Mendelian randomization analysis

doi:10.3969/j.issn.1674-8115.2025.12.006

Abstract

Abstract:

Objective ·To investigate the causal relationship between gut microbiota and cardiovascular diseases (CVDs) using Mendelian randomization (MR). Methods ·Instrumental variables included genetic loci from gut microbiota data provided by the MiBioGen consortium (n=18 340) and CVD data from the IEU Open GWAS database, covering four CVD types: atrial fibrillation (n=1 030 836), coronary artery disease (n=547 261), hypertension (n=20 526), and heart failure (n=977 323). The inverse variance weighted (IVW) method was employed as the primary analytical approach. Additionally, Cochran's Q test was used to assess heterogeneity of genetic instruments, the MR-Egger intercept test to evaluate horizontal pleiotropy, and leave-one-out analysis to examine the sensitivity of single-nucleotide polymorphisms (SNPs) on the exposure-outcome causal relationship. The MR Steiger test was applied to validate the causal direction between gut microbiota and CVDs. Results ·The IVW analysis indicated that Victivallales (OR=0.939), Howardella (OR=0.939), Anaerostipes (OR=0.922), Bifidobacteriaceae (OR=0.916), Lentisphaeria (OR=0.936), Odoribacter (OR=0.909), Intestinibacter (OR=0.933), Lentisphaerae (OR=0.926), and Bifidobacteriales (OR=0.916) were protective factors against atrial fibrillation, while Catenibacterium (OR=1.057), Lachnospiraceae UCG008 (OR=1.051), Streptococcus (OR=1.089), and Victivallis (OR=1.038) were risk factors. For coronary artery disease, Lactobacillales (OR=0.919) and Parabacteroides (OR=0.866) were protective factors, while Veillonellaceae (OR=1.065), Lachnoclostridium (OR=1.093), Lachnospiraceae (OR=1.094), Oxalobacter (OR=1.062), and Odoribacter (OR=1.160) were risk factors. For hypertension, Mollicutes RF9 (OR=0.851), Coriobacteriia (OR=0.803), Coriobacteriales (OR=0.803), Coriobacteriaceae (OR=0.803), and Intestinibacter (OR=0.819) were protective factors, while Christensenellaceae R7 group (OR=1.218), Desulfovibrio (OR=1.167) ,and Peptococcaceae (OR=1.230) were risk factors. For heart failure, Bacillales (OR=0.955) and Anaerostipes (OR=0.899) were protective factors, while Ruminococcus UCG009 (OR=1.107), Eubacterium oxidoreducens group (OR=1.117), Selenomonadales (OR=1.106), Negativicutes (OR=1.107), Eubacterium eligens group (OR=1.139), and Flavonifractor (OR=1.144) were risk factors. Cochran's Q test showed no heterogeneity among SNPs of gut microbiota causally associated with CVDs (all P>0.05). The pleiotropy test found no evidence of horizontal pleiotropy (all P>0.05). Leave-one-out sensitivity analysis confirmed the robustness of the results. The MR Steiger directionality test supported the causal direction from gut microbiota (exposure) to CVDs (outcome). Conclusion ·Some gut microbiota have significant causal effects on CVDs; altering their abundance may influence CVD risk, providing potential targets for microbiota-based intervention strategies.

Key words: gut microbiota, cardiovascular disease, Mendelian randomization, genome-wide association study (GWAS), single-nucleotide polymorphism (SNP)

CLC Number:

R541

MA Huihua, YAN Kuipo, LIU Gang, XU Yazhou, ZHANG Lei, SUN Yanqin. Causal relationship between gut microbiota and cardiovascular diseases: a bidirectional Mendelian randomization analysis[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(12): 1606-1619.

