Abstract
Background
Oral health is strongly related to diet, and pro-inflammatory dietary patterns may worsen dental diseases through systemic inflammation. This study examined the relationship between the Food-based Dietary Inflammatory Index (FDII) and dental health, assessed by the DMFT index (Decayed, Missing, and Filled Teeth index), in Iranian adults.
Method
This cross-sectional study analyzed baseline data from the Bandare-Kong cohort, a subset of the prospective PERSIAN study, including 3,630 Iranian adults aged 35–75 years. Dietary intake was assessed using a validated food frequency questionnaire, and FDII was used to assess the inflammatory potential of participants ‘dietary patterns. Participants were classified into tertiles based on their E-FDII scores. The DMFT index was calculated as the sum of decayed, missing (due to caries), and filled permanent teeth. Generalized linear regression models (GLR) were used to examine the association between FDII scores and dental health outcomes.
Results
In the total population, higher dietary inflammatory potential was suggestively associated with poorer oral health. Compared with T1, T2 and T3 of E-FDII were related to fewer teeth [β = − 0.9 (–1.2 to − 0.6) and − 1.0 (–1.5 to − 0.8)], a higher number of decayed teeth [0.3 (0.1 to 0.5) and 0.4 (0.2 to 0.8)], more missing teeth [0.9 (0.6 to 1.3) and 1.1 (0.8 to 1.4)], and higher DMFT scores [1.1 (0.6 to 1.5) and 1.5 (1.0 to 1.9), p-trend = 0.001] in the adjusted model, while the number of filled teeth showed no clear association. Stratified analyses indicated that these suggestive trends were more apparent in women and middle-aged adults.
Conclusion
Our findings indicate that higher diet-related inflammatory potential is associated with poorer dental health in a representative sample of Iranian adults. Given the small effect sizes, the clinical relevance may be limited. Longitudinal studies are warranted to clarify the temporal relationship and underlying mechanisms.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12903-026-09001-2.
Keywords: Dietary inflammatory index, Inflammatory, Diet, Oral health, Dental health survey
Introduction
Oral health is a vital component of overall health, significantly influencing physical, social, and psychological well-being [1]. Dental caries, as the most prevalent non-communicable oral disease worldwide, poses a substantial burden across the lifespan [2, 3]. This progressive microbial disease irreversibly damages the mineralized tissues of teeth, through demineralization of inorganic components (e.g., hydroxyapatite crystals) and degradation of the organic matrix (e.g., collagen and other proteins), processes driven by acids produced by cariogenic bacteria [2]. Added sugar intake is one of the most well-established dietary determinants of dental caries, as frequent consumption of free sugars promotes acid production by cariogenic bacteria and accelerates enamel demineralization [4]. This condition develops through a complex interaction among teeth, saliva, and diet, with systemic health factors occasionally modifying this relationship [5]. Diet plays a fundamental role in oral health, affecting not only hard dental tissues—leading to tooth decay, wear, malocclusion, and mobility—but also soft tissues. Poor dietary patterns may result in conditions such as gingivitis, gum recession, and oral lesions, and in severe cases, may even contribute to oral cancers [6].
Chronic systemic inflammation driven by pro-inflammatory diets can worsen oral diseases by intensifying gum inflammation, delaying tissue healing, and increasing susceptibility to infections [7–9]. Diets with a high inflammatory index typically include large amounts of processed foods, saturated fats, simple sugars, and red meat, whereas anti-inflammatory diets include fruits, vegetables, whole grains, fish, and healthy oils such as olive oil [10, 11].
