Association between type 2 diabetes mellitus and gastric cancer: A Mendelian randomization study

doi:10.21203/rs.3.rs-4498488/v1

Download PDF

Article

Association between type 2 diabetes mellitus and gastric cancer: A Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4498488/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background & aims:

Type 2 diabetes mellitus (T2DM) has been proven to be associated with the risk of developing gastric cancer (GC) in observational studies. However, the result was affected by many potential confounding factors, so a Mendelian randomization (MR) study was conducted to assess the association.

Method

We performed bidirectional MR analyses to explore the causal relationship between T2DM and GC, as well as the risk factors for GC. To examine the effect caused by confounding factors, we took part of the risk factors into research. We also conducted a meta-analysis of the results from different data sources.

Result

The MR analysis revealed a negative correlation between T2DM and GC (odds ratio = 0.10, 95% confidence interval, 0.01–0.69; P = .02). Regarding the relationship between risk factors for GC and T2DM, we found that BMI (OR, 1.04; 95% CI, 1.04–1.05; P < .001), physical activity (OR, 0.98; 95% CI, 0.97-1.00; P = .01), ever smoking (OR, 1.03; 95% CI, 1.01–1.06; P < .001) and alcohol consumption (OR, 1.01; 95% CI, 1.00-1.02; P = .01) could increase the occurrence of T2DM. On the other hand, T2DM could increase the rate of HP infection (OR, 2.06; 95% CI, 1.16–3.66; P = .01) and EBV infection (OR, 6.31; 95% CI, 1.12–35.64; P = .04) to affect the development of GC. These factors could affect the outcome of observational studies, which differed from our research.

Conclusion

The results showed that T2DM had a negative correlation with GC. Confounding factors, including high BMI, physical activity, smoking, alcohol consumption, HP infection, and EBV infection, could reverse the tendency. Further research should be conducted to explore the specific mechanism regarding the causality between the two diseases.

Biological sciences/Cancer/Gastrointestinal cancer

Health sciences/Endocrinology/Endocrine system and metabolic diseases

Mendelian randomization

type 2 diabetes mellitus

gastric cancer

meta-analysis

Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in adulthood, is often associated with metabolic syndrome and is responsible for the cause of multisystemic complications ^[1]. Gastric cancer (GC) is one of the most common malignancies worldwide and the third most common cause of cancer-related death worldwide, which is caused by a combination of genetic and environmental factors ^[2]. Observational studies have shown a higher absolute risk of GC in adults with T2DM ^[3]. One meta-analysis revealed no significant associations between T2DM and GC ^[4]. However, the mechanism by which T2DM may promote GC tumorigenesis is poorly understood. As an observational study, we cannot rule out the possibility of reverse causality and uncontrolled confounding. Furthermore, it is a challenge to control all potential confounders in a randomized controlled trial.

Mendelian randomization (MR) is now widely used to investigate whether an observational association between an exposure and an outcome represents a causal relationship ^[5]. MR uses genetic variants as a proxy for risk factors to minimize confounding and reverse causation in observational data ^[6]. MR analysis could overcome reverse causation bias since allelic variation always precedes the onset of disease ^[7]. Moreover, it can minimize the impact caused by confounding factors by introducing genetic markers as instrumental variables (IVs) of exposures. Genome-wide association studies (GWAS) were used to assess evidence for causality ^[8].

In this study, we aimed to investigate the causal relationship between T2DM and GC by a two-sample MR framework to reduce the biases that are commonly encountered in observational studies. We considered T2DM as exposure and used information from different GWAS data sources to guarantee the robustness of findings by using a meta-analysis. Finally, to exclude reverse causation, we used a bidirectional approach to the available data, considering GC as the exposure and T2DM as the outcome. The study was conducted following the STROBE-MR (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for transparent reporting of MR studies ^[9].

2.1 Study Design

Three key assumptions of MR are shown in the schematic view of the study design. The assumptions are as follows: (A) single nucleotide polymorphisms (SNPs) are strongly associated with T2DM; (B) SNPs are independent of known confounders; and (C) SNPs affect GC only via T2DM.

