Association between education inequality and lung cancer: a two- sample Mendelian randomization study

doi:10.21203/rs.3.rs-3932255/v1

Background: While numerous studies have explored the correlation between educational disparities and lung cancer, the definitive determination of a causal link between these factors is still pending. This study seeks to clarify the potential causal role of educational disparities in the development of lung cancer.

Methods: This study employed a two-sample Mendelian randomization (MR) analysis by using genetic data from genome-wide association studies (GWAS) to explore the hypothesized causal connection between educational attainment and lung cancer. The investigation specifically concentrated on its major subtypes: Lung Squamous Cell Carcinoma (LUSC) and Lung Adenocarcinoma (LUAD). In order to enhance our understanding and address potential confounding factors, a Multivariable MR analysis was utilized. Additionally, an advanced Two-step MR methodology was applied to clarify the biological pathways through which educational attainment might impact the risk of lung cancer.

Results: This study employed the Multiplicative Random-Effect Inverse Variance Weighted (IVW-MRE) as primary method in Univariable Mendelian Randomization (UVMR) analysis. The IVW-MRE approach yielded MR estimates indicating an odds ratio (OR) of 0.153 (95% Confidence Interval [CI]: 0.125-0.186) for overall Lung Cancer (LUCA), 0.195 (95% CI: 0.154–0.246) for Lung Squamous Cell Carcinoma (LUSC), and 4.860 (95% CI: 3.584–6.589) for Lung Adenocarcinoma (LUAD). Additionally, Multivariable Mendelian Randomization (MVMR) analysis confirmed the persistent significance of educational inequalities as risk determinants. A subsequent two-step MR analysis further delineated the mediating roles of smoking, alcohol consumption, and Body Mass Index (BMI) in this association, and only found alcohol consumption has mediation effect between the two traits.

Conclusion: Our findings indicate a genetic connection between educational inequality and susceptibility to lung cancer. This emphasizes the need for more extensive research to unravel the complex socio-genetic interactions involved. Importantly, these results underscore the crucial role of addressing educational disparities in the epidemiology and progression of lung cancer.

Biological sciences/Biotechnology

Biological sciences/Cancer

Biological sciences/Cell biology

Biological sciences/Computational biology and bioinformatics

Biological sciences/Genetics

Biological sciences/Molecular biology

Earth and environmental sciences/Environmental social sciences

Health sciences/Diseases

Health sciences/Health care

Health sciences/Medical research

Health sciences/Risk factors

Physical sciences/Mathematics and computing

Lung Cancer

Educational Inequality

Mendelian Randomization

Genome-Wide Association Studies

Lung Squamous Cell Carcinoma

Lung Adenocarcinoma

In the ongoing global health discussion, there is a growing acknowledgment of the complex connection between lung cancer and socio-economic and educational factors. In the Nordic region, lifestyle changes among non-Western immigrant women have led to notable shifts in cancer patterns, particularly an escalation in the risk factors associated with lung cancer.^[1–3]. In Spain, the impact of educational disparities is clearly evident in mortality rates and life expectancy, reflecting the profound socio-economic effects on health outcomes^{[4, 5]}. In non-small cell lung cancer (NSCLC), the impact of demographic and socio-economic factors on diagnostic and therapeutic outcomes is particularly significant. This phenomenon frequently contributes to the advancement of the disease to later stages and the postponement of surgical interventions. Consequently, it adversely affects survival rates ^[6–10]. In addressing this scenario, it becomes evident that a thorough comprehension of the socio-educational factors influencing the onset and treatment of lung cancer is crucial. This underscores the necessity for tailored interventions aimed at alleviating these discrepancies.

