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Genetic liability to obesity and peptic ulcer disease: a Mendelian randomization study



Epidemiological evidence relating obesity to peptic ulcer disease (PUD) has been mixed. Here we sought to determine the causality in the association of obesity with PUD risk using the Mendelian randomization (MR) approach.


This study was based on summary-level data for body mass index (BMI), waist-to-hip ratio (WHR), and PUD derived from large genome-wide association studies (GWASs). Single nucleotide polymorphisms significantly associated with BMI and WHR (P < 5 × 10–8) were leveraged as instrumental variables. Causal estimates were pooled using several meta-analysis methods. In addition, multivariable MR was employed to account for covariation between BMI and WHR, as well as to explore potential mediators.


Genetically predicted higher BMI has a causal effect on PUD, with an OR of 1.34 per SD increase in BMI (~ 4.8 kg/m2) (P = 9.72 × 10–16). Likewise, there was a 35% higher risk of PUD (P = 2.35 × 10–10) for each SD increase in WHR (0.09 ratio). Complementary analyses returned consistent results. Multivariable MR demonstrated that adjustment for WHR largely attenuated the BMI-PUD association. However, the causal association of WHR with PUD risk survived adjustment for BMI. Both the associations remained robust upon adjustment for several traditional risk factors. Replication analyses using different instrumental variants further strengthened the causal inference. Besides, we found no evidence for the causal association in the reverse analyses from PUD to BMI/WHR.


This MR study revealed that obesity (notably abdominal obesity) is causally associated with higher PUD risk. Programs aimed at weight loss may represent therapeutic opportunities for PUD.

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Peptic ulcer disease (PUD) is a common gastrointestinal disorder that significantly impacts the quality of life [1, 2]. Management has become more challenging than ever before due to the increasing prevalence of PUD not caused by the use of NSAIDs or Helicobacter Pylori infection [2]. The identification of modifiable risk factors for PUD is clinically important in reducing the burden of the disease.

Obesity has been associated with an alteration in gut microbiota [3], gut inflammation, and the breakdown of the gastrointestinal mucosal epithelial barrier [4]. Epidemiological studies suggested that obesity may increase the incidence of PUD [5,6,7]. However, evidence of the association was contradictory. In another observational study, Jeung Hui Pyo et al. reported that obesity was not related to PUD [8] upon multiple adjustment. In addition, observational associations may be biased by reverse causation and residual confounders, thus distorting true relationships.

Clarifying the causal link between obesity and PUD may offer new avenues for the treatment against PUD. Mendelian randomization (MR) has been widely used as an epidemiological tool for causality inference in associations of exposures with outcomes [9]. With genetic variants leveraged as instruments, this technique can minimize the biases inherent in observational studies, thereby strengthening the causal inference. The present study sought to evaluate the potential causal effect of total obesity and abdominal obesity on PUD risk by the MR approach.


Study design

First, we performed two-sample MR analyses to assess the effect of body mass index (BMI) (total obesity) and waist-to-hip ratio (WHR) (abdominal obesity) as exposures on PUD as an outcome trait. Then, we performed reverse MR analyses, where PUD was used as exposure and obesity characteristics (BMI/WHR) were used as outcome. The following three key assumptions should be considered when performing MR analysis. First, instrumental variables (IVs) are linked to BMI or WHR at a genome-wide significance level; Second, IVs are not associated with potential confounding factors; Third, IVs should lead to PUD exclusively via the selected exposure. The current study did not require specific ethical approval or written informed consent.

Data sources and instruments variables (IVs) selection

Summary-level data for PUD was derived from a genome-wide association study (GWAS) including up to 16,666 cases and 439,661 controls from UK biobank (Table 1) [10]. Individuals with gastric ulcer, duodenal ulcer, other site peptic ulcer, or astro-jejunal were defined as PUD cases [10].

