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. Author manuscript; available in PMC: 2024 Sep 1.
Published in final edited form as: J Clin Periodontol. 2023 Jul 18;50(9):1140–1153. doi: 10.1111/jcpe.13852

Epigenome-wide association study using peripheral blood leukocytes identifies genomic regions associated with periodontal disease and edentulism in the Atherosclerosis Risk in Communities Study

Naisi Zhao 1, Flavia Teles 2, Jiayun Lu 3, Devin C Koestler 5,6, James Beck 7, Eric Boerwinkle 8,9, Jan Bressler 8, Karl T Kelsey 10,11, Elizabeth A Platz 3,4, Dominique S Michaud 1
PMCID: PMC10528731  NIHMSID: NIHMS1915883  PMID: 37464577

Abstract

Aim:

Investigate individual susceptibility to periodontitis by conducting an epigenome-wide association study using peripheral blood.

Materials and Methods

We included 1077 African American and 457 European American participants of the Atherosclerosis Risk in Communities (ARIC) study who had completed a dental examination or reported being edentulous at visit 4 and had available data on DNA methylation from visit 2 or 3. DNA methylation levels were compared by periodontal disease severity and edentulism through discovery analyses and subsequent testing of individual CpGs.

Results

Our discovery analysis replicated findings from a previous study reporting a region in gene ZFP57 (6p22.1) that was significantly hypomethylated in severe periodontal disease compared to no/mild periodontal disease in European American participants. Higher methylation levels in a separate region in an unknown gene (located in Chr10: 743,992–744,958) was associated with significantly higher odds of edentulism compared to no/mild periodontal disease in African American participants. In subsequent CpG testing, four CpGs in a region previously associated with periodontitis located within HOXA4 were significantly hypermethylated in severe periodontal disease compared to no/mild periodontal disease in African American participants (OR per 1 SD increase in methylation level: cg11015251: 1.28 (1.02, 1.61); cg14359292: 1.24 (1.01, 1.54); cg07317062: 1.30 (1.05, 1.61); cg08657492: 1.25 (1.01, 1.55)).

Conclusions

Our study highlights epigenetic variations in ZPF57 and HOXA4 that were significantly and reproducibly associated with periodontitis. Future studies should evaluate gene regulatory mechanisms in the candidate regions of these loci.

Keywords: Periodontitis, periodontal disease, DNA methylation, Illumina Infinium HumanMethylation450K, epigenome-wide association study (EWAS), peripheral blood

Introduction

Over 47% of US adults have some form of periodontitis, and the incidence of periodontitis rises steeply in adults 50 and older.1–3 Given the public health impact of periodontal disease4,5, studying individuals’ susceptibility to periodontitis could be crucial to building effective disease prevention strategies. While genome-wide association studies (GWAS) have discovered new genes that may contribute to the pathogenesis of periodontitis, only a few common variants identified reached genome-wide significance, highlighting the complex interplay between genetic and environmental factors in the development and progression of the disease.6–10 In addition to genetic factors, epigenetic modifications of DNA can affect the genetic blueprint of host responses, either directly in lesion-associated target tissue or associated with an altered immune response. Studies have found that blood methylation levels can correlate with tissue levels, making blood-based DNA methylation an attractive target for biomarker discovery.11 Therefore, assessing epigenetic alterations in whole blood may provide valuable insights into the development and progression of periodontitis and help identify individuals who are at higher risk for the disease.

In the context of periodontal disease, host response plays a critical role in individual susceptibility to infections,12 as revealed by several studies investigating DNA methylation changes in gingival tissues of patients with periodontitis. These studies have identified differential methylation patterns in genes involved in inflammation, immune response, and extracellular matrix organization.13,14 Recent study by Kim et al.15 collected gingival tissue samples from 14 individuals with healthy periodontium and 14 individuals with chronic periodontitis and reported that hypermethylated CpGs in individuals with periodontitis were enriched in genes related to inflammation and immune response, while the hypomethylated CpGs were enriched in genes related to cellular and tissue development. Despite the insights gained from gingival tissue studies, the use of DNA methylation in whole blood offers subject level sampling instead of site level specimens. This approach enables the identification of systemic contributors to periodontitis and the reflection of immunologic susceptibility to periodontitis. Two epigenome-wide studies (EWAS) of peripheral blood leukocytes in periodontitis have been published. A cross-sectional study in adult female twins by Kurushima et al.16 identified several CpGs, including one in ZNF804A, associated with gingival bleeding and tooth mobility. A pilot study by Hernandez et al.17, conducted in 8 periodontitis patients and 8 matched controls, reported statistically significant differences in peripheral blood DNA methylation in multiple regions, including the ZNF718, HOXA4, and ZFP57 genes.

To expand on previous work of periodontitis-related DNA methylation changes, we conducted both discovery analyses and subsequent testing of CpGs in specific gene regions. The discovery analysis involved an epigenome-wide association study (EWAS) and differential methylated regions analysis to discover aberrant patterns of blood-derived DNA methylation in periodontal disease severity and edentulism among 1534 Black and White older adults who underwent a clinical dental examination as a component of the community-based ARIC study. Additionally, we examined relationships with CpGs based on a priori knowledge of specific gene regions that may be important for periodontitis, including the HOXA4 and ZFP57 genes. Our study aimed to identify CpG sites associated with periodontal disease severity and edentulism, controlling for relevant health conditions, socioeconomic status, self-reported smoking, and methylation-predicted packyears.

Material and Methods

Study Population

Study participants were enrolled in the Atherosclerosis Risk in Communities (ARIC) Study (RRID: SCR_021769), a prospective cohort study of cardiovascular disease risk that enrolled 15,792 participants between 1987 and 1989 from four US communities (Jackson, MS; Washington County, MD; suburban Minneapolis, MN; and Forsyth County, NC).18,19 Participants underwent a baseline clinical examination (Visit 1) and subsequent follow-up clinical exams (Visits 2–9 completed & Visit 10 planned). Participants provided written informed consent at each visit. The ARIC Study protocol was approved by institutional review boards at the four field centers and coordinating center.

At Visit 4 (1996–1998), participants who had at least one tooth or dental implant and were otherwise eligible and consented underwent a clinical dental examination by trained personnel (n = 6017). Edentulous participants were not eligible for the dental examination. Our analysis focused on a sample of ARIC participants with available data on leukocyte DNA methylation levels collected at Visit 2 (1990–92) or Visit 3 (1993–1995). Among the participants with methylation data, those who attended a dental examination20 (n = 1174) or who self-reported being edentulous (n = 360) during Visit 4 were eligible for our study.

DNA Methylation Assessing and Preprocessing

The procedures for DNA methylation assessment have been previously described.21,22 Briefly, Illumina HumanMethylation450 BeadChip array was used for genome wide profiling of DNA methylation in 2,853 African American participants at 483,525 CpG sites and in 1,104 European American participants at 482,815 CpG sites. Thus, we treated methylation data of African American and European American participants as two separate cohorts. Details are summarized in the Supplemental Methods File.

Periodontal Measures

Periodontal severity was determined based on standardized dental measurements, including clinical attachment loss (CAL) calculated based on probing depth and gingival recession, which were measured at six sites on all teeth present.23,24 To ensure comprehensive assessment, we utilized two different measures of periodontitis severity. The first definition, “periodontitis (ARIC),” categorizes individuals based on the percentage of sites with CAL>= 3 mm into no/mild, moderate, or severe periodontitis, as previously used in the ARIC study.25 The second measure, “periodontitis (CDC-AAP),” was developed for population-based surveillance of periodontitis by the US Centers for Disease Control and Prevention–American Academy of Periodontology (CDC-AAP). This measure uses CAL, pocket depth (PD), and the site location (interproximal) to classify participants into no, mild, moderate, or severe periodontitis.26 Beyond using traditional definitions of periodontal disease severity, we used edentulism as a surrogate for the most severe type of periodontitis, since tooth loss is a common outcome of periodontitis and is often used as a clinical marker of disease severity.27 In addition, individuals who are edentulous have likely experienced significant periodontal breakdown and tooth loss. ARIC participants who self-reported as both not having any natural teeth and not having any dental implants were classified as edentulous for this analysis.

