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Leisure television watching exerts a causal effect on gastroesophageal reflux disease: evidence from a two-step mendelian randomization study

BMC Medical Genomics volume 17, Article number: 204 (2024) Cite this article

Abstract

Background

Previous studies have shown that physical activity (PA) and leisure sedentary behaviors (LSB, including leisure television watching) are linked to gastroesophageal reflux disease (GERD). However, the associations between PA/LSB and GERD remain controversial. In this study, we aimed to reveal whether these associations reflect causal relationships and reveal the potential mechanisms of these relationships using bidirectional and two-step Mendelian randomization (MR) analyses.

Methods

We obtained genome-wide association study (GWAS) summary statistics for PA/LSB, four common risk factors (including cigarettes smoked per day, alcoholic drinks per week, triglycerides, total cholesterol) and GERD from published GWASs. A bidirectional MR analysis was performed to identify causal relationships between PA/LSB and GERD. Then, a series of sensitivity analyses were performed to verify the robustness of the results. Finally, a mediation analysis via two-step MR was conducted to investigate any effects explained by common risk factors in these relationships.

Results

Genetically predicted per 1-SD increase in leisure time television watching significantly increased the risk of GERD in the bidirectional MR analysis (OR = 1.33; 95% CI: 1.14–1.56; P = 2.71 × 10− 4). Sensitivity analyses successfully verified the robustness of the causal relationship. Further mediation analysis showed that this effect was partly mediated by increasing cigarettes smoked per day, with mediated proportions of 18.37% (95% CI: 11.94-39.79%).

Conclusion

Our findings revealed a causal relationship between leisure television watching and an increased risk of GERD, notably, the causal effect was partially mediated by cigarettes smoked per day. These findings may inform prevention and management strategies directed toward GERD.

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Introduction

Gastroesophageal reflux disease (GERD) is a common gastrointestinal disorder, in which the contents of the stomach and duodenum enter the esophagus to cause symptoms, such as heartburn and regurgitation [1]. An increase in GERD incidence has been reported since 1995 [2], with approximately 20% of adults in the Western world being affected [3]. GERD impairs the patient’s quality of life and increases the risk of other esophageal complications, such as esophagitis, esophageal strictures, Barrett’s esophagus, and esophageal adenocarcinoma [4,5,6]. Therefore, determining the potentially modifiable risk factors of GERD is important to prevent and manage the disease in clinical practice.

Studies have shown that certain lifestyles, namely, physical activity (PA) and leisure sedentary behaviors (LSB), are possible risk factors for GERD. However, the relevant evidence of PA is inconsistent. Dağlı et al. thought that excessive PA was a significant risk factor for GERD, and regular and mild-moderate PA can reduce the symptoms [7]. Conversely, meta-analysis of Lam et al. found that high levels of PA may decrease the risk of GERD [8]. Moreover, cross-sectional survey of Djärv et al. did not find evidence for the effects of PA on GERD in individuals with normal weight [9]. For LSB, defined as any waking behavior characterized by an energy expenditure ≤ 1.5 metabolic equivalents, while in a sitting, reclining or lying position [10], a recent study reported its relationship with the prevalence of GERD [11]. However, the studies of LSB are limited [12, 13]. Furthermore, traditional studies are limited to entirely precluding the possibility of reverse causality and confounding factors, which may hinder causal inference, due to inherent defects [14].

To overcome these limitations, Mendelian randomization (MR) is used as a research methodology for causal inference. It involves using exposure-related genetic variations as instrumental variables (IVs) to assess whether the association between exposures and outcomes is consistent with a causal effect, given that genetic variants are randomly assigned during conception before disease onset [15]. Therefore, MR analysis could efficiently identify causal determinants of a particular outcome and exclude reverse causality and confounding factors when pleiotropy is well controlled [16, 17].

In this study, we dissected the potential causal relationships between PA/LSB and GERD in a bidirectional MR analysis with subsequent mediation analysis investigating the potential mechanisms of these relationships, using large-scale genome-wide association study (GWAS) summary data.

