两样本孟德尔随机化法分析1型糖尿病对结直肠癌的影响

doi:10.3969/j.issn.1674-8115.2024.06.011

摘要/Abstract

摘要：

目的·利用孟德尔随机化法（Mendelian randomization，MR）研究1型糖尿病与结直肠癌间潜在的因果关系。方法·采用两样本双向MR分析评估1型糖尿病与结直肠癌的因果关系。研究数据均来自IEU Open GWAS Project数据库。1型糖尿病的数据集包括患者9 266例和对照人群15 574例，包含12 783 129个单核苷酸多态性（single nucleotide polymorphism，SNP）的关联分析；结直肠癌的数据集包括患者5 657例和对照人群372 016例，包含29 999 696个SNP的关联分析。筛选工具变量SNP，以逆方差加权（inverse variance weighted，IVW）法结果作为效应的主要指标，同时将MR-Egger回归、加权中位数法、基于众数的简单估计、基于众数的加权估计4种方法结果作为参考。采用留一法检验敏感性，采用IVW法和MR-Egger法进行Cochran's Q检验判断异质性，MR-pleiotropy函数检验多效性，采用Steiger检验进行方向性研究。采用共定位分析评估1型糖尿病和结直肠癌之间的效应是否由相同的SNP引起，采用交叉性状连锁不平衡得分回归（linkage disequilibrium score regression，LDSC）分析2种疾病之间的遗传相关性。所有检验采用R语言软件（4.3.1版本）执行分析。结果·经筛选后，共采用工具变量（SNP）33个。异质性检验结果发现，SNP之间存在一定的异质性（IVW法和MR-Egger法结果均P<0.05），因此效应评估采用随机效应模型的结果。正向MR分析结果显示，IVW法、MR-Egger法、加权中位数法、基于众数的加权估计均发现1型糖尿病对结直肠癌存在显著的因果效应（均P<0.05）；敏感性分析显示，结果稳定。多效性检验未检测到多效性（P>0.05）。Steiger检验发现，1型糖尿病对结直肠癌的效应未受反向作用干扰。反向MR分析未发现结直肠癌对1型糖尿病存在因果效应（均P>0.05）。共定位分析结果显示，H₄假设概率为45.7%，2种疾病间的因果关系不是由两者基因序列中相同的SNP引起的。LDSC分析显示2种疾病不存在遗传相关性。结论·1型糖尿病可能促进结直肠癌发生，但结直肠癌对1型糖尿病不存在影响。

关键词: 结直肠癌, 1型糖尿病, 孟德尔随机化

Abstract:

Objective ·To investigate the potential causal relationship between type 1 diabetes and colorectal cancer by using Mendelian randomization (MR). Methods ·Two-sample bidirectional MR was used to investigate the causal relationship between type 1 diabetes and colorectal cancer. All research data were collected from the IEU Open GWAS Project database. The dataset of type 1 diabetes included 9 266 cases and 15 574 controls, with correlation analysis in 12 783 129 single nucleotide polymorphisms (SNPs); the dataset of colorectal cancer included 5 657 cases and 372 016 controls, with correlation analysis in 29 999 696 SNPs. The instrumental variables SNPs were screened. The results derived from the inverse-variance weighted (IVW) method were used as the main indicator of effect. The results derived from other four methods, namely MR-Egger regression, weighted median, simple mode, and weighted mode, were used as reference. Sensitivity was analyzed with the leave-one-out method. Heterogeneity was analyzed with Cochran's Q test by using both IVW and MR-Egger methods. Pleiotropy was analyzed with MR-pleiotropy function, and Steiger test was used for directional research. The colocation analysis was used to find out whether the causal relationship between type 1 diabetes and colorectal cancer was caused by the same SNP. The genetic correlation between 2 diseases was analyzed by using the linkage disequilibrium score regression (LDSC). All tests were analyzed by using R language software (version 4.3.1). Results ·After being screened, a total of 33 instrumental variables (SNPs) were used. The heterogeneity test results showed that there was heterogeneity among the SNPs (IVW and MR-Egger: P<0.05), so the effect evaluation was based on the results of the random effect model. MR analysis showed that type 1 diabetes had a significant causal effect on colorectal cancer (P<0.05) by using IVW, MR-Egger, weighted median and weighted mode. Sensitivity analysis showed that the results were stable. Pleiotropy was not detected in pleiotropy test (P>0.05). Steiger test showed that the effect of type 1 diabetes on colorectal cancer was not interfered with by the reverse effect. Reverse MR analysis showed no causal effect of colorectal cancer on type 1 diabetes (P>0.05). The results of colocalization analysis showed that the probability of H₄ hypothesis was 45.7%, and the causal relationship between the 2 diseases was not caused by the same SNP in the gene sequences. LDSC analysis demonstrated that there was no genetic correlation between the two diseases. Conclusion ·Type 1 diabetes may promote colorectal cancer, but colorectal cancer has no effect on type 1 diabetes.

