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Mediators of the association between education and periodontitis: Mendelian randomization study

BMC Oral Health volume 25, Article number: 647 (2025) Cite this article

Abstract

Aim

To estimate the causal link between the risk of chronic periodontitis and educational attainment (EA).

Methods

The biggest genome-wide association studies (GWAS) were used to conduct two-sample univariable Mendelian randomization (MR) analyses to evaluate the direct and combined effects of body mass index (BMI), smoking, household income, alcohol drinking, major depression, and EA on chronic periodontitis. To determine if putative mediators are causally involved in the pathway that mediates the relationship between EA and chronic periodontitis, a two-step MR analysis is performed.

Results

MR evidence suggested a causal relationship between higher educational level and lower chronic periodontitis risk (OR: 0.72; 95% confidence interval (CI), 0.63 to 0.82; P < 0.001). The proportions mediated of the total effect of genetically predicted education on chronic periodontitis were 12.9%, 30.7%, 89.9%, 9.7%, and 16.4% for BMI, smoking, household income, alcohol drinking, and major depression, respectively.

Conclusion

The risk of chronic periodontitis is protected by higher EA. Obesity, smoking, income, alcohol drinking, major depression seem to be significant factors. Measures to alleviate the risk burden of chronic periodontitis caused by educational disparities may be achieved by addressing these factors.

Peer Review reports

Introduction

Periodontitis is a chronic inflammatory disease affecting the supporting structures of the teeth, leading to tooth loss and systemic health complications [1,2,3]. It is estimated that 62% of dentate individuals had periodontitis between 2011 and 2020, with 23.6% experiencing severe periodontitis [4]. As a prevalent global health burden, periodontitis has been associated with various socioeconomic and behavioral factors [4], among which education attainment (EA) has emerged as a key factor [5]. Empirical research have shown a significant association between EA and periodontitis, revealing that individuals with lower educational levels are at a higher risk of developing the condition compared to those with higher levels of education [6, 7]. However, the causal relationship between EA and periodontitis remains unclear, as previous observational studies are susceptible to confounding and reverse causation.

The development of periodontal can be understood within a framework from distal to proximal determinants. According to the Social Determinants of Health (SDOH) model, periodontitis develops through a pathway where distal factors progressively influence proximal factors. Distal factors, such as socioeconomic status—including education and income—affect health literacy, access to healthcare, and the availability of oral health resources. Intermediate factors, such as psychosocial stress and depression, further contribute by linking lower socioeconomic status with greater economic and social pressures, which may lead to chronic inflammation, immune dysregulation, and restricted access to healthcare. Proximal factors, such as behavioral habits like smoking and alcohol drinking, as well as biological mechanisms such as inflammatory responses, directly drive the progression of periodontitis. Although previous observational studies have identified associations between modifiable risk factors (such as income, body mass index (BMI), smoking, alcohol consumption, and depression) and periodontitis, the mediating role of these factors in the pathway linking educational attainment to periodontitis remains largely unexplored [7,8,9,10,11,12].

Mendelian randomization (MR) offers a robust approach to infer causal relationships by using genetic variants as instrumental variables. Since these variants are randomly allocated and are not affected by lifestyle factors and chronic conditions, MR reduces the risk of confounding and reverse causation, providing more reliable estimates of causal effects [13, 14].

In this study, we aimed to investigate the causal pathways linking EA to chronic periodontitis, with a particular focus on assessing the mediating role of income, BMI, smoking, alcohol drinking, and depression.

Methods

Ethics statement

This study is a secondary study based on free data, and the included research have been approved by the relevant institutional review committee. All participants have given informed consent. This study followed the STROBE-MR guideline [15]. In addition, the GWAS data used in this study were obtained from publicly available databases, which do not require specific permissions for academic use. All data were accessed in compliance with the terms of use of the respective databases.

Study design

Using single nucleotide polymorphisms (SNPs) as instruments for the risk factor, we conducted a genetic instrumental variable analysis based on summary-level data using a two-sample MR design. Initially, a two-sample MR study was conducted to evaluate the relationship between BMI, smoking, alcohol drinking, family income, major depression and EA and the risk of chronic periodontitis. Multivariable MR (MVMR) was used to figure out the precise direct effect of EA on chronic periodontitis, irrespective of other factors. To determine if intermediary variables, including BMI, smoking, alcohol drinking, family income, major depression, play a causal role in the mediating route between EA and chronic periodontitis, we lastly performed a two-step MR analysis (Summarized in Fig. 1).

