肠道微生物群对代谢综合征的遗传预测因果效应研究

doi:10.12114/j.issn.1007-9572.2024.0327

摘要/Abstract

摘要： 背景许多观察性研究发现肠道微生物群与代谢综合征（MetS）及其组成部分存在相关性，但两者之间的因果关系尚不明确。目的本研究旨在探讨肠道微生物群与MetS及其组成部分之间的双向因果关系。方法从MiBioGen获得肠道微生物群相关的单核苷酸多态性，在英国生物样本库、Complex Trait Genetics（CTG）和其他联盟研究中获得了MetS及其组成部分的汇总统计数据，采用双向两样本孟德尔随机化分析，评估两者的因果关系。除此之外，通过一系列敏感性分析验证孟德尔随机化分析结果的稳健性。为了获得更严格的因果关系解释，使用Bonferroni校正检验肠道菌群和MetS之间因果关系的强度。结果逆方差加权估计结果表明，双歧杆菌（Bifidobacteriaceae）（OR=0.96，95%CI=0.93~0.98，P=1.49×10-3）与MetS有明显的负向因果关系。部分肠道菌群与腰围之间存在明显的正向因果关系，如酶莱纳菌纲（class.Melainabacteria）（OR=1.02，95%CI=1.01~1.03，P=1.90×10-3）、胃厌氧杆菌目（order.Gastranaerophilales）（OR=1.02，95%CI=1.01~1.03，P=1.61×10-3）、NB1n目（order.NB1n）（OR=1.02，95%CI=1.01~1.03，P=2.00×10-3）和霍氏真杆菌群（genus..Eubacteriumhalliigroup）（OR=1.03，95%CI=1.01~1.04，P=6.97×10-4）等，但是反向孟德尔随机化分析不支持两者之间的因果关系。敏感性分析表明不存在异质性或水平多效性。结论这项双向孟德尔随机化研究提供了肠道微生物群与MetS及其组成部分有明显的因果关系证据，但是不支持反向因果关系。这个发现为MetS的防治提供了新的思路。未来仍需要通过进一步的随机对照试验来阐明益生菌等微生物制剂与MetS的关系。

关键词: 肠道微生物群, 代谢综合征, 孟德尔随机化, 因果关系

Abstract:

Background

Many observational studies have found an association between the gut microbiome and metabolic syndrome (MetS) and its components, but the causal relationship between them is not yet clear.

Objective

To explore a bidirectional causal relationship between the gut microbiota and the MetS and its components.

Methods

This study obtained gut microbiota-associated single nucleotide polymorphisms from MiBioGen, obtained summary statistics of MetS and its components in the UK Biobank, Complex Trait Genetics (CTG), and other consortiums studies, and performed bidirectional two-sample Mendelian randomization analyses to assess the relationship between causal relationships. In addition, a series of sensitivity analyses were performed to verify the robustness of the Mendelian randomization analysis results. To obtain a more rigorous causal interpretation, Bonferroni correction was used to test the strength of the causal relationship between gut microbiota and MetS.

Results

Inverse variance weighted estimation showed that Bifidobacteriaceae (OR=0.96, 95%CI=0.93-0.98, P=1.49×10^-3) had a significant negative causal relationship with MetS. There was a significant positive causal relationship between some gut microbiota and waist circumference, such as class.Melainabacteria (OR=1.02, 95%CI=1.01-1.03, P=1.90×10^-3), order.Gastranaerophilales (OR=1.02, 95%CI=1.01-1.03, P=1.61×10^-3), order.NB1n (OR=1.02, 95%CI=1.01-1.03, P=2.00×10^-3), and genus..Eubacteriumhalliigroup (OR=1.03, 95%CI=1.01-1.04, P=6.97×10^-4), among others. However, inverse Mendelian randomization analysis did not support a causal relationship. Sensitivity analyses showed there was no heterogeneity or horizontal pleiotropy.

Conclusion

This bidrectional Mendelian randomization study provides evidence of a clear causal relationship of gut microbiota on MetS and components, but does not support reverse causality. This finding provides new ideas for the management of MetS. Further randomized controlled trials are still needed in the future to elucidate the relationship between the effects of microbial agents such as probiotics on MetS.

