==== Front Cancer Med Cancer Med 10.1002/(ISSN)2045-7634 CAM4 Cancer Medicine 2045-7634 John Wiley and Sons Inc. Hoboken 37148539 10.1002/cam4.5974 CAM45974 CAM4-2022-10-4637.R1 Research Article RESEARCH ARTICLES Cancer Prevention Genetic associations of leisure sedentary behaviors and the risk of 15 site‐specific cancers: A Mendelian randomization study Chen et al. Chen Jinwei 1 2 Yang Kaibin 1 Qiu Youyu 1 3 Lai Weijie 2 Qi Sifan 2 Wang Gaoyuan 1 Chen Lin 1 Li Kunpeng 1 Zhou Dan 1 Liu Qing 4 Tang Linglong https://orcid.org/0000-0002-8561-1454 1 Liu Xu https://orcid.org/0000-0003-4827-5524 1 Du Xiaojing 1 duxj@sysucc.org.cn Guo Rui 1 guorui@sysucc.org.cn Ma Jun https://orcid.org/0000-0002-1137-9349 1 majun2@mail.sysu.edu.cn 1 Department of Radiation Oncology Sun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou P. R. China 2 Zhongshan School of Medicine Sun Yat‐sen University Guangzhou P. R. China 3 Department of Radiation Oncology Sixth Affiliated Hospital, Kunming Medical University Yuxi Yunan China 4 Department of Medical Statistics and Epidemiology, School of Public Health Sun Yat‐sen University Guangzhou P. R. China * Correspondence Jun Ma, Rui Guo and Xiaojing Du, Department of Radiation Oncology, Sun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, 651 Dongfeng Road East, Guangzhou 510060, P. R. China. Email: majun2@mail.sysu.edu.cn; guorui@sysucc.org.cn; duxj@sysucc.org.cn 06 5 2023 6 2023 12 12 10.1002/cam4.v12.12 1362313636 31 3 2023 21 10 2022 08 4 2023 © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Abstract Background and Aims Leisure sedentary behavior (LSB) is associated with the risk of cancer, but the causal relationship between them has not been clarified. The aim of this study was to assess the potential causal association between LSB and risk of 15 site‐specific cancers. Methods The causal association between LSB and cancer were assessed with univariate Mendelian randomization (UVMR) and multivariate Mendelian randomization (MVMR). 194 SNPs associated with LSB (from the UK Biobank 408,815 individuals) were adopted as the instrument variables. Sensitivity analyses were performed to ensure the robustness of the results. Results UVMR analysis revealed that television watching significantly increased the risk of endometrial cancer (OR = 1.29, 95% CI = 1.02–1.64, p = 0.04) (mainly the endometrioid histology [OR = 1.28, 95% CI = 1.02–1.60, p = 0.031])，breast cancer (OR = 1.16, 95% CI = 1.04–1.30, p = 0.007) (both ER+ breast cancer [OR = 1.17, 95% CI = 1.03–1.33, p = 0.015], and ER− breast cancer [OR = 1.55, 95% CI = 1.26–1.89, p = 2.23 × 10−5]). Although causal association was not found between television watching and ovarian cancer, it was seen in low grade and low malignant potential serous ovarian cancer (OR = 1.49, 95% CI = 1.07–2.08, p = 0.018). However, significant results were not obtained in the UVMR analysis between driving, computer use and the 15 types of cancer. Further MVMR analysis indicated that the above results are independent from most metabolic factors and dietary habits, but mediated by educational attainment. Conclusion LSB in form of television watching has independent causal association with the risk of endometrial cancer, breast cancer, and ovarian cancer. cancer risk factors epidemiology and prevention genome‐wide association women's cancer National Natural Science Foundation of China 10.13039/501100001809 81930072 82172870 the Key‐Area Research and Development Program of Guangdong Province2019B020230002 the Overseas Expertise Introduction Project for Discipline Innovation111 Project B14035 source-schema-version-number2.0 cover-dateJune 2023 details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.3.0 mode:remove_FC converted:03.07.2023 Chen J , Yang K , Qiu Y , et al. Genetic associations of leisure sedentary behaviors and the risk of 15 site‐specific cancers: A Mendelian randomization study. Cancer Med. 2023;12 :13623‐13636. doi:10.1002/cam4.5974 Jinwei Chen, Kaibin Yang and Youyu Qiu contributed equally to this work. ==== Body pmc1 INTRODUCTION Cancer is one of the leading causes of death globally and poses a major health threat to humans. 1 There were an estimated 19.3 million new cancer cases and 10.0 million cancer deaths in 2020. 2 Premature deaths from cancer have significantly reduced life expectancy in both developing and developed countries. 3 An increasing volume of evidence has shown the benefits of early prevention of cancer, emphasizing the tremendous potential for the development of good lifestyle habits to prevent the incidence of cancer. 4 , 5 , 6 , 7 Leisure sedentary behaviors (LSBs) are any activity during which one is seated, reclined, or lying, and does not exert much energy (≤1.5 metabolic equivalents). 8 , 9 On average, a British adult spends 5 h a day sedentary, 10 a French adult sits for 12 h on weekdays, 11 and an American adult spends 55% of their waking time sedentary, or 7.7 h per day. 12 Owing to their high prevalence, LSBs have become an important public health issue. High levels of sedentary time have been reported to be associated with an increased risk of many cancers in numerous previous observational studies. 5 , 13 , 14 , 15 , 16 Although much observational research has focused on understanding the association between LSB and cancer, 14 , 17 , 18 exploring causation between LSB and cancer is difficult with observational studies, in which confounding factors may affect the conclusions. 