Fig. 1
figure 1

Mediation analysis of the effect of educational attainment on chronic periodontitis via potential mediators. ‘Direct effect’ indicates the effect of educational attainment on chronic periodontitis risk after adjusting for the mediators

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Data sources

The instrumental educational variables came from the Social Science Genetic Association Consortium (SSGAC), which comprised 1, 131, 881 people of European descent [16]. EA was categorized according to the International Standard Classification of Education (ISCED) 2011, converted to US years of schooling and standardized, with each unit representing 4.2 years of schooling [17].

The data on chronic periodontitis was obtained from the FinnGen consortium. The data for chronic periodontitis included 4,784 cases and 272,252 controls, in which the case was determined by the presence of chronic periodontitis ICD codes (ICD8:5234; ICD-9:5234; ICD-10: K05.30; K05.31). Details on the demographics of the cohorts and the measurements can be found in the FinnGen research project (https://risteys.finngen.fi/endpoints/K11_PERIODON_CHRON). Self-reported or Centers for Disease Control and Prevention (CDC)/American Academy of Periodontology (AAP) comparable criteria assessed by probing depth were used to identify patients with periodontitis.

Data of summary level GWAS for daily cigarette smoking and alcoholic drinks per week were obtained from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Household income summary statistics from the MRC-IEU. BMI data were obtained from the Genetic Investigation of Anthropometric Traits consortium (GIANT) GWAS meta-analysis of 681,275 participants of European decent [18]. The major depressive disorder summary statistics from the Psychiatric Genomics Consortium (PGC) included 500,199 participants [19]. The details of above data were provided in Table 1.

Table 1 Summary of each GWAS
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Instrumental variables selection and data harmonization

To obtain reliable MR anlysis, three key assumptions for instrumental variables (IVs) must be met: (1) Relevance: the genetic IVs must strongly correlated with the exposure; (2) Independence: the genetic IVs should not be linked to any confounders that influence both the exposure and the outcome. (3) Exclusion restriction: the genetic IVs should affect the outcome only through the exposure, without alternative biological pathways, ensuring the absence of horizontal pleiotropy [20].

Before conducting MR analyses, SNPs strongly associated with the exposure were rigorously selected as IVs. Independent SNPs were identified using a genome-wide significance threshold of p < 5 × 10⁻⁸ to control for false positives. To minimize linkage disequilibrium (LD) bias, SNPs were pruned using an r² threshold of < 0.001 within a 10,000 kb window based on European ancestry populations [21]. To further ensure the validity of the IVs, we utilized LDlink (https://ldlink.nih.gov/) to identify SNPs associated with potential confounders that could influence both EA and chronic periodontitis. SNPs strongly associated with smoking, alcohol consumption, BMI, income, and type 2 diabetes were excluded to mitigate pleiotropy-driven bias. To maintain consistency across exposure and outcome datasets, data harmonization was performed, ensuring alignment of the effect allele. Palindromic SNPs were excluded unless their effect allele frequency (EAF) deviated sufficiently from 0.5 to resolve strand ambiguity. To satisfy the exclusion restriction assumption, only SNPs where pexposure < poutcome were retained after harmonization. To mitigate weak instrument bias, SNPs with an F-statistic (F = β²/SE²) < 10 were excluded, following standard recommendations to ensure instrument strength [22]. The proportion of variance explained (R²) for each IV was calculated using the formula: R2 = 2*EAF*(1 − EAF)*β2, where EAF represents the effect allele frequency, and β is the estimated genetic effect on exposure. The cumulative R² for multiple IVs was obtained by summing the individual R² values [23].

Statistical analysis

The total causal effect was estimated primarily using the inverse variance weighting (IVW) method for univariate MR analysis, which calculates the weighted average of SNP-specific effect estimates [24]. Additional robust methods, including MR-Egger regression, weighted median, weighted mode, and simple mode approaches, were implemented to validate the stability and consistency of the primary IVW estimates [25,26,27].