Covariate Assessment

Modifiable risk factors associated with periodontitis and edentulism include: tobacco smoking28, nutrition29, psychological stress and depression30, obesity, diabetes31, and metabolic syndrome.32 At Visit 2 (1990–92) and Visit 3 (1993–1995), cigarette smoking status (current, former, never) and cigarette smoking dose were measured. To account for potential confounding by leukocyte cell composition, we used a DNA methylation deconvolution procedure (see Supplemental Methods) to estimate the proportions of different cell types in each sample.33 We used the R function EstDimRMT to determine that five principal components with non-zero eigenvalues were needed to correct batch effects in each cohort.34,35 Additionally, we included age, sex, smoking category (never, former, current), packyears, and BMI as covariates in the EWAS and DMR analyses. In contrast, our subsequent testing of CpGs from specific gene regions included additional covariates such as methylation-predicted packyears smoked, ARIC field center, SES, and diabetes status. Diabetes at baseline was defined as fasting plasma glucose ≥126 mg/dl, non-fasting plasma glucose ≥200 mg/dl, receiving diabetes treatment, or self-report of a diabetes diagnosis by a physician. Participants with high life-course SES was identified using lifetime socioeconomic status (SES) variable, calculated based on 12 factors associated with SES.36

Statistical Analysis

To investigate the association between DNA methylation and periodontal disease, we conducted an epigenome-wide association analysis (EWAS) using multivariable logistic regression models. This analysis aimed to identify CpG sites associated with severe vs. no/mild periodontitis (ARIC definition), moderate vs. no/mild periodontitis (ARIC definition), and edentulous vs. no/mild periodontitis (ARIC definition), or severe vs. no periodontitis (CDC-AAP definition), mild/moderate vs. no periodontitis (CDC-AAP definition), and edentulous vs. no periodontitis (CDC-AAP definition) per 1 SD increase in methylation level at single CpG sites. All models were adjusted for age, sex, five surrogate variables for batch effects, smoking category (never, former, current), packyears, BMI, and leukocyte cell composition. To control for potential residual confounding by smoking, we included a methylation-predicted packyears developed based on a meta-analysis of association results between DNA methylation and cigarette smoking in individuals from 16 cohorts.37 The Benjamini-Hochberg method was used to calculate the false discovery rate (FDR) for multiple comparison adjustments (FDR q-value < 0.05). Lastly, we conducted EWAS separately for African American and European American participants.

We also performed a differential methylated regions analysis using the DMRcate R/Bioconductor package to identify differentially methylated regions (DMRs) associated with periodontal disease status. The DMR analysis was conducted separately for African American and European American participants. A linear model that adjusted for the same covariates as the EWAS was fit using the DMRcate function along with these parameters: lambda = 1,000 and kernel adjustment C = 2.38 Statistically significant DMRs were required to have a minimum of two statistically significant single CpGs and to meet the multiple testing adjustment criteria of FDR (q-value) <0.05 (calculated using the Benjamini-Hochberg method).39

Supplemental Figure 1 provides a summary of the discovery analyses described above and subsequent CpG testing. CpGs belonging to the statistically significant DMRs were further evaluated using a baseline category logit model to test for association with periodontitis status according to both the CDC-AAP and the ARIC periodontitis definitions. 42 CpGs were selected based on prior knowledge from both our own DMRcate results and previously published pilot study by Hernandez et al.17 The regions of HOXA4 and ZFP57 genes were of interest because they were found to be significant with multiple consecutive CpGs in the same direction after controlling for cell composition in the Hernandez study model.17 We compiled CpGs from the three DMRs identified by Hernandez et al. and by our own DMRcate and tested them in the ARIC dataset. All models controlled for age, sex, SES, BMI, diabetes status, smoking category (never, former, current), packyears, methylation-predicted packyears smoked, leukocyte cell composition, ARIC field center, and five surrogate variables for batch effects. Subgroup analysis was also conducted for those 58 years and older vs. under, male vs. female participants, participants of higher vs. lower SES. Analyses were conducted separately for African American and European American participants. In this subsequent CpG testing, we did not correct for multiple comparisons since the statistical tests for the individual CpGs were pre-specified and were not exploratory in nature, reducing the risk of obtaining spurious results due to chance.

Results

Population Characteristics

Table 1 summarizes the characteristics of ARIC participants included in this study by periodontal disease status defined by the CDC-AAP and by the ARIC periodontitis definitions. The average time between blood drawn and Visit 4 was 6 years. Participants with severe periodontitis were more likely to be male, black, obese, less educated, current smoker, and have lower life-course SES (Table 1). Compared to those of European ancestry, African American participants in this study were more likely to be diabetic, have low SES, are less educated, have fewer teeth, and are more likely to have severe periodontitis (Supplemental Table 1). African American participants were younger, with a median age of 55, compared to 58 years old for European American participants. 28.6% of African American participants had severe periodontal disease, with 45.7% having no/mild periodontal disease, while 11.4% and 60.2% of European American participants had severe or no/mild periodontal disease, respectively.

Table 1.

Characteristics across periodontal disease categories among ARIC participants, separately by racea

