
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312382
MD-D-24-05170
00083
10.1097/MD.0000000000039584
3
5700
Research Article
Observational Study
The causal effect of telomere length on the risk of malignant lymphoma: A Mendelian randomization study
https://orcid.org/0009-0005-8326-7222
Song Teng MSc songteng2013@163.com
abc
Liu Jie MSc liuejie201413@163.com
d
Zhao Ke MSc 1782786572@qq.com
abc
Li Shuping MSc li578116468@163.com
abc
Qiu Minghan MSc qiuminghan@163.com
abc
Zhang Miao MSc zhangmiao810208@126.com
abc
Wang Huaqing MD abc*
a Department of Oncology, Tianjin Union Medical Center, Nankai University, Tianjin, China
b Tianjin Cancer Institute of lntegrative Traditional Chinese and Western Medicine, Tianjin, China
c The Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, China
d Department of Cardiology, Tianjin Bei Chen Hospital, Tianjin, China.
* Correspondence: Huaqing Wang, Department of Oncology, Tianjin Union Medical Center, Nankai University, Hongqiao District, Tianjin, China (e-mail: huaqingw@163.com).
20 9 2024
20 9 2024
103 38 e3958419 5 2024
11 7 2024
15 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Telomere length (TL) has been implicated in the risk assessment of numerous cancers in observational studies. Nevertheless, the relationship between TL and malignant lymphoma remains unclear, displaying inconsistent patterns across different studies. A summary dataset for genome-wide association study of TL and malignant lymphoma was acquired from the OpenGWAS website. An extensive 2-sample Mendelian randomization (MR) analysis was performed, encompassing various methodologies such as MR-Egger, weighted median, weighted mode, simple mode, and the primary method of inverse-variance weighting (IVW). Sensitivity evaluations were performed using the Cochran Q test, MR-Egger regression, and leave-one-out analysis. The main method IVW revealed that TL substantially increased the risk of Hodgkin lymphoma (HL; odds ratio [OR] = 2.135; 95% confidence interval [CI] = 1.181–3.859; P = .012). Both the IVW and weighted median methods indicated statistical associations between genetically predicted TL and other types of non-HL (OR = 1.671, 95% CI = 1.009–2.768, P = .045; OR = 2.310, 95% CI = 1.033–5.169, P = .042). However, there was no association between TL and diffuse large B-cell lymphoma, follicular lymphoma, or mature T/natural Killer-cell lymphoma, and sensitivity analysis revealed no heterogeneity or horizontal pleiotropy, indicating that the causal effect was robust. Our study shows that TL plays different roles in different types of lymphomas. A longer TL significantly increases the risk of HL and other types of non-HL.

Hodgkin lymphoma
Mendelian randomization
non-Hodgkin lymphoma
single-nucleotide polymorphisms
telomere length
National Natural Science Foundation of China 10.13039/501100001809 Grant No. 82070206 Huaqing WangOPEN-ACCESSTRUE
SDCT
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pmc1. Introduction

Malignant lymphoma is a malignant tumor originating from the lymph nodes or lymph tissues and is more common in the lymph nodes.[1] According to pathological characteristics, it can be segregated into Hodgkin lymphoma (HL) and non-HL (NHL), among which the incidence of NHL is relatively high.[2] The exact etiology and pathogenesis of lymphoma have not been fully elucidated and are the result of a combination of factors.[3] Previous studies have shown that the incidence of malignant lymphoma may be related to infection (bacterial or viral),[4,5] chronic inflammation,[6,7] immunodeficiency,[8] and genetic predisposition.[9] Other possible risk factors include environmental factors, socioeconomic status, education, diet, and smoking.[10]

Telomeres are DNA-protein complexes at the ends of eukaryotic chromosomes that can prevent degradation, fusion, and rearrangement of chromosome ends, thus playing a pivotal role in maintaining chromosome integrity and stability.[11] Several studies have shown that telomerase is reactivated in the majority of human neoplasms (85%–90%) and cancer cells are characterized by stable telomere length (TL) and immortalization.[12] Therefore, TL is essential for tumorigenesis.[13] TL is frequently estimated in the leukocytes. Leukocyte TL is hereditary, with heritability ranging from 36% to 84%.[14] Recent studies have suggested that longer TL may increase the risk of lymphoma, especially NHL.[15,16] In previous case-control studies, TL measurements were often performed after the onset of disease, and malignant tumors may have affected TL changes, resulting in reverse causal bias.[17] In addition, environmental factors (such as age, smoking, and lifestyle) may also affect TL and increase the risk of lymphoma, creating an illusion of a relationship between TL and disease. Currently, the relationship between TL and the risk of lymphoma remains unclear. Therefore, an advanced methodology is required to assess the causal relationships between them.