To detect and address potential pleiotropy bias, comprehensive sensitivity analyses were conducted, including the MR-PRESSO test for identifying and correcting horizontal pleiotropy, MR-Egger intercept test for directional pleiotropy, Cochran’s Q test for heterogeneity among genetic variants, and leave-one-out analysis to determine the influence of individual SNPs [28,29,30]. Cochran’s Q statistic was specifically employed in the fixed-effects IVW analysis, and a P-value ≤ 0.05 was considered indicative of significant heterogeneity. When substantial heterogeneity was detected, random-effects IVW analysis was adopted instead. MR-Egger regression’s intercept term was evaluated to assess the presence of horizontal pleiotropy (intercept significantly deviating from zero, P < 0.05). In the presence of directional pleiotropy, MR-Egger’s slope coefficient was used as a consistent estimator of the causal effect. Weighted median and weighted mode analyses provided additional sensitivity analyses under varying assumptions regarding pleiotropy.

Furthermore, this study validates the exclusivity hypothesis through a two-step mediation analysis [31]. Using IVs for EA, the initial stage was estimating the causal effect of exposure on the putative mediators. The second phase was evaluating the possible mediators’ causal impact on the likelihood of chronic periodontitis using genetic tools for the mediators that had been identified. We used the “product of coefficients” method [32] to evaluate the indirect effect of EA on the risk of chronic periodontitis via each putative mediator, where there was evidence that EA influenced the mediator, which in turn influenced the risk of chronic periodontitis. The mediation effect was derived by using the delta method. MVMR was used to concurrently assess the direct effect of EA on chronic periodontitis conditioned on other factors, taking into account the interdependence of socioeconomic factors. We constructed MV-IVW model, supplemented with sensitivity analyses using MVMR-Egger regression and MVMR-Lasso method.

All statistical analyses were conducted using the “TwoSampleMR” package (version 0.6.6) and “MendelianRandomization” package (version 0.10.0) within R software version 4.4.0.

Results

Genetic instruments

A total of 1129 SNPs were selected as IVs for EA (Supplementary Table 1), 43 SNPs for household income (Supplementary Table 2), 23 SNPs for daily cigarette smoking (Supplementary Table 3), 32 SNPs for alcoholic drinks per week (Supplementary Table 4), 71 SNPs for major depression (Supplementary Table 5), and 297 SNPs for BMI (Supplementary Table 6). The proportion of variance explained by the selected SNPs was as follows: EA (5.1%), BMI (3.1%), smoking (3.5%), income (0.6%), drinking (0.6%), and depression (2.6%) (Table 1). The F-statistics for all selected genetic instruments exceeded the conventional threshold of 10, indicating strong instrument validity and minimizing the risk of weak instrument bias in the MR analyses (Table 1).

Univariable MR

Effect of EA on chronic periodontitis

Univariable MR analysis revealed a significant protective effect of EA on chronic periodontitis, with higher EA associated with a lower risk of chronic periodontitis (IVW: β = -0.33, 95CI: -0.47 to -0.20, P < 0.001). Sensitivity analyses supported the robustness of this finding. No evidence of pleiotropy was detected, as indicated by Cochran’s Q statistic (P = 0.22). Additionally, MR-PRESSO did not identify any potential outliers, and MR-Egger intercept analysis showed no signs of directional pleiotropy (P = 0.56) (Table 2).

Table 2 Sensitivity analysis results in this study
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The weighted median method produced consistent estimates (β = -0.30, 95% CI: -0.50 to -0.10, P = 0.003), further supporting the protective role of EA against chronic periodontitis. The leave-one-out analysis demonstrated that the exclusion of any single SNP did not substantially alter the overall estimates, as all remaining SNPs yielded effect sizes in the same direction. This result confirms the robustness of the MR analysis.

Effect of EA on mediators

The MR analysis consistently indicated a negative causal effect of EA on BMI. The IVW method demonstrated a significant inverse association between EA and BMI (β = -0.23; 95% CI: -0.28 to -0.18; P < 0.001). Similar estimates were observed using the weighted median, weighted mode, and simple mode methods (all P < 0.001) (Fig. 2).