Numerous observational studies have consistently indicated a negative association between the level of educational attainment and the incidence of lung cancer ^[11–14]. Higher educational levels are generally linked to a reduced risk of developing lung cancer, a pattern that persists even after accounting for smoking habits ^[11]. However, it is important to acknowledge that these connections could be subject to biases inherent in conventional observational approaches, including confounding variables, reverse causation, and measurement errors ^[15]. The constraints of these studies in establishing causation emphasize the need for alternative research designs to draw causal inferences. In this context, Mendelian Randomization (MR) studies investigating the association between education and lung cancer have gained prominence ^[15]. These studies suggest that achieving a higher level of education could potentially lead to a causal reduction in the risk of lung cancer.

To enhance our comprehension and account for potential confounding factors, our study employed Univariable and Multivariable Mendelian Randomization (MR) analysis. Furthermore, we utilized a sophisticated two-step MR approach to elucidate the biological mechanisms by which educational attainment might influence the risk of lung cancer. The MR framework adeptly addresses concerns related to potential confounders and mediation effect, which are often encountered in conventional observational studies. By employing substantial sample sizes and instrumental variables that are strongly associated (F statistics > 10), our study effectively examines the association between educational parity and the risk of lung cancer.

2.1 Two-sample MR study design

In order to bolster the credibility of causal inferences drawn from Mendelian Randomization (MR) studies, it is imperative to uphold three fundamental assumptions: (1) The genetic variants under consideration must demonstrate a robust association with the exposure of interest. (2) These variants should be devoid of significant correlations with confounding variables that could potentially distort the exposure-outcome relationship. (3) The influence of these genetic variants on the outcome should be exclusively through the exposure, without any independent effect.

As shown in Fig. 1, In this study, we employed a multifaceted Mendelian randomization approach to elucidate the relationship between education attainment and lung cancer. The primary analysis, designated as Univariable MR, was conducted to estimate the direct effect of educational attainment on the incidence of lung cancer. In order to enhance our understanding and address potential confounding factors, we implemented Multivariable MR as an additional analysis (Additional Analysis 1). This analysis aimed to isolate the independent effect of education al attainment, mitigating influences from confounding variables. Lastly, in Additional Analysis 2, a Two-step MR approach was utilized to delineate the biological pathways through which educational attainment might exert its influence on the risk of lung cancer, providing a more comprehensive understanding of the underlying mechanisms.

2.2 Data sources and instruments

Information about the data sources and sample sizes used in this study is summarized in Table 1. The study relied on summary-level data that have been made publicly available.

Table 1

Details on the characteristics of each included dataset.
Phenotype	Data source	Total sample size	Population	# SNPs
Lung cancer, Lung squamous cell carcinoma, Lung adenocarcinoma	Wang, Yufei et al. “Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer.” Nature genetics vol. 46,7 (2014): 736 − 41.	11,348 cases, 15,861 controls, Sum:27,209	European	8.9M
Education attainment	Lee JJ, Wedow R, Okbay A, et al. Gene discovery and polygenic prediction from a genome-wide association study of education al attainment in 1.1 million individuals. Nat Genet 50, 1112–1121(2018).	1,100,000	European	8.1M
BMI	Pulit SL, Stoneman C, Morris AP et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 2019 Jan 1;28(1):166–174.	694, 648	European	27.4M
Smoking, Drinking	Liu M, Jiang Y, Wedow R. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019 Feb;51(2):237–244.	1,232,091	European	11.8M
SNP: single nucleotide polymorphism

2.3 Lung cancer and its subtypes

In a comprehensive genome-wide association study (GWAS), conducted by Wang et al.^[16] on behalf of the International Lung Cancer Consortium (ILCCO), an extensive meta-analysis was carried out utilizing advanced inverse-variance methodologies. This thorough examination incorporated data from four prominent European cohorts focused on GWAS for lung cancer: the MDACC GWAS, the ICR GWAS, the NCI GWAS, and the IARC GWAS. The primary focus of the research was on lung cancer, with specific attention given to its subtypes, particularly lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

Crucial GWAS summary data encompassing overall lung cancer (LUCA), as well as the LUAD and LUSC subtypes, were meticulously extracted from the IEU-OpenGWAS online platform. This extraction utilized the specific inquiry codes "ieu-a-966" for LUCA, "ieu-a-967" for LUAD, and "ieu-a-965" for LUSC. Such a methodical approach was instrumental in ensuring the reliability and accuracy of the data, which forms the foundation of our investigation into the complex interplay between lung cancer and educational variables.