Table 1 Detailed information on data sources

Primary analyses were performed using genome-wide significant (P < 5 × 10–8) single nucleotide polymorphisms (SNPs) identified from the largest GWAS that combined data from UK biobank and the Genetic Investigation of Anthropometric Traits (GIANT) consortium, including up to 694,649 individuals of European descent (Table 1) [11]. We set a cut-off for minor allele frequency to > 1%, leaving 85,044 and 39,427 SNPs for BMI and WHR (unadjusted for BMI), respectively. To obtain valid IVs, these SNPs were then pruned at r2 < 0.001 across a window size of 10000 kb (based on the 1000 Genomes Project population [12]) to avoid bias of linkage disequilibrium. For those SNPs not available in the outcome dataset, proxies (r2 > 0.8) were found by searching the publicly available website ( (Additional file 1: Table S1). Finally, 531 and 343 SNPs were included for BMI and WHR, respectively (Additional file 1: Table S2 and 3). In addition, we calculated F-statistics to assess whether there was a weak IV bias (\(F={R}^{2}\frac{N-2}{1-{R}^{2}}\)). R2 refers to the percentage of the variation explained by SNPs and is calculated as described by Shim et al. [13]; N represents the total sample size [14].

However, there was a sample overlap (UK Biobank mainly) in the participants included in the primary analyses (Table 1). To test the robustness of the results, we then retrieved summary-level data for BMI and WHR from other GWASs where only individuals of the GIANT consortium were included (339,224 samples for BMI and 224,459 samples for WHR), so that there would be no sample overlap with the outcome data (Table 1) [15, 16]. These SNPs underwent similar quality-control steps, leaving 78 SNPs and 29 SNPs for BMI and WHR, respectively. Considering 2 SNPs for BMI not available in the outcome datasets with no suitable proxies found, both SNPs were excluded from the study. Here, we provided the characteristics of the remaining 76 SNPs and 29 SNPs in Additional file 1: Table S4 and S5, with R2 and F-statistics calculated as well.

In the reverse analyses, we extracted 8 independent SNPs as IVs for PUD using the above method. SNPs not found in the outcome datasets were replaced with their proxies if any (Additional file 1: Table S6). Detailed information for these SNPs were provided in Additional file 1: Table S7.

Mendelian randomization analyses

Inverse-variance weighted (IVW) in the multiplicative random-effects model was employed as the main method given the presence of heterogeneity among SNPs [17, 18]. A series of complementary analyses were conducted: The weighted median [19] and MR-Egger [20] provided more conservative causal estimates; and the MR-pleiotropy residual sum and outlier (MR-PRESSO) methods [21] was employed to identify pleiotropic outliers that may bias the results. Heterogeneity among IVs was measured by the heterogeneity Q test and I2 statistics. Horizontal pleiotropy was assessed using the MR-Egger regression intercept [22].

Since BMI and WHR are correlated covariates to each other, collider bias is likely to affect our results. To avoid this problem, we performed the multivariable MR method [23] to adjust BMI for WHR, and likewise, to adjust WHR for BMI. In addition, we included Helicobacter Pylori (H.P.) infection, type 2 diabetes (T2D), dyslipidemia, smoking, and alcohol use in multivariable MR to determine whether the causal association between adiposity and PUD, if any, was mediated by these traditional risk factors. Summary statistic for H.P. infection was derived from UK Biobank database (, ID: ukb-b-531), T2D from the GWAS conducted by DIAGRAM consortium (452,244 individuals; 81,412 cases) [24], circulating lipid levels (LDL-C, HDL-C, TC, and TG) from the Global Lipids Genetics Consortium (188,578 individuals) [25], and smoking and alcohol use from the GWAS & Sequencing Consortium of Alcohol and Nicotine use (1,232,091 individuals) [26].

We calculated the statistical power using a publicly available tool ( based on a type 1 error of 5% [27]. There was over 80% power to detect an OR of 1.09 for the BMI-PUD association, and 1.12 for the WHR-PUD association in the primary analyses; an OR of 1.15 for the BMI-PUD association, and 1.25 for the WHR-PUD association in the replication analyses. The estimates were considered significant at P < 0.025 (0.05/2 exposures). MR analyses were performed using packages including TwoSampleMR [28], MendelianRandomization [29], and MR-PRESSO [21] within software R (version 4.1.0).