Fig. 2
figure 2

Forest plot of the mediating effect of BMI

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Higher EA was also significantly associated with increased household income (IVW: β = 0.66, 95% CI: 0.64 to 0.68, P < 0.001) and decreased smoking frequency (IVW: β = -0.35, 95% CI: -0.40 to -0.30, P < 0.001) (Figs. 3 and 4). Higher EA was linked to a modest increase in alcohol consumption (IVW: β = 0.04, 95% CI: 0.02 to 0.06, P = 0.009) and a lower risk of major depression (IVW: β = -0.24, 95% CI: -0.28 to -0.19, P < 0.001) (Figs. 5 and 6).

Fig. 3
figure 3

Forest plot of the mediating effect of cigarettes smoked per day

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Fig. 4
figure 4

Forest plot of the mediating effect of household income

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Fig. 5
figure 5

Forest plot of the mediating effect of alcoholic drinks per week

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Fig. 6
figure 6

Forest plot of the mediating effect of major depression

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Effect of mediators on chronic periodontitis

The analysis identified significant associations between several mediators and chronic periodontitis. There was moderate evidence to suggest that a higher BMI was associated with an increased risk of chronic periodontitis (IVW: β = 0.19, 95% CI: 0.02 to 0.36, P = 0.026) (Fig. 2).

Daily cigarette smoking exhibited a strong positive relationship with chronic periodontitis, with higher smoking frequency correlating with a greater risk of the condition (IVW: β = 0.29, 95% CI: 0.13 to 0.46, P < 0.001) (Fig. 3). An inverse association was observed between average household income and chronic periodontitis, indicating that individuals with higher income levels had a lower risk of developing the condition (IVW: β = -0.46, 95% CI: -0.86 to -0.05, P = 0.028) (Fig. 4). Similarly, alcohol consumption was positively associated with chronic periodontitis, with more frequent alcohol intake correlating with a heightened risk of the disease (IVW: β = 0.74, 95% CI: 0.18 to 1.29, P = 0.009) (Fig. 5). For major depression, moderate evidence suggests that major depression may increase the risk of periodontitis (IVW: β = 0.23, 95% CI: 0.02 to 0.44, P = 0.028) (Fig. 6).

Multivariable MR

The MVMR-Lasso results indicated EA (β=−2.42, 95% CI: −4.41 to − 0.41; p = 0.018) and income (β=−1.01, 95% CI: -1.72 to -0.31; p = 0.005) remained correlated with a reduced risk of chronic periodontitis, while alcohol drinking (β = 1.08, 95% CI: 0.25 to 1.91; p = 0.011) remained correlated with an increased risk of chronic periodontitis (Supplementary Table 7). The MR-Lasso method effectively accounted for potential weak instruments and provided robust causal estimates. These findings suggest that EA, income and drinking independently influenced chronic periodontitis.

After adjusting for smoking, neither EA (p = 0.199) nor smoking (p = 0.471) significantly affected chronic periodontitis. Similarly, after accounting for depression, EA (p = 0.302) and depression (p = 0.210) showed no significant impact. Additionally, after adjusting for BMI, neither EA (p = 0.842) nor BMI (p = 0.415) had a significant effect (Supplementary Table 7). Heterogeneity tests and MR-Egger intercept tests revealed no substantial evidence of horizontal pleiotropy (Supplementary Table 8). The correlation between the genetic associations of EA and mediators (BMI, smoking, income, drinking, and major depression) was moderate (r = 0.553), indicating some shared genetic architecture but not severe multicollinearity.

Mediation analysis

Given that BMI, smoking, income, alcohol drinking, and depression are critical in the prevention and management of chronic periodontitis, socioeconomic-related factors could be mediators underlying the protective effect of EA on chronic periodontitis. We conducted an estimation of the indirect impact of EA on chronic periodontitis through mediators (Table 3). BMI explained 12.9% (95% CI 8.6–17.2) of the total effect of EA on chronic periodontitis, cigarettes smoked per day explained 30.7% (95% CI 23.7–37.8), household income explained 89.9% (95% CI 84.8–94.9), alcoholic drinks per week explained 9.7% (95% CI 5.5–14.0), and major depression explained 16.4% (95% CI 11.8–21.1) (Table 3).

Table 3 The mediation effect of educational attainment on chronic periodontitis via household income and cigarettes smoked per day
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Discussion

Our two-step MR mediation analysis identified several modifiable factors through which education exerts its protective influence. Specifically, household income accounted for 89.9% of the effect, smoking for 30.7%, BMI for 12.9%, alcohol consumption for 9.7%, and major depression for 16.4% (Table 3). These findings suggest that a substantial portion of the benefits of higher education on periodontal health is mediated through improved socioeconomic status, healthier behaviors, and enhanced psychological well-being.

A main source of potential bias in MR studies is horizontal pleiotropy; we examined this using myriad MR methods that provided consistent results to the main analysis. Sensitivity analyses were conducted to assess the robustness of MR results, including Weighted mode, Weighted median, MR-PRESSO and MR-Egger. Moreover, Cochran’s Q test was used to test heterogeneity, and the MR-PRESSO global test detected outlier SNPs. Additionally, the analysis of horizontal pleiotropy using the MR-Egger intercept was used to check the analysis of horizontal pleiotropy. Although in our results, the Cochran Q-test p-values of EA and BMI, smoking, income, alcohol consumption, and depression in MR analysis were less than 0.05, indicating heterogeneity, and the MR-PRESSO global test p-value < 0.05 suggested the possibility of outliers, we calculated the distortion coefficient (quantifying the degree of influence of outliers on causal estimates) [30]. The p-values of the distortion coefficient were all greater than 0.05, indicating that the distortion coefficient was not significant. This means that although there are some outliers, their impact on causal estimates is not significant enough to cause systematic bias [30]. Additionally, we did not detect horizontal pleiotropy using the MR Egger method (all p > 0.05) (Table 2). Overall, the causal estimates obtained from pleiotropy-robust methods consistently indicated that there was no causal relationship.

The results of this study likely reflect a combination of biological mechanisms and socioeconomic pathways. Education is a distal determinant that shapes individuals’ life trajectories, health literacy, and access to resources [33]. Systematic reviews have consistently demonstrated that lower socioeconomic status is associated with an increased risk and progression of periodontitis [6, 7], which is in line with our results. Multiple mechanisms likely contribute to the role of EA in reducing the risk of chronic periodontitis, primarily through its influence on lifestyle behaviors, mental health, inflammatory pathways, and immune regulation [34,35,36]. Higher EA enhances an individual’s ability to make informed health-related decisions, promotes better health literacy, and fosters a greater awareness of disease prevention strategies [37]. Individuals with higher education levels are more likely to adopt healthier behaviors, such as improved oral hygiene practices, regular dental visits, and reduced engagement in risk behaviors like smoking and excessive alcohol consumption.

Our finding that household income mediated the largest proportion of the education effect on periodontitis, approximately 90%, underscores the critical role of financial and material advantages in promoting oral health. Increased income facilitates regular dental visits, professional cleanings, timely treatment of gum disease, and access to healthier diets and living environments that support good oral hygiene [37]. Beyond economic factors, socioeconomic status also intersects with psychosocial determinants. Individuals with lower education and income levels often experience greater chronic stress and have fewer resources to invest in health-promoting behaviors, amplifying their risk of disease. Major depression mediated 16.4% of the association suggests a psychosocial pathway, wherein lower education heightens the risk of major depression, which may, in turn, worsen periodontal health through stress-induced immune dysfunction and neglect of oral hygiene. Depression is a frequently occurring mental health disorder that significantly influences health conditions and their medical management [38]. Depression can elevate systematic inflammation and immune dysregulation responses, potentially accelerating periodontal tissue breakdown [11, 39]. Thus, psychosocial stressors act as intermediate factors connecting socioeconomic disadvantage with biological susceptibility to periodontitis.

At the more proximal level, behavioral and biological risk factors play a direct role in periodontal pathology. Smoking emerged as a significant mediator (30.7%), reinforcing its well-established causal role in the link between education and periodontitis. A cross-sectional study by Sutton et al. found a 28% higher periodontitis risk in individuals with tobacco exposure [8]. Mendelian randomization studies by Gage et al. confirmed that higher EA reduces smoking initiation, heavy smoking, and increases cessation rates [40]. Smoking impairs gingival blood flow, immune response, and healing capacity, thereby increasing periodontal destruction [8, 41]. Similarly, obesity is a risk factor for periodontal disease, likely through systemic inflammatory pathways [42, 43]. Adipose tissue secretes pro-inflammatory cytokines that exacerbate periodontal inflammationns [44]. Our finding that BMI mediates approximately 13% of the effect suggests that higher educational attainment contributes to better periodontal health by reducing obesity rates. Alcohol consumption had a relatively small mediation effect of approximately 10%, but excessive intake can contribute to poor oral hygiene, nutritional deficiencies, and direct toxic effects on gum tissu [45]. More broadly, education shapes a network of downstream factors, including healthier behaviors such as reduced smoking and harmful drinking, improved diet and weight management, enhanced psychological well-being, and greater socioeconomic stability, all of which collectively promote better periodontal health. This multi-layered mechanism aligns with the Social Determinants of Health framework, in which distal social factors, such as education and income, shape intermediate psychosocial stressors and proximal lifestyle choices, ultimately influencing disease outcomes.

