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山东大学学报 (医学版) ›› 2025, Vol. 63 ›› Issue (5): 86-94.doi: 10.6040/j.issn.1671-7554.0.2024.0658

• 临床医学 • 上一篇    

代谢综合征及其组分与消化系统恶性肿瘤的因果关联:两样本孟德尔随机化研究

黄馨,王梦雪,付书璠,张琦悦,徐力   

  1. 南京中医药大学附属医院肿瘤内科, 江苏 南京 210029
  • 发布日期:2025-05-07
  • 通讯作者: 徐力. E-mail:13913887528@163.com

Causal association of metabolic syndrome and its components with digestive system malignancies: a two-sample Mendelian randomized study

HUANG Xin, WANG Mengxue, FU Shufan, ZHANG Qiyue, XU Li   

  1. Department of Medical Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu, China
  • Published:2025-05-07

摘要: 目的 采用两样本孟德尔随机化(two-sample Mendelian randomization, TSMR)方法,从遗传学角度探索代谢综合征(metabolic syndrome, MetS)及其组分与消化系统恶性肿瘤之间的因果关联,为后者的预防提供新线索。 方法 基于全基因组关联研究(genome wide association study, GWAS)的汇总数据,将MetS及其组分作为暴露因素,消化系统恶性肿瘤作为结局变量,采用逆方差加权法(inverse variance weighted, IVW)为主要分析方法,加权中位数(weighted median, WM)和MR-Egger为补充分析方法评估因果效应;采用敏感性分析验证研究结果的可靠性。 结果 IVW结果显示, MetS与肝癌(OR=1.357, 95%CI=1.004~1.834, P=0.047)和食管癌(OR=1.001, 95%CI=1.000~1.001, P=0.037)的发生风险增加相关。MetS各组分中,腰围(waist circumference, WC)与胃癌(OR=1.809, 95%CI=1.024~3.196, P=0.041)和食管癌(OR=1.001, 95%CI=1.000~1.002, P=0.020)的发生风险增加相关;高密度脂蛋白(high density lipoprotein, HDL)与结直肠癌(OR=0.789, 95%CI=0.633~0.984, P=0.035)的发生风险降低相关;敏感性分析提示研究结果稳健。 结论 MetS会增加肝癌及食管癌的发生风险。在其各组分中,WC是胃癌及食管癌的发生危险因素,HDL结直肠癌发生的保护因素。

关键词: 孟德尔随机化, 代谢综合征, 消化系统恶性肿瘤, 全基因组关联研究, 因果关联

Abstract: Objective To provide new clues for the prevention and treatment of digestive system malignancies by using two sample Mendelian randomization(TSMR)method so as to explore the causal relationship between metabolic syndrome(MetS)and its components and digestive system malignancies from a genetic perspective. Methods Based on the summary data of genome-wide association study(GWAS), MetS and its components were used as exposure factors, and digestive system maligancies were used as outcome variables. The inverse variance weighted(IVW)method was used as the main analysis method, and weighted median(WM)and MR Egger were used as supplementary analysis methods to evaluate causal effects. The sensitivity analysis was used to verify the reliability of the research results. Results The IVW method showed that MetS is associated with an increased risk of liver cancer(OR=1.357, 95%CI=1.004-1.834, P=0.047)and esophageal cancer(OR=1.001, 95%CI=1.000-1.001, P=0.037). Among the components of MetS, waist circumference(WC)is associated with an increased risk of gastric cancer(OR=1.809, 95%CI=1.024-3.196, P=0.041)and esophageal cancer(OR=1.001, 95%CI=1.000-1.002, P=0.020); High density lipoprotein(HDL)is associated with a reduced risk of colorectal cancer(OR=0.789, 95%CI=0.633-0.984, P=0.035); Sensitivity analysis suggests that the research results are robust. Conclusion MetS increases the risk of liver and esophageal cancer, with WC being a risk factor for gastric and esophageal cancer, while HDL is a protective factor for colorectal cancer.

