Association between Clonal Hematopoiesis and Periodontitis: A Two-Sample Mendelian Randomization Study

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Recently, a series of studies mentioned a possible link between CH and periodontitis; however, the exact cause-effect relationship has not yet been clarified. Methods We performed MR analyses with two samples to investigate a possible causal relationship between different clonal hematopoiesis phenotypes and susceptibility to periodontitis.Variants of clonal hematopoiesis phenotypes included overall clonal hematopoiesis (CH-overall), CH with a DNMT3A mutation (CH-DNMT3A), CH with a TET2 mutation (CH-TET2), large clones (CH-large), and small clones (CH-small). Genetic instruments were derived from genome-wide association study data. The main method of analysis used was the IVW method. Sensitivity analyses included the weighted median, MR-Egger regression and MR-PRESSO. Reverse MR analyses were also performed to look for bidirectional effects. Results MR analysis revealed a significant association between CH-DNMT3A and periodontitis, with an odds ratio of 0.084 (95% confidence interval: 0.007–0.972, P = 0.047). These findings suggest that CH-DNMT3A may play a protective role in the development of periodontitis.No other CH phenotypes, such as CH-overall, CH-TET2, CH-large, or CH-small, were associated with periodontitis. The inverse MR analyses provided no statistical evidence for an association of periodontitis with any of the CH phenotypes. There was no evidence of pleiotropy or heterogeneity. Conclusions Our findings indicate a novel protective role for DNMT3A-mutated clonal hematopoiesis (CH) in periodontitis.However, additional research is necessary to clarify the underlying mechanisms of this association. Periodontitis Clonal Hematopoiesis Mendelian randomization analysis inflammation Figures Figure 1 Figure 2 Background Periodontitis is a long-term inflammatory condition that affects tooth-supporting structures and has a high prevalence with age[1]. It is defined by the continuous breakdown of periodontal ligament and alveolar bone, leading to the loss of teeth if left unmanaged[2]. Various studies have estimated that the global prevalence of periodontitis is between 20% and 50%[3]. The etiology of periodontitis is multifactorial, as it results from the interaction of microbial, environmental, and genetic factors. Recent studies have shown that systemic diseases such as hematopoietic abnormalities may influence periodontitis development and progression[4–6]. Clonal hematopoiesis is an aging-associated disorder stemming from the clonal amplification of blood cells with somatic mutations in genes regulating hematopoiesis and epigenetics, including DNMT3A and TET2[6–9]. It arises as a result of somatic mutations occurring in HSCs that confer a selective growth advantage to cells with these mutations, thus leading to the disproportional expansion of mutant clones over unmutated HSCs[9, 10]. The prevalence of CH increases with age and affects approximately 10–20% of individuals above 70 years of age[11]. Research indicates that CH is associated with various inflammatory disorders and a heightened risk of developing blood cancers [12–14]. However, the causality between CH and chronic inflammatory diseases, especially periodontitis, is still not fully known. Insight into the causes of CH and periodontitis will help elucidate the biological pathways and possible therapeutic points involved. This study aimed at investigating bidirectional causality between the phenotypes of CH and periodontitis using a two-sample MR approach. In MR, genetic variants are used as instrumental variables to aid in making inferences about causality between an exposure and an outcome[15, 16]. Because this approach exploits the random assignment of genetic variants at conception, it mitigates many of the limitations of observational studies, such as confounding and reverse causality[17]. To date, in this analysis, we have considered the use of two-sample MR to probe the possible causal associations of various CH phenotypes with periodontitis: overall clonal hematopoiesis (CH-overall), CH with DNMT3A mutation (CH-DNMT3A), CH with TET2 mutation (CH-TET2), large clones (CH-large), and small clones (CH-small). To complement the above methods, reverse MR analyses were implemented to identify the effect of periodontitis on CHs to further understand the interactions between them. Methods Study design The analysis was a two-sample MR design, in which genetic data from two independent genome-wide association study datasets, one for clonal hematopoiesis and one for periodontitis,were used. This can allow for the examination of the causal relationship between CH and periodontitis through the use of genetic variants strongly associated with the phenotype of CH as instrumental variables. These analyses were all based on three ERMs: there is a statistically significant association between instrumental variable(s) and exposure; instrument variables are unrelated to any confounder; and outcomes can be influenced only by instrumental variables through their influence on exposure. Data sources In the present analysis, summary statistics for genetic association data in clonal hematopoiesis were obtained from a large-scale genome-wide association study on a large scale by Siddhartha et al[18]. Genetic data from 200,453 individuals of European ancestry were analyzed to determine the prevalence of clonal hematopoiesis and its association with various somatic mutations, including those in DNMT3A and TET2, in the general population. WES-derived genomic data of these participants were obtained from the UK Biobank. Periodontitis-associated data were derived from a genome-wide association study meta-analysis by the FinnGen consortium[19], including genomic data from 346,731 Finnish participants comprising 87,497 cases of chronic periodontitis and 259,234 controls. The UK Biobank is a publicly available database. The FinnGen project is maintained under the auspices of the University of Helsinki. Each of these studies with these datasets was approved by local institutional review boards and ethics committees. Details of the data sources are provided in Table 1 . Table 1 Data sources for clonal hematopoiesis and periodontitis. Trait name ID Population Samplesize PMID or GWAS exposure Clonal hematopoiesis (overall) GCST90102618 European 184121 35,835,912 Clonal hematopoiesis (DNMT3A mutation) GCST90102619 European 179103 35,835,912 Clonal hematopoiesis (TET2 mutation) GCST90102620 European 175959 35,835,912 Clonal hematopoiesis (large