Key words: Gut microbiota, Metabolic syndrome, Mendelian randomization, Causal relationship

中图分类号:

R 378.2　R 589

罗秀,麻钊丽,黄梦宇,等. 肠道微生物群对代谢综合征的遗传预测因果效应研究[J]. 中国全科医学, 2026, 29(05): 612-622. DOI: 10.12114/j.issn.1007-9572.2024.0327.
LUO Xiu,MA Zhaoli,HUANG Mengyu, et al. A Study of the Causal Effect of Gut Microbiota on Genetic Prediction of Metabolic Syndrome[J]. Chinese General Practice, 2026, 29(05): 612-622. DOI: 10.12114/j.issn.1007-9572.2024.0327.

图/表 5

参考文献 64

[1]	HUANG Y L, ZHANG L F, WANG Z W, et al. The prevalence and characteristics of metabolic syndrome according to different definitions in China: a nationwide cross-sectional study, 2012-2015[J]. BMC Public Health, 2022, 22(1): 1869. DOI: 10.1186/s12889-022-14263-w.
[2]	SAKLAYEN M G. The global epidemic of the metabolic syndrome[J]. Curr Hypertens Rep, 2018, 20(2): 12. DOI: 10.1007/s11906-018-0812-z.
[3]	LAN Y, MAI Z L, ZHOU S Y, et al. Prevalence of metabolic syndrome in China: an up-dated cross-sectional study[J]. PLoS One, 2018, 13(4): e0196012. DOI: 10.1371/journal.pone.0196012.
[4]	DABKE K, HENDRICK G, DEVKOTA S. The gut microbiome and metabolic syndrome[J]. J Clin Invest, 2019, 129(10): 4050-4057. DOI: 10.1172/JCI129194.
[5]	TILG H, ADOLPH T E, TRAUNER M. Gut-liver axis: pathophysiological concepts and clinical implications[J]. Cell Metab, 2022, 34(11): 1700-1718. DOI: 10.1016/j.cmet.2022.09.017.
[6]	TICINESI A, MILANI C, GUERRA A, et al. Understanding the gut-kidney axis in nephrolithiasis: an analysis of the gut microbiota composition and functionality of stone formers[J]. Gut, 2018, 67(12): 2097-2106. DOI: 10.1136/gutjnl-2017-315734.
[7]	FAN Y, PEDERSEN O. Gut microbiota in human metabolic health and disease[J]. Nat Rev Microbiol, 2021, 19(1): 55-71. DOI: 10.1038/s41579-020-0433-9.
[8]	PEDERSEN H K, FORSLUND S K, GUDMUNDSDOTTIR V, et al. A computational framework to integrate high-throughput '- omics' datasets for the identification of potential mechanistic links[J]. Nat Protoc, 2018, 13(12): 2781-2800. DOI: 10.1038/s41596-018-0064-z.
[9]	YANG T, SANTISTEBAN M M, RODRIGUEZ V, et al. Gut dysbiosis is linked to hypertension[J]. Hypertension, 2015, 65(6): 1331-1340. DOI: 10.1161/HYPERTENSIONAHA.115.05315.
[10]	MESLIER V, LAIOLA M, ROAGER H M, et al. Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake[J]. Gut, 2020, 69(7): 1258-1268. DOI: 10.1136/gutjnl-2019-320438.
[11]	FUJISAKA S, USSAR S, CLISH C, et al. Antibiotic effects on gut microbiota and metabolism are host dependent[J]. J Clin Invest, 2016, 126(12): 4430-4443. DOI: 10.1172/JCI86674.
[12]	WANG Z, KOONEN D, HOFKER M, et al. Gut microbiome and lipid metabolism: from associations to mechanisms[J]. Curr Opin Lipidol, 2016, 27(3): 216-224. DOI: 10.1097/MOL.0000000000000308.
[13]	YUAN S, CHEN J, RUAN X X, et al. Smoking, alcohol consumption, and 24 gastrointestinal diseases: Mendelian randomization analysis[J]. eLife, 2023, 12: e84051. DOI: 10.7554/eLife.84051.
[14]	SEKULA P, FABIOLA GRECO M, 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. DOI: 10.1681/ASN.2016010098.
[15]	PAGONI P, DIMOU N L, MURPHY N, et al. Using Mendelian randomisation to assess causality in observational studies[J]. Evid Based Ment Health, 2019, 22(2): 67-71. DOI: 10.1136/ebmental-2019-300085.
[16]	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. DOI: 10.1038/s41588-020-00763-1.
[17]	PALMER T M, LAWLOR D A, HARBORD R M, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors[J]. Stat Methods Med Res, 2012, 21(3): 223-242. DOI: 10.1177/0962280210394459.
[18]	LEVIN M G, JUDY R, GILL D, et al. Genetics of height and risk of atrial fibrillation: a Mendelian randomization study[J]. PLoS Med, 2020, 17(10): e1003288. DOI: 10.1371/journal.pmed.1003288.
[19]	GILL D, EFSTATHIADOU A, CAWOOD K, et al. Education protects against coronary heart disease and stroke independently of cognitive function: evidence from Mendelian randomization[J]. Int J Epidemiol, 2019, 48(5): 1468-1477. DOI: 10.1093/ije/dyz200.
[20]	LI P S, WANG H Y, GUO L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study[J]. BMC Med, 2022, 20(1): 443. DOI: 10.1186/s12916-022-02657-x.
[21]	BURGESS S, THOMPSON S G. Interpreting findings from Mendelian randomization using the MR-Egger method[J]. Eur J Epidemiol, 2017, 32(5): 377-389. DOI: 10.1007/s10654-017-0255-x.
[22]	VERBANCK M, CHEN C Y, NEALE B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-698. DOI: 10.1038/s41588-018-0099-7.
