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. 2025 Sep 19;104(38):e44485. doi: 10.1097/MD.0000000000044485

Causal relationship between the duration of mobile phone use and risk of stroke: A Mendelian randomization study

Ruisong Jin a, Xutang Jiang b,c, Qingxin Lin b,c, Xinyue Huang b,c,d, Wen Gao b,c,e, Bihuan Wang a, Feng Zheng b,c,*
PMCID: PMC12459444  PMID: 40988177

Abstract

This study investigates the causal relationship between the duration of mobile phone use (DMPU) and risk of stroke using Mendelian randomization (MR) analysis. Independent single nucleotide polymorphisms from genome-wide association study datasets were employed as instrumental variables to estimate the effects of DMPU on the risk of stroke, intracerebral hemorrhage, ischemic stroke, and its subtypes (cardioembolic infarction, small-vessel disease, large artery atherosclerosis [LAAS]). Inverse-variance weighting was utilized as the primary MR method and sensitivity analyses were performed. Ninety single nucleotide polymorphisms associated with stroke from genome-wide association study datasets were selected as instrumental variables. Inverse-variance weighted analysis showed a significant causality between DMPU and an increased risk of LAAS (odds ratio [OR] = 1.120; 95% confidence interval [CI] = 1.005–1.248; P = .040). No genetic association was found for stroke (OR = 1.000; 95% CI = 0.999–1.001, P = .677), intracerebral hemorrhage (OR = 1.020; 95% CI = 0.912–1.140, P = .734), ischemic stroke (OR = 1.020; 95% CI = 0.979–1.062, P = .344), cardioembolic infarction (OR = 1.066; 95% CI = 0.974–1.166, P = .166), and small-vessel disease (OR = 1.052; 95% CI = 0.944–1.173, P = .356). MR-Egger regression (intercept = 2.75 × 10⁻3; P = .888) suggested multidimensionality was unlikely to bias the results; Cochran Q test and funnel plot showed no heterogeneity or asymmetry, indicating the robustness of present findings. The current investigation confirmed a causal relationship between DMPU and an increased risk of LAAS, suggesting significant implications for public health initiatives and policy development.

Keywords: causal relationship, duration of mobile phone use, Mendelian randomization, stroke

1. Introduction

Stroke is one of the major causes of death and disability worldwide, and the primary prevention is therefore of great importance.[1] A growing number of evidence indicates that stroke is caused by multiple factors, however, traditional risk factors such as diabetes, smoking, and a sedentary lifestyle, do not account for all the risks of stroke.[2–4] With the increasing global burden of stroke, identifying other potential risk factors is crucial, which may contribute to stroke prevention.[5]

Recently, average mobile phone use time has been reported to increase from 2.25 to 4.8 hours per day.[6] The dramatic increase in the use of mobile phones has also raised concerns about their possible adverse effects on human health.[7] Several occupational studies have shown that long-term exposure to low-intensity electromagnetic fields is associated with an increased risk of cardiac arrhythmias and cardiovascular mortality,[8] as well as diurnal changes in blood pressure.[9,10] The prevalence of cell phone overuse is high in socially excluded areas and has been reported to be associated with sleep disorders, academic failure, and obesity.[11] Nevertheless, the relationship between the duration of mobile phone use (DMPU) and stroke is yet to be fully elucidated.[12–14]

To address this gap, we introduced Mendelian randomization (MR), which provides a strong statistical component in epidemiological studies. This study utilizes genetic variants as long-term stable instrumental variable (IV), ensuring that the DMPU precedes stroke and its subtypes, thereby circumventing the influence of reverse causality. Compared to observational studies, MR analysis is less susceptible to potential reverse causality and confounding bias, as genetic variation is randomly distributed at the time of fertilization.[15]

In the present investigation employing a 2-sample MR design, we utilized a random assignment of genetic variants that are linked to DMPU as IVs to scrutinize the causal relationship between DMPU and stroke risk.