Periodontitis (CDC-AAP) Periodontitis (ARIC)
African American Participants Edentulism No Mild Moderate Severe No/Mild Moderate Severe
(N=309) (N=318) (N=18) (N=263) (N=169) (N=183) (N=277) (N=308)
Age
Mean (SD) 57.4 (5.56) 54.4 (5.31) 55.2 (5.99) 55.3 (5.61) 54.8 (5.24) 54.4 (5.16) 54.8 (5.62) 55.0 (5.38)
Median [Min, Max] 57.0 [47.0, 69.0] 53.0 [47.0, 68.0] 52.5 [49.0, 67.0] 54.0 [47.0, 70.0] 54.0 [47.0, 67.0] 53.0 [47.0, 68.0] 53.0 [47.0, 68.0] 54.0 [47.0, 70.0]
Gender, %
Female 76.1 78.9 66.7 58.9 36.7 82.0 66.4 47.4
Male 23.9 21.1 33.3 41.1 63.3 18.0 33.6 52.6
Teeth Number
Mean (SD) NA 16.3 (7.45) 17.2 (6.76) 17.2 (6.90) 17.6 (6.77) 17.3 (7.19) 18.8 (7.59) 16.9 (7.90)
Median [Min, Max] NA 17.5 [3.00, 28.0] 18.5 [3.00, 26.0] 19.0 [2.00, 28.0] 18.0 [2.00, 28.0] 18.0 [3.00, 30.0] 20.0 [3.00, 32.0] 17.0 [2.00, 32.0]
Cigarette Smoking Status, %
Current 24.9 10.1 22.2 20.5 42.0 8.2 16.6 32.5
Former 26.5 25.2 27.8 28.1 21.9 27.3 24.2 25.6
Never 48.5 64.8 50.0 51.3 36.1 64.5 59.2 41.9
Accumulated Cigarette Smoking Packyears
Mean (SD) 11.7 (19.8) 4.49 (10.2) 5.38 (9.07) 9.26 (16.3) 14.7 (18.1) 4.00 (9.27) 6.53 (14.5) 12.7 (16.9)
Median [Min, Max] 0.300 [0, 160] 0 [0, 81.0] 0.750 [0, 36.3] 0 [0, 146] 7.20 [0, 88.9] 0 [0, 81.0] 0 [0, 146] 4.15 [0, 88.9]
Drinking Status, %
Current 22.7 28.0 44.4 34.6 46.7 30.6 31.8 39.9
Former 37.2 29.9 33.3 28.9 28.4 29.5 29.6 28.9
Never 40.1 41.8 22.2 36.5 23.7 39.9 38.6 30.2
Diabetes Status, %
No 74.4 87.1 66.7 86.3 81.1 85.2 87.7 82.5
Yes 25.6 12.3 33.3 12.9 18.9 14.2 11.9 16.9
High SES Status, %
No 82.2 47.5 72.2 53.6 60.9 44.8 45.8 64.6
Yes 17.8 52.5 27.8 46.4 39.1 55.2 54.2 35.4
Education Level, %
High school (no degree) or less  55.0 20.1 44.4 32.7 34.9 18.6 23.8 38.0
High school graduate or vocational school 29.4 33.0 38.9 25.5 30.2 33.9 27.1 30.2
College or graduate School 15.2 46.9 16.7 41.4 34.9 47.5 48.7 31.8
BMI Categories, %
BMI < 18.5 1.0 0.3 0.0 0.8 0.0 0.5 0.4 0.3
BMI ≥18.5 and <25 14.9 16.0 22.2 17.5 20.7 12.0 19.5 19.5
BMI ≥25 and <30 35.0 36.8 16.7 40.3 42.0 37.2 37.5 40.6
BMI ≥30 and <40 40.5 39.0 44.4 32.7 33.7 41.0 34.3 34.1
BMI ≥40 8.7 7.9 11.1 8.4 3.6 8.7 7.9 5.5
ARIC Field Center, %
Forsyth County, NC 4.9 3.1 33.3 7.2 11.2 7.7 6.9 6.8
Jackson, MS 95.1 96.9 66.7 92.8 88.8 92.3 93.1 93.2
Periodontitis (CDC-AAP) Periodontitis (ARIC)
European American Participants Edentulism No Mild Moderate Severe No/Mild Moderate Severe
(N=51) (N=171) (N=28) (N=162) (N=45) (N=224) (N=130) (N=52)
Age
Mean (SD) 60.2 (4.74) 58.2 (5.04) 57.0 (4.95) 58.7 (5.47) 57.5 (5.36) 57.9 (5.04) 58.9 (5.50) 58.0 (5.47)
Median [Min, Max] 61.0 [48.0, 67.0] 58.0 [48.0, 68.0] 56.5 [49.0, 65.0] 59.0 [47.0, 67.0] 58.0 [48.0, 67.0] 58.0 [47.0, 68.0] 59.0 [47.0, 67.0] 58.0 [48.0, 67.0]
Gender, %
Female 62.7 77.2 57.1 50.6 26.7 71.0 49.2 36.5
Male 37.3 22.8 42.9 49.4 73.3 29.0 50.8 63.5
Teeth Number
Mean (SD) NA 23.8 (5.06) 26.0 (2.05) 22.5 (6.23) 22.1 (5.42) 24.8 (4.40) 24.0 (4.97) 17.9 (8.37)
Median [Min, Max] NA 25.0 [2.00, 28.0] 27.0 [20.0, 28.0] 25.0 [1.00, 28.0] 24.0 [5.00, 28.0] 26.0 [2.00, 32.0] 26.0 [4.00, 30.0] 19.5 [1.00, 31.0]
Cigarette Smoking Status, %
Current 35.3 11.1 3.6 18.5 24.4 10.3 18.5 26.9
Former 21.6 29.2 35.7 36.4 35.6 30.8 35.4 38.5
Never 43.1 59.6 60.7 45.1 40.0 58.9 46.2 34.6
Accumulated Cigarette Smoking Packyears
Mean (SD) 20.9 (30.4) 7.95 (14.2) 7.68 (13.2) 16.7 (23.8) 17.6 (20.0) 8.64 (16.1) 13.5 (17.1) 26.5 (30.4)
Median [Min, Max] 9.40 [0, 141] 0 [0, 65.8] 0 [0, 44.0] 3.28 [0, 148] 11.0 [0, 82.0] 0 [0, 84.0] 3.28 [0, 73.5] 22.9 [0, 148]
Drinking Status, %
Current 27.5 59.1 67.9 59.9 68.9 61.6 60.0 61.5
Former 15.7 10.5 7.1 9.3 17.8 8.9 13.1 11.5
Never 56.9 30.4 25.0 30.9 13.3 29.5 26.9 26.9
Diabetes Status, %
No 94.1 97.1 100.0 96.9 95.6 98.2 95.4 96.2
Yes 5.9 2.9 0.0 3.1 4.4 1.8 4.6 3.8
High SES Status, %
No 58.8 13.5 7.1 16.7 11.1 11.2 13.8 26.9
Yes 41.2 86.5 92.9 83.3 88.9 88.8 86.2 73.1
Education Level, %
High school (no degree) or less  39.2 5.3 3.6 14.8 6.7 5.8 10.0 21.2
High school graduate or vocational school 47.1 44.4 53.6 41.4 37.8 46.4 42.3 30.8
College or graduate School 13.7 50.3 42.9 43.8 55.6 47.8 47.7 48.1
BMI Categories, %
BMI < 18.5 0.0 1.2 3.6 1.9 0.0 0.9 1.5 3.8
BMI ≥18.5 and <25 45.1 51.5 32.1 35.2 37.8 49.1 33.8 32.7
BMI ≥25 and <30 33.3 33.3 42.9 45.1 53.3 35.7 46.2 50.0
BMI ≥30 and <40 19.6 12.9 21.4 17.9 8.9 13.8 17.7 13.5
BMI ≥40 2.0 1.2 0.0 0.0 0.0 0.4 0.8 0.0
ARIC Field Center, %
Forsyth County, NC 94.1 90.6 67.9 84.0 71.1 85.3 86.2 75.0
Minneapolis, MN 3.9 8.2 21.4 13.6 15.6 12.1 12.3 11.5
Washington County, MD 2.0 1.2 10.7 2.5 13.3 2.7 1.5 13.5
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a

Results are reported separately by race because methylation profiling was performed separately by race. Characteristics were from the same ARIC visit as the methylation data (mostly Visit 2 or 3), periodontal disease categories were from the dental examination performed at Visit 4, and edentulism status was assessed at Visit 4.

EWAS & DMRcate Analysis

Using the ARIC periodontitis definition, the single CpG EWAS did not identify differentially methylated CpGs that were statistically significant after multiple comparison corrections (q < 0.05). After adjusting for methylation-predicted packyears, we identified one statistically significant DMR (ENSG00000231601 manual annotation Chr10: 743,992–744,958) in edentulism when compared to no/mild periodontal disease in African American participants and one statistically significant DMR (ZFP57 6p22.1) in severe periodontal disease compared to no/mild periodontal disease in European American participants (Supplemental Table 2). The EWAS and DMRcate results did not vary from those presented above when using the CDC-AAP periodontitis definition for analysis.

Zinc Finger Protein Gene 57 (ZFP57)

The statistically significant DMR located on Chr6 (ZFP57 gene), consisting of 17 CpGs, differed between European American participants with severe periodontal disease and those with no/mild periodontal disease. The 17 CpGs perfectly overlapped with a 21-CpG ZFP57 region which was reported by Hernandez et al. as hypomethylated when comparing periodontitis patients with age-matched and sex-matched periodontally healthy controls living in the Republic of Colombia.17 Overlapping CpGs located in the ZFP57 DMR region were found to be associated with a lower risk of severe periodontal disease compared to no/mild periodontal disease (ARIC definition) in European American participants (Table 2). This association was stronger in European American participants 58 years or older (Table 3). It was also comparable for male and female participants and participants of higher or lower SES (Supplemental Table 3). In addition, there was a statistically significant decreased risk of moderate vs. no/mild periodontal disease among European American participants 58 years or older (Table 3). The ZFP57 CpGs were not significantly associated with risk of moderate or severe periodontal disease, as compared to no/mild periodontal disease in African American participants. However, increasing methylation of three ZFP57 CpGs (cg11383134, cg15570656, and cg13835168) was associated with a significant decreased risk of severe vs. no/mild periodontal disease in African American participants 58 years or older (Table 3). When we used the CDC-AAP definition to categorize periodontal disease status, increasing methylation of the ZFP57 CpGs was found to be associated with a significant decreased risk of mild/moderate vs. no periodontal disease in European American ARIC participants. The pattern of associations for those 58 years and older, male and female participants, participants of higher or lower SES, and for edentulism were all similar to what we found using the ARIC definition (Table 2 and Supplemental Table 3).

Table 2.

