Systemic inflammation at the oral–ocular interface: a 3P medicine perspective on the relationship between periodontitis and eye diseases

Abstract

Rationale and Purpose

Periodontitis is a common source of chronic systemic inflammation, driven by bacterial translocation and cytokine release through ulcerated gingival epithelium, and may contribute to the development of various chronic ocular diseases. This study investigated the association between periodontitis and multiple ocular conditions, interpreting the findings within the framework of Predictive, Preventive, and Personalized Medicine (3PM).

Working Hypothesis and Methods

We hypothesized that periodontitis, as a treatable inflammatory condition, serves as a predictive marker and modifiable risk factor for ocular diseases involving vascular and immune-mediated mechanisms. Data from 11,448 adults aged ≥ 40 years from the Korea National Health and Nutrition Examination Survey (2008–2010) were analyzed. Periodontal status was classified using the Community Periodontal Index (CPI): no periodontitis (CPI ≤ 2), moderate (CPI = 3), and severe (CPI = 4). Ophthalmologists assessed ocular diseases, including cataract, pterygium, diabetic retinopathy, glaucoma suspect, blepharoptosis, and age-related macular degeneration (AMD), using standardized diagnostic protocols. Multivariable logistic regression adjusted for key covariates, and Pearson correlation analysis was conducted.

Results

Periodontitis was independently associated with higher risks of cataract (adjusted OR = 1.26), diabetic retinopathy (OR = 1.66), pterygium (OR = 1.22), glaucoma suspect (OR = 1.10), and blepharoptosis (OR = 1.16). These associations were more pronounced in individuals with severe periodontitis. No significant association was observed with early or late AMD. CPI scores showed weak but significant positive correlations with several ocular conditions, particularly cataract and diabetic retinopathy.

Conclusions and Expert Recommendations in the Framework of 3PM

Periodontitis may serve as a predictive biomarker for ocular diseases with shared inflammatory and vascular pathways. Early identification and management of periodontal disease offer a targeted preventive strategy to reduce systemic inflammatory burden and ocular comorbidities. Personalized care models incorporating periodontal status into ocular screening protocols may improve diagnostic precision and enable risk-adapted interventions. These findings support the integration of oral health into multidisciplinary care frameworks, advancing the paradigm shift from reactive to predictive and personalized ophthalmology.

Introduction

Periodontitis is a chronic inflammatory disease of the gum and supporting tissues, initiated by bacterial biofilm dysbiosis and sustained by an exaggerated host immune response [1]. Although it primarily affects the oral cavity, periodontitis is now recognized as a source of systemic low-grade inflammation and oxidative stress [2]. It has been linked to a variety of systemic conditions, including cardiovascular disease, type 2 diabetes, rheumatoid arthritis, and adverse pregnancy outcomes [3]. This systemic involvement is believed to result from the translocation of bacterial components such as lipopolysaccharides and the release of pro-inflammatory cytokines like interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) into the bloodstream through ulcerated gingival epithelium [4]. Periodontitis is highly prevalent, affecting approximately 40 to 50 percent of adults worldwide, with moderate to severe forms present in about 10 to 15 percent of the population [5]. Recently, there has been growing interest in the relationship between periodontal disease and ocular health [6]. Clinical observations have reported that patients with periodontal inflammation may also present with retinal inflammation or intraocular conditions such as uveitis [7]. These findings have led to increasing research into the possible role of chronic periodontal inflammation in the development or progression of eye diseases through immune, vascular, and inflammatory mechanisms.

Understanding the potential connection between oral and ocular health is especially important in the context of aging societies, where the burden of both periodontal disease and chronic eye conditions is rapidly increasing [8]. As life expectancy rises, more individuals are living with age-associated diseases that share common inflammatory and vascular pathways. Periodontitis and major ocular diseases such as cataract, glaucoma, diabetic retinopathy (DR), and age-related macular degeneration (AMD) are among the leading causes of disability and diminished quality of life in older adults [9, 10]. Because both the oral cavity and the eye are highly vascularized and immunologically responsive, systemic inflammation originating from the periodontium may contribute to the onset or progression of ocular pathology [11, 12]. Identifying oral health as a modifiable risk factor offers an opportunity for early intervention and integrated care. Investigating the oral–ocular axis may lead to novel strategies for preserving vision, reducing systemic inflammatory burden, and improving overall health outcomes in aging populations [13].

Despite increasing evidence linking periodontitis to various systemic diseases, including ocular conditions, the causal mechanisms underlying these associations remain insufficiently defined [14]. Most existing studies are cross-sectional or retrospective in nature, limiting the ability to establish temporal or mechanistic relationships. Furthermore, the complex interplay between microbial factors, host immune responses, and systemic inflammation is rarely investigated in a multidisciplinary or integrative manner [15]. There is a pressing need for predictive frameworks that incorporate clinical, immunological, and imaging data to identify individuals at risk of developing inflammation-mediated ocular diseases secondary to chronic periodontal inflammation. Within the context of predictive, preventive, and personalized medicine (3PM), such frameworks should aim to stratify risk, enable early detection, and guide individualized intervention strategies [16, 17]. Advancing research in this direction will not only help clarify causality but also support the development of comprehensive models of care that integrate dental and ophthalmic health in routine preventive medicine [18].

To address this gap, the present study analyzes data from the Korea National Health and Nutrition Examination Survey (KNHANES), a large-scale and nationally representative dataset that includes standardized assessments of both oral and ophthalmologic health [19]. This approach enables the evaluation of associations between periodontitis, categorized by severity, and various ocular diseases while accounting for major confounding factors. Furthermore, the study incorporates current biological and clinical evidence to enhance understanding of the systemic mechanisms that may link oral inflammation to ocular pathology.