2.4 Educational attainment

In our endeavor to elucidate the complex interplay between educational attainment and lung cancer risk, we meticulously sourced summary statistics from the comprehensive Genome-Wide Association Study (GWAS) on educational attainment, conducted by Lee JJ et al^[17].

The presented data underwent rigorous quality control procedures, adhering to the highest standards of data integrity in accordance with local language conventions and medical reviewing practices. The final dataset, inclusive of more than 1.1 million individuals of European descent, underwent meticulous analysis to identify 1,271 independent single nucleotide polymorphisms (SNPs) that achieved genome-wide significance. This comprehensive analysis not only highlights the methodological strength of our study but also emphasizes the crucial influence of genetic factors in mediating the complex relationship between educational levels and susceptibility to lung cancer.

2.5 BMI

In our study, we strategically employed Genome-Wide Association Studies (GWAS) as a source of potential mediators, focusing specifically on phenotypes related to obesity. The main goal was to optimize the sample sizes from these datasets while carefully ensuring the exclusion of any duplicated samples. A crucial mediator under investigation was Body Mass Index (BMI), calculated from a cohort consisting of 694,648 individuals of European descent. This dataset was obtained from the comprehensive meta-analysis carried out by Pulit SL et al. in 2019^[18].In utilizing this methodology, we successfully leveraged a substantial and unique dataset, thereby greatly reinforcing the robustness of our analysis. This approach played a crucial role in refining the accuracy of our exploration into the potential mediating role of factors associated with obesity, such as BMI, in the context of lung cancer risk and its association with educational attainment.

2.6 Smoking (SMK) and Drinking (DRK)

In the groundbreaking meta-analysis carried out by Liu M et al. in 2019, an intricate exploration was undertaken to unravel the genetic factors influencing tobacco and alcohol consumption in individuals of European descent, shedding light on the intricacies of these behaviors ^[19]. This comprehensive study, involving a cohort of 1.2 million individuals, provided profound insights into the genetic underpinnings of smoking and drinking tendencies.

In this analysis, we identified 378 genetic variants linked to the initiation of regular smoking (SmkInit; n = 1,232,091). Simultaneously, alcohol consumption was measured using a more direct metric – the number of drinks per week (DrinkWk; n = 941,280), revealing 99 associated genetic variants. This comprehensive research represents a significant advancement in uncovering the genetic factors that impact these prevalent behaviors within the European demographic. It contributes to a more nuanced comprehension of their potential role in the etiology of lung cancer, particularly in the context of educational disparities.

2.7 Testing instrument strength and statistical power

In this investigation, the F-statistic was calculated using the formulas: F = R²(n-2)/(1-R²), where 'F' represents the F-statistic, 'R²' denotes the proportion of phenotypic variance explained by a genetic instrument, 'n' indicates the cohort size, 'β' reflects the estimated genetic association of the single nucleotide polymorphism (SNP) with the exposure, and 'MAF' stands for the minor allele frequency. In the realm of medical genetics research, specifically in the context of lung cancer, an F-statistic of 10 or above typically suggests a notably diminished likelihood of encountering weak instrument bias in Mendelian Randomization (MR) analyses. This benchmark plays a pivotal role in safeguarding the credibility of causal inferences drawn from genetic associations.

Furthermore, we meticulously evaluated the statistical power of our study utilizing the methodology outlined by Burgess et al. This method combines the sample size from the European Population Genome Study with the degree to which genetic instruments elucidate the variance in the exposure under scrutiny. Such a thorough assessment is imperative for unraveling the genetic underpinnings of lung cancer, providing significant support to the scientific and educational communities in understanding and addressing this crucial public health issue.

