Jung-Eun Park

doi:10.5720/kjcn.2025.00325

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Research Article
Association between number of teeth and oxidative balance score in Korean adults: a population-based study: Jung-Eun Park^†; Korean Journal of Community Nutrition 2026;31(1):64-74.
DOI: https://doi.org/10.5720/kjcn.2025.00325
Published online: February 28, 2026

Professor, Department of Dental Hygiene, College of Health Science, Dankook University, Cheonan, Korea

†Corresponding author: Jung-Eun Park Department of Dental Hygiene, College of Health Science, Dankook University, 119 Dandae-ro, Dongnam-gu, Cheonan 31116, Korea Tel: +82-41-550-1494 Fax: +82-41-550-1494 Email: jepark@dankook.ac.kr

• Received: October 31, 2025 • Revised: December 5, 2025 • Accepted: January 28, 2026

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract
INTRODUCTION
METHODS
RESULTS
DISCUSSION
NOTES
REFERENCES

Abstract

Objectives
This study aimed to evaluate the relationship between oxidative balance score (OBS), a metric indicating an individual’s oxidative balance status, and the number of teeth in a sample of Korean adults.
Methods
This cross-sectional study included 13,199 adults aged 19 and older who participated in a health survey and oral examination. Subsequent to the adjustment for confounding factors, a logistic regression analysis was employed to evaluate the probability of a subject belonging to a number of teeth category based on OBS level.
Results
In the group with OBS level T2, the likelihood of having NT1 (0–10 teeth) was found to be significant adjusted for all variables (odds ratios: 1.51, 95% confidence intervals: 1.195–1.908). In the multinomial model, a significant association was observed for the NT1 category, whereas no significant association was found for the NT2 (11–20 teeth) category after adjustment.
Conclusion
In the group with OBS level T2, the likelihood of having NT1 (0–10 teeth) was found to be significant. As this study examines cross-sectional associations, the necessity of conducting longitudinal research as subsequent studies is evident to ascertain the existence of causality.
Keywords: oxidative balance score; oral health; Korea National Health and Nutrition Examination Survey; tooth loss

INTRODUCTION

The oxidative balance score (OBS) is a comprehensive indicator of an individual’s oxidative balance status, and it is evaluated based on various dietary and lifestyle factors [1-3]. An individual’s diet, physical activity, and health-related behaviors collectively influence their level of oxidative stress. Many studies have reported an association between systemic diseases and OBS, including dietary intake and lifestyle behaviors, in various conditions such as periodontitis [4, 5], hearing loss and tinnitus [3], gastrointestinal cancers [6], and cardiovascular diseases [7].

An examination of the effects on oral tissues during the oxidation process, when molecular oxygen is reduced to water, reveals the release of a large amount of free energy. This free energy has the potential to induce free radicals and reactive oxygen species (ROS). In this instance, oxidative stress, stemming from an imbalance between free radicals and ROS, is identified as a primary contributor to oral inflammatory diseases and dental caries [8]. In particular, related systematic literature reviews and meta-analysis studies have indicated a significant association between periodontitis and local oxidative stress. These results suggest that oxidative stress may be involved in the development and progression of chronic periodontitis [9]. Furthermore, oxidative stress has been reported to be involved in the onset and progression of diseases mediated by dental biofilm, such as dental caries [10].

Consequently, oxidative stress can serve as a risk factor for major oral diseases and potentially contribute to tooth loss, depending on the condition and prognosis of the oral disease.

Research on the relationship between OBS and periodontitis has been conducted both nationally and internationally [4, 5]. However, to the best of our knowledge, no study has yet demonstrated a relationship between number of teeth and OBS using large-scale national data. The number of remaining teeth is an objective indicator of the oral condition of an individual and a factor that can reflect the deterioration of oral health or cumulative exposure to systemic diseases over time [11, 12]. Therefore, determining the OBS of individuals, as well as the number of teeth, which is a cumulative oral indicator is essential.

This study aimed to evaluate the relationship between number of teeth and OBS among Korean adults using nationally representative data from the Korea National Health and Nutrition Examination Survey (KNHANES 7th, 2016–2018). We hypothesized that a correlation would exist between the number of teeth and OBS even after controlling for major confounding factors.

METHODS

Ethics statement

The first and second years (2016–2017) of the 7th KNHANES were exempted from review by the Institutional Review Board (IRB) of the Korea Disease Control and Prevention Agency, as they constitute research directly conducted by the state for public welfare under the Bioethics and Safety Act. The third year (2018) was subject to IRB review considering the collection of human-derived materials and the provision of raw data to third parties (IRB No. 2018-01-03-P-A).

1. Study design

This study was conducted as a cross-sectional study using data from the 7th KNHANES (2016–2018) [13] and, reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (https://www.strobe-statement.org/).

2. Study participants

The data used in this study were obtained from the 7th KNHANES, a nationally representative cross-sectional survey conducted between 2016 and 2018. KNHANES data are publicly available [13], and the study participants were selected using a complex sample design with proportional allocation and systematic sampling applied in stages. The requirement for informed consent was waived because the study utilized data were already accessible to the public. The oral examination data of the study participants collected during the health screening survey were utilized. The total number of individuals selected for the study was 13,199, out of a total sample of 16,489 individuals. The discrepancy in overall frequency of the research results was due to missing values.