Figures/Tables 8

TrendMD

References 56

[1]	LI Y, CAO G Y, JING W Z, et al. Global trends and regional differences in incidence and mortality of cardiovascular disease, 1990‒2019: findings from 2019 global burden of disease study[J]. Eur J Prev Cardiol, 2023, 30(3): 276-286.
[2]	刘明波, 何新叶, 杨晓红, 等. 《中国心血管健康与疾病报告2023》要点解读[J]. 临床心血管病杂志, 2024, 40(8): 599-616.
	LIU M B, HE X Y, YANG X H, et al. Interpretation of Report on Cardiovascular Health and Diseases in China 2023[J]. Journal of Clinical Cardiology, 2024, 40(8): 599-616.
[3]	SHI H Q, TER HORST R, NIELEN S, et al. The gut microbiome as mediator between diet and its impact on immune function[J]. Sci Rep, 2022, 12(1): 5149.
[4]	OJO O, FENG Q Q, OJO O O, et al. The role of dietary fibre in modulating gut microbiota dysbiosis in patients with type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials[J]. Nutrients, 2020, 12(11): 3239.
[5]	TELLE-HANSEN V H, GAUNDAL L, BASTANI N, et al. Replacing saturated fatty acids with polyunsaturated fatty acids increases the abundance of Lachnospiraceae and is associated with reduced total cholesterol levels: a randomized controlled trial in healthy individuals[J]. Lipids Health Dis, 2022, 21(1): 92.
[6]	WANG Z N, KLIPFELL E, BENNETT B J, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease[J]. Nature, 2011, 472(7341): 57-63.
[7]	UFNAL M, JAZWIEC R, DADLEZ M, et al. Trimethylamine-N-oxide: a carnitine-derived metabolite that prolongs the hypertensive effect of angiotensin Ⅱ in rats[J]. Can J Cardiol, 2014, 30(12): 1700-1705.
[8]	LI D D, LU Y, YUAN S, et al. Gut microbiota-derived metabolite trimethylamine-N-oxide and multiple health outcomes: an umbrella review and updated meta-analysis[J]. Am J Clin Nutr, 2022, 116(1): 230-243.
[9]	BÄCKHED F, LEY R E, SONNENBURG J L, et al. Host-bacterial mutualism in the human intestine[J]. Science, 2005, 307(5717): 1915-1920.
[10]	SAVAGE D C. Associations of indigenous microorganisms with gastrointestinal mucosal epithelia[J]. Am J Clin Nutr, 1970, 23(11): 1495-1501.
[11]	MAZMANIAN S K, LIU C H, TZIANABOS A O, et al. An immunomodulatory molecule of symbiotic bacteria directs maturation of the host immune system[J]. Cell, 2005, 122(1): 107-118.
[12]	LI M, WANG B H, ZHANG M H, et al. Symbiotic gut microbes modulate human metabolic phenotypes[J]. Proc Natl Acad Sci USA, 2008, 105(6): 2117-2122.
[13]	WANG H R, REN S J, LV H L, et al. Gut microbiota from mice with cerebral ischemia-reperfusion injury affects the brain in healthy mice[J]. Aging (Albany NY), 2021, 13(7): 10058-10074.
[14]	WAN S Z, NIE Y, ZHANG Y, et al. Gut microbial dysbiosis is associated with profibrotic factors in liver fibrosis mice[J]. Front Cell Infect Microbiol, 2020, 10: 18.
[15]	JIE Z Y, XIA H H, ZHONG S L, et al. The gut microbiome in atherosclerotic cardiovascular disease[J]. Nat Commun, 2017, 8(1): 845.
[16]	TANG W H W, BÄCKHED F, LANDMESSER U, et al. Intestinal microbiota in cardiovascular health and disease: JACC state-of-the-art review[J]. J Am Coll Cardiol, 2019, 73(16): 2089-2105.
[17]	WAN Y, WANG F L, YUAN J H, et al. Effects of dietary fat on gut microbiota and faecal metabolites, and their relationship with cardiometabolic risk factors: a 6-month randomised controlled-feeding trial[J]. Gut, 2019, 68(8): 1417-1429.
[18]	DELANNOY-BRUNO O, DESAI C, RAMAN A S, et al. Evaluating microbiome-directed fibre snacks in gnotobiotic mice and humans[J]. Nature, 2021, 595(7865): 91-95.