Key words: Mendelian randomization, Metabolic syndrome, Digestive system malignancies, Genome wide association study, Causal association

中图分类号: 

  • R735
[1] Arnold M, Abnet CC, Neale RE, et al. Global burden of 5 major types of gastrointestinal cancer[J]. Gastroenterology, 2020, 159(1): 335-349.
[2] Fock KM. Review article: the epidemiology and prevention of gastric cancer[J]. Aliment Pharmacol Ther, 2014, 40(3): 250-260.
[3] Valle L, Vilar E, Tavtigian SV, et al. Genetic predisposition to colorectal cancer: syndromes, genes, classification of genetic variants and implications for precision medicine[J]. J Pathol, 2019, 247(5): 574-588.
[4] Achike FI, To NP, Wang HD, et al. Obesity, metabolic syndrome, adipocytes and vascular function: a holistic viewpoint[J]. Clin Exp Pharmacol Physiol, 2011, 38(1): 1-10.
[5] Shen XD, Wang Y, Zhao R, et al. Metabolic syndrome and the risk of colorectal cancer: a systematic review and meta-analysis[J]. Int J Colorectal Dis, 2021, 36(10): 2215-2225.
[6] Zhong L, Liu JF, Liu S, et al. Correlation between pancreatic cancer and metabolic syndrome: a systematic review and meta-analysis[J]. Front Endocrinol, 2023, 14: 1116582. doi: 10.3389/fendo.2023.1116582
[7] Zhan ZQ, Chen YZ, Huang ZM, et al. Metabolic syndrome, its components, and gastrointestinal cancer risk: a meta-analysis of 31 prospective cohorts and Mendelian randomization study[J]. J Gastroenterol Hepatol, 2024, 39(4): 630-641.
[8] Lin YL, Ness-Jensen E, Hveem K, et al. Metabolic syndrome and esophageal and gastric cancer[J]. Cancer Causes Control, 2015, 26(12): 1825-1834.
[9] Emdin CA, Khera AV, Kathiresan S. Mendelian randomization[J]. Jama, 2017, 318(19): 1925-1926.
[10] Li YJ, Li QX, Cao ZQ, et al. The causal association of polyunsaturated fatty acids with allergic disease: a two-sample Mendelian randomization study[J]. Front Nutr, 2022, 9: 962787. doi:10.3389/fnut.2022.962787
[11] Lind L. Genome-wide association study of the metabolic syndrome in UK biobank[J]. Metab Syndr Relat Disord, 2019, 17(10): 505-511.
[12] Guerrero-Romero F, Rodríguez-Morán M. Concordance between the 2005 International Diabetes Federation definition for diagnosing metabolic syndrome with the National Cholesterol Education Program Adult Treatment Panel III and the World Health Organization definitions[J]. Diabetes Care, 2005, 28(10): 2588-2589.
[13] Liberopoulos EN, Mikhailidis DP, Elisaf MS. Diagnosis and management of the metabolic syndrome in obesity[J]. Obes Rev, 2005, 6(4): 283-296.
[14] Elsworth B, Mitchell R, Raistrick C A, et al. MRC-IEU UK Biobank gwas pipeline version 1[EB/OL].(2017-12-14)[2024-05-21]. https://data.bris.ac.uk/data/dataset/2fahpksont1zi26xosyamqo8rr
[15] Manning AK, Hivert MF, Scott RA, et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance[J]. Nat Genet, 2012, 44(6): 659-669.
[16] Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels[J]. Nat Genet, 2013, 45(11): 1274-1283.
[17] Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome[J]. eLife, 2018, 7: e34408. doi:10.7554/eLife.34408
[18] Burgess S, Davey SG, Davies NM, et al. Guidelines for performing mendelian randomization investigations: update for summer 2023[J]. Wellcome Open Res, 2023, 4: 186. doi:10.12688/wellcomeopenres.15555.3
[19] Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomization: the STROBE-MR statement[J]. JAMA, 2021, 326(16): 1614-1621.
[20] Burgess S, Thompson SG, CHD Genetics Collaboration CRP. Avoiding bias from weak instruments in Mendelian randomization studies[J]. Int J Epidemiol, 2011, 40(3): 755-764.
[21] de Klerk JA, Beulens JWJ, Mei HL, et al. Altered blood gene expression in the obesity-related type 2 diabetes cluster may be causally involved in lipid metabolism: a Mendelian randomisation study[J]. Diabetologia, 2023, 66(6): 1057-1070.
[22] Agudo A, Bonet C, Travier N, et al. Impact of cigarette smoking on cancer risk in the European prospective investigation into cancer and nutrition study[J]. J Clin Oncol, 2012, 30(36): 4550-4557.