5 , 19 , 20 , 21 , 22 , 23 Mendelian randomization (MR) determines whether an observational association between a risk factor and an outcome is causal, by using genetic variants. 24 Typically, individuals inherit genetic variants that affect a risk factor at birth, and these variants are not confounded by other factors. Hence, differences in outcomes between carriers of variants and those without variants can be attributed to differences in risk factors. As this process is similar to random allocation of treatment in randomized controlled trials, reverse causation and the confounding problems in observational studies can be overcome, thus identifying a causal effect and providing evidence for it. 25 MR has three core assumptions. First, genetic variants must be associated with exposure, but single nucleotide polymorphisms (SNPs) need not be functional variants that are responsible for SNP‐exposure relationships. Second, genetic variants should not be associated with exposure‐outcome confounders. Third, genetic variants should be associated with outcome only through the exposure being studied. 26 On the basis of existing studies, we carried out this study in an effort to investigate the causal effects of LSB on the risk of 15 site‐specific cancers using univariable MR (UVMR) and multivariable MR (MVMR) methods. 2 METHODS 2.1 Genetic variants for leisure sedentary behaviors The instrumental variables (IVs) for LSBs were derived from a recent genome‐wide association analysis (GWAS) of sedentary behaviors in 408,815 volunteers of European ancestry registered in UK Biobank (Table 1). 27 During the study, LSB was measured as watching TV, interacting with a computer (excluding work‐related computer use), and driving, according to self‐reported questionnaires. We excluded participants whose sedentary phenotypes were outside of the 99.5% range on the right side of the normal distribution, because of the right‐skewness of sedentary phenotypes. This GWAS analysis revealed 193 independent genetic variants at 169 genetic loci associated with LSB, of which 152 genetic variants at 145 loci were associated with television watching, 37 genetic variants at 36 loci were associated with computer use, and 4 genetic variants at 4 loci were associated with driving (p < 1 × 10−8). TABLE 1 Overview of the genetic data for leisure sedentary behaviors, covariate variables, and 10 site‐specific cancers a . Trait GWAS ID Year Cohort Population PMID Gender Sample size Cases Controls Leisure sedentary behaviors NA 2020 UKB European 32317632 Female and male 408,815 12,136 396,679 Endometrial cancer ebi‐a‐GCST006464 2018 ECAC+E2C2+UKB European 30093612 Female 121,885 12,906 108,979 Endometrial cancer (NEE) ebi‐a‐GCST006466 2018 ECAC+E2C2+UKB European 30093612 Female 36,677 1230 35,447 Endometrial cancer (EE) ebi‐a‐GCST006465 2018 ECAC+E2C2+UKB European 30093612 Female 54,884 8758 46,126 Breast cancer ieu‐a‐1126 2017 BCAC+DRIVE European 29059683 Female 228,951 122,977 105,974 Breast cancer (ER+) ieu‐a‐1127 2017 BCAC+DRIVE European 29059683 Female 175,475 69,501 105,974 Breast cancer (ER‐) ieu‐a‐1128 2017 BCAC+DRIVE European 29059683 Female 127,442 21,468 105,974 Ovarian cancer ieu‐a‐1120 2017 OCAC+CIMBA European 28346442 Female 66,450 25,509 40,941 Ovarian cancer (LGS+LMPS) ieu‐a‐1229 2017 OCAC+CIMBA European 28346442 Female 43,907 2966 40,941 Ovarian cancer (HGS) ieu‐a‐1121 2017 OCAC+CIMBA European 28346442 Female 53,978 13,037 40,941 Ovarian cancer (MO) ieu‐a‐1231 2017 OCAC+CIMBA European 28346442 Female 43,507 2566 40,941 Ovarian cancer (EO) ieu‐a‐1125 2017 OCAC+CIMBA European 28346442 Female 43,751 2810 40,941 Ovarian cancer (CCO) ieu‐a‐1124 2017 OCAC+CIMBA European 28346442 Female 42,307 1366 40,941 Cervical cancer ukb‐b‐8777 2018 UKB European NA Female 462,933 1889 461,044 Oral cavity and pharyngeal cancer ieu‐b‐89 2016 INHANCE European 27749845 Female and male 5425 2497 2928 Esophageal cancer finn‐b‐C3_OESOPHAGUS 2021 FB European NA Female and male 218,792 232 218,560 Cancer of liver and intrahepatic bile ducts finn‐b‐C3_LIVER_INTRAHEPATIC_BILE_DUCTS 2021 FB European NA Female and male 218,792 304 218,488 Pancreatic cancer ieu‐a‐822 2009 PanScan1 European 19648918 Female and male 3835 1896 1939 Cancer of urinary organs finn‐b‐C3_URINARY_TRACT 2021 FB European NA Female and male 218,792 2168 216,624 Prostate cancer ieu‐b‐85 2018 PRACTICAL European 29892016 Male 140,254 79,148 61,106 Melanoma ukb‐a‐58 2017 UKB European NA Female and male 337,159 2677 334,482 Bladder cancer ukb‐b‐8193 2018 UKB European NA Female and male 462,933 1101 461,832 Glioma ieu‐a‐1013 2013 GliomaScan European 22,886,559 Female and male 6811 1856 4955 Thyroid cancer ieu‐a‐1082 2013 NA European 23894154 Female and male 1080 649 431 Colon cancer ukb‐d‐C3_COLON 2018 UKB European NA Female and male 361,194 2437 358,757 Triglycerides ebi‐a‐GCST002216 2013 GLGC European 24097068 Female and male 94,595 NA NA Cholesterol, total ebi‐a‐GCST002221 2013 GLGC European 24097068 Female and male 94,595 NA NA LDL cholesterol ebi‐a‐GCST002222 2013 GLGC European 24097068 Female and male 94,595 NA NA HDL cholesterol ebi‐a‐GCST002223 2013 GLGC European 24097068 Female and male 94,595 NA NA Type 2 diabetes ebi‐a‐GCST006867 2018 DIAGRAM+GERA+UKB European 30054458 Female and male 