clone) GCST90102621 European 177967 35,835,912 Clonal hematopoiesis (small clone) GCST90102622 European 180072 35,835,912 outcome periodontitis Chronic periodontitis European 277036 FinnGen (K11_PERIODON_CHRON) Instrumental variable (IV) We constructed reliable MR instrumental variables by selecting SNPs that were significantly associated with each type of clonal hematopoiesis at p < 1×10^-5. We performed further linkage disequilibrium pruning to ensure that the selected SNPs were independent from each other,with r² < 0.001, and did not have chain reactions with each other. The selected SNPs were used as instrumental variables. Statistical analysis for Mendelian randomization First, the strengths of the IVs were determined by computing the F-statistic via the following formula: F = ((R2 * (NK-1)) / (1 - R2 * K) where (R²) is the proportion of variance in exposure explained by genetic variation; (N) is the sample size, and (K) denotes the number of instruments. It has been suggested that an F-statistic > 10 indicates that no significant weak instrument bias will be present[20]. We conducted two-way two-sample Mendelian randomization analyses via the inverse variance weighting method as the main analytical approach. The IVW method provides a weighted average of the Wald ratios for each genetic variant[21]. We performed sensitivity analyses, including MR-Egger and weighted median methods, to assess the robustness of our findings. We used the MR-Egger intercept test-a method that can detect directional pleiotropy-as a way of investigating horizontal pleiotropy[22]. Weighted median estimation could yield valid casual estimates even if up to 50% of the genetic instruments are invalid[23]. Furthermore,the MR-PRESSO method was applied for the detection of and adjustment for horizontal pleiotropy[24]. The heterogeneity was tested by Cochran's Q statistic, which evaluates heterogeneity in the estimates of the causal effects originating from different genetic variants[25]. We estimated the summary OR and 95% CI for periodontitis risk for every subtype of CH. We considered a P value of less than 0.05 as representative of a significant causal effect. In addition to the causal effect of CH on periodontitis, we also conducted an inverse MR analysis to test the effect of periodontitis on the risk of a CH phenotype. In this latter analysis, genetic variants associated with periodontitis at p < 5×10^-5 were used as instrumental variables to assess their causal effect on CH. Other types of analyses were conducted as described previously. All the statistical analyses were performed in R, version 4.2.1, via the two-sample MR package, version 0.5.6[26] and the MR-PRESSO package, version 1.0. Results Selection of instrumental variables According to the instrumental variable (IV) selection criteria (p < 1×10 − 5 ), a total of 159 single nucleotide polymorphisms (SNPs) were used as IVs associated with five subtypes of clonal hematopoiesis. Details regarding the selected instrumental variables are provided in Additional File Table S1 . Statistical analysis Among the five causal associations, the F-statistic of the instrumental variables (IVs) ranged from 19.42–222.54, thereby eliminating the bias associated with weak IVs. In the primary IVW analysis in the forward MR analysis, a significant causal effect of CH with DNMT3A mutations on periodontitis risk was observed (OR = 0.084, 95% CI: 0.007–0.972, P = 0.047) (Table 2 , Fig. 1 ). These findings suggest that this particular CH phenotype is protective against periodontitis. For the remaining subtypes of CH, no significant causal estimates were observed (Fig. 2 ). The sensitivity analyses by MR-Egger regression and weighted median estimators supported the original IVW analytical result (Table 3 ). The MR-Egger regression revealed no evidence of pleiotropy, represented by the nonsignificant intercept term (P > 0.05). Weighted median analysis further strengthens this protective association of CH with DNMT3A mutations against periodontitis and suggests the results are robust against any potential violation of the MR assumptions. No significant heterogeneity in the instrumental variables was found in Cochran's IVW Q test results(Table S2 ). In the leave-one-out method, there are potential outliers on gross inspection (Fig S1 ).Furthermore, MR-PRESSO analysis did not report any significant outliers in the current analysis (overall test P > 0.05, Table S3 ). However, there is insufficient evidence for horizontal pleiotropy in the association of clonal hematopoiesis with periodontitis. Table 2 MR estimates for the association between clonal hematopoiesis and periodontitis exposure method nsnp b se pval or or_lci95 or_uci95 Clonal hematopoiesis (overall) MR Egger 35 1.017 1.348 0.456 2.766 0.197 38.813 Weighted median 35 0.448 1.064 0.674 1.565 0.194 12.610 Inverse variance weighted 35 0.481 0.754 0.524 1.617 0.369 7.088 Simple mode 35 1.812 1.924 0.353 6.124 0.141 265.932 Weighted mode 35 0.524 1.156 0.653 1.688 0.175 16.270 Clonal hematopoiesis (DNMT3A mutation) MR Egger 27 -5.866 2.990 0.061 0.003 0.000 0.994 Weighted median 27 -3.379 1.762 0.055 0.034 0.001 1.078 Inverse variance weighted 27 -2.475 1.248 0.047 0.084 0.007 0.972 Simple mode 27 -5.554 3.486 0.123 0.004 0.000 3.592 Weighted mode 27 -5.675 2.613 0.039 0.003 0.000 0.575 Clonal hematopoiesis (TET2 mutation) MR Egger 28 1.674 4.875 0.734 5.332 0.000 75263.793 Weighted median 28 -0.447 3.017 0.882 0.640 0.002 236.719 Inverse variance weighted 28 -0.559 2.193 0.799 0.572 0.008 42.060 Simple mode 28 -2.444 5.550 0.663 0.087 0.000 4602.418 Weighted mode 28 -1.008 3.956 0.801 0.365 0.000 850.292 Clonal hematopoiesis (large clone) MR Egger 24 3.393 3.360 0.324 29.748 0.041 21544.824 Weighted median 24 2.028 2.191 0.355 7.599 0.104 557.363 Inverse variance weighted 24 2.546 1.570 0.105 12.751 0.588 276.649 Simple mode 24 2.350 3.834 0.546 10.488 0.006 19231.291 Weighted mode 24 2.242 2.676 0.411 9.408 0.050 1782.738 Clonal hematopoiesis (small clone) MR Egger 27 3.215 2.098 0.138 24.905 0.408 1520.919 Weighted median 27 0.666 1.654 0.687 1.947 0.076 49.833 Inverse variance weighted 27 0.143 1.168 0.903 1.154 0.117 11.386 Simple mode 27 3.581 2.885 0.226 35.919 0.126 10255.016 Weighted mode 27 0.960 2.095 0.650 2.613 0.043 158.605 Table 3 Pleiotropy test of clonal hematopoiesis genetic variants in the risk of periodontitis. outcome egger_intercept se pval periodontitis -0.005 0.010 0.634 periodontitis 0.016 0.013 0.224 periodontitis -0.007 0.014 0.611 periodontitis -0.005 0.017 0.777 periodontitis -0.020 0.011 0.090 There is no evidence for a causal effect of