3. Number of teeth

The standardized oral examination in the KNHANES was performed by dentists with extensive training. The methods used to ensure the reliability of the oral examination included training using dental models, education for calibration, web-based photo instruction, simulation of oral health examination with human participants, field instruction, and reproducing examinations. The number of remaining teeth was calculated by summing the number of teeth in the anterior region—comprising the central incisor, lateral incisor, and canine, each with four surfaces (buccal, distal, mesial, and lingual)—and the posterior region—comprising the first premolar, second premolar, first molar, and second molar, each with five surfaces (buccal, distal, occlusal, mesial, and lingual). Missing teeth and wisdom teeth were excluded from the calculation. The number of teeth in the study participants was categorized into three groups: NT1 (0–10 teeth), NT2 (11–20 teeth), and NT3 (21–28 teeth) [14].

4. Oxidative balance score

OBS components were categorized as follows based on previous research: five types of antioxidants (physical activity, n-3 fatty acids, vitamin C, retinol, β-carotene) and five types of pro-oxidants (obesity, alcohol, smoking, n-6 fatty acids, and polyunsaturated fatty acids [PUFAs]) [4, 5, 15, 16]. Dietary intake was assessed using the 24-hour recall method, wherein trained interviewers documented dietary information. Furthermore, data concerning lifestyle habits were collected via questionnaires completed by the study participants.

Physical activity scores were calculated using the metabolic equivalent of task (MET; MET-min/week). The scores for high-intensity physical activities, exercises, sports, and leisure activities (8.0 × min × day); moderate-intensity physical activities, exercises, sports, and leisure activities (4.0 × min × day); and walking (3.3 × min × day) were summed and categorized into tertiles [17]. The nutrient intake scores for the six nutrients were categorized into tertiles based on the minimum and maximum values. Alcohol consumption data were classified as < 1 drink per month (3), 1–4 drinks per month (2), or ≥ 2 drinks per week (1). Smoking data were classified as non-smoker (3), occasional smoker (2), or daily smokers (1). Body mass index (BMI) data were classified as normal weight (18.5–22.9 kg/m²) (3), overweight (23–24.9 kg/m²) (2), or obese (≥ 25 kg/m²) (1). Each antioxidant and pro-oxidant was scored from 1 to 3 points, ranging from the lowest tertile to the highest tertile, with 30 points being the maximum score. Higher scores indicated greater antioxidant effects. This study categorized the OBS into tertiles based on the lowest and highest scores.

5. Covariates

Covariates were reclassified as follows for the purpose of analysis. The demographic factors considered included gender, age, household income, and education. The covariates applied in this study were selected based on prior research as key factors associated with oral health and OBS levels [5].

Age was categorized into 19–29, 30–39, 40–49, 50–59, and ≥ 60 years. Household income was classified as < 25% (the lowest quartile group), 25%–49%, 50%–74%, and 75%–100% (the highest quartile group). It was subsequently categorized into upper, middle (upper-middle, lower-middle), and lower levels. Education was categorized as primary school or below, middle school, high school, and college or above.

The general health variables considered included hypertension, diabetes, and tooth brushing frequency per day. Hypertension was classified as hypertension, pre-hypertension, or normal [18]. Hypertension was defined as a systolic blood pressure of 140 mmHg or higher, diastolic blood pressure of 90 mmHg or higher, or taking antihypertensive medication. Pre-hypertension was defined as a systolic blood pressure of 120 mmHg or higher but lower than 140 mmHg and diastolic blood pressure of 80 mmHg or higher but lower than 90 mmHg. The rest were defined as normal.

Diabetes was classified into diabetes, pre-diabetes, and normal [19]. Diabetes was defined as having a fasting blood glucose level of 126 mg/dL or higher, having received a diagnosis from a physician, or being on oral hypoglycemic agents or insulin injections. Pre-diabetes was defined as a fasting blood glucose level of 100 mg/dL or higher but lower than 126 mg/dL, with the rest classified as normal.

Finally, tooth brushing frequency per day was categorized as once or less, or twice or more.

6. Statistical analysis

KNHANES data were collected using a complex sample design method. A complex sample analysis was performed after applying weights, stratification variables (kstrata), and survey units (psu). The number of teeth according to the general characteristics of the study participants was subjected to a chi-squared test. The mean scores for each OBS item according to the categories of the number of teeth were analyzed using analysis of variance in a complex sampling general linear model. To determine the relationship between OBS and number of teeth, the multivariate logistic regression model was adjusted stepwise for confounding variables. Model 1 was an unadjusted model, while Model 2 was adjusted for demographic factors including gender, age, household income, and education. Model 3 was additionally adjusted for hypertension, diabetes, and tooth brushing frequency per day from Model 2.

The analysis results are presented as odds ratios (OR) and 95% confidence intervals (CI). All statistical analyses conducted in this study were performed using IBM SPSS Statistics for Windows, version 28.0 (IBM Corp.), and significance testing was based on a Type I error level of 0.05.

RESULTS

1. Number of teeth by general characteristics

Table 1 shows the number of teeth by the general characteristics of the study participants. In the NT1 (0–10 teeth) group, women (50.9%) outnumbered men by a slight margin (P < 0.001). The group was associated with advanced age, low household income, low education level, hypertension, and brushing teeth once or less per day (P < 0.001). Furthermore, the highest percentage of OBS T3 was observed in the NT3 (21–28 teeth) group at 63.5% (P < 0.001).