Methods

Study design

An overview of the study design is presented in Fig. 1. First, we performed a bidirectional MR analysis to explore possible causal relationships between PA/LSB and GERD. Then, a series of sensitivity analyses including the Cochran’s Q test, the MR-Egger intercept test, the leave one out (LOO) analysis, and the scatter plot, were performed to verify the robustness of the significant relationships. Finally, a mediation analysis via two-step MR was conducted to assess whether four common risk factors (cigarettes smoked per day, alcoholic drinks per week, triglycerides, total cholesterol) have causal roles in the mediating pathway between PA/LSB and GERD. Meanwhile, the MR analysis had to fulfill the following three assumptions: (i) IVs are associated with the exposure; (ii) IVs are independent of potential confounders; (iii) IVs are associated with the outcome through the studied exposure only [18].

Fig. 1
figure 1

An overview of the study design. (A) Flow chart of the bidirectional MR, sensitivity analyses, and mediation analysis. (B) Schematic chart of the basic principles of MR. MR, Mendelian randomization; PA, physical activity; LSB, leisure sedentary behaviors; GERD, gastroesophageal reflux disease; LOO, leave one out; IVs, instrumental variables

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Data source

Summary data for PA were obtained from the published GWAS dataset with age, sex, genotyping chip, first ten genomic principal components, center, season (month) during the center visit or wearing accelerometers as covariates [19]. Three phenotypes were included in PA: self-reported moderate to vigorous physical activity (MVPA) including 377,234 participants of European descent, accelerometer-assessed acceleration average including 91,084 participants of European descent, and strenuous sports or other exercises (SSOE) including 350,492 participants of European descent. Summary data for LSB were obtained from the published GWAS dataset with age, sex, BMI (body mass index), smoking status, hypertension, diabetes, Townsend deprivation index, PA levels, alcohol use per week and years of education as covariates [20]. Three phenotypes were included in LSB incorporating 422,218 participants of European descent: leisure television watching, leisure computer use, and driving. Summary data for GERD were obtained from the “fin-ngen_R8_K11_REFLUX” GWAS dataset (http://www.finngen.fi), which included 315,123 participants of European descent.

Finally, summary level data of four common risk factors were retrieved from different consortia based on populations not overlapping with PA/LSB and GERD [21, 22].

Selection of instrumental variables

For IVs, only single-nucleotide polymorphisms (SNPs) that met the following screening criteria were selected as IVs. First, only SNPs that met the genome-wide significance threshold of P < 5 × 10− 8 were selected as potential IVs. Second, SNPs were discarded by linkage disequilibrium (LD; r2 = 0.001, kb = 10,000) with the 1,000 European Genome data being used as a reference to calculate LD between the SNPs. Third, the strength of each selected SNP was assessed by calculating the F statistic (F = βexposure^2/SEexposure^2), where an F statistic ≥ 10 indicated no strong evidence of weak instrument bias [23, 24]. Fourth, all palindromic SNPs were eliminated. Fifth, SNPs associated with potential confounders (including BMI, waist circumference, caffeine consumption, depression, and type 2 diabetes) were excluded by “phenoscanner v2” (www.phenoscanner.medschl.cam.ac.uk) [25]. Finally, the MR pleiotropy residual sum and outlier (MR-PRESSO) test was utilized to detect any potential horizontal pleiotropy and to remove outliers, thereby mitigating the effects of pleiotropy [26].

MR analysis

We performed three MR analytical methods, including random effects/fixed effects inverse variance weighted (RE/FE-IVW), weighted median (WM), and MR-Egger regression to calculate the causal effect. IVW was the main method because IVW was usually more efficient [27, 28]. IVW is calculated by regressing the coefficient from an outcome regression on the IV on that from an exposure regression on the variant, and weighting each estimate by the inverse variance of the association between the instrument and the outcome [29]. WM can provide consistent estimates when at least 50% of the weighted variance is from valid IVs [30]. The MR-Egger regression method allows pleiotropy to be present in more than 50% of IVs with less precision [29].