655,666 61,714 1178 Body mass index ieu‐a‐835 2015 GIANT European 25673413 Female and male 322,154 NA NA Year of schooling ieu‐a‐1001 2016 SSGAC European 27225129 Female and male 293,723 NA NA Cheese intake ukb‐b‐1489 2018 MRC‐IEU European NA Female and male 451,486 NA NA Alcoholic drinks intake ukb‐b‐13,978 2018 MRC‐IEU European NA Female and male 34,317 NA NA Chocolate‐covered raisin intake ukb‐b‐1160 2018 MRC‐IEU European NA Female and male 64,949 NA NA Chocolate‐covered biscuits intake ukb‐b‐5068 2018 MRC‐IEU European NA Female and male 64,949 NA NA Tea intake ukb‐b‐6066 2018 MRC‐IEU European NA Female and male 447,485 NA NA Ice‐cream intake ukb‐b‐17,189 2018 MRC‐IEU European NA Female and male 64,949 NA NA Coffee intake ukb‐b‐5237 2018 MRC‐IEU European NA Female and male 428,860 NA NA Cereal intake ukb‐b‐15,926 2018 MRC‐IEU European NA Female and male 441,640 NA NA Oatcakes intake ukb‐b‐6298 2018 MRC‐IEU European NA Female and male 64,949 NA NA Fresh fruit intake ukb‐b‐3881 2018 MRC‐IEU European NA Female and male 446,462 NA NA Fried potatoes intake ukb‐b‐12,836 2018 MRC‐IEU European NA Female and male 64,949 NA NA Drinking water intake ukb‐b‐3975 2018 MRC‐IEU European NA Female and male 64,949 NA NA Sweets intake ukb‐b‐10,217 2018 MRC‐IEU European NA Female and male 64,949 NA NA Muesli intake ukb‐b‐11,004 2018 MRC‐IEU European NA Female and male 64,949 NA NA Fizzy drink intake ukb‐b‐2832 2018 MRC‐IEU European NA Female and male 64,949 NA NA Savory biscuits intake ukb‐b‐1503 2018 MRC‐IEU European NA Female and male 64,949 NA NA Abbreviations: BCAC, Breast Cancer Association Consortium; CCO, clear cell ovarian cancer; CIMBA, Consortium of Investigators of Modifiers of BRCA1/2; DIAGRAM, Diabetes Genetics Replication and Meta‐analysis Consortium; DRIVE, Discovery, Biology and Risk of Inherited Variants in Breast Cancer Consortium; E2C2, the Epidemiology of Endometrial Cancer Consortium; ECAC, Endometrial Cancer Association Consortium; EE, endometrioid endometrial cancer; EO, endometrioid ovarian cancer; ER–, estrogen receptor negative; ER+, estrogen receptor positive; FB, Finnish Biobank; GERA, Genetic Epidemiology Research on Aging; GIANT, Genetic Investigation of Anthropometric Traits Consortium; HDL, high‐density lipoprotein; HGS, high‐grade serous; INHANCE, International Head and Neck Cancer Epidemiology Consortium; LDL, low‐density lipoprotein; LGS+LMPS, low‐grade and low malignant potential serous; MO, mucinous ovarian cancer; MRC‐IEU, MRC Integrative Epidemiology Unit; NEE, non‐endometrioid endometrial cancer; OCAC, Ovarian Cancer Association Consortium; PanScan1, Pancreatic Cancer Cohort Consortium; PRACTICAL, Prostate Cancer Association Group to Investigate Cancer‐Associated Alterations in the Genome Consortium; GLGC, Global Lipid Genetics Consortium; SSGAC, Social Science Genetic Association Consortium; UKB, UK Biobank. a Overview of genetic data used in the Mendelian randomization analyses. The study population were all European. For case–control studies, the number of cases and controls was reported, and for all studies, the sample size was reported. SNPs that met the following criteria were selected for MR analysis in the summary data of this GWAS: p < 5 × 10−8, independent of each other within 5000G, absence of linkage disequilibrium (r 2  ≤ 0.005, p < 0.05), and no moderate allele frequency (Table S1). 2.2 Instrumental variables for cancers The IVs associated with 15 cancers (ovarian, endometrial, breast, cervical, prostate, pancreatic, oral and pharyngeal, esophageal, liver and intrahepatic bile ducts, urinary organs, colon, bladder, thyroid, glioma, and melanoma), 6 metabolic factors (triglycerides [TC], total cholesterol, high‐density lipoprotein [HDL] cholesterol level, low‐density lipoprotein [LDL] cholesterol level, body mass index [BMI], and type 2 diabetes [T2D]), 11 dietary factors (intake of fizzy drink, coffee, tea, cereal, fresh fruit, chocolate‐covered raisin, chocolate‐covered biscuits, ice‐cream, fried potatoes, sweets, savory biscuits) and year of schooling were derived from published large GWAS. The detailed information of these studies is shown in Table 1. 2.3 Weak instrument bias analyses Weak instrument bias refers to bias produced by genetic variants with low power to explain exposure owing to insufficient sample size. According to the literature, the F‐statistic, equal to (n–2)R 2/(1–R 2), can be used to evaluate the effect of weak instrumental variables. 28 R 2 was the degree of sedentary behaviors explained by the SNP, and was calculated using the formula R 2 = 2 × (1 – EAF) × EAF×β 2/(se2 × n) (EAF refers to effect allele frequency, n was the sample size of the GWAS). Weak instrument bias is considered when F > 10. In addition, variation between individual genetic variant (I 2 GX) was calculated to assess potential weak instrument bias in the MR‐Egger regression analysis. Low risk of measurement bias was considered at I 2 GX > 95%. 29 2.4 Univariable Mendelian randomization analysis The causal relationship between individual SNPs and cancers was estimated by β‐value (Table S3). Then an inverse‐variance‐weighted (IVW) (fixed‐effects) method was used to summarize the β‐values of individual SNPs to obtain the total effect of LSB on 15 site‐specific cancers. Further sensitivity analyses, including IVW (random‐effects), weighted‐median, MR‐Egger, MR‐PRESSO, and weighted‐mode, were performed to account for presence of horizontal pleiotropy, heterogeneity, potential violations of the MR assumptions, and invalid instrument‐exposure associations (Figure 1). 