All IVs in the present study had F-statistics above the threshold of 10, suggesting sufficient IV strength for MR analyses. These SNPs were estimated to account for 5.51% and 3.43% of the phenotypic variation of BMI and WHR, respectively (Additional file 1: Table S2 and S3). In the replication analyses, genetic variants IVs explained 2.32% and 0.78% of the variation of BMI and WHR, respectively (Additional file 1: Table S4 and S5). The selected SNPs for PUD were estimated to account for 2.32% of the variation of PUD (Additional file 1: Table S7).

For per SD increase in genetically determined BMI (~ 4.8 kg/m2) there was 34% increased odds of PUD (95% confidence interval [CI], 25–44%; P = 9.72 × 10–16; Fig. 1 and Additional file 1: Fig S1). In an analogous analysis, we observed that each 0.09 ratio higher WHR was associated with a 35% increase in risk for PUD (95% CI, 23–49%; P = 2.35 × 10–10; Fig. 1 and Additional file 1: Fig S1). The results remained broadly consistent in the complementary analyses such as weighted median and MR-Egger regression (Fig. 1). There were 3 outliers for WHR (rs1680490, rs668871, and rs6861681) identified by the MR-PRESSO method. The results did not substantially change after correcting for these outliers (Fig. 1). Importantly, little evidence for heterogeneity and horizontal pleiotropy was found (Additional file 1: Table S8).

Fig. 1
figure 1

Associations of genetically determined BMI and WHR with PUD risk. BMI: body mass index; WHR: waist-to-hip ratio; PUD, peptic ulcer disease; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; IVW (mre), multiplicative random-effects inverse-variance weighted; MR-Egger, Mendelian Randomization-Egger; MR-PRESSO, MR-Pleiotropy Residual Sum and Outlier. Datasets for BMI and WHR were extracted from GWAS conducted by Pulit et al. *Excluding the outliers for WHR (rs1680490, rs668871, and rs6861681)

Multivariable MR demonstrated that the effect of BMI on PUD was attenuated upon adjustment for WHR (Table 2). Nevertheless, the causal association of WHR with PUD persisted after adjusting for BMI (Table 2). Besides, both BMI-PUD and WHR-PUD associations remained consistent following adjustment for genetically determined H.P. infection, T2D, circulating lipid levels, smoking, or alcohol use (Table 2). Furthermore, the results were robust under adjustment for all these lifestyle factors (Table 2).

Table 2 Multivariable Mendelian randomization for the associations between obesity traits and PUD adjusting for potential mediators

In the replication analyses, we used IVs based on summary-level data for both obesity traits from individuals included in the GIANT consortium [15, 16]. IVW analysis showed that genetically-predicted higher BMI (per SD) was associated with an 17% increase in risk for PUD (95% CI = 5–31; P = 0.006; Fig. 2 and Additional file 2: Fig S2). The MR estimate for WHR-PUD association was also positive and, notably, of a greater magnitude; Genetically-predicted higher WHR was associated with 35% higher risk of PUD (95% CI = 13–61%; P = 0.001; Fig. 2 and Additional file 2: Fig S2). Complementary analyses returned broadly consistent results (Fig. 2 and Additional file 2: Fig S2). No pleiotropic outliers were detected by the MR-PRESSO test. The risk of heterogeneity and horizontal pleiotropy should be low according to multiple tests listed in Additional file 1: Table S9.

Fig. 2
figure 2

Replication analyses showing the associations of genetically determined BMI and WHR with PUD risk. BMI: body mass index; WHR: waist-to-hip ratio; PUD, peptic ulcer disease; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; IVW (mre), multiplicative random-effects inverse-variance weighted; MR-Egger, Mendelian Randomization-Egger; MR-PRESSO, MR-Pleiotropy Residual Sum and Outlier. Datasets for BMI and WHR were extracted from GWAS conducted by Locke et al. and Shungin et al., respectively

Reverse MR analyses provided no evidence for the causal effect of PUD on BMI or WHR (Fig. 3 and Additional file 2: Fig S3). The association pattern persisted when different datasets for BMI and WHR were used (Fig. 3 and Additional file 2: Fig S4). Besides, complementary analyses yielded broadly consistent results (Additional file 1: Table S10). SNP heterogeneity was high for BMI and WHR in the original analyses but not in the replication analyses. (Additional file 1: Table S11 and S12). MR Egger regression indicated a low risk of pleiotropy in both the original and replication analyses (Additional file 1: Table S11 and S12).

