Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2024 Sep 20;103(38):e39813. doi: 10.1097/MD.0000000000039813

Sedentary and 21 gastrointestinal disorders: A Mendelian randomization study

Yunzhi Lin a, Jun He a, Zhen Ding a,*
PMCID: PMC11419558  PMID: 39312361

Abstract

Sedentary behavior (SB) has been linked in the past by observational studies to gastrointestinal illnesses, although the exact cause of the link is still unknown. To deal with this problem, we carried out a Mendelian randomization (MR) study to thoroughly examine the connection between SB and common gastrointestinal illnesses. We selected instrumental variables representing the SB from the UK Biobank study, including watching television viewing, playing computer, and driving. In addition, we obtained genetic associations of 21 common gastrointestinal disorders from the FinnGen research. After adjusting for common risk factors associated with gastrointestinal diseases, we analyzed the independent association between genetic. Furthermore, we used the inverse-variance weighted (IVW) method in conjunction with complementing techniques like MR-Egger (Mendelian randomization based on Egger Regression) and weighted median to assure the accuracy and dependability of the results. Our findings suggest that genetic susceptibility to prolonged television viewing is significantly associated with an increased risk of 9 out of 21 gastrointestinal disorders. Specifically, these disorders include gastroesophageal reflux disease, chronic gastritis, cholelithiasis, acute pancreatitis, chronic pancreatitis, gastroduodenal ulcer, fatty liver, irritable bowel syndrome, and acute appendicitis. These associations remained significant even after correcting for potential confounding factors. The replication analysis confirms the same conclusion. The results of this study demonstrate a causal relationship between cachexia and genetically predicted SB. To further understand the underlying pathogenic mechanisms at play, more study is required.

Keywords: Gastrointestinal diseases, Mendelian randomization, sedentary, single nucleotide polymorphisms, watching television

1. Introduction

Any activity with an intensity of <1.5 metabolic equivalents is considered sedentary behavior (SB), which has a variety of negative effects on human health.[1,2] The sedentary lifestyle, which includes a variety of activities like watching television, using computers, and driving, is quickly becoming prevalent throughout the world.[3,4] A growing number of research have shown that SB has a significant impact on human health, indicating that there has been tremendous progress in the study of SB and its effects on health. As a result, providing appropriate advice to limit inactive time may help in lowering the incidence of diseases, a global public health concern that is on the rise.[1,2,5–8]

The relationship between sedentary time and cognitive function in older individuals was explored in a cross-sectional research of older persons from Japan. The study proved that extended periods of inactivity were detrimental to one’s capacity to orient oneself and that this association was held even after controlling for potential confounding variables like moderate to strenuous physical exercise. This result implies that decreasing sedentary time may benefit older persons’ cognitive function.[9] Reducing the amount of time people spend watching television may be a goal for public health interventions, as evidenced by a large prospective American study demonstrating long-term television viewing is significantly linked to the risk of 8 different forms of death.[10] A different investigation involving 430,584 participants from the UK Biobank discovered that SB was linked to a higher risk of colorectal cancer and that cutting out on sedentary time may help to lower this risk.[11] Furthermore, a review of the National Association of Retired Persons Diet and Health Study revealed a slight association between SB and stomach and esophageal cancers.[12]

Although earlier studies have shown that SB is a risk factor for gastrointestinal disorders, it is critical to recognize that confounding factors and the possibility of reverse causal relationships cannot be discounted. These studies frequently have intrinsic shortcomings as well. As a result, it is unclear if there is a causative link between SB and gastrointestinal disorders and what specific function it plays in aggravating these problems. Mendelian Randomization (MR) analysis, leveraging the comprehensive Genome wide association study (GWAS) database, offers a robust approach to address these complexities. By taking into account allele distribution randomization during gamete production, MR performs similarly to randomized controlled trials and is in accordance with Mendel’s second rule of genetics. This technique uses genetic variants as instrumental factors to examine causal relationships between illness phenotypes and phenotypes. Notably, it endeavors to mitigate potential confounders and biases stemming from reverse causal relationships, thereby enhancing the credibility of findings to a significant extent.[13,14]

MR procedures have advanced significantly as a result of the recent creation of large-scale GWAS databases, which provide associated genetic variants and single nucleotide polymorphisms (SNPs) through DNA and whole genome sequencing. While previous MR studies have explored the link between recreational SB and certain diseases,[15,16] the relationship between SB and gastrointestinal diseases has been less explored. In this study, we aim to fill this gap by investigating the causal association between SB and 21 gastrointestinal diseases using 3 types of leisurely SBs (watching television, playing on the computer, and driving) as exposures. Through MR analysis methods using existing GWAS datasets, we aim to provide further evidence to support global public health efforts.