To verify the robustness of the identified causal associations, we carried out a series of sensitivity analyses, including the Cochran’s Q test, the MR-Egger intercept test, the LOO analysis, and the scatter plot. The Cochran’s Q test was performed to estimate the heterogeneity among IVs associated with each phenotype. The RE-IVW method was used when P < 0.05, and the FE-IVW method was used when P > 0.05 to provide a more conservative but robust estimate [31]. The MR-Egger intercept test was performed to estimate the presence of horizontal pleiotropy [29]. The LOO analysis was performed to determine whether the significant results were driven by any single SNP [32]. The scatter plot was used to observe the heterogeneity and consistent effects estimated by these three MR analytical methods [33].

To investigate the potential mechanisms of the causal relationships between PA/LSB and GERD, we further conducted a two-step MR analysis to investigate the potential mediating pathway by four common risk factors, including cigarettes smoked per day, alcoholic drinks per week, triglycerides and total cholesterol. Meanwhile, the indirect effect of PA/LSB on GERD via potential mediators was assessed with the method previously described [34].

Statistical analysis

All MR analyses were conducted in R statistical software (version 4.2.2) using the “TwoSampleMR” package (version 0.5.6) and “MRPRESSO” package (version 1.0). The results of forward MR analysis were expressed as odds ratios (OR) with 95% confidence intervals (CI) to quantify the association between PA/LSB, as the exposure, and GERD as the outcome. In the reverse MR analysis with GERD as the exposure and PA/LSB as the outcome, the results were expressed as β with SE. In the two-step MR analysis, the results were expressed as β with 95% CI. Given the multiple testing between PA/LSB and GERD, we considered the MR analysis results verifying the causal effect between PA/LSB and GERD to be statistically significant only if the Bonferroni corrected P < 0.0042 (0.05/12). In the process of the two-step MR analysis, the threshold for significance was set at Bonferroni corrected P < 0.0063 (0.05/8).

Results

Selection of instrumental variables

After conducting a series of screening steps, 13 SNPs associated with MVPA were selected as IVs, 4 IVs for acceleration average, 10 IVs for SSOE, 98 IVs for leisure television watching, 31 IVs for leisure computer use, and 2 IVs for driving, respectively. The F statistic of each IV was higher than > 10, indicating no evidence of weak instrument bias (Supplementary Table S1). Notably, IVs discarded during the MR-PRESSO test are listed in Supplementary Table S2.

For GERD, there was no SNP associated with GERD at the threshold of P < 5 × 10− 8. Hence, we used a more liberal P value threshold of P < 5 × 10− 6 as the IVs for GERD according to the previously described method [35]. 26 SNPs were screened as IVs for GERD to PA/LSB. All of these SNPs are listed in Supplementary Table S3, and the F statistic of each IV was higher than 10. Meanwhile, no IVs were discarded during the MR-PRESSO test.

For common risk factors, 18 IVs for cigarettes smoked per day, 27 IVs for alcoholic drinks per week, 53 IVs for triglycerides, and 81 IVs for total cholesterol, respectively. All of these SNPs are listed in Supplementary Table S4, and the F statistic of each IV was higher than 10. Meanwhile, no IVs were discarded during the MR-PRESSO test.

Bidirectional MR analysis

The forward MR results from PA/LSB to GERD are listed in Fig. 2. Among the tested PA/LSB phenotypes, the RE-IVW method indicated that per 1-SD increase in leisure time television watching significantly increased the risk for GERD (OR = 1.33; 95% CI: 1.14–1.56; P = 2.71 × 10− 4), whereas the WM result was not significant (OR = 1.18; 95% CI: 0.95–1.45; P = 1.31 × 10− 1). Meanwhile, no evidence of a causal association between genetic liability for MVPA, acceleration average, SSOE, computer use, driving and GERD risk was shown.