2.2 Data Sources

This study is mainly based on GWAS summary data conducted in the past 5 years (predominantly in European individuals and including both males and females). GWAS summary statistics for T2DM (n = 462,933) were obtained from Ben Elsworth’s study ^[10], which evaluated the relationship between T2DM and SNPs. Gastric cancer (n = 202,308) was obtained from Ishigaki K’s study ^[11]. Gastroesophageal reflux disease with esophagitis (n = 463,010), gastric/stomach ulcers (n = 462,933), irritable bowel syndrome (n = 462,933), infective/viral hepatitis (n = 462,933), calculus of gallbladder with acute cholecystitis (n = 463,010), acute pancreatitis (n = 463,010), body mass index (BMI, n = 461,460), ever smoked (n = 461,066), alcohol intake frequency (n = 462,346), moderate physical activity (n = 440, 266), fresh fruit intake (n = 446,462), salt added to food (n = 462,630), chronic gastritis (n = 463,010), and hypertension (n = 463,010) were obtained from MRC Integrative Epidemiology Unit (MRC-IEU) Consortium^[45–55]. Colorectal cancer (n = 377,673) and low-density lipoprotein (LDL) cholesterol (n = 440,546) were obtained from the UK Biobank ^{[12, 13]}. Chronic kidney disease (CKD, n = 117,165) data were obtained from CKDGen ^[14]. Coronary heart disease (CHD, n = 184,305) was obtained from the CARDIoGRAMplusC4D Consortium ^[15]. Anti-Helicobacter pylori IgG seropositivity (Anti-HP IgG, n = 8,735) and Anti-Epstein‒Barr virus viral capsid antigen (VCA) IgG levels (Anti-EBV IgG, n = 956) were obtained from eBioscience Consortium. Our research was based on publicly available summary data, and ethical approval was not needed for this study.

2.3 Selection and Validation of SNPs

Two principal criteria were used to select suitable SNPs. 1) We selected SNPs associated with T2DM as instruments such that they were strongly associated with the exposure (p < 5 × 10 − 8, F-statistic > 10). 2) The independence among the selected SNPs was evaluated according to the linkage disequilibrium (LD) (r2 < 0.001, clumping window of 10,000 kb) (Fig. 1) ^[16].

Figure. 1. Exposure data source: T2DM. Outcome data sources included esophagitis, ulcers, gastric cancer, irritable bowel syndrome, hepatitis, cholecystitis, pancreatitis and colorectal cancer. C. Confounders included BMI, Smoke, Alcohol, Chronic gastritis, Physical activity, Fruit intake, Salt, Anti-HP IgG, Anti-EBV IgG and T2DM related complications.

2.4 MR analysis and meta-analysis

The inverse-variance weighted (IVW) method was used as the primary analysis in our research. MR Egger was used to test for directional pleiotropy. Meta-analysis was used to evaluate the results from different data sources to ensure the stability of our study. Heterogeneity among selected IVs was indicated by Cochrane’s Q-value. A leave-one-out sensitivity analysis was conducted to determine whether the overall estimates were disproportionately affected by an individual SNP that would be removed. We removed from the initial set of instruments those genetic variants for which we found biological or statistical evidence of pleiotropy to check the robustness of our research. All analyses were performed using the “TwoSampleMR” and “metafor” packages in R version 4.3.1.

3.1 SNP Selection and Validation

In summary, the SNPs were included in the study published in 2018 and were mainly based on the European population. Sixty-three IVs achieved genome-wide significance levels, and all F-statistics > 10.