[23] Rumgay H, Shield K, Charvat H, et al. Global burden of cancer in 2020 attributable to alcohol consumption: a population-based study[J]. Lancet Oncol, 2021, 22(8): 1071-1080.
[24] He SY, Xia CF, Li H, et al. Cancer profiles in China and comparisons with the USA: a comprehensive analysis in the incidence, mortality, survival, staging, and attribution to risk factors[J]. Sci China Life Sci, 2024, 67(1): 122-131.
[25] Burgess S, Scott RA, Timpson NJ, et al. Using published data in mendelian randomization: a blueprint for efficient identification of causal risk factors[J]. Eur J Epidemiol, 2015, 30(7): 543-552.
[26] Bowden J, Davey Smith G, Haycock PC, et al. Consis-tent estimation in mendelian randomization with some invalid instruments using a weighted median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-314.
[27] Bowden J, Hemani G, Davey Smith G. Invited commentary: detecting individual and global horizontal pleiotropy in mendelian randomization-a job for the humble heterogeneity statistic[J]. Am J Epidemiol, 2018, 187(12): 2681-2685.
[28] Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression[J]. Inter-national journal of epidemiology, 2015, 44(2): 512-525.
[29] Verbanck M, Chen CY, 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.
[30] Reynolds CJ, Fabiola Greco M, Allen RJ, et al. The causal relationship between gastro-oesophageal reflux disease and idiopathic pulmonary fibrosis: a bidirectional two-sample Mendelian randomisation study[J]. Eur Respir J, 2023, 61(5): 2201585.
[31] Yao F, Bo YC, Zhao LY, et al. Prevalence and influencing factors of metabolic syndrome among adults in China from 2015 to 2017[J]. Nutrients, 2021, 13(12): 4475. doi: 10.3390/nu13124475
[32] 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2020年版)[J]. 中华糖尿病杂志, 2021, 13(4): 315-409. Chinese Medical Association Diabetes Society. Reaction to guideline for the prevention and treatment of type 2 diabetes mellitus in China(2020 edition)[J]. Chinese Journal of Diabetes Mellitus, 2021, 13(4):315-409.
[33] Tahergorabi Z, Moodi M, Zardast M, et al. Metabolic syndrome and the risk of gastrointestinal cancer: a case-control study[J]. Asian Pac J Cancer Prev, 2018, 19(8): 2205-2210.
[34] Mili N, Paschou SA, Goulis DG, et al. Obesity, metabolic syndrome, and cancer: pathophysiological and therapeutic associations[J]. Endocrine, 2021, 74(3): 478-497.
[35] 谢同辉, 陈志强, 赵丹文, 等. 代谢综合征与消化道癌症关系的研究进展[J]. 中国现代普通外科进展, 2022, 25(8): 633-635, 638. XIE Tonghui, CHEN Zhiqiang, ZHAO Danwen, et al. Research progress in metabolic syndrome and digestive tract cancer[J]. Chinese Journal of Current Advances in General Surgery, 2022, 25(8): 633-635, 638.
[36] Belladelli F, Montorsi F, Martini A. Metabolic syndrome, obesity and cancer risk[J]. Curr Opin Urol, 2022, 32(6): 594-597.
[37] Szablewski L. Changes in cells associated with insulin resistance[J]. Int J Mol Sci, 2024, 25(4): 2397. doi: 10.3390/ijms25042397
[38] 扈艳雯, 王志媛, 郁万江, 等. 52例肥胖患者脂肪分布与代谢综合征及糖代谢指标的相关性[J]. 山东大学学报(医学版), 2020, 58(8): 101-106. HU Yanwen, WANG Zhiyuan, YU Wanjiang, et al. Correlation of fat distribution with metabolic syndrome and glucose metabolism in 52 obese patients[J]. Journal of Shandong University(Health Sciences), 2020, 58(8): 101-106.
[39] Giovannucci E, Harlan DM, Archer MC, et al. Diabetes and cancer: a consensus report[J]. CA Cancer J Clin, 2010, 60(4): 207-221.
[40] Siegel AB, Zhu AX. Metabolic syndrome and hepatoce-llular carcinoma: two growing epidemics with a potential link[J]. Cancer, 2009, 115(24): 5651-5661.
[41] Shi YW, Yang RX, Fan JG. Chronic hepatitis B infection with concomitant hepatic steatosis: current evidence and opinion[J]. World J Gastroenterol, 2021, 27(26): 3971-3983.
[42] Selby LV, Ejaz A, Brethauer SA, et al. Fatty liver disease and primary liver cancer: disease mechanisms, emerging therapies and the role of bariatric surgery[J]. Expert Opin Investig Drugs, 2020, 29(2): 107-110.