The Food-based Dietary Inflammatory Index (FDII) was developed to assess the inflammatory potential of an individual’s diet by analyzing intake across food groups [11–13]. Higher FDII scores have been positively associated with systemic inflammatory markers, including C-reactive protein (CRP) [14]. Given that systemic inflammation is a known contributor to oral and dental diseases, and diet is a modifiable risk factor, understanding the relationship between diet-induced inflammation and dental health is essential [15, 16]. Although previous studies have linked dietary inflammatory indices to various chronic diseases [17], research examining FDII to dental health outcomes, such as the DMFT index and oral health, remains limited. The DMFT index (Decayed, Missing, and Filled Teeth) is a widely accepted quantitative measure used to evaluate dental health status, particularly the prevalence of dental caries [18]. To our knowledge [19], this is the first large-scale study in an Iranian population examining FDII in relation to dental health indicators, using data from the Bandar Kong Non-Communicable Disease (BKNCD) cohort.
Therefore, the primary aim of the present study was to investigate the association between the FDII and dental health, including the number of decayed, missing, and filled teeth, as well as the DMFT index, in a large sample of Iranian adults from the BKNCD cohort. Secondary aims included exploring whether this association varied according to demographic factors, such as age and sex. By addressing these objectives, this study seeks to provide new insights into the role of diet-induced inflammation in oral health and the risk of dental caries.
Materials and methods
Participant
This cross-sectional, population-based research employed baseline data from the BKNCD cohort, which is part of the larger, prospective PERSIAN cohort study (http://persiancohort.com). Recruitment of participants took place in Bandare-Kong, Iran, from October 2016 to November 2018. A detailed description of the cohort has been reported previously [20]. Ethical approval for this research was granted by the Ethics Committee of Hormozgan University of Medical Sciences (Ethics code: IR.HUMS.REC.1401.170). In accordance with the study protocol, participants were enrolled only after voluntarily signing informed consent and meeting the eligibility requirements. The inclusion criteria included: (1) willingness to participate, (2) age ranging from 35 to 75 years, (3) residency in Bandare-Kong for at least one full year and a minimum of nine months per year, (4) capacity to provide written informed consent, and (5) Iranian nationality. Subjects were excluded from the study if they were unwilling to take part, were temporary residents, had any physical or cognitive impairments that could interfere with participation, or reported total energy intake that was considered unreliable. Among 4,099 individuals initially recruited for this cross-sectional study, 3,630 participants aged 35–75 years were included in the final analysis. Recruitment was carried out using registries from local health centers and was stratified by urban and rural populations. A comprehensive flowchart illustrating participant inclusion is shown in Figure S1.
Data collection
Data collection was carried out via face-to-face interviews administered by trained and experienced interviewers, using a standardized questionnaire to obtain comprehensive information on participants’ sociodemographic characteristics, lifestyle behaviors, dietary habits, physical activity levels, and medical history. To evaluate socioeconomic status, Multiple Correspondence Analysis (MCA) was applied to generate a Wealth Score Index (WSI). The index incorporated a wide range of household assets and facilities such as refrigerator, washing machine, dishwasher, computer, internet access, motorcycle, type and value of car, vacuum cleaner, and television (categorized as no TV, standard color TV, or plasma TV). It also considered mobile phone and laptop ownership, as well as international travel history (classified as none, pilgrimage only, or both pilgrimage and non-pilgrimage).
Oral health status
Oral and dental examinations were conducted in the cohort center by a trained dentist, using sterilized dental mirrors, probes, and cotton rolls to isolate teeth. Each participant was evaluated for DMFT status through direct visual and tactile examination under artificial lighting conditions. All procedures adhered strictly to the diagnostic criteria and assessment protocols recommended by the World Health Organization [21].
The assessment of oral health included a comprehensive set of indicators: (1) the frequency of tooth brushing, (2) the total number of natural teeth excluding implants, (3) counts of decayed and filled teeth, and (4) teeth lost specifically due to dental caries (excluding congenitally missing teeth and those lost from trauma or other causes). Additionally, the presence of oral lesions and the use of dental hygiene aids such as dental floss, dentures, or mouthwash were recorded. The DMFT index (Decayed, Missing (due to caries), and Filled Teeth index) was calculated as the sum of decayed, missing (due to caries), and filled permanent teeth.