3.2 Association between T2DM and Digestive System Diseases

The IVW analysis indicated that genetically predicted T2DM was inversely associated with one of the eight digestive system diseases, including gastroesophageal reflux disease with esophagitis (odds ratio, (OR), 1.00; 95% confidence interval, (CI), 0.99–1.02; P = .77), gastric/stomach ulcers (OR, 1.00; 95% CI, 0.98–1.01; P = .63), gastric cancer (OR, 0.10, 95% CI, 0.01–0.69; P = .02), irritable bowel syndrome (OR, 0.99; 95% CI, 0.96–1.02; P = .43), infective/viral hepatitis (OR, 1.00; 95% CI, 0.98–1.01; P = .55), calculus of gallbladder with acute cholecystitis (OR, 1.01; 95% CI, 1.00-1.03; P = .08), acute pancreatitis (OR, 1.00; 95% CI, 0.99–1.01; P = .81), and colorectal cancer (OR, 1.03; 95% CI, 1.00-1.05 P = .09)(Table 1). No eligible SNPs were found in ulcerative colitis and Crohn's disease. Possible evidence of an inverse association between genetically predicted T2DM and GC was observed. The scatter plot and forest plot, which reveal similar results of the association between T2DM and GC, are shown in Supplementary Figure S1 and Supplementary Figure S2. The leave-one-out sensitivity analysis, as shown in Supplementary Figure S3, revealed that the overall estimates were not disproportionately affected by any individual SNP. The funnel plot also indicated no evidence of horizontal pleiotropy in Supplementary Figure S4. Bidirectional MR analyses were followed to interrogate the association between T2DM and GC, and the result represented no statistical significance. Next, data from different sources were included in our study, a meta-analysis was conducted to avoid contingency, and the outcome showed a negative correlation between T2DM and GC by the IVW method (Fig. 2).

Figure 2 Meta-analysis Results: T2DM has a statistically significant association with GC. The size of the data markers indicates the weight of the odds ratio (OR), using random-effects analysis with instrumental variables.

Table 1

Association between T2DM and Digestive System Diseases
Exposure	Outcomes	Method	nSNPs	OR(95%CI)	P-value
Diabetes	Gastro-esophageal reflux disease with esophagitis	IVW	47	1.00(0.99–1.02)	0.77
Diabetes	Gastric/stomach ulcers	IVW	54	1.00(0.98–1.01)	0.63
Diabetes	Gastric cancer	IVW	48	0.10(0.01–0.69)	0.02*
Diabetes	Irritable bowel syndrome	IVW	61	0.99(0.96–1.02)	0.43
Diabetes	Infective/viral hepatitis	IVW	22	1.00(0.98–1.01)	0.55
Diabetes	Calculus of gallbladder with acute cholecystitis	IVW	24	1.01(1.00-1.03)	0.08
Diabetes	Acute pancreatitis	IVW	31	1.00(0.99–1.01)	0.81
Diabetes	Colorectal cancer	IVW	55	1.03(1.00-1.05)	0.09
IVW:Inverse-Variance Weighted;

3.3 Risk Factors of GC Effect on T2DM

Further research is conducted to explore the causes of why a positive correlation between T2DM and GC was observed. We included risk factors for GC to explore the relationship between factors and T2DM. The IVW analysis indicates that genetically predicted BMI, smoking, and alcohol consumption had positive correlations with T2DM and that physical activity had a negative correlation with T2DM. The specific results were as follows: BMI (OR, 1.04; 95% CI, 1.04–1.05; P < .001), ever smoking (OR, 1.03; 95% CI, 1.01–1.06; P < .001), alcohol consumption (OR, 1.01; 95% CI, 1.00-1.02; P = .01), physical activity (OR, 0.98; 95% CI, 0.97-1.00; P = .01), fruit intake (OR, 1.00; 95% CI, 0.98–1.03; P = .43) and salt intake (OR, 1.00; 95% CI, 0.99–1.01; P = .83) (Table 2). Some of the risk factors, such as vegetable intake, nitrite intake, and high-fat diet, were not included because no SNP conformed to the criteria and no sources corresponded to the factors.

3.4 Effect of T2DM on Risk Factors for GC

Next, we explored whether genetically predicted T2DM will have a positive effect on the risk factors for GC (e.g., HP infection, EBV infection, chronic gastritis, and BMI). The IVW analysis indicated that T2DM correlated positively with anti-HP IgG (OR, 2.06; 95% CI, 1.16–3.66; P = .01) and anti-EBV IgG (OR, 6.31; 95% CI, 1.12–35.64; P = .04). No associations were observed for chronic gastritis and BMI (Table 2). The results indicated that T2DM could increase the incidence of GC by increasing the rate of HP and EBV infection.