Odds ratio for CpGs in the ZFP57a differentially methylated region and using two definitions of periodontal disease severity in ARICb

Odds Ratio per 1 SD increase in methylation level (95% CI)
Periodontitis (CDC-AAP definition)c Periodontitis (ARIC definition)c
CpG mild/moderate vs. no severe vs. no edentulism vs. no moderate vs. no/mild severe vs. no/mild edentulism vs. no/mild
EA Participants (Visit 02)
cg07134666 0.69 (0.54, 0.88) 1.09 (0.72, 1.64) 1.00 (0.66, 1.50) 0.84 (0.66, 1.07) 0.66 (0.47, 0.92) 1.05 (0.71, 1.55)
cg11383134 0.65 (0.50, 0.84) 1.04 (0.66, 1.65) 1.06 (0.66, 1.72) 0.76 (0.60, 0.98) 0.65 (0.46, 0.91) 1.12 (0.71, 1.78)
cg22494932 0.62 (0.47, 0.82) 1.08 (0.65, 1.79) 1.04 (0.60, 1.79) 0.74 (0.58, 0.94) 0.69 (0.50, 0.96) 1.14 (0.68, 1.90)
cg15570656 0.70 (0.55, 0.89) 1.09 (0.72, 1.65) 1.01 (0.66, 1.54) 0.89 (0.70, 1.13) 0.67 (0.48, 0.94) 1.09 (0.72, 1.63)
cg16885113 0.68 (0.52, 0.87) 1.09 (0.71, 1.69) 1.09 (0.70, 1.70) 0.82 (0.64, 1.05) 0.66 (0.47, 0.92) 1.15 (0.75, 1.76)
cg20228636 0.70 (0.54, 0.90) 1.13 (0.74, 1.74) 1.11 (0.72, 1.72) 0.83 (0.65, 1.06) 0.67 (0.48, 0.94) 1.14 (0.75, 1.73)
cg25699073 0.65 (0.49, 0.86) 1.16 (0.69, 1.94) 1.05 (0.62, 1.79) 0.74 (0.58, 0.95) 0.69 (0.49, 0.96) 1.10 (0.67, 1.81)
cg25978138 0.70 (0.54, 0.91) 0.99 (0.62, 1.58) 1.19 (0.72, 1.96) 0.74 (0.58, 0.96) 0.63 (0.46, 0.88) 1.17 (0.73, 1.90)
cg13835168 0.79 (0.63, 1.00) 1.24 (0.83, 1.85) 1.10 (0.75, 1.63) 0.97 (0.77, 1.23) 0.70 (0.49, 0.98) 1.12 (0.77, 1.63)
AA Participants (Visit 02)
cg07134666 1.11 (0.93, 1.31) 1.00 (0.81, 1.23) 0.96 (0.81, 1.14) 1.10 (0.90, 1.33) 1.02 (0.84, 1.24) 0.96 (0.79, 1.17)
cg11383134 1.06 (0.89, 1.25) 0.98 (0.79, 1.20) 0.96 (0.81, 1.14) 1.04 (0.85, 1.26) 0.96 (0.79, 1.17) 0.93 (0.76, 1.14)
cg22494932 1.06 (0.88, 1.26) 0.95 (0.77, 1.17) 0.94 (0.79, 1.12) 1.07 (0.87, 1.30) 0.96 (0.79, 1.18) 0.93 (0.76, 1.14)
cg15570656 1.09 (0.92, 1.29) 0.97 (0.79, 1.19) 0.93 (0.78, 1.10) 1.09 (0.90, 1.32) 1.00 (0.82, 1.22) 0.93 (0.76, 1.13)
cg16885113 1.10 (0.92, 1.30) 0.98 (0.80, 1.21) 0.97 (0.82, 1.15) 1.08 (0.89, 1.32) 1.00 (0.82, 1.21) 0.96 (0.79, 1.17)
cg20228636 1.11 (0.94, 1.32) 1.00 (0.82, 1.23) 0.95 (0.81, 1.13) 1.09 (0.90, 1.32) 1.02 (0.84, 1.25) 0.95 (0.78, 1.15)
cg25699073 1.09 (0.92, 1.30) 1.01 (0.82, 1.24) 0.99 (0.83, 1.17) 1.08 (0.89, 1.31) 1.01 (0.83, 1.23) 0.98 (0.80, 1.19)
cg25978138 1.06 (0.88, 1.26) 1.01 (0.81, 1.25) 0.92 (0.77, 1.09) 1.09 (0.88, 1.33) 0.99 (0.81, 1.22) 0.91 (0.75, 1.12)
cg13835168 1.07 (0.90, 1.27) 1.00 (0.81, 1.23) 0.94 (0.79, 1.12) 1.07 (0.88, 1.30) 1.02 (0.84, 1.24) 0.94 (0.77, 1.15)
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a

A ZFP57 DMR identified by our DMRcate analysis and also reported in Hernandez et al.

b

two definitions of PD were used CDC-AAP and ARIC. See Methods.

c

Model adjusted for age, sex, diabetes, SES, pack-years, smoking status (never, former, current), pack-years based methylation score, BMI, surrogate variables for batch effects, and cell proportions.

NOTE: Bold OR and CI values indicate statistical significance.

Table 3.

Odds ratio for CpGs in the ZFP57a differentially methylated region and periodontitis severity stratified by age at blood draw at Visit 2, ARIC participants

Odds Ratio per 1 SD increase in methylation level (95% CI) for periodontitis (ARIC definition)
Age<58a Age>=58a
CpG moderate vs. no/mild severe vs. no/mild edentulism vs. no/mild moderate vs. no/mild severe vs. no/mild edentulism vs. no/mild
EA Participants (Visit 02)
cg07134666 1.01 (0.69, 1.46) 1.09 (0.60, 1.97) 0.84 (0.33, 2.10) 0.59 (0.41, 0.85) 0.36 (0.22, 0.60) 0.79 (0.48, 1.30)
cg11383134 0.87 (0.60, 1.26) 0.93 (0.52, 1.67) 1.03 (0.37, 2.89) 0.58 (0.39, 0.86) 0.36 (0.22, 0.61) 0.82 (0.46, 1.46)
cg22494932 0.80 (0.56, 1.16) 1.01 (0.56, 1.82) 1.10 (0.37, 3.27) 0.59 (0.40, 0.87) 0.40 (0.24, 0.66) 0.85 (0.46, 1.60)
cg15570656 1.00 (0.69, 1.45) 1.00 (0.56, 1.79) 0.84 (0.33, 2.12) 0.69 (0.48, 0.99) 0.39 (0.23, 0.65) 0.88 (0.52, 1.48)
cg16885113 0.95 (0.65, 1.37) 1.04 (0.58, 1.87) 1.01 (0.38, 2.70) 0.60 (0.41, 0.88) 0.36 (0.21, 0.59) 0.83 (0.48, 1.43)
cg20228636 0.94 (0.64, 1.36) 0.89 (0.49, 1.60) 0.90 (0.35, 2.34) 0.63 (0.44, 0.92) 0.42 (0.25, 0.70) 0.90 (0.53, 1.53)
cg25699073 0.75 (0.51, 1.09) 0.96 (0.53, 1.74) 1.17 (0.37, 3.70) 0.64 (0.44, 0.94) 0.42 (0.26, 0.70) 0.80 (0.44, 1.45)
cg25978138 0.86 (0.57, 1.30) 0.84 (0.44, 1.61) 1.10 (0.36, 3.40) 0.61 (0.42, 0.89) 0.46 (0.30, 0.72) 0.97 (0.54, 1.74)
cg13835168 1.27 (0.87, 1.85) 1.05 (0.58, 1.91) 1.01 (0.40, 2.54) 0.66 (0.46, 0.94) 0.42 (0.25, 0.70) 0.91 (0.56, 1.49)
AA Participants (Visit 02)
cg07134666 1.22 (0.97, 1.55) 1.18 (0.93, 1.50) 1.02 (0.80, 1.30) 0.81 (0.55, 1.21) 0.68 (0.46, 1.01) 0.68 (0.46, 1.00)
cg11383134 1.12 (0.89, 1.41) 1.10 (0.86, 1.40) 0.96 (0.75, 1.23) 0.83 (0.55, 1.25) 0.65 (0.44, 0.98) 0.70 (0.47, 1.04)
cg22494932 1.09 (0.86, 1.38) 1.06 (0.83, 1.35) 0.93 (0.73, 1.19) 1.00 (0.65, 1.53) 0.74 (0.49, 1.11) 0.77 (0.52, 1.15)
cg15570656 1.18 (0.94, 1.50) 1.16 (0.91, 1.48) 0.98 (0.76, 1.26) 0.85 (0.58, 1.26) 0.67 (0.45, 0.99) 0.68 (0.46, 0.99)
cg16885113 1.17 (0.93, 1.48) 1.13 (0.89, 1.43) 0.98 (0.77, 1.26) 0.87 (0.58, 1.29) 0.70 (0.47, 1.04) 0.73 (0.50, 1.07)
cg20228636 1.13 (0.90, 1.43) 1.15 (0.91, 1.47) 0.95 (0.74, 1.22) 0.93 (0.64, 1.36) 0.73 (0.50, 1.06) 0.75 (0.52, 1.09)
cg25699073 1.12 (0.89, 1.41) 1.13 (0.89, 1.44) 0.98 (0.77, 1.24) 0.94 (0.62, 1.42) 0.74 (0.49, 1.11) 0.79 (0.53, 1.18)
cg25978138 1.14 (0.89, 1.45) 1.05 (0.82, 1.35) 0.93 (0.73, 1.20) 0.98 (0.65, 1.47) 0.82 (0.55, 1.22) 0.74 (0.50, 1.08)
cg13835168 1.16 (0.92, 1.47) 1.19 (0.93, 1.52) 0.98 (0.76, 1.26) 0.85 (0.58, 1.23) 0.67 (0.46, 0.99) 0.72 (0.49, 1.03)
Open in a new tab
a