periodontitis on CH phenotypes in reverse MR analysis (Figure S2 ). The heterogeneity test for periodontitis to overall CH was significant (Table S4 ). Although the MR-Egger regression intercept did not detect horizontal pleiotropy (Table S5 ), MR-PRESSO analysis revealed p < 0.05 (Table S6 ). After the removal of outliers to reduce heterogeneity and horizontal pleiotropy, the adjusted p-value was above 0.05. Overall, periodontitis-related genetic variants did not significantly the risk of any CH phenotype. P > 0.05 for all CH outcomes, which means that the relationship is unidirectional: CH affects the risk of periodontitis. Discussion In the present two-sample MR study, summary statistics for CH were retrieved from GWAS meta-analyses by the UKB consortium, whereas periodontitis summary statistics were obtained from the FinnGen Consortium R10 publication data. Our results shed new light, in particular, on the significant inverse association between periodontitis risk and CH driven by DNMT3A mutations. Nevertheless, associations with other CH phenotypes did not reach the threshold for statistical significance. Additioanally, MR inversion revealed no evidence for reverse causality: periodontitis does not cause CH. The protective effect of DNMT3A-mutated clonal hematopoiesis (CH) on periodontitis risk presents an intriguing and somewhat unexpected finding. This observation challenges the prevailing view of CH as primarily a detrimental condition linked to increased inflammation and age-related diseases[27]. Previous research has indicated that an inflammatory environment can improve the development and persistence of CH, creating a feedback loop that exacerbates both conditions[28, 29]. For example, proinflammatory cytokines such as TNF-α and IL-6 promote the expansion of specific CH clones, particularly those with mutations in genes such as TET2 and DNMT3A, which increase inflammatory responses, including those observed in periodontitis[30].However, recent studies have suggested that DNMT3A mutations may result in epigenetic modifications due to changes in DNA methylation patterns, potentially regulating the expression of genes involved in inflammatory pathways[31, 32]. Observational studies have noted that DNA methylation alterations are more pronounced in individuals with periodontitis than in healthy individuals[33–35].These changes in DNA methylation could lead to a reduced inflammatory response in periodontal tissues, potentially mitigating tissue destruction and alveolar bone loss. Clonal hematopoiesis (CH) with DNMT3A mutations can significantly influence the differentiation and function of immune cells, particularly those involved in the pathogenesis of periodontitis. DNMT3A is known to regulate the differentiation and function of various immune cell types, including T cells and bone marrow-derived cells[36]. Alterations in T-cell subsets, such as a shift towards regulatory T cells (Tregs), may contribute to a more balanced immune response in periodontal tissues, potentially reducing the risk of developing periodontitis[37–39].Furthermore, the presence of DNMT3A mutant clones may also affect the oral microbiome, potentially promoting symbiosis with periodontal tissues[40, 41]. Recent studies have emphasized the critical role of the oral microbiome in the development of periodontitis[40, 42, 43]. Changes in the immune system induced by CH could indirectly influence the composition or virulence of the oral microbiome, thereby mitigating the risk of periodontitis. Further research is needed to elucidate the underlying biological mechanisms and potential clinical implications of these findings. Strikingly, the protective effect conferred by DNMT3A-mutated clonal hematopoiesis was specific as compared with that of the subtypes. This finding may imply that the protective mechanism is related to the special function of DNMT3A in epigenetic regulation and hematopoietic stem cell biology[36]. Since the results are significant in the case of only one subtype-the one mediated by DNMT3A-the lack of significant associations with the other subtypes of CH, represented by TET2 mutations and overall CH, would then provide evidence for the hypothesis that the protective effect is specific to DNMT3A-mediated epigenetic alterations. Our results further highlight the complexity of CH and its heterogeneous effects on various inflammatory diseases. Given that prior studies have suggested a possible link between CH and increased cardiovascular mortality with all-cause mortality[12], our findings suggest that CH and, in particular, CH driven by DNMT3A mutations may be protective against certain inflammatory conditions such as periodontitis. This further higtlights the need to elucidate in greater detail the biology of CH and its influence on a plethora of age-related diseases. We have identified a few strengths of this study: First, MR allows an approximation of causality with minimal confounding and reverse causality. Second, the large sample size of the GWAS data provided robust genetic associations for CH subtypes and periodontitis. Third, the consistency of estimates from different MR methods enhances reliability. However, several limitations should be considered. First, although MR analysis diminishes confounding and reverse causality, its efficiency is related to the validity of the genetic instruments. Although we carefully selected SNPs strongly associated with the CH phenotype, one must consider potential pleiotropy: action through pathways other than the exposure of interest. Second, our study focused predominantly on populations of European ancestry, which may not allow for the generalization of findings to other ethnic groups. Third, although the MR analysis possibly provides evidence for causality, its exact magnitude should be interpreted with caution. Larger data and more powerful analyses will be needed to refine these estimates and provide a more precise understanding of the relationships. Conclusions This bidirectional two-sample Mendelian randomization (MR) study provides evidence for a potential causal relationship between clonal hematopoiesis (CH) and DNMT3A mutations and a reduced risk of periodontitis. These findings underscore the complex interplay between clonal hematopoiesis and inflammatory conditions, warranting further investigation into the underlying biological mechanisms and potential therapeutic implications. The unexpected protective effect of DNMT3A-mutated CH on periodontitis risk opens new avenues for research in hematology and periodontal medicine, potentially leading to novel approaches for understanding and managing