[19]	SMITH G D, EBRAHIM S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?[J]. Int J Epidemiol, 2003, 32(1): 1-22.
[20]	DAVEY SMITH G, HEMANI G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies[J]. Hum Mol Genet, 2014, 23(R1): R89-R98.
[21]	SEKULA P, DEL GRECO M F, PATTARO C, et al. Mendelian randomization as an approach to assess causality using observational data[J]. J Am Soc Nephrol, 2016, 27(11): 3253-3265.
[22]	WANG J, KURILSHIKOV A, RADJABZADEH D, et al. Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative[J]. Microbiome, 2018, 6(1): 101.
[23]	KURILSHIKOV A, MEDINA-GOMEZ C, BACIGALUPE R, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition[J]. Nat Genet, 2021, 53(2): 156-165.
[24]	WU F S, HUANG Y, HU J L, et al. Mendelian randomization study of inflammatory bowel disease and bone mineral density[J]. BMC Med, 2020, 18(1): 312.
[25]	BURGESS S, BUTTERWORTH A, THOMPSON S G. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol, 2013, 37(7): 658-665.
[26]	LONG Y W, TANG L H, ZHOU Y Y, et al. Causal relationship between gut microbiota and cancers: a two-sample Mendelian randomisation study[J]. BMC Med, 2023, 21(1): 66.
[27]	LAWLOR D A, HARBORD R M, STERNE J A C, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology[J]. Stat Med, 2008, 27(8): 1133-1163.
[28]	HEMANI G, TILLING K, DAVEY SMITH G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data[J]. PLoS Genet, 2017, 13(11): e1007081.
[29]	TANG W H, WANG Z N, LEVISON B S, et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk[J]. N Engl J Med, 2013, 368(17): 1575-1584.
[30]	KOH A, DE VADDER F, KOVATCHEVA-DATCHARY P, et al. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites[J]. Cell, 2016, 165(6): 1332-1345.
[31]	WAHLSTRÖM A, SAYIN S I, MARSCHALL H U, et al. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism[J]. Cell Metab, 2016, 24(1): 41-50.
[32]	GUO Y, LUO S Y, YE Y X, et al. Intermittent fasting improves cardiometabolic risk factors and alters gut microbiota in metabolic syndrome patients[J]. J Clin Endocrinol Metab, 2021, 106(1): 64-79.
[33]	KOREN O, SPOR A, FELIN J, et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis[J]. Proc Natl Acad Sci USA, 2011, 108(Suppl 1): 4592-4598.
[34]	HUTCHINSON A N, TINGÖ L N, BRUMMER R J. The potential effects of probiotics and ω-3 fatty acids on chronic low-grade inflammation[J]. Nutrients, 2020, 12(8): 2402.
[35]	BRON P A, KLEEREBEZEM M, BRUMMER R J, et al. Can probiotics modulate human disease by impacting intestinal barrier function?[J]. Br J Nutr, 2017, 117(1): 93-107.
[36]	RUSCICA M, PAVANELLO C, GANDINI S, et al. Nutraceutical approach for the management of cardiovascular risk—a combination containing the probiotic Bifidobacterium longum BB536 and red yeast rice extract: results from a randomized, double-blind, placebo-controlled study[J]. Nutr J, 2019, 18(1): 13.
[37]	LIU J L, AN N, MA C, et al. Correlation analysis of intestinal flora with hypertension[J]. Exp Ther Med, 2018, 16(3): 2325-2330.
[38]	MAYERHOFER C C K, KUMMEN M, HOLM K, et al. Low fibre intake is associated with gut microbiota alterations in chronic heart failure[J]. ESC Heart Fail, 2020, 7(2): 456-466.
[39]	WANG M, XIONG H, LU L, et al. Serum lipopolysaccharide is associated with the recurrence of atrial fibrillation after radiofrequency ablation by increasing systemic inflammation and atrial fibrosis[J]. Oxid Med Cell Longev, 2022, 2022: 2405972.