Fig. 3
figure 3

Associations of genetically determined PUD with risk of BMI and WHR. BMI: body mass index; WHR: waist-to-hip ratio; PUD, peptic ulcer disease; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval


This bidirectional MR study demonstrated that genetically predicted obesity (notably abdominal obesity) was causally associated with PUD risk. The results were consistent across complementary analyses and survived adjustment for several traditional risk factors. Besides, no evidence was found to support the causal effect of PUD on obesity.

The association between obesity and PUD remain a subject of ongoing debate. Generally speaking, PUD is divided into gastric ulcers (GU) and duodenal ulcers (DU). Our results collaborated with a series of previous observational studies. Evidence from a cross-sectional study reported a positive association between obesity and GU (but not DU) (OR = 4.15; 95% CI, 1.31–13.13) among northern Sweden individuals [7]. A retrospective cohort study enrolling 32,472 Korean individuals found a pattern of relationships of higher BMI with increased risk of GU (OR, 1.32; 95% CI, 1.16–1.49; P < 0.001), but not with the risk of DU [8]. Likewise, another large prospective cohort study with 47,120 U.S. individuals reported an increased prevalence of GU in subjects with higher BMI upon multivariate adjustment (OR, 1.83; 95% CI, 1.20–2.78; P < 0.01) [5]. Similarly, the same research group showed that BMI was not associated with a greater tendency in suffering from DU [5]. In this study, we provided evidence supporting the positive association of obesity with PUD risk in European populations and demonstrated a potential causality of this relationship. Considering that significance was observed only in the BMI-GU relationship in previous studies, we speculated that the positive results observed in this MR study may be largely attributable to the fact that BMI increased the risk of GU, but not DU. Future MR study using separate GWAS datasets for GU and DU should shed light on this important issue.

The multivariable MR study suggested a great attenuation of the association between BMI and PUD after adjusting for WHR. Indeed, BMI is considered an imprecise obesity classification method since it ignores the distribution of adiposity and cannot distinguish between lean body mass and fat mass [30, 31]. Therefore, it may be a relatively poor tool for exploring associations between obesity and diseases. On the other hand, our results demonstrated that the association between WHR and PUD was not significantly attenuated after adjusting for BMI. As previously reported, WHR is an independent biological tool for abdominal adiposity and visceral fat measurement [32]. The results from the multivariable MR specifically demonstrated that the causal association between WHR and PUD was independent of BMI. In other words, individuals with higher abdominal adiposity are more likely to suffer from PUD, even if their BMI is relatively low.

The pathophysiology of PUD has been associated with the use of NSAIDs and Helicobacter Pylori infection. However, the underlying mechanisms linking obesity to PUD is not yet well-elucidated. Observationally, obesity was strongly associated with PUD in non-NSAIDs/aspirin users and Helicobacter Pylori-negative subjects [5]. Therefore, the observed causal effect of obesity on PUD is likely to be independent of anti-inflammatory drug use or Helicobacter Pylori infection. Recent studies have linked obesity to mucosal dysfunction [33, 34], which is one of the potential mechanisms associated with PUD [35]. In our study, we performed multivariable MR analyses to explore potential mediators. Obesity has been associated with metabolic diseases like T2D [36] and dyslipidemia [37], and unhealthy behaviors like smoking [38] and alcohol use [39]. However, this MR study demonstrated that the association of obesity with PUD risk persisted upon adjustment for these risk factors. In addition, obesity-induced alteration in gut microbiota [3, 4] and gastrointestinal inflammation [40] are also likely to mediate the causal associations. However, detailed research is insufficient. Further studies into how obesity is involved in the development of PUD are warranted.