Mendelian randomization (MR) is an analytical technique used to investigate causal associations between exposures and outcomes, utilizing genetic variants as instrumental variables (IVs).[18] Compared with traditional epidemiological studies, MR analyses utilize single-nucleotide polymorphisms (SNPs) as IVs, which prevents the results from being affected by confounding factors such as environmental factors.[19] In addition, SNPs are randomly distributed to individuals along with gametes, similar to the requirements of randomized controlled trials, and are preceded by the onset of disease, which has a temporal nature that avoids the effects of reverse causality. However, a valid IV needs to fulfill 3 important assumptions. First, the underlying assumption is that the IV is strongly correlated with exposure. Second, IV is independent of the confounding factors. Third, IV can only influence the outcome through exposures.[20] When IV satisfies these assumptions, the MR method provides reliable results.

This study explored the causality between TL and different types of lymphomas using MR analysis, providing a scientific basis for the prevention and management of lymphomas.

2. Materials and methods

2.1. Data sources for TL and malignant lymphoma Genome-Wide Association Study

The Integrative Epidemiology Unit OpenGWAS database (genome-wide association study [GWAS] identity document: ieu-b-4879) presented the summarized statistics for TL utilized in this study, with a total sample size of 472,174 cases from the European population and 20,134,421 SNPs. The FinnGen website (https://www.finngen.fi/en) provided GWAS datasets pertaining to malignant lymphoma. Concerning HL, the dataset comprised 369 cases alongside 180,756 controls. In the context of NHL, the dataset included 209 cases of diffuse large B-cell lymphoma (DLBCL), 522 cases of follicular lymphoma (FL), 150 cases of mature T/natural killer (NK)-cell lymphoma, and 533 cases of other and unspecified types of NHL for a total of 180,756 controls, which can also be accessed and downloaded from the well-known OpenGWAS website (https://gwas.mrcieu.ac.uk/), which is a part of the public domain.

2.2. Genetic IV selection for TL

To better explore the causal connection between TL and lymphoma, we needed to select SNPs that satisfied the following criteria: (1) SNPs are highly associated with TL and have genome-wide research significance (P < 5 × 10−8). (2) SNPs were independent of each other (r2 < 0.001; clumping distance > 10,000 kb), thus avoiding any potential bias caused by linkage disequilibrium (LD). (3) SNPs exhibiting an LD of r2 > 0.8 were delineated, while palindromic SNPs were excluded during the harmonization of the TL and outcomes. (4) The PhenoScanner database (The PhenoScanner website) was used to judge and remove SNPs associated with the potential confounders and mediators. (5) The MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) test was employed to discern potential outlier SNPs for correcting potential horizontal pleiotropy. Ultimately, the remaining SNPs were employed for MR analysis. Utilizing the F statistic, the statistical strength of the relationship between SNPs and exposures was evaluated. IVs showing an F statistic > 10 do not suggest a weak genetic instrument. The formula for the F statistic is as follows:

F=R21−R2⋅N−K−1K

Here, R2 represents the percentage of exposure elucidated by the genetic instrument, N denotes the sample size, and K denotes the number of SNPs.

2.3. MR analysis

MR analysis was conducted to examine the causal relationship between TL and malignant lymphoma, using inverse-variance weighted (IVW) analysis, complemented by the weighted median (WM) and MR-Egger regression as supplementary methodologies.[21] IVW combines the Wald estimates for each SNP to obtain an overall estimate.

2.4. Sensitivity analysis

In this study, various sensitivity analysis methods were employed to demonstrate the reliability of IVW outcomes.[22] Horizontal pleiotropy was detected using an MR-Egger regression model. This indicated that there were no confounding factors if P > .05. The heterogeneity of the IVs was evaluated using Cochran Q statistic. If Q_Pval > 0.05, there was no heterogeneity between the 2 groups. Finally, to observe whether the SNPs caused significant changes in the outcome, the IVW method was used to conduct a leave-one-out (LOO) test for SNPs. The relationship between TL and lymphoma risk was expressed as a dominance ratio (odds ratio [OR]) and its 95% confidence interval (CI), which provided evidence for a possible causal relationship if P ≤ .05. A flowchart of this study is shown in Figure 1.