[40]	KONG B, FU H, XIAO Z, et al. Gut microbiota dysbiosis induced by a high-fat diet increases susceptibility to atrial fibrillation[J]. Can J Cardiol, 2022, 38(12): 1962-1975.
[41]	INZAUGARAT M E, JOHNSON C D, HOLTMANN T M, et al. NLR family pyrin domain-containing 3 inflammasome activation in hepatic stellate cells induces liver fibrosis in mice[J]. Hepatology, 2019, 69(2): 845-859.
[42]	LIU P N, YU S S, LIU J R, et al. Effects of Lactobacillus on hyperlipidemia in high-fat diet-induced mouse model[J]. Arch Med Sci, 2020, 19(3): 792-799.
[43]	CHEN L H, LIU W E, LI Y M, et al. Lactobacillus acidophilus ATCC 4356 attenuates the atherosclerotic progression through modulation of oxidative stress and inflammatory process[J]. Int Immunopharmacol, 2013, 17(1): 108-115.
[44]	HUANG Y, WANG J F, QUAN G H, et al. Lactobacillus acidophilus ATCC 4356 prevents atherosclerosis via inhibition of intestinal cholesterol absorption in apolipoprotein E-knockout mice[J]. Appl Environ Microbiol, 2014, 80(24): 7496-7504.
[45]	AHN H Y, KIM M, CHAE J S, et al. Supplementation with two probiotic strains, Lactobacillus curvatus HY7601 and Lactobacillus plantarum KY1032, reduces fasting triglycerides and enhances apolipoprotein A-V levels in non-diabetic subjects with hypertriglyceridemia[J]. Atherosclerosis, 2015, 241(2): 649-656.
[46]	李亚梦, 崔美泽, 孙婧, 等. 肠道菌群及其代谢产物氧化三甲胺: 冠心病治疗的新靶点[J]. 生物工程学报, 2021, 37(11): 3745-3756.
	LI Y M, CUI M Z, SUN J, et al. Gut microbiota and its metabolite trimethylamine-N-oxide (TMAO): a novel regulator in coronary artery disease[J]. Chinese Journal of Biotechnology, 2021, 37(11): 3745-3756.
[47]	KATSIMICHAS T, THEOFILIS P, TSIOUFIS K, et al. Gut microbiota and coronary artery disease: current therapeutic perspectives[J]. Metabolites, 2023, 13(2): 256.
[48]	WANG Y, XU Y Y, YANG M, et al. Butyrate mitigates TNF-α-induced attachment of monocytes to endothelial cells[J]. J Bioenerg Biomembr, 2020, 52(4): 247-256.
[49]	CHEN W J, ZHANG S, WU J F, et al. Butyrate-producing bacteria and the gut-heart axis in atherosclerosis[J]. Clin Chim Acta, 2020, 507: 236-241.
[50]	YU Y J, RAKA F, ADELI K. The role of the gut microbiota in lipid and lipoprotein metabolism[J]. J Clin Med, 2019, 8(12): 2227.
[51]	TORTELOTE G G. Therapeutic strategies for hypertension: exploring the role of microbiota-derived short-chain fatty acids in kidney physiology and development[J]. Pediatr Nephrol, 2025.
[52]	LUO Q, HU Y L, CHEN X, et al. Effects of gut microbiota and metabolites on heart failure and its risk factors: a two-sample Mendelian randomization study[J]. Front Nutr, 2022, 9: 899746.
[53]	CHIONCEL O, AMBROSY A P. Trimethylamine N-oxide and risk of heart failure progression: marker or mediator of disease[J]. Eur J Heart Fail, 2019, 21(7): 887-890.
[54]	SUN X L, JIAO X F, MA Y R, et al. Trimethylamine N-oxide induces inflammation and endothelial dysfunction in human umbilical vein endothelial cells via activating ROS-TXNIP-NLRP3 inflammasome[J]. Biochem Biophys Res Commun, 2016, 481(1/2): 63-70.
[55]	KEITEL V, REINEHR R, GATSIOS P, et al. The G-protein coupled bile salt receptor TGR5 is expressed in liver sinusoidal endothelial cells[J]. Hepatology, 2007, 45(3): 695-704.
[56]	ROMANO K A, MARTINEZ-DEL CAMPO A, KASAHARA K, et al. Metabolic, epigenetic, and transgenerational effects of gut bacterial choline consumption[J]. Cell Host Microbe, 2017, 22(3): 279-290.e7.