There are three issues that can violate the MR assumption and lead to biased causal inferences: (1) biological mechanism; (2) genetic coinheritance; and (3) population effects. Pleiotropy represents one of the most important biological mechanisms. In the present study, MR-Egger intercept test was carried out to assess pleiotropy. As shown in Additional file 1: Table S8, S9, S11 and S12, there was no evidence for pleiotropy for all associations considered. Secondly, we ruled out the effect of non-Mendelian inheritance by performing the clumping process with R2 threshold of < 0.001 and window size of 10,000 kb. Thereby, the influence of genetic coinheritance would be minimal in this study. Third, the population stratification is unlikely to bias our results since all of the participants involved in these original GWASs were of European ancestry.

The MR design that we used is the first strength of this study, which can minimize biases such as residual confounders and reverse causation. Reverse causation describes a scenario where post-event measurement of the exposure can be affected by the outcome event. For example, one can hardly tell whether event A cause higher risk of event B or event B increased incidence of event A in the observational studies. The MR approach can avoid reverse causation because genetic variants were randomly assorted during conception and unlikely to be influenced by disease status. Besides, we here performed reverse MR analyses from PUD to obesity and no evidence was found for the causal association in this direction, which further minimized the influence of reverse causation. Second, the present study leveraged a high statistical power (above the threshold of 80%) enabled by the use of the largest GWAS meta-analyses to date. Third, all the complementary approaches returned consistent results, strengthening the causal inference. Finally, the analyses were less likely to be affected by the bias of population structure since they were restricted to individuals of European descent.

However, several limitations deserved consideration. First, we did not evaluate the associations between obesity and different subtypes of PUD (GU and DU) due to a lack of related GWASs. Second, the potential non-linear association cannot be assessed since this study relies on summary statistics. Third, sample overlap in the GWASs of obesity and PUD (UK Biobank mainly) are likely to bias the causal estimates and inflate Type 1 error rates [41] in the primary analysis. However, given that all genetic variants that we used were confirmed to be strong (F statistics > 10), we did not expect substantial bias here [41]. In addition, the results were consistent in the replication analyses where no sample overlap was present between the exposure and outcome. Finally, the limitation of participants in this study to Europeans might limit the generalizability of this study.


In the present study, we provided genetic evidence showing that obesity (notably abdominal obesity) is causally associated with increased PUD risk. Programs aimed at weight loss may play an important role in preventing PUD.

Availability of data and materials

The summary statistics of GWAS for peptic ulcer disease are derived from a GWAS conducted by Yeda Wu et al. (; Full GWAS summary statistics for BMI and WHR are publicly available through; In replication analysis, summary level data for BMI and WHR can be accessed at; Summary statistics for H.P. infection was derived from UK Biobank database (, ID: ukb-b-531); Summary statistics for type 2 diabetes is available at the DIAGRAM consortium website (; Summary statistics for LDL-C, HDL-C, TC and TG are available through http:/; Summary statistics for smoking and alcohol use can be download from



Peptic ulcer disease


Mendelian randomization


Body mass index


Waist-to-hip ratio


Genome-wide association study


Odds ratio


Confidence interval


Non-steroidal anti-inflammatory drugs


Instrumental instruments


Single nucleotide polymorphisms


MR-pleiotropy residual sum and outlier


Type 2 diabetes


Low-density lipoprotein cholesterol


High-density lipoprotein cholesterol


Total cholesterol




Gastric ulcer


Duodenal ulcer


  1. Malfertheiner P, Chan FKL, McColl KEL. Peptic ulcer disease. Lancet. 2009;374(9699):1449–61.

    Article  CAS  Google Scholar 

  2. Lanas A, Chan FKL. Peptic ulcer disease. Lancet. 2017;390(10094):613–24.

    Article  Google Scholar 

  3. Tilg H, Moschen AR, Kaser A. Obesity and the microbiota. Gastroenterology. 2009;136(5):1476–83.

    Article  Google Scholar 

  4. Raybould HE. Gut microbiota, epithelial function and derangements in obesity. J Physiol. 2012;590(3):441–6.

    Article  CAS  Google Scholar 

  5. Boylan MR, Khalili H, Huang ES, Chan AT. Measures of adiposity are associated with increased risk of peptic ulcer. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2014;12(10):1688–94.