2. Methods

The present research was performed in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology Mendelian Randomization guideline, utilizing the checklist provided in the Supplementary Material, Supplemental Digital Content, http://links.lww.com/MD/Q206. Due to the public availability of the data from the genome-wide association studies (GWASs), all primary investigations were granted specific ethical approval and informed consent. Any supplementary ethical authorization was therefore deemed unnecessary for the purpose of this study.[16]

2.1. Study overview

The aim of this study was to investigate the causal relationship between the DMPU and stroke risk, using a 2-sample MR method. In the present analysis, stroke included intracerebral hemorrhage (ICH), ischemic stroke (IS), and its subtypes (cardioembolic infarction [CEI], small-vessel disease [SVD], large artery atherosclerosis [LAAS]).[17] And different MR methods were used and sensitivity analyses were performed to test the validity and robustness of the findings.

These IVs in MR need to fulfill 3 assumptions: the assumption of relevance: the genetic variants selected as IVs should be significantly associated with exposure; the assumption of exclusivity: IVs should not be associated with any confounding factors; and the assumption of independence: IVs should influence outcomes only through exposure and not through other pathways.[18] The approximate analysis flow and MR assumptions were shown in Figure 1.

Figure 1.

Figure 1.

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Study design of the present MR analysis on the causal relationship between DMPU and stroke risk. DMPU = the duration of mobile phone use, LAAS = large artery atherosclerosis, MR = Mendelian randomization, SNPs = single nucleotide polymorphisms.

2.2. Data source

We searched for publicly available summary statistics datasets that encompass a vast amount of summary statistics from GWASs. And all data of the present study were obtained from the IEU Open GWAS project platform (https://gwas.mrcieu.ac.uk/). GWAS summary datasets were mined for DMPU (Table 1).

Table 1.

Detailed information of the GWAS data in the present analysis.

Study phenotypes GWAS ID Sample size Number of SNPs Population Year
DMPU ukb-e-1110_AFR 6320 15,532,406 African American or Afro-Caribbean 2020
Stroke ebi-a-GCST90038613 484,598 9,587,836 NA 2021
ICH ebi-a-GCST90018870 473,513 24,191,284 European 2021
IS ebi-a-GCST90018864 484,121 24,174,314 European 2021
CEI ebi-a-GCST006910 211,763 8,271,294 European 2018
SVD ebi-a-GCST006909 198,048 8,280,845 European 2018
LAAS ebi-a-GCST006907 150,765 8,418,349 European 2018
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CEI = cardioembolic infarction, DMPU = duration of mobile phone use, GWAS = genome-wide association study, ICH = intracerebral hemorrhage, IS = ischemic stroke, LAAS = large artery atherosclerosis, NA = not available, SNP = single nucleotide polymorphism, SVD = small-vessel disease.

2.3. Genetic variants selection criteria

Based on 2 criteria, we selected and validated independent single nucleotide polymorphism (SNPs) associated with DMPU: SNPs with a P value of <5 × 10⁻8 were considered to be significantly associated with exposures and were included in the study. If sufficiently viable IVs could not be extracted, the P-value was increased to 5 × 10⁻6[19,20]; the value of the parameter r2 to 0.001 and the kilobase pairs to 10,000 were set to eliminate the interference in linkage disequilibrium.[21] Then the echo SNPs and incompatible SNPs were eliminated.[22]

2.4. Statistical analysis

We investigated the intrinsic casual association between DMPU and the risk of stroke with 3 MR methods: inverse-variance weighting (IVW), weighted median, and MR-Egger method.[23] To test the heterogeneity of the IVs, Cochran Q test and funnel plot were employed.[24] In Cochran Q test, if significant heterogeneity (P < .05) is detected among the IVs, the random effects IVW method is applied. On the other hand, if no heterogeneity is observed between the IVs, the fixed-effects IVW method is used. The intercept test in MR-Egger regression was employed to evaluate the potential influence of pleiotropic effects of genetic variations on causality estimates. A pleiotropic effect was deemed present if the P value for the intercept test was below 0.05. To determine if causality was predominantly affected by specific SNPs, a leave-one-out analysis was conducted, which involved systematically excluding each IV and assessing its impact on the overall causality.