2.8 Univariable MR

In the assessment of each exposure variable, our study employed the Inverse Variance Weighting (IVW) method within a multiplicative random-effects model for the principal Mendelian randomization (MR) analysis. This approach involved aggregating Wald ratio estimates from individual Single Nucleotide Polymorphisms (SNPs) to generate a consolidated causal estimate for each risk factor. These estimates were calculated by dividing the association of each SNP with the outcome by its association with the exposure. Given the dichotomous nature of the outcome, effect estimates were transformed into odds ratios (ORs) to more intuitively represent the relationship between educational attainment and lung cancer risk.

Acknowledging the potential bias in Inverse Variance Weighted (IVW) estimates stemming from pleiotropic instrumental variables, we undertook a series of sensitivity analyses. These analyses were designed to mitigate the impact of pleiotropy on the causal estimates. Specifically, we employed MR-Egger regression to examine the presence of horizontal pleiotropy, with a particular focus on its intercept term. A deviation from zero, reaching significance at P < 0.05, was interpreted as suggestive of directional pleiotropic bias.

Ultimately, a leave-one-SNP-out analysis was undertaken to assess the impact of individual genetic variants on the identified correlations. This additional step enhances the resilience and dependability of our findings within the realm of medical research.

2.9 Multivariable MR

In this study, we explored the possible intersection of Single Nucleotide Polymorphisms (SNPs) in relation to both educational attainment and lung cancer. We took into account the complicating factors of smoking, alcohol consumption, and Body Mass Index (BMI). To unravel this complexity, a comprehensive Mendelian randomization (MR) analysis was performed. This analysis aimed to discern the specific influence of educational attainment on the risk of lung cancer while considering the influences of smoking, alcohol consumption, and BMI.

Our methodology employed the multivariable extension of the Inverse Variance Weighting (IVW) Mendelian Randomization (MR) approach. This sophisticated technique, as elucidated in the referenced literature, adeptly addresses both overt and latent pleiotropic effects. The incorporation of this method played a crucial role in advancing our comprehension of the genetic interactions among educational attainment, smoking, alcohol consumption, BMI, and the risk of lung cancer. The objective of this approach was to provide a more nuanced perspective on the etiological pathways involved, thereby enriching the current understanding of the multifaceted nature of factors contributing to lung cancer risk.

2.10 Mediation analysis

To clarify potential mediating effects within the context of significant Mendelian randomization (MR) associations, a bifurcated MR analysis strategy was employed. In the first phase, genetic instruments associated with educational attainment were utilized to determine the causal impact of this exposure on potential mediators. In the subsequent stage of the analysis, genetic instruments specific to the identified mediators were applied. This procedural step was crucial for a comprehensive assessment of their role in influencing susceptibility to lung cancer.

As medical researchers, we have employed the "product of coefficients" approach to assess the indirect impact of educational attainment on the risk of lung cancer through potential mediators. This method was specifically utilized when substantial evidence indicated that educational attainment had a notable effect on the mediator, which in turn impacted lung cancer risk. The standard errors associated with these indirect effects were meticulously calculated employing the delta method, ensuring precision and reliability in our estimations.

The F-statistics for the genetic instruments used in this study corroborated the absence of weak instrument bias, as detailed in Table S1

3.1 Univariable MR

The univariate Mendelian Randomization (MR) analysis revealed a protective causal association between educational attainment and lung cancer risk. The Inverse Variance-Weighted method indicated a significant odds ratio (OR) of 0.153 (95% Confidence Interval [CI]: 0.125–0.186; P < 0.001), suggesting a substantial inverse relationship. Complementing this, the MR-Egger analysis yielded an OR of 0.036 (95% CI: 0.012–0.108; P < 0.001), while the weighted median approach produced an OR of 0.509 (95%CI: 0.313–0.829; P < 0.001). These results, consistently demonstrating a robust positive correlation between educational attainment and reduced lung cancer risk, are detailed in Table 2. Leave on out as shown in Table S2.