age-related inflammatory diseases. Declarations Acknowledgements We thank all the researchers and participants of the studies involved in the Mendelian randomization. Funding Information Hua Lu is supported by Wuxi Taihy Talent Medical and HealthCare Project (Project number453205005THGD). Ran Xu is supported by Wuxi Municipal Health Commission Youth Project (Project number Q202108) Availability of data and materials The datasets analyzed during the current study are available in the GWAS Catalog (https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90102001-GCST90103000/) using the study accession numbers GCST90102618 (overall CH; https://www.ebi.ac.uk/gwas/studies/GCST90102618), GCST90102619 ( DNMT3A -CH; https://www.ebi.ac.uk/gwas/studies/GCST90102619), GCST90102620 ( TET2 -CH; https://www.ebi.ac.uk/gwas/studies/GCST90102620), GCST90102621 (small clone CH; https://www.ebi.ac.uk/gwas/studies/GCST90102621) and GCST90102622 (large clone CH; https://www.ebi.ac.uk/gwas/studies/GCST90102622)[18]. and the FinnGen repository, https://r10.finngen.fi/ [19]. Competing interests The authors declare no competing interests. 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Supplementary Files SupplementaryTableandfigures.docx Cite Share Download PDF Status: Published Journal Publication published 09 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Nov, 2024 Reviews received at journal 07 Nov, 2024 Reviews received at journal 04 Nov, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviewers agreed at journal 23 Oct, 2024 Reviewers invited by journal 23 Oct, 2024 Editor assigned by journal 23 Oct, 2024 Editor invited by journal 15 Oct, 2024 Submission checks completed at journal 14 Oct, 2024 First submitted to journal 26 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5158598","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":377481642,"identity":"7df9d2d1-fbfb-4fb1-aaf6-05c4f43acc70","order_by":0,"name":"Feng Qiu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Qiu","suffix":""},{"id":377481643,"identity":"1bddf9cd-f714-4ee4-95d5-657fc733cf37","order_by":1,"name":"Wei Shao","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Shao","suffix":""},{"id":377481645,"identity":"0d6c9e55-6070-4236-aa8b-ba6cc55782c3","order_by":2,"name":"Xue Qin","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Qin","suffix":""},{"id":377481646,"identity":"66454377-bb0b-4960-98c7-c7c8edee5c6d","order_by":3,"name":"Ran Xu","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Xu","suffix":""},{"id":377481648,"identity":"9e7ceedc-0713-4e2e-987a-e5dd48fd3d75","order_by":4,"name":"Hua Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACAwYeAyBpI8fP3gDkFjAwSBCnpSDNWLLnAAPDAQOitXw4nGhwI4FYLRI5ZhI/DA4nGNx8fEz6A9CFkg3MDx/dwKsl/5tkj0F6nuTttDSJAwZpxtIMbMbGOYRs4TGwLua7DWQcMDicOI+Bh02akBbJPwbMiQ03z5CgRZrHwDlxwg0eiJbZBLXwvDG2ljEABXJassUZEKOZgF/s23MMb775A4rKwwdvVFTYyEkcb374GJ8WLICZNOWjYBSMglEwCrAAAFK3RnDtMNLFAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":true,"prefix":"","firstName":"Hua","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-09-26 12:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5158598/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5158598/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-87040-5","type":"published","date":"2025-04-09T16:04:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70531268,"identity":"121907b8-39b9-4b7a-a740-e7809fda845a","added_by":"auto","created_at":"2024-12-04 06:02:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":383710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plots for the causal association between clonal hematopoiesis andperiodontitis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5158598/v1/e24b29c23a908a3818e21543.png"},{"id":70531267,"identity":"c6a104cb-10c4-4d61-90df-7bbad7b36c87","added_by":"auto","created_at":"2024-12-04 06:02:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":163541,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of univariable Mendelian randomization analvsis of the relationship between clonal hematopoiesis and periodontitis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5158598/v1/b071c6bb5a9b706198b4f019.png"},{"id":80559055,"identity":"5c3eb7ce-1314-435a-abba-2de45ba33735","added_by":"auto","created_at":"2025-04-14 16:17:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1344869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5158598/v1/c04fe397-f8b9-4071-afea-512737c1c8f3.pdf"},{"id":70531265,"identity":"6c254c35-d2c7-424a-9c35-895c9db98f1c","added_by":"auto","created_at":"2024-12-04 06:02:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":596453,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableandfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5158598/v1/dbcf8fd8a86d2f194da6e6a3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Clonal Hematopoiesis and Periodontitis: A Two-Sample Mendelian Randomization Study","fulltext":[{"header":"Background","content":"\u003cp\u003ePeriodontitis is a long-term inflammatory condition that affects tooth-supporting structures and has a high prevalence with age[1]. It is defined by the continuous breakdown of periodontal ligament and alveolar bone, leading to the loss of teeth if left unmanaged[2]. Various studies have estimated that the global prevalence of periodontitis is between 20% and 50%[3]. The etiology of periodontitis is multifactorial, as it results from the interaction of microbial, environmental, and genetic factors. Recent studies have shown that systemic diseases such as hematopoietic abnormalities may influence periodontitis development and progression[4\u0026ndash;6].\u003c/p\u003e \u003cp\u003eClonal hematopoiesis is an aging-associated disorder stemming from the clonal amplification of blood cells with somatic mutations in genes regulating hematopoiesis and epigenetics, including DNMT3A and TET2[6\u0026ndash;9]. It arises as a result of somatic mutations occurring in HSCs that confer a selective growth advantage to cells with these mutations, thus leading to the disproportional expansion of mutant clones over unmutated HSCs[9, 10]. The prevalence of CH increases with age and affects approximately 10\u0026ndash;20% of individuals above 70 years of age[11]. Research indicates that CH is associated with various inflammatory disorders and a heightened risk of developing blood cancers [12\u0026ndash;14]. However, the causality between CH and chronic inflammatory diseases, especially periodontitis, is still not fully known.