Oral–ocular axis and the need for 3PM integration

Emerging insights from systems medicine highlight that localized inflammation in the oral cavity can exert widespread effects through systemic immune and metabolic pathways [20, 21]. The interplay between oral microbiota, host immune responses, and metabolic alterations can chronically influence distant organs, including the eye [22]. The oral–ocular axis exemplifies this mechanism, as chronic periodontitis contributes to microvascular dysfunction and immune dysregulation implicated in ocular disease development. These findings support the adoption of predictive models that incorporate periodontal health as an early indicator of ophthalmic risk. Importantly, these effects are often potentiated by systemic metabolic conditions such as diabetes mellitus, hypertension, and dyslipidemia, which exacerbate both periodontal inflammation and ocular tissue vulnerability [23, 24]. Targeted prevention through periodontal management may reduce systemic inflammatory burden, while personalized ophthalmic screening guided by oral health status can enhance early detection and treatment precision. Recognizing and integrating these connections is essential for transitioning from fragmented, symptom-based care to a holistic medical framework grounded in 3PM [25].

Working hypothesis in the framework of 3PM

This study is grounded in the hypothesis that periodontitis, as a chronic source of systemic inflammation, acts as a predictive biomarker for ocular diseases involving vascular or immune-mediated mechanisms [26]. We propose that the presence and severity of periodontitis are significantly associated with an increased risk of developing conditions such as DR, cataract, glaucoma, blepharoptosis, and pterygium. Within the 3PM framework, we further hypothesize that oral health assessments can inform early identification (predictive), guide risk-based monitoring and interventions (preventive), and support individualized patient care plans (personalized), thereby contributing to integrated strategies for reducing ocular disease burden (Fig. 1).

Fig. 1.

Pathophysiological pathway linking periodontitis-induced systemic inflammation to ocular diseases. The left panel shows how periodontitis, triggered by microbial dysbiosis and epithelial disruption, leads to immune activation and sustained systemic inflammation. The right panel illustrates eye diseases potentially linked to this inflammatory state, including cataract, pterygium, glaucoma, diabetic retinopathy, and blepharoptosis. This highlights the systemic impact of periodontitis on ocular health

Methods

Data collection

This research utilized data from the KNHANES, a nationally representative, cross-sectional survey administered by the Korea Disease Control and Prevention Agency (KDCA). The dataset is publicly accessible for research purposes via the KNHANES website (https://knhanes.kdca.go.kr). Ethical approval for data collection was granted by the Institutional Review Board of the KDCA (approval numbers: 2008-01EXP-08-P, 2009-01CON-03-2C, and 2010-02CON-21-C). Participants were selected through a stratified multistage probability sampling method that accounted for variables such as age, sex, and region, ensuring national representativeness [27]. The survey comprises digitized health information, including demographic profiles, medical histories, laboratory test results, and findings from both oral and ophthalmic examinations. For this analysis, we extracted variables including age, sex, body mass index, and the presence of hypertension and diabetes mellitus.

Figure 2 depicts the flowchart of participant selection from the KNHANES 2008–2010 dataset. From a total of 29,235 individuals, 5,794 were excluded due to missing oral examination data, and an additional 3,883 were excluded for lacking ophthalmologic examination data. Among the remaining 19,558 participants, those under the age of 39 (n = 8,015) and those with other missing data (n = 95) were further excluded. The final study population consisted of 11,448 individuals aged 39 years or older with complete data for both oral and ophthalmologic assessments. Data from 2007 and earlier were excluded due to the absence of ophthalmologic examinations, while data from 2011 and later were excluded due to incomplete oral or ocular disease assessments. This rigorous selection ensured the inclusion of a well-defined cohort for analyzing the association between periodontitis and ocular diseases.

Fig. 2.

Selection of the study population from the Korea National Health and Nutrition Examination Survey (KNHANES) 2008–2010. The study included participants from the 2008 to 2010 KNHANES cycles, during which both periodontal and ophthalmologic examinations were performed

Oral examination

Periodontal status was assessed using the Community Periodontal Index (CPI), based on clinical examinations conducted by trained dentists in accordance with the World Health Organization (WHO) guidelines [28, 29]. The evaluation included probing for gingival bleeding using approximately 20 g of force with a WHO CPI probe, and identifying the presence of dental plaque and periodontal pockets. Examinations were performed across six sextants of the mouth (posterior right maxilla, anterior maxilla, posterior left maxilla, posterior right mandible, anterior mandible, and posterior left mandible), with the highest score recorded in each region. The CPI scoring system ranged from 0 to 4, with scores defined as follows: 0 indicated healthy periodontal tissue, 1 indicated bleeding on probing, 2 indicated the presence of supra- or subgingival calculus without deep pockets, 3 indicated shallow periodontal pockets (3.5–5.5 mm), and 4 indicated deep periodontal pockets (≥ 5.5 mm) [30]. In this study, periodontitis was defined as the presence of a CPI score of 3 or higher in at least one sextant, and severe periodontitis was defined as a CPI score of 4. Examiner calibration and training were implemented to reduce inter-examiner variability and ensure consistency in the assessment of periodontal pocket depth.

Ophthalmologic examinations

Standardized ophthalmologic assessments in the KNHANES were conducted according to established national protocols [27, 31]. Retinal fundus images were obtained for both eyes using a non-mydriatic digital fundus camera (TRC-NW6S, Topcon, Tokyo, Japan), centered on the macula and fovea. Trained ophthalmologists interpreted the images to determine the presence of DR, AMD, and glaucomatous features based on validated diagnostic criteria. DR was diagnosed if any characteristic lesion—such as microaneurysms, retinal hemorrhages, hard exudates, cotton wool spots, intraretinal microvascular abnormalities, venous beading, or neovascularization—was present, in accordance with the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. AMD classification followed the criteria of the international Age-Related Maculopathy Epidemiological Study Group. Glaucoma suspect was defined by the presence of at least one structural sign of glaucomatous damage, including neuroretinal rim thinning or notching, disc hemorrhage, vertical cup-to-disc ratio ≥ 0.7, or a retinal nerve fiber layer defect. Slit-lamp examinations were performed using the Haag-Streit BQ-900 (Haag-Streit AG, Koeniz, Switzerland) to evaluate cataracts and pterygium. Cataracts were graded using the Lens Opacities Classification System III (LOCS III), and eyes with pseudophakia or a history of cataract surgery were also categorized as having cataracts. Pterygium was defined clinically as a fibrovascular growth crossing the nasal or temporal limbus. Ptosis assessment was included, with blepharoptosis defined as a marginal reflex distance 1 (MRD1) of less than 2 mm in either eye. A participant was considered positive for a given ocular condition if at least one pathological finding was observed in either eye. The quality control and standardization of ophthalmologic evaluations were supervised by the Epidemiologic Survey Committee of the Korean Ophthalmologic Society (KOS), with regular training of examining personnel. All procedures were monitored and validated by the KDCA.