30 , 31 , 32 First, Cochran's Q and Rucker's Q statistic values were calculated to test for the presence of heterogeneity in the IVW (fixed‐effects) and MR‐Egger, respectively. Heterogeneity was considered at p < 0.05. I 2 index, equal to (Q–dfQ×100%) (Q represents the quantitative value of Cochran's Q test, df represents degree of freedom) was also calculated, and heterogeneity was considered as significant if I 2  > 25%. 33 Once heterogeneity exists, either SNPs with very small p‐value for outcomes should be excluded, or random‐effects models and weight‐median are more appropriate for estimating MR effect (Figure 2). 30 , 34 Second, the Egger bias intercept test was used to detect the presence of horizontal pleiotropy. 31 Significant difference between MR‐Egger intercept and 0 was evidence of horizontal pleiotropy. MR‐Egger could appropriately estimate the causal effect in this situation (Figure 2). The MR‐PRESSO is also a commonly used R package for pleiotropy. It can test for horizontal pleiotropy, remove pleiotropic outliers, and test for estimation differences before and after the outliers are removed. 35 FIGURE 1 Overview of Mendelian randomization analyses. Solid gray lines represent the direct effect of instrument variables on outcomes through the exposure under study (television watching, computer use, and driving). Dashed red lines represent indirect effects of instrument variables on outcomes that have potential violation of Mendelian randomization assumptions. IVW, inverse‐variance‐weighted method. FIGURE 2 Selection of the most appropriate causal estimation. It was reported that different Mendelian randomization sensitivity analyses made different assumptions about horizontal pleiotropy, heterogeneity, and error in the instrument‐exposure associations. Thus, the most appropriate Mendelian randomization sensitivity analysis should be selected for estimating the associations according to differing horizontal pleiotropy and heterogeneity. Horizontal pleiotropy, outliers, and heterogeneity were visually analyzed by using scatterplots, leave‐one‐out plots, and funnel plots. 36 MR‐Steiger analysis was performed to validate the direction of causality between exposure and outcomes. 37 To determine if the results were robust to the p‐value threshold, we added genetic variants for sedentary behavior with higher p‐value (<1 × 10−7, < 1 × 10−6) and repeated the MR analysis. 2.5 Multivariable Mendelian randomization analysis GWAS studies of LSB have revealed some degree of genetic association between sedentary behaviors and other traits including triglycerides, total cholesterol, HDL, LDL, BMI, T2D, and educational attainment. 27 Other studies revealed the association between sedentary behaviors and dietary habits. 38 , 39 The effects of LSB on these traits were estimated using UVMR analyses and the significantly associated traits were included in MVMR analysis. To investigate whether the effect of LSB on cancers may be mitigated by its effect on other traits and prevent potential MR assumption violations, MVMR was used to estimate the direct effect of multiple exposures on different outcomes (Figure 1). 40 The SNPs used for MVMR analysis were linked to both LSB and a second exposure; exclusion criteria were consistent with UVMR analysis. 2.6 Statistical analysis Primary univariate and multivariate analyses of the relationship between LSB and cancers were conducted with a two‐sided significance threshold of p < 0.05. Correction for p‐value was made using the Bonferroni method in the secondary analysis of LSB on metabolic factors (p < 0.05/9 = 0.006). Associations were considered suggestive at 0.006 < p < 0.05. Causal associations were estimated using the odds ratio (OR). All analyses were finished using the R package TwoSampleMR (version 0.5.6). Data analyses were conducted between January 2021 and June 2022. 3 RESULTS 3.1 Instrument variables A total of 194 IVs were included in this study, of which 4 were related to driving, 37 to computer use, and 152 to television watching. Heritability referred to the proportion of genetic variance of all SNPs in the total variance and it was used to assess the degree to which SNPs affected the trait. Heritability was estimated from the literature to be 16.1%, 9.3%, and 4.4% for television watching, computer use, and driving, respectively. 27 In this study, the F‐value of IVs related to television watching, computer use, and driving ranged from 23.94 to 144.19, 24.07 to 79.56, and 24.00 to 45.01, respectively. None of them were considered to have weak instrument bias (F > 10) (Table S1). As calculated in the original literature, I 2 GX for television watching, computer use, and driving were 0.98, 0.98, and 0, respectively, indicating a low chance of weak instrument bias except for driving. 27 Statistical power of these IVs was calculated using mRnd power calculator. 41 Statistical power of television watching was 100%. However, statistical power of computer use and driving remained scarce (<80%) because of the low heritability. (Table S2). 