[43] Chettouh H, Lequoy M, Fartoux L, et al. Hyperinsu-linaemia and insulin signalling in the pathogenesis and the clinical course of hepatocellular carcinoma[J]. Liver Int, 2015, 35(10): 2203-2217.
[44] Polyzos SA, Chrysavgis L, Vachliotis ID, et al. Nonalcoholic fatty liver disease and hepatocellular carcinoma: Insights in epidemiology, pathogenesis, imaging, prevention and therapy[J]. Semin Cancer Biol, 2023, 93: 20-35.
[45] Lee YB, Ha Y, Chon YE, et al. Association between hepatic steatosis and the development of hepatocellular carcinoma in patients with chronic hepatitis B[J]. Clin Mol Hepatol, 2018, 25(1): 52-64.
[46] Peleg N, Issachar A, Arbib OS, et al. Liver steatosis is a strong predictor of mortality and cancer in chronic he-patitis B regardless of viral load[J]. JHEP Reports, 2019, 1(1): 9-16.
[47] Zhang JJ, Wu HD, Wang RY. Metabolic syndrome and esophageal cancer risk: a systematic review and meta-analysis[J]. Diabetol Metab Syndr, 2021, 13(1): 8. doi: 10.1186/s13098-021-00627-6
[48] Rothwell JA, Jenab M, Karimi M, et al. Metabolic syndrome and risk of gastrointestinal cancers: an investigation using large-scale molecular data[J]. Clin Gastroenterol Hepatol, 2022, 20(6): e1338-e1352.
[49] Lee JE, Han K, Yoo J, et al. Association between metabolic syndrome and risk of esophageal cancer: a nationwide population-based study[J]. Cancer Epidemiol Biomarkers Prev, 2022, 31(12): 2228-2236.
[50] Du X, Hidayat K, Shi BM. Abdominal obesity and gastroesophageal cancer risk: systematic review and meta-analysis of prospective studies[J]. Biosci Rep. 2017 May 11;37(3):BSR20160474. doi: 10.1042/BSR20160474
[51] 王罡强, 袁金秋, 张常华, 等. 代谢综合征及其组分与胃癌发生风险前瞻性队列研究[J]. 热带医学杂志, 2021, 21(9): 1096-1102. WANG Gangqiang, YUAN Jinqiu, ZHANG Changhua, et al. Associations of metabolic syndrome and its components with risk of gastric cancer: a prospective cohort study[J]. Journal of Tropical Medicine, 2021, 21(9): 1096-1102.
[52] Sanikini H, Muller DC, Chadeau-Hyam M, et al. Anthropometry, body fat composition and reproductive factors and risk of oesophageal and gastric cancer by subtype and subsite in the UK Biobank cohort[J]. PLoS One, 2020, 15(10): e0240413. doi:10.1371/journal.pone.0240413
[53] Harvey AE, Lashinger LM, Hursting SD. The growing challenge of obesity and cancer: an inflammatory issue[J]. Ann N Y Acad Sci, 2011, 1229: 45-52. doi:10.1111/j.1749-6632.2011.06096.x
[54] van Kruijsdijk RCM, van der Wall E, Visseren FLJ. Obesity and cancer: the role of dysfunctional adipose tissue[J]. Cancer Epidemiol Biomarkers Prev, 2009, 18(10): 2569-2578.
[55] Choi YJ, Lee DH, Han KD, et al. Abdominal obesity, glucose intolerance and decreased high-density lipoprotein cholesterol as components of the metabolic syndrome are associated with the development of colorectal cancer[J]. Eur J Epidemiol, 2018, 33(11): 1077-1085.
[56] 杨刚, 张乐杨, 周雨迪, 等. 代谢综合征及其组份与结直肠癌根治术后短期预后的相关性[J]. 中国肿瘤临床, 2022, 49(19): 982-987. YANG Gang, ZHANG Leyang, ZHOU Yudi, et al. Relationship between metabolic syndrome and its components and short-term out-come after radical resection of colorectal cancer[J]. Chinese Journal of Clinical Oncology, 2022, 49(19): 982-987.
[57] van Duijnhoven FJB, Bas Bueno-De-Mesquita H, Calligaro M, et al. Blood lipid and lipoprotein concentrations and colorectal cancer risk in the European prospective investigation into cancer and nutrition[J]. Gut, 2011, 60(8): 1094-1102.
[58] Ossoli A, Wolska A, Remaley AT, et al. High-density lipoproteins: a promising tool against cancer[J]. Biochim Biophys Acta BBA Mol Cell Biol Lipds, 2022, 1867(1): 159068. doi:10.1016/j.bbalip.2021.159068
[59] Zhao TJ, Zhu N, Shi YN, et al. Targeting HDL in tumor microenvironment: new hope for cancer therapy[J]. J Cell Physiol, 2021, 236(11): 7853-7873.
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