Statistical analysis

All analyses were conducted using the complex sampling weights provided by KNHANES to ensure nationally representative estimates. Categorical variables were compared across periodontitis groups using the chi-square test, and continuous variables were assessed using one-way analysis of variance (ANOVA). Multivariable logistic regression was employed to evaluate the associations between periodontitis and ocular conditions, including cataract, pterygium, DR, early and late AMD, glaucoma suspect, and blepharoptosis. Covariates included age, sex, body mass index, hypertension, and diabetes mellitus, selected based on established associations with both periodontal and ocular diseases in prior literature. Adjusted odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated. Additional analyses assessed the impact of periodontitis severity using CPI scores, examining dose–response trends. Pearson correlation coefficients were computed to assess the linear relationships between CPI and ocular outcomes. Subgroup analyses stratified by sex and age were performed to explore potential effect modifiers. A two-sided p-value < 0.05 was considered statistically significant.

Results

Demographics and baseline characteristics

A total of 11,448 participants were included in the analysis, comprising 6,897 individuals without periodontitis (CPI ≤ 2) and 4,551 individuals with periodontitis (CPI ≥ 3). The distribution of baseline demographic and clinical characteristics is presented in Table 1. The mean age distribution differed significantly between groups (p = 0.001), with a higher proportion of older individuals (≥ 65 years) in the periodontitis group. The proportion of males was significantly greater in the periodontitis group (51.5%) compared to the non-periodontitis group (37.8%) (p < 0.001). Body mass index also differed significantly (p < 0.001), with a lower percentage of participants with BMI < 22.9 kg/m² in the periodontitis group. Notably, the prevalence of diabetes mellitus and hypertension was significantly higher among participants with periodontitis (p < 0.001 for both). In terms of ocular diseases, cataract, pterygium, DR, glaucoma suspect, and blepharoptosis were all significantly more prevalent in the periodontitis group (all p < 0.01), whereas there were no significant differences observed for early or late age-related macular degeneration.

Table 1.

Comparison of demographic and clinical characteristics between subjects with and without periodontitis in the study population

Variables	Subjects without periodontitis* (N = 6897)	Subjects with periodontitis* (N = 4551)	P-value
Age (years)			0.001
40 to 64	4611 (66.9)	3054 (67.1)
65 to 74	1494 (21.7)	1111 (24.4)
More than 75	792 (11.5)	386 (8.5)
Sex			< 0.001
Female	4293 (62.2)	2208 (48.5)
Male	2604 (37.8)	2343 (51.5)
Body mass index (kg/m2)			< 0.001
Less than 22.9	2864 (41.5)	1689 (37.1)
23.0 to 24.9	1739 (25.2)	1195 (26.3)
More than 25.0	2294 (33.3)	1667 (36.6)
Diabetes Mellitus	619 (9.0)	565 (12.4)	< 0.001
Hypertension	1888 (27.4)	1429 (31.4)	< 0.001
Ocular diseases
Cataract	3164 (45.9)	2283 (50.2)	< 0.001
Pterygium	650 (9.4)	511 (11.2)	0.002
Diabetic retinopathy	82 (1.2)	93 (2.0)	< 0.001
Early AMD	412 (6.0)	263 (5.8)	0.665
Late AMD	36 (0.5)	25 (0.5)	0.844
Glaucoma suspect	1324 (19.2)	965 (21.2)	0.009
Blepharoptosis	998 (14.5)	749 (16.5)	0.004
Maximum CPI			< 0.001
0	2417 (35.0)	-
1	666 (9.7)	-
2	3814 (55.3)	-
3	-	3549 (78.0)
4	-	1002 (22.0)

AMD = age-related macular degeneration; CPI = Community Periodontal Index

^* Subjects with periodontitis were defined as those with a CPI ≥ 3, while subjects without periodontitis were defined as those with a CPI score ≤ 2

Associations between periodontitis and ocular diseases

Multivariable logistic regression analyses revealed that periodontitis was independently associated with increased odds of several ocular diseases, even after adjusting for age, sex, BMI, diabetes, and hypertension. For cataract, periodontitis was significantly associated with higher odds (OR: 1.26, 95% CI: 1.15–1.38, p < 0.001; Table 2). In addition, cataract was strongly associated with increasing age (OR for ≥ 75 years: 32.24), diabetes mellitus (OR: 2.13), and hypertension (OR: 1.63), while sex and BMI were not significant.

Table 2.

Weighted odds ratios from logistic regression estimating the association between periodontitis and cataract

Variables	Model 1*		Model 2†
	OR (95% CI)	P-value	OR (95% CI)	P-value
Age (years)
40 to 64	Reference	-	Reference	-
65 to 74	13.18 (11.74–14.80)	< 0.001	11.46 (10.19–12.89)	< 0.001
More than 75	37.47 (29.57–47.49)	< 0.001	32.24 (25.38–40.94)	< 0.001
Sex
Female	-	-	Reference
Male	-	-	0.94 (0.86–1.03)	0.943
Body mass index (kg/m2)
Less than 22.9	-	-	Reference	-
23.0 to 24.9	-	-	0.91 (0.81–1.02)	0.109
More than 25.0	-	-	0.93 (0.84–1.04)	0.225
Diabetes Mellitus	-	-	2.13 (1.82–2.50)	< 0.001
Hypertension	-	-	1.63 (1.47–1.82)	< 0.001
Periodontitis	1.31 (1.20–1.43)	< 0.001	1.26 (1.15–1.38)	< 0.001

OR = odds ratio; CI = confidence interval

^*Model 1: Adjusted for age only

^†Model 2: Adjusted for age, sex, body mass index, diabetes, and hypertension

In pterygium, periodontitis was associated with an OR of 1.22 (95% CI: 1.07–1.38, p = 0.002; Table 3). Other significant factors included older age (OR for ≥ 75 years: 3.74) and male sex (OR: 1.28), whereas BMI, DM, and HTN were not significant.