[23]	HE L J, YU T T, ZHANG W, et al. Causal associations of obesity with Achilles tendinopathy: a two-sample Mendelian randomization study[J]. Front Endocrinol, 2022, 13: 902142. DOI: 10.3389/fendo.2022.902142.
[24]	SAVAGE J E, JANSEN P R, STRINGER S, et al. Genome-wide association meta-analysis in 269, 867 individuals identifies new genetic and functional links to intelligence[J]. Nat Genet, 2018, 50(7): 912-919. DOI: 10.1038/s41588-018-0152-6.
[25]	DONG S S, ZHANG K, GUO Y, et al. Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study[J]. Genome Med, 2021, 13(1): 48. DOI: 10.1186/s13073-021-00865-3.
[26]	BINDA C, LOPETUSO L R, RIZZATTI G, et al. Actinobacteria: a relevant minority for the maintenance of gut homeostasis[J]. Dig Liver Dis, 2018, 50(5): 421-428. DOI: 10.1016/j.dld.2018.02.012.
[27]	KIJMANAWAT A, PANBURANA P, REUTRAKUL S, et al. Effects of probiotic supplements on insulin resistance in gestational diabetes mellitus: a double-blind randomized controlled trial[J]. J Diabetes Investig, 2019, 10(1): 163-170. DOI: 10.1111/jdi.12863.
[28]	EBRAHIMZADEH LEYLABADLO H, SANAIE S, SADEGHPOUR HERAVI F, et al. From role of gut microbiota to microbial-based therapies in type 2-diabetes[J]. Infect Genet Evol, 2020, 81: 104268. DOI: 10.1016/j.meegid.2020.104268.
[29]	HU C L, RZYMSKI P. Non-photosynthetic melainabacteria (cyanobacteria) in human gut: characteristics and association with health[J]. Life, 2022, 12(4): 476. DOI: 10.3390/life12040476.
[30]	SHANG J Y, GUO H, LI J, et al. Exploring the mechanism of action of Sanzi formula in intervening colorectal adenoma by targeting intestinal flora and intestinal metabolism[J]. Front Microbiol, 2022, 13: 1001372. DOI: 10.3389/fmicb.2022.1001372.
[31]	NASCIMENTO ARAUJO C D, AMORIM A T, BARBOSA M S, et al. Evaluating the presence of Mycoplasma hyorhinis, Fusobacterium nucleatum, and Helicobacter pylori in biopsies of patients with gastric cancer[J]. Infect Agent Cancer, 2021, 16(1): 70. DOI: 10.1186/s13027-021-00410-2.
[32]	ZHANG J, HU Y Q, WU L D, et al. Causal effect of gut microbiota on gastroduodenal ulcer: a two-sample Mendelian randomization study[J]. Front Cell Infect Microbiol, 2023, 13: 1322537. DOI: 10.3389/fcimb.2023.1322537.
[33]	ALMEIDA A, MITCHELL A L, BOLAND M, et al. A new genomic blueprint of the human gut microbiota[J]. Nature, 2019, 568(7753): 499-504. DOI: 10.1038/s41586-019-0965-1.
[34]	SHETTY S A, ZUFFA S, BUI T P N, et al. Reclassification of Eubacterium hallii as Anaerobutyricum hallii gen. nov., comb. nov., and description of Anaerobutyricum soehngenii sp. nov., a butyrate and propionate-producing bacterium from infant faeces[J]. Int J Syst Evol Microbiol, 2018, 68(12): 3741-3746. DOI: 10.1099/ijsem.0.003041.
[35]	MUKHERJEE A, LORDAN C, ROSS R P, et al. Gut microbes from the phylogenetically diverse genus Eubacterium and their various contributions to gut health[J]. Gut Microbes, 2020, 12(1): 1802866. DOI: 10.1080/19490976.2020.1802866.
[36]	DE VOS W M, TILG H, VAN HUL M, et al. Gut microbiome and health: mechanistic insights[J]. Gut, 2022, 71(5): 1020-1032. DOI: 10.1136/gutjnl-2021-326789.
[37]	VERDAM F J, FUENTES S, DE JONGE C, et al. Human intestinal microbiota composition is associated with local and systemic inflammation in obesity[J]. Obesity (Silver Spring), 2013, 21(12): E607-E615. DOI: 10.1002/oby.20466.
[38]	MUNUKKA E, WIKLUND P, PEKKALA S, et al. Women with and without metabolic disorder differ in their gut microbiota composition[J]. Obesity (Silver Spring), 2012, 20(5): 1082-1087. DOI: 10.1038/oby.2012.8.
[39]	SIMÕES C D, MAUKONEN J, KAPRIO J, et al. Habitual dietary intake is associated with stool microbiota composition in monozygotic twins[J]. J Nutr, 2013, 143(4): 417-423. DOI: 10.3945/jn.112.166322.
[40]	SEPP E, LÕIVUKENE K, JULGE K, et al. The association of gut microbiota with body weight and body mass index in preschool children of Estonia[J]. Microb Ecol Health Dis, 2013, 24. DOI: 10.3402/mehd.v24i0.19231.
[41]	TURPIN W, ESPIN-GARCIA O, XU W, et al. Association of host genome with intestinal microbial composition in a large healthy cohort[J]. Nat Genet, 2016, 48(11): 1413-1417. DOI: 10.1038/ng.3693.
[42]	KOVATCHEVA-DATCHARY P, SHOAIE S, LEE S, et al. Simplified intestinal microbiota to study microbe-diet-host interactions in a mouse model[J]. Cell Rep, 2019, 26(13): 3772-3783.e6. DOI: 10.1016/j.celrep.2019.02.090.