Figure 1. The flow diagram of the MR study. The MR approach relies on the following 3 assumptions: assumption 1: the genetic variants must be strongly correlated with the exposure; assumption 2: the genetic variants must be unrelated to any confounding factors that are associated with the outcome; assumption 3: the genetic variants can only affect the outcome through the exposure and not through other pathways. MR = Mendelian randomization, SNP = single-nucleotide polymorphism, TL = telomere length.

2.5. Statistical software version and name

All MR analyses were performed using R (version 4.3.1) in conjunction with the TwoSampleMR and MR-PRESSO packages.

3. Results

3.1. Genetic IVs for TL

From the TL GWAS, 154 independent genome-wide significant (P < 5 × 10−8) SNPs were identified. Fifteen proxy SNPs with an LD of r2 > 0.8 were determined when the exposure and outcome GWAS were harmonized, as they were not included in the outcome GWAS. Furthermore, 20 palindromic SNPs (rs10768683, rs10840270, rs111950327, rs11769630, rs11991877, rs145114957, rs16978028, rs17677991, rs1957937, rs2276182, rs2306646, rs28577594, rs41269079, rs4731541, rs56178008, rs611646, rs670180, rs75664430, rs762679, and rs77231040) and 1 incompatible SNP (rs9940099) were excluded from further analysis. Following the MR-PRESSO test, it was observed that no outlier SNPs were detected among the selected IVs mentioned above. Ultimately, a set of 120 genetic variants exhibiting strong associations with TL was utilized for subsequent MR analysis. All the F statistics for these instrumental SNPs were >10, which suggests that there are no weak IVs. The comprehensive details pertaining to the selected genetic IVs are outlined in Table S1, Supplemental Digital Content, http://links.lww.com/MD/N534.

3.2. Causal effect of TL on HL

Our study indicated a significant association between genetically predicted TL and the risk of HL in the IVW model (OR = 2.135; 95% CI = 1.181–3.859; P = .012). Nevertheless, the WM model (OR = 1.752; 95% CI = 0.684–4.489; P = .243) and MR-Egger regression model (OR = 1.590; 95% CI = 0.569–4.438; P = .377) did not show a causal association (Table 1). The scatter plot (Fig. 2A) showed that increased TL led to an elevated risk of HL. The forest plot (Fig. 2B) showed the causal impact of each independent SNP on HL, along with the overall causal assessments derived from both the MR-Egger and IVW models.

Table 1 Results of MR analysis of the causal effects between TL and different lymphoma subtypes.

Outcome	Method	Number of SNPs	β	SE	P value	OR	95% CI	
HL	MR-Egger	120	0.464	0.524	.377	1.590	0.569–4.438	
WM	120	0.561	0.480	.243	1.752	0.684–4.489	
IVW	120	0.758	0.302	.012	2.135	1.181–3.859	
DLBCL	MR-Egger	120	0.569	0.746	.447	1.767	0.409–7.620	
WM	120	0.326	0.629	.604	1.386	0.404–4.755	
IVW	120	0.273	0.429	.525	1.313	0.566–3.045	
FL	MR-Egger	120	0.532	0.456	.245	1.703	0.697–4.161	
WM	120	0.244	0.403	.544	1.276	0.579–2.811	
IVW	120	−0.186	0.267	.485	0.830	0.492–1.400	
Mature
T/NK-cell lymphomas	MR-Egger	120	−0.125	0.837	.881	0.882	0.171–4.544	
WM	120	−0.187	0.776	.809	0.829	0.181–3.798	
IVW	120	−0.937	0.484	.053	0.391	0.151–1.011	
Other and unspecified types of NHL	MR-Egger	120	0.847	0.446	.060	2.332	0.972–5.593	
WM	120	0.837	0.411	.042	2.310	1.033–5.169	
IVW	120	0.514	0.257	.045	1.671	1.009–2.768	
CI = confidence interval, DLBCL = diffuse large B-cell lymphoma, FL = follicular lymphoma, HL = Hodgkin lymphoma, IVW = inverse-variance weighted, MR = Mendelian randomization, NHL = non-Hodgkin lymphoma, NK = natural killer, OR = odds ratio, SE = standard error, SNP = single-nucleotide polymorphism, TL = telomere length, WM = weighted median.

Figure 2. (A) The scatter plot from genetically predicted TL on HL. (B) The forest plot of causality effect sizes of both single and merged SNPs for TL on HL. (C) The results of LOO methods for sensitivity analysis. (D) The funnel plot from genetically predicted TL on HL. HL = Hodgkin lymphoma, LOO = leave one out, MR = Mendelian randomization, SE = standard error, SNP = single-nucleotide polymorphism, TL = telomere length.