2. Materials and methods

To establish a causal connection, MR analysis makes 3 assumptions instrumental variables must have a strong correlation with SB, instrumental variables can only affect gastrointestinal diseases through SB, and instrumental variables cannot be influenced by other confounding factors in the occurrence and progression of gastrointestinal diseases.[13,17] Public GWAS databases will be used as the data source in this study, and all available data will be obtained and used. No new ethical approval is needed for this investigation because all participants in the original studies gave their informed consent and got ethical approval from the relevant ethics committee.

3. SB data sources

A total of 437,887, 360,895, and 310,555 samples were included in this study, utilizing GWAS data from the UK Biobank database as genetic predictive instrumental variables for TV watching, computer playing, and driving (Table 1). The UK Biobank is a large prospective research cohort that enrolled approximately half a million subjects from 2006 to 2010 to identify the causes of disease through self-report, medical records, and follow-up studies to benefit global human health.[18] Using Wang et al’s GWAS meta-analysis of leisure screen time for 526,725 Europeans as a validation group.[15] To access the corresponding instrumental variables, we applied a gene-wide significance threshold of P < 5E−08, while also removing the effect of linkage disequilibrium (clump r2 < 0.001 and clump distance > 100,00kb). In cases where the number of screened SNPs was less than 3, we adjusted the threshold to P < 5E−06. Ultimately, we identified 113, 83, and 59 SNPs from the GWAS dataset that represented SB as instrumental variables for TV watching, computer playing, and driving time, respectively. Additionally, we removed SNPs with inverted repeat sequences to ensure that the effects of these SNPs on exposure corresponded to the same alleles as the effects.

Table 1.

Sources of exposure and outcome data.

Exposure/outcome Cases Controls/sample size Data sources
Time spent watching television – 437,887 https://gwas.mrcieu.ac.uk/
Leisure screen time – 526,725 https://www.ebi.ac.uk/gwas/
Time spent using computer – 360,895 https://gwas.mrcieu.ac.uk/
Time spent driving – 310,555 https://gwas.mrcieu.ac.uk/
Body mass index – 339,224 https://portals.broadinstitute.org/collaboration/giant
Cigarettes smoked per day – 249,752 https://genome.psych.umn.edu/index.php/GSCAN
Type 2 diabetes – 655,666 http://cnsgenomics.com/data.html
Gastroesophageal reflux 22,867 292,256 https://r8.finngen.fi/
Esophageal cancer 503 259,583 https://r8.finngen.fi/
Acute gastritis 2120 292,256 https://r8.finngen.fi/
Chronic gastritis 8621 292,256 https://r8.finngen.fi/
Gastroduodenal ulcer 8240 292,256 https://r8.finngen.fi/
Gastric cancer 1227 259,583 https://r8.finngen.fi/
Fatty liver 1908 340,591 https://r8.finngen.fi/
Liver fibrosis 130 338,951 https://r8.finngen.fi/
Cirrhosis 3548 338,951 https://r8.finngen.fi/
Hepatoma 648 259,583 https://r8.finngen.fi/
Cholelithiasis 34,461 301,383 https://r8.finngen.fi/
Acute pancreatitis 5509 301,383 https://r8.finngen.fi/
Chronic pancreatitis 3002 301,383 https://r8.finngen.fi/
Pancreatic cancer 1249 259,583 https://r8.finngen.fi/
Celiac Disease 3320 328,204 https://r8.finngen.fi/
Ulcerative colitis 4460 337,399 https://r8.finngen.fi/
Crohn’s disease 1484 340,875 https://r8.finngen.fi/
Irritable bowel syndrome 8116 276,683 https://r8.finngen.fi/
Acute appendicitis 25,706 314,763 https://r8.finngen.fi/
Colon cancer 3292 259,583 https://r8.finngen.fi/
Rectal cancer 2017 259,583 https://r8.finngen.fi/
Open in a new tab