Table 2

Association between T2DM and risk factors for GC
Exposure	Outcome	Method	nSNPs	OR(95%CI)	P-value
BMI	Diabetes	IVW	438	1.04(1.04–1.05)	0.00
Smoke	Diabetes	IVW	79	1.03(1.01–1.06)	0.00
Alcohol	Diabetes	IVW	95	1.01(1.00-1.02)	0.01
Physical activity	Diabetes	IVW	18	0.98(0.97-1.00)	0.01
Fruit intake	Diabetes	IVW	52	1.00(0.98–1.03)	0.43
Salt	Diabetes	IVW	102	1.00(0.99–1.01)	0.83
diabetes	Anti-EBV IgG	IVW	55	6.31(1.12–35.64)	0.04
diabetes	Anti-Hp IgG	IVW	61	2.06(1.16–3.66)	0.01
diabetes	Chronic gastritis	IVW	37	1.00(0.99–1.01)	0.58
diabetes	BMI	IVW	63	1.61(0.61–4.24)	0.34
BMI: body mass index; Anti-Hp IgG: Anti-helicobacter pylori immune globulin; Anti-EBV IgG: Anti-Epstein-Barr virus viral capsid antigen (VCA) IgG levels;

3.5 Association between Diabetes-Related Complications and Incidence of GC

Patients with long-term T2DM often have many complications, such as high levels of blood sugar, insulin resistance, coronary heart disease, hypertension, chronic kidney disease, diabetic neuropathy, vascular disease, and dyslipidemia (Table 3). These complications would also affect the results of observational studies. No associations were observed for the complications. No suitable sources correspond to insulin resistance and vascular disease.

Table 3

Association between Comorbidities/Complications of T2DM and GC
Exposure	Outcome	Method	nSNPs	OR(95%CI)	P-value
Blood sugar	GC	IVW	15	0.82(0.53–1.26)	0.37
hypertension	GC	IVW	55	0.36(0.12–1.06)	0.06
Coronary artery disease	GC	IVW	33	0.87(0.80-1.00)	0.05
Chronic kidney disease	GC	IVW	3	0.82(0.52–1.30)	0.40
Diabetic neuropathy	GC	IVW	3	0.96(0.87–1.07)	0.48
LDL cholesterol	GC	IVW	124	0.95(0.82–1.09)	0.43
LDL cholesterol: Low Density Lipoprotein;

In a two-sample MR analysis, we assessed the causal association between T2DM and digestive system diseases. Our research revealed that T2DM was inversely associated with GC. Further research was conducted to explore the causal association between the risk factors and T2DM, and we hoped to find the reason why MR analysis achieved a result different from past observational studies.

T2DM is an endocrine disease that is caused by a combination of genetic and environmental factors ^[17]. It is reported as a risk factor for cardiovascular disease, chronic kidney disease, and neuropathy ^[18–20]. The association between T2DM and digestive system diseases was not clarified. T2DM could lead to multiorgan damage; naturally, we considered that it is positively correlated with GC. Prior observational studies have indeed obtained similar results ^[21]. On the other hand, GC is a multifactorial disease caused by multiple genetic and environmental factors, and they both share common risk factors (e.g., high BMI, physical activity, smoking, alcohol consumption, unhealthy diet) ^[22–24]. Observational studies may be biased by confounding and reverse causality ^[25], thereby weakening the reliability of findings. Exploring the relationship between T2DM and GC relying upon the results of observational studies seems inappropriate. Our research considered T2DM as an IV to analyze the association by applying the MR-IVW method. The results showed that the two were inversely correlated (OR, 0.10; 95% CI, 0.01–0.69; P = .02) after the deletion of the discrete value (rs10830963). Analysis of heterogeneity and horizontal pleiotropy indicated that no heterogeneity (P = .09) or horizontal pleiotropy (P = .99) was observed in our research. To ensure the stability of the experiment, IVs from different data sources were included, and a meta-analysis was conducted to observe the combined effect, which indicated a similar correlation between the two. This result differed from previous observational studies that pointed out that insulin resistance, hyperglycemia, and hyperinsulinemia caused by T2DM are closely related to the development of GC ^[3]. Similarly, some observational studies pointed out that both type 1 diabetes mellitus (T1DM) and T2DM have a higher risk of GC ^[4]. A reverse MR analysis was conducted to assess the causal relationship between GC and T2DM, and the outcome revealed no obvious correlation (OR, 1.00 (1.00–1.00), P = .65). Theoretically, MR analysis could derive a more reliable result owing to not being affected by confounding factors ^[26]. Our research would further explore why observational studies ended up with opposite conclusions. It is reported that T2DM could promote the occurrence of GC through different mechanisms ^{[3, 27, 28]}. The potential links between T2DM and GC may involve interactions with shared risk factors, HP ^[29] or other pathogen infections ^[30], medication intake ^[31], and complications ^{[3, 32]}. Therefore, we took the shared risk factors, HP, and part of the complications into our research.