Table 3.

Weighted odds ratios from logistic regression estimating the association between periodontitis and pterygium

Variables	Model 1*		Model 2†
	OR (95% CI)	P-value	OR (95% CI)	P-value
Age (years)
40 to 64	Reference	–	Reference	–
65 to 74	2.49 (2.17–2.86)	< 0.001	2.56 (2.22–2.96)	< 0.001
More than 75	3.60 (3.05–4.26)	< 0.001	3.74 (3.14–4.46)	< 0.001
Sex
Female	-	-	Reference	–
Male	-	-	1.28 (1.13–1.45)	< 0.001
Body mass index (kg/m2)		-
Less than 22.9	-	-	Reference	–
23.0 to 24.9	-	-	0.91 (0.79–1.09)	0.378
More than 25.0	-	-	1.08 (0.93–1.25)	0.294
Diabetes Mellitus	-	-	0.84 (0.68–1.03)	0.085
Hypertension	-	-	0.98 (0.85–1.13)	0.748
Periodontitis	1.25 (1.10–1.41)	< 0.001	1.22 (1.07–1.38)	0.002

OR = odds ratio; CI = confidence interval

^*Model 1: Adjusted for age only

^†Model 2: Adjusted for age, sex, body mass index, diabetes, and hypertension

For DR, among participants with diabetes, periodontitis showed a significant association (OR: 1.66, 95% CI: 1.22–2.24, p = 0.001; Table 4). Hypertension was also significantly associated with DR (OR: 2.00), while age, sex, and BMI did not show significance in the fully adjusted model.

Table 4.

Weighted odds ratios from logistic regression estimating the association between periodontitis and diabetic retinopathy. Diabetes mellitus was excluded from the models because all participants with diabetic retinopathy had diabetes

Variables	Model 1*		Model 2†
	OR (95% CI)	P-value	OR (95% CI)	P-value
Age (years)
40 to 64	Reference	–	Reference	–
65 to 74	2.01 (1.45–2.78)	< 0.001	1.67 (1.19–2.33)	0.003
More than 75	1.48 (0.91–2.41)	0.115	1.18 (0.71–1.95)	0.529
Sex
Female	–	–	Reference	–
Male	–	–	1.04 (0.77–1.41)	0.791
Body mass index (kg/m2)
Less than 22.9	–	–	Reference	–
23.0 to 24.9	–	–	1.02 (0.70–1.51)	0.903
More than 25.0	–	–	1.00 (0.70–1.43)	0.978
Hypertension	–	–	2.00 (1.45–2.76)	< 0.001
Periodontitis	1.72 (1.27–2.32)	< 0.001	1.66 (1.22–2.24)	0.001

OR = odds ratio; CI = confidence interval

^*Model 1: Adjusted for age only

^†Model 2: Adjusted for age, sex, body mass index, and hypertension

In contrast, no significant association was observed between periodontitis and early AMD (OR: 0.97, p = 0.675; Table 5) or late AMD (OR: 1.02, p = 0.940; Table 6). For both AMD types, age remained the dominant predictor (ORs > 3.0 for older age groups). Additionally, male sex and absence of diabetes were associated with higher odds of late AMD, while higher BMI and hypertension were associated with lower or higher odds of early AMD, respectively.

Table 5.

Weighted odds ratios from logistic regression estimating the association between periodontitis and early age-related macular degeneration (AMD)

Variables	Model 1*		Model 2†
	OR (95% CI)	P-value	OR (95% CI)	P-value
Age (years)
40 to 64	Reference	–	Reference	–
65 to 74	3.61 (3.04–4.30)	< 0.001	3.46 (2.88–4.14)	< 0.001
More than 75	3.51 (2.81–4.38)	< 0.001	3.24 (2.57–4.08)	< 0.001
Sex
Female	–	–	Reference	–
Male	–	–	1.03 (0.88–1.21)	0.741
Body mass index (kg/m2)
Less than 22.9	–	–	Reference	–
23.0 to 24.9	–	–	0.90 (0.74–1.09)	0.284
More than 25.0	–	–	0.79 (0.65–0.95)	0.013
Diabetes Mellitus	–	–	0.94 (0.73–1.19)	0.590
Hypertension	–	–	1.20 (1.01–1.43)	0.038
Periodontitis	0.97 (0.82–1.14)	0.679	0.97 (0.82–1.14)	0.675

OR = odds ratio; CI = confidence interval

^*Model 1: Adjusted for age only

^†Model 2: Adjusted for age, sex, body mass index, diabetes, and hypertension

Table 6.

Weighted odds ratios from logistic regression estimating the association between periodontitis and late age-related macular degeneration (AMD)

Variables	Model 1*		Model 2†
	OR (95% CI)	P-value	OR (95% CI)	P-value
Age (years)
40 to 64	Reference	–	Reference	–
65 to 74	3.25 (1.77–5.96)	< 0.001	3.15 (1.68–5.90)	< 0.001
More than 75	6.31 (3.35–11.87)	< 0.001	5.92 (3.04–11.56)	< 0.001
Sex
Female	–	–	Reference	–
Male	–	–	2.14 (1.27–3.61)	0.004
Body mass index (kg/m2)
Less than 22.9	–	–	Reference	–
23.0 to 24.9	–	–	1.16 (0.63–2.13)	0.649
More than 25.0	–	–	0.81 (0.42–1.53)	0.511
Diabetes Mellitus	–	–	0.30 (0.09–0.97)	0.045
Hypertension	–	–	1.52 (0.88–2.61)	0.134
Periodontitis	1.10 (0.66–1.85)	0.706	1.02 (0.61–1.71)	0.944

OR = odds ratio; CI = confidence interval

^*Model 1: Adjusted for age only

^†Model 2: Adjusted for age, sex, body mass index, diabetes, and hypertension

In glaucoma suspect cases, periodontitis was significantly associated (OR: 1.10, 95% CI: 1.00–1.22, p = 0.044; Table 7). Other significant factors included male sex (OR: 1.34), diabetes (OR: 1.43), hypertension (OR: 1.25), and a lower BMI category (OR: 0.87 for BMI 23.0–24.9).

