INTRODUCTION

Cardiovascular disease (CVD) is among the leading causes of mortality worldwide [1] and represents a major cause of death among older adults [2]. A variety of factors, including hypertension, smoking, and diabetes, influence the incidence of CVD [3]. Recent research has highlighted periodontal disease (PD) as a significant risk factor for CVD [4]. Poor oral health is associated with an elevated risk of CVD, along with higher mortality rates [5]. Furthermore, self-reported gum issues have been linked to an increased risk of death from ischemic heart disease and peripheral vascular disease [6]. Oral biofilms, a key factor in PD, have been implicated in systemic diseases, including CVD, through mechanisms involving systemic inflammation and immune responses [7]. These findings suggest a close connection between oral health and CVD, underscoring the importance of self-assessment of oral health status in evaluating CVD risk.

In older individuals with PD, the condition can promote atherosclerosis, thereby increasing the risk of CVD [4]. Interventions aimed at treating periodontitis have been shown to improve endothelial function, which is vital in the pathogenesis of CVD [8]. Additionally, PD can lead to reduced masticatory function and pain during chewing, which impedes adequate nutritional intake [9] and further heightens the risk of CVD and mortality [10,11]. Indeed, oral health in older adults significantly impacts overall health [12] and quality of life [13]. Poor oral health is associated with an increased risk of hospitalization from both infectious and non-communicable diseases [14]. Since oral infections can influence susceptibility to systemic diseases and represent key risk factors, timely oral health management interventions are essential [15].

However, limited research has been conducted on the early detection and intervention of oral health issues to prevent the progression to PD. Most of the existing literature has instead focused on the association between periodontitis and CVD. Sanz et al. [16] identified a bidirectional relationship between periodontitis and CVD, underscoring the need for integrated management of these conditions. Additionally, longitudinal research in this area is scarce. While several longitudinal studies have examined the relationship between PD and CVD, these have primarily explored the effects of periodontal treatment in reducing systemic inflammation and endothelial dysfunction; alternatively, they have investigated the association between diagnosed periodontitis and CVD outcomes. This gap in research underscores the need for studies examining the impact of early oral health indicators, such as measures of oral health-related quality of life (OHRQoL), on the risk of developing CVD. Therefore, we focused on OHRQoL indicators that emerge at the onset of periodontal issues, rather than on the diagnosis of periodontitis itself. In particular, we believed it essential to examine whether the Geriatric Oral Health Assessment Index (GOHAI), a tool used to quantify OHRQoL, can be used as a meaningful predictor of CVD.

While periodontal treatment can reduce endothelial dysfunction and systemic inflammation, evidence supporting its role in CVD prevention remains limited [17]. Comprehensive periodontal assessments are essential for identifying early oral health issues that may have systemic implications, including cardiovascular risk factors [18]. Thus, it is crucial to evaluate oral health using indicators before the onset of periodontitis, at the initial stages of discomfort, to facilitate early intervention. By identifying and addressing oral health problems early, we can mitigate the progression to periodontitis and its associated risks, potentially reducing the burden of CVD. This approach underscores the importance of preventive measures and timely interventions in oral health to improve overall cardiovascular outcomes.

In the present study, we employed panel data analysis to examine the association between oral health, as measured by GOHAI scores, and CVD in older adults. OHRQoL is increasingly recognized as a key outcome in evaluating treatments and health-related interventions [19]. The GOHAI is one of the most extensively researched tools for measuring OHRQoL, alongside the Oral Health Impact Profile (OHIP)-14 and OHIP-49 [20]. It is widely used to assess oral health, particularly in older adult populations. In a review of OHRQoL instruments, the GOHAI was the only tool with more internal validity studies than external ones [21], underscoring its reliability and validity.

Most OHRQoL questionnaires were developed in English-speaking countries [22], thus necessitating cultural adaptation beyond linguistic translation alone for their use in diverse cultural contexts [23]. This adaptation is vital for preserving the content validity of the instrument. Among studies examining the GOHAI, a high proportion (34.6%) have engaged in cross-cultural validation [21], indicating its effectiveness for use with older adult populations from various cultural backgrounds. Consequently, the GOHAI was selected for this study to evaluate OHRQoL among the older adult population.

In this study, we evaluated the predictive value of GOHAI scores for CVD risk; their comparative utility alongside established risk factors such as smoking, hypertension, and diabetes; and their potential role in informing public health strategies for CVD prevention and management. By highlighting the importance of early oral health indicators, this research aims to support integrated approaches to reducing the burden of CVD in aging populations.