Fig. 2
figure 2

Results of the forward MR analysis from PA/LSB to GERD. MVPA, moderate to vigorous physical activity; SSOE, strenuous sports or other exercises; No. IVs, the number of IVs; WM, weighted median; FE-IVW, fixed effects inverse variance weighted; RE-IVW, random effects inverse variance weighted. a: indicates OR for GERD per 1-SD increase in time spent on PA/LSB; *: P of statistical significance (< 0.0042) after Bonferroni correction

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In addition, the results from the Cochran’s Q test can be found in Supplementary Table S5. Heterogeneity was observed between the genetic IVs for leisure television watching to GERD (Q = 124.11; P = 0.03), leading to the utilization of the RE-IVW, while no evidence of heterogeneity was observed between the genetic IVs for other phenotypes to GERD (P > 0.05), resulting in the FE-IVW being used. In addition, MR-Egger regression intercepts did not detect any pleiotropy, indicating a lack of potential horizontal pleiotropy (all intercepts P > 0.05) (Supplementary Table S5). No results for the Cochran’s Q test and MR-Egger regression of driving were obtained due to insufficient IVs. As shown in Fig. 3, the LOO analysis showed no significant difference in the causal effect of leisure television watching on GERD, indicating that the noteworthy results were not driven by any single SNP. The scatter plot is shown in Fig. 4, presenting consistent direction of the MR analytical methods.

Fig. 3
figure 3

The leave one out sensitivity analysis for leisure television watching on GERD. GERD, gastroesophageal reflux disease

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Fig. 4
figure 4

The scatter plot for the effect of leisure television watching on GERD through genetic variants. GERD, gastroesophageal reflux disease

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The bidirectional MR results from GERD to PA/LSB are listed in Supplementary Table S6. In this reverse direction, there was no causal association from GERD to PA/LSB. The results from the Cochran’s Q test can be found in Supplementary Table S6. Heterogeneity was observed between the genetic IVs for GERD to leisure television watching (Q = 45.01; P = 0.01), resulting in the RE-IVW being used, while no evidence of heterogeneity was observed between the genetic IVs for GERD to other phenotypes (P > 0.05), which led to the utilization of the FE-IVW. Moreover, MR-Egger regression intercepts did not detect any pleiotropy, indicating a lack of potential horizontal pleiotropy (all intercepts P > 0.05) (Supplementary Table S6).

Mediation analysis

According to the results of bidirectional MR analysis, leisure television watching seemed to be associated with GERD. Nevertheless, considering that leisure television watching may play a role in several common risk factors, and factors-related measurements could be mediators underlying the effect of television watching on GERD, we performed a two-step MR analysis to estimate the proportion of leisure television watching’s effect on GERD mediated through these potential mediators. In the first step, among the four potential mediators, we identified causal evidence for effects of per 1-SD increase in leisure time television watching on cigarettes smoked per day (RE-IVW β = 0.32; 95% CI: 0.23–0.41; P = 6.79 × 10− 12) and triglycerides (FE-IVW β = 0.19; 95% CI: 0.13–0.25; P = 6.25 × 10− 9) (Supplementary Table S7). In the second step, we identified a causal relationship between per 1-SD increase in cigarettes smoked per day (FE-IVW β = 0.17; 95% CI: 0.06–0.27; P = 1.46 × 10− 3) and GERD risk (Supplementary Table S8). Moreover, as shown in Supplementary Table S7 and Supplementary Table S8, we simultaneously performed the Cochran’s Q test and MR-Egger intercept test to estimate the heterogeneity and horizontal pleiotropy, respectively. Hence, cigarettes smoked per day served as a mediator mediating the effect of leisure television watching on GERD. Finally, we estimated the indirect effect of leisure television watching on GERD via the above mediators, and the results demonstrated that the mediation effect of cigarettes smoked per day was 0.05 (95% CI: 0.02–0.09; P = 3.88 × 10− 3) with a mediated proportion of 18.37% (95% CI: 11.94-39.79%) (Table 1).

Table 1 Mediation effect of television watching on GERD via mediators
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Discussion

In this study, we analyzed the causal effect of PA/LSB on GERD utilizing MR analyses. We first explored the possible associations between six phenotypes of PA/LSB and GERD using the bidirectional MR analysis, and we found that per 1-SD increase in leisure time television watching was significantly associated with an increased risk of GERD (OR = 1.33; 95% CI: 1.14–1.56; P = 2.71 × 10− 4) in the IVW method, whereas the WM result was not significant (OR = 1.18; 95% CI: 0.95–1.45; P = 1.31 × 10− 1) because heterogeneity was found in the IVs for leisure television watching to GERD (P < 0.05). Besides, no causal effect of other phenotypes on GERD was observed. Then, no association was observed from GERD to PA/LSB in the reverse direction. Finally, to investigate the potential mechanisms through which genetically predicted leisure television watching affects the risk of GERD, we further assessed leisure television watching’s effect on common risk factors, which potentially serve as mediators, using a two-step MR analysis. Our mediation analysis demonstrated that this causal relationship is mediated by increasing cigarettes smoked per day with a mediated proportion of 18.37% (95% CI: 11.94-39.79%).