Table 7.

Weighted odds ratios from logistic regression estimating the association between periodontitis and glaucoma suspect (glaucomatous optic disc)

Variables	Model 1*		Model 2†
	OR (95% CI)	P-value	OR (95% CI)	P-value
Age (years)
40 to 64	Reference	–	Reference	–
65 to 74	1.66 (1.49–1.85)	< 0.001	1.51 (1.35–1.69)	< 0.001
More than 75	3.02 (2.64–3.45)	< 0.001	2.75 (2.39–3.16)	< 0.001
Sex
Female	–	–	Reference	–
Male	–	–	1.34 (1.22–1.48)	< 0.001
Body mass index (kg/m2)
Less than 22.9	–	–	Reference	–
23.0 to 24.9	–	–	0.87 (0.77–0.98)	0.024
More than 25.0	–	–	0.92 (0.82–1.03)	0.135
Diabetes Mellitus	–	–	1.43 (1.24–1.65)	< 0.001
Hypertension	–	–	1.25 (1.13–1.39)	< 0.001
Periodontitis	1.17 (1.06–1.28)	0.001	1.10 (1.00–1.22)	0.044

OR = odds ratio; CI = confidence interval

^*Model 1: Adjusted for age only

^†Model 2: Adjusted for age, sex, body mass index, diabetes, and hypertension

For blepharoptosis, individuals with periodontitis had a significantly increased risk (OR: 1.16, 95% CI: 1.04–1.30, p = 0.005; Table 8). The risk also increased with age (OR: 4.54 for ≥ 75 years), high BMI (OR: 1.29 for BMI > 25.0), diabetes (OR: 1.34), and hypertension (OR: 1.25), while sex did not show a significant effect.

Table 8.

Weighted odds ratios from logistic regression estimating the association between periodontitis and blepharoptosis

Variables	Model 1*		Model 2†
	OR (95% CI)	P-value	OR (95% CI)	P-value
Age (years)
40 to 64	Reference	-	Reference	-
65 to 74	3.16 (2.81–3.55)	< 0.001	2.91 (2.58–3.29)	< 0.001
More than 75	4.81 (4.16–5.56)	< 0.001	4.54 (3.91–5.29)	< 0.001
Sex
Female	–	–	Reference	–
Male	–	–	1.05 (0.94–1.17)	0.364
Body mass index (kg/m2)
Less than 22.9	–	–	Reference	-
23.0 to 24.9	–	–	1.12 (0.98–1.29)	0.084
More than 25.0	–	–	1.29 (1.13–1.46)	< 0.001
Diabetes Mellitus	–	–	1.34 (1.15–1.57)	< 0.001
Hypertension	–	–	1.25 (1.11–1.41)	< 0.001
Periodontitis	1.20 (1.08–1.34)	< 0.001	1.16 (1.04–1.30)	0.005

OR = odds ratio; CI = confidence interval

^*Model 1: Adjusted for age only

^†Model 2: Adjusted for age, sex, body mass index, diabetes, and hypertension

Association between severity of periodontitis and ocular diseases

The analysis revealed that increasing severity of periodontitis was associated with a higher risk of several ocular diseases (Fig. 3). Compared to participants without periodontitis (CPI ≤ 2), those with moderate periodontitis (CPI = 3) showed significantly increased odds of cataract (OR 1.209, 95% CI 1.094–1.335, p < 0.001), pterygium (OR 1.183, 95% CI 1.033–1.355, p = 0.015), and DR (OR 1.435, 95% CI 1.028–2.004, p = 0.034). These associations were stronger in individuals with severe periodontitis (CPI = 4), with adjusted odds ratios of 1.483 for cataract (p < 0.001), 1.353 for pterygium (p = 0.005), and 2.498 for DR (p < 0.001). Glaucoma suspects and blepharoptosis were also significantly associated with severe periodontitis. The odds ratio for glaucoma suspect in the severe group was 1.224 (95% CI 1.039–1.441, p = 0.016), and for blepharoptosis, it was 1.198 (95% CI 1.068–1.344, p = 0.002). In contrast, early and late age-related macular degeneration did not show significant associations with periodontitis at any severity level.

Fig. 3.

Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for associations between periodontitis severity and various ocular diseases. Bars represent the ORs for moderate periodontitis (CPI = 3, green) and severe periodontitis (CPI = 4, orange), compared to the reference group with no periodontitis (CPI ≤ 2). Error bars indicate 95% confidence intervals. Asterisks (*) denote statistical significance (p < 0.05)

The correlation-enhanced network (Fig. 4) showed that diseases with significant associations also had positive Pearson correlation coefficients with CPI, such as DR (r = 0.042, p < 0.001), cataract (r = 0.041, p < 0.001), pterygium (r = 0.030, p = 0.001), and glaucoma suspect (r = 0.028, p = 0.003). In contrast, blepharoptosis (r = 0.018, p = 0.058), early AMD (r = –0.005, p = 0.608), and late AMD (r = 0.001, p = 0.935) did not show significant correlations with CPI. These results were supported by the heatmap analysis, which visualized the strength and significance of these correlations. Collectively, these findings indicate a consistent trend where the risk of ocular diseases with inflammatory or vascular components increases with the severity of periodontitis, while conditions such as AMD or blepharoptosis may have different underlying pathophysiology less influenced by periodontal status.

Fig. 4.