3.2 Causal effects of television watching on 15 site‐specific cancers Complete results of UVMR of the association between 3 sedentary behaviors and 15 site‐specific cancers are summarized in Figure 3. It was obvious that significant positive results were obtained mainly for associations between television watching and female cancers. FIGURE 3 Summary of the univariable Mendelian randomization results for leisure sedentary behaviors and 10 site‐specific cancers. Total effect sizes for associations between leisure sedentary behaviors and 10 site‐specific cancers were estimated using seven different methods. Asterisks indicate that the association is nominally significant (p < 0.05). Color is scaled based on the Mendelian randomization odds ratio estimates, and associations for which no instrument was available are presented as white tiles. Using an IVW fixed‐effects approach, a one‐standard deviation (1‐SD) increase in watching television increased endometrial cancer risk by nearly 30% (OR, 1.28; 95% CI, 1.06–1.55; p = 0.010). It occurred in endometrioid subtype (OR, 1.28; 95% CI, 1.02–1.60; p = 0.031) rather than non‐endometrioid subtype (OR, 1.15; 95% CI, 0.72–1.86; p = 0.56) (Table S3, Figure S1). There was no evidence of horizontal pleiotropy (p‐intercept >0.05). However, there was significant heterogeneity except for non‐endometrioid subtype (Table S4, Figures S2–S13). After controlling the heterogeneity by using a weighted‐median and IVW random‐effects approach, the causal effect of television watching on overall endometrial cancer and endometrioid subtype remained significant. Similarly, television watching was significantly associated with breast cancer (OR, 1.16; 95% CI, 1.04–1.30; p = 0.007) in the IVW fixed‐effects analyses. The association appeared to be stronger for the estrogen receptor negative (ER–) subtype (OR, 1.55; 95% CI, 1.26–1.89; p = 2.23 × 10−5) than for estrogen receptor positive (ER+) subtype (OR, 1.17; 95% CI, 1.03–1.33; p = 0.015) (Table S3, Figure S1). In the following sensitivity analyses, we used the outlier (MR‐PRESSO) method to test and correct for 3, 3, and 2 horizontal pleiotropic outliers for overall, ER+, and ER– breast cancer, respectively. After excluding these outliers, the causal estimates showed no significant differences (Tables S5, S6). Given that the average pleiotropic effect was minor and the intercept from the MR‐Egger regression was not statistically significant, the influence of pleiotropy may have been minimal (Table S4, Figures S14–S25). In addition, although heterogeneity was significant, the results of control analysis with breast cancer and two subtypes as the outcome remained significant. The results for low‐grade and low malignant potential serous ovarian cancer demonstrated an approximately 49% increase in risk, 1‐SD increase in television watching (OR, 1.49; 95% CI, 1.07–2.08; p = 0.018) without significant pleiotropy and heterogeneity (Tables S3–S6, Figures S26–S49). This association was also supported by MR‐Egger and weighted‐median analyses. However, overall and other subtype analyses of ovarian cancer yielded statistically significant results in all UVMR sensitivity analyses (Figure S1). Although the causal effect between television watching and cervical cancer was significant in all sensitivity analyses, it was extremely weak (OR, 1.003; 95% CI, 1.001–1.005; p = 4.7 × 10−4) (Tables S3 and S4, Figures S50–S53). This association might be explained by the lack of sufficient adjustment and residual confusion, although it seemed to be a new finding. Aside from the above results, television watching made no significant difference to the other 11 cancers. There was no evidence of horizontal pleiotropy or heterogeneity except for prostate cancer. But even when we used control analyses to correct the heterogeneity, no significant results were obtained (Tables S3–S6, Figures S54–S99). 3.3 Causal effects of computer use and driving on 15 site‐specific cancers There were no significant results in any of the UVMR analyses of relationship between driving and the 15 site‐specific cancers. UVMR analysis of computer use also failed to obtain any significant results except for non‐endometrioid endometrial cancer (OR, 2.99; 95% CI, 1.19–7.49; p = 0.019) and cervical cancer (OR, 1.002; 95% CI, 0.99–1.005; p = 0.208). Both of the latter findings, however, were supported by only one MR method, whereas all other methods consistently showed null results for computer use and endometrial and cervical cancer (Tables S3–S6). Thus, we considered them as false positive results. A lowered p‐value threshold for IVs was used and the UVMR analyses were repeated, considering the small number of driving and computer use variants used in the MR analyses. The genetic association was independent of p‐value thresholds, as the results remained non‐significant with the change of p‐value thresholds (Tables S3–S6). Therefore, caution should be exercised in considering driving and computer use as causal risk factors for cancers. 