    Google Scholar 

  6. Kim J, Kim KH, Lee BJ. Association of peptic ulcer disease with obesity, nutritional components, and blood parameters in the Korean population. PLoS ONE. 2017;12(8): e0183777.

    Article  Google Scholar 

  7. Aro P, Storskrubb T, Ronkainen J, Bolling-Sternevald E, Engstrand L, Vieth M, Stolte M, Talley NJ, Agréus L. Peptic ulcer disease in a general adult population: the Kalixanda study: a random population-based study. Am J Epidemiol. 2006;163(11):1025–34.

    Article  Google Scholar 

  8. Pyo JH, Lee H, Kim JE, Choi YH, Kim TJ, Min YW, Min BH, Lee JH, Rhee PL, Yoo H, et al. Obesity and risk of peptic ulcer disease: a large-scale health check-up cohort study. Nutrients. 2019;11(6):1288.

    Article  CAS  Google Scholar 

  9. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63.

    Article  Google Scholar 

  10. Wu Y, Murray GK, Byrne EM, Sidorenko J, Visscher PM, Wray NR. GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression. Nat Commun. 2021;12(1):1146.

    Article  CAS  Google Scholar 

  11. Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, Yengo L, Ferreira T, Marouli E, Ji Y, et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 2019;28(1):166–74.

    Article  CAS  Google Scholar 

  12. Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature. 2010;467(7319):1061–73.

    Article  Google Scholar 

  13. Shim H, Chasman DI, Smith JD, Mora S, Ridker PM, Nickerson DA, Krauss RM, Stephens M. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS ONE. 2015;10(4): e0120758.

    Article  Google Scholar 

  14. Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–64.

    Article  Google Scholar 

  15. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206.

    Article  CAS  Google Scholar 

  16. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, Strawbridge RJ, Pers TH, Fischer K, Justice AE, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518(7538):187–96.

    Article  CAS  Google Scholar 

  17. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36(11):1783–802.

    Article  Google Scholar 

  18. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658–65.

    Article  Google Scholar 

  19. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14.

    Article  Google Scholar 

  20. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377–89.

    Article  Google Scholar 

  21. Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8.

    Article  CAS  Google Scholar 

  22. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25.

    Article  Google Scholar 

  23. Day FR, Loh P-R, Scott RA, Ong KK, Perry JRB. A robust example of collider bias in a genetic association study. Am J Hum Genet. 2016;98(2):392–3.

    Article  CAS  Google Scholar 

  24. Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR, Chu AY, Gan W, Kitajima H, Taliun D, Rayner NW, et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet. 2018;50(4):559–71.

    Article  CAS  Google Scholar 

  25. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83.

    Article  CAS  Google Scholar 

  26. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, Datta G, Davila-Velderrain J, McGuire D, Tian C, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51(2):237–44.

    Article  CAS  Google Scholar 

  27. Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42(5):1497–501.

    Article  Google Scholar 

  28. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9.

    Article  Google Scholar 

  30. Gallagher D, Visser M, Sepúlveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol. 1996;143(3):228–39.

    Article  CAS  Google Scholar 

  31. Ruiz JR, Sui X, Lobelo F, Morrow JR, Jackson AW, Sjöström M, Blair SN. Association between muscular strength and mortality in men: prospective cohort study. BMJ Clin Res. 2008;337: a439.

    Article  Google Scholar 

  32. Seidell JC, Björntorp P, Sjöström L, Sannerstedt R, Krotkiewski M, Kvist H. Regional distribution of muscle and fat mass in men–new insight into the risk of abdominal obesity using computed tomography. Int J Obes. 1989;13(3):289–303.

    CAS  PubMed  Google Scholar 

  33. Thaiss CA, Levy M, Grosheva I, Zheng D, Soffer E, Blacher E, Braverman S, Tengeler AC, Barak O, Elazar M, et al. Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection. Science. 2018;359(6382):1376–83.

    Article  CAS  Google Scholar 

  34. Paris S, Ekeanyanwu R, Jiang Y, Davis D, Spechler SJ, Souza RF. Obesity and its effects on the esophageal mucosal barrier. Am J Physiol Gastrointest Liver Physiol. 2021;321(3):G335–43.