GWAS data ID	Disease	Sample size/n	Case/n	Control/n	Population
ebi-a-GCST006414	AF	1 030 836	60 620	970 216	European
ebi-a-GCST005195	CAD	547 261	122 733	424 528	European
ebi-a-GCST008036	Hypertension	20 526	11 863	8 663	European
ebi-a-GCST009541	HF	977 323	47 309	930 014	European

GWAS data ID	Disease	Sample size/n	Case/n	Control/n	Population
ebi-a-GCST006414	AF	1 030 836	60 620	970 216	European
ebi-a-GCST005195	CAD	547 261	122 733	424 528	European
ebi-a-GCST008036	Hypertension	20 526	11 863	8 663	European
ebi-a-GCST009541	HF	977 323	47 309	930 014	European

Outcome	Exposure	MR method	SNP/n	β	se	OR (95%CI)	P value	Correct causal direction	P value (Steiger test)
AF
	Catenibacterium	IVW	5	0.05	0.025	1.057 (1.005‒1.112)	0.030	True	4.85×10^-6
	Victivallales	IVW	8	-0.06	0.023	0.939 (0.896‒0.984)	0.008	True	1.52×10^-6
	Howardella	IVW	10	-0.06	0.019	0.939 (0.904‒0.977)	0.001	True	3.04×10^-5
	Lachnospiraceae UCG008	IVW	11	0.04	0.025	1.051 (1.001‒1.104)	0.047	True	2.52×10^-7
	Anaerostipes	IVW	13	-0.08	0.037	0.922 (0.857‒0.993)	0.030	True	5.20×10^-6
	Bifidobacteriaceae	IVW	11	-0.08	0.034	0.916 (0.855‒0.980)	0.011	True	1.42×10^-5
	Lentisphaeria	IVW	8	-0.06	0.024	0.936 (0.887‒0.983)	0.003	True	3.96×10^-6
	Streptococcus	IVW	15	0.08	0.039	1.089 (1.008‒1.177)	0.029	True	9.37×10^-6
	Victivallis	IVW	10	0.03	0.018	1.038 (1.001‒1.077)	0.043	True	4.62×10^-6
	Odoribacter	IVW	7	-0.09	0.046	0.909 (0.831‒0.996)	0.040	True	1.81×10^-5
	Intestinibacter	IVW	15	-0.06	0.031	0.933 (0.877‒0.993)	0.028	True	2.28×10^-6
	Lentisphaerae	IVW	9	-0.07	0.022	0.926 (0.886‒0.968)	<0.001	True	5.06×10^-6
	Bifidobacteriales	IVW	11	-0.08	0.034	0.916 (0.855‒0.980)	0.011	True	1.35×10^-5
CAD
	Lactobacillales	IVW	15	-0.08	0.032	0.919 (0.862‒0.980)	0.010	True	9.28×10^-7
	Veillonellaceae	IVW	19	0.06	0.026	1.065 (1.011‒1.122)	0.018	True	1.21×10^-5
	Parabacteroides	IVW	6	-0.14	0.050	0.866 (0.784‒0.956)	0.004	True	6.31×10^-5
	Lachnospiraceae	IVW	17	0.09	0.038	1.094 (1.012‒1.183)	0.023	True	7.23×10^-6
	Lachnoclostridium	IVW	13	0.08	0.039	1.093 (1.011‒1.181)	0.025	True	9.14×10^-5
	Oxalobacter	IVW	11	0.05	0.020	1.062 (1.019‒1.106)	0.004	True	6.05×10^-6
	Odoribacter	IVW	7	0.14	0.047	1.160 (1.056‒1.275)	0.001	True	6.42×10^-7
Hypertension
	Mollicutes RF9	IVW	13	-0.16	0.068	0.851 (0.743‒0.974)	0.019	True	7.23×10^-5
	Peptococcaceae	IVW	9	0.20	0.086	1.230 (1.038‒1.458)	0.016	True	2.07×10^-5
	Christensenellaceae R7 group	IVW	10	0.19	0.100	1.218 (1.001‒1.483)	0.049	True	9.97×10^-5
	Coriobacteriales	IVW	14	-0.21	0.085	0.803 (0.679‒0.950)	0.010	True	0.001
	Coriobacteriia	IVW	14	-0.21	0.085	0.803 (0.679‒0.950)	0.010	True	0.002
	Desulfovibrio	IVW	10	0.15	0.062	1.167 (1.033‒1.318)	0.012	True	0.007
	Coriobacteriaceae	IVW	14	-0.21	0.085	0.803 (0.679‒0.950)	0.010	true	0.007
	Intestinibacter	IVW	15	-0.19	0.083	0.819 (0.696‒0.965)	0.010	True	0.015
HF
	Ruminococcaceae UCG009	IVW	12	0.06	0.030	1.107 (1.009‒1.137)	0.022	True	6.12×10^-6
	Eubacterium oxidoreducens group	IVW	4	0.11	0.043	1.117 (1.026‒1.215)	0.010	True	3.26×10^-5
	Bacillales	IVW	9	-0.04	0.022	0.955 (0.913‒0.998)	0.010	True	5.84×10^-6
	Selenomonadales	IVW	12	0.10	0.044	1.106 (1.013‒1.208)	0.023	True	1.53×10^-5
	Anaerostipes	IVW	13	-0.10	0.043	0.899 (0.825‒0.974)	0.013	True	3.77×10^-6
	Negativicutes	IVW	12	0.10	0.044	1.107 (1.014‒1.208)	0.023	True	4.48×10^-5
	Eubacterium eligens group	IVW	7	0.13	0.057	1.139 (1.019‒1.274)	0.022	True	6.43×10^-6
	Flavonifractor	IVW	5	0.13	0.053	1.144 (1.031‒1.270)	0.011	True	1.06×10^-5