Associations between periodontitis severity and ocular diseases. (A) Correlation-enhanced network centered on severe periodontitis (CPI = 4). Node size reflects adjusted odds ratios (OR); red nodes indicate statistically significant correlations with CPI (P-value < 0.05). Edges represent Pearson’s correlation coefficients. ORs for both moderate (CPI = 3) and severe periodontitis are shown for each disease. (B) Pearson correlation heatmap of CPI and ocular diseases. Asterisks (*) denote statistically significant correlations (P-value < 0.05)

Discussion

This study identified a consistent association between periodontitis and multiple ocular diseases, particularly those characterized by microvascular impairment and chronic inflammation. The strongest associations were observed with DR and cataract, suggesting that systemic inflammatory responses triggered by periodontitis may contribute to retinal vascular leakage and lens opacity. Severe periodontitis (CPI = 4) conferred greater risk than moderate periodontitis (CPI = 3), supporting a dose–response relationship and reinforcing the role of cumulative inflammatory burden. Glaucoma suspects and blepharoptosis also showed significant associations with periodontitis, implicating potential links to optic nerve susceptibility and periorbital tissue degeneration through inflammatory or vascular pathways. In contrast, early and late AMD did not show significant associations with periodontitis, even after adjusting for key covariates. This divergence may reflect the unique pathogenesis of AMD, which is more strongly associated with local complement activation and genetic susceptibility rather than systemic inflammation alone. These findings highlight the importance of differentiating ocular diseases based on their underlying mechanisms when evaluating systemic contributors such as periodontitis. Overall, the results suggest that periodontitis may serve as a surrogate marker of systemic vascular and inflammatory risk, particularly relevant for retinal and anterior segment disorders.

Based on our findings, effective management of periodontitis may be essential not only for preserving oral health but also for mitigating systemic inflammation and potentially reducing the risk of ocular diseases. As a modifiable inflammatory condition, periodontitis responds well to structured therapeutic interventions that target microbial dysbiosis, immune dysregulation, and tissue breakdown [32]. According to recent literature, comprehensive care involves both mechanical and adjunctive therapies. Mechanical debridement, including scaling and root planing, remains the cornerstone of treatment by physically removing subgingival biofilm and calculus. Adjunctive treatments such as locally delivered antimicrobials and systemic antibiotics may enhance outcomes in advanced cases, though their long-term use requires careful consideration. More recently, the integration of host modulation therapies, including anti-inflammatory agents and antioxidants, has shown promise in dampening the exaggerated immune responses characteristic of chronic periodontitis [33]. Specifically, targeting mitochondrial dysfunction, which is implicated in the pathogenesis of periodontitis, may provide novel therapeutic angles [34]. For instance, strategies that regulate oxidative stress and apoptosis pathways may improve periodontal outcomes in systemically compromised individuals [35]. Moreover, probiotic approaches and microbiome-modulating interventions are gaining interest for their ability to rebalance dysbiotic oral ecosystems. Emphasizing regular dental checkups, rigorous plaque control, tobacco cessation, and dietary modifications can prevent disease progression [36]. As such, periodontal care should be integrated into systemic disease management protocols to help lower the inflammatory burden and reduce ocular comorbidity risks [37].

The observed associations between periodontitis and several ocular diseases may reflect shared inflammatory and vascular mechanisms, particularly for DR and cataract, as illustrated in Fig. 5. Chronic periodontitis induces systemic inflammation characterized by elevated levels of TNF-α, IL-6, IL-1β, and CRP [38], which disrupt endothelial integrity and increase oxidative stress [26]. In diabetic retinopathy, these mediators promote pericyte apoptosis and endothelial dysfunction in the retinal microvasculature, resulting in blood-retinal barrier breakdown [13]. This cascade facilitates leukostasis, capillary leakage, and ischemia, which in turn stimulate vascular endothelial growth factor (VEGF) expression and pathologic neovascularization [39]. In parallel, periodontitis worsens glycemic control, further contributing to retinal damage [40]. In cataract formation, circulating cytokines penetrate the vascularized uveal tract and ciliary body, compromising the blood-ocular barrier. This induces oxidative stress, accumulation of glucose and advanced glycation end products (AGEs), and depletion of glutathione within the lens [41]. These changes lead to protein aggregation, lens epithelial apoptosis, and lens opacification [42]. In glaucoma, systemic inflammation and microbial translocation may impair optic nerve perfusion and promote neurodegenerative changes. The association with pterygium may be explained by systemic amplification of local conjunctival inflammation and remodeling. In blepharoptosis, chronic inflammatory burden may contribute to progressive myopathic or connective tissue degeneration. These findings support the hypothesis that periodontitis functions as a systemic contributor to both retinal and anterior segment ocular diseases.

Fig. 5.

Shared inflammatory and metabolic pathways linking periodontitis to diabetic retinopathy and cataract formation, amplified by systemic metabolic diseases. (A) Chronic periodontitis induces systemic inflammation, characterized by elevated pro-inflammatory mediators such as TNF-α, IL-6, CRP, and IL-1β. These cytokines promote endothelial dysfunction and oxidative stress in retinal microvasculature, leading to pericyte apoptosis, blood-retinal barrier breakdown, and leukostasis. The resulting ischemia and VEGF upregulation contribute to macular edema, fragile neovascularization, and ultimately, diabetic retinopathy. (B) Systemic inflammation also affects the anterior segment of the eye. Breakdown of the blood-ocular barrier and cytokine diffusion through the vascular uveal tract (connected to the ciliary body) induces chronic oxidative stress in the lens. This cascade leads to glucose accumulation, depletion of glutathione, aggregation of lens proteins, and apoptosis of lens epithelial cells, which are key pathophysiological processes in cataractogenesis. Representative clinical images of diabetic retinopathy and cataract are provided

Furthermore, the relationship between periodontitis and ocular disease is likely mediated and amplified by systemic metabolic comorbidities such as diabetes mellitus, hypertension, and dyslipidemia, which frequently coexist with both periodontal and ocular conditions [43, 44]. Rather than acting in isolation, periodontitis may contribute to ocular disease as part of a broader systemic inflammatory network. Systemic metabolic diseases are well recognized for their role in promoting endothelial dysfunction, oxidative stress, and immune dysregulation, all of which can influence both periodontal degradation and ocular tissue damage [45]. Additionally, there is a bidirectional association between periodontitis and these systemic diseases, wherein chronic periodontal inflammation exacerbates metabolic dysfunction, while metabolic disorders further amplify periodontal breakdown [23, 46]. This interplay suggests a syndromic mechanism in which periodontitis serves as both a marker and modulator of systemic inflammatory burden, thereby contributing to ocular complications through converging pathogenic pathways. These insights highlight the importance of multidisciplinary approaches that integrate oral health within the broader context of systemic and ocular disease prevention and management.