    Article  CAS  Google Scholar 

  35. Mertz HR, Walsh JH. Peptic ulcer pathophysiology. Med Clin North Am. 1991;75(4):799–814.

    Article  CAS  Google Scholar 

  36. Yuan S, Larsson SC. An atlas on risk factors for type 2 diabetes: a wide-angled Mendelian randomisation study. Diabetologia. 2020;63(11):2359–71.

    Article  CAS  Google Scholar 

  37. Vekic J, Zeljkovic A, Stefanovic A, Jelic-Ivanovic Z, Spasojevic-Kalimanovska V. Obesity and dyslipidemia. Metab Clin Exp. 2019;92:71–81.

    Article  CAS  Google Scholar 

  38. Carreras-Torres R, Johansson M, Haycock PC, Relton CL, Davey Smith G, Brennan P, Martin RM. Role of obesity in smoking behaviour: Mendelian randomisation study in UK Biobank. BMJ Clin Res. 2018;361: k1767.

    Article  Google Scholar 

  39. Traversy G, Chaput J-P. Alcohol consumption and obesity: an update. Curr Obes Rep. 2015;4(1):122–30.

    Article  Google Scholar 

  40. Ding S, Chi MM, Scull BP, Rigby R, Schwerbrock NMJ, Magness S, Jobin C, Lund PK. High-fat diet: bacteria interactions promote intestinal inflammation which precedes and correlates with obesity and insulin resistance in mouse. PLoS ONE. 2010;5(8): e12191.

    Article  Google Scholar 

  41. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40(7):597–608.

    Article  Google Scholar 

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Summary-level data for PUD was obtained from a GWAS meta-analysis based on UK Biobank, and instrumental variants for BMI and WHR were derived from a GWAS meta-analysis based on UK Biobank and GIANT consortium. We thank all investigators for sharing these data. This work was supported by Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare and Alibaba Cloud. We are also grateful to the Core Facility Platform of Zhejiang University School of Medicine for technical assistance. We extend sincere thanks to Shuai Yuan (Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden) for valuable advice on this study.


This work was supported by a grant from the National Natural Science Foundation of China (82170489), a grant from the Natural Science Foundation of Zhejiang Province (LR22H020001).

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ZL and HC designed the study, conducted the analyses, and wrote the manuscript; TC contributed to study supervision and revisions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ting Chen.

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Supplementary Information

Additional file 1. Tabel S1

. Proxies for SNPs not found in the outcome dataset (Forward MR); Tabel S2. Characteristics of the genetic variants associated with body mass index; Tabel S3. Characteristics of the genetic variants associated with waist-to-hip ratio; Tabel S4. Characteristics of the genetic variants associated with body mass index in replication analyses; Tabel S5. Characteristics of the genetic variants associated with waist-to-hip ratio in replication analyses; Tabel S6. Proxies for SNPs not found in the outcome dataset (Reverse MR); Tabel S7. Characteristics of the genetic variants associated with peptic ulcer disease; Tabel S8. Evaluation of heterogeneity and directional pleiotropy using different methods (Forward MR); Tabel S9. Evaluation of heterogeneity and directional pleiotropy in replication analyses (Forward MR); Table S10. Associations of genetic predisposition to PUD with risk of obesity in complementary Mendelian Randomization analyses (Reverse MR); Tabel S11. Evaluation of heterogeneity and directional pleiotropy using different methods (Reverse MR); Tabel S12. Evaluation of heterogeneity and directional pleiotropy in replication analyses (Reverse MR).

Additional file 2. Figure S1

. Scatter plots for MR estimates of BMI (A) and WHR (B) on PUD; Figure S2. Scatter plots for MR estimates of BMI (A) and WHR (B) on PUD in the replication analysis; Figure S3. Scatter plots for MR estimates of PUD on BMI (A) and WHR (B); Figure S4. Scatter plots for MR estimates of PUD on BMI (A) and WHR (B) in the replication analysis.

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Li, Z., Chen, H. & Chen, T. Genetic liability to obesity and peptic ulcer disease: a Mendelian randomization study. BMC Med Genomics 15, 209 (2022).

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