2. Oxidative balance score by general characteristics

Table 2 shows the OBS by the general characteristics of the study participants. In the T3 group, women over men, advanced age, higher household income, college graduates or higher, normal blood pressure, and non-diabetics were more likely to be represented (P < 0.001).

3. Detailed items of oxidative balance score by number of teeth

Table 3 shows the mean OBS by the number of teeth. Total MET, n-3 fatty acids, vitamin C, retinol, and β-carotene, belonging to the antioxidant group in OBS, were significantly higher in the NT3 (21–28 teeth) group (P < 0.001). However, in the pro-oxidants group, variables such as alcohol, n-6 fatty acids, and PUFAs, excluding BMI, showed higher oxidative promotion scores for pro-oxidants in the NT3 (21–28 teeth) group (P < 0.001).

4. Distribution of oxidative balance score categories according to sociodemographic and health-related characteristics

Table 4 presents the likelihood of being classified into the lower (T1) or intermediate (T2) OBS categories according to sociodemographic and health-related characteristics. The distribution of OBS exhibited disparities in accordance with the participants’ sociodemographic and health-related characteristics. Household income (lower) and education (≤ primary school) levels were significantly associated with belonging to OBS T2 (OR: 1.70, 95% CI: 1.482–1.951, OR: 1.86, 95% CI: 1.642–2.112, respectively). Additionally, health-related characteristics such as hypertension, diabetes, and ≤ 1 daily toothbrushing were associated with a higher likelihood of belonging to OBS T2 (OR: 1.55, 95% CI: 1.392–1.733, OR: 1.37, 95% CI: 1.190–1.585, OR: 1.20, 95% CI: 1.025–1.421, respectively).

5. Relationship between number of teeth and oxidative balance score

Table 5 shows the likelihood of belonging to lower tooth count categories according to OBS levels. In the group with OBS level T2, the likelihood of having NT1 teeth (0–10) was found to be significant (OR: 1.65, 95% CI: 1.378–1.989). Model 2, adjusted for demographic variables, yielded a significant result (OR: 1.48, 95% CI: 1.205–1.831), as did Model 3, adjusted for all variables (OR: 1.51, 95% CI: 1.195–1.908). In the multinomial model, a significant association was observed for the NT1 category, whereas no significant association was found for the NT2 category after adjustment.

DISCUSSION

This study used raw data from the 7th KNHANES, representative of Korean adults, for the analysis to determine the association between number of teeth and OBS. A significant association was identified between number of teeth and OBS in the study involving 13,199 study participants. In the group with OBS as T2, the likelihood of belonging to tooth count NT1 (0–10 teeth) was found to be OR: 1.51, 95% CI: 1.195–1.908 in the model adjusted for all variables. Additionally, the likelihood of belonging to NT2 (11–20 teeth) was OR: 1.22, 95% CI: 1.056–1.423 in the unadjusted model, but was not significant in the adjusted model.

These results suggest that the difference between the NT1 (0–10) group, which experienced extreme tooth loss, and the NT2 (11–20 teeth) group could be explained by confounding variables such as socioeconomic and health variables. The number of teeth was influenced by age and socioeconomic factors, suggesting that residual confounding may have remained [20].

The primary causes of tooth loss include dental caries and periodontal disease. The development of dental caries is driven by a complex interplay between acid-producing bacteria in the oral cavity, host saliva components, and carbohydrate intake [21]. Therefore, oxidative stress plays a role in the mechanism that induces intracellular signaling, thereby promoting the differentiation and growth of acid-producing bacteria that cause dental caries [22]. Dental caries forms and progresses over time, reaching deeper tissues and ultimately leading to tooth collapse. Periodontal disease is also a multifactorial disorder. When activated phagocytes produce excess ROS in the gingival sulcus, the antioxidant capacity decreases. Ultimately, increased oxidative stress in affected tissues leads to the destruction of periodontal tissue and tooth loss [23, 24].

Previous studies have demonstrated a negative, linear relationship between OBS and periodontitis [4, 5]. In contrast, our study results showed partial agreement, revealing a correlation between the number of teeth and the T2 group, which is defined as the median score relative to the highest OBS score.

In this study, a significant association with the number of remaining teeth was observed only in the OBS T2 group. While OBS T2 indicates a medium level of oxidative balance, T1 and T3 reflect low and high levels, respectively. This phenomenon may also be interpreted as reflecting the characteristic that, within the extreme OBS (T1, T3) ranges, dental condition has already been affected to a certain degree, thereby hindering the statistical detection of changes based on differences in OBS levels. Furthermore, the distribution of the sample and statistical power may have contributed to the lack of significant association observed in the extreme OBS ranges.

Furthermore, this study found that pro-oxidants, such as alcohol, n-6 fatty acids, and PUFAs, had higher oxidative promotion scores in the NT3 (21–28 teeth) group. Previous studies have highlighted the positive effects of alcohol consumption, as evidenced by changes in biomarkers. Specifically, as reported in a systematic literature review, higher levels of high-density lipoprotein cholesterol and adiponectin as well as lower levels of fibrinogen have been observed with moderate alcohol consumption (up to one drink per day for women and up to two drinks per day for men) in relation to coronary artery disease and biological markers [25]. However, the aforementioned biomarkers are not specifically related to oral diseases. A previous systematic review of tooth loss and mortality from cardiovascular disease found a significant association in cases with 0–9 teeth [26]. Consequently, oral diseases exhibit shared risk factors with systemic diseases and are associated with them, indicating the necessity for a comprehensive approach to their management and prevention.