Cochran Q test in the MR-Egger and IVW methods demonstrated no significant heterogeneity between the 2 datasets (Table 2) (P = .852), indicating consistency in the results. MR-Egger regression analysis indicated no indications of potential pleiotropy (Table 2) (intercept = 0.010; P = .492), suggesting that the observed association was not driven by confounding factors. Furthermore, in the LOO analyses, the observed association did not change dramatically even when a single SNP was removed (Fig. 2C). The funnel plot (Fig. 2D) exhibited approximate symmetry, indicating the absence of directional pleiotropy. In summary, the sensitivity analysis provided further support for the reliability of MR analysis results.

Table 2 Heterogeneity tests and MR-Egger intercept of TL causally linked to HL and the other types of NHL.

Outcome	Heterogeneity	MR-Egger	
	IVW			MR-Egger		
Cochrane Q	Q_df	P value	Cochrane Q	Q_df	P value	Intercept	P value	
HL	102.5346	119	.8592970	102.0605	118	.8518575	0.01032031	.4924609	
Other types of NHL	125.0708	119	.3335743	124.1937	118	.3301283	−0.01166654	.3631633	
HL = Hodgkin lymphoma, IVW = inverse-variance weighted, MR = Mendelian randomization, NHL = non-Hodgkin lymphoma, TL = telomere length.

3.3. Causal effect of TL on NHL

The estimations from both the IVW and WM methods indicated statistical associations between genetically predicted longer TL and the other types of NHL (OR = 1.671, 95% CI = 1.009–2.768, P = .045; OR = 2.310, 95% CI = 1.033–5.169, P = .042) (Table 1). Therefore, increased TL is a risk factor for other NHL types (Fig. 3A and 3B).

Figure 3. (A) The scatter plot from genetically predicted TL on the other types of NHL. (B) The forest plot of causality effect sizes of both single and merged SNPs for TL on the other types of NHL. (C) The results of LOO methods for sensitivity analysis. (D) The funnel plot from genetically predicted TL on the other types of NHL. LOO = leave one out, MR = Mendelian randomization, NHL = non-Hodgkin lymphoma, SE = standard error, SNP = single-nucleotide polymorphism, TL = telomere length.

The MR-Egger intercept test did not provide evidence of directional pleiotropy (Table 2) (intercept = −0.011; P = .363). Furthermore, the Cochran Q test did not provide any indication of increased heterogeneity among studies (Table 2) (P = .333). In the LOO analyses, the MR estimates remained stable even after sequentially removing each of the 120 SNPs used as IVs (Fig. 3C). The funnel plot (Fig. 3D) showed that the SNPs were roughly symmetrical on the left and right sides of the IVW line.

As indicated in Table 1, the IVW results showed that TL was not causally related to DLBCL, FL, or NK/T-cell lymphomas (P > .05). Consistently, the other 2 analyses provided no incidence of potential pleiotropy (Table 1).

4. Discussion

Recently, a multitude of studies have substantiated the predictive and prognostic value of TL in relation to cancer, underscoring its significance in facilitating precise diagnostics and treatment strategies.[23,24] Observational studies have assessed the correlation between TL and susceptibility to various types of cancers. Indeed, the majority of retrospective investigations have indicated that individuals with shorter TL tend to have an elevated risk of developing cancer.[25,26] In contrast, several large prospective studies in recent years have revealed that a longer TL is a risk factor for certain types of cancer.[27–29] An MR study indicated that the correlation between TL and tumor risk was significantly variable in multiple cancer categories, with increased TL significantly associated with a higher risk of glioma, lung adenocarcinoma, neuroblastoma, bladder cancer, melanoma, kidney cancer, and endometrial cancer.[30]

Telomeres play a dual role in human carcinogenesis, with both short and long TL promoting oncogenesis; however, the precise molecular mechanisms are currently unknown. The progressive shortening of telomeres in response to the cell division cycle serves as a molecular mechanism for cell death through senescence, acting as a tumor suppressor mechanism that prevents the early onset of tumorigenesis in organisms.[31] However, gradual erosion of telomeres and biological aging can give rise to chromosomal aberrations and genomic instability, thereby further increasing susceptibility to cancer.[32,33] On the other hand, in tumor cells, excessive TL and upregulation of telomerase activity evade the normal process of programmed cellular senescence, giving the cells the ability to divide and proliferate indefinitely and enter a state of immortality, which promotes tumorigenesis.[34] The intricate interplay between TL and cancer risk frequently exhibits a discernible U-shaped pattern.[35] This suggests that maintaining an optimal TL is crucial for maintaining genomic stability and reducing cancer risk.