GERD is a common disease worldwide, and prior studies have confirmed that factors such as depression and eating habits are related to the occurrence and progression of GERD [3]. However, there is no consensus on PA/LSB. For PA, our study revealed that there was no causal effect of PA on GERD, which is consistent with the result by Djärv et al. [9], indicating that previous studies may have been affected by confounding factors. For LSB, our results, combining evidence from the bidirectional MR analysis and the two-step MR analysis, supported that more leisure time spent on television viewing was associated with a significantly increased risk of GERD. Some potential mechanisms have been proposed, which may help to explain the positive effect. First, it was demonstrated that the pathophysiology of GERD was multifactorial including the function of the anti-reflux barrier [36]. The mechanism of this effect could be by weakening the crural diaphragm, which is an important component of the anti-reflux barrier, thus possibly compromising the anti-reflux function due to prolonged sedentary behaviors [37, 38]. Additionally, leisure sedentary behavior induces low-grade chronic systemic inflammation, and sedentary time has been shown to increase the levels of multiple inflammatory factors including tumor necrosis factor-alpha, interleukin-6, and leptin, which may contribute to the development of GERD [39,40,41,42]. Finally, compared with computer use and driving, television watching is a more immersive, less reflective and communicative form of leisure entertainment, hence, sustained television watching is consistently accompanied by poor physical and mental health, such as anxiety and depression [32], which might partially contribute to GERD [43]. As a result, leisure television watching increases the risk of GERD.

Notably, the causal of effect leisure television watching on GERD was partially mediated by cigarettes smoked per day. As a common risk factor, smoking is considered as the environmental trigger of GERD [44, 45]. Specifically, cigarette smoking exposes the esophagus to a lot of toxins and chemicals that damage the membranes in the lower esophageal sphincter (LES), causing it to be loose [46]. Besides, cigarette smoking stimulates the secretion of gastric acid, leading to an increase in gastroesophageal reflux symptoms [47, 48]. Meanwhile, longer time spent on TV viewing is associated with higher smoking intention and higher cigarette volume smoked [49, 50]. Hence, smoking can potentially mediate the positive effect of leisure TV watching on GRED.

However, several limitations should be taken into account in our study. First, while our sensitivity analyses incorporating the MR-Egger intercept test failed to find evidence of horizontal pleiotropy, the possibility of vertical pleiotropy cannot be ruled out since there are many factors influencing PA/LSB and GERD, which would potentially bias the results [51]. Second, although we found a significant positive effect of leisure television watching on GERD in the RE-IVW method, the result of the WM method was not statistically significant since heterogeneity existed in the IVs for leisure television watching to GERD. Hence, our results may be subject to potential bias and need to be further validated in larger GWAS summary data in the future work. Third, despite the mediation analysis indicated that the causal effect of leisure television viewing on GERD was partially mediated by cigarettes smoked per day, the joint genetic impact of potential mediators was not revealed and requires further investigation. Fourth, it is better to obtain triangulating evidence (e.g. an additional independent observational study or basic medical study) to enhance the robustness of the causal inference discovered in our study [52], thus, we need to further exploration to validate our findings. Fifth, we were unable to explore the potential non-linear association between PA/LSB and GERD since this study was based on summary data. Finally, as our study was performed mainly on individuals of European descent, the generalizability of our results to other ethnic groups may be limited.

Conclusion

In conclusion, our study revealed a causal relationship between PA/LSB and GERD. Genetically predicted leisure television watching increased the risk of GERD, and to a lesser extent, enhancing smoking behavior mediated this effect. These findings suggest that reducing leisure time spent on watching television may be a beneficial approach for preventing GERD, especially in smoking populations.