Another previous study on the antibacterial activity of wine against periodontal pathogens reported moderate antimicrobial impact on Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, and Fusobacterium nucleatum [27]. The results revealed that the polyphenol components in wine exerted an antimicrobial effect. Given that the drinking variables in this study utilized data on drinking frequency but did not account for alcohol consumption volume or type of alcohol consumed, further research could benefit from considering a broader range of drinking variables.

Furthermore, previous studies on dietary n-6 fatty acids and PUFAs have underscored the importance of examining the ratio between n-6 and n-3, as opposed to merely evaluating the intake of individual fatty acids. A three-year longitudinal study of Japanese older adults examined the relationship between fatty acids and periodontal disease, revealing that a higher ratio of n-6 to n-3 PUFAs was significantly associated with an increased incidence of periodontal disease [28]. Conversely, a Mendelian randomization study using data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014 on the association between PUFAs and periodontitis reported no evidence that heightened n-3 fatty acid concentrations or diminished n-6 to n-3 fatty acid ratios hinder periodontitis [29].

As demonstrated in the findings of this study, previous research has frequently yielded contradictory results regarding the relationship between nutrient composition and inflammatory diseases [4, 30, 31]. Therefore, a more integrated consideration of dietary antioxidant and pro-oxidant exposure is imperative for further research in this area. Although this study considered single n-6 fatty acids and total PUFAs as pro-oxidant factors, future research should incorporate OBS that accounts for the n-6 to n-3 ratio.

It is a well-established fact that smoking and obesity, as measured by BMI, have deleterious effects on both oral and chronic diseases. Smoking has been demonstrated to exacerbate inflammation in the oral mucosa and periodontal tissues and may potentially cause malignant tumors [32]. A correlation with the number of decayed teeth was observed among those who smoked daily over four years [33]. Individuals with obesity exhibit elevated levels of pro-inflammatory adipokines, such as tumor necrosis factor-α (TNF-α) and leptin [34, 35]. These inflammatory cytokines have been shown to increase the activity of proteases and matrix metalloproteinases, which can lead to a loss of attachment in periodontal tissues and bone resorption [36, 37].

On the other hand, the five antioxidant components used in this study attained high antioxidant scores in the NT3 (21–28 teeth) group with statistical significance. A previous study that utilized data from 2009 to 2014 under the NHANES investigated the correlation between the composite dietary antioxidant index and periodontitis. This finding aligns with previous studies that also examined the effects of vitamin C, retinol, and carotene, reporting a significant association between the composite dietary antioxidant index and clinical attachment loss of teeth, as well as the number of remaining teeth [38]. These findings suggest that dietary antioxidant intake could contribute to eliminating excessive free radicals and protecting periodontal tissues from oxidative stress [38, 39].

Another previous study confirmed that administering n-3 fatty acid supplements to patients with chronic periodontitis reduced periodontal pocket depth and inflammation [40]. N-3 fatty acids have been shown to mitigate inflammation in gingival tissues through the suppression of pro-inflammatory mediators, specifically TNF-α and interleukin-1 beta (IL-1β) [41].

Physical activity also reduces the levels of inflammatory cytokines such as TNF-α and IL-6 and increases the levels of anti-inflammatory cytokines such as adiponectin [42]. Consequently, consistent engagement in physical activity has been demonstrated to exert a positive influence on the prevention and management of inflammation within the body.

The repercussions of oxidative stress and imbalance have been demonstrated to exert a detrimental influence on dental caries and periodontitis. Amelioration of this imbalance can be achieved by implementing health behaviors that promote oxidative balance and the dietary intake of antioxidants. This study makes a significant contribution to the field by employing a cross-sectional investigation strategy, utilizing data from the KNHANES, a nationally representative survey, to present the potential for number of teeth categories based on OBS levels among Korean adults.

Limitations

A potential limitation of this study is its cross-sectional design, which precludes the ability to ascertain cause-and-effect relationships. Moreover, a discerning interpretation of the results of this study is warranted. The etiology of periodontitis and dental caries is multifactorial, and the process of tooth loss may be protracted. Furthermore, among individual socioeconomic factors, household income and education level in particular may act as factors influencing OBS levels and the number of remaining teeth. Therefore, future studies should implement a stratified analysis based on socioeconomic factors to achieve a more precise evaluation of the relationship between OBS and the number of remaining teeth. The dependent variable in this study, the number of remaining teeth (NT), is a significant indicator of oral health. However, the method is limited in its capacity to adequately reflect detailed intraoral conditions, such as the presence or absence of prosthetics, implants, and the state of periodontal tissues. Since the applied dietary data signifies the aggregate of nutrient intakes from foods consumed by an individual over the course of a day, there may be errors in reporting during the quantitative estimation of nutrient intake, including fatty acid intake. In particular, while the amount of lipids such as PUFAs and n-6 fatty acids is being evaluated, it is crucial to consider the potential for unexpected associations to exist between fatty acid intake and oxidation scores due to constraints on factors that directly influence oxidative stress, such as cooking methods (e.g., the use of oxidized oils).

Notwithstanding, this study is among the first to propose the potential categorization of OBS levels and tooth counts. Subsequent longitudinal studies may be necessary to identify improvements in specific OBS items and ascertain the causal relationship between OBS and number of teeth.

Conclusion

In conclusion, this study demonstrated a correlation between number of teeth and OBS in Korean adults. Specially in the group with OBS level T2, the likelihood of having NT1 teeth (0–10 teeth) was found to be significant. As this study examines cross-sectional associations, the necessity of conducting longitudinal research as subsequent studies is evident to ascertain the existence of causality.