Outcome	Exposure	MR method	SNP/n	β	se	OR (95%CI)	P value	Correct causal direction	P value (Steiger test)
AF
	Catenibacterium	IVW	5	0.05	0.025	1.057 (1.005‒1.112)	0.030	True	4.85×10^-6
	Victivallales	IVW	8	-0.06	0.023	0.939 (0.896‒0.984)	0.008	True	1.52×10^-6
	Howardella	IVW	10	-0.06	0.019	0.939 (0.904‒0.977)	0.001	True	3.04×10^-5
	Lachnospiraceae UCG008	IVW	11	0.04	0.025	1.051 (1.001‒1.104)	0.047	True	2.52×10^-7
	Anaerostipes	IVW	13	-0.08	0.037	0.922 (0.857‒0.993)	0.030	True	5.20×10^-6
	Bifidobacteriaceae	IVW	11	-0.08	0.034	0.916 (0.855‒0.980)	0.011	True	1.42×10^-5
	Lentisphaeria	IVW	8	-0.06	0.024	0.936 (0.887‒0.983)	0.003	True	3.96×10^-6
	Streptococcus	IVW	15	0.08	0.039	1.089 (1.008‒1.177)	0.029	True	9.37×10^-6
	Victivallis	IVW	10	0.03	0.018	1.038 (1.001‒1.077)	0.043	True	4.62×10^-6
	Odoribacter	IVW	7	-0.09	0.046	0.909 (0.831‒0.996)	0.040	True	1.81×10^-5
	Intestinibacter	IVW	15	-0.06	0.031	0.933 (0.877‒0.993)	0.028	True	2.28×10^-6
	Lentisphaerae	IVW	9	-0.07	0.022	0.926 (0.886‒0.968)	<0.001	True	5.06×10^-6
	Bifidobacteriales	IVW	11	-0.08	0.034	0.916 (0.855‒0.980)	0.011	True	1.35×10^-5
CAD
	Lactobacillales	IVW	15	-0.08	0.032	0.919 (0.862‒0.980)	0.010	True	9.28×10^-7
	Veillonellaceae	IVW	19	0.06	0.026	1.065 (1.011‒1.122)	0.018	True	1.21×10^-5
	Parabacteroides	IVW	6	-0.14	0.050	0.866 (0.784‒0.956)	0.004	True	6.31×10^-5
	Lachnospiraceae	IVW	17	0.09	0.038	1.094 (1.012‒1.183)	0.023	True	7.23×10^-6
	Lachnoclostridium	IVW	13	0.08	0.039	1.093 (1.011‒1.181)	0.025	True	9.14×10^-5
	Oxalobacter	IVW	11	0.05	0.020	1.062 (1.019‒1.106)	0.004	True	6.05×10^-6
	Odoribacter	IVW	7	0.14	0.047	1.160 (1.056‒1.275)	0.001	True	6.42×10^-7
Hypertension
	Mollicutes RF9	IVW	13	-0.16	0.068	0.851 (0.743‒0.974)	0.019	True	7.23×10^-5
	Peptococcaceae	IVW	9	0.20	0.086	1.230 (1.038‒1.458)	0.016	True	2.07×10^-5
	Christensenellaceae R7 group	IVW	10	0.19	0.100	1.218 (1.001‒1.483)	0.049	True	9.97×10^-5
	Coriobacteriales	IVW	14	-0.21	0.085	0.803 (0.679‒0.950)	0.010	True	0.001
	Coriobacteriia	IVW	14	-0.21	0.085	0.803 (0.679‒0.950)	0.010	True	0.002
	Desulfovibrio	IVW	10	0.15	0.062	1.167 (1.033‒1.318)	0.012	True	0.007
	Coriobacteriaceae	IVW	14	-0.21	0.085	0.803 (0.679‒0.950)	0.010	true	0.007
	Intestinibacter	IVW	15	-0.19	0.083	0.819 (0.696‒0.965)	0.010	True	0.015
HF
	Ruminococcaceae UCG009	IVW	12	0.06	0.030	1.107 (1.009‒1.137)	0.022	True	6.12×10^-6
	Eubacterium oxidoreducens group	IVW	4	0.11	0.043	1.117 (1.026‒1.215)	0.010	True	3.26×10^-5
	Bacillales	IVW	9	-0.04	0.022	0.955 (0.913‒0.998)	0.010	True	5.84×10^-6
	Selenomonadales	IVW	12	0.10	0.044	1.106 (1.013‒1.208)	0.023	True	1.53×10^-5
	Anaerostipes	IVW	13	-0.10	0.043	0.899 (0.825‒0.974)	0.013	True	3.77×10^-6
	Negativicutes	IVW	12	0.10	0.044	1.107 (1.014‒1.208)	0.023	True	4.48×10^-5
	Eubacterium eligens group	IVW	7	0.13	0.057	1.139 (1.019‒1.274)	0.022	True	6.43×10^-6
	Flavonifractor	IVW	5	0.13	0.053	1.144 (1.031‒1.270)	0.011	True	1.06×10^-5