3.4 Multiple variables Mendelian randomization We investigated the causal association between sedentary behaviors and metabolic factors, educational attainment and dietary habits through UVMR. TV viewing was found to be causally associated with BMI (OR, 1.29; 95% CI, 1.19–1.40; p < 0.001), TC (OR, 1.19; 95% CI, 1.07–1.31; p < 0.001), LDL (OR, 1.13; 95% CI, 1.00–1.27; p = 0.04), and T2D (OR, 1.87; 95% CI, 1.61–2.19; p < 0.001), while negatively associated with HDL (OR, 0.82; 95% CI, 0.75–0.90; p < 0.001) and years of schooling (OR, 0.60; 95% CI, 0.56–0.64; p < 0.001). For dietary habits, TV viewing was a risk factor of most unhealthy dietary habits (intake of chocolate‐covered raisin [OR, 1.02; 95% CI, 1.00–1.04; p = 0.02], fizzy drink [OR, 1.07; 95% CI, 1.02–1.12; p = 0.006], fried potatoes [OR, 1.15; 95% CI, 1.05–1.26; p = 0.003], and sweets [OR, 1.06; 95% CI, 1.00–1.11; p = 0.03]), but was negatively associated with other dietary habits (intake of cereal [OR, 0.85; 95% CI, 0.82–0.89; p < 0.001], fresh fruit [OR, 0.91; 95% CI, 0.88–0.93; p < 0.001], and coffee [OR, 0.91; 95% CI, 0.88–0.94; p < 0.001]). Computer use was showed to be positively associated with years of schooling (OR, 1.78; 95% CI, 1.48–2.14; p < 0.001) and intake of cereal (OR, 1.15; 95% CI, 1.02–1.30; p = 0.02). However, there were no significant associations between computer use and all metabolic factors as well as all unhealthy dietary habits. As for driving, it was only showed to be associated with year of schooling (OR, 0.59; 95% CI, 0.37–0.95; p = 0.03) (Tables S7–S9). Interestingly, educational attainment was inversely associated with TV viewing, while positively associated with computer use. MVMR analysis was performed to ensure that the causal association between television watching and endometrial, breast, and ovarian cancer came from the direct influence of television watching rather than significant confounding variables. For endometrial cancer, adjustment of BMI, T2D, and educational attainment made the previously causal association non‐significant, indicating that BMI, T2D, and educational attainment were mediators in this association. Adjustment of BMI also indicated it as the mediators in the association between television watching and low‐grade serous ovarian cancer. Besides, only educational attainment changed the association between television watching and breast cancer, indicating it as a mediator (Figure 4, Table S9). FIGURE 4 Forest plot of additional multivariate Mendelian randomization analyses of the relationship between sedentary behaviors and three female cancers. Direct effect sizes for associations between leisure sedentary behaviors and (A) breast cancer, (B) endometrial cancer, and (C) ovarian cancer were estimated by multivariable randomization analysis. Confounding factors included body mass index, triglycerides, cholesterol, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol in serum, and type 2 diabetes. ER+, estrogen receptor positive breast cancer; ER–, estrogen receptor negative breast cancer; EE, endometrioid endometrial cancer; LGS + LMPS, low‐grade and low malignant potential serous ovarian cancer. 4 DISCUSSION Our study explored the causal relationship between LSBs and 15 site‐specific cancers, using MR analyses. After complete sensitivity analyses, the associations were converged on three common female cancers – endometrial, breast, and ovarian cancer. Furthermore, the associations varied among subtypes of the three female cancers. However, computer use and driving were not seen as risk factors for the selected cancers. Further MVMR analyses indicated the direct effect of television watching on the three female cancers. Given the high stability of the positive results, we considered television watching as a risk factor for endometrial cancer, breast cancer, and ovarian cancer. Whether computer use and driving were risk factors was not conclusive. We found that television watching increased the risk of endometrial cancer, especially in endometroid subtype. This is in accordance with the previous large meta‐analyses that found a consistent association between television watching and incidence of endometrial cancer. 15 , 16 , 42 Having said that, the novelty of our study lies in that we obtained causal associations between LSB and endometrial cancer instead of observational associations. It should also be noted that another meta‐analysis previously reported a non‐significant result [risk ratio (RR), 1.05; 95% CI, 0.51–2.15]. 14 However, only one cohort study, with a low quality rating of evidence, was included in analysis. The incomplete literature search and different definitions of television watching might be the reasons for this discrepancy. We indicated television watching as a risk factor for breast cancer. Contrary to our findings, no significant causal association was shown in two other meta‐analyses. 15 , 43 However, the definition and measurement methods of sedentary behaviors in these studies were highly heterogeneous, which might account for this discrepancy. Other studies have established the causal association between LSB and breast cancer risk. 16 , 42 , 44 , 45 , 46 But the association between LSB and breast cancer incidence by ER status was still unclear. 46 , 47 , 48 Even if LSB increased the risk of ER–breast cancer more obviously in a study similar to ours, 48 that study only included a small number of cases for analysis owing to the lack of subtype information in all the breast cancer cases, limiting its validity. With more rigorous and complete analyses, our study effectively fills the gaps in existing research. We also demonstrated causal associations between LSB and ovarian cancer in line with a large meta‐analysis. 