Key words: colorectal cancer, type 1 diabetes, Mendelian randomization

中图分类号:

R735.3

俞洋, 孟丹, 仇奕文, 袁见, 朱莹杰. 两样本孟德尔随机化法分析1型糖尿病对结直肠癌的影响[J]. 上海交通大学学报（医学版）, 2024, 44(6): 755-761.

YU Yang, MENG Dan, QIU Yiwen, YUAN Jian, ZHU Yingjie. Analysis of impact of type 1 diabetes on colorectal cancer by using two-sample Mendelian randomization[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(6): 755-761.

图/表 6

表1 5种MR方法检验1型糖尿病对结直肠癌的效应

Tab 1 Effect of type 1 diabetes on colorectal cancer using five MR methods

Exposure	Outcome	MR method	P value	OR (95%CI)
Type 1 diabetes	Colorectal cancer	IVW	0.003 4	1.000 7 (1.000 2‒1.001 1)
		MR-Egger	0.001 3	1.001 1 (1.000 5‒1.001 8)
		Weighted median	3.39×10^-5	1.001 0 (1.000 5‒1.001 4)
		Simple mode	0.671 9	1.000 3 (0.998 8‒1.001 9)
		Weighted mode	0.000 1	1.001 0 (1.000 6‒1.001 4)

图1 SNP对1型糖尿病和结直肠癌效应的散点图

Fig 1 Scatterplot of SNP effects on type 1 diabetes and colorectal cancer

图2 1型糖尿病对结直肠癌效应森林图

Fig 2 Forest plot of effect of type 1 diabetes on colorectal cancer

图3 1型糖尿病对结直肠癌效应的敏感性分析（留一法）

Fig 3 Sensitivity analysis for effect of type 1 diabetes on colorectal cancer（leave-one-out analysis）

图4 1型糖尿病对结直肠癌效应的漏斗图

Fig 4 Funnel plot of effect of type 1 diabetes on colorectal cancer

表2 5种MR方法检验结直肠癌对1型糖尿病的效应

Tab 2 Effect of colorectal cancer on type 1 diabetes using five MR methods

Exposure	Outcome	MR method	P value	OR (95%CI)
Colorectal cancer	Type 1 diabetes	IVW	0.373 2	0.013 8 (1.12×10^-6‒171.093 6)
		MR-Egger	0.643 6	0.000 3 (3.23×10^-18‒2.96×10^-10)
		Weighted median	0.273 9	0.000 8 (2.34×10^-9‒279.941 3)
		Simple mode	0.247 1	3.36×10^-6 (1.82×10^-14‒745.124 1)
		Weighted mode	0.266 8	4.42×10^-5 (4.18×10^-12‒426.783 2)