4. Gastrointestinal disease data sources

The 21 gastrointestinal diseases used in this study were obtained from the latest version (r8) of the FinnGen database (https://r8.finngen.fi/, RRID: SCR_022254), which is a large-scale biospecimen database that employs reliable diagnostic technologies and public health systems (Table 1). The database has been collecting data from the national health registry of the Finnish population since 1969, enabling a deeper understanding of genome-health associations and facilitating the medical community’s exploration of the causes of disease in the population.[19,20] We utilized GWAS data for 21 gastrointestinal diseases from the FinnGen database. The sample populations for the exposure and outcome were of European ethnic origin and showed no apparent overlap.

5. Analysis of MR

In the present study, we utilized MR analysis to investigate the genetically predicted causal links between leisure SBs, namely television watching, computer playing, and driving time, and 21 gastrointestinal disorders. Simultaneously using replication analysis to validate our findings. The multiplicative random effects inverse-variance weighted (IVW) method, which uses weighted linear regression and presumes that the chosen instrumental variables are independent of one another, was the primary statistical technique used. This approach is robust even in the presence of heterogeneity.[21] We used IVW in conjunction with complementing techniques like Mendelian randomization based on Egger Regression (MR-Egger) and weighted median to assure the accuracy and dependability of the results.[22–24] The MR-Egger approach checks the horizontal pleiotropy of instrumental variables and determines whether an intercept term is present. When the P-value is <.05, we consider the study results to suggest potential horizontal pleiotropy.[25] Heterogeneity in the IVW method was assessed by estimating Cochran’s Q value, and we assume the presence of heterogeneity between instrumental variables when the P-value is <.05.[26] We identified aberrant SNPs that affect the outcomes of the MR analysis using the MR-PRESSO tool, and then we repeated the MR analysis after eliminating those SNPs to remove any outliers among the instrumental variables.[27]

Additionally, we used a multivariate MR approach to evaluate the independent effects of television viewing and gastrointestinal disorders while controlling for variables frequently associated with gastrointestinal disorders (Table 1), such as body mass index (BMI), daily smoking, and type 2 diabetes, to investigate the causal association between genetically predicted television viewing and gastrointestinal disorders.[28–31] In this study, we employed a 2-sample MR approach to estimate the effects of the SB on the intermediate factors. Subsequently, we used multivariable MR to adjust for SB and evaluate the impact of the intermediate factors on gastrointestinal diseases. Finally, the proportion of the mediation effect is calculated by multiplying these 2 effect estimates and dividing the result by the total effect. This comprehensive approach provides valuable insights into the mediating role of the intermediate factors between exposure and gastrointestinal diseases. Besides, we applied the Benjamin-Hochberg method to control the False Discovery Rate (FDR) for correcting multiple tests. Correlations with a corrected P-value < .05 using the Benjamin–Hochberg method were considered statistically significant. To avoid the bias of weak instrumental variables, we calculated the F-value statistic of instrumental variables by the formula β²/SE² and excluded it when the F-value was <10.[32] Analyses were performed in R software (RRID: SCR_000432) and carried out with TwoSampleMR, MendelianRandomization, and MR-PRESSO packages.[27,33,34]

6. Results

In this study, the instrumental variables selected for analysis had F-statistics ranging from 20 to 716 for BMI, 29 to 961 for daily smoking, and 29 to 1578 for type 2 diabetes. For the time spent watching television, the F-statistics ranged from 30 to 151, while for the time spent playing on the computer, they ranged from 29 to 89, and for the time spent driving, they ranged from 20 to 39. LSB Instrumental variables estimation F statistical value is between 29 and 97. Notably, all of these instrumental variables had F-values >10, indicating that no bias was found for weak instrumental variables (Tables S1–S5, Supplemental Digital Content, http://links.lww.com/MD/N612).