[43]	DUNCAN S H, LOBLEY G E, HOLTROP G, et al. Human colonic microbiota associated with diet, obesity and weight loss[J]. Int J Obes, 2008, 32(11): 1720-1724. DOI: 10.1038/ijo.2008.155.
[44]	FEI N, ZHAO L P. An opportunistic pathogen isolated from the gut of an obese human causes obesity in germfree mice[J]. ISME J, 2013, 7(4): 880-884. DOI: 10.1038/ismej.2012.153.
[45]	ZHOU W, XU H Y, ZHAN L B, et al. Dynamic development of fecal microbiome during the progression of diabetes mellitus in zucker diabetic fatty rats[J]. Front Microbiol, 2019, 10: 232. DOI: 10.3389/fmicb.2019.00232.
[46]	WANG Z N, TANG W H, BUFFA J A, et al. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide[J]. Eur Heart J, 2014, 35(14): 904-910. DOI: 10.1093/eurheartj/ehu002.
[47]	WILSON TANG W H, WANG Z N, LI X S, et al. Increased trimethylamine N-oxide portends high mortality risk independent of glycemic control in patients with type 2 diabetes mellitus[J]. Clin Chem, 2017, 63(1): 297-306. DOI: 10.1373/clinchem.2016.263640.
[48]	ZHUANG R L, GE X Y, HAN L, et al. Gut microbe-generated metabolite trimethylamine N-oxide and the risk of diabetes: a systematic review and dose-response meta-analysis[J]. Obes Rev, 2019, 20(6): 883-894. DOI: 10.1111/obr.12843.
[49]	LIANG Y Y, CHEN J, PENG M G, et al. Association between sleep duration and metabolic syndrome: linear and nonlinear Mendelian randomization analyses[J]. J Transl Med, 2023, 21(1): 90. DOI: 10.1186/s12967-023-03920-2.
[50]	ZHANG M, CHEN J, YIN Z Q, et al. The association between depression and metabolic syndrome and its components: a bidirectional two-sample Mendelian randomization study[J]. Transl Psychiatry, 2021, 11(1): 633. DOI: 10.1038/s41398-021-01759-z.
[51]	LIU Y, WANG Y, NI Y Q, et al. Gut microbiome fermentation determines the efficacy of exercise for diabetes prevention[J]. Cell Metab, 2020, 31(1): 77-91.e5. DOI: 10.1016/j.cmet.2019.11.001.
[52]	LIU R X, HONG J, XU X Q, et al. Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention[J]. Nat Med, 2017, 23(7): 859-868. DOI: 10.1038/nm.4358.
[53]	KOOTTE R S, LEVIN E, SALOJÄRVI J, et al. Improvement of insulin sensitivity after lean donor feces in metabolic syndrome is driven by baseline intestinal microbiota composition[J]. Cell Metab, 2017, 26(4): 611-619.e6. DOI: 10.1016/j.cmet.2017.09.008.
[54]	SUN J, BUYS N J. Glucose- and glycaemic factor-lowering effects of probiotics on diabetes: a meta-analysis of randomised placebo-controlled trials[J]. Br J Nutr, 2016, 115(7): 1167-1177. DOI: 10.1017/S0007114516000076.
[55]	ZHANG Q Q, WU Y C, FEI X Q. Effect of probiotics on glucose metabolism in patients with type 2 diabetes mellitus: a meta-analysis of randomized controlled trials[J]. Medicina (Kaunas), 2016, 52(1): 28-34. DOI: 10.1016/j.medici.2015.11.008.
[56]	NICOLUCCI A C, HUME M P, MARTÍNEZ I, et al. Prebiotics reduce body fat and alter intestinal microbiota in children who are overweight or with obesity[J]. Gastroenterology, 2017, 153(3): 711-722. DOI: 10.1053/j.gastro.2017.05.055.
[57]	STANISLAWSKI M A, FRANK D N, BORENGASSER S J, et al. The gut microbiota during a behavioral weight loss intervention[J]. Nutrients, 2021, 13(9): 3248. DOI: 10.3390/nu13093248.
[58]	SZENTIRMAI É, MILLICAN N S, MASSIE A R, et al. Butyrate, a metabolite of intestinal bacteria, enhances sleep[J]. Sci Rep, 2019, 9(1): 7035. DOI: 10.1038/s41598-019-43502-1.
[59]	HARTSTRA A V, BOUTER K E, BÄCKHED F, et al. Insights into the role of the microbiome in obesity and type 2 diabetes[J]. Diabetes Care, 2015, 38(1): 159-165. DOI: 10.2337/dc14-0769.
[60]	SCHWIERTZ A, TARAS D, SCHÄFER K, et al. Microbiota and SCFA in lean and overweight healthy subjects[J]. Obesity (Silver Spring), 2010, 18(1): 190-195. DOI: 10.1038/oby.2009.167.
[61]	PENG L Y, HE Z J, CHEN W, et al. Effects of butyrate on intestinal barrier function in a Caco-2 cell monolayer model of intestinal barrier[J]. Pediatr Res, 2007, 61(1): 37-41. DOI: 10.1203/01.pdr.0000250014.92242.f3.
[62]	DUVALLET C, GIBBONS S M, GURRY T, et al. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses[J]. Nat Commun, 2017, 8(1): 1784. DOI: 10.1038/s41467-017-01973-8.
[63]	GALA H, TOMLINSON I. The use of Mendelian randomisation to identify causal cancer risk factors: promise and limitations[J]. J Pathol, 2020, 250(5): 541-554. DOI: 10.1002/path.5421.
[64]	ZHENG J, BAIRD D, BORGES M C, et al. Recent developments in Mendelian randomization studies[J]. Curr Epidemiol Rep, 2017, 4(4): 330-345. DOI: 10.1007/s40471-017-0128-6.