Furthermore, parts of the risk factors for GC that occurred before the incidence of T2DM would affect T2DM events. We found that high BMI, physical activity, smoking, and alcohol consumption could increase the incidence of T2DM. There was no statistical significance observed in high salt intake and low vegetable and fruit intake; in other words, high BMI, physical activity, smoking, and alcohol consumption could affect the outcomes of observational studies as potential confounding factors. Previous studies have indicated that gastric cancer risk increases for men with a BMI ≥ 27 kg/m2 ^[33]. It was reported that physical activity is a protective factor for GC development ^[34]. The potential mechanism is probably the suppression of tumor growth and immunologic control of tumors ^[35]. Our research showed that smoking increases gastric cancer, which was similar to a previous observational study. A possible mechanism was that smoking could aggravate HP infections by weakening the surveillance and defensive functions of the immune system and increasing the failure rate of H. pylori eradication treatment ^{[36, 37]}. In terms of alcohol drinking, previous studies revealed that chronic alcohol drinking could combine HP infection with IL-10 (interleukin-10) inhibition to induce gastric tumorigenesis ^[38]. Additionally, alcohol consumption could lead to direct cellular injury and gene mutation by enhancing the penetration of carcinogens into cells ^[39].

We searched the GWAS to find SNPs that met the criteria to predict the factors. As shown in our results, anti-HP IgG and anti-EBV IgG were proven to be positively affected by T2DM. This means that T2DM could increase the occurrence by improving the rate of HP infection and EBV infection. HP infection is categorized as a class I carcinogen for the development of GC ^[40]. It was reported that HP could drive p53 degradation and induce genome instability to accelerate gastric carcinogenesis ^{[41, 42]}. In regard to EBV infection, EBV could promote immune escape through the enhancer-mediated transcription of programmed cell death 1 ligand 1 (PD-L1) and promote the progression of GC via Sam68-dependent activation of methyltransferase-like 3 (METTL3) ^{[43, 44]}.

In regard to T2DM-related complications and comorbidities, the results revealed that no complications or comorbidities were significantly associated with the incidence of GC.

Nevertheless, this study also has several limitations. First, cases from GWAS were mostly European, so the results might not be applicable across races. Second, the horizontal pleiotropic effect was unavoidable in all MR studies. Third, MR outcomes were obtained by using some genetic variants in most situations to minimize the effect of weak instruments and LD and inevitably lower the potency of the test. Fourth, different types of GC might require a separate discussion because they might share different risk factors. Fifth, some of the risk factors could not match data sources that are compliant with the criteria, and more research needs to be conducted to explore the association with other risk factors.

In summary, our study suggests that T2DM is inversely associated with GC using MR analysis. In regard to the mechanism, T2DM could raise the rate of HP and EBV infection to increase the incidence of GC. On the other hand, GC risk factors (high BMI, physical activity, smoking, and alcohol consumption) could increase the incidence of T2DM. Both could lead to the difference between the results of observational studies and our research. Many complications and comorbidities tend to accompany T2DM, so we should not just focus on the comprehensive effects revealed by observational studies, and more studies need to be done to explore the association between T2DM itself and GC.