7. Univariate results

When conducting a univariate MR study investigating the relationship between television viewing time and gastrointestinal diseases, we observed a significant correlation between television viewing time and 9 distinct gastrointestinal diseases after applying FDR correction (Fig. 1). Specifically, genetically predicted TV watching time was associated with gastroesophageal reflux disease(GERD) (OR = 1.454; 95% CI: 1.160–1.824; P = 1.19E−04), chronic gastritis (OR = 1.933; 95% CI: 1.421–2.630; P = 2.73E−05), gastroduodenal ulcer (OR = 1.712; 95% CI: 1.199–2.446; P = .003), fatty liver (OR = 2.347; 95% CI: 1.203–4.579; P = .012), cholelithiasis (OR = 1.464; 95% CI: 1.200–1.787; P = 1.77E−04), acute pancreatitis (OR = 2.725; 95% CI: 1.863–3.985; P = 2.35E−07), chronic pancreatitis (OR = 2.821; 95% CI: 1.539–5.169; P = 7.90E−04), irritable bowel syndrome (IBS) (OR = 1.619; 95% CI: 1.164–2.253; P = .004), and acute appendicitis (OR = 1.339; 95% CI: 1.339–1.649; P = .005) with significant positive associations (Table S6, Supplemental Digital Content, http://links.lww.com/MD/N612). The replication analysis of LST also demonstrated consistent results with our initial discovery (Fig. 2, Table S7, Supplemental Digital Content, http://links.lww.com/MD/N612). The findings of the MR study between computer use time and gastrointestinal disorders showed evidence of a negative association between genetically predicted computer play time and GERD (OR = 0.646; 95% CI: 0.503–0.831; P = 6.453E−04), IBS (OR = 0.606; 95% CI: 0.408–0.902; P = .013) (Table S8, Supplemental Digital Content, http://links.lww.com/MD/N612). What is more, the results of our MR analysis indicated that genetically predicted driving times were not linked to gastrointestinal disorders (Table S9, Supplemental Digital Content, http://links.lww.com/MD/N612). The complementary methods MR-Egger method and Weighted median in this univariate analysis also sustain the concept of the IVW method.

Figure 1.

Figure 1.

Open in a new tab

Genetic susceptibility to TV viewing time and association with 21 gastrointestinal diseases.

Figure 2.

Figure 2.

Open in a new tab

Genetic susceptibility to leisure screen time and association with 21 gastrointestinal diseases in the Validation analysis.

8. Sensitivity analysis

Heterogeneity and pleiotropy in the MR analyses were assessed using Cochran’s Q test and the MR-Egger (Mendelian randomization based on Egger Regression) intercept test. In the univariate analysis, we observed slight to moderate heterogeneity and horizontal pleiotropy. In the univariate analysis, we observed mild to moderate heterogeneity and identified horizontal pleiotropy in the associations between television viewing and duodenal ulcer (P = .025), computer use and acute appendicitis (P = .020), as well as driving and IBS (P = .023). However, even when the MR analysis was performed again after the potential outliers were removed by MR-PRESSO, the conclusions remained unchanged (Tables S10–S12, Supplemental Digital Content, http://links.lww.com/MD/N612).

9. Mediation MR analysis

We analyzed genetically predicted independent influences on television watching time after adjustment for BMI, daily smoking, and diabetes to probe the pathological mechanisms between television watching and gastrointestinal disorders. The MR analysis results indicated a significant correlation between the predicted TV watching time based on Gene prediction and an increased risk of BMI, smoking, and diabetes (Table S13, Supplemental Digital Content, http://links.lww.com/MD/N612). Table 2 shows the results of multivariate MR analysis and the proportion of mediating factors. In our multivariate analysis, we observed that a substantial proportion (30.51%) of the relationship between TV watching time and gallstone was mediated by BMI. Additionally, the combined effects of BMI and 3 other mediators accounted for a considerable proportion (41.75%) of this relationship (Table 2, Table S14, Supplemental Digital Content, http://links.lww.com/MD/N612). Our discovery of genetically predicted time spent watching TV after adjustment for BMI was associated with fatty liver (OR = 1.294; 95% CI: 0.474–3.538; P = .616) was no longer relevant. The replication analysis unequivocally corroborates the identical conclusion (Table S15, Supplemental Digital Content, http://links.lww.com/MD/N612). In addition to this, when watching TV time was causally affiliated with 9 gastrointestinal disorders in univariate MR analysis, genetically predicted TV time was still relevant to these 9 gastrointestinal disorders, whether corrected for BMI, daily smoking, or diabetes.