暴露因素	结局指标	方法	SNP（个）	OR（95%CI）	P值	Q_pval	Egger_intercept	p-Egger_intercept
双歧杆菌目	MetS	MR Egger	8	0.89（0.79~1.01）	0.11
		IVW	8	0.96（0.93~0.98）	1.49×10^-3	0.92	4.70×10^-3	0.28
		WME	8	0.96（0.92~0.99）	0.01
		SM	8	0.96（0.91~1.01）	0.14
		WM	8	0.96（0.91~1.01）	0.13
双歧杆菌科	MetS	MR Egger	8	0.89（0.79~1.01）	0.11
		IVW	8	0.96（0.93~0.98）	1.49×10^-3	0.92	4.70×10^-3	0.28
		WME	8	0.96（0.92~0.99）	0.01
		SM	8	0.96（0.91~1.01）	0.14
		WM	8	0.96（0.91~1.01）	0.13
梅莱纳菌纲	WC	MR Egger	10	1.02（0.98~1.05）	0.38
		IVW	10	1.02（1.01~1.03）	1.90×10^-3	0.81	3.81×10^-4	0.85
		WME	10	1.02（1.00~1.04）	0.02
		SM	10	1.01（0.99~1.04）	0.36
		WM	10	1.01（0.99~1.04）	0.38
胃厌氧杆菌目	WC	MR Egger	9	1.01（0.98~1.05）	0.53
		IVW	9	1.02（1.01~1.03）	1.61×10^-3	0.88	1.08×10^-3	0.63
		WME	9	1.02（1.00~1.04）	0.02
		SM	9	1.02（0.99~1.04）	0.18
		WM	9	1.01（0.99~1.04）	0.28
NB1n目	WC	MR Egger	16	1.01（0.96~1.07）	0.62
		IVW	16	1.02（1.01~1.03）	2.00×10^-3	0.42	-6.02×10^-4	0.87
		WME	16	1.02（1.01~1.03）	0.01
		SM	16	1.02（1.00~1.04）	0.06
		WM	16	1.02（1.00~1.04）	0.08
霍氏真杆菌群	WC	MR Egger	15	1.03（0.99~1.06）	0.13
		IVW	15	1.03（1.01~1.04）	6.97×10^-4	0.43	-7.35×10^-5	0.96
		WME	15	1.03（1.01~1.05）	0.01
		SM	15	1.04（1.00~1.08）	0.07
		WM	15	1.04（1.01~1.07）	0.03