All statistical analyses in this MR study were performed in R Studio with R version 4.3.3 (MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK) and P-value <.05 was regarded as statistically significant.[25]

3. Results

The populations in these datasets included male and female individuals. A total of 90 suitable SNPs associated with stroke from GWAS datasets were identified with F-statistics >10, suggesting that there was no weak bias in the results of the present study (Table S1, Supplemental Digital Content, https://links.lww.com/MD/Q49).

IVW analyses showed that DMPU had a positive causal effect on susceptibility to LAAS (odds ratio [OR] = 1.120; 95% confidence interval [CI] = 1.005–1.248; P = .040). No genetically predicted association was detected between DMPU and the risk of stroke (OR = 1.000; 95% CI = 0.999–1.001; P = .677), ICH (OR = 1.020; 95% CI = 0.912–1.140; P = .734), IS (OR = 1.020; 95% CI = 0.979–1.062; P = .344), CEI (OR = 1.066; 95% CI = 0.974–1.166; P = .166), and SVD (OR = 1.052; 95% CI = 0.944–1.173; P = .356). The weighted median and MR-Egger analyses failed to show causal influences between DMPU and stroke (Table 2). Based on the exceptional precision of IVW method in comparison to the weighted median and MR-Egger analyses, the results of the MR analysis propose the possibility of a causal association between DMPU and LAAS.

Table 2.

MR analyses of the causal relationship between DMPU and stroke risk.

Exposure Outcome Method Number of SNPs Association P-value OR (95% Cl) Cochran Q statistic Cochran Q P‐value MR-Egger intercept MR-Egger intercept P-value
DMPU Stroke IVW 14 .677 1.000 (0.999–1.001) 14.662 .329
MR Egger 14 .465 1.001 (0.999–1.003) 14.185 .289 0.0001 .537
WM 14 .406 1.001 (0.999–1.002)
ICH IVW 16 .734 1.020 (0.912–1.140) 7.521 .942
MR Egger 16 .185 1.174 (0.937–1.470) 5.528 .977 0.026 .180
WM 16 .607 1.040 (0.896–1.207)
IS IVW 16 .344 1.020 (0.979–1.062) 13.652 .552
MR Egger 16 .854 1.008 (0.925–1.010) 13.566 .482 0.002 .775
WM 16 .866 0.995 (0.941–1.053)
CEI IVW 14 .166 1.066 (0.974–1.166) 16.215 .238
MR Egger 14 .889 1.013 (0.845–1.216) 15.695 .206 0.010 .540
WM 14 .149 1.087 (0.971–1.216)
SVD IVW 15 .356 1.052 (0.944–1.173) 17.304 .240
MR Egger 15 .372 1.108 (0.891–1.378) 16.924 .203 0.010 .598
WM 15 .397 1.063 (0.923–1.224)
LAAS IVW 15 .040 1.120 (1.005–1.248) 14.468 .415
MR Egger 15 .284 1.136 (0.908–1.421) 14.445 .343 0.003 .883
WM 15 .259 1.092 (0.937–1.273)
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P value < 0.05 was shown in bold.

CEI = cardioembolic infarction, CI = confidence interval, DMPU = duration of mobile phone use, ICH = intracerebral hemorrhage, IS = ischemic stroke, IVW = inverse-variance weight, LAAS = large artery atherosclerosis, MR = Mendelian randomization, OR = odd ratio, SNP = single nucleotide polymorphism, SVD = small-vessel disease, WM = weighted median.