Table 2.

The association and intermediary proportion of Gene prediction TV watching time adjusted by mediators with 9 gastrointestinal diseases.

Outcome Unadjusted Adjust BMI Adjust cigarettes per day Adjust type 2 diabetes Adjust BMI, cigarettes per day, Type 2 diabetes
P P Mediation effect (%) P Mediation effect (%) P Mediation effect (%) P Mediation effect (%)
Gastroesophageal reflux 1.19E−03 .010 7.16% .031 10.77% .001 11.59% .045 19.35%
Chronic gastritis 2.73E−05 .123 0.97% .001 4.83% .015 10.67% .563 15.97%
Gastroduodenal ulcer .003 .026 2.47% .005 10.61% .019 12.09% .825 27.56%
Nonalcoholic fatty liver disease .012 .616 22.08% .006 −7.55% .030 21.24% .413 32.91%
Cholelithiasis 1.77E−04 .268 30.51% .001 13.75% .058 8.05% .772 41.75%
Acute pancreatitis 2.35E−07 .002 1.54% 1.11E−04 2.14% .009 5.11% .278 9.30%
Chronic pancreatitis 7.90E−04 .046 −5.46% .009 5.55% .027 11.27% .079 16.95%
Irritable bowel syndrome .004 .032 6.15% .045 8.06% .026 3.65% .046 12.41%
Acute appendicitis .005 9.95E−04 −2.17% .003 7.36% .008 3.45% .023 10.75%
Open in a new tab

BMI = body mass index.

10. Discussion

In this work, we investigated the potential causal association between SB and 21 different gastrointestinal disorders using open-source GWAS databases. Our findings revealed that 9 of the gastrointestinal conditions we looked at were at risk due to watching television, a type of SB, the replication analysis also validated this finding. Both univariate and multivariate MR studies that corrected for potentially confounding variables such as BMI, smoking, and diabetes came to the same conclusions. These findings may offer important new understandings of the genetic pathways underpinning the association between gastrointestinal disorders and SB.

SB may be a substantial risk factor for more than a dozen malignancies, including esophageal, oropharyngeal, lung, endometrial, gastric, and colon cancers, according to a recent large prospective cohort study.[35,36] However, no such potential association was found in the MR analysis conducted by Zhang et al and the current study.[37] The disparities in the results are probably caused by the influence of prior observational research with confounding variables and unavoidable reverse causal relationships. Another reasonable explanation is that the limited sample size used in this study made it impossible to identify an association and necessitated additional research to determine the causal link between SB and cancer.

That SB promotes bad eating patterns and lifestyle choices that can eventually result in obesity is 1 explanation for the molecular linkages linking SB and cancer. As a risk factor for the development of cancer, obesity can encourage carcinogenesis and progression through a variety of pathways: altering signaling pathways and the insulin-like growth factor system, which are separate risk factors for the growth of tumor cells; [38] obese people produce more estrogen, which is linked to a higher risk of breast cancer in women after menopause;[39] functioning as a persistent inflammatory biomarker that increases leptin and lipocalin release from adipose tissue, which promotes the growth of cancer by causing DNA and mitochondrial damage[40,41]; and dramatically raising the incidence of cancer through several routes, including adipocytokines, changed gut flora, and increased ectopic fat deposition.[42,43]