Data availability

Publicly available datasets were analyzed in this study. The PA summary statistics can be found at https://www.ebi.ac.uk/gwas/labs/publications/29899525. The LSB summary statistics can be found at https://www.ebi.ac.uk/gwas/labs/publications/29899525. The GERD summary statistics can be found in FinnGen database http://www.finngen.fi (accessed on 25 April, 2023).

Abbreviations

GERD:

Gastroesophageal reflux disease

PA:

Physical activity

LSB:

Leisure sedentary behaviors

MR:

Mendelian randomization

IVs:

Instrumental variables

GWAS:

Genome-wide association study

LOO:

Leave one out

MVPA:

Moderate to vigorous physical activity

SSOE:

Strenuous sports or other exercises

BMI:

Body mass index

SNPs:

Single-nucleotide polymorphisms

LD:

Linkage disequilibrium

MR-PRESSO:

MR pleiotropy residual sum and outlier

RE/FE-IVW:

Random effects/fixed effects inverse variance weighted

WM:

Weighted median

OR:

Odds ratios

CI:

Confidence intervals

LES:

Lower esophageal sphincter

References

  1. Chen J, Brady P. Gastroesophageal reflux disease: pathophysiology, diagnosis, and treatment. Gastroenterol Nurs. 2019;42(1):20–8.

    Article  PubMed  Google Scholar 

  2. El-Serag HB, Sweet S, Winchester CC, Dent J. Update on the epidemiology of gastro-oesophageal reflux disease: a systematic review. Gut. 2014;63(6):871–80.

    Article  PubMed  Google Scholar 

  3. Maret-Ouda J, Markar SR, Lagergren J. Gastroesophageal reflux disease: a review. JAMA. 2020;324(24):2536–47.

    Article  CAS  PubMed  Google Scholar 

  4. Mittal R, Vaezi MF. Esophageal motility disorders and gastroesophageal reflux disease. N Engl J Med. 2020;383(20):1961–72.

    Article  CAS  PubMed  Google Scholar 

  5. Yuan S, Larsson SC. Adiposity, diabetes, lifestyle factors and risk of gastroesophageal reflux disease: a mendelian randomization study. Eur J Epidemiol. 2022;37(7):747–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Thrift AP. Global burden and epidemiology of Barrett oesophagus and oesophageal cancer. Nat Rev Gastroenterol Hepatol. 2021;18(6):432–43.

    Article  PubMed  Google Scholar 

  7. Dağlı Ü, Kalkan İH. The role of lifestyle changes in gastroesophageal reflux diseases treatment. Turk J Gastroenterol. 2017;28(Suppl 1):S33–7.

    PubMed  Google Scholar 

  8. Lam S, Hart AR. Does physical activity protect against the development of gastroesophageal reflux disease, Barrett’s esophagus, and esophageal adenocarcinoma? A review of the literature with a meta-analysis. Dis Esophagus. 2017;30(11):1–10.

    Article  PubMed  Google Scholar 

  9. Djärv T, Wikman A, Nordenstedt H, Johar A, Lagergren J, Lagergren P. Physical activity, obesity and gastroesophageal reflux disease in the general population. World J Gastroenterol. 2012;18(28):3710–4.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behavior Research Network (SBRN) - terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Alsuwat OB, Alzahrani AA, Alzhrani MA, Alkhathami AM, Mahfouz MEM. Prevalence of Gastroesophageal Reflux Disease in Saudi Arabia. J Clin Med Res. 2018;10(3):221–5.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Zhang M, Hou ZK, Huang ZB, Chen XL, Liu FB. Dietary and lifestyle factors related to gastroesophageal reflux disease: a systematic review. Ther Clin Risk Manag. 2021;17:305–23.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kumar S, Sharma S, Norboo T, Dolma D, Norboo A, Stobdan T, et al. Population based study to assess prevalence and risk factors of gastroesophageal reflux disease in a high altitude area. Indian J Gastroenterol. 2011;30(3):135–43.

    Article  CAS  PubMed  Google Scholar 

  14. Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian randomization as an Approach to assess causality using Observational Data. J Am Soc Nephrol. 2016;27(11):3253–65.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Burgess S, Thompson SG. Multivariable mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181(4):251–60.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhu X. Mendelian randomization and pleiotropy analysis. Quant Biol. 2021;9(2):122–32.