Anti-EBV IgG

Anti-Epstein-Barr virus IgG

Anti-HP IgG

Anti-helicobacter pylori IgG

BMI

Body mass index

CKD

Chronic kidney disease

CHD

Coronary heart disease

Confidence interval

Gastric cancer

GWAS

Genome-wide association studies

IVs

Instrumental variables

IL-10

Interleukin-10

IVW

Inverse-Variance Weighted

Linkage disequilibrium

LDL

Low-density lipoprotein

Mendelian randomization

METTL3

Methyltransferase-like 3

MRC-IEU

MRC Integrative Epidemiology Unit

Odds ratio

PD-L1

Programmed cell death 1 ligand 1

SNPs

Single nucleotide polymorphisms

STROBE-MR

Strengthening the Reporting of Observational Studies in Epidemiology

T1DM

Type 1 diabetes mellitus

T2DM

Type 2 diabetes mellitus

VCA

Viral capsid antigen

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Publicly available datasets were analyzed in this study. All data can be found in the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk/).

Declaration of competing interest

The authors declare that they have no competing interests.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors’ Contributions

DJ was involved in the study conception and study design. All authors were involved in the implementation of the study and interpreting the data. YY, LT, and GP participated in the data acquisition and bioinformatics analysis of the study. YY and LT, with the help of DJ, wrote the manuscript. DJ drafted and critically revised the work. All authors have read and approved the final manuscript.

Acknowledgments

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We thank Freepik for free medical images (https://www.freepik.com/). The authors thank the IEU Open GWAS Project for free data sources (https://gwas.mrcieu.ac.uk/).