\u003c/p\u003e \u003cp\u003eInsight into the causes of CH and periodontitis will help elucidate the biological pathways and possible therapeutic points involved. This study aimed at investigating bidirectional causality between the phenotypes of CH and periodontitis using a two-sample MR approach. In MR, genetic variants are used as instrumental variables to aid in making inferences about causality between an exposure and an outcome[15, 16]. Because this approach exploits the random assignment of genetic variants at conception, it mitigates many of the limitations of observational studies, such as confounding and reverse causality[17].\u003c/p\u003e \u003cp\u003eTo date, in this analysis, we have considered the use of two-sample MR to probe the possible causal associations of various CH phenotypes with periodontitis: overall clonal hematopoiesis (CH-overall), CH with DNMT3A mutation (CH-DNMT3A), CH with TET2 mutation (CH-TET2), large clones (CH-large), and small clones (CH-small). To complement the above methods, reverse MR analyses were implemented to identify the effect of periodontitis on CHs to further understand the interactions between them.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003eThe analysis was a two-sample MR design, in which genetic data from two independent genome-wide association study datasets, one for clonal hematopoiesis and one for periodontitis,were used. This can allow for the examination of the causal relationship between CH and periodontitis through the use of genetic variants strongly associated with the phenotype of CH as instrumental variables. These analyses were all based on three ERMs: there is a statistically significant association between instrumental variable(s) and exposure; instrument variables are unrelated to any confounder; and outcomes can be influenced only by instrumental variables through their influence on exposure.\u003c/p\u003e \u003cp\u003eData sources\u003c/p\u003e \u003cp\u003eIn the present analysis, summary statistics for genetic association data in clonal hematopoiesis were obtained from a large-scale genome-wide association study on a large scale by Siddhartha et al[18]. Genetic data from 200,453 individuals of European ancestry were analyzed to determine the prevalence of clonal hematopoiesis and its association with various somatic mutations, including those in DNMT3A and TET2, in the general population. WES-derived genomic data of these participants were obtained from the UK Biobank. Periodontitis-associated data were derived from a genome-wide association study meta-analysis by the FinnGen consortium[19], including genomic data from 346,731 Finnish participants comprising 87,497 cases of chronic periodontitis and 259,234 controls. The UK Biobank is a publicly available database. The FinnGen project is maintained under the auspices of the University of Helsinki. Each of these studies with these datasets was approved by local institutional review boards and ethics committees. Details of the data sources are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData sources for clonal hematopoiesis and periodontitis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTrait name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSamplesize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePMID or GWAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClonal hematopoiesis (overall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90102618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e184121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35,835,912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClonal hematopoiesis (DNMT3A mutation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90102619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e179103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35,835,912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClonal hematopoiesis (TET2 mutation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90102620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35,835,912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClonal hematopoiesis (large clone)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90102621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e177967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35,835,912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClonal hematopoiesis (small clone)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90102622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e180072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35,835,912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eperiodontitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChronic periodontitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e277036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003cp\u003e(K11_PERIODON_CHRON)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInstrumental variable (IV)\u003c/p\u003e \u003cp\u003eWe constructed reliable MR instrumental variables by selecting SNPs that were significantly associated with each type of clonal hematopoiesis at p\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10^-5. We performed further linkage disequilibrium pruning to ensure that the selected SNPs were independent from each other,with r\u0026sup2; \u0026lt; 0.001, and did not have chain reactions with each other. The selected SNPs were used as instrumental variables.\u003c/p\u003e \u003cp\u003eStatistical analysis for Mendelian randomization\u003c/p\u003e \u003cp\u003eFirst, the strengths of the IVs were determined by computing the F-statistic via the following formula:\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eF = ((R2 * (NK-1)) / (1 - R2 * K)\u003c/h2\u003e \u003cp\u003ewhere (R\u0026sup2;) is the proportion of variance in exposure explained by genetic variation; (N) is the sample size, and (K) denotes the number of instruments. It has been suggested that an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 indicates that no significant weak instrument bias will be present[20].\u003c/p\u003e \u003cp\u003eWe conducted two-way two-sample Mendelian randomization analyses via the inverse variance weighting method as the main analytical approach. The IVW method provides a weighted average of the Wald ratios for each genetic variant[21]. We performed sensitivity analyses, including MR-Egger and weighted median methods, to assess the robustness of our findings. We used the MR-Egger intercept test-a method that can detect directional pleiotropy-as a way of investigating horizontal pleiotropy[22]. Weighted median estimation could yield valid casual estimates even if up to 50% of the genetic instruments are invalid[23]. Furthermore,the MR-PRESSO method was applied for the detection of and adjustment for horizontal pleiotropy[24]. The heterogeneity was tested by Cochran's Q statistic, which evaluates heterogeneity in the estimates of the causal effects originating from different genetic variants[25].