Our results align with and extend previous findings on the association between periodontitis and ocular diseases (Table 9). Prior large-scale cohort studies have reported a significant link between periodontitis and cataract formation, attributing this relationship to chronic systemic inflammation and oxidative stress [47, 48]. Our study supports this association, demonstrating a significantly increased risk of cataract in individuals with periodontitis. For diabetic retinopathy, cross-sectional analyses have consistently shown that periodontal inflammation exacerbates glycemic dysregulation and retinal microvascular damage [19, 49]. Our analysis supports this relationship, revealing a significant association between periodontitis and DR after adjusting for relevant confounding factors. These findings are further supported by previous systematic reviews and meta-analyses that highlight the link between periodontal disease and diabetic retinal complications [50, 51]. In contrast, while earlier reports suggested a possible relationship between periodontal disease and age-related macular degeneration [52, 53], particularly via microbial mechanisms or systemic inflammation [54], we observed no significant association in either early or late AMD subtypes. This discrepancy may reflect differing pathophysiologic mechanisms or study population characteristics. The observed association with glaucoma is consistent with epidemiological and microbiome studies suggesting that systemic inflammation and oral dysbiosis may contribute to optic nerve damage [55–57]. Notably, our study adds novel evidence supporting a modest but statistically significant link between periodontitis and blepharoptosis, a condition with no prior direct epidemiologic evaluation. Similarly, our findings suggest a potential association with pterygium, which has previously only been hypothesized through mechanistic pathways involving systemic inflammatory burden. While uveitis and dry eye disease were not assessed in our dataset, previous studies have reported weak but statistically significant associations with periodontitis [22, 58, 59], suggesting that systemic inflammatory mediators from periodontal disease may contribute to ocular surface or intraocular immune dysregulation. Together, these findings highlight the relevance of periodontal health in the broader context of ocular disease prevention and systemic inflammatory control.

Table 9.

Summary of associations between periodontitis and ocular diseases

Eye disease	Reported association	Supporting evidence	Proposed Mechanisms	Key References	Our Study Findings
Cataract	Positive	Large cohort and case–control studies	Systemic inflammation and oxidative stress promote lens opacity	Taiwan cohort [47]; Korean cohort [48]	Significantly increased risk (adjusted OR: 1.26, p < 0.001)
Pterygium	Unclear	No direct studies; theoretical link	Systemic inflammation may aggravate ocular surface inflammation	None (hypothesis based)	Increased risk observed (adjusted OR: 1.22, p = 0.002)
Diabetic retinopathy	Positive	Systematic review and cross-sectional studies	Periodontitis worsens glycemic control and promotes vascular damage	Systematic review [50, 51]; Cross-sectional studies [19, 49]	Strong association (adjusted OR: 1.66, p = 0.001)
Age-related macular degeneration (early/late)	Positive (weak)	Cross-sectional surveys; large cohort study	Systemic inflammation; P. gingivalis invasion of retinal pigment epithelium	NHANES [52]; Taiwan cohort [53]	No significant association (early p = 0.675; late p = 0.940)
Glaucoma (POAG)	Positive	Cohort and microbiome studies	Inflammatory cytokines and microbial dysbiosis damage optic nerve	Taiwan cohort [55]; Korean cohort [56]; microbiome study [57]	Mild but significant association (adjusted OR: 1.10, p = 0.044)
Blepharoptosis	Unclear	No direct studies; theoretical link	Chronic inflammation may weaken levator support tissues	None (hypothesis based)	Weak but significant association (adjusted OR: 1.16, p = 0.005)
Uveitis	Positive (very weak)	Large cohort studies	Blood-ocular barrier disruption by systemic cytokines	Taiwan cohort [22]	Not evaluated
Dry eye disease	Positive	Community surveys; clinical findings	Systemic inflammation affects tear film stability	Japan study [58]; case–control study [59]	Not evaluated

NHANES = National Health and Nutrition Examination Survey; OR = odds ratio; POAG = primary open-angle glaucoma

Figure 6 provides a structured illustration of how the findings of this study align with the principles of 3PM. At the center, periodontitis is depicted as a chronic source of systemic inflammation [2], connected by arrows to five associated ocular conditions: cataract, diabetic retinopathy, glaucoma, pterygium, and blepharoptosis. These links represent the observed associations in our analysis and reflect shared inflammatory and vascular pathways. Surrounding this core, the circular layout is divided into three colored segments that represent each component of the 3PM framework. The segment labeled predictive medicine highlights the role of periodontitis as a clinical biomarker of systemic inflammation. It predicts an increased risk of various ocular diseases, and this risk is amplified with greater periodontal severity. Periodontitis may serve as an early indicator for conditions affecting both the retina and anterior segment. This section emphasizes concepts such as biomarkers, inflammation, and periodontal severity [60]. The preventive medicine segment highlights the potential to reduce ocular disease burden through oral health interventions. Strategies include regular periodontal screening, improved hygiene practices, and early control of inflammation. These efforts support systemic disease prevention and align with public health goals. The personalized medicine segment focuses on individualized care. Risk stratification based on periodontal status, including CPI scoring, can guide tailored ophthalmic screening and monitoring for high-risk individuals. Interdisciplinary collaboration between dental and eye care professionals is essential to implement integrated care models. Key ideas in this domain include risk profiling and coordinated oral and ocular evaluations.

Fig. 6.