This study used the MR method to provide evidence for the causal relationship between EA and chronic periodontitis. When screening SNPs, LD was removed, and palindromic sequences and incompatible alleles were excluded to ensure the accuracy of the research results. A significant strength of this study is that, compared with traditional observational studies, the MR method, which uses genetic variations as IVs, effectively minimizes potential biases from confounding factors and reverse causality, thereby providing clearer and more reliable evidence of causal relationships. This study also has some limitations. Firstly, the population included in this study is limited to Europe, which reduces population bias but may also limit the applicability of the research results to other populations. Secondly, efforts were made to identify pleiotropic effects through Cochran’s Q test, MR-PRESSO, and MR-Egger. Moreover, it is still possible that pleiotropic effects were not captured by the measurements used in this study. Thirdly, the results of this study only indicate that EA can reduce the risk of chronic periodontitis. Finally, this study has not yet focused on the diversity of chronic periodontitis, and EA may have different correlations with different types of periodontitis with varying degrees of severity.

Conclusion

This study provides robust evidence supporting a causal relationship between higher EA and a reduced risk of chronic periodontitis, mediated primarily through income, smoking, obesity, alcohol consumption, and depression. These findings have significant public health implications, emphasizing the need to address socioeconomic and behavioral determinants of oral health.

While education itself is a distal factor, interventions targeting its key mediators—such as smoking cessation, obesity prevention, mental health support, and policies to reduce income disparities—could significantly lower periodontitis risk among lower-educated populations. Public health initiatives should also focus on improving access to dental care and integrating oral health education into broader health promotion programs.

Future research should explore these causal pathways in diverse populations and refine intervention strategies to mitigate the impact of educational disparities on oral health. By addressing these modifiable risk factors, it may be possible to reduce the burden of periodontitis and promote more equitable health outcomes.

Data availability

The datasets generated and/or analyzed during the current study are available in the SSGAC, IEU, FinnGen repository, PGC, and GIANT consortium. SSGAC Download Data, IEU OpenGWAS project, https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_K11_PERIODON_CHRON.gz, https://pgc.unc.edu/for-researchers/download-results/, https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files.

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Acknowledgements

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Funding

①Postgraduate Research & Practice Innovation Program of Jiangsu Province.

②Provincial College Students’ Innovation and Entrepreneurship Training Program Funding Project: 202410304135Y.

③2024 Nantong Natural Science Foundation: MS2024059.

④Jiangsu Province University Philosophy and Social Science Research Project: 2023SJYB1683.

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Authors and Affiliations

  1. School of Nursing and Rehabilitation, Nantong University, Nantong, Jiangsu, PR China

    Yuan-Yuan Chen, Lu-Lu Wang, Shu-Qi Mo, Dan-Yan Zhao, Yu-Zhu Fan, Rui-Nan Zhang & Wang-Qin Shen

  2. School of Nursing, Fudan University, Shanghai, PR China

    Zheng Zhu

  3. School of Nursing, Jiangsu Vocational College of Medicine, Yancheng, Jiangsu, PR China

    Ling-Ling Guo

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  1. Yuan-Yuan Chen
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Contributions

YY.C. wrote the main manuscript text. LL.W.， SQ.M., and LL.G. prepared Table 1. YZ.F. RN.Z. and DY.Z. prepared Figs. 1, 2 and 3. WQ.S. and Z.Z. were responsible for reviewing and revising articles. All authors reviewed the manuscript.

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Chen, YY., Wang, LL., Mo, SQ. et al. Mediators of the association between education and periodontitis: Mendelian randomization study. BMC Oral Health 25, 647 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12903-025-06006-1

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BMC Oral Health

ISSN: 1472-6831

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