\u003c/p\u003e \u003cp\u003eWe estimated the summary OR and 95% CI for periodontitis risk for every subtype of CH. We considered a P value of less than 0.05 as representative of a significant causal effect. In addition to the causal effect of CH on periodontitis, we also conducted an inverse MR analysis to test the effect of periodontitis on the risk of a CH phenotype. In this latter analysis, genetic variants associated with periodontitis at p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10^-5 were used as instrumental variables to assess their causal effect on CH. Other types of analyses were conducted as described previously. All the statistical analyses were performed in R, version 4.2.1, via the two-sample MR package, version 0.5.6[26] and the MR-PRESSO package, version 1.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSelection of instrumental variables\u003c/p\u003e\n\u003cp\u003eAccording to the instrumental variable (IV) selection criteria (p\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), a total of 159 single nucleotide polymorphisms (SNPs) were used as IVs associated with five subtypes of clonal hematopoiesis. Details regarding the selected instrumental variables are provided in Additional File Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAmong the five causal associations, the F-statistic of the instrumental variables (IVs) ranged from 19.42\u0026ndash;222.54, thereby eliminating the bias associated with weak IVs.\u003c/p\u003e\n \u003cp\u003eIn the primary IVW analysis in the forward MR analysis, a significant causal effect of CH with DNMT3A mutations on periodontitis risk was observed (OR\u0026thinsp;=\u0026thinsp;0.084, 95% CI: 0.007\u0026ndash;0.972, P\u0026thinsp;=\u0026thinsp;0.047) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings suggest that this particular CH phenotype is protective against periodontitis. For the remaining subtypes of CH, no significant causal estimates were observed (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The sensitivity analyses by MR-Egger regression and weighted median estimators supported the original IVW analytical result (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The MR-Egger regression revealed no evidence of pleiotropy, represented by the nonsignificant intercept term (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Weighted median analysis further strengthens this protective association of CH with DNMT3A mutations against periodontitis and suggests the results are robust against any potential violation of the MR assumptions. No significant heterogeneity in the instrumental variables was found in Cochran\u0026apos;s IVW Q test results(Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). In the leave-one-out method, there are potential outliers on gross inspection (Fig \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).Furthermore, MR-PRESSO analysis did not report any significant outliers in the current analysis (overall test P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). However, there is insufficient evidence for horizontal pleiotropy in the association of clonal hematopoiesis with periodontitis.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMR estimates for the association between clonal hematopoiesis and periodontitis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eexposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ensnp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epval\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eor_lci95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eor_uci95\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClonal hematopoiesis (overall)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClonal hematopoiesis (DNMT3A mutation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClonal hematopoiesis (TET2 mutation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75263.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4602.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e850.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClonal hematopoiesis\u003c/p\u003e\n \u003cp\u003e(large clone)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21544.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e557.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e276.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19231.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1782.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClonal hematopoiesis (small clone)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1520.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10255.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePleiotropy test of clonal hematopoiesis genetic variants in the risk of periodontitis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eoutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eegger_intercept\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eperiodontitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eperiodontitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eperiodontitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eperiodontitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eperiodontitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThere is no evidence for a causal effect of periodontitis on CH phenotypes in reverse MR analysis (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). The heterogeneity test for periodontitis to overall CH was significant (Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e). Although the MR-Egger regression intercept did not detect horizontal pleiotropy (Table \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e), MR-PRESSO analysis revealed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e). After the removal of outliers to reduce heterogeneity and horizontal pleiotropy, the adjusted p-value was above 0.05. Overall, periodontitis-related genetic variants did not significantly the risk of any CH phenotype. P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all CH outcomes, which means that the relationship is unidirectional: CH affects the risk of periodontitis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present two-sample MR study, summary statistics for CH were retrieved from GWAS meta-analyses by the UKB consortium, whereas periodontitis summary statistics were obtained from the FinnGen Consortium R10 publication data. Our results shed new light, in particular, on the significant inverse association between periodontitis risk and CH driven by DNMT3A mutations. Nevertheless, associations with other CH phenotypes did not reach the threshold for statistical significance. Additioanally, MR inversion revealed no evidence for reverse causality: periodontitis does not cause CH.