    Article  CAS  PubMed  Google Scholar 

  18. Boef AG, Dekkers OM, le Cessie S. Mendelian randomization studies: a review of the approaches used and the quality of reporting. Int J Epidemiol. 2015;44(2):496–511.

    Article  PubMed  Google Scholar 

  19. Klimentidis YC, Raichlen DA, Bea J, Garcia DO, Wineinger NE, Mandarino LJ, et al. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int J Obes (Lond). 2018;42(6):1161–76.

    Article  CAS  PubMed  Google Scholar 

  20. van de Vegte YJ, Said MA, Rienstra M, van der Harst P, Verweij N. Genome-wide association studies and mendelian randomization analyses for leisure sedentary behaviours. Nat Commun. 2020;11(1):1770.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, 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  PubMed  PubMed Central  Google Scholar 

  22. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chen L, Yang H, Li H, He C, Yang L, Lv G. Insights into modifiable risk factors of cholelithiasis: a mendelian randomization study. Hepatology. 2022;75(4):785–96.

    Article  CAS  PubMed  Google Scholar 

  24. Deng Z, Wang H, Huang K, Li Y, Ran Y, Chen Y et al. Association between vascular risk factors and idiopathic normal pressure hydrocephalus: a mendelian randomization study. J Neurol. 2023.

  25. Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 2019;35(22):4851–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Verbanck M, Chen CY, Neale B, Do R. Publisher correction: detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(8):1196.

    Article  CAS  PubMed  Google Scholar 

  27. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016;45(6):1961–74.

    PubMed  PubMed Central  Google Scholar 

  28. Meng Y, Tan Z, Liu C, Dong W, Chen C. Association between Inflammatory Bowel Disease and Iridocyclitis: a mendelian randomization study. J Clin Med. 2023;12(4):1282.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 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  PubMed  PubMed Central  Google Scholar 

  30. 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  PubMed  PubMed Central  Google Scholar 

  31. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195–208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen X, Hong X, Gao W, Luo S, Cai J, Liu G, et al. Causal relationship between physical activity, leisure sedentary behaviors and COVID-19 risk: a mendelian randomization study. J Transl Med. 2022;20(1):216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang T, Wang X, Wu J, Li X. Causal relationship between thyroid dysfunction and ovarian cancer: a two-sample mendelian randomization study. BMC Cancer. 2024;24(1):629.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gao Y, Mi J, Liu Z, Song Q. Leisure sedentary behavior and risk of Lung Cancer: a two-sample mendelian randomization study and mediation analysis. Front Genet. 2021;12:763626.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wootton RE, Lawn RB, Millard LAC, Davies NM, Taylor AE, Munafò MR, et al. Evaluation of the causal effects between subjective wellbeing and cardiometabolic health: mendelian randomisation study. BMJ. 2018;362:k3788.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Nurko S. Pathophysiology of GER. In: Vandenplas Y, editor. Gastroesophageal reflux in children. Cham: Springer International Publishing; 2022. pp. 15–28.

    Chapter  Google Scholar 

  37. Nobre e Souza M, Lima MJ, Martins GB, Nobre RA, Souza MH, de Oliveira RB, et al. Inspiratory muscle training improves antireflux barrier in GERD patients. Am J Physiol Gastrointest Liver Physiol. 2013;305(11):G862–867.

    Article  CAS  PubMed  Google Scholar 

  38. Nilsson M, Johnsen R, Ye W, Hveem K, Lagergren J. Lifestyle related risk factors in the aetiology of gastro-oesophageal reflux. Gut. 2004;53(12):1730–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Henson J, Yates T, Edwardson CL, Khunti K, Talbot D, Gray LJ, et al. Sedentary time and markers of chronic low-grade inflammation in a high risk population. PLoS ONE. 2013;8(10):e78350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rieder F, Biancani P, Harnett K, Yerian L, Falk GW. Inflammatory mediators in gastroesophageal reflux disease: impact on esophageal motility, fibrosis, and carcinogenesis. Am J Physiol Gastrointest Liver Physiol. 2010;298(5):G571–581.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Pardak P, Filip R, Woliński J, Krzaczek M. Associations of Obstructive Sleep Apnea, Obestatin, Leptin, and Ghrelin with Gastroesophageal Reflux. J Clin Med. 2021;10(21).