METHODS

Data

The Korean Longitudinal Study of Aging (KLoSA), initiated in 2006, established a nationally representative panel of 10 254 individuals aged 45 years and older from across Korea. The sampling framework employed a multistage, stratified, probability-proportional-to-size method that accounted for the regional distribution and housing characteristics. Data are collected biennially through structured computer-assisted personal interviews performed by trained interviewers. The survey gathers extensive information on health, socioeconomic status, and aging, providing a robust dataset for longitudinal public health research.

In 2018, the KLoSA introduced a new oral health questionnaire, which incorporated the GOHAI, to evaluate OHRQoL. The present study utilizes data from the seventh (2018) and eighth (2020) waves of the KLoSA, focusing on participants who completed the survey in both years (n=5413). Of these individuals, 824 respondents in 2018 and 919 in 2020 self-reported a diagnosis of CVD.

Variables

Dependent variables (CVD)

The dependent variable in this study is the diagnosis of CVD, which encompasses both CVD (or heart disease) and cerebrovascular disease, such as stroke and transient ischemic attack (TIA). Regarding cerebrovascular disease, 3 options were available: “yes,” “suspected stroke or TIA,” and “no.” In this study, responses of “yes” and “suspected stroke or TIA” were combined into a single category labeled “diagnosed with cerebrovascular disease.” This decision was based on the clinical significance of TIA as a precursor to stroke.

Accordingly, participants were classified as either “diagnosed with CVD” if they reported a diagnosis of heart disease or cerebrovascular disease (including suspected stroke or TIA) or “not diagnosed with CVD” if they did not report either condition. To maintain consistency in variable coding, missing or ambiguous responses were excluded from the analysis.

Independent variables (GOHAI score)

The GOHAI was used in this study to evaluate OHRQoL among older adults. Based on receiver operating characteristic analysis, the Youden index was maximized at a threshold of 38.5. However, the sensitivity and specificity at a cutoff of 39.5 were clinically acceptable and did not differ significantly from those at 38.5. Therefore, a cutoff of 39.5 was adopted, with scores below 40 categorized as “poor” and scores of 40 or above as “not poor.” This decision was made to maintain consistency with prior research and to improve clinical applicability, in line with studies that use 40 as a reference point for categorizing oral health status [24–26].

Control variables

The control variables included in the analysis were age group, sex, smoking status, household status, hypertension, diabetes, obesity, and alcohol consumption. These variables were selected due to their established associations with CVD risk, as evidenced by prior epidemiological and clinical research [27]. Household status was divided into “single-person households” for individuals living alone and “non–single-person households” for those residing with family members or non-relatives, enabling an examination of potential differences in CVD risk based on living arrangement. Regarding smoking status, each participant was classified as a “non-smoker,” “former smoker,” or “current smoker” based on self-reported lifetime smoking and current smoking behavior. Similarly, alcohol consumption included categories of “non-drinker,” “former drinker,” and “current drinker,” reflecting self-reported lifetime alcohol use and recent drinking patterns. Obesity was assessed using body mass index (BMI) categories as defined by the KLoSA: underweight (BMI <18.5 kg/m²), normal weight (BMI 18.5 to 22.9 kg/m²), overweight (BMI 23.0 to 24.9 kg/m²), obesity (BMI 25.0 to 29.9 kg/m²), and severe obesity (BMI ≥30.0 kg/m²). Hypertension and diabetes were identified through self-reports of physician diagnoses or ongoing treatment for these conditions.

To ensure that multicollinearity did not pose a significant issue among the variables included in the model, correlation analysis and variance inflation factor (VIF) tests were conducted. All variables had correlation coefficients below the commonly accepted threshold of 0.8 and VIF values within the acceptable range (≤10), confirming the absence of multicollinearity.

Statistical Analysis

In this study, we utilized panel data to examine the relationship between GOHAI scores and the prevalence of CVD. Analyses were performed using Stata version 17 (StataCorp., College Station, TX, USA). The general characteristics of the study participants were summarized as unweighted counts and percentages for categorical variables. Chi-square tests were employed to compare differences in characteristics by CVD status (Table 1).

To examine the impact of GOHAI scores on the prevalence of CVD in 2018 and 2020, we utilized a generalized estimating equation (GEE) model with a binary logistic link function. The GEE method was selected for its capacity to account for the correlated nature of repeated measures within individuals by employing an exchangeable correlation structure, yielding robust standard errors for population-averaged estimates. The results are presented in Table 2. By accommodating the within-subject correlation, the GEE approach facilitates the analysis of longitudinal data, thus enabling the derivation of population-averaged estimates.

The GEE model incorporated adjustments for potential confounders, including sex, smoking status, household status, hypertension, diabetes, obesity, and alcohol consumption. All variables except for sex were treated as time-varying covariates to account for possible changes in participants’ health and lifestyle over time. Sex was considered a time-invariant variable, as it was determined from baseline characteristics.