\u003c/p\u003e \u003cp\u003eThe protective effect of DNMT3A-mutated clonal hematopoiesis (CH) on periodontitis risk presents an intriguing and somewhat unexpected finding. This observation challenges the prevailing view of CH as primarily a detrimental condition linked to increased inflammation and age-related diseases[27]. Previous research has indicated that an inflammatory environment can improve the development and persistence of CH, creating a feedback loop that exacerbates both conditions[28, 29]. For example, proinflammatory cytokines such as TNF-α and IL-6 promote the expansion of specific CH clones, particularly those with mutations in genes such as TET2 and DNMT3A, which increase inflammatory responses, including those observed in periodontitis[30].However, recent studies have suggested that DNMT3A mutations may result in epigenetic modifications due to changes in DNA methylation patterns, potentially regulating the expression of genes involved in inflammatory pathways[31, 32]. Observational studies have noted that DNA methylation alterations are more pronounced in individuals with periodontitis than in healthy individuals[33\u0026ndash;35].These changes in DNA methylation could lead to a reduced inflammatory response in periodontal tissues, potentially mitigating tissue destruction and alveolar bone loss.\u003c/p\u003e \u003cp\u003eClonal hematopoiesis (CH) with DNMT3A mutations can significantly influence the differentiation and function of immune cells, particularly those involved in the pathogenesis of periodontitis. DNMT3A is known to regulate the differentiation and function of various immune cell types, including T cells and bone marrow-derived cells[36]. Alterations in T-cell subsets, such as a shift towards regulatory T cells (Tregs), may contribute to a more balanced immune response in periodontal tissues, potentially reducing the risk of developing periodontitis[37\u0026ndash;39].Furthermore, the presence of DNMT3A mutant clones may also affect the oral microbiome, potentially promoting symbiosis with periodontal tissues[40, 41]. Recent studies have emphasized the critical role of the oral microbiome in the development of periodontitis[40, 42, 43]. Changes in the immune system induced by CH could indirectly influence the composition or virulence of the oral microbiome, thereby mitigating the risk of periodontitis. Further research is needed to elucidate the underlying biological mechanisms and potential clinical implications of these findings.\u003c/p\u003e \u003cp\u003eStrikingly, the protective effect conferred by DNMT3A-mutated clonal hematopoiesis was specific as compared with that of the subtypes. This finding may imply that the protective mechanism is related to the special function of DNMT3A in epigenetic regulation and hematopoietic stem cell biology[36]. Since the results are significant in the case of only one subtype-the one mediated by DNMT3A-the lack of significant associations with the other subtypes of CH, represented by TET2 mutations and overall CH, would then provide evidence for the hypothesis that the protective effect is specific to DNMT3A-mediated epigenetic alterations.\u003c/p\u003e \u003cp\u003eOur results further highlight the complexity of CH and its heterogeneous effects on various inflammatory diseases. Given that prior studies have suggested a possible link between CH and increased cardiovascular mortality with all-cause mortality[12], our findings suggest that CH and, in particular, CH driven by DNMT3A mutations may be protective against certain inflammatory conditions such as periodontitis. This further higtlights the need to elucidate in greater detail the biology of CH and its influence on a plethora of age-related diseases.\u003c/p\u003e \u003cp\u003eWe have identified a few strengths of this study: First, MR allows an approximation of causality with minimal confounding and reverse causality. Second, the large sample size of the GWAS data provided robust genetic associations for CH subtypes and periodontitis. Third, the consistency of estimates from different MR methods enhances reliability.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be considered. First, although MR analysis diminishes confounding and reverse causality, its efficiency is related to the validity of the genetic instruments. Although we carefully selected SNPs strongly associated with the CH phenotype, one must consider potential pleiotropy: action through pathways other than the exposure of interest. Second, our study focused predominantly on populations of European ancestry, which may not allow for the generalization of findings to other ethnic groups. Third, although the MR analysis possibly provides evidence for causality, its exact magnitude should be interpreted with caution. Larger data and more powerful analyses will be needed to refine these estimates and provide a more precise understanding of the relationships.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis bidirectional two-sample Mendelian randomization (MR) study provides evidence for a potential causal relationship between clonal hematopoiesis (CH) and DNMT3A mutations and a reduced risk of periodontitis. These findings underscore the complex interplay between clonal hematopoiesis and inflammatory conditions, warranting further investigation into the underlying biological mechanisms and potential therapeutic implications. The unexpected protective effect of DNMT3A-mutated CH on periodontitis risk opens new avenues for research in hematology and periodontal medicine, potentially leading to novel approaches for understanding and managing age-related inflammatory diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank all the researchers and participants of the studies involved in the Mendelian randomization. \u003c/p\u003e\n\u003ch2\u003eFunding Information\u003c/h2\u003e\n\u003cp\u003eHua Lu is supported by Wuxi Taihy Talent Medical and HealthCare Project (Project number453205005THGD). \u003c/p\u003e\n\u003cp\u003eRan Xu is supported by Wuxi Municipal Health Commission Youth Project (Project number Q202108)\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the GWAS Catalog (https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90102001-GCST90103000/) using the study accession numbers GCST90102618 (overall CH; https://www.ebi.ac.uk/gwas/studies/GCST90102618), GCST90102619 (\u003cem\u003eDNMT3A\u003c/em\u003e-CH; https://www.ebi.ac.uk/gwas/studies/GCST90102619), GCST90102620 (\u003cem\u003eTET2\u003c/em\u003e-CH; https://www.ebi.ac.uk/gwas/studies/GCST90102620), GCST90102621 (small clone CH; https://www.ebi.ac.uk/gwas/studies/GCST90102621) and GCST90102622 (large clone CH; https://www.ebi.ac.uk/gwas/studies/GCST90102622)[18]. and the FinnGen repository, https://r10.finngen.fi/ [19].