NOTES

CONFLICT OF INTEREST

There are no financial or other issues that might lead to conflict of interest.

FUNDING

The present research was supported by the research fund of Dankook University in 2024 (43898).

DATA AVAILABILITY

The data used is publicly available at the Korean National Health and Nutrition Examination Survey (https://knhanes.kdca.go.kr/knhanes/eng/intr/dataIntr.do.).

Table 1.

Number of teeth by the general characteristics of the study participants

Characteristics	Number of teeth			P-value¹⁾
Characteristics	NT1 (0-10)	NT2 (11-20)	NT3 (21-28)	P-value¹⁾
Gender (n = 13,199)
Men	555 (49.1)	560 (44.7)	4,678 (41.5)	< 0.001
Women	570 (50.9)	702 (55.3)	6,134 (58.5)
Age (year) (n = 13,199)
19–29	0 (0.0)	3 (0.2)	1,559 (15.1)	< 0.001
30–39	3 (0.3)	12 (1.0)	2,087 (18.7)
40–49	9 (0.6)	42 (3.3)	2,368 (21.1)
50–59	65 (5.7)	206 (17.3)	2,222 (21.6)
≥ 60	1,048 (93.4)	999 (78.2)	2,576 (23.6)
Household income (n = 13,161)
Lower	630 (54.5)	527 (40.3)	1,484 (13.8)	< 0.001
Median	404 (37.4)	573 (46.8)	5,834 (53.4)
Upper	82 (8.0)	160 (13.0)	3,467 (32.8)
Education (n = 12,566)
≤ Primary school	664 (63.7)	598 (47.4)	1,367 (12.9)	< 0.001
Middle school	142 (14.3)	196 (17.0)	895 (8.8)
High school	162 (16.3)	258 (24.2)	3,608 (35.3)
≥ College	59 (5.7)	128 (11.4)	4,489 (43.0)
Hypertension (n = 13,169)
Hypertension	702 (62.9)	714 (56.9)	2,854 (25.7)	< 0.001
Pre-hypertension	230 (19.7)	277 (21.3)	2,687 (25.1)
Normal	187 (17.4)	268 (21.8)	5,250 (49.1)
Diabetes (n = 12,384)
Diabetes	279 (27.2)	305 (26.0)	1,006 (9.4)	< 0.001
Pre-diabetes	285 (29.4)	335 (29.9)	2,342 (22.4)
Normal	400 (43.4)	513 (44.1)	6,919 (68.2)
Tooth brushing (times/day) (n = 12,759)
≤ 1	244 (26.4)	170 (13.2)	778 (6.8)	< 0.001
≥ 2	670 (73.6)	1,033 (86.8)	9,864 (93.2)
OBS (n = 13,199)
T1	72 (9.3)	97 (11.7)	1,305 (12.5)	< 0.001
T2	373 (35.5)	309 (29.5)	2,734 (24.0)
T3	584 (55.2)	573 (58.8)	7,152 (63.5)

n (weighted %).

OBS, oxidative balance score.

¹⁾P-value was calculated by complex sample chi-square test.

Table 2.

OBS levels by the general characteristics of the study participants

Characteristics	OBS			P-value¹⁾
Characteristics	T1	T2	T3	P-value¹⁾
Gender (n = 13,199)
Men	763 (47.5)	1,114 (32.6)	3,916 (45.5)	< 0.001
Women	711 (52.5)	2,302 (67.4)	4,393 (54.5)
Age (year) (n = 13,199)
19–29	243 (16.8)	356 (11.5)	963 (11.9)	< 0.001
30–39	261 (16.2)	478 (13.5)	1,363 (16.2)
40–49	285 (17.0)	546 (15.7)	1,588 (18.7)
50–59	311 (22.5)	614 (19.2)	1.568 (19.6)
≥ 60	374 (27.6)	1,422 (40.1)	2,827 (33.6)
Household income (n = 13,161)
Lower	241 (17.1)	854 (24.9)	1,546 (18.0)	< 0.001
Median	768 (52.3)	1.711 (50.5)	4,332 (51.6)
Upper	450 (30.6)	838 (24.6)	2,421 (30.3)
Education (n = 12,566)
≤ Primary school	210 (16.4)	946 (28.3)	1,473 (17.6)	< 0.001
Middle school	120 (10.7)	310 (9.8)	803 (10.0)
High school	485 (35.0)	891 (28.6)	2,652 (33.9)
≥ College	529 (37.8)	1,058 (33.2)	3,089 (38.5)
Hypertension (n = 13,169)
Hypertension	442 (30.9)	1,308 (37.0)	2,520 (29.8)	< 0.001
Pre-hypertension	395 (26.4)	845 (25.6)	1,954 (23.4)
Normal	627 (42.7)	1,248 (37.4)	3,830 (46.8)
Diabetes (n = 12,384)
Diabetes	171 (13.5)	488 (14.4)	931 (11.3)	< 0.001
Pre-diabetes	352 (24.4)	792 (24.6)	1,818 (23.1)
Normal	814 (62.1)	1,885 (61.0)	5,133 (65.7)
Tooth brushing (times/day) (n = 12,759)
≤ 1	132 (8.8)	338 (9.9)	722 (8.4)	0.068
≥ 2	1,279 (91.2)	2,888 (90.1)	7,400 (91.6)

n (weighted %).

OBS, oxidative balance score.

¹⁾P-value was calculated by complex sample chi-square test.

Table 3.

Detailed items average of OBS by number of teeth

Variables	Number of teeth			P-value²⁾
Variables	NT1 (0-10)	NT2 (11-20)	NT3 (21-28)	P-value²⁾
Antioxidants
Total MET	306.22 ± 23.18	467.67 ± 27.31	726.86 ± 14.83	< 0.001
n-3 fatty acids (g)	1.34 ± 0.06	1.65 ± 0.07	1.91 ± 0.02	< 0.001
Vitamin C (mg)	46.72 ± 2.42	53.52 ± 2.10	63.72 ± 1.02	< 0.001
Retinol (μg)	68.41 ± 6.66	103.83 ± 15.23	149.57 ± 4.82	< 0.001
β-carotene (μg)	2,229.38 ± 100.19	2,630.90 ± 103.66	2,793.91 ± 53.32	< 0.001
Pro-oxidants
Alcohol¹⁾	2.22 ± 0.03	2.23 ± 0.03	2.14 ± 0.00	0.003
Smoking¹⁾	2.20 ± 0.05	2.20 ± 0.04	2.13 ± 0.02	0.221
BMI¹⁾	2.03 ± 0.03	1.90 ± 0.03	2.07 ± 0.01	< 0.001
n-6 fatty acids (g)	5.58 ± 0.22	6.97 ± 0.22	9.79 ± 0.10	< 0.001
PUFA (g)	6.93 ± 0.27	8.62 ± 0.26	11.72 ± 0.12	< 0.001

Mean ± SE.

OBS, oxidative balance score; MET, metabolic equivalent of task; BMI, body mass index; PUFA, polyunsaturated fatty acid.

¹⁾Maximum value (score 3): alcohol: less than 1 drink per month, smoking: non-smoker, BMI: normal weight (18.5–22.9kg/m²).

²⁾P-value was calculated by analysis of variance (ANOVA) in a complex sampling general linear model.

Table 4.

Distribution of OBS categories according to sociodemographic and health-related characteristics

Characteristics	OBS (Ref. tertile3)
Characteristics	T1	T2
Gender
Men	1.086 (0.949–1.242)	0.578 (0.526–0.637)^**
Women	Ref.	Ref.
Age (year)
19-29	1.710 (1.364–2.144)^**	0.806 (0.680–0.955)^*
30-39	1.216 (0.982–1.505)	0.699 (0.606–0.806)^**
40-49	1.104 (0.891–1.367)	0.702 (0.604–0.816)^**
50-59	1.394 (1.146–1.696)^**	0.822 (0.721–0.936)^*
≥ 60	Ref.	Ref.
Household income
Lower	0.942 (0.744–1.192)	1.700 (1.482–1.951)^**
Median	1.004 (0.848–1.189)	1.203 (1.077–1.344)^*
Upper	Ref.	Ref.
Education
≤ Primary school	0.952 (0.763–1.189)	1.863 (1.642–2.112)^**
Middle school	1.093 (0.825–1.448)	1.141 (0.969–1.343)
High school	1.050 (0.880–1.253)	0.977 (0.872–1.095)
≥ College	Ref.	Ref.
Hypertension
Hypertension	1.139 (0.975–1.329)	1.553 (1.392–1.733)^**
Pre-hypertension	1.232 (1.048–1.447)^*	1.364 (1.205–1.544)^**
Normal	Ref.	Ref.
Diabetes
Diabetes	1.270 (1.046–1.542)^*	1.373 (1.190–1.585)^**
Pre-diabetes	1.117 (0.944–1.321)	1.148 (1.016–1.297)^*
Normal	Ref.	Ref.
Tooth brushing (times/day)
≤ 1	1.064 (0.868–1.316)	1.207 (1.025–1.421)^*
≥ 2	Ref.	Ref.

OR (95% CI).

ORs indicate the likelihood of belonging to the T1 or T2 OBS categories.

OBS, oxidative balance score; Ref, reference; OR, odds ratio; CI, confidence interval.

^*P < 0.05,

^**P < 0.001.

Table 5.

Relationship between number of teeth and OBS

Variables [Ref. NT3 (21-28)]	Model 1		Model 2		Model 3
Variables [Ref. NT3 (21-28)]	Adjusted OR (95% Cl)	P-value	Adjusted OR (95% Cl)	P-value	Adjusted OR (95% Cl)	P-value
NT1 (0-10)
OBS T1	0.851 (0.616–1.174)	0.325	1.159 (0.769–1.747)	0.48	1.169 (0.719–1.898)	0.528
OBS T2	1.655 (1.378–1.989)	< 0.001	1.485 (1.205–1.831)	< 0.001	1.510 (1.195–1.908)	< 0.001
OBS T3	Ref.		Ref.		Ref.
NT2 (11-20)
OBS T1	0.841 (0.666–1.061)	0.144	0.869 (0.645–1.171)	0.356	0.785 (0.572–1.077)	0.133
OBS T2	1.226 (1.056–1.423)	0.007	1.118 (0.937–1.333)	0.215	1.086 (0.893–1.320)	0.406
OBS T3	Ref.		Ref.		Ref.

Results of multiple multinomial logistic regression models.

ORs indicate the likelihood of belonging to the NT1 or NT2 number of teeth categories.

Model 1 unadjusted model. Model 2 adjusted for socioeconomic variables (gender, age, household income and education). Model 3 adjusted for the same factors as Model 2 plus general health variables (hypertension, diabetes mellitus and toothbrushing).

OBS, oxidative balance score; Ref, reference; OR, odds ratio; CI, confidence interval.

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Association between number of teeth and oxidative balance score in Korean adults: a population-based study

Korean J Community Nutr. 2026;31(1):64-74. Published online February 28, 2026

DOI: https://doi.org/10.5720/kjcn.2025.00325

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Association between number of teeth and oxidative balance score in Korean adults: a population-based study

Characteristics	Number of teeth			P-value¹⁾
Characteristics	NT1 (0-10)	NT2 (11-20)	NT3 (21-28)	P-value¹⁾
Gender (n = 13,199)
Men	555 (49.1)	560 (44.7)	4,678 (41.5)	< 0.001
Women	570 (50.9)	702 (55.3)	6,134 (58.5)
Age (year) (n = 13,199)
19–29	0 (0.0)	3 (0.2)	1,559 (15.1)	< 0.001
30–39	3 (0.3)	12 (1.0)	2,087 (18.7)
40–49	9 (0.6)	42 (3.3)	2,368 (21.1)
50–59	65 (5.7)	206 (17.3)	2,222 (21.6)
≥ 60	1,048 (93.4)	999 (78.2)	2,576 (23.6)
Household income (n = 13,161)
Lower	630 (54.5)	527 (40.3)	1,484 (13.8)	< 0.001
Median	404 (37.4)	573 (46.8)	5,834 (53.4)
Upper	82 (8.0)	160 (13.0)	3,467 (32.8)
Education (n = 12,566)
≤ Primary school	664 (63.7)	598 (47.4)	1,367 (12.9)	< 0.001
Middle school	142 (14.3)	196 (17.0)	895 (8.8)
High school	162 (16.3)	258 (24.2)	3,608 (35.3)
≥ College	59 (5.7)	128 (11.4)	4,489 (43.0)
Hypertension (n = 13,169)
Hypertension	702 (62.9)	714 (56.9)	2,854 (25.7)	< 0.001
Pre-hypertension	230 (19.7)	277 (21.3)	2,687 (25.1)
Normal	187 (17.4)	268 (21.8)	5,250 (49.1)
Diabetes (n = 12,384)
Diabetes	279 (27.2)	305 (26.0)	1,006 (9.4)	< 0.001
Pre-diabetes	285 (29.4)	335 (29.9)	2,342 (22.4)
Normal	400 (43.4)	513 (44.1)	6,919 (68.2)
Tooth brushing (times/day) (n = 12,759)
≤ 1	244 (26.4)	170 (13.2)	778 (6.8)	< 0.001
≥ 2	670 (73.6)	1,033 (86.8)	9,864 (93.2)
OBS (n = 13,199)
T1	72 (9.3)	97 (11.7)	1,305 (12.5)	< 0.001
T2	373 (35.5)	309 (29.5)	2,734 (24.0)
T3	584 (55.2)	573 (58.8)	7,152 (63.5)

Characteristics	OBS			P-value¹⁾
Characteristics	T1	T2	T3	P-value¹⁾
Gender (n = 13,199)
Men	763 (47.5)	1,114 (32.6)	3,916 (45.5)	< 0.001
Women	711 (52.5)	2,302 (67.4)	4,393 (54.5)
Age (year) (n = 13,199)
19–29	243 (16.8)	356 (11.5)	963 (11.9)	< 0.001
30–39	261 (16.2)	478 (13.5)	1,363 (16.2)
40–49	285 (17.0)	546 (15.7)	1,588 (18.7)
50–59	311 (22.5)	614 (19.2)	1.568 (19.6)
≥ 60	374 (27.6)	1,422 (40.1)	2,827 (33.6)
Household income (n = 13,161)
Lower	241 (17.1)	854 (24.9)	1,546 (18.0)	< 0.001
Median	768 (52.3)	1.711 (50.5)	4,332 (51.6)
Upper	450 (30.6)	838 (24.6)	2,421 (30.3)
Education (n = 12,566)
≤ Primary school	210 (16.4)	946 (28.3)	1,473 (17.6)	< 0.001
Middle school	120 (10.7)	310 (9.8)	803 (10.0)
High school	485 (35.0)	891 (28.6)	2,652 (33.9)
≥ College	529 (37.8)	1,058 (33.2)	3,089 (38.5)
Hypertension (n = 13,169)
Hypertension	442 (30.9)	1,308 (37.0)	2,520 (29.8)	< 0.001
Pre-hypertension	395 (26.4)	845 (25.6)	1,954 (23.4)
Normal	627 (42.7)	1,248 (37.4)	3,830 (46.8)
Diabetes (n = 12,384)
Diabetes	171 (13.5)	488 (14.4)	931 (11.3)	< 0.001
Pre-diabetes	352 (24.4)	792 (24.6)	1,818 (23.1)
Normal	814 (62.1)	1,885 (61.0)	5,133 (65.7)
Tooth brushing (times/day) (n = 12,759)
≤ 1	132 (8.8)	338 (9.9)	722 (8.4)	0.068
≥ 2	1,279 (91.2)	2,888 (90.1)	7,400 (91.6)

Variables	Number of teeth			P-value²⁾
Variables	NT1 (0-10)	NT2 (11-20)	NT3 (21-28)	P-value²⁾
Antioxidants
Total MET	306.22 ± 23.18	467.67 ± 27.31	726.86 ± 14.83	< 0.001
n-3 fatty acids (g)	1.34 ± 0.06	1.65 ± 0.07	1.91 ± 0.02	< 0.001
Vitamin C (mg)	46.72 ± 2.42	53.52 ± 2.10	63.72 ± 1.02	< 0.001
Retinol (μg)	68.41 ± 6.66	103.83 ± 15.23	149.57 ± 4.82	< 0.001
β-carotene (μg)	2,229.38 ± 100.19	2,630.90 ± 103.66	2,793.91 ± 53.32	< 0.001
Pro-oxidants
Alcohol¹⁾	2.22 ± 0.03	2.23 ± 0.03	2.14 ± 0.00	0.003
Smoking¹⁾	2.20 ± 0.05	2.20 ± 0.04	2.13 ± 0.02	0.221
BMI¹⁾	2.03 ± 0.03	1.90 ± 0.03	2.07 ± 0.01	< 0.001
n-6 fatty acids (g)	5.58 ± 0.22	6.97 ± 0.22	9.79 ± 0.10	< 0.001
PUFA (g)	6.93 ± 0.27	8.62 ± 0.26	11.72 ± 0.12	< 0.001

Characteristics	OBS (Ref. tertile3)
Characteristics	T1	T2
Gender
Men	1.086 (0.949–1.242)	0.578 (0.526–0.637)^**
Women	Ref.	Ref.
Age (year)
19-29	1.710 (1.364–2.144)^**	0.806 (0.680–0.955)^*
30-39	1.216 (0.982–1.505)	0.699 (0.606–0.806)^**
40-49	1.104 (0.891–1.367)	0.702 (0.604–0.816)^**
50-59	1.394 (1.146–1.696)^**	0.822 (0.721–0.936)^*
≥ 60	Ref.	Ref.
Household income
Lower	0.942 (0.744–1.192)	1.700 (1.482–1.951)^**
Median	1.004 (0.848–1.189)	1.203 (1.077–1.344)^*
Upper	Ref.	Ref.
Education
≤ Primary school	0.952 (0.763–1.189)	1.863 (1.642–2.112)^**
Middle school	1.093 (0.825–1.448)	1.141 (0.969–1.343)
High school	1.050 (0.880–1.253)	0.977 (0.872–1.095)
≥ College	Ref.	Ref.
Hypertension
Hypertension	1.139 (0.975–1.329)	1.553 (1.392–1.733)^**
Pre-hypertension	1.232 (1.048–1.447)^*	1.364 (1.205–1.544)^**
Normal	Ref.	Ref.
Diabetes
Diabetes	1.270 (1.046–1.542)^*	1.373 (1.190–1.585)^**
Pre-diabetes	1.117 (0.944–1.321)	1.148 (1.016–1.297)^*
Normal	Ref.	Ref.
Tooth brushing (times/day)
≤ 1	1.064 (0.868–1.316)	1.207 (1.025–1.421)^*
≥ 2	Ref.	Ref.

Variables [Ref. NT3 (21-28)]	Model 1		Model 2		Model 3
Variables [Ref. NT3 (21-28)]	Adjusted OR (95% Cl)	P-value	Adjusted OR (95% Cl)	P-value	Adjusted OR (95% Cl)	P-value
NT1 (0-10)
OBS T1	0.851 (0.616–1.174)	0.325	1.159 (0.769–1.747)	0.48	1.169 (0.719–1.898)	0.528
OBS T2	1.655 (1.378–1.989)	< 0.001	1.485 (1.205–1.831)	< 0.001	1.510 (1.195–1.908)	< 0.001
OBS T3	Ref.		Ref.		Ref.
NT2 (11-20)
OBS T1	0.841 (0.666–1.061)	0.144	0.869 (0.645–1.171)	0.356	0.785 (0.572–1.077)	0.133
OBS T2	1.226 (1.056–1.423)	0.007	1.118 (0.937–1.333)	0.215	1.086 (0.893–1.320)	0.406
OBS T3	Ref.		Ref.		Ref.

Table 1. Number of teeth by the general characteristics of the study participants

n (weighted %).

OBS, oxidative balance score.

P-value was calculated by complex sample chi-square test.

Table 2. OBS levels by the general characteristics of the study participants

n (weighted %).

OBS, oxidative balance score.

P-value was calculated by complex sample chi-square test.

Table 3. Detailed items average of OBS by number of teeth

Mean ± SE.

OBS, oxidative balance score; MET, metabolic equivalent of task; BMI, body mass index; PUFA, polyunsaturated fatty acid.

Maximum value (score 3): alcohol: less than 1 drink per month, smoking: non-smoker, BMI: normal weight (18.5–22.9kg/m²).

P-value was calculated by analysis of variance (ANOVA) in a complex sampling general linear model.

Table 4. Distribution of OBS categories according to sociodemographic and health-related characteristics

OR (95% CI).

ORs indicate the likelihood of belonging to the T1 or T2 OBS categories.

OBS, oxidative balance score; Ref, reference; OR, odds ratio; CI, confidence interval.

P < 0.05,

P < 0.001.

Table 5. Relationship between number of teeth and OBS

Results of multiple multinomial logistic regression models.

ORs indicate the likelihood of belonging to the NT1 or NT2 number of teeth categories.

OBS, oxidative balance score; Ref, reference; OR, odds ratio; CI, confidence interval.