According to the findings of a prior prospective study looking at the relationship between physical activity and Barrett’s esophagus, being frequently sedentary is probably a risk factor for gastroesophageal reflux disease(GERD) conditions.[44] Similarly to this, an MR analysis of 29,975 GERD cases showed a connection between TV viewing and computer use.[45] Using MR techniques from several GWAS databases, both univariate and multivariate, the results of the current study were verified. A significant population-based cohort study further supported the link between a sedentary lifestyle and an increased risk of IBS.[46] Additionally, a cross-sectional study of medical students in Peru indicated that stress and a sedentary lifestyle were independent risk factors for IBS.[47] We confirmed the importance of SB as an independent factor in IBS using MR techniques. A sedentary lifestyle decreases the peristaltic action of the intestine, which can lead to decreased gravitational force for descending colon dynamics and emptying as well as chronic intestinal obstruction made worse by protracted colon distension. This is the most likely pathogenic reason for this.[47,48] More studies are required to substantiate the claim that physical activity and exercise can help prevent IBS by increasing intestinal motility and absorptive capacity. Notably, this study showed that SB, such as using a computer, maybe a protective factor against IBS, but its statistical significance was not significant, and there aren’t any controlled studies that examine the relationship between the 2. So, it is advised to proceed with caution when interpreting these data.

Studies on the connection between SB and digestive disorders are scarce. The most likely explanation at this time is that SB can cause gastrointestinal issues due to a variety of reasons, including obesity, poor lifestyles, and ability intake. Adult TV consumption has been linked in numerous studies to obesity, and cutting back on TV time can result in lower calorie intake and healthier lifestyle choices.[49,50] This MR study reinforces these findings.

By accounting for common risk factors for gastrointestinal disorders, this study sought to analyze the independent impact of sedentary activity while examining the relationship between SB and various gastrointestinal diseases. Gastrointestinal diseases cause significant economic losses to individuals and the nation as a whole, making it critical to raise public awareness of the harmful effects of SB,[51] as demonstrated in this study. Our findings can help improve clinicians’ prevention strategies for gastrointestinal disorders and provide further guidance for patients at high risk.

The main advantage of this study is the use of MR analysis methods and the correction of BMI, smoking, and diabetes risk factors, which greatly reduces confounding factors and bias in reverse causal associations in observational studies, thereby providing accurate causal inference between SB and gastrointestinal diseases. Additionally, by restricting the GWAS population to those with European ancestry, we avoided population stratification bias. This study does, however, have certain drawbacks. First, the potential for multiplicity, which cannot be completely excluded, is the main drawback of MR analysis. To reduce the bias caused by horizontal multiplicity, we used 1-column approaches, multivariate methods, and sensitivity analysis. Second, due to the unavailability of instrumental variables estimation for gastrointestinal diseases, we were unable to investigate the 2-way association between SB and gastrointestinal diseases in the Gene prediction analysis. Third, because we used a linear approach, it is possible that we won’t be able to determine the causal link when SB and disease have a nonlinear association.

11. Conclusion

In summary, this MR study offers compelling evidence that limiting SB, particularly television viewing time, may be able to halt the onset and progression of some gastrointestinal disorders. Our analysis validates the hypothesis that sedentary activity has an independent impact on gastrointestinal health by controlling for common risk factors and confounding variables. Our findings imply that encouraging physical activity and minimizing SB might be a useful strategy for avoiding and managing gastrointestinal disorders, while further study is required to fully understand the underlying mechanisms.

Acknowledgments

Thanks to the researchers who have contributed to Genome wide association study (GWAS) data.

Author contributions

Conceptualization: Zhen Ding.

Formal analysis: Zhen Ding.

Investigation: Yunzhi Lin.

Software: Yunzhi Lin.

Validation: Jun He.

Visualization: Yunzhi Lin.

Writing – original draft: Yunzhi Lin.

Writing – review & editing: Yunzhi Lin.

Supplementary Material

Abbreviations:

BMI
body mass index
FDR
false discovery rate
GERD
gastroesophageal reflux disease
GWAS
genome wide association study
IBS
irritable bowel syndrome
IVW
inverse-variance weighted
MR
Mendelian randomization
MR-Egger
Mendelian randomization based on Egger regression
SB
sedentary behavior
SNPs
single nucleotide polymorphisms

The authors have no funding and conflicts of interest to disclose.

All participants in the original studies gave their informed consent.

All data used in this study were obtained from publicly available databases; further ethical approval was not required.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Lin Y, He J, Ding Z. Sedentary and 21 gastrointestinal disorders: A Mendelian randomization study. Medicine 2024;103:38(e39813).

Contributor Information

Yunzhi Lin, Email: 1136778488@qq.com.