\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLuo, L.S., et al., \u003cem\u003eSecular trends in severe periodontitis incidence, prevalence and disability-adjusted life years in five Asian countries: A comparative study from 1990 to 2017.\u003c/em\u003e J Clin Periodontol, 2021. \u003cstrong\u003e48\u003c/strong\u003e(5): p. 627-637.\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;n\u0026ouml;nen, E., M. 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Walsh, \u003cem\u003eSomatic Mutations and Clonal Hematopoiesis: Unexpected Potential New Drivers of Age-Related Cardiovascular Disease.\u003c/em\u003e Circ Res, 2018. \u003cstrong\u003e122\u003c/strong\u003e(3): p. 523-532.\u003c/li\u003e\n\u003cli\u003eMcClatchy, J., et al., \u003cem\u003eClonal hematopoiesis related TET2 loss-of-function impedes IL1\u0026beta;-mediated epigenetic reprogramming in hematopoietic stem and progenitor cells.\u003c/em\u003e Nature Communications, 2023. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 8102.\u003c/li\u003e\n\u003cli\u003eVenugopal, K., et al., \u003cem\u003eAlterations to DNMT3A in Hematologic Malignancies.\u003c/em\u003e Cancer Res, 2021. \u003cstrong\u003e81\u003c/strong\u003e(2): p. 254-263.\u003c/li\u003e\n\u003cli\u003eJurdziński, K.T., J. Potempa, and A.M. 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Luo, and M.J. Rauh, \u003cem\u003eClonal hematopoiesis and inflammation: Partners in leukemogenesis and comorbidity.\u003c/em\u003e Exp Hematol, 2020. \u003cstrong\u003e83\u003c/strong\u003e: p. 85-94.\u003c/li\u003e\n\u003cli\u003eChen, H., et al., \u003cem\u003eExploring the causal relationship between periodontitis and gut microbiome: Unveiling the oral-gut and gut-oral axes through bidirectional Mendelian randomization.\u003c/em\u003e J Clin Periodontol, 2024. \u003cstrong\u003e51\u003c/strong\u003e(4): p. 417-430.\u003c/li\u003e\n\u003cli\u003eSulaiman, Y., et al., \u003cem\u003eOral and Gut Microbiota Dysbiosis Due to Periodontitis: Systemic Implications and Links to Gastrointestinal Cancer: A Narrative Review.\u003c/em\u003e Medicina, 2024. \u003cstrong\u003e60\u003c/strong\u003e(9): p. 1416.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Periodontitis, Clonal Hematopoiesis, Mendelian randomization analysis, inflammation","lastPublishedDoi":"10.21203/rs.3.rs-5158598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5158598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClonal hematopoiesis is an age-related condition in which somatically mutated blood cells clonally expand. Recently, a series of studies mentioned a possible link between CH and periodontitis; however, the exact cause-effect relationship has not yet been clarified.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed MR analyses with two samples to investigate a possible causal relationship between different clonal hematopoiesis phenotypes and susceptibility to periodontitis.Variants of clonal hematopoiesis phenotypes included overall clonal hematopoiesis (CH-overall), CH with a DNMT3A mutation (CH-DNMT3A), CH with a TET2 mutation (CH-TET2), large clones (CH-large), and small clones (CH-small). Genetic instruments were derived from genome-wide association study data. The main method of analysis used was the IVW method. Sensitivity analyses included the weighted median, MR-Egger regression and MR-PRESSO. Reverse MR analyses were also performed to look for bidirectional effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMR analysis revealed a significant association between CH-DNMT3A and periodontitis, with an odds ratio of 0.084 (95% confidence interval: 0.007\u0026ndash;0.972, P\u0026thinsp;=\u0026thinsp;0.047). These findings suggest that CH-DNMT3A may play a protective role in the development of periodontitis.No other CH phenotypes, such as CH-overall, CH-TET2, CH-large, or CH-small, were associated with periodontitis. The inverse MR analyses provided no statistical evidence for an association of periodontitis with any of the CH phenotypes. There was no evidence of pleiotropy or heterogeneity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings indicate a novel protective role for DNMT3A-mutated clonal hematopoiesis (CH) in periodontitis.However, additional research is necessary to clarify the underlying mechanisms of this association.\u003c/p\u003e","manuscriptTitle":"Association between Clonal Hematopoiesis and Periodontitis: A Two-Sample Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-04 06:02:43","doi":"10.21203/rs.3.rs-5158598/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-13T05:37:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-07T08:53:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-04T11:06:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66837341144450387482752875211850013692","date":"2024-10-25T07:40:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180282630533947346381375362630891763490","date":"2024-10-23T05:15:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-23T05:12:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-23T05:08:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-16T02:32:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-14T09:28:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-09-26T12:03:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44d88a60-0cbe-46ca-8877-c29124833858","owner":[],"postedDate":"December 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:14:23+00:00","versionOfRecord":{"articleIdentity":"rs-5158598","link":"https://doi.org/10.1038/s41598-025-87040-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-09 16:04:51","publishedOnDateReadable":"April 9th, 2025"},"versionCreatedAt":"2024-12-04 06:02:43","video":"","vorDoi":"10.1038/s41598-025-87040-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-87040-5","workflowStages":[]},"version":"v1","identity":"rs-5158598","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5158598","identity":"rs-5158598","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}