Summary of the study findings within the framework of Predictive, Preventive, and Personalized Medicine (3PM). Periodontitis serves as a clinical marker of systemic inflammation and a predictive risk factor for various ocular diseases, including cataract, diabetic retinopathy, glaucoma, blepharoptosis, and pterygium. The central diagram illustrates the identified associations, with arrows linking periodontitis to the affected ocular conditions. The surrounding 3PM framework highlights key implications: predictive value for ocular comorbidity, opportunities for preventive strategies via oral health interventions, and personalized approaches enabling risk stratification and interdisciplinary care

Despite the strengths of this large, population-based analysis, several limitations should be acknowledged. First, the cross-sectional design restricts causal inference; while associations between periodontitis and ocular diseases were identified, temporal relationships cannot be established. Prospective longitudinal studies are needed to confirm the directionality and progression of these associations. Second, the data are derived from a Korean national health survey, which may limit generalizability to other ethnic or geographic populations with differing oral health profiles, healthcare access, and genetic predispositions. Third, although multiple confounders such as age, sex, body mass index, diabetes, and hypertension were adjusted for, unmeasured variables—such as socioeconomic status, nutritional intake, sun exposure, or medication use—could still influence the observed associations. Notably, corticosteroid use is a known risk factor for both cataract and periodontal disease [61, 62], but was not directly assessed in this analysis. Finally, the diagnosis of ocular diseases was based on standardized survey assessments, which, while robust, may not capture subclinical disease or variations in clinical practice.

Expert recommendations

In alignment with the principles of 3PM, the findings of this study support the following recommendations, which contribute to a paradigm shift from reactive care toward proactive, integrative strategies in both dental and ophthalmic practice.

• Predictive Medical Approach: This study demonstrates that periodontal status, particularly the severity indicated by the CPI or other indicators [29], is significantly associated with several ocular diseases that share systemic inflammatory and vascular mechanisms, including DR, cataract, glaucoma suspect, and pterygium. As such, periodontitis may serve as a clinically accessible and modifiable predictive biomarker for ocular comorbidities. By integrating CPI scores and oral health data into ophthalmologic risk stratification tools, clinicians can more effectively identify individuals at heightened risk before clinical manifestations occur [2]. This adds new value by positioning periodontal health as a measurable parameter for early disease prediction in ophthalmology.

• Targeted Prevention: The results also suggest that managing periodontal inflammation may reduce systemic inflammatory burden and thereby mitigate the onset or progression of ocular diseases. Routine periodontal screening and early intervention should be prioritized, particularly in aging populations and patients with diabetes or other metabolic disorders [21, 26]. Public health strategies should emphasize the systemic implications of periodontitis and advocate for integrated screening protocols. This preventive approach not only addresses oral disease burden but also serves as a cost-effective strategy to delay or prevent the development of vision-threatening conditions, thereby going beyond current reactive ophthalmic care models.

• Personalized Treatment: Personalized care can be enhanced by using individual periodontal profiles to guide the frequency, timing, and type of ophthalmologic assessments. In patients with severe periodontitis or high CPI scores, clinicians may consider more intensive monitoring for retinal or anterior segment diseases. Furthermore, targeted interventions such as microbiome modulation, anti-inflammatory therapies, or adjunctive nutritional and metabolic support for periodontal disease may provide systemic benefits, including ocular protection. These approaches facilitate individualized, interdisciplinary treatment planning, thus exemplifying the application of personalized medicine in real-world settings.

• Added Value and Paradigm Shift Contribution: This manuscript advances the field by demonstrating, through a nationally representative cohort, that periodontitis is not only a dental concern but a systemic condition with clear predictive relevance for ocular disease. It highlights a novel, evidence-based opportunity to incorporate oral health metrics into ophthalmologic prevention and care. By doing so, it provides a practical framework for implementing 3PM in routine clinical settings and contributes directly to the paradigm shift from reactive, disease-based treatment to anticipatory, patient-centered, and interdisciplinary healthcare [17].

Conclusion and outlook in the framework of 3PM

This study provides epidemiological and mechanistic evidence that periodontitis is a significant systemic contributor to various ocular diseases, particularly those involving inflammatory and vascular pathways such as diabetic retinopathy, cataract, glaucoma suspect, blepharoptosis, and pterygium. The findings show the predictive role of periodontitis as a marker of chronic systemic inflammation and its potential utility in forecasting ocular comorbidities.

In the context of predictive medicine, periodontitis may serve as an early warning signal, helping to stratify patients at increased risk for vision-threatening conditions. Preventive strategies can be implemented through collaborative oral–ocular health programs, encouraging routine dental care and periodontal management to reduce systemic inflammatory burden. Personalized approaches are also warranted, where individualized ocular screening and monitoring protocols can be tailored for patients with chronic periodontal disease, particularly those with additional risk factors such as diabetes or hypertension. Future research should explore longitudinal trajectories, assess intervention effectiveness, and further develop risk models incorporating oral health metrics into ophthalmic screening frameworks. These interdisciplinary strategies represent a practical and clinically actionable pathway toward implementing 3PM in both dental and ophthalmological settings.

Author Contributions

Joon Yul Choi and Tae Keun Yoo are co-corresponding authors. Conceptualization & Methodology: EO, JHJ, TKY Data collection: EO, TKY Data processing: EO, JHJ, JYC Investigation & Visualization: JYC, TKY Supervision: TKY Writing—original draft: EO, TKY Writing—review & editing: EO, JHJ, TKY.

Data collection: EO, TKY.

Data processing: EO, JHJ, JYC.

Investigation & Visualization: JYC, TKY.

Supervision: TKY.

Writing—original draft: EO, TKY.

Writing—review & editing: EO, JHJ, TKY.

Funding

TKY is an advisory board member of MediWhale and has received consultant fees as part of the standard compensation package. The remaining authors declare no conflicts of interest.

Data availability

KNHANES is a nationwide, cross-sectional survey conducted by the Korea Disease Control and Prevention Agency (KDCA). The data are available to the public (https://knhanes.kdca.go.kr/) for research purposes.

Declarations

Ethics approval

KNHANES is a nationwide, cross-sectional survey conducted by the Korea Disease Control and Prevention Agency (KDCA). The study protocol was approved by the Institutional Review Board of the KDCA, and informed consent was obtained from all participants before the survey. The data are available to the public for research purposes. This study adhered to the Declaration of Helsinki.

Competing interests

TKY is an advisory board member of MediWhale and has received consultant fees as part of the standard compensation package. The remaining authors declare no conflicts of interest.

Consent to participate

Not applicable.