A ZFP57 DMR identified by our DMRcate analysis and also reported in Hernandez et al.

b

Model adjusted for age, sex, diabetes, SES, pack-years, smoking status (never, former, current), pack-years based methylation score, BMI, surrogate variables for batch effects, and cell proportions.

NOTE: Bold OR and CI values indicate statistical significance.

HOXA4

A HOXA4 DMR, consisting of 13 CpGs, was found to be hypermethylated in periodontitis patients with age-matched and sex-matched periodontally healthy controls.17 This finding is consistent with Hernandez et al. While the HOXA4 DMR was not statistically significant in the DMRcate analyses using both of our periodontitis definitions, when we tested the CpGs contained in the HOXA4 DMR individually, increasing methylation of cg11015251, cg14359292, cg07317062, and cg08657492 was statistically significantly associated with a higher risk of severe periodontal disease compared to no/mild periodontal disease in African American participants (Table 4). This association was comparable for African American participants 58 years or older and younger than 58-years-old, male and female participants, and participants of higher or lower life-course SES (Supplemental Table 4). In contrast, these HOXA4 CpGs were not significantly associated with risk of moderate or severe periodontal disease, as compared to no/mild periodontal disease, in European American participants (Table 4). In addition, increasing methylation of cg14359292 and cg24169822 was associated with decreased risk of edentulism vs. no periodontal disease among European American participants (Table 4; stronger association in females Supplemental Table 4). Using the CDC-AAP definition, we also found significant positive associations between increasing methylation of selected HOXA4 CpGs and periodontitis that were largely consistent with what we found using the ARIC definition.

Table 4.

Odds ratio for CpGs in the HOXA4a differentially methylated region and periodontitis severity

Odds Ratio per 1 SD increase in methylation level (95% CI)
Periodontitis (CDC-AAP definition)b Periodontitis (ARIC definition)b
CpG mild/moderate vs. no severe vs. no edentulism vs. no moderate vs. no/mild severe vs. no/mild edentulism vs. no/mild
EA Participants (Visit 02)
cg06942814 0.92 (0.70, 1.20) 1.03 (0.66, 1.61) 0.89 (0.60, 1.34) 1.03 (0.78, 1.35) 0.99 (0.66, 1.49) 0.91 (0.61, 1.35)
cg11015251 0.83 (0.61, 1.12) 0.70 (0.43, 1.13) 0.88 (0.55, 1.39) 0.97 (0.71, 1.31) 0.83 (0.55, 1.26) 0.94 (0.6, 1.45)
cg14359292 0.85 (0.65, 1.11) 0.91 (0.60, 1.39) 0.64 (0.42, 0.99) 1.00 (0.77, 1.30) 1.01 (0.68, 1.50) 0.70 (0.46, 1.05)
cg07317062 0.91 (0.71, 1.15) 1.10 (0.74, 1.64) 0.93 (0.64, 1.36) 1.05 (0.82, 1.33) 1.15 (0.81, 1.65) 0.99 (0.69, 1.44)
cg08657492 0.86 (0.66, 1.11) 0.94 (0.62, 1.42) 0.70 (0.47, 1.05) 1.03 (0.79, 1.33) 0.96 (0.66, 1.42) 0.75 (0.50, 1.12)
cg11410718 0.94 (0.74, 1.20) 1.00 (0.73, 1.66) 0.86 (0.58, 1.28) 1.05 (0.82, 1.35) 1.06 (0.74, 1.51) 0.89 (0.60, 1.30)
cg22997113 0.97 (0.75, 1.25) 1.11 (0.72, 1.71) 0.91 (0.61, 1.37) 1.10 (0.85, 1.43) 1.01 (0.68, 1.49) 0.92 (0.62, 1.37)
cg24169822 0.77 (0.59, 1.00) 0.83 (0.55, 1.25) 0.59 (0.39, 0.90) 1.03 (0.79, 1.33) 0.87 (0.59, 1.28) 0.66 (0.44, 1.01)
AA Participants (Visit 02)
cg06942814 1.20 (1.00, 1.44) 1.02 (0.81, 1.27) 0.94 (0.78, 1.13) 1.15 (0.93, 1.42) 1.18 (0.95, 1.46) 0.98 (0.79, 1.22)
cg11015251 1.18 (0.97, 1.43) 0.98 (0.78, 1.24) 0.93 (0.76, 1.13) 1.29 (1.03, 1.61) 1.28 (1.02, 1.61) 1.07 (0.85, 1.34)
cg14359292 1.18 (0.98, 1.41) 1.08 (0.87, 1.34) 0.98 (0.81, 1.17) 1.17 (0.95, 1.44) 1.24 (1.01, 1.54) 1.05 (0.85, 1.30)
cg07317062 1.17 (0.98, 1.40) 1.10 (0.88, 1.37) 0.98 (0.81, 1.17) 1.23 (1.00, 1.51) 1.30 (1.05, 1.61) 1.09 (0.88, 1.34)
cg08657492 1.25 (1.04, 1.49) 1.07 (0.85, 1.33) 0.99 (0.82, 1.19) 1.22 (0.99, 1.50) 1.25 (1.01, 1.55) 1.06 (0.86, 1.31)
cg11410718 1.15 (0.96, 1.38) 1.10 (0.88, 1.38) 0.95 (0.79, 1.15) 1.19 (0.96, 1.46) 1.20 (0.97, 1.49) 1.02 (0.82, 1.26)
cg22997113 1.14 (0.95, 1.36) 1.04 (0.83, 1.29) 0.93 (0.78, 1.11) 1.10 (0.90, 1.35) 1.13 (0.92, 1.39) 0.96 (0.78, 1.18)
cg24169822 1.20 (1.00, 1.44) 1.08 (0.86, 1.34) 0.99 (0.82, 1.19) 1.19 (0.96, 1.47) 1.23 (0.99, 1.52) 1.05 (0.85, 1.31)
Open in a new tab
a

A HOXA4 DMR reported as periodontitis-related in Hernandez et al.

b

Model adjusted for age, sex, diabetes, SES, pack-years, smoking status (never, former, current), pack-years based methylation score, BMI, surrogate variables for batch effects, and cell proportions.

NOTE: Bold OR and CI values indicate statistical significance.