Kahn R, Davidson M B. The Reality of Type 2 Diabetes Prevention [J]. Diabetes Care, 2014, 37(4): 943–949.
Sexton R E, Al Hallak M N, Diab M, et al. Gastric cancer: a comprehensive review of current and future treatment strategies [J]. Cancer and Metastasis Reviews, 2020, 39(4): 1179–1203.
Tseng C-H. The Relationship between Diabetes Mellitus and Gastric Cancer and the Potential Benefits of Metformin: An Extensive Review of the Literature [J]. Biomolecules, 2021, 11(7): 1022.
Guo J, Liu C, Pan J, et al. Relationship between diabetes and risk of gastric cancer: A systematic review and meta-analysis of cohort studies [J]. Diabetes Res Clin Pract. 2022, May;187:109866.
Li P, Wang H, Guo L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study [J]. BMC Medicine, 2022, 20(1): 443.
Xu J, Zhang S, Tian Y, et al. Genetic Causal Association between Iron Status and Osteoarthritis: A Two-Sample Mendelian Randomization [J] Nutrients, 2022;14(18):3683.
Ference B A, Holmes M V, Smith G D. Using Mendelian Randomization to Improve the Design of Randomized Trials [J]. Cold Spring Harbor Perspectives in Medicine, 2021, 11(7): a040980.
Abdellaoui A, Yengo L, Verweij K J H, et al. 15 years of GWAS discovery: Realizing the promise [J]. The American Journal of Human Genetics, 2023, 110(2): 179–194.
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.
Lyon M S, Andrews S J, Elsworth B, et al. The variant call format provides efficient and robust storage of GWAS summary statistics [J]. Genome Biology, 2021, 22(1): 32.
Zang Z, Shao Y, Nakyeyune R, et al. Association of Body Mass Index and the Risk of Gastro-Esophageal Cancer: A Mendelian Randomization Study in a Japanese Population [J]. Nutrition and Cancer, 2023, 75(2): 542–551.
Richardson T G, Sanderson E, Palmer T M, et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomization analysis [J]. PLOS Medicine, 2020, 17(3): e1003062.
Morales Berstein F, McCartney D L, Lu A T, et al. Assessing the causal role of epigenetic clocks in the development of multiple cancers: a Mendelian randomization study [J]. eLife, 2022, 11: e75374.
Zheng J, Zhang Y, Rasheed H, et al. Trans-ethnic Mendelian-randomization study reveals causal relationships between cardiometabolic factors and chronic kidney disease [J]. International Journal of Epidemiology, 2022, 50(6): 1995–2010.
Majid Nikpay, Anuj Goel, Hong-Hee Won, et al. A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease [J]. Nature Genetics, 2015, 47(10): 1121–1130.
Riaz H, Khan M S, Siddiqi T J, et al. Association Between Obesity and Cardiovascular Outcomes: A Systematic Review and Meta-analysis of Mendelian Randomization Studies [J]. JAMA Network Open, 2018, 1(7): e183788.
Sørensen T I A, Metz S, Kilpeläinen T O. Do gene–environment interactions have implications for the precision prevention of type 2 diabetes? [J]. Diabetologia, 2022, 65(11): 1804–1813.
Yun J-S, Ko S-H. Current trends in epidemiology of cardiovascular disease and cardiovascular risk management in type 2 diabetes [J]. Metabolism, 2021, 123: 154838.
Ruilope L M, Agarwal R, Anker S D, et al. Blood Pressure and Cardiorenal Outcomes With Finerenone in Chronic Kidney Disease in Type 2 Diabetes [J]. Hypertension, 2022, 79(12): 2685–2695.
Geng T, Zhu K, Lu Q, et al. Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study [J]. PLOS Medicine, 2023, 20(1): e1004135.
Demir S, Nawroth P P, Herzig S, et al. Emerging Targets in Type 2 Diabetes and Diabetic Complications [J]. Advanced Science, 2021, 8(18): 2100275.
Reginald F. The Prevention of Diabetes Mellitus [J]. The New England Journal of Medicine, 2009, 222(15): 623–627.
Zhang R, Li H, Li N, et al. Risk factors for gastric cancer: a large-scale, population-based case–control study [J]. Chinese Medical Journal, 2021, 134(16): 1952–1958.
Van Den Brandt P A. The impact of a healthy lifestyle on the risk of esophageal and gastric cancer subtypes [J]. European Journal of Epidemiology, 2022, 37(9): 931–945.
Hui Y, Tu C, Liu D, et al. Risk factors for gastric cancer: A comprehensive analysis of observational studies [J]. Frontiers in Public Health, 2023, 10: 892468.
Bowden J, Holmes M V. Meta-analysis and Mendelian randomization: A review [J]. Research Synthesis Methods, 2019, 10(4): 486–496.
Dabo B, Pelucchi C, Rota M, et al. The association between diabetes and gastric cancer: results from the Stomach Cancer Pooling Project Consortium [J]. European Journal of Cancer Prevention, 2022, 31(3): 260–269.
Cheung K S, Chan E W, Chen L, et al. Diabetes Increases Risk of Gastric Cancer After Helicobacter pylori Eradication: A Territory-Wide Study With Propensity Score Analysis [J]. Diabetes Care, 2019, 42(9): 1769–1775.
Polyzos S A, Papaefthymiou A, Doulberis M, et al. Helicobacter pylori infection and diabetes mellitus [J]. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2021, 15(3): 845–846.