暴露因素	结局指标	方法	SNP（个）	OR（95%CI）	P值	Q_pval	Egger_intercept	p-Egger_intercept
双歧杆菌目	MetS	MR Egger	8	0.89（0.79~1.01）	0.11
		IVW	8	0.96（0.93~0.98）	1.49×10^-3	0.92	4.70×10^-3	0.28
		WME	8	0.96（0.92~0.99）	0.01
		SM	8	0.96（0.91~1.01）	0.14
		WM	8	0.96（0.91~1.01）	0.13
双歧杆菌科	MetS	MR Egger	8	0.89（0.79~1.01）	0.11
		IVW	8	0.96（0.93~0.98）	1.49×10^-3	0.92	4.70×10^-3	0.28
		WME	8	0.96（0.92~0.99）	0.01
		SM	8	0.96（0.91~1.01）	0.14
		WM	8	0.96（0.91~1.01）	0.13
梅莱纳菌纲	WC	MR Egger	10	1.02（0.98~1.05）	0.38
		IVW	10	1.02（1.01~1.03）	1.90×10^-3	0.81	3.81×10^-4	0.85
		WME	10	1.02（1.00~1.04）	0.02
		SM	10	1.01（0.99~1.04）	0.36
		WM	10	1.01（0.99~1.04）	0.38
胃厌氧杆菌目	WC	MR Egger	9	1.01（0.98~1.05）	0.53
		IVW	9	1.02（1.01~1.03）	1.61×10^-3	0.88	1.08×10^-3	0.63
		WME	9	1.02（1.00~1.04）	0.02
		SM	9	1.02（0.99~1.04）	0.18
		WM	9	1.01（0.99~1.04）	0.28
NB1n目	WC	MR Egger	16	1.01（0.96~1.07）	0.62
		IVW	16	1.02（1.01~1.03）	2.00×10^-3	0.42	-6.02×10^-4	0.87
		WME	16	1.02（1.01~1.03）	0.01
		SM	16	1.02（1.00~1.04）	0.06
		WM	16	1.02（1.00~1.04）	0.08
霍氏真杆菌群	WC	MR Egger	15	1.03（0.99~1.06）	0.13
		IVW	15	1.03（1.01~1.04）	6.97×10^-4	0.43	-7.35×10^-5	0.96
		WME	15	1.03（1.01~1.05）	0.01
		SM	15	1.04（1.00~1.08）	0.07
		WM	15	1.04（1.01~1.07）	0.03