ENSG00000231601

Using the ARIC periodontitis definition, our DMRcate analysis identified one statistically significant DMR located on Chr10 (ENSG0000023160 gene), consisting of three CpGs (cg22954052, cg25580864, and cg05495790), that differed between African American participants with edentulism and those with no/mild periodontal disease (Table 5). Among African American participants, increasing methylation of cg22954052 and cg05495790 was significantly associated with increased risks of moderate periodontal disease, severe periodontal disease, and edentulism, when compared to no/mild periodontal disease. These associations were stronger among African American participants under age 58 (Supplemental Table 5). cg22954052 and cg05495790 were associated with an increased risk of edentulism vs. no periodontal disease in African American participants. These CpGs were not associated with PD or edentulism in European American participants. When we used the CDC-AAP definition, increasing methylation of cg22954052 and cg05495790 was also significantly associated with increased risks of mild/moderate vs. no periodontal disease and edentulism vs. no periodontal disease, but not severe PD vs no PD, in African American participants.

Table 5.

Odds ratio for CpGs in the ENSG00000231601a differentially methylated region and periodontitis severity

Odds Ratio per 1 SD increase in methylation level (95% CI)
Periodontitis (CDC-AAP definition)b Periodontitis (ARIC definition)b
CpG mild/moderate vs. no severe vs. no edentulism vs. no moderate vs. no/mild severe vs. no/mild edentulism vs. no/mild
EA Participants (Visit 02)
cg22954052 1.11 (0.86, 1.43) 1.33 (0.87, 2.04) 0.88 (0.60, 1.29) 1.08 (0.84, 1.40) 1.06 (0.74, 1.51) 0.84 (0.58, 1.23)
cg25580864 1.10 (0.84, 1.43) 0.98 (0.64, 1.49) 1.01 (0.66, 1.53) 1.03 (0.78, 1.34) 1.17 (0.77, 1.76) 1.01 (0.67, 1.52)
cg05495790 1.12 (0.86, 1.44) 1.76 (1.16, 2.67) 0.85 (0.56, 1.31) 1.22 (0.94, 1.58) 1.21 (0.85, 1.72) 0.84 (0.56, 1.27)
AA Participants (Visit 02)
cg22954052 1.23 (1.02, 1.48) 1.18 (0.94, 1.49) 1.29 (1.07, 1.56) 1.54 (1.24, 1.91) 1.44 (1.16, 1.79) 1.56 (1.25, 1.94)
cg25580864 1.17 (0.96, 1.44) 1.11 (0.87, 1.43) 1.22 (0.99, 1.50) 1.16 (0.91, 1.46) 1.28 (1.01, 1.63) 1.30 (1.03, 1.65)
cg05495790 1.34 (1.10, 1.63) 1.22 (0.95, 1.56) 1.38 (1.13, 1.69) 1.43 (1.13, 1.80) 1.36 (1.08, 1.73) 1.53 (1.21, 1.94)
Open in a new tab
a

A de novo ENSG00000231601 DMR identified by our DMRcate analysis.

b

Model adjusted for age, sex, diabetes, SES, pack-years, smoking status (never, former, current), pack-years based methylation score, BMI, surrogate variables for batch effects, and cell proportions.

NOTE: Bold OR and CI values indicate statistical significance.

Discussion

To our knowledge, this is the first large-scale epigenome-wide DNA methylation study of clinically assessed periodontitis. Using epigenome-wide analyses in 1534 ARIC participants, we identified differential methylation patterns comparing various clinically assessed periodontal disease classifications, suggesting that epigenetic changes may contribute to variation in susceptibility to periodontitis. Our DMR analysis replicated findings from a previous study reporting an association between DNA methylation levels measured in whole blood at CpG sites in the Zinc Finger Protein Gene 57 (ZFP57) and periodontitis using two clinically assessed periodontal disease definitions. We also found DNA methylation differences near ENSG00000231601 (Chr10) between African American participants with edentulism and those with no or mild periodontal disease.

To account for the complexity of measuring periodontitis, our analysis used two periodontitis classifications: the ARIC definition and the CDC-AAP definition. As a chronic disease, periodontitis can present with a wide range of symptom severity and progression. We observed stronger associations using the ARIC definition for each of the identified regions. The ARIC definition for “severe” is based on having extensive periodontal disease (30% of teeth with attachment loss ≥3 mm), while the CDC-AAP definition for “severe” requires a greater degree of attachment loss (>6 mm) but fewer teeth. Our results suggest that the genetic regions identified may be more strongly linked to extensive periodontal disease.

Overall, we found comparable results across gender and socioeconomic status, but observed greater associations among participants 58 or older, likely due to a higher prevalence of extensive periodontal disease among older ARIC participants. Our analysis also revealed significant associations between DNA methylation patterns in the HOXA4 gene and an increased risk of severe periodontal disease in African American participants. These associations were consistent across various African American subgroups, including those stratified by age, sex, and SES. As a transcription factor, HOXA4 plays a crucial role in cell differentiation and embryonic development by regulating expression of various target genes.40,41 The observed associations are in line with previous studies that have linked DNA methylation in HOXA4 to other diseases, such as cancer and heart disease.42,43 While the specific role of HOXA4 in inflammatory diseases such as periodontitis and heart disease has not been fully elucidated, it is plausible that the gene could play a role in these conditions given its involvement in developmental processes and its ability to regulate the expression of various target genes.43,44 However, further studies are warranted to replicate our findings and investigate this periodontal connection. Differences we observed between African Americans and European Americans may be attributed to population-specific factors. Most African American participants are from the Jackson Field Center. Participants from this region experienced very different exposures during their lifetimes and have limited access to health and dental care (compared to other ARIC participants). These factors, which can influence periodontal disease development and progression, may have impacted findings in a manner similar to differences we observed with different disease classifications. Consequently, it is not possible to determine whether our results are due to genetic effects or correlates of socioeconomic factors.

Prior to the present study, there were only two published studies of genome-wide DNA methylation of peripheral blood leukocytes in periodontitis. Kurushima et al. (2019) conducted an epigenome-wide study using Illumina DNA methylation 450K data and a retrospective dental phenotype collection of self-reported dental mobility and gingival bleeding information obtained from 528 older female twins in the Twins UK cohort (mean age: 58 years old, age range: 19–82 years old; compared to African American ARIC participants mean age: 55 years old, age range: 47–70 years old, European American ARIC participants mean age: 58 years old, age range: 47–68 years old).16 This study detected a hypomethylated region in ZNF804A in individuals who experience gingival bleeding, in conjunction with an increased level of ornithine, a metabolite related to gingival inflammation, in blood metabolomics profile. There were no concordances between our results and the results of Kurushima et al., possibly due to clear differences in study design, including the use of clinically examined vs. self-reported periodontitis data. Hernandez et al. (2021) performed an analysis to identify differentially methylated positions and regions with a small number of clinically diagnosed periodontitis patients and gingivally healthy controls17. Results of Hernandez et al. identified ZNF718, HOXA4, and ZFP57 genes related to systemic immune-related epigenetic patterns in periodontitis17, including a ZFP57 region of 21 CpGs that was found to be hypomethylated when comparing periodontitis patients with age-matched and sex-matched periodontally healthy controls. While Hernandez et al. also carried out clinical examination, its study participants were much younger than ours (age range: 25–55 years old), suggesting a more extreme comparison of phenotype in Hernandez et al. Consistent with Hernandez et al., our study identified a ZFP57 DMR to be periodontitis-related. In addition, the 17-CpGs ZFP57 DMR we identified perfectly overlapped with the 21-CpG ZFP57 DMR identified by Hernandez and colleagues.

Existing evidence supports the relevance of some of the identified ZFP57 CpGs for periodontitis. ZFP57 is an imprint regulator, and DNA methylation in this exact ZFP57 region has been shown to be environmentally modulated in human blood and related to transient neonatal diabetes, Parkinson’s disease, and post-traumatic stress disorder.45–47 The Zinc Finger Proteins (ZNFs) are also important transcription factors. They are involved in tissue development, and their alterations may promote chronic conditions, including oral cancer and periodontitis.48 Recent studies have shown ZNFs playing a role in the progression of periodontitis.49 For instance, ZNFA20 can inhibit NF-κβ pathway activation via inflammatory cytokines49,50 and thus support the disruption of local inflammatory responses by P. gingivalis, the keystone pathogen of periodontitis.51,52 The replication of the ZNF57 DMR in our study suggests a potential sequence of events in periodontitis development, where hypomethylation of the ZNF57 DMR may upregulate the expression of ZNF57, which is related to antigen presentation and immune response regulation.

Observed DNA methylation levels could precede periodontal disease development or be the consequence of periodontal disease. We cannot establish whether the associations observed between differential methylation in whole blood and periodontitis are the cause or the consequence of the disease. While the ARIC study is prospective and the methylation data were obtained up to six years before the dental examination, a dental examination was not performed concurrently with the blood draw, such that we likely studied the combination of prevalent and incident periodontal disease. Moreover, we could not detect single CpG associations at the stringent epigenome-wide significance level, and the use of the 450K array, which covers only ~2% of human CpG sites, may limit the discovery. In subsequent CpG testing, the limited number of severe periodontitis cases in certain subgroups could have reduced the statistical power to detect significant associations, even though we included all available subjects with DNA methylation data. Additionally, the new findings were not replicated in independent samples. Finally, the use of edentulism as a surrogate for the most severe type of periodontitis may not be entirely accurate. Edentulism can result from a variety of causes, including periodontal disease, dental caries, and poor access to dental care. Therefore, unless patients have specifically reported losing their teeth due to periodontal disease, the assumption that edentulism is a direct result of severe periodontitis may not be entirely valid. Consequently, our analysis cannot distinguish between DNA methylation patterns of those who are edentulous due to periodontitis versus other causes.

Despite the limitations, several strengths of this study warrant attention, including the use of clinically examined periodontal phenotypes, large sample size, ethnic diversity, and rigorous analytical methods. We also adjusted for SES, diabetes status, imputed cell composition, self-reporting smoking history, and a methylation-predicted packyears smoked to further reduce concern about confounding. Additionally, we utilized two definitions of periodontal disease to address the complexity of disease classification. Analyses in individuals of different ancestries support the need for future large-scale EWAS of periodontitis to investigate ethnicity-specific and age-specific periodontitis-related CpGs.

Along with previously published EWAS, our results suggest that gene regulation mechanisms may influence the susceptibility to and severity of periodontitis. Our well-powered EWAS and DMRcate analysis in participants drawn from the general population identified several genomic regions for which differential DNA methylation sites were associated with periodontitis. This study supports leukocyte DNA methylation for the evaluation of epigenetic patterns in periodontitis. Our study, in conjunction with a prior pilot study, implicates ZFP57 as a promising candidate for future studies to illuminate the underlying gene regulatory mechanisms linking differential DNA methylation to periodontal disease.

Supplementary Material

f2
f1

CLINICAL RELEVANCE.

Scientific Rationale for Study:

Without altering the DNA sequence, epigenetic effects (e.g., DNA methylation changes) can alter gene activity and influence host response to periodontal infections. Our well-powered study investigates individual susceptibility to periodontitis by conducting a thorough assessment of periodontitis-related DNA methylation levels in blood.

Principal Findings:

We identified two gene regions, ZPF57 and HOXA4, that are differentially methylated in individuals with periodontitis compared to those without periodontitis.

Practical implications:

Studying differential leukocyte DNA methylation patterns may point to candidate regions and underlying gene regulatory mechanisms that play a key role in the progression and/or susceptibility to periodontitis.

Acknowledgments

The authors thank the staff and participants of the ARIC study for their important contributions. We thank the ARIC study for data sharing.

Funding

The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I,). The Dental ARIC Study was supported by the National Institute of Dental and Craniofacial Research (R01 DE11551). Additional support to complete the analysis was provided by the 2018 American Association for Cancer Research (AACR)-Johnson & Johnson Lung Cancer Innovation Science Award (MPIs: Michaud, Platz, and Kelsey). The content of this work is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was also supported by the Maryland Cigarette Restitution Fund at Johns Hopkins.

Footnotes

Ethics approval and consent to participate

This study reports analyses conducted solely using de-identified data from the ARIC study. The ARIC study protocol was approved by the Institutional Review Boards at each study site. The ARIC participants gave written informed consent.

Competing interests

Dr. Kelsey is a founder and scientific advisor for Cellintec, which had no role in this research.

Availability of data and materials

The data that support the findings of this study are available from the Atherosclerosis Risk in Communities (ARIC) Study. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from https://sites.cscc.unc.edu/aric/distribution-agreements with the permission of the Atherosclerosis Risk in Communities (ARIC) Study.

Reference:

  • 1.Eke P, Dye B, Wei L, Thornton-Evans G, Genco R. CDC Periodontal Disease Surveillance workgroup: James Beck (University of North Carolina, Chapel Hill, USA). Gordon Douglass (Past President, American Academy of Periodontology), Roy Page (University of Washin Prevalence of periodontitis in adults in the United States: 2009 and 2010 J Dent Res. 2012;91(10):914–920. [DOI] [PubMed] [Google Scholar]
  • 2.Eke PI, Dye BA, Wei L, et al. Update on prevalence of periodontitis in adults in the United States: NHANES 2009 to 2012. Journal of periodontology. 2015;86(5):611–622. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Eke PI, Borgnakke WS, Genco RJ. Recent epidemiologic trends in periodontitis in the USA. Periodontology 2000. 2020;82(1):257–267. [DOI] [PubMed] [Google Scholar]
  • 4.Slots J Periodontitis: facts, fallacies and the future. Periodontology 2000. 2017;75(1):7–23. [DOI] [PubMed] [Google Scholar]
  • 5.Petersen PE, Ogawa H. The global burden of periodontal disease: towards integration with chronic disease prevention and control. Periodontology 2000. 2012;60(1):15–39. [DOI] [PubMed] [Google Scholar]
  • 6.Laine ML, Jepsen S, Loos BG. Progress in the identification of genetic factors in periodontitis. Current Oral Health Reports. 2014;1(4):272–278. [Google Scholar]
  • 7.Divaris K, Monda KL, North KE, et al. Exploring the genetic basis of chronic periodontitis: a genome-wide association study. Human molecular genetics. 2013;22(11):2312–2324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Schaefer AS, Richter GM, Nothnagel M, et al. A genome-wide association study identifies GLT6D1 as a susceptibility locus for periodontitis. Human molecular genetics. 2010;19(3):553–562. [DOI] [PubMed] [Google Scholar]
  • 9.Munz M, Willenborg C, Richter GM, et al. A genome-wide association study identifies nucleotide variants at SIGLEC5 and DEFA1A3 as risk loci for periodontitis. Human molecular genetics. 2017;26(13):2577–2588. [DOI] [PubMed] [Google Scholar]
  • 10.Shungin D, Haworth S, Divaris K, et al. Genome-wide analysis of dental caries and periodontitis combining clinical and self-reported data. Nature Communications. 2019;10(1):2773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Do WL, Sun D, Meeks K, et al. Epigenome-wide meta-analysis of BMI in nine cohorts: Examining the utility of epigenetically predicted BMI. Am J Hum Genet. 2023;110(2):273–283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Hajishengallis G Periodontitis: from microbial immune subversion to systemic inflammation. Nature reviews immunology. 2015;15(1):30–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Schulz S, Immel UD, Just L, Schaller H-G, Gläser C, Reichert S. Epigenetic characteristics in inflammatory candidate genes in aggressive periodontitis. Human immunology. 2016;77(1):71–75. [DOI] [PubMed] [Google Scholar]
  • 14.de Faria Amormino SA, Arão TC, Saraiva AM, et al. Hypermethylation and low transcription of TLR2 gene in chronic periodontitis. Human Immunology. 2013;74(9):1231–1236. [DOI] [PubMed] [Google Scholar]
  • 15.Kim H, Momen‐Heravi F, Chen S, Hoffmann P, Kebschull M, Papapanou PN. Differential DNA methylation and mRNA transcription in gingival tissues in periodontal health and disease. Journal of Clinical Periodontology. 2021;48(9):1152–1164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Kurushima Y, Tsai P-C, Castillo-Fernandez J, et al. Epigenetic findings in periodontitis in UK twins: a cross-sectional study. Clinical epigenetics. 2019;11(1):1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Hernández HG, Hernández‐Castañeda AA, Pieschacón MP, Arboleda H. ZNF718, HOXA4, and ZFP57 are differentially methylated in periodontitis in comparison with periodontal health: epigenome‐wide DNA methylation pilot study. Journal of Periodontal Research. 2021;56(4):710–725. [DOI] [PubMed] [Google Scholar]
  • 18.Wright JD, Folsom AR, Coresh J, et al. The ARIC (atherosclerosis risk in communities) study: JACC focus seminar 3/8. Journal of the American College of Cardiology. 2021;77(23):2939–2959. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Jackson R, Chambless LE, Yang K, et al. Differences between respondents and nonrespondents in a multicenter community-based study vary by gender and ethnicity. Journal of clinical epidemiology. 1996;49(12):1441–1446. [DOI] [PubMed] [Google Scholar]
  • 20.Naorungroj S, Slade G, Divaris K, Heiss G, Offenbacher S, Beck J. Racial differences in periodontal disease and 10‐year self‐reported tooth loss among late middle‐aged and older adults: the dental Aric study. Journal of public health dentistry. 2017;77(4):372–382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Bose M, Wu C, Pankow JS, et al. Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study. BMC bioinformatics. 2014;15(1):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Navas-Acien A, Domingo-Relloso A, Subedi P, et al. Blood DNA Methylation and Incident Coronary Heart Disease: Evidence From the Strong Heart Study. JAMA Cardiology. 2021;6(11):1237–1246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Michaud DS, Lu J, Peacock-Villada AY, et al. Periodontal disease assessed using clinical dental measurements and cancer risk in the ARIC study. JNCI: Journal of the National Cancer Institute. 2018;110(8):843–854. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Beck JD, Eke P, Heiss G, et al. Periodontal disease and coronary heart disease: a reappraisal of the exposure. Circulation. 2005;112(1):19–24. [DOI] [PubMed] [Google Scholar]
  • 25.Beck JD, Elter JR, Heiss G, Couper D, Mauriello SM, Offenbacher S. Relationship of periodontal disease to carotid artery intima-media wall thickness: the atherosclerosis risk in communities (ARIC) study. Arteriosclerosis, thrombosis, and vascular biology. 2001;21(11):1816–1822. [DOI] [PubMed] [Google Scholar]
  • 26.Eke PI, Page RC, Wei L, Thornton‐Evans G, Genco RJ. Update of the case definitions for population‐based surveillance of periodontitis. Journal of periodontology. 2012;83(12):1449–1454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Papapanou PN, Sanz M, Buduneli N, et al. Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri‐Implant Diseases and Conditions. Journal of periodontology. 2018;89:S173–S182. [DOI] [PubMed] [Google Scholar]
  • 28.Lazăr L, Dakó T, Cozma A, Lazăr A-P. The influence of smoking on the periodontal biome. A review. Acta Stomatologica Marisiensis Journal.5(1):6–11. [Google Scholar]
  • 29.Martinon P, Fraticelli L, Giboreau A, Dussart C, Bourgeois D, Carrouel F. Nutrition as a key modifiable factor for periodontitis and main chronic diseases. Journal of Clinical Medicine. 2021;10(2):197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Spector AM, Postolache TT, Akram F, Scott AJ, Wadhawan A, Reynolds MA. Psychological stress: a predisposing and exacerbating factor in periodontitis. Current Oral Health Reports. 2020;7(3):208–215. [Google Scholar]
  • 31.Genco RJ, Borgnakke WS. Diabetes as a potential risk for periodontitis: association studies. Periodontology 2000. 2020;83(1):40–45. [DOI] [PubMed] [Google Scholar]
  • 32.Madiba TK, Bhayat A. Periodontal disease-risk factors and treatment options. South African Dental Journal. 2018;73(9):571–575. [Google Scholar]
  • 33.Salas LA, Zhang Z, Koestler DC, et al. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nature Communications. 2022;13(1):761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Plerou V, Gopikrishnan P, Rosenow B, Amaral LAN, Guhr T, Stanley HE. Random matrix approach to cross correlations in financial data. Physical Review E. 2002;65(6):066126. [DOI] [PubMed] [Google Scholar]
  • 35.Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics. 2010;11(10):733–739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lu J, Zaimi I, Barber JR, et al. SES and correlated factors do not explain the association between periodontal disease, edentulism, and cancer risk. Ann Epidemiol. 2019;38:35–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Joehanes R, Just AC, Marioni RE, et al. Epigenetic Signatures of Cigarette Smoking. Circ Cardiovasc Genet. 2016;9(5):436–447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Peters TJ, Buckley MJ, Statham AL, et al. De novo identification of differentially methylated regions in the human genome. Epigenetics & chromatin. 2015;8(1):1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological). 1995;57(1):289–300. [Google Scholar]
  • 40.Krumlauf R Hox genes in vertebrate development. Cell. 1994;78(2):191–201. [DOI] [PubMed] [Google Scholar]
  • 41.Akam M Hox genes: from master genes to micromanagers. Current Biology. 1998;8(19):R676–R678. [DOI] [PubMed] [Google Scholar]
  • 42.Elias MH, Azlan H, Sulong S, Baba AA, Ankathil R. Aberrant DNA methylation at HOXA4 and HOXA5 genes are associated with resistance to imatinib mesylate among chronic myeloid leukemia patients. Cancer Reports. 2018;1(2):e1111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Lillvis JH, Erdman R, Schworer CM, et al. Regional expression of HOXA4 along the aorta and its potential role in human abdominal aortic aneurysms. BMC physiology. 2011;11(1):1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Aonuma T, Moukette B, Kawaguchi S, et al. MiR-150 attenuates maladaptive cardiac remodeling mediated by long noncoding RNA MIAT and directly represses profibrotic Hoxa4. Circulation: Heart Failure. 2022;15(4):e008686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Bak M, Boonen SE, Dahl C, et al. Genome-wide DNA methylation analysis of transient neonatal diabetes type 1 patients with mutations in ZFP57. BMC medical genetics. 2016;17(1):1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Henderson AR, Wang Q, Meechoovet B, et al. DNA methylation and expression profiles of whole blood in parkinson’s disease. Frontiers in Genetics. 2021;12:640266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Vinkers CH, Geuze E, van Rooij SJ, et al. Successful treatment of post-traumatic stress disorder reverses DNA methylation marks. Molecular psychiatry. 2021;26(4):1264–1271. [DOI] [PubMed] [Google Scholar]
  • 48.Agnihotri R, Gaur S. The Role of Zinc Finger Proteins in Various Oral Conditions. The Scientific World Journal. 2022;2022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Hong JY, Bae WJ, Yi JK, Kim GT, Kim EC. Anti‐inflammatory and anti‐osteoclastogenic effects of zinc finger protein A 20 overexpression in human periodontal ligament cells. Journal of periodontal research. 2016;51(4):529–539. [DOI] [PubMed] [Google Scholar]
  • 50.Wertz IE, O’rourke KM, Zhou H, et al. De-ubiquitination and ubiquitin ligase domains of A20 downregulate NF-κB signalling. Nature. 2004;430(7000):694–699. [DOI] [PubMed] [Google Scholar]
  • 51.Ohshima J, Wang Q, Fitzsimonds ZR, et al. Streptococcus gordonii programs epithelial cells to resist ZEB2 induction by Porphyromonas gingivalis. Proceedings of the National Academy of Sciences. 2019;116(17):8544–8553. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Matsui S, Zhou L, Nakayama Y, et al. MiR-200b attenuates IL-6 production through IKKβ and ZEB1 in human gingival fibroblasts. Inflammation Research. 2018;67(11):965–973. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

f2
NIHMS1915883-supplement-f2.docx (16.7KB, docx)
f1
NIHMS1915883-supplement-f1.xlsx (127.9KB, xlsx)

Data Availability Statement

The data that support the findings of this study are available from the Atherosclerosis Risk in Communities (ARIC) Study. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from https://sites.cscc.unc.edu/aric/distribution-agreements with the permission of the Atherosclerosis Risk in Communities (ARIC) Study.

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