In the context of a homogeneous tumor phenotype, the relationship between TL and carcinogenesis is complex and inconsistent. Hosnijeh et al conducted a nested case-control study. They measured the TL of monocytic DNA in peripheral blood samples of 464 lymphoma cases and 464 healthy controls before diagnosis. The results revealed a positive association between elongated TL and augmented susceptibility to BCL.[16] Another prospective cohort study comparing TL in peripheral White blood cell DNA from 107 NHL patients and controls found that increased TL may be a potential predictor of the future risk of NHL.[36] Machiela et al[15] substantiated this assertion by documenting that longer telomeres confer enhanced replication potential to blood-borne cancer cells. Nonetheless, an additional small case-control study of 40 NHL cases and 40 healthy controls showed that the risk of NHL was higher with shorter TL and that TL shortening predominantly occurred subsequent to the onset of cancer.[37] Therefore, it is plausible to consider reverse causality in this result. Longitudinal observations and randomized controlled trials may be useful to study this relationship. However, cohort follow-up may take a long time as lymphomas accumulate gradually from pathological alterations in tissues.[38]

In this study, the GWAS dataset was employed for MR analysis to evaluate the causative link between TL and lymphoma. These results suggest that a longer TL increases the risk of HL and other types of NHL. Nonetheless, no causal association was discovered between TL and DLBCL, FL, or mature NK/T-cell lymphomas. Significantly, this pioneering MR study serves as the first to demonstrate that a longer TL predisposes individuals to HL. However, Gao et al[39] reported a correlation between TL and a decreased risk of HL in their MR studies, suggesting distinct causal effects of TL on diverse lymphoma subtypes.

This MR study offers notable benefits, as it amalgamates data from an extensive sample of published GWAS, minimizing the limitations of observational research, such as confounding factors and reverse causality. Furthermore, by employing GWAS data limited to a European population for both exposure and outcome, potential bias arising from population heterogeneity was significantly reduced. Nonetheless, there are certain limitations to be considered in this study. First, the restricted number of instances encompassing diverse subcategories of lymphoma within the FinnGen cohort could potentially undermine the veracity of the analysis findings. Furthermore, the majority of the population included in this study was European, which may impose limitations on the application of the conclusions to other ethnic populations. Therefore, further studies in other populations are warranted. Once more, this study is only a statistical result; TL may have different effects on different pathological subtypes, so it is imperative to further explore the specific biological mechanism between TL and different pathological types of lymphoma. In addition, observational studies on TL and lymphoma are scarce and cannot be adequately compared and discussed.

5. Conclusion

Our investigation revealed that TL plays a heterogeneous role in various lymphoma subtypes. However, there is a need for further validation in large-scale prospective studies and exploration of relevant biological mechanisms.

Acknowledgments

The authors acknowledge the participants and investigators of the FinnGen study.

Author contributions

Writing – original draft: Teng Song, Jie Liu.

Data curation: Jie Liu, Ke Zhao.

Visualization: Shuping Li, Minghan Qiu.

Software: Miao Zhang.

Funding acquisition: Huaqing Wang.

Writing – review & editing: Huaqing Wang.

Supplementary Material

Abbreviations:

CI confidence interval

DLBCL diffuse large B-cell lymphoma

FL follicular lymphoma

GWAS genome-wide association study

HL Hodgkin lymphoma

IV instrumental variable

IVW inverse-variance weighted

LD linkage disequilibrium

LOO leave one out

MR Mendelian randomization

MR-PRESSO MR Pleiotropy Residual Sum and Outlier

NHL non-Hodgkin lymphoma

OR odds ratio

SNP single-nucleotide polymorphism

TL telomere length

WM weighted median

This work was supported by the National Natural Science Foundation of China (grant No. 82070206) and Tianjin Key Medical Discipline (Specialty) Construction Project (grant No.TJYXZDXK-053B).

Ethical approval and consent to participate is not applicable for this study.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Song T, Liu J, Zhao K, Li S, Qiu M, Zhang M, Wang H. The causal effect of telomere length on the risk of malignant lymphoma: A Mendelian randomization study. Medicine 2024;103:38(e39584).
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