暴露因素	结局指标	方法	SNP （个）	OR（95%CI）	P值
MetS	双歧杆菌目	MR Egger	164	1.18（0.81~1.73）	0.39
		IVW	164	1.00（0.87~1.14）	0.97
		WME	164	0.97（0.81~1.18）	0.78
		SM	164	0.84（0.51~1.37）	0.48
		WM	164	0.90（0.65~1.25）	0.53
MetS	双歧杆菌科	MR Egger	164	1.18（0.81~1.73）	0.39
		IVW	164	1.00（0.87~1.14）	0.97
		WME	164	0.97（0.81`1.18）	0.78
		SM	164	0.84（0.51~1.37）	0.48
		WM	164	0.90（0.65~1.25）	0.53
WC	梅莱纳菌纲	MR Egger	303	1.03（0.66~1.59）	0.90
		IVW	303	0.94（0.81~1.10）	0.46
		WME	303	0.78（0.60~1.02）	0.07
		SM	303	0.58（0.29~1.19）	0.14
		WM	303	0.73（0.45~1.19）	0.21
WC	胃厌氧杆菌目	MR Egger	303	1.03（0.66~1.59）	0.91
		IVW	303	0.95（0.81~1.10）	0.47
		WME	303	0.82（0.63~1.06）	0.13
		SM	303	0.63（0.29~1.38）	0.25
		WM	303	0.76（0.48~1.21）	0.25
WC	NB1n目	MR Egger	303	0.68（0.42~1.08）	0.10
		IVW	303	0.90（0.77~1.06）	0.23
		WME	303	0.90（0.69~1.17）	0.41
		SM	303	0.81（0.35~1.92）	0.64
		WM	303	0.69（0.42~1.14）	0.15
WC	霍氏真杆菌群	MR Egger	303	0.99（0.76~1.28）	0.94
		IVW	303	1.02（0.93~1.11）	0.73
		WME	303	1.02（0.88~1.18）	0.80
		SM	303	1.28（0.82~1.98）	0.28
		WM	303	0.83（0.61~1.11）	0.20

暴露因素	结局指标	方法	SNP （个）	OR（95%CI）	P值
MetS	双歧杆菌目	MR Egger	164	1.18（0.81~1.73）	0.39
		IVW	164	1.00（0.87~1.14）	0.97
		WME	164	0.97（0.81~1.18）	0.78
		SM	164	0.84（0.51~1.37）	0.48
		WM	164	0.90（0.65~1.25）	0.53
MetS	双歧杆菌科	MR Egger	164	1.18（0.81~1.73）	0.39
		IVW	164	1.00（0.87~1.14）	0.97
		WME	164	0.97（0.81`1.18）	0.78
		SM	164	0.84（0.51~1.37）	0.48
		WM	164	0.90（0.65~1.25）	0.53
WC	梅莱纳菌纲	MR Egger	303	1.03（0.66~1.59）	0.90
		IVW	303	0.94（0.81~1.10）	0.46
		WME	303	0.78（0.60~1.02）	0.07
		SM	303	0.58（0.29~1.19）	0.14
		WM	303	0.73（0.45~1.19）	0.21
WC	胃厌氧杆菌目	MR Egger	303	1.03（0.66~1.59）	0.91
		IVW	303	0.95（0.81~1.10）	0.47
		WME	303	0.82（0.63~1.06）	0.13
		SM	303	0.63（0.29~1.38）	0.25
		WM	303	0.76（0.48~1.21）	0.25
WC	NB1n目	MR Egger	303	0.68（0.42~1.08）	0.10
		IVW	303	0.90（0.77~1.06）	0.23
		WME	303	0.90（0.69~1.17）	0.41
		SM	303	0.81（0.35~1.92）	0.64
		WM	303	0.69（0.42~1.14）	0.15
WC	霍氏真杆菌群	MR Egger	303	0.99（0.76~1.28）	0.94
		IVW	303	1.02（0.93~1.11）	0.73
		WME	303	1.02（0.88~1.18）	0.80
		SM	303	1.28（0.82~1.98）	0.28
		WM	303	0.83（0.61~1.11）	0.20