参考文献 27

1	URBANO F, FARELLA I, BRUNETTI G, et al. Pediatric type 1 diabetes: mechanisms and impact of technologies on comorbidities and life expectancy[J]. Int J Mol Sci, 2023, 24(15): 11980.
2	罗飞宏. 儿童1型糖尿病的诊治与展望[J]. 临床儿科杂志, 2022, 40(5): 321-327.
	LUO F H. Diagnosis, treatment and future of childhood type 1 diabetes mellitus[J]. Journal of Clinical Pediatrics, 2022, 40(5): 321-327.
3	YANG Y, HAN Z H, LI X, et al. Epidemiology and risk factors of colorectal cancer in China[J]. Chin J Cancer Res, 2020, 32(6): 729-741.
4	孟令华, 常湛, 王丽慧, 等. 结直肠癌与2型糖尿病的相关性及临床特征分析[J]. 现代中西医结合杂志, 2016, 25(9): 999-1000.
	MENG L H, CHANG K, WANG L H, et al. Correlation and clinical characteristics of colorectal cancer and type 2 diabetes[J]. Modern Journal of Integrated Traditional Chinese and Western Medicine, 2016, 25(9): 999-1000.
5	SKRIVANKOVA V W, RICHMOND R C, WOOLF B A R, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement[J]. JAMA, 2021, 326(16): 1614-1621.
6	FORGETTA V, MANOUSAKI D, ISTOMINE R, et al. Rare genetic variants of large effect influence risk of type 1 diabetes[J]. Diabetes, 2020, 69(4): 784-795.
7	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.
8	LIU N Y, WANG G, LIU C, et al. Non-alcoholic fatty liver disease and complications in type 1 and type 2 diabetes: a Mendelian randomization study[J]. Diabetes Obes Metab, 2023, 25(2): 365-376.
9	LIU Z R, WANG H C, YANG Z K, et al. Causal associations between type 1 diabetes mellitus and cardiovascular diseases: a Mendelian randomization study[J]. Cardiovasc Diabetol, 2023, 22(1): 236.
10	KAMAT M A, BLACKSHAW J A, YOUNG R, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations[J]. Bioinformatics, 2019, 35(22): 4851-4853.
11	BRION M J A, SHAKHBAZOV K, VISSCHER P M. Calculating statistical power in Mendelian randomization studies[J]. Int J Epidemiol, 2013, 42(5): 1497-1501.
12	BOWDEN J, DEL GRECO M F, MINELLI C, et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization[J]. Stat Med, 2017, 36(11): 1783-1802.
13	BOWDEN J, DAVEY SMITH G, BURGESS S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression[J]. Int J Epidemiol, 2015, 44(2): 512-525.
14	BOWDEN J, DAVEY SMITH G, HAYCOCK P C, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-314.
15	HARTWIG F P, DAVEY SMITH G, BOWDEN J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption[J]. Int J Epidemiol, 2017, 46(6): 1985-1998.
16	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.
17	YUN Z J, GUO Z W, LI X, et al. Genetically predicted 486 blood metabolites in relation to risk of colorectal cancer: a Mendelian randomization study[J]. Cancer Med, 2023, 12(12): 13784-13799.
18	BULIK-SULLIVAN B K, LOH P R, FINUCANE H K, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies[J]. Nat Genet, 2015, 47(3): 291-295.
19	CHEN C, WANG P, ZHANG R D, et al. Mendelian randomization as a tool to gain insights into the mosaic causes of autoimmune diseases[J]. Autoimmun Rev, 2022, 21(12): 103210.
20	牛相英. 2型糖尿病与结直肠癌临床病理特征的研究[D]. 银川: 宁夏医科大学, 2020.
	NIU X Y. Study on the relationship between type 2 diabetes mellitus and colorectal cancer in clinicopathological characteristics[D]. Yinchuan: Ningxia Medical University, 2020.
21	杨千洪, 雷新键, 李峰, 等. 结直肠癌与2型糖尿病的相关性及临床特征分析[J]. 中国卫生标准管理, 2022, 13(24): 80-83.
	YANG Q H, LEI X J, LI F, et al. Correlation and clinical characteristics of colorectal cancer onset and type 2 diabetes mellitus[J]. China Health Standard Management, 2022, 13(24): 80-83.
22	张欣怡, 林翔, 刘希樵, 等. 基于网络药理学和分子对接探究葛根芩连汤“异病同治”结直肠癌和2型糖尿病作用机制[J]. 中国中医药信息杂志, 2022, 29(7): 24-32.
	ZHANG X Y, LIN X, LIU X Q, et al. Study on mechanism of treating different diseases with same method of Gegen Qinlian Decoction in treating colorectal cancer and type 2 diabetes mellitus based on network pharmacology and molecular docking[J]. Chinese Journal of Information on Traditional Chinese Medicine, 2022, 29(7): 24-32.
23	JOH H K, LEE D H, HUR J, et al. Simple sugar and sugar-sweetened beverage intake during adolescence and risk of colorectal cancer precursors[J]. Gastroenterology, 2021, 161(1): 128-142.e20.
24	HUR J, OTEGBEYE E, JOH H K, et al. Sugar-sweetened beverage intake in adulthood and adolescence and risk of early-onset colorectal cancer among women[J]. Gut, 2021, 70(12): 2330-2336.
25	LAMB M M, FREDERIKSEN B, SEIFERT J A, et al. Sugar intake is associated with progression from islet autoimmunity to type 1 diabetes: the Diabetes Autoimmunity Study in the Young[J]. Diabetologia, 2015, 58(9): 2027-2034.
26	柳健, 廖斐, 董卫国. 早发性结直肠癌相关危险因素的研究进展[J]. 医学研究杂志, 2022, 51(5): 26-29.
	LIU J, LIAO F, DONG W G, et al. Research progress on risk factors related to early-onset colorectal cancer[J]. Journal of Medical Research, 2022, 51(5): 26-29.
27	GONZALEZ-GRONOW M, PIZZO S V. Physiological roles of the autoantibodies to the 78-kilodalton glucose-regulated protein (GRP78) in cancer and autoimmune diseases[J]. Biomedicines, 2022, 10(6): 1222.

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