Additionally, we conducted stratified analyses by sex to explore potential variations in the relationship between GOHAI scores and CVD prevalence. These analyses were adjusted for sex-specific factors, with certain variables (such as household status) included for female participants only due to data availability. The results of these analyses are summarized in Table 3.

Ethics Statement

To ensure participant privacy, this study used de-identified secondary data from the KLoSA, as approved by the Bioethics Committee (approval No. 336002, Statistics Korea). The dataset was meticulously reviewed to ensure that no individual could be directly or indirectly identified.

RESULTS

This study examined the association of self-reported oral health, as measured by GOHAI scores (the independent variable), with the prevalence of CVD (the dependent variable) among older adults between 2018 and 2020. After adjusting for age, sex, and other confounding factors, poor GOHAI scores (<40) were associated with higher odds of CVD, with an especially pronounced effect in female. These results indicate possible sex-specific variations in the influence of systemic inflammation and health behaviors on CVD risk.

Table 1 presents the baseline characteristics of the study population in 2018 and 2020. Over this timeframe, the proportion of participants with poor GOHAI scores increased significantly, rising from 51.2% to 70.3% (p<0.001). This coincided with an increase in the prevalence of CVD, from 15.2% to 17.0%. The aging of the cohort, in which the percentage of participants older than 75 years grew from 31.0% to 36.2%, likely contributed to the decline in oral health and its implications for systemic health. Furthermore, socioeconomic shifts and disruptions in healthcare systems during the coronavirus disease 2019 (COVID-19) pandemic may have accelerated this trend by restricting access to routine dental care and altering daily health behaviors.

Table 2 summarizes the results of the multivariate analysis evaluating the relationship between GOHAI scores and the prevalence of CVD. Participants with poor GOHAI scores (<40) were found to face significantly higher odds of CVD (odds ratio [OR], 1.13; 95% confidence interval [CI], 1.07 to 1.19; p<0.001), even after adjusting for age, sex, smoking status, and other covariates. Regarding age group, individuals aged 75 and older exhibited 1.52 times the odds of CVD compared to those aged 55 to 64 (p<0.001). These findings support the conclusion that poor GOHAI scores are indicative of compromised oral health and can serve as a meaningful predictor of cardiovascular risk in older adults.

Table 3 presents a stratified analysis by sex, revealing notable differences in the association between poor GOHAI scores and CVD. In males, poor GOHAI scores were significantly associated with higher odds of CVD (OR, 1.12; 95% CI, 1.03 to 1.22; p=0.008). The same association was even stronger in females (OR, 1.36; 95% CI, 1.06 to 1.22; p=0.001). These findings suggest that sex may modify the pathway linking oral health and systemic diseases, potentially reflecting underlying biological factors such as more pronounced inflammatory responses in females or variations in lipid metabolism.

DISCUSSION

In this study, poor GOHAI scores (<40) were significantly associated with higher odds of CVD, even after adjusting for confounding factors including age, sex, and smoking status. This association was stronger among male and female.

The results highlight the potential of employing self-reported oral health indices as accessible and cost-effective tools in public health strategies for the early identification of CVD risk, especially in resource-limited settings. Considering the pronounced relationships among age, oral health, and CVD, additional studies are warranted to investigate the longitudinal interplay of these factors. This research should incorporate inflammatory biomarkers and clinical outcomes to deepen our understanding of the impact of oral health on cardiovascular aging. Such insights could inform the development of tailored interventions that emphasize oral health as a key component of healthy aging frameworks.

The strong association between age and CVD risk underscores the importance of addressing oral health within the broader context of aging [1,2]. This relationship suggests that self-reported oral health, as measured by the GOHAI, may be indicative of systemic inflammation and other chronic disease pathways that accelerate with age [11]. Moreover, the cumulative burden of poor oral health in older adults likely interacts with other age-related vulnerabilities, including weakened immunity and heightened exposure to risk factors such as hypertension or diabetes.

Previous studies have established that poor oral health, particularly periodontitis, is associated with increased cardiovascular risk through systemic inflammation, mediated by markers such as high-sensitivity C-reactive protein and lipoprotein-associated phospholipase A2 (Lp-PLA2) [16,28]. Clinical evaluations, including periodontal probing depth and clinical attachment loss, have been commonly used to explore the link between periodontitis and CVD [29,30]. In contrast, the present study employed the self-reported GOHAI, which offers scalability for population-based research, to investigate its potential as a predictor of CVD risk. Self-reported tools, such as the Mini-Mental State Examination, have been successfully implemented for dementia screening in large-scale studies [31]. Similarly, the GOHAI offers an efficient and cost-effective method for identifying individuals at risk of CVD in resource-limited settings. While prior research has relied on professional dental assessments [32], our findings indicate that self-reported measures like the GOHAI could serve as a complement to these evaluations in large-scale public health interventions.