Anthropometry measurement
Following the completion of the questionnaire, participants’ anthropometric measurements were obtained. Height was measured in centimeters, standardized to the nearest 0.5 cm using a wall-mounted stadiometer (Height Rod Wall, FAZZINI S208, Italy, 2015). Weight was measured in kilograms to the nearest 10 g, using a portable Weighing scale, RGZ 160, China.
Assessment of dietary intake
Dietary intake was assessed using a validated semi-quantitative Food Frequency Questionnaire (FFQ) specifically adapted for the Iranian population. The 132-item FFQ was administered by trained nutritionists to estimate participants’ habitual intake over the previous year. For each food item, participants reported both the frequency of consumption (per day, week, or month) and the typical portion size, based on standard Iranian serving sizes and household measures. All data were then converted to grams per day using appropriate conversion factors and categorized into 32 food groups based on nutrient content and prior research (Supplementary Table 1). Nutrient and energy intakes were calculated using NUTRITIONIST IV software (version 7.0; N-Squared Computing, Salem, OR, USA).
Development of energy-adjusted food-based dietary inflammatory index (E-FDII)
For the development of the FDII score, we utilized previous studies conducted among Iranian [11, 13, 22] adults. In that study, the contribution of various foods and food groups to systemic inflammation was assessed. Systemic inflammation was evaluated by measuring serum concentrations of high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) [11, 13]. Food groups that loaded highly on the “healthy” dietary pattern were classified as anti-inflammatory, while those associated with the “Western” dietary pattern were considered pro-inflammatory. This classification was based on observed associations: the healthy dietary pattern showed an inverse relationship with inflammatory biomarkers, whereas the Western dietary pattern demonstrated a positive association [22]. In the current study, we calculated the mean daily intake of 28 predefined food groups—12 anti-inflammatory (including fruits, fruit juices, fish, poultry, cruciferous vegetables, yellow vegetables, green leafy vegetables, other vegetables, tomatoes, legumes, whole grains, and tea) and 16 pro-inflammatory (including processed meats, red meats, eggs, butter, dairy products, coffee, potatoes, French fries, refined grains, pizza, snacks, mayonnaise, soft drinks, sweets and desserts, hydrogenated fats, and hydrogenated oils). First, we calculated energy-adjusted amounts of these food groups using the residual method [23]. Then, the mean daily intakes were multiplied by their given factor loadings for each participant. For each participant, the overall E-FDII score was computed by summing the weighted scores of all relevant foods and food groups. To facilitate interpretation and reduce the scale of the values, the total score was divided by 100. A more positive E-FDII score reflects a more pro-inflammatory dietary pattern, whereas a more negative score indicates a less pro-inflammatory (i.e., more anti-inflammatory) diet.
Statistical analysis
All statistical analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY, USA). The Kolmogorov–Smirnov test was applied to assess the normality of continuous variables. Descriptive statistics are reported as means with 95% confidence intervals (CIs) for continuous variables and as frequencies and percentages for categorical variables.
Participants were classified into tertiles based on their E-FDII scores. Differences in demographic characteristics, health outcomes, and dietary intake across E-FDII tertiles were evaluated using analysis of covariance (ANCOVA), adjusting for relevant confounding variables.
Generalized linear regression models (GLR) were used to examine the association between energy-adjusted Food-Based Dietary Inflammatory Index (E-FDII) scores and dental health outcomes. Covariates for adjustment were selected based on Directed Acyclic Graph (DAG) theory to identify the minimally sufficient adjustment set, as illustrated in Figure S2 [24]. To assess linear trends across tertiles of E-FDII, linear regression models were applied, and p-values for trend (p-trend) were reported accordingly.
In sensitivity analyses, potential interactions with age, gender, and obesity status were assessed for all associations, and stratified analyses were conducted accordingly. Participants were categorized as follows: age groups: ≤ 46 years (n = 1,838) vs. ≥ 47 years (n = 1,792); sex: women (n = 2,069) vs. men (n = 1,561); and Body Mass Index (BMI) categories: < 25 kg/m² (n = 1,227) vs. ≥ 25 kg/m² (n = 2,303). Additionally, participants were categorized into middle-aged (45–60 years) and elderly (60–75 years) groups after excluding younger individuals, to better capture age-related differences in dental health outcomes. Also, the associations between E-FDII and dental health were adjusted for added sugar intake as a further sensitivity analysis.