49 But it must be pointed out that the observed estimates of the association in this study were inflated because women who volunteered as controls in this study were generally healthier. Thus observed evidence on LSB and ovarian cancer in our study is an important finding, particularly as no meaningful and reliable association has been found between sedentary behaviors and ovarian cancer risk in several large studies. 8 , 49 , 50 For other 12 cancers, only colon cancer had observational associations with LSB in previous studies, 14 , 15 but we could not identify any meaningful causal associations by rigorous MR analyses despite this. Another key point to note is that causal effects of driving and computer use on the selected cancers were either non‐significant or unestablished in several sensitivity analyses. For driving trait, inadequate SNPs and low statistical power might be the reasons. For computer use, the follow‐up pre‐UVMR and MVMR analysis gave the possible explanation. Pre‐UVMR analysis revealed high genetic correlations between sedentary behaviors and educational attainment, which were negative for TV viewing and driving, and positive for computer use. In addition, causal associations were found between TV viewing and cardio‐metabolic factors as well as unhealthy dietary habits, which were insignificant for computer use. It seemed that people spending more time in using computer had higher educational attainment and healthier dietary habits. And education had been presumed to causally influence health because it generally confers greater access to salubrious resources such as economic security, healthy lifestyles, social ties, fulfilling jobs, a sense of personal control, and learned effectiveness. 51 That said, volunteering bias in the questionnaires data was one possible explanation for the difference. We therefore take caution in determining driving and computer use as causal risk factors for cancer. The multivariable MR analyses showed an effect of television watching on female cancer independent of most cardio‐metabolic factors and dietary habits. It also indicated vertical pleiotropy of BMI, T2D, intake of fizzy drink and biscuits, as the direct effects of TV viewing on cancers were attenuated compared with the total effects. This provides genetic insights in how sedentary behaviors are associated with cancers. LSB could cause increased snack intake and decreased energy expenditure, accompanied by weight gain and obesity, which could increase risk of cancer. Obesity facilitates carcinogenesis through a number of pathways, including insulin resistance, perturbations in the insulin‐like growth factor axis, and low‐grade systemic inflammation. 52 , 53 , 54 , 55 , 56 In postmenopausal women, adipose tissue is the main site for aromatization of androgen precursors to produce estrogen, which would increase the risk for endometrial cancer. 57 It was also shown that reducing physical activity can increase serum levels of estradiol and decreased sex hormone binding globulin, thereby affecting the development of many female cancers. 14 , 43 , 58 Other mechanisms include influence on BRCA1 gene status, decreased vitamin D levels, imbalance of inflammatory factors, and altered telomere length. 18 , 59 , 60 , 61 , 62 , 63 The multivariable MR in which we corrected for education indicated pleiotropy due to education. Pre‐UVMR analysis in our study revealed correlations between sedentary behaviors and educational attainment in line with previous study. Besides, we have also verified the inverse association between educational attainment and sedentary behaviors in line with a previous study. This study also found that higher educational attainment levels were positively with vigorous physical activity levels and alcohol consumption. 64 In summary, traits like education, sedentary behaviors, and dietary habits are correlated and it is therefore difficult to disentangle their complex interrelationships. Both causal directions taken together would point to education having a complex dual mediating and confounding role in the association between television watching with cancer risk. The strengths of our study are, initially, that it is the first to explore and find a causal association between sedentary behavior and three common female cancers using MR and that different associations were found within different subtypes with reliable evidence. The results suggest a potential role of LSB in cancer prevention. We may need to promote lifestyle changes that reduce sedentary time for the general public. Second, the instrumental variables used in this study were derived from a large GWAS with significant statistical power. In addition, this study carried out sensitivity analyses using several different methods to test the heterogeneity and pleiotropy of IVs and correct the directionality of causal associations. The test results confirmed one another, making the results more reliable. Finally, MVMR was performed on the positive results obtained from UVMR, which revealed the direct effect of LSB on the corresponding cancers. The first limitation of this study is that the population included in this study was European, and it is not clear whether the conclusions of the study can be extended to other ethnic groups. Second, SNPs obtained by statistical methods rather than biological methods inevitably have pleiotropy, for which we used multiple sensitivity analyses to minimize the impact of pleiotropy. We also performed MVMR to correct the effects of confounders. However, this does not rule out the effects of other unknown, potentially confounding variables. In addition, the acquisition of leisure sedentary time was based on subjective measurements rather than with measurement tools; although this avoided measurement bias, it was pointed out that subjective and objective measurement standards are non‐uniform. 