Systemic inflammation, particularly elevated levels of interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α), has been shown to exert a disproportionate impact on cardiovascular health in female [33,34]. This aligns with our findings, which indicate a stronger association between poor GOHAI scores and CVD risk in female participants. The longer life expectancy of females may contribute to this stronger association by increasing their cumulative exposure to poor oral health. Additionally, social determinants, such as higher rates of living alone among older females, may interact with biological vulnerabilities to exacerbate the impact of poor oral health on cardiovascular outcomes. While previous studies have focused on traditional risk factors like smoking and hypertension, few have explored sex-specific differences in oral health-related predictors of CVD [35]. Future research should target this gap by integrating longitudinal data and biomarker analyses to clarify how sex-specific factors influence these relationships. These findings highlight the need for sex-sensitive public health interventions to address oral health disparities and their systemic implications.

To fully grasp the impact of poor oral health on cardiovascular outcomes, the underlying mechanisms must be understood. Shared inflammatory pathways may partially explain the link between poor oral health and CVD. For instance, periodontitis induces chronic inflammation, which leads to the release of cytokines such as TNF-α and IL-6. These cytokines promote endothelial dysfunction and lipid accumulation in arterial walls [36]. IL-6 also affects lipid metabolism and coagulation, exacerbating atherogenesis and contributing to systemic inflammation [37]. Specifically, IL-6 influences platelet activation and fibrinogen levels, while TNF-α impacts the expression of plasminogen activator inhibitor in hepatocytes, endothelial cells, and adipose tissue, establishing a procoagulant state [38]. Oxidative stress and atherogenic dyslipidemia further amplify these processes, accelerating the progression of CVD [38]. Lp-PLA2, an independent risk factor for CVD, is also believed to contribute to the degradation of platelet-activating factor, linking periodontal inflammation and CVD outcomes [39].

This study has several limitations. First, the reliance on self-reported oral health data may have introduced measurement bias. This phenomenon is particularly applicable to aging populations, in which question comprehension and response consistency can vary due to the interview process and environment [40]. Second, the use of only 2 years of panel data limited our ability to observe long-term trends and the cumulative effects of behaviors like smoking and alcohol consumption on oral health. Third, although the GOHAI assesses OHRQoL, it does not capture clinical indicators, such as the severity of periodontitis, which could provide deeper insights into the connection between oral and systemic health. Lastly, the observed significant increase in poor GOHAI scores (<40) between 2018 and 2020 may be attributable to population aging and external factors, such as the COVID-19 pandemic, which disrupted access to dental care and daily oral health routines. These limitations underscore the need for future research with longer follow-up periods, integrated clinical indicators, and more robust survey methods to improve the reliability and applicability of the data.

Despite its limitations, this study suggests that self-reported oral health screening tools like the GOHAI may effectively identify individuals at elevated cardiovascular risk who might be overlooked by traditional screening methods. Research on the relationship between oral health and CVD often requires professional diagnoses, necessitating collaboration with dentists or dental hygienists, which can be resource-intensive [30]. In contrast, the GOHAI, a self-reported measure of OHRQoL, offers an efficient and cost-effective approach for large-scale public health applications, especially in settings with limited resources. Furthermore, self-reported discomfort related to oral health, as measured by the GOHAI, may serve as an early indicator of cardiovascular risk, prompting timely medical evaluations that include cardiovascular screening. These findings highlight the potential of the GOHAI to facilitate proactive public health interventions that connect oral and systemic health by identifying early warning signs through accessible self-reported tools.

Further research is necessary to validate these mechanisms by incorporating inflammatory biomarkers, such as TNF-α and IL-6, alongside clinical cardiovascular outcomes. Longitudinal studies that combine biomarkers and clinical assessments will aid in determining the reliability of the GOHAI in predicting cardiovascular outcomes and guiding effective interventions. Moreover, conducting stratified analyses by sex will help to uncover any differential impacts of oral health on cardiovascular risk, thereby contributing to more targeted and sex-sensitive public health strategies.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Author Contributions

Conceptualization: Lim KT. Data curation: Choe JW. Formal analysis: Lim KT. Funding acquisition: None. Methodology: Choe JW, Lim KT. Project administration: Hwang SS. Visualization: Choe JW. Writing – original draft: Lim KT. Writing – review & editing: Lim KT, Choe JW, Hwang SS.

Acknowledgements

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Figure & Data

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