Heterogeneity and sensitivity analyses, using Cochran Q test, found no evidence of heterogeneity in the IV estimates based on individual genetic variants (Q = 14.17, P = .415) (Table 2). Heterogeneity reflects the variability in causal estimates derived from each SNP, indicating the consistency of causal effects across all SNPs. To account for potential heterogeneity, a random-effects IVW approach was applied (Fig. 2). The MR-Egger regression intercept showed no signs of pleiotropy among the SNPs (Egger intercept = 2.75 × 10⁻3, P = .888) (Table 2). Additionally, funnel plots revealed no significant outliers or asymmetries (Fig. 3). The results of leave-one-out analysis showed that the relationship between genetically predicted DMPU and susceptibility to LAAS was not driven by any single SNP (Fig. 4).

Figure 2.

Figure 2.

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Forest plots assessing heterogeneity of the causal relationship between DMPU on LAAS. DMPU = duration of mobile phone use, LAAS = large artery atherosclerosis, MR = Mendelian randomization.

Figure 3.

Figure 3.

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Funnel plots assessing the heterogeneity of the causal relationship between DMPU on LAAS. DMPU = duration of mobile phone use, LAAS = large artery atherosclerosis, MR = Mendelian randomization.

Figure 4.

Figure 4.

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“Leave-one-out” analysis confirmed the robustness of the causal relationship between DMPU and LAAS. DMPU = duration of mobile phone use, LAAS = large artery atherosclerosis, MR = Mendelian randomization.

4. Discussion

This study aimed to investigate the causal relationship between DMPU and stroke risk with MR methods by analyzing summary statistical data from GWAS datasets. We found a causal relationship between DMPU and LAAS risk. Our results suggest that reducing DMPU may be beneficial in preventing LAAS occurrence. These findings could influence public health strategies designed to reduce stroke risk.

In the present analysis, a significant inverse association was observed between DMPU and susceptibility to LAAS, which is different from a previous study.[26] Our study provides stronger evidence for a causal relationship using MR, which helps to overcome confounding factors and reverse causality bias that may affect observational studies. To the best of our knowledge, previous investigations have not employed a 2-sample MR approach to thoroughly examine the causal relationship between DMPU and the risk of stroke. Our findings suggest that decreased DMPU may serve as a potential preventive measure against the onset of LAAS.

It has been reported that DMPU may increase the risk of cardiovascular disease (CVD).[27] Electromagnetic radiation, such as that emitted by mobile phones, can enhance oxidative stress, suggesting a possible biological pathway for CVD development. However, epidemiological studies on the long-term cardiovascular effects of electromagnetic radiation from mobile phones have yet to be established.[28] More conclusive evidence with valid measurements of DMPU is needed before this association becomes a concern for the general public.[27] Therefore, in the present study, we utilized MR analysis to investigate the causal relationship between DMPU and stroke risk. To our best knowledge, this is the first study using 2-sample MR approach to examine the causal relationship between DMPU and the risk of stroke and its subtypes.

Our findings suggested that more DMPU may be causally associated with an increased risk of LAAS. This may be due to: First, mobile phone use affects the quality of sleep,[29] and there is a correlation between sleep deprivation and increased risk of LAAS.[30] An unhealthy sleep pattern may negatively impact CVD development by disrupting the circadian rhythm, altering endocrine and metabolic functions, increasing sympathetic nervous activity and inflammation, and contributing to elevated carotid intima-media thickness, plaque formation, and atherosclerosis.[31] Second, previous study found that youth who engaged in extensive gaming using their mobile phones were at a higher risk of living with overweight or obesity.[32] An unhealthy diet linked to mobile phone use can elevate blood lipid levels, which tend to form lipid plaques that gradually clog arteries and increase the risk of LAAS.[33] Third, excessive sedentary time associated with mobile phone use can cause fat and cholesterol to accumulate in the lining of blood vessels, forming plaque that can narrow the arteries and eventually lead to LAAS.[34] Fourth, similarly, poor mental health can disrupt cardiometabolic parameters, such as blood pressure, lipid profile, and blood glucose levels, cause autonomic dysfunction, and enhance immune and inflammatory responses, ultimately leading to a higher risk of LAAS.[31]

In the present analysis, no causal association was detected between DMPU and stroke, ICH, IS, CEI, and SVD, which is different from an observational study.[27] This discrepancy might stem from differences in analytical approaches, as our study employed a genetic perspective to investigate the causal relationship between DMPU and stroke, which avoided the affection by confounding variables that may bias the direct assessment of causality.