Table 2

Causal effects of Education Attainment on Lung Cancer and its subtypes
Outcome	Method	nSNP	OR (95%CI)	P-value
Lung cancer	Inverse variance weighted	377	0.153 (0.125–0.186)	1.40E-77
	MR Egger	377	0.036 (0.012–0.108)	4.70E-09
	Weighted median	377	0.509 (0.313–0.829)	6.70E-03
Lung adenocarcinoma	Inverse variance weighted	374	4.860 (3.584–6.589)	2.40E-24
	MR Egger	374	10.115 (1.548–66.083)	1.60E-02
	Weighted median	374	1.331 (0.637–2.779)	0.45
Squamous cell lung cancer	Inverse variance weighted	366	0.195 (0.154–0.246)	6.10E-43
	MR Egger	366	1.028 (0.212–4.996)	0.97
	Weighted median	366	1.338 (0.657–2.724)	0.42

3.2 Multivariable MR

We conducted a MVMR to discern the independent effect of educational attainment on lung cancer risk, adjusting for smoking, drinking, and BMI. By integrating these variables, the study sought to isolate the specific impact of educational attainment from these intertwined lifestyle and physiological factors, thereby providing a more accurate assessment of its relationship with lung cancer risk.

In a series of MVMRanalyses, the impact of educational attainment on lung cancer risk was systematically evaluated, accounting for various confounding factors. The first analysis (Fig. 2-Model 1) revealed a significant inverse association between educational attainment and lung cancer risk (OR: 0.12; 95% CI: 0.07, 0.21; P < 0.0001), after adjusting for smoking variables. This suggests a protective effect of higher educational levels against lung cancer in the context of smoking.

The second analysis (Fig. 2-Model 2) focused on the relationship between educational attainment and lung cancer, considering alcohol consumption as a confounder. Post-adjustment, a notable inverse relationship was again observed, with an OR of 0.15 (95% CI: 0.09, 0.25; P < 0.0001), indicating that higher educational attainment potentially reduces lung cancer risk even when accounting for alcohol consumption.

Lastly, the third analysis (Fig. 2-Model 3) assessed the independent effect of educational attainment on lung cancer risk, controlling for Body Mass Index (BMI). The results indicated a less pronounced but still observable protective effect of education (OR: 0.48; 95% CI: 0.24, 0.94; P > 0.05), suggesting that the relationship between educational attainment and lung cancer risk is somewhat moderated by BMI.

Overall, these findings underscore the multifaceted nature of lung cancer etiology, highlighting the significant role of educational attainment as a potential protective factor, even when considering the influence of smoking, alcohol consumption, and BMI.

3.3 Mediation analysis

Given that educational attainment can have a significant impact on the prevention and management of Lung cancer, Lung cancer-related measurements may be a potential mediator of the protective effect of educational attainment on lung cancer. We conducted a two-step MR analysis to investigate the mediating pathways between educational attainment and lung cancer through two obesity-related phenotypes, namely smoking, drinking and body mass index (BMI). (Fig. 3) In the first step, a genetic instrument for educational attainment was used to estimate the causal effect of exposure on potential mediators. Among the three potential mediators, we found three causal relationships between educational attainment and mediators. In the subsequent phase of our study, we conducted an evaluation of the causative impact of mediators on the risk of lung cancer. Finally, we have conducted an assessment of the indirect impact of educational attainment on the development of lung cancer through the intermediary factors of drinking. Our analysis revealed that the mediation effect of drinking was estimated to be -0.09 (standard error = 0.044; P-value = 0.041), constituting approximately 5% (95% confidence interval, 0.169–9.43%) of the total effect. As for smoking and BMI, there is no mediation effect between educational attainment and lung cancer.