Erener S. Diabetes, infection risk and COVID-19 [J]. Molecular Metabolism, 2020, 39: 101044.
Abrahami D, McDonald E G, Schnitzer M E, et al. Proton pump inhibitors and risk of gastric cancer: population-based cohort study [J]. Gut, 2022, 71(1): 16–24.
Ali M K, Pearson-Stuttard J, Selvin E, et al. Interpreting global trends in type 2 diabetes complications and mortality [J]. Diabetologia, 2022, 65(1): 3–13.
Hirabayashi M, Inoue M, Sawada N, et al. Effect of body-mass index on the risk of gastric cancer: A population-based cohort study in A Japanese population [J]. Cancer Epidemiology, 2019, 63: 101622.
Poorolajal J, Moradi L, Mohammadi Y, et al. Risk factors for stomach cancer: a systematic review and meta-analysis [J]. Epidemiology and Health, 2020, 42: e2020004.
Pedersen L, Idorn M, Olofsson G H, et al. Voluntary Running Suppresses Tumor Growth through Epinephrine- and IL-6-Dependent NK Cell Mobilization and Redistribution [J]. Cell Metabolism, 2016, 23(3): 554–562.
Bagaitkar J, Demuth D R, Scott D A. Tobacco use increases susceptibility to bacterial infection [J]. Tobacco Induced Diseases, 2008, 4(1): 12.
Yu J, Yang P, Qin X, et al. Impact of smoking on the eradication of Helicobacter pylori [J]. Helicobacter, 2022, 27(1): e12860.
Aziz F, Chakraborty A, Liu K, et al. Gastric tumorigenesis induced by combining Helicobacter pylori infection and chronic alcohol through IL-10 inhibition [J]. Carcinogenesis, 2022, 43(2): 126–139.
Jayasekara H, MacInnis R J, Lujan-Barroso L, et al. Lifetime alcohol intake, drinking patterns over time and risk of stomach cancer: A pooled analysis of data from two prospective cohort studies [J]. International Journal of Cancer, 2021, 148(11): 2759–2773.
Tshibangu-Kabamba E, Yamaoka Y. Helicobacter pylori infection and antibiotic resistance — from biology to clinical implications [J]. Nature Reviews Gastroenterology & Hepatology, 2021, 18(9): 613–629.
Imai S, Ooki T, Murata-Kamiya N, et al. Helicobacter pylori CagA elicits BRCAness to induce genome instability that may underlie bacterial gastric carcinogenesis [J]. Cell Host & Microbe, 2021, 29(6): 941–958.e10.
Costa L, Corre S, Michel V, et al. USF1 defect drives p53 degradation during Helicobacter pylori infection and accelerates gastric carcinogenesis [J]. Gut, 2020, 69(9): 1582–1591.
Zhang J, Du Y, Gong L, et al. ebv-circRPMS1 Promotes the Progression of EBV-Associated Gastric Carcinoma via Sam68 Complex Dependent Activation of METTL3 [R]. Cancer Lett. 2022;535:215646.
Wang J, Ge J, Wang Y, et al. EBV miRNAs BART11 and BART17-3p promote immune escape through the enhancer-mediated transcription of PD-L1 [J]. Nature Communications, 2022, 13(1): 866.
Ben Elsworth, Gastro-oesophageal reflux disease with oesophagitis [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-10144/.
Ben Elsworth, self-reported: gastric/stomach ulcers [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-20078/.
Ben Elsworth, self-reported: irritable bowel syndrome [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-2592/.
Ben Elsworth, self-reported: infective/viral hepatitis [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-7901/.
Ben Elsworth, Calculus of gallbladder with acute cholecystitis [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-10362/.
Ben Elsworth, acute pancreatitis [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-19388/.
Ben Elsworth, Body mass index (BMI) [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-19953/.
Ben Elsworth, ever smoked [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-20261/.
Ben Elsworth, Alcohol intake frequency [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-5779/.
Ben Elsworth, Number of days/week of moderate physical activity 10 + minutes [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-4710/.
Ben Elsworth, Fresh fruit intake [database], UKBiobank, 2018, PIDs, https://gwas.mrcieu.ac.uk/datasets/ukb-b-3881/.

No competing interests reported.

SupplementaryFiles.doc

Download PDF

Version 1

posted

You are reading this latest preprint version

Association between type 2 diabetes mellitus and gastric cancer: A Mendelian randomization study

Status:

Version 1

Abstract

Background & aims:

Method

Result

Conclusion

Figures

Introduction

Materials and methods

2.1 Study Design

2.2 Data Sources

2.3 Selection and Validation of SNPs

2.4 MR analysis and meta-analysis

Result

3.1 SNP Selection and Validation

3.2 Association between T2DM and Digestive System Diseases

3.3 Risk Factors of GC Effect on T2DM

3.4 Effect of T2DM on Risk Factors for GC

3.5 Association between Diabetes-Related Complications and Incidence of GC

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1