Our study has several advantages: First, to our knowledge, this is the first study to explore the casual relationship between DMPU and the risk of stroke and its subtypes at the genetic level. Second, stroke is a disease with a long-term cumulative risk, and short-term studies may fail to capture the potential health impacts of DMPU. Genetic variants remain stable throughout an individual’s life, ensuring the research accurately reflects DMPU’s impact on stroke, unaffected by short-term behavioral changes or measurement errors. Third, all the IVs were obtained from publicly available global GWASs with extensive data, ensuring statistical validity in evaluating the causal relationship between DMPU and the risk of stroke and its subtypes.

The present study has several limitations. First, the data were presented in a summarized form without classification by age and gender. Therefore, the findings in the present study should be used carefully when addressing specific age groups or genders. Second, although the MR-Egger intercept indicates minimal horizontal pleiotropy, potential pleiotropic effects possibly hidden by the limited number of genetic instruments or small sample sizes continue to be a concern. Third, due to the lack of GWAS data based on the categorization of DMPU, the effect of different use duration of mobile phone on stroke risk cannot be determined in the present study. Future studies are warranted to be focused on this issue.

5. Conclusion

In summary, the current investigation substantiates a causal relationship between DMPU and the risk of stroke. More DMPU may be causally associated with an increased LAAS risk, which has implications for clinical and public health practices and policies. Further longitudinal and experimental studies are warranted to investigate the mechanisms and pathways through which the different DMPU influences stroke risk.

Acknowledgments

The authors would like to thank all participants and researchers for contributing and sharing the GWAS summary data.

Author contributions

Conceptualization: Qingxin Lin.

Data curation: Ruisong Jin, Xutang Jiang, Wen Gao.

Methodology: Bihuan Wang.

Visualization: Ruisong Jin, Xutang Jiang, Wen Gao.

Writing – original draft: Ruisong Jin, Xutang Jiang, Wen Gao.

Writing – review & editing: Xinyue Huang, Feng Zheng.

Supplementary Material

medi-104-e44485-s001.pdf (161.5KB, pdf)
medi-104-e44485-s002.docx (37.2KB, docx)

Abbreviations:

CEI
cardioembolic infarction
CI
confidence interval
CVD
cardiovascular disease
DMPU
duration of mobile phone use
GWASs
genome-wide association studies
ICH
intracerebral hemorrhage
IS
ischemic stroke
IVs
instrumental variables
IVW
inverse-variance weighted
LAAS
large artery atherosclerosis
MR
Mendelian randomization
OR
odds ratio
SNP
single nucleotide polymorphism
SVD
small-vessel disease

This work was supported by Fujian Medical University Student Innovation and Entrepreneurship Training Project (Grant Number C2025111), Doctoral Startup Fund of the Second Affiliated Hospital of Fujian Medical University (Grant Number BS202205), Natural Science Foundation of Fujian Province (Grant Number 2023J01754), Health Technology Program Project of Fujian Province (Grant Number 2023GGA046), and Technology Innovation Joint Fund Project of Fujian Province (Grant Number 2023Y9235).

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: Jin R, Jiang X, Lin Q, Huang X, Gao W, Wang B, Zheng F. Causal relationship between the duration of mobile phone use and risk of stroke: A Mendelian randomization study. Medicine 2025;104:38(e44485).

RJ, XJ, QL, XH, and WG contributed to this article equally.

Contributor Information

Ruisong Jin, Email: 3243973057@qq.com.

Xutang Jiang, Email: jiangxutang2001@163.com.

Wen Gao, Email: 2458063564@qq.com.

Bihuan Wang, Email: 813203690@qq.com.

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Supplementary Materials

medi-104-e44485-s001.pdf (161.5KB, pdf)
medi-104-e44485-s002.docx (37.2KB, docx)

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