65 LSB owing to occupational factors is also an important form of LSB, but the GWAS of IVs do not include it because of the lack of relevant data, which may have resulted in underestimation of LSB. The use of more precise means to determine sedentary time should therefore be considered in the future. An additional limitation of our study is that the GWAS studies for LSB involved both male and female, whereas female cancer was assessed only in women. Therefore, our results might be biased if the effects of the genetic variants are different between two sexes. However, the jury is still out on whether genetic variants of LSB had sex‐specific effects. Under this situation, applying sex‐combined IVs makes an implicit hypothesis that no effect differences exist between males and females, which, however, is not necessarily satisfied. Nevertheless, the use of sex‐combined IVs in MR analysis is not without advantages if such assumption can be well‐established. In this case, one of the greatest benefits is that more IVs would be exploited on account of a larger sample size for the exposure GWAS, which can potentially improve statistical power due to more phenotypic variances explained. Another study indicated that MR analysis might still provide evidence on whether a causal association exists but not necessarily on the precise magnitude of the causal effect when sex‐combined IVs were used. 66 Additionally, we carefully examined heterogeneity in instruments and performed rigorous sensitivity analyses to ensure the robustness of the results according to the suggestions provided by a recent study. 67 Finally, in the present study, we failed to perform stratified analysis of the study subjects by age, gender, and other demographic characteristics. AUTHOR CONTRIBUTIONS Jinwei Chen: Conceptualization (lead); data curation (lead); formal analysis (lead); visualization (lead); writing – original draft (lead). Kai‐Bin Yang: Conceptualization (lead); formal analysis (lead); visualization (lead); writing – original draft (lead). Youyu Qiu: Conceptualization (supporting); formal analysis (lead); visualization (equal); writing – original draft (lead); writing – review and editing (equal). Weijie Lai: Data curation (lead); visualization (equal). Sifan Qi: Data curation (lead); visualization (supporting). Gaoyuan Wang: Data curation (equal); visualization (supporting). Lin Chen: Data curation (equal); writing – original draft (equal). Kunpeng Li: Data curation (supporting); writing – original draft (equal). Dan Zhou: Data curation (supporting); writing – original draft (supporting). Qing Liu: Formal analysis (equal); visualization (supporting); writing – original draft (lead). Ling‐Long Tang: Formal analysis (supporting); visualization (supporting); writing – original draft (equal). Xu Liu: Formal analysis (supporting); visualization (supporting); writing – review and editing (equal). Xiao‐Jing Du: Conceptualization (lead); project administration (lead); writing – review and editing (equal). Rui Guo: Conceptualization (supporting); supervision (lead); writing – review and editing (equal). Jun Ma: Conceptualization (lead); funding acquisition (lead); project administration (lead); supervision (lead); writing – review and editing (lead). FUNDING INFORMATION This study was supported by grants from the National Natural Science Foundation of China (81930072, 82172870), the Key‐Area Research and Development Program of Guangdong Province (2019B020230002), and the Overseas Expertise Introduction Project for Discipline Innovation (111 Project, B14035). CONFLICT OF INTEREST STATEMENT No potential conflict of interest was reported by the authors. CONSENT FOR PUBLICATION Not applicable. ETHICS APPROVAL AND CONSENT TO PARTICIPATE The study strictly followed the Declaration of Helsinki. The data were anonymized and publicly available, thus institutional ethics committee approval and consent from the participants were not required. Supporting information Figures S1–S99. Click here for additional data file. Tables S1–S9. Click here for additional data file. ACKNOWLEDGMENTS Not applicable. DATA AVAILABILITY STATEMENT The original data supporting the findings of this study are available in the MR BASE database at https://www.mrbase.org/ or included in the supplementary materials. Data directly related to the results and code for data cleaning and analysis in this study are available from the corresponding authors upon reasonable request. ==== Refs REFERENCES 1 Chen H , Qiao C , Miao TT , Li AL , Wang WY , Gu W . Synthesis and biological evaluation of novel N‐(piperazin‐1‐yl)alkyl‐1H‐dibenzo[a,c]carbazole derivatives of dehydroabietic acid as potential MEK inhibitors. J Enzyme Inhib Med Chem. 2019;34 (1 ):1544‐1561.31448648 2 Cancer IAfRo . Biennial Report 2020–2021. 2022 (International Agency for Research on Cancer). 3 Bray F , Ferlay J , Soerjomataram I , Siegel RL , Torre LA , Jemal A . 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