Jun He, Email: 2861194191@qq.com.

References

  • [1].Salmon J, Tremblay MS, Marshall SJ, Hume C. Health risks, correlates, and interventions to reduce sedentary behavior in young people. Am J Prev Med. 2011;41:197–206. [DOI] [PubMed] [Google Scholar]
  • [2].Park JH, Moon JH, Kim HJ, Kong MH, Oh YH. sedentary lifestyle: overview of updated evidence of potential health risks. Korean J Fam Med. 2020;41:365–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [3].Pate RR, O’Neill JR, Lobelo F. The evolving definition of “sedentary”. Exerc Sport Sci Rev. 2008;36:173–8. [DOI] [PubMed] [Google Scholar]
  • [4].Hamer M, Chida Y. Physical activity and risk of neurodegenerative disease: a systematic review of prospective evidence. Psychol Med. 2009;39:3–11. [DOI] [PubMed] [Google Scholar]
  • [5].Owen N, Healy GN, Matthews CE, Dunstan DW. Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38:105–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Saunders TJ, McIsaac T, Douillette K, et al. Sedentary behaviour and health in adults: an overview of systematic reviews. Appl Physiol Nutr Metab. 2020;45(10 Suppl. 2):S197–217. [DOI] [PubMed] [Google Scholar]
  • [7].Park S, Lee S, Kim Y, et al. Causal effects of physical activity or sedentary behaviors on kidney function: an integrated population-scale observational analysis and Mendelian randomization study. Nephrol Dial Transplant. 2022;37:1059–68. [DOI] [PubMed] [Google Scholar]
  • [8].Lee J, Chang RW, Ehrlich-Jones L, et al. Sedentary behavior and physical function: objective evidence from the osteoarthritis initiative. Arthritis Care Res (Hoboken). 2015;67:366–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Chen S, Chen T, Honda T, Nofuji Y, Kishimoto H, Narazaki K. Associations of objectively-measured sedentary time and patterns with cognitive function in non-demented Japanese older adults: a cross-sectional study. Int J Environ Res Public Health. 2022;19:1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [10].Keadle SK, Moore SC, Sampson JN, Xiao Q, Albanes D, Matthews CE. Causes of death associated with prolonged TV viewing: NIH-AARP diet and health study. Am J Prev Med. 2015;49:811–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Morris JS, Bradbury KE, Cross AJ, Gunter MJ, Murphy N. Physical activity, sedentary behaviour and colorectal cancer risk in the UK Biobank. Br J Cancer. 2018;118:920–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Cook MB, Matthews CE, Gunja MZ, Abid Z, Freedman ND, Abnet CC. Physical activity and sedentary behavior in relation to esophageal and gastric cancers in the NIH-AARP cohort. PLoS One. 2013;8:e84805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318:1925–6. [DOI] [PubMed] [Google Scholar]
  • [14].Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22. [DOI] [PubMed] [Google Scholar]
  • [15].Wang Z, Emmerich A, Pillon NJ, et al. Lifelines Cohort Study. Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention. Nat Genet. 2022;54:1332–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Bahls M, Leitzmann MF, Karch A, et al. Physical activity, sedentary behavior and risk of coronary artery disease, myocardial infarction and ischemic stroke: a two-sample Mendelian randomization study. Clin Res Cardiol. 2021;110:1564–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Sekula P, Del Greco MF, Pattaro C, Kottgen A. Mendelian Randomization as an approach to assess causality using observational data. J Am Soc Nephrol. 2016;27:3253–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Peltonen L, Jalanko A, Varilo T. Molecular genetics of the Finnish disease heritage. Hum Mol Genet. 1999;8:1913–23. [DOI] [PubMed] [Google Scholar]
  • [20].Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;615:E19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].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;36:1783–802. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].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;40:304–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].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;45:1961–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology. 2017;28:30–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181:251–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Locke AE, Kahali B, Berndt SI, et al. LifeLines Cohort Study. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Liu M, Jiang Y, Wedow R, et al. 23andMe Research Team. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51:237–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Xue A, Wu Y, Zhu Z, et al. eQTLGen Consortium. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 2018;9:2941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Burgess S, Thompson SG, Collaboration CCG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755–64. [DOI] [PubMed] [Google Scholar]