  42. Bergens O, Nilsson A, Papaioannou KG, Kadi F. Sedentary patterns and systemic inflammation: sex-specific links in older adults. Front Physiol. 2021;12:625950.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Miao Y, Yuan S, Li Y, Chen J, Li X, Larsson SC, et al. Bidirectional Association between Major Depressive Disorder and Gastroesophageal Reflux Disease: mendelian randomization study. Genes (Basel). 2022;13(11):2010.

    Article  CAS  PubMed  Google Scholar 

  44. Slater BJ, Collings A, Dirks R, Gould JC, Qureshi AP, Juza R et al. Multi-society consensus conference and guideline on the treatment of gastroesophageal reflux disease (GERD). Surg Endosc. 2023;37(2):781–806.

  45. Ness-Jensen E, Hveem K, El-Serag H, Lagergren J. Lifestyle intervention in gastroesophageal reflux disease. Clin Gastroenterol Hepatol. 2016;14(2):175–e182171.

    Article  PubMed  Google Scholar 

  46. Thomas GA, Rhodes J, Ingram JR. Mechanisms of disease: nicotine–a review of its actions in the context of gastrointestinal disease. Nat Clin Pract Gastroenterol Hepatol. 2005;2(11):536–44.

    Article  CAS  PubMed  Google Scholar 

  47. Berkowitz L, Schultz BM, Salazar GA, Pardo-Roa C, Sebastián VP, Álvarez-Lobos MM, et al. Impact of cigarette smoking on the gastrointestinal tract inflammation: Opposing effects in Crohn’s Disease and Ulcerative Colitis. Front Immunol. 2018;9:74.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Thakore J, Gopalswamy N, Brown D, Agrawal S. The Acute effects of Nicotine on the esophagus of smokers and non-smokers: 66. Official J Am Coll Gastroenterol | ACG. 2005;100.

  49. Johnson EK, Len-Ríos M, Shoenberger H, Han KJ. A fatal attraction: the Effect of TV viewing on smoking initiation among Young Women. Communication Res. 2019;46(5):688–707.

    Article  Google Scholar 

  50. Gutschoven K, Van den Bulck J. Television viewing and smoking volume in adolescent smokers: a cross-sectional study. Prev Med. 2004;39(6):1093–8.

    Article  PubMed  Google Scholar 

  51. 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  PubMed  PubMed Central  Google Scholar 

  52. Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45(6):1866–86.

    PubMed  Google Scholar 

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Acknowledgements

We want to thank all the investigators and participants for contributing to the GWAS datasets and making the summary data publicly available.

Funding

This research was funded by the National Natural Science Foundation of China (grant number: 82272212) and the Natural Science Foundation of Hubei Province of China (grant number: 2021CFB317).

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Author notes

  1. Qinglu Fan and Zhihao Nie contributed equally to this work.

Authors and Affiliations

  1. Department of Thoracic Surgery, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, Hubei Province, People’s Republic of China

    Qinglu Fan, Zhihao Nie & Songping Xie

  2. Department of Ophthalmology, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, Hubei Province, People’s Republic of China

    Yi Lu

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  1. Qinglu Fan
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Contributions

QF designed and analyzed the data; ZN analyzed the data and prepared the original draft; YL analyzed the data; SX designed, supervised and acquired funding for the work. All authors read and approved the final manuscript.

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Correspondence to Songping Xie.

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Not applicable. We used summary-level data from publicly available GWAS studies which have received ethical approval from their respective institutional review boards and informed consent from all participants. No administrative permissions were required to access the data.

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Fan, Q., Nie, Z., Lu, Y. et al. Leisure television watching exerts a causal effect on gastroesophageal reflux disease: evidence from a two-step mendelian randomization study. BMC Med Genomics 17, 204 (2024). https://doi.org/10.1186/s12920-024-01986-5

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Keywords

BMC Medical Genomics

ISSN: 1755-8794

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