Causal effect between telomere length and thirteen types of cancer in Asian population: a bidirectional mendelian randomization study

Causal effect between telomere length and thirteen types of cancer in Asian population: a bidirectional mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal effect between telomere length and thirteen types of cancer in Asian population: a bidirectional mendelian randomization study Bowen Yang, Junming Bi, Weinan Zeng, Mingquan Chen, Zhihao Yao, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5053163/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Aging Clinical and Experimental Research → Version 1 posted 8 You are reading this latest preprint version Abstract Background The relationship between leukocyte telomere length (LTL) and the risk of developing various cancers has always been controversial and predominantly focused on European populations. Hence, Mendelian randomization (MR) was applied to the Asian population to explore the causal relationships between LTL and the risk of developing various cancers. Methods We explored the causal connection between LTL and the risk of developing thirteen types of cancer in Asian populations using freely available genetic variation data. The primary analytical method employed was the inverse variance weighted (IVW) method, complemented by sensitivity and validation analyses. Following Bonferroni correction, P < 0.0038 was considered to indicate statistical significance, and P values ranging from 0.0038 to 0.05 were considered to indicate a nominally significant association. Results The findings indicated significant positive associations between LTL and the risk of developing lung cancer (odds ratio [OR] = 1.6009, 95% confidence interval [CI]: 1.3056–1.9629, P=6.08×10 − 6) and prostate cancer (OR = 1.4200, 95% CI: 1.1489–1.7550, P༝0.0012). Additionally, there was a nominally significant association between LTL and the risk of developing hematological malignancy (OR = 1.5119, 95% CI: 1.0810–2.1146, P༝0.0157). No statistically significant relationships between LTL and the risk of developing the other ten kinds of cancer were detected. No causal link between the risk of developing various cancers and LTL was discovered. Conclusions Asians with longer telomeres are more prone to developing lung and prostate cancer. There is also a nominally significant association between longer telomeres and the risk of developing hematological malignancy. Telomere length Cancer Mendelian randomization Asian population Figures Figure 1 Figure 2 Introduction Telomeres are intricate structures consisting of DNA and proteins that are located at the termini of eukaryotic chromosomes [ 1 ]. Telomeric DNA consists of two single-stranded DNA sequences of different lengths that fold inward to create a ring-shaped structure called a T-loop, which inhibits chromosomal rearrangements and end fusions. Therefore, chromosome stability depends mainly on telomeres for maintenance [ 2 , 3 ]. Whenever a cell undergoes division, there is a slight decrease in telomere length (TL) due to the inability of the enzyme polymerase to completely extend the DNA ends. When TL becomes dangerously short, the cell is prompted to undergo senescence, which ultimately results in cell growth inhibition or apoptosis [ 4 ]. Thus, the potential for TL to serve as a predictor for aging and age-related illnesses is very promising [ 5 , 6 ]. A complicated genetic characteristic, TL heritability varies between 34% and 50% within families [ 7 ]. However, the associations between TL and the risk of developing various cancers has been controversial in previous research, with varying and inconclusive findings observed across different forms of cancer and even within the same type of cancer [ 8 ]. For instance, several studies have shown that longer TL is consistently correlated with several malignancies, such as lung cancer, glioma, and renal cell carcinoma [ 9 – 11 ]. Conversely, specific recent investigations have indicated that bladder cancer, gastric cancer, and esophageal squamous cell carcinoma may be caused by shorter TL [ 12 , 13 ]. Different nations, types of cancer, study designs, and measurement techniques are among the reasons why the outcomes of these investigations have varied. Because leukocyte telomere length (LTL) is more accessible to quantify and comparable to TL in other human tissues, in research, LTL is frequently utilized instead of TL [ 14 , 15 ]. On the other hand, recent genetic studies have identified several single-nucleotide polymorphisms (SNPs) associated with LTL, offering a solid platform for investigating the associations between LTL and the risk of developing various types of tumors [ 16 – 20 ]. Past studies of this relationship have focused on European populations [ 21 , 22 ]. However, we are also interested in exploring the relationship between LTL and the risk of developing cancer in Asian populations to determine whether there are similarities or differences in that relationship compared with that in European populations. Asian populations may exhibit distinct genetic architectures and environmental exposures that modify LTL dynamics and cancer risk, underscoring the need for population-specific studies. In the current study, we aimed to close this gap by focusing on Asian populations, thereby improving worldwide awareness of the association between LTL and the risk of developing cancer. We studied the potential causal associations between LTL and the risk of developing various types of tumors in Asian communities by using an epidemiological approach known as Mendelian randomization (MR). This concept is derived from the second law of Mendel, which stresses the separate inheritance of features. By concentrating on the random segregation of genetic variation from parent to child during meiosis, MR shields against the influence of many other confusing factors [ 23 ]. Reverse causation bias is also absent from MR since a person's genetic composition cannot be altered after conception [ 24 ]. Materials & Methods Study design In this investigation, we used a two-sample MR methodology to evaluate the causal connection between LTL and the risk of developing thirteen distinct types of tumors with genome-wide association study (GWAS) data from individuals of Asian descent. This study utilized genetic data exclusively from the Singapore Chinese Health Study (SCHS) and the Biobank Japan Project (BBJ), focusing on Asian populations. Instrumental variables (IVs) must satisfy three assumptions: (1) the IVs must exhibit a reliable association with LTL; (2) the IVs should be unrelated to confounding variables in the association between LTL and the risk of developing cancer; and (3) the only variable by which the IVs should affect malignancy is LTL, excluding any other variables (Fig. 1 ). This study was conducted according to the STROBE-MR guidelines (Additional file 1: Table S1 ) [ 25 ]. LTL data source Our analysis incorporated data from the SCHS, a comprehensive epidemiological survey that included 63,257 Chinese individuals of both sexes. Among this population, a subset of 23,096 individuals underwent GWAS analysis of LTL. Among the samples, a total of 16,759 individuals were chosen for analysis, while 6,337 individuals were utilized for validation [ 26 ]. The LTL measurements for SCHS participants were conducted by researchers who utilized professional DNA blood kits to extract DNA from peripheral blood samples. To determine the relative length of telomeres in the samples, researchers have employed the monochrome multiplex quantitative polymerase chain reaction (qPCR) technique [ 27 ]. Based on the comparison of the telomere-to-albumin gene copy numbers to a reference sample, TL was measured. All qPCR experiments were conducted twice, and the coefficient of variation for the repetitions was 3.5%, which indicates the high reliability of the method and provides essential data support for further exploration of the relationship between TL and specific SNPs [ 26 ]. Participants in the SCHS were informed of the study details and consented to participate in this study, and approval was obtained from the National University of Singapore Institutional Review Board. The data source of cancers All of the cancer summary statistics were retrieved from BBJ (Table 1 ). BBJ is one of the largest Asian biobanks. It includes a vast cohort of more than 200,000 individuals and gathers DNA and blood samples from 12 Japanese medical institutes [ 28 , 29 ]. Doctors at designated medical institutions selected types of cancer based on their clinical significance in terms of incidence or mortality rates in Japan and adhered to the diagnostic criteria for all types of cancer, excluding patients who had received bone marrow transplants and those of non-Asian descent. Control samples were obtained from four prospective population-based cohorts in Japan. Approval for the GWAS related to BBJ was obtained from the relevant institutional ethics committee [ 30 ]. Table 1 Details of the cancers included in the study Cancer types Sample size Case samples Control samples n (total) n (male) n (female) n (total) n (male) n (female) Biliary tract cancer 196084 339 211 128 195745 97655 98090 Breast cancer 95283 5552 0 5552 89731 0 89731 Cervical cancer 90336 605 0 605 89731 0 89731 Colorectal cancer 202807 7062 4496 2566 195745 97655 98090 Endometrial cancer 90730 999 0 999 89731 0 89731 Esophageal cancer 197045 1300 1132 168 195745 97655 98090 Gastric cancer 202308 6563 4885 1678 195745 97655 98090 Hematological malignancy 212453 1236 701 535 211217 108646 102571 Hepatocellular carcinoma 197611 1866 1384 482 195745 97655 98090 Lung cancer 212453 4050 2710 1340 208403 106637 101766 Ovarian cancer 90451 720 0 720 89731 0 89731 Pancreatic cancer 196187 442 288 154 195745 97655 98090 Prostate cancer 109347 5408 5408 0 103939 103939 0 Selection of IVs The study flow diagram is displayed in Fig. 1 . In summary, LTL served as an exposure factor, whereas cancer was an outcome factor. When choosing the most effective IVs, we used these quality control measures to guarantee that our findings about the causal connection between LTL and the risk of developing cancer were genuine and accurate. As IVs, we initially chose SNPs that exhibited a strong correlation ( P <5×10 − 8 ) with LTL and showed no evidence of linkage disequilibrium (LD, r 2 <0.001 and window size=10,000 kb). Second, the minor allele frequency (MAF) criterion was 0.01 for the relevant variant. Third, to maintain consistency in the influence of SNPs on both exposure and outcome in MR, we specifically eliminated palindromic SNPs. This was done to avoid any potential changes in the coding of alleles or the orientation of DNA strands. Fourth, we calculated the F-statistic to assess the strength of the IVs. An F value greater than 10 indicates that the IVs are suitable for investigation [ 31 ]. The F statistic was calculated using the following formula: F ༝ R 2 ( N − 2)/(1 − R 2 ), where R 2 is the proportion of the variability of the LTL explained by each instrument and N is the sample size of the GWAS for the SNP-LTL association. R 2 can be calculated by using the following formula: 2EAF × β 2 ×(1-EAF)∕2EAF × β 2 ×(1།EAF)་2(SE( β ) 2 )× N ×EAF×(1།EAF), where EAF, β , N , and SE( β ) represent the effect allele frequency, the estimated genetic effect on LTL, the sample size of the GWAS for the SNP-LTL association, and the standard error of the genetic effect, respectively [ 32 ]. Horizontal pleiotropy was rigorously tested using MR-PRESSO and MR-Egger regression. SNPs with significant pleiotropy (P < 0.05 in MR-PRESSO outlier tests) were removed iteratively until no global pleiotropy remained. Moreover, SNPs connected to cancer-related variables or risk factors, including obesity, daily cigarette smoking, weekly alcohol use, and insufficient physical activity, were examined and excluded using the online resource: LDtrait ( https://ldlink.nih.gov/?tab༝ldtrait ) [ 33 ]. The SNPs that were carefully chosen after the aforementioned stages were utilized in our investigation. Statistical analysis Our research primarily employed the IVW (random-effects model) approach to evaluate the causal association between LTL and the risk of developing thirteen different types of malignant tumors. The IVW approach exhibits the best statistical power when every assumption is satisfied [ 34 ]. The weighted median method may reliably estimate causal effects even when there are half-valid IVs [ 35 ]. The MR‒Egger method can provide more accurate estimates of causal effects, accounting for possible heterogeneity [ 36 ]. After Bonferroni adjustment (0.05 divided by 13 outcomes), P <0.0038 was considered to indicate strong evidence of an association. On the other hand, 0.0038< P <0.05 was considered to indicate a nominally significant association. Next, we performed sensitivity analyses. MR studies frequently apply Cochran's Q test to evaluate disparities among different IVs and determine the presence of heterogeneity [ 37 ]. Horizontal pleiotropy was assessed using the MR–PRESSO and MR‒Egger regression tests. Horizontal pleiotropy is regarded as nonexistent when the MR–Egger regression intercept is near zero [ 36 ]. We frequently carried out the "leave-one-out" procedure to check the reliability of our research results. Additionally, the MR–Steiger directionality test is often applied to identify any reverse causal association [ 38 ]. Finally, we utilized an online tool ( https://shiny.cnsgenomics.com/mRnd/)t o quantify the statistical power of this MR study [ 39 ]. The statistical analyses were performed with R version 4.3.3 and the R packages (TwoSampleMR and MR-PRESSO). The code utilized in this investigation is available in Additional file 2. Results Selection of IVs Following the completion of several quality control procedures as previously described, we found nine LTL-SNPs in the Asian populations that met the generally recognized genome-wide significance level ( P <5×10 − 8 ) for exposure. The F-statistics for each SNP exceeded 10, suggesting the absence of weak IVs (Additional file 1: Table S2 ). None of these nine SNPs were associated with cancer-related confounding factors (Additional file 1: Table S3 ). Additionally, after removing pleiotropic SNPs discovered by MR–PRESSO (rs10857352 and rs2293607 for breast cancer, rs41293836 for gastric cancer, rs7705526 for lung and prostate cancer, and rs41309367 for prostate cancer), not a single IV showed horizontal pleiotropy. The genetic variations in exposure and outcome are characterized as shown in Additional file 1: Table S4. Causal effects between LTL and the risk of developing various cancers Based on the IVW approach results (Fig. 2), we found a significant positive association between LTL and the risk of developing lung cancer (OR＝1.6009, 95% CI: 1.3056-1.9629, P ＝6.08×10 −6 ) and prostate cancer (OR＝1.4200, 95% CI: 1.1489-1.7550, P ＝0.0012). The exact magnitude and direction results were obtained using the weighted median approach as with the IVW method. A nominally significant association was observed between and LTL and the risk of developing hematological malignancy (OR＝1.5119 95% CI: 1.0810-2.1146, P ＝0.0157). For the remaining ten cancer types, there was no statistically significant correlation. The estimated dependence of LTL and tumors on IVs is depicted in the scatter plots (Additional file 3: Figure S1). As illustrated by the ascending lines in the plot, LTL is positively associated with the risk of developing lung cancer, prostate cancer, and hematological malignancy. Sensitivity analyses We evaluated the dependability of the MR results by means of numerous sensitivity analyses. The Cochran's Q test revealed some heterogeneity related to breast cancer and gastric cancer (Table 2). However, the random-effects model of IVW can mitigate the impact of minor heterogeneity. MR‒Egger regression analysis revealed intercepts close to zero for all cancer types (e.g., lung cancer: intercept = 0.0115, P = 0.6283; prostate cancer: intercept = -0.0394, P = 0.0990), and no significant global horizontal pleiotropy was detected by MR–PRESSO ( P > 0.05 for all outcomes, Table 3), supporting the absence of directional pleiotropic effects in our instrumental variables. The results of the MR‒Egger regression analysis are shown graphically with funnel plots (Additional file 3: Figure S2). The validity of the causality estimates of the link was improved by the leave-one-out plots, showing that the lack of any one SNP used in the analysis had no discernible impact on the causal association (Additional file 3: Figure S3). Furthermore, the results of the MR–Steiger directionality test did not provide any evidence supporting a causal relationship between the risk of developing each type of cancer and LTL ( P ＜0.001) (Additional file 1: Table S5). For the risk of developing lung cancer, prostate cancer, and hematological malignancy in this study, our statistical power was 1, 1, and 0.93, respectively (Additional file 1: Table S6). Table 2 Heterogeneity study results on cancers for LTL. Cancer types Cochran’s Q test Method Q P value Biliary tract cancer MR Egger 5.0239 0.6570 IVW 5.0269 0.7547 Breast cancer MR Egger 11.2254 0.0060 IVW 11.3349 0.0471 Cervical cancer MR Egger 4.5603 0.0781 IVW 7.7203 0.4613 Colorectal cancer MR Egger 6.4394 0.4895 IVW 6.5192 0.5893 Endometrial cancer MR Egger 7.0082 0.4280 IVW 7.4687 0.4870 Esophageal cancer MR Egger 11.4881 0.1187 IVW 12.1281 0.1456 Gastric cancer MR Egger 13.6039 0.0344 IVW 13.7193 0.0564 Hematological malignancy MR Egger 7.5204 0.3768 IVW 9.0804 0.3356 Hepatocellular carcinoma MR Egger 11.8565 0.1054 IVW 12.1480 0.1447 Lung cancer MR Egger 7.5683 0.2715 IVW 7.8962 0.3418 Ovarian cancer MR Egger 10.5820 0.1579 IVW 10.6605 0.2217 Pancreatic cancer MR Egger 6.0292 0.5363 IVW 8.5011 0.3861 Prostate cancer MR Egger 4.3575 0.4992 IVW 8.4502 0.2069 Abbreviations: IVW inverse variance weighted, LTL leukocyte telomere length Table 3 Results of the horizontal pleiotropy between LTL and the risk of developing cancers Cancer types MR-PRESSO P value MR-Egger Intercept SE P value Biliary tract cancer 0.766 0.0038 0.0681 0.9576 Breast cancer 0.102 -0.0065 0.0277 0.8232 Cervical cancer 0.493 -0.0910 0.0512 0.1187 Colorectal cancer 0.556 0.0043 0.0153 0.7857 Endometrial cancer 0.587 -0.0271 0.0400 0.5194 Esophageal cancer 0.144 0.0280 0.0448 0.5521 Gastric cancer 0.091 -0.0064 0.0282 0.8291 Hematological malignancy 0.359 -0.0446 0.0371 0.2674 Hepatocellular carcinoma 0.159 -0.0158 0.0381 0.6907 Lung cancer 0.457 0.0115 0.0225 0.6283 Ovarian cancer 0.243 -0.0132 0.0578 0.8263 Pancreatic cancer 0.487 0.0935 0.0595 0.1599 Prostate cancer 0.284 -0.0394 0.0195 0.0990 Abbreviations: SE standard error, LTL leukocyte telomere length, MR Mendelian randomization Discussion In this study, we investigated the causal relationships between LTL and the risk of developing thirteen different cancer types in Asian populations via the two-sample MR method. The results showed that the risks of developing lung cancer (OR＝1.6009, 95% CI: 1.3056-1.9629, P ＝6.08×10 −6 ) and prostate cancer (OR＝1.4200, 95% CI: 1.1489-1.7550, P ＝0.0012) were strongly correlated with genetically determined LTL. A nominally significant association between LTL and the risk of developing hematological malignancy (OR＝1.5119 95% CI: 1.0810-2.1146, P ＝0.0157) was detected, which suggests that LTL and hematological malignancy may be causally related. No causal correlation was identified between LTL and the risk of developing tumors from the remaining ten cancer categories. Using the MR–Steiger directionality test, we likewise failed to establish causal links between the risk of developing various cancers and LTL. This study highlights the causal relationship between longer TL and the risk of developing cancer in Asian populations and may provide new ideas for global cancer detection and prevention. The association between longer TL and the risk of developing lung cancer has received the most attention among many cancers. Our findings are comparable to those derived in Western populations, which indicate that individuals with longer TL have increased susceptibility to lung cancer [5]. Nevertheless, our research on lung cancer frequently contradicts the findings of numerous prior studies, which commonly indicate a heightened likelihood of lung cancer in individuals with shorter TL [12, 40]. This disparity could be related to insufficient case‒control studies, which are susceptible to intrinsic flaws such as reverse causality and confounding. For example, in retrospective case‒control studies, the timing of exposure and results are ambiguous. Blood samples are typically collected after lung cancer diagnosis or treatment, potentially confounding this relationship. Additionally, some studies have suggested that tumor chemotherapy can shorten telomeres [41, 42]. On the other hand, specific investigations have indicated that distinct pathophysiological subtypes may influence the correlation between TL and the risk of developing lung cancer [43, 44]. However, due to the lack of available histological subtyping data in the BBJ, we could not further explore the relationship between histological subtypes and TL. Beyond telomere length per se, emerging evidence implicates epigenetic dysregulation, such as DNA methylation, in modulating cancer risk. For instance, A recent meta-analysis further identified specific methylation signatures as independent predictors of lung cancer risk, suggesting that telomere length and epigenetic alterations may act through convergent pathways to promote oncogenesis [45]. Future studies integrating multi-omics data (e.g., telomere length, methylation profiles, and somatic mutations) are needed to dissect these interactions. Previous studies have demonstrated that TL has a significant impact on the process of prostate cancer development and progression [46, 47]. A longer TL was substantially linked to higher overall death rates, according to a new Australian study including 533 prostate cancer patients with a median follow-up of 149 months [48]. However, an extensive prospective survey with up to 20 years of follow-up for the identification of cancer and mortality encompassing 47,102 participants from a European population revealed that shorter TL was linked to a greater risk of early mortality for all malignancy except prostate cancer [49]. A variety of factors may contribute to different study results. There are various explanations, including overall mortality, technical discrepancies in real-time PCR measurements of LTL, patient demographic heterogeneity, and the limited sample size. Moreover, prostate tissue-specific TL is rare, so we used TL-associated data obtained from peripheral blood leukocytes rather than prostate tissue. This limitation may reduce the likelihood of detecting causal linkage. However, our results might still be somewhat valid because peripheral blood leukocytes are readily available and helpful for screening and risk prediction, and specific research has demonstrated a significant relationship between the two tissue types [14, 15]. Our research emphasizes the significance of TL in hematological malignancy across Asian populations, adding to the current pool of evidence. According to earlier research, the TLs of patients with blood cancers are shorter than those of patients in the control group [50-53]. The correlation between TL and the risk of developing cancers of hematological malignancy can be attributed to two factors. First, cancer cells typically increase the production of telomerase, an enzyme that allows them to maintain or even extend telomeres. This enables cancer cells to continue proliferating without any limitations [54]. Second, the irregular activity of telomerase can result in variations in TL among individuals, which subsequently impacts their vulnerability to hematological malignancy [55]. Studies examining TL in patients with hematological malignancy may not provide an accurate representation of the actual TL. The reason for this is that blood samples contain a combination of healthy and diseased cells, which change in proportion depending on the individual's illness status. In this study, utilizing an MR design, we investigated the causal associations between TL and the risk of developing various cancers while reducing residual confounding, which is frequently observed in observational studies and prevents reverse causality. A critical contribution of our work is the application of large datasets from a homogeneous population for MR analysis, which improves the accuracy of our results. Nevertheless, there are certain limitations to our research. First, we could only perform causal association MR analysis because there were no data on individuals available. Hence, we were no longer able to investigate the sensitivity and specificity of the results. Second, telomere length may be affected by factors such as drugs or inflammation, resulting in its shortening or lengthening, and such confounding factors may introduce bias, which this study failed to fully correct[47, 56]. In addition, Since telomerase mainly controls TL, it is imperative to explore more how telomerase activity directly or indirectly affects the genesis of malignancy. This work might provide new information about how telomeres accelerate the course of cancer. Nevertheless, the absence of thorough telomerase-related GWAS data prevents us from analyzing the connection between telomerase and malignancy. More studies must be performed on this subject. Finally, the possible biological mechanism responsible for the association between longer TL and the risk of developing cancer is yet unknown. Thus, additional molecular studies are necessary to confirm the results of this work. Conclusions Overall, we conducted a thorough evaluation of the cause-and-effect relationship between TL and the risk of developing several types of cancer. The findings of our study indicate that among Asian individuals, having longer TL increases the likelihood of developing lung cancer and prostate cancer. Additionally, there is some evidence of a nominally significant association between longer TL and the risk of developing hematological malignancy. MR‒Egger regression analysis revealed intercepts close to zero for all cancer types (e.g., lung cancer: intercept = 0.0115, P = 0.6283; prostate cancer: intercept = -0.0394, P = 0.0990), and no significant global horizontal pleiotropy was detected by MR–PRESSO (P > 0.05 for all outcomes, Table 3), supporting the absence of directional pleiotropic effects in our instrumental Declarations Competing interests The authors affirm that they do not possess any identifiable personal relationships or competing financial interests that might have appeared to exert an influence on the research presented in this article. Ethics approval and consent to participate The study utilized data from publicly accessible sources and was ethically approved. Funding This research was supported by the Project of Department of Finance of Guangdong Province (Grant No. KS0120220268), the Science-Technology-Medicine Collaborative Project of Ganzhou Science and Technology Bureau, Jiangxi Province (Grant No. 2023LNS26880), the Foshan Nanhai District "14th Five-Year Plan" Key Specialty Development Program (Specialty Category), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515111219), and the Guangzhou Science and Technology Program (Grant No. 202201010897). Author Contribution Conceptualization, B.Y., Z.X, and Y.Y.; methodology, B.Y. and J.B.; software, B.Y. and M.C.; validation, B.Y. and Z.Y.; formal analysis, B.Y.; investigation, B.Y.; resources, B.Y. and W.Z.; data curation, B.Y. and S.C.; writing—original draft preparation, B.Y. and Z.J.; writing—review and editing, B.Y. and C.Z.; visualization, B.Y., and C.Z.; supervision, B.Y., H.L. and X.G.; project administration, B.Y., Z.X. and Y.Y.; funding acquisition, Z.X. and Y.Y.; All authors have read and agreed to the published version of the manuscript. Acknowledgments We extend our sincere appreciation to all the GWAS cohort participants and the investigators of the Singapore Chinese Health Study and Biobank Japan Project for their assistance in collaborating on the dissemination of the GWAS summary statistics. Availability of data and materials The datasets supporting the conclusions of this article are available in the SCHS repository ( https://doi.org/10.6084/m9.figshare.8066999 ) and BBJ repository ( http://jenger.riken.jp/en/ ) References de Lange T (2005) Shelterin: the protein complex that shapes and safeguards human telomeres. Genes Dev 19(18):2100–2110. https://doi.org/10.1101/gad.1346005 Griffith JD, Comeau L, Rosenfield S, Stansel RM, Bianchi A, Moss H et al (1999) Mammalian telomeres end in a large duplex loop. Cell 97(4):503–514. https://doi.org/10.1016/s0092-8674(00)80760-6 Hug N, Lingner J (2006) Telomere length homeostasis. Chromosoma 115(6):413–425. https://doi.org/10.1007/s00412-006-0067-3 Shay JW, Wright WE (2019) Telomeres and telomerase: three decades of progress. Nat Rev Genet 20(5):299–309. https://doi.org/10.1038/s41576-019-0099-1 Haycock PC, Burgess S, Nounu A, Zheng J, Okoli GN, Bowden J et al (2017) Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study. JAMA Oncol 3(5):636–651. https://doi.org/10.1001/jamaoncol.2016.5945 Armanios M (2022) The Role of Telomeres in Human Disease. Annual review of genomics and human genetics. 23:363–381. https://doi.org/10.1146/annurev-genom-010422-091101 Srinivas N, Rachakonda S, Kumar R (2020) Telomeres and Telomere Length: A General Overview. Cancers 12(3). https://doi.org/10.3390/cancers12030558 Nelson CP, Codd V (2020) Genetic determinants of telomere length and cancer risk. Curr Opin Genet Dev 60:63–68. https://doi.org/10.1016/j.gde.2020.02.007 Seow WJ, Cawthon RM, Purdue MP, Hu W, Gao YT, Huang WY et al (2014) Telomere length in white blood cell DNA and lung cancer: a pooled analysis of three prospective cohorts. Cancer Res 74(15):4090–4098. https://doi.org/10.1158/0008-5472.Can-14-0459 Saunders CN, Kinnersley B, Culliford R, Cornish AJ, Law PJ, Houlston RS (2022) Relationship between genetically determined telomere length and glioma risk. Neurooncology 24(2):171–181. https://doi.org/10.1093/neuonc/noab208 Machiela MJ, Hofmann JN, Carreras-Torres R, Brown KM, Johansson M, Wang Z et al (2017) Genetic Variants Related to Longer Telomere Length are Associated with Increased Risk of Renal Cell Carcinoma. Eur Urol 72(5):747–754. https://doi.org/10.1016/j.eururo.2017.07.015 Ma H, Zhou Z, Wei S, Liu Z, Pooley KA, Dunning AM et al (2011) Shortened telomere length is associated with increased risk of cancer: a meta-analysis. PLoS ONE 6(6):e20466. https://doi.org/10.1371/journal.pone.0020466 Pan W, Du J, Shi M, Jin G, Yang M (2017) Short leukocyte telomere length, alone and in combination with smoking, contributes to increased risk of gastric cancer or esophageal squamous cell carcinoma. Carcinogenesis 38(1):12–18. https://doi.org/10.1093/carcin/bgw111 Daniali L, Benetos A, Susser E, Kark JD, Labat C, Kimura M et al (2013) Telomeres shorten at equivalent rates in somatic tissues of adults. Nat Commun 4:1597. https://doi.org/10.1038/ncomms2602 Gadalla SM, Cawthon R, Giri N, Alter BP, Savage SA (2010) Telomere length in blood, buccal cells, and fibroblasts from patients with inherited bone marrow failure syndromes. Aging 2(11):867–874. https://doi.org/10.18632/aging.100235 Pooley KA, Bojesen SE, Weischer M, Nielsen SF, Thompson D, Amin Al Olama A et al (2013) A genome-wide association scan (GWAS) for mean telomere length within the COGS project: identified loci show little association with hormone-related cancer risk. Hum Mol Genet 22(24):5056–5064. https://doi.org/10.1093/hmg/ddt355 Mangino M, Hwang SJ, Spector TD, Hunt SC, Kimura M, Fitzpatrick AL et al (2012) Genome-wide meta-analysis points to CTC1 and ZNF676 as genes regulating telomere homeostasis in humans. Hum Mol Genet 21(24):5385–5394. https://doi.org/10.1093/hmg/dds382 Gu J, Chen M, Shete S, Amos CI, Kamat A, Ye Y et al (2011) A genome-wide association study identifies a locus on chromosome 14q21 as a predictor of leukocyte telomere length and as a marker of susceptibility for bladder cancer. Cancer Prev Res (Philadelphia Pa) 4(4):514–521. https://doi.org/10.1158/1940-6207.Capr-11-0063 Levy D, Neuhausen SL, Hunt SC, Kimura M, Hwang SJ, Chen W et al (2010) Genome-wide association identifies OBFC1 as a locus involved in human leukocyte telomere biology. Proc Natl Acad Sci USA 107(20):9293–9298. https://doi.org/10.1073/pnas.0911494107 Codd V, Nelson CP, Albrecht E, Mangino M, Deelen J, Buxton JL et al (2013) Identification of seven loci affecting mean telomere length and their association with disease. Nat Genet 45(4):422. https://doi.org/10.1038/ng.2528 Li C, Stoma S, Lotta LA, Warner S, Albrecht E, Allione A et al (2020) Genome-wide Association Analysis in Humans Links Nucleotide Metabolism to Leukocyte Telomere Length. Am J Hum Genet 106(3):389–404. https://doi.org/10.1016/j.ajhg.2020.02.006 Zhang C, Doherty JA, Burgess S, Hung RJ, Lindström S, Kraft P et al (2015) Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study. Hum Mol Genet 24(18):5356–5366. https://doi.org/10.1093/hmg/ddv252 Wehby GL, Ohsfeldt RL, Murray JC (2008) Mendelian randomization' equals instrumental variable analysis with genetic instruments. Stat Med 27(15):2745–2749. https://doi.org/10.1002/sim.3255 Davey Smith G, Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 23(R1):R89–98. https://doi.org/10.1093/hmg/ddu328 Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA et al (2021) Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA 326(16):1614–1621. https://doi.org/10.1001/jama.2021.18236 Dorajoo R, Chang X, Gurung RL, Li Z, Wang L, Wang R et al (2019) Loci for human leukocyte telomere length in the Singaporean Chinese population and trans-ethnic genetic studies. Nat Commun 10(1):2491. https://doi.org/10.1038/s41467-019-10443-2 Cawthon RM (2009) Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res 37(3):e21. https://doi.org/10.1093/nar/gkn1027 Hirata M, Kamatani Y, Nagai A, Kiyohara Y, Ninomiya T, Tamakoshi A et al (2017) Cross-sectional analysis of BioBank Japan clinical data: A large cohort of 200,000 patients with 47 common diseases. J Epidemiol 27(3s):S9–s21. https://doi.org/10.1016/j.je.2016.12.003 Nagai A, Hirata M, Kamatani Y, Muto K, Matsuda K, Kiyohara Y et al (2017) Overview of the BioBank Japan Project: Study design and profile. J Epidemiol 27(3s):S2–s8. https://doi.org/10.1016/j.je.2016.12.005 Ishigaki K, Akiyama M, Kanai M, Takahashi A, Kawakami E, Sugishita H et al (2020) Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat Genet 52(7):669–679. https://doi.org/10.1038/s41588-020-0640-3 Burgess S, Thompson SG (2011) Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40(3):755–764. https://doi.org/10.1093/ije/dyr036 Papadimitriou N, Dimou N, Tsilidis KK, Banbury B, Martin RM, Lewis SJ et al (2020) Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat Commun 11(1):597. https://doi.org/10.1038/s41467-020-14389-8 Lin SH, Brown DW, Machiela MJ (2020) LDtrait: An Online Tool for Identifying Published Phenotype Associations in Linkage Disequilibrium. Cancer Res 80(16):3443–3446. https://doi.org/10.1158/0008-5472.Can-20-0985 Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG (2015) Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30(7):543–552. https://doi.org/10.1007/s10654-015-0011-z Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40(4):304–314. https://doi.org/10.1002/gepi.21965 Burgess S, Thompson SG (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32(5):377–389. https://doi.org/10.1007/s10654-017-0255-x Bowden J, Spiller W, Del Greco MF, Sheehan N, Thompson J, Minelli C et al (2018) Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol 47(4):1264–1278. https://doi.org/10.1093/ije/dyy101 Hemani G, Tilling K, Davey Smith G (2017) Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet 13(11):e1007081. https://doi.org/10.1371/journal.pgen.1007081 Brion MJ, Shakhbazov K, Visscher PM (2013) Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 42(5):1497–1501. https://doi.org/10.1093/ije/dyt179 Anic GM, Sondak VK, Messina JL, Fenske NA, Zager JS, Cherpelis BS et al (2013) Telomere length and risk of melanoma, squamous cell carcinoma, and basal cell carcinoma. Cancer Epidemiol 37(4):434–439. https://doi.org/10.1016/j.canep.2013.02.010 Lee JJ, Nam CE, Cho SH, Park KS, Chung IJ, Kim HJ (2003) Telomere length shortening in non-Hodgkin's lymphoma patients undergoing chemotherapy. Ann Hematol 82(8):492–495. https://doi.org/10.1007/s00277-003-0691-4 Chakraborty S, Sun CL, Francisco L, Sabado M, Li L, Chang KL et al (2009) Accelerated telomere shortening precedes development of therapy-related myelodysplasia or acute myelogenous leukemia after autologous transplantation for lymphoma. J Clin oncology: official J Am Soc Clin Oncol 27(5):791–798. https://doi.org/10.1200/jco.2008.17.1033 Gu J, Wu X (2013) Re: short telomere length, cancer survival, and cancer risk in 47 102 individuals. J Natl Cancer Inst 105(15):1157. https://doi.org/10.1093/jnci/djt154 Sanchez-Espiridion B, Chen M, Chang JY, Lu C, Chang DW, Roth JA et al (2014) Telomere length in peripheral blood leukocytes and lung cancer risk: a large case-control study in Caucasians. Cancer Res 74(9):2476–2486. https://doi.org/10.1158/0008-5472.Can-13-2968 Dolcini J, Chiavarini M, Firmani G, Brennan KJM, Cardenas A, Baccarelli AA et al (2025) Methylation Biomarkers of Lung Cancer Risk: A Systematic Review and Meta-Analysis. Cancers 17(4). https://doi.org/10.3390/cancers17040690 Ding Z, Wu CJ, Jaskelioff M, Ivanova E, Kost-Alimova M, Protopopov A et al (2012) Telomerase reactivation following telomere dysfunction yields murine prostate tumors with bone metastases. Cell 148(5):896–907. https://doi.org/10.1016/j.cell.2012.01.039 Ornish D, Lin J, Chan JM, Epel E, Kemp C, Weidner G et al (2013) Effect of comprehensive lifestyle changes on telomerase activity and telomere length in men with biopsy-proven low-risk prostate cancer: 5-year follow-up of a descriptive pilot study. Lancet Oncol 14(11):1112–1120. https://doi.org/10.1016/s1470-2045(13)70366-8 Renner W, Krenn-Pilko S, Gruber HJ, Herrmann M, Langsenlehner T (2018) Relative telomere length and prostate cancer mortality. Prostate Cancer Prostatic Dis 21(4):579–583. https://doi.org/10.1038/s41391-018-0068-3 Weischer M, Nordestgaard BG, Cawthon RM, Freiberg JJ, Tybjærg-Hansen A, Bojesen SE (2013) Short telomere length, cancer survival, and cancer risk in 47102 individuals. J Natl Cancer Inst 105(7):459–468. https://doi.org/10.1093/jnci/djt016 Haydeé Cottliar AS, Noriega MF, Narbaitz M, Rodríguez A, Slavutsky IR (2006) Association between telomere length and BCL2 gene rearrangements in low- and high-grade non-Hodgkin lymphomas. Cancer Genet Cytogenet 171(1):1–8. https://doi.org/10.1016/j.cancergencyto.2006.05.016 Widmann TA, Herrmann M, Taha N, König J, Pfreundschuh M (2007) Short telomeres in aggressive non-Hodgkin's lymphoma as a risk factor in lymphomagenesis. Exp Hematol 35(6):939–946. https://doi.org/10.1016/j.exphem.2007.03.009 Song DY, Kim JA, Jeong D, Yun J, Kim SM, Lim K et al (2019) Telomere length and its correlation with gene mutations in chronic lymphocytic leukemia in a Korean population. PLoS ONE 14(7):e0220177. https://doi.org/10.1371/journal.pone.0220177 Jebaraj BM, Kienle D, Lechel A, Mertens D, Heuberger M, Ott G et al (2013) Telomere length in mantle cell lymphoma. Blood 121(7):1184–1187. https://doi.org/10.1182/blood-2012-08-452649 Maciejowski J, de Lange T (2017) Telomeres in cancer: tumour suppression and genome instability. Nat Rev Mol Cell Biol 18(3):175–186. https://doi.org/10.1038/nrm.2016.171 Vakonaki E, Tsiminikaki K, Plaitis S, Fragkiadaki P, Tsoukalas D, Katsikantami I et al (2018) Common mental disorders and association with telomere length. Biomedical Rep 8(2):111–116. https://doi.org/10.3892/br.2018.1040 Jurk D, Wilson C, Passos JF, Oakley F, Correia-Melo C, Greaves L et al (2014) Chronic inflammation induces telomere dysfunction and accelerates ageing in mice. Nat Commun 2:4172. https://doi.org/10.1038/ncomms5172 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xls Additionalfile2.docx Additionalfile3.docx Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Aging Clinical and Experimental Research → Version 1 posted Editorial decision: Accepted 17 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviews received at journal 29 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5053163","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435643234,"identity":"a6388aa3-9029-4bf7-9f7e-da98cb877e7d","order_by":0,"name":"Bowen Yang","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bowen","middleName":"","lastName":"Yang","suffix":""},{"id":435643235,"identity":"3f0e892d-d5e0-4811-b55d-19818ffacba5","order_by":1,"name":"Junming Bi","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junming","middleName":"","lastName":"Bi","suffix":""},{"id":435643236,"identity":"3a07108a-a48c-494d-9390-3b50e531b889","order_by":2,"name":"Weinan Zeng","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weinan","middleName":"","lastName":"Zeng","suffix":""},{"id":435643237,"identity":"676f80ca-589a-4c35-af72-168b19e6f52b","order_by":3,"name":"Mingquan Chen","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mingquan","middleName":"","lastName":"Chen","suffix":""},{"id":435643238,"identity":"b8477a67-4111-4352-ab1b-a73be0a3d760","order_by":4,"name":"Zhihao Yao","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Yao","suffix":""},{"id":435643239,"identity":"18dbd11a-bd06-4aeb-b38f-43ff585ab84c","order_by":5,"name":"Shouyu Cheng","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shouyu","middleName":"","lastName":"Cheng","suffix":""},{"id":435643240,"identity":"1e2635df-8565-4378-b4e6-cd16d698d7c7","order_by":6,"name":"Zhaoqiang Jiang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoqiang","middleName":"","lastName":"Jiang","suffix":""},{"id":435643241,"identity":"e73a9996-4bed-4748-9dfd-c16bc6cb1371","order_by":7,"name":"Changzheng Zhang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changzheng","middleName":"","lastName":"Zhang","suffix":""},{"id":435643242,"identity":"ea397ccc-96e7-458f-958d-c01c9998135d","order_by":8,"name":"Hangyu Liao","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hangyu","middleName":"","lastName":"Liao","suffix":""},{"id":435643243,"identity":"33c7dea1-d02d-4ec2-b96e-de9b83207686","order_by":9,"name":"Xiaokang Gu","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaokang","middleName":"","lastName":"Gu","suffix":""},{"id":435643244,"identity":"edcc2027-7fdd-40aa-9dff-4999130310d9","order_by":10,"name":"Zhiyong Xian","email":"","orcid":"","institution":"Ganzhou Hospital of Guangdong Provincial People's Hospital, Ganzhou Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Xian","suffix":""},{"id":435643245,"identity":"e060445c-dd05-437f-96c7-acf1c27721c4","order_by":11,"name":"Yuming Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYNACAwYGfiDJTJoWyQbStIB0HSBWi8GNHDOpGwV37DafX7ztcQGDTb68A/OzB4S0SOcYPEveduNZufEMhjTLjQfYzA2I0HI42ezGGTNpHobDBoYNPGwSRGkxnkGqFjsD/h6IFnkGAlokzzwrtgZqSZC4wVZuzGOQZmDAzGaGVwvf8eSNt3P+HLbn7z+87TFPhY2BfHvzM7xaFA5wgIMnsUEigQ0cpwaH8akHAvkG9gcg2p6B/wAbVISAllEwCkbBKBhxAACXikV0L+0jygAAAABJRU5ErkJggg==","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yuming","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-09-08 14:41:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5053163/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5053163/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40520-025-03046-z","type":"published","date":"2025-04-29T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79696700,"identity":"689de873-2cd7-4f59-bedd-3f2d33fac145","added_by":"auto","created_at":"2025-04-01 15:39:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112504,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology and procedures of this study. GWAS: genome-wide association study, LTL: leukocyte telomere length, MR: Mendelian randomization, SNPs: single-nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5053163/v1/194f330d6d9465d01451b2be.png"},{"id":79696703,"identity":"8cda8e6c-193e-44c7-b8b7-e55898127b98","added_by":"auto","created_at":"2025-04-01 15:39:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":323088,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization study of the influence of LTL on the risk of developing various\u003c/p\u003e\n\u003cp\u003ecancers\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5053163/v1/697f7a5d5f92665d56a3250e.png"},{"id":81988093,"identity":"26f61f41-4b8d-4bbb-84ea-e8b4322e75cf","added_by":"auto","created_at":"2025-05-05 16:07:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1259712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5053163/v1/7db2b754-f7b4-4b43-809d-350ace73ddeb.pdf"},{"id":79696706,"identity":"70a61478-cb49-4896-b893-230f9e727055","added_by":"auto","created_at":"2025-04-01 15:39:22","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":105984,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xls","url":"https://assets-eu.researchsquare.com/files/rs-5053163/v1/09e7178a218d0b31483b4439.xls"},{"id":79696704,"identity":"4316b0ff-cda6-4e4a-80ef-e1a4f5738c74","added_by":"auto","created_at":"2025-04-01 15:39:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27991,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5053163/v1/4e816989f81c75c1c4dada37.docx"},{"id":79698038,"identity":"c8d98056-2883-44a0-a2ab-f014576a5e3e","added_by":"auto","created_at":"2025-04-01 15:55:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":474686,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5053163/v1/199a00dde922ececfe39ce4d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal effect between telomere length and thirteen types of cancer in Asian population: a bidirectional mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTelomeres are intricate structures consisting of DNA and proteins that are located at the termini of eukaryotic chromosomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Telomeric DNA consists of two single-stranded DNA sequences of different lengths that fold inward to create a ring-shaped structure called a T-loop, which inhibits chromosomal rearrangements and end fusions. Therefore, chromosome stability depends mainly on telomeres for maintenance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Whenever a cell undergoes division, there is a slight decrease in telomere length (TL) due to the inability of the enzyme polymerase to completely extend the DNA ends. When TL becomes dangerously short, the cell is prompted to undergo senescence, which ultimately results in cell growth inhibition or apoptosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, the potential for TL to serve as a predictor for aging and age-related illnesses is very promising [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A complicated genetic characteristic, TL heritability varies between 34% and 50% within families [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the associations between TL and the risk of developing various cancers has been controversial in previous research, with varying and inconclusive findings observed across different forms of cancer and even within the same type of cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For instance, several studies have shown that longer TL is consistently correlated with several malignancies, such as lung cancer, glioma, and renal cell carcinoma [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conversely, specific recent investigations have indicated that bladder cancer, gastric cancer, and esophageal squamous cell carcinoma may be caused by shorter TL [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Different nations, types of cancer, study designs, and measurement techniques are among the reasons why the outcomes of these investigations have varied. Because leukocyte telomere length (LTL) is more accessible to quantify and comparable to TL in other human tissues, in research, LTL is frequently utilized instead of TL [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, recent genetic studies have identified several single-nucleotide polymorphisms (SNPs) associated with LTL, offering a solid platform for investigating the associations between LTL and the risk of developing various types of tumors [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Past studies of this relationship have focused on European populations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, we are also interested in exploring the relationship between LTL and the risk of developing cancer in Asian populations to determine whether there are similarities or differences in that relationship compared with that in European populations. Asian populations may exhibit distinct genetic architectures and environmental exposures that modify LTL dynamics and cancer risk, underscoring the need for population-specific studies. In the current study, we aimed to close this gap by focusing on Asian populations, thereby improving worldwide awareness of the association between LTL and the risk of developing cancer.\u003c/p\u003e \u003cp\u003eWe studied the potential causal associations between LTL and the risk of developing various types of tumors in Asian communities by using an epidemiological approach known as Mendelian randomization (MR). This concept is derived from the second law of Mendel, which stresses the separate inheritance of features. By concentrating on the random segregation of genetic variation from parent to child during meiosis, MR shields against the influence of many other confusing factors [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Reverse causation bias is also absent from MR since a person's genetic composition cannot be altered after conception [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eIn this investigation, we used a two-sample MR methodology to evaluate the causal connection between LTL and the risk of developing thirteen distinct types of tumors with genome-wide association study (GWAS) data from individuals of Asian descent. This study utilized genetic data exclusively from the Singapore Chinese Health Study (SCHS) and the Biobank Japan Project (BBJ), focusing on Asian populations. Instrumental variables (IVs) must satisfy three assumptions: (1) the IVs must exhibit a reliable association with LTL; (2) the IVs should be unrelated to confounding variables in the association between LTL and the risk of developing cancer; and (3) the only variable by which the IVs should affect malignancy is LTL, excluding any other variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was conducted according to the STROBE-MR guidelines (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLTL data source\u003c/h3\u003e\n\u003cp\u003eOur analysis incorporated data from the SCHS, a comprehensive epidemiological survey that included 63,257 Chinese individuals of both sexes. Among this population, a subset of 23,096 individuals underwent GWAS analysis of LTL. Among the samples, a total of 16,759 individuals were chosen for analysis, while 6,337 individuals were utilized for validation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The LTL measurements for SCHS participants were conducted by researchers who utilized professional DNA blood kits to extract DNA from peripheral blood samples. To determine the relative length of telomeres in the samples, researchers have employed the monochrome multiplex quantitative polymerase chain reaction (qPCR) technique [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Based on the comparison of the telomere-to-albumin gene copy numbers to a reference sample, TL was measured. All qPCR experiments were conducted twice, and the coefficient of variation for the repetitions was 3.5%, which indicates the high reliability of the method and provides essential data support for further exploration of the relationship between TL and specific SNPs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Participants in the SCHS were informed of the study details and consented to participate in this study, and approval was obtained from the National University of Singapore Institutional Review Board.\u003c/p\u003e\n\u003ch3\u003eThe data source of cancers\u003c/h3\u003e\n\u003cp\u003eAll of the cancer summary statistics were retrieved from BBJ (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). BBJ is one of the largest Asian biobanks. It includes a vast cohort of more than 200,000 individuals and gathers DNA and blood samples from 12 Japanese medical institutes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Doctors at designated medical institutions selected types of cancer based on their clinical significance in terms of incidence or mortality rates in Japan and adhered to the diagnostic criteria for all types of cancer, excluding patients who had received bone marrow transplants and those of non-Asian descent. Control samples were obtained from four prospective population-based cohorts in Japan. Approval for the GWAS related to BBJ was obtained from the relevant institutional ethics committee [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of the cancers included in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCase samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eControl samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e (total)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e (male)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e (female)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e (total)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e (male)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e (female)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiliary tract cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e195745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e195745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophageal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e195745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e195745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematological malignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e211217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e108646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e102571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e195745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e208403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e106637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e101766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvarian cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e195745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e103939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e103939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSelection of IVs\u003c/h3\u003e\n\u003cp\u003eThe study flow diagram is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In summary, LTL served as an exposure factor, whereas cancer was an outcome factor. When choosing the most effective IVs, we used these quality control measures to guarantee that our findings about the causal connection between LTL and the risk of developing cancer were genuine and accurate. As IVs, we initially chose SNPs that exhibited a strong correlation (\u003cem\u003eP\u003c/em\u003e\u0026lt;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) with LTL and showed no evidence of linkage disequilibrium (LD, r\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.001 and window size=10,000 kb). Second, the minor allele frequency (MAF) criterion was 0.01 for the relevant variant. Third, to maintain consistency in the influence of SNPs on both exposure and outcome in MR, we specifically eliminated palindromic SNPs. This was done to avoid any potential changes in the coding of alleles or the orientation of DNA strands. Fourth, we calculated the F-statistic to assess the strength of the IVs. An \u003cem\u003eF\u003c/em\u003e value greater than 10 indicates that the IVs are suitable for investigation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The \u003cem\u003eF\u003c/em\u003e statistic was calculated using the following formula: \u003cem\u003eF\u003c/em\u003e༝\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2)/(1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e), where \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e is the proportion of the variability of the LTL explained by each instrument and N is the sample size of the GWAS for the SNP-LTL association. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e can be calculated by using the following formula: 2EAF\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026times;(1-EAF)∕2EAF\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026times;(1།EAF)་2(SE(\u003cem\u003eβ\u003c/em\u003e)\u003csup\u003e2\u003c/sup\u003e)\u0026times;\u003cem\u003eN\u003c/em\u003e\u0026times;EAF\u0026times;(1།EAF), where EAF, \u003cem\u003eβ\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e, and SE(\u003cem\u003eβ\u003c/em\u003e) represent the effect allele frequency, the estimated genetic effect on LTL, the sample size of the GWAS for the SNP-LTL association, and the standard error of the genetic effect, respectively [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Horizontal pleiotropy was rigorously tested using MR-PRESSO and MR-Egger regression. SNPs with significant pleiotropy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in MR-PRESSO outlier tests) were removed iteratively until no global pleiotropy remained. Moreover, SNPs connected to cancer-related variables or risk factors, including obesity, daily cigarette smoking, weekly alcohol use, and insufficient physical activity, were examined and excluded using the online resource: LDtrait (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ldlink.nih.gov/?tab༝ldtrait\u003c/span\u003e\u003cspan address=\"https://ldlink.nih.gov/?tab༝ldtrait\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The SNPs that were carefully chosen after the aforementioned stages were utilized in our investigation.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eOur research primarily employed the IVW (random-effects model) approach to evaluate the causal association between LTL and the risk of developing thirteen different types of malignant tumors. The IVW approach exhibits the best statistical power when every assumption is satisfied [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The weighted median method may reliably estimate causal effects even when there are half-valid IVs [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The MR‒Egger method can provide more accurate estimates of causal effects, accounting for possible heterogeneity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. After Bonferroni adjustment (0.05 divided by 13 outcomes), \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0038 was considered to indicate strong evidence of an association. On the other hand, 0.0038\u0026lt;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 was considered to indicate a nominally significant association. Next, we performed sensitivity analyses. MR studies frequently apply Cochran's Q test to evaluate disparities among different IVs and determine the presence of heterogeneity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Horizontal pleiotropy was assessed using the MR\u0026ndash;PRESSO and MR‒Egger regression tests. Horizontal pleiotropy is regarded as nonexistent when the MR\u0026ndash;Egger regression intercept is near zero [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. We frequently carried out the \"leave-one-out\" procedure to check the reliability of our research results. Additionally, the MR\u0026ndash;Steiger directionality test is often applied to identify any reverse causal association [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Finally, we utilized an online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://shiny.cnsgenomics.com/mRnd/)t\u003c/span\u003e\u003cspan address=\"https://shiny.cnsgenomics.com/mRnd/)t\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eo quantify the statistical power of this MR study [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The statistical analyses were performed with R version 4.3.3 and the R packages (TwoSampleMR and MR-PRESSO). The code utilized in this investigation is available in Additional file 2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSelection of IVs\u003c/h2\u003e \u003cp\u003eFollowing the completion of several quality control procedures as previously described, we found nine LTL-SNPs in the Asian populations that met the generally recognized genome-wide significance level (\u003cem\u003eP\u003c/em\u003e\u0026lt;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) for exposure. The F-statistics for each SNP exceeded 10, suggesting the absence of weak IVs (Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). None of these nine SNPs were associated with cancer-related confounding factors (Additional file 1: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Additionally, after removing pleiotropic SNPs discovered by MR\u0026ndash;PRESSO (rs10857352 and rs2293607 for breast cancer, rs41293836 for gastric cancer, rs7705526 for lung and prostate cancer, and rs41309367 for prostate cancer), not a single IV showed horizontal pleiotropy. The genetic variations in exposure and outcome are characterized as shown in Additional file 1: Table S4.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCausal effects between LTL and the risk of developing various cancers\u003c/h3\u003e\n\u003cp\u003eBased on the IVW approach results (Fig. 2), we found a significant positive association between LTL and the risk of developing lung cancer (OR＝1.6009, 95% CI: 1.3056-1.9629, \u003cem\u003eP\u003c/em\u003e＝6.08\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e) and prostate cancer (OR＝1.4200, 95% CI: 1.1489-1.7550, \u003cem\u003eP\u003c/em\u003e＝0.0012). The exact magnitude and direction results were obtained using the weighted median approach as with the IVW method. A nominally significant association was observed between and LTL and the risk of developing hematological malignancy (OR＝1.5119 95% CI: 1.0810-2.1146, \u003cem\u003eP\u003c/em\u003e＝0.0157). For the remaining ten cancer types, there was no statistically significant correlation. The estimated dependence of LTL and tumors on IVs is depicted in the scatter plots (Additional file 3: Figure S1). As illustrated by the ascending lines in the plot, LTL is positively associated with the risk of developing lung cancer, prostate cancer, and hematological malignancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe evaluated the dependability of the MR results by means of numerous sensitivity analyses. The Cochran\u0026apos;s Q test revealed some heterogeneity related to breast cancer and gastric cancer (Table 2). However, the random-effects model of IVW can mitigate the impact of minor heterogeneity. MR‒Egger regression analysis revealed intercepts close to zero for all cancer types (e.g., lung cancer: intercept = 0.0115, \u003cem\u003eP\u003c/em\u003e = 0.6283; prostate cancer: intercept = -0.0394, \u003cem\u003eP\u003c/em\u003e = 0.0990), and no significant global horizontal pleiotropy was detected by MR\u0026ndash;PRESSO (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for all outcomes, Table 3), supporting the absence of directional pleiotropic effects in our instrumental variables. The results of the MR‒Egger regression analysis are shown graphically with funnel plots (Additional file 3: Figure S2). The validity of the causality estimates of the link was improved by the leave-one-out plots, showing that the lack of any one SNP used in the analysis had no discernible impact on the causal association (Additional file 3: Figure S3). Furthermore, the results of the MR\u0026ndash;Steiger directionality test did not provide any evidence supporting a causal relationship between the risk of developing each type of cancer and LTL (\u003cem\u003eP\u003c/em\u003e＜0.001) (Additional file 1: Table S5). For the risk of developing lung cancer, prostate cancer, and hematological malignancy in this study, our statistical power was 1, 1, and 0.93, respectively (Additional file 1: Table S6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Heterogeneity study results on cancers for LTL.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCancer types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCochran\u0026rsquo;s Q test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBiliary tract cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.2254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.3349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCervical cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.5603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.7203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.4394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.5192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEndometrial cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.0082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.4687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEsophageal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.4881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.1281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.6039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.7193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHematological malignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.5204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.0804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.8565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.1480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.5683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.8962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.5820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.6605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePancreatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.0292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.5011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProstate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.3575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.4502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IVW inverse variance weighted, LTL leukocyte telomere length\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Results of the horizontal pleiotropy between LTL and the risk of developing cancers\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMR-PRESSO\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMR-Egger\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eBiliary tract cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.9576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.8232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eCervical cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.1187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.7857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eEndometrial cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.5194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eEsophageal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.5521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.8291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eHematological malignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.2674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.6907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eLung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.6283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.8263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003ePancreatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.1599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eProstate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-0.0394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.0990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SE standard error, LTL leukocyte telomere length, MR Mendelian randomization\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the causal relationships between LTL and the risk of developing thirteen different cancer types in Asian populations via the two-sample MR method. The results showed that the risks of developing lung cancer (OR＝1.6009, 95% CI: 1.3056-1.9629, \u003cem\u003eP\u003c/em\u003e＝6.08×10\u003csup\u003e−6\u003c/sup\u003e) and prostate cancer (OR＝1.4200, 95% CI: 1.1489-1.7550, \u003cem\u003eP\u003c/em\u003e＝0.0012) were strongly correlated with genetically determined LTL. A nominally significant association between LTL and the risk of developing hematological malignancy (OR＝1.5119 95% CI: 1.0810-2.1146, \u003cem\u003eP\u003c/em\u003e＝0.0157) was detected, which suggests that LTL and hematological malignancy may be causally related. No causal correlation was identified between LTL and the risk of developing tumors from the remaining ten cancer categories. Using the MR–Steiger directionality test, we likewise failed to establish causal links between the risk of developing various cancers and LTL. This study highlights the causal relationship between longer TL and the risk of developing cancer in Asian populations and may provide new ideas for global cancer detection and prevention.\u003c/p\u003e\n\u003cp\u003eThe association between longer TL and the risk of developing lung cancer has received the most attention among many cancers. Our findings are comparable to those derived in Western populations, which indicate that individuals with longer TL have increased susceptibility to lung cancer [5]. Nevertheless, our research on lung cancer frequently contradicts the findings of numerous prior studies, which commonly indicate a heightened likelihood of lung cancer in individuals with shorter TL [12, 40]. This disparity could be related to insufficient case‒control studies, which are susceptible to intrinsic flaws such as reverse causality and confounding. For example, in retrospective case‒control studies, the timing of exposure and results are ambiguous. Blood samples are typically collected after lung cancer diagnosis or treatment, potentially confounding this relationship. Additionally, some studies have suggested that tumor chemotherapy can shorten telomeres [41, 42]. On the other hand, specific investigations have indicated that distinct pathophysiological subtypes may influence the correlation between TL and the risk of developing lung cancer [43, 44]. However, due to the lack of available histological subtyping data in the BBJ, we could not further explore the relationship between histological subtypes and TL.\u0026nbsp;Beyond telomere length per se, emerging evidence implicates epigenetic dysregulation, such as DNA methylation, in modulating cancer risk. For instance, A recent meta-analysis further identified specific methylation signatures as independent predictors of lung cancer risk, suggesting that telomere length and epigenetic alterations may act through convergent pathways to promote oncogenesis [45]. Future studies integrating multi-omics data (e.g., telomere length, methylation profiles, and somatic mutations) are needed to dissect these interactions.\u003c/p\u003e\n\u003cp\u003ePrevious studies have demonstrated that TL has a significant impact on the process of prostate cancer development and progression [46, 47]. A longer TL was substantially linked to higher overall death rates, according to a new Australian study including 533 prostate cancer patients with a median follow-up of 149 months [48]. However, an extensive prospective survey with up to 20 years of follow-up for the identification of cancer and mortality encompassing 47,102 participants from a European population revealed that shorter TL was linked to a greater risk of early mortality for all malignancy except prostate cancer [49]. A variety of factors may contribute to different study results. There are various explanations, including overall mortality, technical discrepancies in real-time PCR measurements of LTL, patient demographic heterogeneity, and the limited sample size. Moreover, prostate tissue-specific TL is rare, so we used TL-associated data obtained from peripheral blood leukocytes rather than prostate tissue. This limitation may reduce the likelihood of detecting causal linkage. However, our results might still be somewhat valid because peripheral blood leukocytes are readily available and helpful for screening and risk prediction, and specific research has demonstrated a significant relationship between the two tissue types [14, 15].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our research emphasizes the significance of TL in hematological malignancy across Asian populations, adding to the current pool of evidence. According to earlier research, the TLs of patients with blood cancers are shorter than those of patients in the control group [50-53]. The correlation between TL and the risk of developing cancers of hematological malignancy can be attributed to two factors. First, cancer cells typically increase the production of telomerase, an enzyme that allows them to maintain or even extend telomeres. This enables cancer cells to continue proliferating without any limitations [54]. Second, the irregular activity of telomerase can result in variations in TL among individuals, which subsequently impacts their vulnerability to hematological malignancy [55]. Studies examining TL in patients with hematological malignancy may not provide an accurate representation of the actual TL. The reason for this is that blood samples contain a combination of healthy and diseased cells, which change in proportion depending on the individual's illness status.\u003c/p\u003e\n\u003cp\u003eIn this study, utilizing an MR design, we investigated the causal associations between TL and the risk of developing various cancers while reducing residual confounding, which is frequently observed in observational studies and prevents reverse causality. A critical contribution of our work is the application of large datasets from a homogeneous population for MR analysis, which improves the accuracy of our results. Nevertheless, there are certain limitations to our research. First, we could only perform causal association MR analysis because there were no data on individuals available. Hence, we were no longer able to investigate the sensitivity and specificity of the results. Second, telomere length may be affected by factors such as drugs or inflammation, resulting in its shortening or lengthening, and such confounding factors may introduce bias, which this study failed to fully correct[47, 56]. In addition, Since telomerase mainly controls TL, it is imperative to explore more how telomerase activity directly or indirectly affects the genesis of malignancy. This work might provide new information about how telomeres accelerate the course of cancer. Nevertheless, the absence of thorough telomerase-related GWAS data prevents us from analyzing the connection between telomerase and malignancy. More studies must be performed on this subject. Finally, the possible biological mechanism responsible for the association between longer TL and the risk of developing cancer is yet unknown. Thus, additional molecular studies are necessary to confirm the results of this work.\u003c/p\u003e\n\n"},{"header":"Conclusions","content":"\u003cp\u003eOverall, we conducted a thorough evaluation of the cause-and-effect relationship between TL and the risk of developing several types of cancer. The findings of our study indicate that among Asian individuals, having longer TL increases the likelihood of developing lung cancer and prostate cancer. Additionally, there is some evidence of a nominally significant association between longer TL and the risk of developing hematological malignancy. MR‒Egger regression analysis revealed intercepts close to zero for all cancer types (e.g., lung cancer: intercept = 0.0115, P = 0.6283; prostate cancer: intercept = -0.0394, P = 0.0990), and no significant global horizontal pleiotropy was detected by MR–PRESSO (P \u0026gt; 0.05 for all outcomes, Table 3), supporting the absence of directional pleiotropic effects in our instrumental\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors affirm that they do not possess any identifiable personal relationships or competing financial interests that might have appeared to exert an influence on the research presented in this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThe study utilized data from publicly accessible sources and was ethically approved.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by the Project of Department of Finance of Guangdong Province (Grant No. KS0120220268), the Science-Technology-Medicine Collaborative Project of Ganzhou Science and Technology Bureau, Jiangxi Province (Grant No. 2023LNS26880), the Foshan Nanhai District \"14th Five-Year Plan\" Key Specialty Development Program (Specialty Category), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515111219), and the Guangzhou Science and Technology Program (Grant No. 202201010897).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, B.Y., Z.X, and Y.Y.; methodology, B.Y. and J.B.; software, B.Y. and M.C.; validation, B.Y. and Z.Y.; formal analysis, B.Y.; investigation, B.Y.; resources, B.Y. and W.Z.; data curation, B.Y. and S.C.; writing\u0026mdash;original draft preparation, B.Y. and Z.J.; writing\u0026mdash;review and editing, B.Y. and C.Z.; visualization, B.Y., and C.Z.; supervision, B.Y., H.L. and X.G.; project administration, B.Y., Z.X. and Y.Y.; funding acquisition, Z.X. and Y.Y.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e We extend our sincere appreciation to all the GWAS cohort participants and the investigators of the Singapore Chinese Health Study and Biobank Japan Project for their assistance in collaborating on the dissemination of the GWAS summary statistics.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets supporting the conclusions of this article are available in the SCHS repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6084/m9.figshare.8066999\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.8066999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and BBJ repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://jenger.riken.jp/en/\u003c/span\u003e\u003cspan address=\"http://jenger.riken.jp/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ede Lange T (2005) Shelterin: the protein complex that shapes and safeguards human telomeres. Genes Dev 19(18):2100\u0026ndash;2110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gad.1346005\u003c/span\u003e\u003cspan address=\"10.1101/gad.1346005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffith JD, Comeau L, Rosenfield S, Stansel RM, Bianchi A, Moss H et al (1999) Mammalian telomeres end in a large duplex loop. Cell 97(4):503\u0026ndash;514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0092-8674(00)80760-6\u003c/span\u003e\u003cspan address=\"10.1016/s0092-8674(00)80760-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHug N, Lingner J (2006) Telomere length homeostasis. Chromosoma 115(6):413\u0026ndash;425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00412-006-0067-3\u003c/span\u003e\u003cspan address=\"10.1007/s00412-006-0067-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShay JW, Wright WE (2019) Telomeres and telomerase: three decades of progress. Nat Rev Genet 20(5):299\u0026ndash;309. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41576-019-0099-1\u003c/span\u003e\u003cspan address=\"10.1038/s41576-019-0099-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaycock PC, Burgess S, Nounu A, Zheng J, Okoli GN, Bowden J et al (2017) Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study. JAMA Oncol 3(5):636\u0026ndash;651. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamaoncol.2016.5945\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2016.5945\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmanios M (2022) The Role of Telomeres in Human Disease. Annual review of genomics and human genetics. 23:363\u0026ndash;381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-genom-010422-091101\u003c/span\u003e\u003cspan address=\"10.1146/annurev-genom-010422-091101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrinivas N, Rachakonda S, Kumar R (2020) Telomeres and Telomere Length: A General Overview. Cancers 12(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers12030558\u003c/span\u003e\u003cspan address=\"10.3390/cancers12030558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson CP, Codd V (2020) Genetic determinants of telomere length and cancer risk. Curr Opin Genet Dev 60:63\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gde.2020.02.007\u003c/span\u003e\u003cspan address=\"10.1016/j.gde.2020.02.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeow WJ, Cawthon RM, Purdue MP, Hu W, Gao YT, Huang WY et al (2014) Telomere length in white blood cell DNA and lung cancer: a pooled analysis of three prospective cohorts. Cancer Res 74(15):4090\u0026ndash;4098. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/0008-5472.Can-14-0459\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.Can-14-0459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunders CN, Kinnersley B, Culliford R, Cornish AJ, Law PJ, Houlston RS (2022) Relationship between genetically determined telomere length and glioma risk. Neurooncology 24(2):171\u0026ndash;181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/neuonc/noab208\u003c/span\u003e\u003cspan address=\"10.1093/neuonc/noab208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMachiela MJ, Hofmann JN, Carreras-Torres R, Brown KM, Johansson M, Wang Z et al (2017) Genetic Variants Related to Longer Telomere Length are Associated with Increased Risk of Renal Cell Carcinoma. Eur Urol 72(5):747\u0026ndash;754. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eururo.2017.07.015\u003c/span\u003e\u003cspan address=\"10.1016/j.eururo.2017.07.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa H, Zhou Z, Wei S, Liu Z, Pooley KA, Dunning AM et al (2011) Shortened telomere length is associated with increased risk of cancer: a meta-analysis. PLoS ONE 6(6):e20466. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0020466\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0020466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan W, Du J, Shi M, Jin G, Yang M (2017) Short leukocyte telomere length, alone and in combination with smoking, contributes to increased risk of gastric cancer or esophageal squamous cell carcinoma. Carcinogenesis 38(1):12\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/carcin/bgw111\u003c/span\u003e\u003cspan address=\"10.1093/carcin/bgw111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaniali L, Benetos A, Susser E, Kark JD, Labat C, Kimura M et al (2013) Telomeres shorten at equivalent rates in somatic tissues of adults. Nat Commun 4:1597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ncomms2602\u003c/span\u003e\u003cspan address=\"10.1038/ncomms2602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadalla SM, Cawthon R, Giri N, Alter BP, Savage SA (2010) Telomere length in blood, buccal cells, and fibroblasts from patients with inherited bone marrow failure syndromes. Aging 2(11):867\u0026ndash;874. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.100235\u003c/span\u003e\u003cspan address=\"10.18632/aging.100235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePooley KA, Bojesen SE, Weischer M, Nielsen SF, Thompson D, Amin Al Olama A et al (2013) A genome-wide association scan (GWAS) for mean telomere length within the COGS project: identified loci show little association with hormone-related cancer risk. Hum Mol Genet 22(24):5056\u0026ndash;5064. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/hmg/ddt355\u003c/span\u003e\u003cspan address=\"10.1093/hmg/ddt355\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMangino M, Hwang SJ, Spector TD, Hunt SC, Kimura M, Fitzpatrick AL et al (2012) Genome-wide meta-analysis points to CTC1 and ZNF676 as genes regulating telomere homeostasis in humans. Hum Mol Genet 21(24):5385\u0026ndash;5394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/hmg/dds382\u003c/span\u003e\u003cspan address=\"10.1093/hmg/dds382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu J, Chen M, Shete S, Amos CI, Kamat A, Ye Y et al (2011) A genome-wide association study identifies a locus on chromosome 14q21 as a predictor of leukocyte telomere length and as a marker of susceptibility for bladder cancer. Cancer Prev Res (Philadelphia Pa) 4(4):514\u0026ndash;521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1940-6207.Capr-11-0063\u003c/span\u003e\u003cspan address=\"10.1158/1940-6207.Capr-11-0063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevy D, Neuhausen SL, Hunt SC, Kimura M, Hwang SJ, Chen W et al (2010) Genome-wide association identifies OBFC1 as a locus involved in human leukocyte telomere biology. Proc Natl Acad Sci USA 107(20):9293\u0026ndash;9298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0911494107\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0911494107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCodd V, Nelson CP, Albrecht E, Mangino M, Deelen J, Buxton JL et al (2013) Identification of seven loci affecting mean telomere length and their association with disease. Nat Genet 45(4):422. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.2528\u003c/span\u003e\u003cspan address=\"10.1038/ng.2528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Stoma S, Lotta LA, Warner S, Albrecht E, Allione A et al (2020) Genome-wide Association Analysis in Humans Links Nucleotide Metabolism to Leukocyte Telomere Length. Am J Hum Genet 106(3):389\u0026ndash;404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2020.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2020.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Doherty JA, Burgess S, Hung RJ, Lindstr\u0026ouml;m S, Kraft P et al (2015) Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study. Hum Mol Genet 24(18):5356\u0026ndash;5366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/hmg/ddv252\u003c/span\u003e\u003cspan address=\"10.1093/hmg/ddv252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWehby GL, Ohsfeldt RL, Murray JC (2008) Mendelian randomization' equals instrumental variable analysis with genetic instruments. Stat Med 27(15):2745\u0026ndash;2749. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/sim.3255\u003c/span\u003e\u003cspan address=\"10.1002/sim.3255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavey Smith G, Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 23(R1):R89\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/hmg/ddu328\u003c/span\u003e\u003cspan address=\"10.1093/hmg/ddu328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA et al (2021) Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA 326(16):1614\u0026ndash;1621. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2021.18236\u003c/span\u003e\u003cspan address=\"10.1001/jama.2021.18236\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDorajoo R, Chang X, Gurung RL, Li Z, Wang L, Wang R et al (2019) Loci for human leukocyte telomere length in the Singaporean Chinese population and trans-ethnic genetic studies. Nat Commun 10(1):2491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-019-10443-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-10443-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCawthon RM (2009) Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res 37(3):e21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkn1027\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkn1027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirata M, Kamatani Y, Nagai A, Kiyohara Y, Ninomiya T, Tamakoshi A et al (2017) Cross-sectional analysis of BioBank Japan clinical data: A large cohort of 200,000 patients with 47 common diseases. J Epidemiol 27(3s):S9\u0026ndash;s21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.je.2016.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.je.2016.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagai A, Hirata M, Kamatani Y, Muto K, Matsuda K, Kiyohara Y et al (2017) Overview of the BioBank Japan Project: Study design and profile. J Epidemiol 27(3s):S2\u0026ndash;s8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.je.2016.12.005\u003c/span\u003e\u003cspan address=\"10.1016/j.je.2016.12.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshigaki K, Akiyama M, Kanai M, Takahashi A, Kawakami E, Sugishita H et al (2020) Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat Genet 52(7):669\u0026ndash;679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41588-020-0640-3\u003c/span\u003e\u003cspan address=\"10.1038/s41588-020-0640-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG (2011) Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40(3):755\u0026ndash;764. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyr036\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyr036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapadimitriou N, Dimou N, Tsilidis KK, Banbury B, Martin RM, Lewis SJ et al (2020) Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat Commun 11(1):597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-020-14389-8\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-14389-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin SH, Brown DW, Machiela MJ (2020) LDtrait: An Online Tool for Identifying Published Phenotype Associations in Linkage Disequilibrium. Cancer Res 80(16):3443\u0026ndash;3446. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/0008-5472.Can-20-0985\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.Can-20-0985\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG (2015) Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30(7):543\u0026ndash;552. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10654-015-0011-z\u003c/span\u003e\u003cspan address=\"10.1007/s10654-015-0011-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40(4):304\u0026ndash;314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/gepi.21965\u003c/span\u003e\u003cspan address=\"10.1002/gepi.21965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32(5):377\u0026ndash;389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10654-017-0255-x\u003c/span\u003e\u003cspan address=\"10.1007/s10654-017-0255-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Spiller W, Del Greco MF, Sheehan N, Thompson J, Minelli C et al (2018) Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol 47(4):1264\u0026ndash;1278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyy101\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyy101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani G, Tilling K, Davey Smith G (2017) Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet 13(11):e1007081. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgen.1007081\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1007081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrion MJ, Shakhbazov K, Visscher PM (2013) Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 42(5):1497\u0026ndash;1501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyt179\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyt179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnic GM, Sondak VK, Messina JL, Fenske NA, Zager JS, Cherpelis BS et al (2013) Telomere length and risk of melanoma, squamous cell carcinoma, and basal cell carcinoma. Cancer Epidemiol 37(4):434\u0026ndash;439. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.canep.2013.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.canep.2013.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JJ, Nam CE, Cho SH, Park KS, Chung IJ, Kim HJ (2003) Telomere length shortening in non-Hodgkin's lymphoma patients undergoing chemotherapy. Ann Hematol 82(8):492\u0026ndash;495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00277-003-0691-4\u003c/span\u003e\u003cspan address=\"10.1007/s00277-003-0691-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakraborty S, Sun CL, Francisco L, Sabado M, Li L, Chang KL et al (2009) Accelerated telomere shortening precedes development of therapy-related myelodysplasia or acute myelogenous leukemia after autologous transplantation for lymphoma. J Clin oncology: official J Am Soc Clin Oncol 27(5):791\u0026ndash;798. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/jco.2008.17.1033\u003c/span\u003e\u003cspan address=\"10.1200/jco.2008.17.1033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu J, Wu X (2013) Re: short telomere length, cancer survival, and cancer risk in 47 102 individuals. J Natl Cancer Inst 105(15):1157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jnci/djt154\u003c/span\u003e\u003cspan address=\"10.1093/jnci/djt154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez-Espiridion B, Chen M, Chang JY, Lu C, Chang DW, Roth JA et al (2014) Telomere length in peripheral blood leukocytes and lung cancer risk: a large case-control study in Caucasians. Cancer Res 74(9):2476\u0026ndash;2486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/0008-5472.Can-13-2968\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.Can-13-2968\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolcini J, Chiavarini M, Firmani G, Brennan KJM, Cardenas A, Baccarelli AA et al (2025) Methylation Biomarkers of Lung Cancer Risk: A Systematic Review and Meta-Analysis. Cancers 17(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers17040690\u003c/span\u003e\u003cspan address=\"10.3390/cancers17040690\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing Z, Wu CJ, Jaskelioff M, Ivanova E, Kost-Alimova M, Protopopov A et al (2012) Telomerase reactivation following telomere dysfunction yields murine prostate tumors with bone metastases. Cell 148(5):896\u0026ndash;907. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2012.01.039\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2012.01.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrnish D, Lin J, Chan JM, Epel E, Kemp C, Weidner G et al (2013) Effect of comprehensive lifestyle changes on telomerase activity and telomere length in men with biopsy-proven low-risk prostate cancer: 5-year follow-up of a descriptive pilot study. Lancet Oncol 14(11):1112\u0026ndash;1120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s1470-2045(13)70366-8\u003c/span\u003e\u003cspan address=\"10.1016/s1470-2045(13)70366-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenner W, Krenn-Pilko S, Gruber HJ, Herrmann M, Langsenlehner T (2018) Relative telomere length and prostate cancer mortality. Prostate Cancer Prostatic Dis 21(4):579\u0026ndash;583. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41391-018-0068-3\u003c/span\u003e\u003cspan address=\"10.1038/s41391-018-0068-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeischer M, Nordestgaard BG, Cawthon RM, Freiberg JJ, Tybj\u0026aelig;rg-Hansen A, Bojesen SE (2013) Short telomere length, cancer survival, and cancer risk in 47102 individuals. J Natl Cancer Inst 105(7):459\u0026ndash;468. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jnci/djt016\u003c/span\u003e\u003cspan address=\"10.1093/jnci/djt016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayde\u0026eacute; Cottliar AS, Noriega MF, Narbaitz M, Rodr\u0026iacute;guez A, Slavutsky IR (2006) Association between telomere length and BCL2 gene rearrangements in low- and high-grade non-Hodgkin lymphomas. Cancer Genet Cytogenet 171(1):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cancergencyto.2006.05.016\u003c/span\u003e\u003cspan address=\"10.1016/j.cancergencyto.2006.05.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWidmann TA, Herrmann M, Taha N, K\u0026ouml;nig J, Pfreundschuh M (2007) Short telomeres in aggressive non-Hodgkin's lymphoma as a risk factor in lymphomagenesis. Exp Hematol 35(6):939\u0026ndash;946. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.exphem.2007.03.009\u003c/span\u003e\u003cspan address=\"10.1016/j.exphem.2007.03.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong DY, Kim JA, Jeong D, Yun J, Kim SM, Lim K et al (2019) Telomere length and its correlation with gene mutations in chronic lymphocytic leukemia in a Korean population. PLoS ONE 14(7):e0220177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0220177\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0220177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJebaraj BM, Kienle D, Lechel A, Mertens D, Heuberger M, Ott G et al (2013) Telomere length in mantle cell lymphoma. Blood 121(7):1184\u0026ndash;1187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1182/blood-2012-08-452649\u003c/span\u003e\u003cspan address=\"10.1182/blood-2012-08-452649\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaciejowski J, de Lange T (2017) Telomeres in cancer: tumour suppression and genome instability. Nat Rev Mol Cell Biol 18(3):175\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrm.2016.171\u003c/span\u003e\u003cspan address=\"10.1038/nrm.2016.171\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVakonaki E, Tsiminikaki K, Plaitis S, Fragkiadaki P, Tsoukalas D, Katsikantami I et al (2018) Common mental disorders and association with telomere length. Biomedical Rep 8(2):111\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/br.2018.1040\u003c/span\u003e\u003cspan address=\"10.3892/br.2018.1040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJurk D, Wilson C, Passos JF, Oakley F, Correia-Melo C, Greaves L et al (2014) Chronic inflammation induces telomere dysfunction and accelerates ageing in mice. Nat Commun 2:4172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ncomms5172\u003c/span\u003e\u003cspan address=\"10.1038/ncomms5172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Telomere length, Cancer, Mendelian randomization, Asian population","lastPublishedDoi":"10.21203/rs.3.rs-5053163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5053163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between leukocyte telomere length (LTL) and the risk of developing various cancers has always been controversial and predominantly focused on European populations. Hence, Mendelian randomization (MR) was applied to the Asian population to explore the causal relationships between LTL and the risk of developing various cancers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe explored the causal connection between LTL and the risk of developing thirteen types of cancer in Asian populations using freely available genetic variation data. The primary analytical method employed was the inverse variance weighted (IVW) method, complemented by sensitivity and validation analyses. Following Bonferroni correction, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0038 was considered to indicate statistical significance, and P values ranging from 0.0038 to 0.05 were considered to indicate a nominally significant association.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe findings indicated significant positive associations between LTL and the risk of developing lung cancer (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.6009, 95% confidence interval [CI]: 1.3056\u0026ndash;1.9629, P=6.08\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6) and prostate cancer (OR\u0026thinsp;=\u0026thinsp;1.4200, 95% CI: 1.1489\u0026ndash;1.7550, P༝0.0012). Additionally, there was a nominally significant association between LTL and the risk of developing hematological malignancy (OR\u0026thinsp;=\u0026thinsp;1.5119, 95% CI: 1.0810\u0026ndash;2.1146, P༝0.0157). No statistically significant relationships between LTL and the risk of developing the other ten kinds of cancer were detected. No causal link between the risk of developing various cancers and LTL was discovered.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAsians with longer telomeres are more prone to developing lung and prostate cancer. There is also a nominally significant association between longer telomeres and the risk of developing hematological malignancy.\u003c/p\u003e","manuscriptTitle":"Causal effect between telomere length and thirteen types of cancer in Asian population: a bidirectional mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 15:39:17","doi":"10.21203/rs.3.rs-5053163/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-17T15:57:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T20:29:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-29T07:45:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308997676644419263764508938645082635691","date":"2025-03-28T18:54:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289133747794789471428474599074200708932","date":"2025-03-28T18:37:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-28T18:33:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T02:27:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aging Clinical and Experimental Research","date":"2025-03-21T12:24:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"26e1f298-14a7-4b8d-b3e7-bb4d7ed03cfb","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:06:10+00:00","versionOfRecord":{"articleIdentity":"rs-5053163","link":"https://doi.org/10.1007/s40520-025-03046-z","journal":{"identity":"aging-clinical-and-experimental-research","isVorOnly":false,"title":"Aging Clinical and Experimental Research"},"publishedOn":"2025-04-29 15:57:39","publishedOnDateReadable":"April 29th, 2025"},"versionCreatedAt":"2025-04-01 15:39:17","video":"","vorDoi":"10.1007/s40520-025-03046-z","vorDoiUrl":"https://doi.org/10.1007/s40520-025-03046-z","workflowStages":[]},"version":"v1","identity":"rs-5053163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5053163","identity":"rs-5053163","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}