  • [33].Yavorska OO, Burgess S. Mendelian randomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46:1734–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Hemani G, Zheng J, Elsworth B, et al. The MR-base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Kerr J, Anderson C, Lippman SM. Physical activity, sedentary behaviour, diet, and cancer: an update and emerging new evidence. Lancet Oncol. 2017;18:e457–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Hunter RF, Murray JM, Coleman HG. The association between recreational screen time and cancer risk: findings from the UK Biobank, a large prospective cohort study. Int J Behav Nutr Phys Act. 2020;17:97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Zhang X, Theodoratou E, Li X, et al. Genetically predicted physical activity levels are associated with lower colorectal cancer risk: a Mendelian randomisation study. Br J Cancer. 2021;124:1330–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Kaaks R, Toniolo P, Akhmedkhanov A, et al. Serum C-peptide, insulin-like growth factor (IGF)-I, IGF-binding proteins, and colorectal cancer risk in women. J Natl Cancer Inst. 2000;92:1592–600. [DOI] [PubMed] [Google Scholar]
  • [39].Key TJ, Appleby PN, Reeves GK, et al. Endogenous Hormones Breast Cancer Collaborative Group. Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women. J Natl Cancer Inst. 2003;95:1218–26. [DOI] [PubMed] [Google Scholar]
  • [40].Sabharwal SS, Schumacker PT. Mitochondrial ROS in cancer: initiators, amplifiers or an Achilles’ heel? Nat Rev Cancer. 2014;14:709–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].van Kruijsdijk RC, van der Wall E, Visseren FL: Obesity and cancer: the role of dysfunctional adipose tissue. Cancer Epidemiol Biomarkers Prev. 2009;18:2569–78. [DOI] [PubMed] [Google Scholar]
  • [42].Lynch BM. Sedentary behavior and cancer: a systematic review of the literature and proposed biological mechanisms. Cancer Epidemiol Biomarkers Prev. 2010;19:2691–709. [DOI] [PubMed] [Google Scholar]
  • [43].Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: emerging biological mechanisms and perspectives. Metabolism. 2019;92:121–35. [DOI] [PubMed] [Google Scholar]
  • [44].Lam S, Alexandre L, Luben R, Hart AR. The association between physical activity and the risk of symptomatic Barrett’s oesophagus: a UK prospective cohort study. Eur J Gastroenterol Hepatol. 2018;30:71–5. [DOI] [PubMed] [Google Scholar]
  • [45].Sun Y, Cao X, Cao D, et al. Genetic estimation of correlations and causalities between multifaceted modifiable factors and gastro-oesophageal reflux disease. Front Nutr. 2022;9:1009122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [46].Gao X, Tian S, Huang N, Sun G, Huang T. Associations of daily sedentary behavior, physical activity, and sleep with irritable bowel syndrome: a prospective analysis of 362,193 participants. J Sport Health Sci. 2023;13:72–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [47].Vasquez-Rios G, Machicado JD, Ticse R, et al. Stress and a sedentary lifestyle are associated with irritable bowel syndrome in medical students from Peru: a cross-sectional study. Eur J Gastroenterol Hepatol. 2019;31:1322–7. [DOI] [PubMed] [Google Scholar]
  • [48].Robbins SE. A common cause of irritable bowel syndrome and diverticulitis: chronic distal colon distention from sedentary behavior and excessive dietary fiber. Expert Rev Gastroenterol Hepatol. 2013;7:413–9. [DOI] [PubMed] [Google Scholar]
  • [49].Hu FB, Li TY, Colditz GA, Willett WC, Manson JE. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA. 2003;289:1785–91. [DOI] [PubMed] [Google Scholar]
  • [50].Xie YJ, Stewart SM, Lam TH, Viswanath K, Chan SS. Television viewing time in Hong Kong adult population: associations with body mass index and obesity. PLoS One. 2014;9:e85440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [51].Peery AF, Crockett SD, Murphy CC, et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: Update 2021. Gastroenterology. 2022;162:621–44. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

medi-103-e39813-s001.xlsx (119KB, xlsx)

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES