Abstract
Objective
The study aimed to provide evidence for a relationship between a high dietary intake of advanced glycation end products, and periodontitis.
Materials and methods
A total of 2334 adults from the National Health and Nutrition Examination Survey (NHANES) during 2003–2004 were included in this study. Binary regression analysis was conducted to measure the association between periodontitis and dietary advanced glycation end products (AGEs), and two adjusted models were constructed to further explore the relationship.
Results
Participants with AGEs intake above 21.41 U/kcal had a higher prevalence of periodontitis compared to those with lower AGEs intake. After fully adjusting for associated factors, the odds ratios for periodontitis in relation to higher AGEs intake were 1.229 (95% confidence interval 1.015–1.488, p = .034), 1.349 (95% confidence interval 1.157–1.642, p = .003), and 1.331 (95% confidence interval 1.088–1.630 p = .006), respectively.
Conclusions
Our cross-sectional study reveals a strong association between periodontitis and AGEs.
Clinical relevance
An association between advanced glycation end products in the diet and periodontitis implies the importance of the quality of food intake for good oral health.
Principal findings
The consumption of dietary advanced glycation end products is associated with an increased susceptibility to periodontitis development.
Practical implications
These findings contribute to recognizing the harm of advanced glycation end products in various foods to periodontitis, and guiding clinical oral education.
Key words: Periodontitis, NHANES, dietary advanced glycation end products, oral health
Introduction
Periodontitis is a complex inflammatory condition originating from dental plaque, characterized by gingival inflammation, clinical attachment loss, alveolar bone destruction, and subsequent tooth loss. This condition significantly affects the quality of life for patients.1,2 As of 2022 the global prevalence of periodontitis has reached up to 61.6% and continues to exhibit an upward trend.3 Therefore a clinical treatment guideline for periodontitis has been proposed to establish an S3 Level Clinical Practice Guideline for the management of Stage I–III periodontitis, encompassing behavioural modifications and plaque control.4 In recent years a supplement to the guideline on the treatment of Stage IV periodontitis has also been shared.5 Furthermore, implementing daily preventive measures for periodontitis, such as maintaining proper oral hygiene habits and adhering to a nutritious diet, can effectively mitigate the occurrence and severity of this condition.6
Advanced glycation end products (AGEs), which result from the glycation of amino groups in amino acids, represent a heterogeneous and proinflammatory group of compounds.7 The process of AGEs formation is referred to as the “Maillard reaction”, which contributes to the development of food's flavour and aromatic attributes.8 AGEs were first discovered as endogenous substances from diabetes. However, a large body of evidence points to the fact that exogenous AGEs (primarily derived from food) make a great contribution to the overall pool of AGEs in the human body.9 Dietary AGEs (dAGEs) mainly come from food rich in sugars, lipid, and protein.10 The process of AGEs formation can induce protein cross-linking and activate intracellular signalling pathways, both of which play crucial roles in the pathogenesis of AGEs-related diseases.8 Many epidemiological and animal studies have consistently demonstrated a significant association between high dAGEs intake and the development as well as complications of various diseases, including obesity, diabetes, hypertension, cardiovascular diseases, metabolic diseases, osteoporotic fractures, Alzheimer's disease, etc.11, 12 Moreover, AGEs have been validated as an important factor in exacerbating periodontitis in individuals with diabetes,13 and the level of serum AGEs has been found to be associated with the extent of periodontal inflammation.14 Despite these findings, the relationship between dAGEs intake and periodontitis remains inconclusive. Therefore a population study is warranted in this area.
In this study a cross-sectional study was conducted based on data from National Health and Nutrition Examination Survey (NHANES) dataset (2003–2004) to evaluate the relationship between dAGEs and periodontitis and uncover potential confounding factors in this relationship.
Materials and methods
Study design and population
NHANES, a cross-sectional survey that assesses the health and nutritional status of the US population, is conducted by the National Centre for Health Statistics (NCHS) with approval from the NCHS Ethics Review Board. The sample for the survey is selected to represent all age groups in the US population. To ensure reliable statistical analysis, NHANES employs an oversampling strategy targeting individuals aged 60 and above, as well as African Americans and Hispanics. More information is available at https://www.cdc.gov/nchs/nhanes.15
Only participants from NHANES 2003–2004 were included, as food frequency questionnaire was available only in 2003–2006, while the periodontal data was not available during 2005–2006. The periodontal data was directly obtained from the NHANES, while the dietary patterns were assessed by using the 139-item Food Frequency Questionnaire (FFQ).16 The eligibility criteria for participants include a minimum age requirement of >19 years old, for chronic periodontitis tends to occur in adults. Figure 1 presents a flow chart illustrating the standard selection and inclusion process, resulting in the analysis of 2334 individuals (Figure 2).
Fig. 1.
Flow chart of procedures for selection and inclusion of participants.
Fig. 2.
The odds ratio (OR) of periodontitis in participants exposed to high-level dietary advanced glycation end products (more than 21.41 U/kcal) with those exposed to low-level dietary advanced glycation end products (less than 21.41 U/kcal), and the interaction between variables and dietary advanced glycation end products showed in the right column.
The diagnosis of periodontitis
The assessment of periodontal conditions in the NHANES 2003–2004 data followed a partial periodontal examination protocol, which involved evaluating three specific sites (mesiobuccal, midbuccal, and distobuccal).17 These examinations were conducted by dentists who possess a valid state dental license in a US jurisdiction.18 The conventional definition of periodontitis provided by the Centers for Disease Control and Prevention and the American Academy of Periodontology (CDC-AAP) was deemed inadequate. Eke et al.19 explored potential bias in the prevalence of periodontitis over several years in the NHANES dataset and found that the relative bias is lowest when periodontitis is defined as clinical attachment loss (CAL) of at least 3 millimetres at one site. Therefore, for this study, the classification of grade and severity of periodontitis were categorized according to Eke, Thornton Evans, Wei, Borgnakke, and Dye (2010). Mild periodontitis was defined as CAL between 3 and 4 mm, and moderate and severe periodontitis was defined as CAL from 4 to 6 mm and more than 6 mm, respectively.
Calculation of advanced glycation end product scores
The consumption of AGEs was evaluated using a standardized and validated method according to the previously published strategy for estimating dietary intake of AGEs.20 The strategy involved the daily calculation of individual AGEs intake by assessing an AGEs score for each food item published in the comprehensive database, which contains 549 food items with recorded AGEs content. This was then multiplied by the frequency of consumption.20 The frequencies of food consumption were converted into a multiplication factor, which was ruled as 0 for consumption less than once a week, 1.5 for 1–2 times a week, 2.5 for 2–3 times a week, 3.5 for 3 to 3–4 times a week, 4.5 for 4–5 times a week, and 5.5 for 5–6 times a week, with 7 and 14 respectively representing once or more than twice daily. Subsequently, select the information from the previous database corresponding to the food frequency questionnaire.21 Finally, standardize the statistics by taking into account the reported total daily kilocalorie intake from the 24-hour dietary recall, making up for the differences in portion sizes.
Definition of covariables
According to previous studies, potential confounding factors for adjustment encompasses gender, age, race/ethnicity (including Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other race), educational levels, body mass index (BMI), poverty income ratio (PIR), smoking status, diabetes mellitus (DM), and alcohol consumption.11,22 Participants under the age of 19 were initially excluded, as it was reported that individuals below this age had a low prevalence of periodontitis.23 As for age, participants were categorized into five groups (19–29, 30–39, 40–49, 50–59, and more than 60). Participants were classified based on their education levels as less than high school level, high school level, and more than high school level. The BMI was divided into three groups: underweight and normal weight (BMI < 25 kg/m2), overweight (BMI = 25–30 kg/m2), and obese (BMI > 30 kg/m2). The PIR was calculated based on the guidelines provided by the US Department of Health and Human Services, with three artificial classifications: PIR < 1.3 as low household income, PIR 1.3–3.5 as medium household income, and PIR > 3.5 as high household income. Besides demographic characteristics, social environment factors also make great contributions to periodontitis. Diabetes mellitus was defined based on self-reported diagnosis. However, when we dropped the missing data for the alcohol consumption and smoking status, the number of participants decreased sharply, so we decided to abandon both variables.7,24, 25, 26
Statistical analysis
The NHANES data collection method employs stratified sampling, which may cause sampling errors to some extent. Therefore, in order to mitigate this discrepancy, it is necessary to apply appropriate weighting techniques. Besides, a limited number of participants who took part in the questionnaire survey also participated in other examinations. For example, some individuals participated in dietary interviews, and subsequently, the corresponding dietary weight (WTDR2D) was applied to adjust the dietary data every 2 years.
Based on the above facts, firstly, we converted continuous variables to categorical variables and presented them as frequencies or percentages. Pearson's chi-squared tests were selected for analyzing categorical variables to compare baseline characteristics between groups with and without periodontitis. Secondly, binary logistic regression was conducted to assess the association between periodontitis and dAGEs. In this analysis progress the dietary consumption frequency questionnaire was weighted. Thirdly, we used the multivariable logistic regression to assess the association among periodontitis, dAGEs, and other factors. The variables that may have an influence on periodontitis were figured out, enhancing the reliability and persuasiveness of the findings. Then, in order to further verify the influence of dAGEs on periodontitis, we built three adjustment models: an unadjusted model, adjusted model 1 (adjusted for sex and race), and adjusted model 2 (adjusted model 1 plus BMI, PIR, education levels, and DM). In this way, further exploration of the potential relationship between dAGEs and periodontitis can be pursued. Last but not least, collinearity diagnostics were necessary on gender, ethnicity, education levels, BMI, PIR, DM, and dAGEs to support our conclusion (with variance inflation factor less than 2), indicating no severe collinearity.27, 28, 29
An interaction between dAGEs and every variable was conducted to verify the independence of covariates. Statistical analysis was performed using SPSS Statistics 27 software, with a significance level set at p < .05.
Results
Descriptive characteristics
The data from NHANES 2003–2004 were utilized, and 2334 participants were enrolled in this study after excluding all missing data. Pearson's chi-squared tests were conducted and the baseline characteristics of participants were presented in Table 1. There were 862 participants (36.9%) with periodontitis and 1472 (63.1%) without, and among the people with periodontitis, there were 361 people with mild periodontitis and 501 people with moderate and severe periodontitis, accounting for 15.5% and 21.5%, respectively. For the age of participants, the median age was 42 years old (Table 2), and the quartile between 25th and 75th ranged from 29 to 60. The prevalence of periodontitis among participants aged 19–29, 30–39, 40–49, 50–59, and ≥60 was 5.7%, 23.5%, 37.1%, 51.7%, and 68.4%, respectively, and the difference in prevalence among the five age groups was statistically significant (p < .001). In addition, the prevalence of periodontitis in males was 45.2%, which was significantly higher than that in females (30.0%). Different races had different prevalence, while significant differences were observed. Meanwhile, the higher the education level, the lower the prevalence of periodontitis. Regarding PIR, participants with more than 3.5 showed a significantly higher prevalence of periodontitis compared to those with a PIR less than 3.5. Among the three BMI categories, individuals classified as underweight/normal only accounted for 31.4% of the cases of periodontitis, which demonstrates a significant difference when compared to overweight and obese participants; however, no significant difference was observed between overweight and obese individuals. The proportion of diabetic participants with periodontitis (61.9%) was higher than that of participants without diabetes (34.8%) and with prediabetes (54.6%), and there was a significant difference among the three groups. At the same time, participants with higher AGEs intake developing periodontitis accounted for 41.3%, which also showed a significant difference when compared to those with lower AGEs intake.
Table 1.
Descriptive characteristics of the study population stratified by periodontitis.
| Periodontitis (N = 862) |
||||||
|---|---|---|---|---|---|---|
| Characteristic | Total (N = 2334) | No periodontitisa (N = 1472) | Mild periodontitis (N = 361) | Moderate + severe periodontitis (N = 501) | p-values* | |
| Age, n (%) | <.001* | |||||
| 19–29 | 600 (25.7) | 566 (94.3) | 27 (4.5) | 7 (1.2) | ||
| 30–39 | 455 (19.5) | 348 (76.5) | 63 (13.8) | 44 (9.7) | ||
| 40–49 | 393 (16.8) | 229 (58.3) | 79 (20.1) | 85 (17.0) | ||
| 50–59 | 294 (12.6) | 142 (48.3) | 60 (20.4) | 92 (31.3) | ||
| ≥60 | 592 (25.4) | 187 (31.6) | 132 (22.3) | 273 (46.1) | ||
| Gender, n (%) | <.001* | |||||
| Male | 1069 (45.8) | 586 (54.8) | 198 (18.5) | 285 (26.7) | ||
| Female | 1265 (54.2) | 886 (70.0) | 163 (12.9) | 216 (17.1) | ||
| Race/ethnicity, n (%) | .147 | |||||
| Mexican American | 473 (20.3) | 296 (62.6) | 71 (15.0) | 106 (22.4) | ||
| Other Hispanic | 73 (3.1) | 42 (57.5) | 15 (20.5) | 16 (21.9) | ||
| Non-Hispanic White | 1256 (53.8) | 796 (63.4) | 204 (16.3) | 256 (20.4) | ||
| Non-Hispanic Black | 437 (18.7) | 284 (65.0) | 50 (11.4) | 103 (23.6) | ||
| Other race | 95 (4.1) | 54 (56.8) | 21 (22.1) | 20 (21.1) | ||
| Education levels, n (%) | <.001* | |||||
| Less than high school | 514 (22.0) | 271 (52.7) | 82 (16.0) | 161 (31.3) | ||
| High school diploma | 601 (25.7) | 369 (61.4) | 90 (15.0) | 142 (23.6) | ||
| More than high school | 1217 (52.1) | 832 (68.4) | 188 (15.4) | 197 (16.2) | ||
| BMI, n (%)b | .001* | |||||
| Underweight/normal | 775 (33.2) | 532 (68.6) | 103 (13.3) | 140 (18.1) | ||
| Overweight | 785 (33.6) | 458 (58.3) | 137 (17.5) | 190 (24.2) | ||
| Obese | 774 (33.2) | 482 (62.3) | 121 (15.6) | 171 (22.1) | ||
| PIR, n (%) | .002* | |||||
| <1.3 | 607 (26.0) | 384 (63.3) | 78 (12.9) | 145 (23.9) | ||
| 1.3–3.5 | 883 (37.8) | 527 (58.7) | 147 (16.6) | 209 (23.7) | ||
| >3.5 | 844 (36.2) | 561 (66.5) | 136 (16.1) | 147 (17.4) | ||
| Diabetes mellitus, n (%) | <.001* | |||||
| DM | 168 (7.2) | 64 (38.1) | 33 (19.6) | 71 (42.3) | ||
| No | 2143 (91.8) | 1397 (65.2) | 320 (14.9) | 426 (19.9) | ||
| Prediabetes | 22 (0.9) | 10 (45.5) | 8 (36.4) | 4 (18.2) | ||
| dAGEs, n (%) | .041* | |||||
| <21.41 | 1849 (79.2) | 1187 (64.2) | 270 (14.6) | 392 (21.2) | ||
| ≥21.41 | 485 (20.8) | 285 (58.8) | 91 (18.8) | 109 (22.5) | ||
Abbreviations: BMI, body mass index; dAGEs, dietary advanced glycation end products; PIR, poverty-income ratio.
Unweighted sample size (n), weighted row % in the columns.
BMI: underweight/normal: BMI less than 25 kg/m2; overweight: BMI between 25 and 30 kg/m2; obese: BMI more than 30 kg/m2.
Significant, p < .05.
Table 2.
Descriptive characteristics of the study population in continuous variable.
| Characteristic | Weighted median (25–75th percentiles) | p-values* |
|---|---|---|
| Age | 42 (29–60) | <.001* |
| Poverty-to-income ratio | 2.48 (1.27–4.40) | <.001* |
| Body mass index percentile | 27.410 (23.905–31.640) | .359 |
Significant, p < .05.
Binary and multiple logistic regression analysis
Binary logistic regression analysis was conducted to analyze the relationship between dAGEs and periodontitis, as presented in Table 3. The score of dAGEs was divided into two parts. Compared with the group of low AGEs score, the OR value in the high AGEs score group was all above 1 in the three models, which indicated that dAGEs had negative effects on periodontitis, and higher AGEs scores were associated with higher risk of periodontitis to some degree. Furthermore, the multiple logistic regression in the association between dAGEs and periodontal status is shown in Table 4. Compared with high AGEs score, the regression coefficient β and 95% confidence interval in low AGEs score could be seen in the chart, and except for the moderate and severe periodontitis in unadjusted model and model 2, all the p values were less than .05.
Table 3.
Adjusted association of dAGEs intake with periodontitis.
| Exposure | Unadjusted modela | Adjust 1b | Adjust 2c | |
|---|---|---|---|---|
| Odds ratio (95% CI) associated with periodontitis | ||||
| dAGEs quartile (U/kcal) | ||||
| <21.41 | Reference | |||
| ≥21.41 | 1.229 (1.015–1.488); .034* | 1.349 (1.109–1.642); .003* | 1.331 (1.088–1.630); .006* | |
Unadjusted model: variable merely including dAGEs.
Adjusted 1: variables including gender, race, and dAGEs.
Adjusted 2: variables including gender, race, BMI, PIR, education levels, DM, and dAGEs.
Significant, p < .05.
Table 4.
Logistic regression analysis of associations between dAGEs and different periodontal status.
| Mild periodontitis |
Moderate + severe periodontitis |
||||||
|---|---|---|---|---|---|---|---|
| Regression model | Normal | Β | 95% CI | p-value | β | 95% CI | p-value |
| Unadjusted model | Reference | −0.400 | 0.518, 0.867 | 0.002* | −0.226 | 0.630, 1.009 | .060 |
| Model 1 | Reference | −0.469 | 0.482, 0.812 | <0.001* | −0.302 | 0.583, 0.938 | .013* |
| Model 2 | Reference | −0.411 | 0.508, 0.865 | 0.002* | −0.228 | 0.623, 1.017 | .068 |
Significant, p < .05.
Subgroup analysis and interaction effects
Periodontitis is a complex disease with numerous associated factors, which may exhibit interdependencies. Based on the results in Table 1, variables that were not significantly associated with periodontitis were excluded from the further analysis. Consequently, the selected factors for interaction analysis included gender, BMI, education level, PIR, and diabetes. What's more, the subgroup analysis was conducted in different groups about the intake of AGEs, comparing those with an intake of more than 21.41 U/kcal to those with the less group. Moreover, the results of the interaction between dAGEs and covariables showed that the p-values for most subgroups were above .05, except for gender, suggesting no significant effect modification of these factors to dAGEs and a consistent effect of dAGEs across different subgroups defined education level, BMI, PIR, and diabetes. However, the variables of gender and dAGEs had a mutual constraint. When excluding gender, the final result didn't show any obvious change. Conversely, age showed a significant difference in our study, thereby interacting with dAGEs. Thus we abandoned this variable as well.
Discussion
The aim of this study was to explore the association between dAGEs and periodontitis. The NHANES 2003–2004 database was used, and covariates like age, gender, ethnicity, education level, BMI, PIR, and diabetes were included. The prevalence of periodontitis in high dAGEs group was significantly higher than that in the low dAGEs group (40.0% vs 35.2%, p = .034). Three adjusted models were constructed to validate the relationship between dAGEs and periodontitis, and the results showed that the OR values associated with periodontitis in the high dAGEs group were 1.229, 1.349, and 1.331, respectively, in the three models compared to those in the low dAGEs group, which demonstrated a higher risk of suffering from periodontitis in individuals with high dAGEs intake group. Therefore the results indicate that high dAGEs intake is associated with an increased prevalence of periodontitis.
Studies have demonstrated that the content of AGEs in gingiva is positively correlated with severity of periodontitis in rats,30 and the breakdown of AGEs can significantly reduce alveolar bone absorption in periodontitis,31 which demonstrates the role of AGEs in disease progress. However, there is still a lack of epidemiological investigation studies about influence of dAGEs on periodontitis. In this study individuals with a daily dAGEs intake above 21.41 U/kcal exhibited a significantly higher risk of suffering from periodontitis compared to those with a daily dAGEs intake below 21.41 U/kcal, which is consistent with previous studies in rats30,31 and filled the gap in epidemiological studies on the influence of dAGEs on periodontitis. The aggravation of AGEs on periodontitis may be partly attributed to interaction with RAGE to promote inflammation. Several possible mechanisms have been uncovered, including promotion of cellular senescence, induction of oxidative stress, and activation of NF-κB signalling pathway to promote proinflammatory cytokines expression.32
In this study the NHANES database from 2003 to 2004 was analyzed, and finally, 2365 participants were enrolled. The nonweighted overall prevalence of periodontitis in our research was approximately 36.4%, which was lower than the nonweighted prevalence reported in a previous study using NHANES 2009–2014 data.24 The discrepancy in the prevalence of periodontitis may be attributed to the variations in the enrolled data and participant inclusion criteria.
Additionally, a few sociodemographic characteristics were analyzed in relation to periodontitis prevalence. The prevalence of periodontitis was higher in males than in females. However, as for age, it shows a significant difference compared with other variables which makes a big influence on other variables so that we have to drop it in the final analysis. Besides, there was an inverse relationship between education levels and the prevalence of periodontitis, with higher education levels associated with lower prevalence. The overweight and obese participants exhibited an increased susceptibility to periodontitis. The participants with PIR above 3.5 had the lowest prevalence of periodontitis, while diabetic participants had the highest prevalence. These findings are partially consistent with previous studies,24 indicating that the data chosen in this study are reliable.
Nowadays, with the increasing prevalence of periodontitis, more and more individuals are suffering from gingival bleeding, tooth displacement, attachment loss, and even tooth loss, thereby exerting a certain impact on people's quality of life.33 Therefore it is imperative to prevent the occurrence of periodontitis. The etiology of periodontitis involves bacterial infection and uncontrolled immune reactions.34,35 Treatments for periodontitis include initial periodontal therapy, surgical therapy, and adjunct systemic treatments such as diabetes control36 and antibiotic application.37 So far, substantial evidence has validated the role of diet in periodontitis,9 and dietary restriction might be an effective way to prevent periodontitis.38 For example, a diet rich in salad, fruit, and vegetables is associated with a lower prevalence of periodontitis.37 This study reveals a significant association between a diet rich in AGEs and periodontitis, which emphasises the importance of food processing and controlling the frequency of the type of meat on oral health.
Periodontitis is a multifactorial disease, and many risk factors could contribute to progression. Various prediction models for the incidence and progression of periodontitis have emerged, with commonly observed risk predictors including age, gender, BMI, bacteria factors, tooth mobility/tooth loss, diabetes, bone loss, PIR, and ethnicity. Related to this study, we simply conducted a cross-sectional study without predictable models, future trials or observational longitudinal studies may be necessary to better address this concern.
Last but not least, there are also limitations of our study. Firstly, the database primarily comprises the individuals from America, potentially introducing a population bias. Additionally, most of the data relies on self-reporting, which may introduce recall bias. Meanwhile, it only includes 14 teeth in one participant, which may introduce a selection bias with a lower prevalence than the normal prevalence. Secondly, what's more, due to the cross-sectional nature of the study, unreliable causation and directionality between dAGEs and periodontitis are the main shortcomings. Furthermore, our study suffers from limited timeliness due to a lack of comprehensive data on the full-mouth periodontal examination protocol, resulting in a partial alignment of the periodontitis diagnosis with the latest definitions. Finally, the endogenic AGEs may have an impact on periodontitis, which can contribute to the error of final results.
Conclusions
This study indicates that dAGEs are associated with the prevalence of periodontitis. However, further longitudinal research is also necessary in the future.
Declaration of competing interest
None disclosed.
Acknowledgments
Authors contributions
Yuehan Zhang and Jiayu Shu analyzed the data; Helin Chen searched the database; Yuehan Zhan, Jiayu Shu, Qiyang Ma, Hongli Gao, and Yufeng Qin conceived and drafted the manuscript; and Helin Chen and Qiang Dong reviewed and modified the manuscript. All authors read and approved the final manuscript.
Funding
This study was supported by the National Natural Science Foundation of China (82160186) and the Science and Technology Program of Guizhou Province (Qiankehe Foundation-ZK [2021] General 433).
Footnotes
Supplementary material associated with this article can be found in the online version at doi:10.1016/j.identj.2024.11.006.
Contributor Information
Qiang Dong, Email: 791400804@qq.com.
Helin Chen, Email: helinchen0509@163.com.
Appendix. Supplementary materials
References
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