Outcome	Exposure	Cochran's Q				MR-Egger intercept
		MR-Egger		IVW		Intercept	P value
		Q value	P value	Q value	P value	Intercept	P value
AF
	Catenibacterium	0.370	0.946	0.488	0.974	0.010	0.753
	Victivallales	3.948	0.683	4.155	0.761	0.005	0.665
	Howardella	7.364	0.497	7.622	0.572	-0.006	0.624
	Lachnospiraceae UCG008	8.051	0.528	8.162	0.612	0.004	0.747
	Anaerostipes	12.334	0.339	12.561	0.402	-0.003	0.661
	Bifidobacteriaceae	8.821	0.453	9.111	0.521	0.004	0.603
	Lentisphaeria	3.948	0.683	4.155	0.761	0.005	0.665
	Streptococcus	20.126	0.092	20.223	0.123	0.002	0.807
	Victivallis	7.007	0.535	8.153	0.518	0.019	0.315
	Odoribacter	3.621	0.605	4.634	0.591	0.010	0.360
	Intestinibacter	14.962	0.309	17.041	0.253	0.010	0.203
	Lentisphaerae	7.455	0.383	7.841	0.449	0.007	0.566
	Bifidobacteriales	8.821	0.454	9.111	0.521	0.004	0.603
CAD
	Lactobacillales	13.551	0.406	14.344	0.424	-0.005	0.398
	Veillonellaceae	14.934	0.528	1.741	0.495	0.005	0.241
	Parabacteroides	4.608	0.329	4.742	0.448	0.005	0.750
	Lachnospiraceae	21.818	0.112	24.485	0.079	-0.008	0.195
	Lachnoclostridium	5.378	0.911	6.793	0.870	0.011	0.259
	Oxalobacter	3.473	0.942	3.692	0.960	-0.006	0.650
	Odoribacter	4.080	0.537	5.288	0.507	0.012	0.321
Hypertension
	Mollicutes RF9	3.784	0.975	0.711	0.877	<0.001	0.957
	Peptococcaceae	5.131	0.643	0.331	0.178	0.055	0.358
	Christensenellaceae R7 group	6.448	0.597	7.960	0.538	0.008	0.575
	Coriobacteriales	8.827	0.717	9.577	0.728	0.012	0.575
	Coriobacteriia	8.827	0.717	9.577	0.728	0.013	0.145
	Desulfovibrio	3.323	0.912	7.960	0.538	0.222	0.253
	Coriobacteriaceae	8.827	0.717	9.577	0.728	-0.020	0.403
	Intestinibacter	19.114	0.119	21.572	0.087	-0.020	0.403
HF
	Ruminococcaceae UCG009	8.939	0.537	10.956	0.446	0.017	0.185
	Eubacterium oxidoreducens group	2.451	0.293	2.483	0.478	-0.002	0.887
	Bacillales	2.705	0.910	2.870	0.942	0.006	0.697
	Selenomonadales	4.459	0.924	5.180	0.922	-0.007	0.415
	Anaerostipes	9.973	0.532	10.057	0.610	-0.002	0.777
	Negativicutes	4.459	0.924	5.180	0.922	-0.007	0.415
	Eubacterium eligens group	1.547	0.907	6.428	0.376	-0.038	0.078
	Flavonifractor	2.919	0.404	2.919	0.404	0.002	0.903