This thorough investigation systematically explored the connection between educational differences and distinct subtypes of lung cancer, specifically adenocarcinoma and squamous cell carcinoma. Despite certain methodological differences in the Inverse Variance Weighted (IVW) and Mendelian Randomization Egger (MRE) approaches, our consolidated analysis revealed a noteworthy causal link between educational disparities and the occurrence of lung cancer. The essential discovery emphasizes the intricate interplay of socio-economic elements, particularly education, in shaping the outcomes of lung cancer. Using multivariable Mendelian randomization, our study centered on investigating the potential protective impact of elevated educational achievement in mitigating the risk of lung cancer, with a particular emphasis on smoking as a crucial factor. The results consistently indicated that higher educational levels are associated with reduced lung cancer risk, an effect that persists even after adjusting for variables like alcohol consumption. Additionally, our study revealed a nuanced protective influence of education on lung cancer risk, moderated by factors such as Body Mass Index (BMI). This finding highlights the intricate relationship between educational attainment and BMI in the context of lung cancer risk reduction.

In a comprehensive cohort study conducted across Denmark, Finland, Iceland, and Norway, significant differences were observed in the rates of incidence and mortality for lung, breast, and colorectal cancers among non-Western immigrant women when compared to the local populations. This discrepancy has become more pronounced over time. These findings are consistent with existing research conducted in European and Nordic contexts ^[20–25]. Various lifestyle and hormonal factors, such as smoking, alcohol consumption, obesity, and physical inactivity, have been identified as primary contributors to lung cancer, collectively accounting for a significant majority of cases ^{[2, 3, 26, 27]}. The study also revealed educational disparities, indicating that higher levels of education were associated with a decrease in lung cancer incidence. This underscores the role of health literacy in the prevention of cancer. Notably, the disparity in lung cancer rates was particularly evident among migrant women with lower levels of education, who encountered challenges in accessing healthcare due to social isolation and limited health literacy.

In a decade-long prospective study conducted in China, which involved half a million adults, a noteworthy positive correlation between alcohol consumption and the risk of developing lung cancer was identified ^[28]. This association was found to be more pronounced in individuals who exhibit flushing reactions to alcohol. The findings from the China Kadoorie Biobank (CKB) study not only reaffirmed this relationship but also established a clear dose-response connection between alcohol intake and lung cancer. Notably, the risk estimates from this study exceeded those reported in the World Cancer Research Fund (WCRF) report ^[29].

In summary, our research supports the implementation of focused interventions aimed at lowering smoking and alcohol consumption to potentially decrease the occurrence of lung cancer^[30]. The Mendelian randomization study specifically underscores the impact of alcohol consumption as a notable factor, particularly in populations with lower levels of education, where there is a higher incidence of lung cancer. Adjusting alcohol intake levels could be a valuable approach to effectively diminish the risk of lung cancer.

In summary, this comprehensive study unveils the complex relationship between socio-economic and educational elements and the outcomes of lung cancer. Our Mendelian randomization analysis strengthens the potential protective influence of achieving higher educational levels in mitigating the risk of lung cancer. This underscores its importance, even in the presence of confounding factors such as smoking and alcohol consumption.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The data used to support the findings of this study are included within the article.

Conflict of interest

The authors declare that there are no conflicts of interest.

Funding

The study did not receive any specific funding from funding agencies in the public, commercial or non-profit sectors.

Author Contributions

All authors contributed to the study conception and design. Hui Nian :Conceptualization, Methodology, Software, Writing- Original draft, Data curation, Visualization were performed; Xiaosang Chen : Investigation, Writing - Original Draft, Writing - Reviewing were performed; Songtao Xu: Conceptualization, Editing,Supervision, Project administration were performed. All authors read and approved the final manuscript.

Acknowledgements

Not applicable

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No competing interests reported.

Association between education inequality and lung cancer: a two- sample Mendelian randomization study

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1 Two-sample MR study design

2.2 Data sources and instruments

2.3 Lung cancer and its subtypes

2.4 Educational attainment

2.5 BMI

2.6 Smoking (SMK) and Drinking (DRK)

2.7 Testing instrument strength and statistical power

2.8 Univariable MR

2.9 Multivariable MR

2.10 Mediation analysis

3. Results

3.1 Univariable MR

3.2 Multivariable MR

3.3 Mediation analysis

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1