To control for false positives arising from multiple comparisons, the false discovery rate (FDR) method was applied using a 5% threshold. This approach ensures that no more than 5% of the statistically significant findings are expected to be false positives, thereby maintaining the integrity of the results [25].
Participants with implausible energy intakes were excluded based on predefined cut-off values. Specifically, men reporting average daily energy intakes below 800 kcal or above 4000 kcal, and women reporting below 500 kcal or above 3500 kcal, were excluded [26]. In total, 71 participants (43 females) were identified as over- or under-reporters and removed from the final dataset (Figure S1).
Results
This study used baseline data from the BKNCD cohort, part of the PERSIAN study, with participants recruited in Bandare-Kong, Iran, from October 2016 to November 2018.
Participants characteristic
Among the 3,630 participants, 2,069 were women (57.0%), and 1,561 were men, with a mean age of 47.6 years. Women had a higher mean BMI (27.5 kg/m²) than men (25.7 kg/m²), and obesity was more prevalent in women (37.7%) than in men (25.8%). Men more frequently reported alcohol use (10.95% vs. 0.14%), smoking (22.1% vs. 13.1%), and drug use (8.7% vs. 0.3%) compared to women. WSI distribution showed more men in the low-level group and more women in the high-level group. Most participants were married (89.6%), and tooth brushing habits were similar across sexes, indicates in Table 1. The mean differences in anthropometric and oral health indicators across tertiles of the E-FDII are presented in Table 2. Participants in the most anti-inflammatory tertile (T1) were older and had significantly higher BMI and weight compared to those in more pro-inflammatory tertiles. Oral health outcomes also varied: individuals in T1 had more teeth, fewer decayed, and missed teeth, while DMFT scores had greater dietary inflammatory potential, all p < 0.05.
Table 1.
Baseline demographic characteristics of participants, n = 3630, Bandare-Kong Non-Communicable Disease (BKNCD) cohort, 2016–2018
| Variables | Total population | Women | Men |
|---|---|---|---|
| Mean (95% CI) or n (%) | Mean (95% CI) or n (%) | Mean (95% CI) or n (%) | |
| Subjects | 3630 | 2069 | 1561 |
| Women, n (%) | 2069 (57.0%) | - | - |
| Age (year) | 47.6 (37.3–47.9) | 47.5 (47.1–48.9) | 47.9 (47.5–48.3) |
| Wealth score index (WSI) n (%) | |||
| Low | 1229 (33.8) | 603 (29.1) | 626 (40.1) |
| Medium | 1216 (33.5) | 685 (33.1) | 531 (34.0) |
| High | 1185 (32.7) | 781 (37.8) | 404 (25.9) |
| Body mass index (BMI) (kg/m2) | 26.7 (26.6–26.9) | 27.5 (27.3–27.7) | 25.7 (25.5–25.9) |
| Overweight (25 ≤ BMI > 30) n (%) | 1439 (39.6) | 820 (39.6) | 619 (39.6) |
| Obese (BMI ≥ 30) n (%) | 864 (23.8) | 604 (37.7) | 242 (25.8) |
| Weight (kg) | 70.6 (70.1–71.1) | 67.63 (67.1–68.2) | 74.5 (73.9–75.2) |
| Marriage status | |||
| Married (yes) n (%) | 3255 (89.6) | 1729 (83.6) | 1526 (97.7) |
| Lifestyle factors | |||
| Alcohol consumption (yes) n (%) | 174 (4.8) | 3 (0.1) | 171 (10.9) |
| Smoking (yes) n (%) | 617 (16.9) | 272 (13.1) | 345 (22.1) |
| Drug (yes) n (%) | 144 (3.9) | 8 (0.4) | 136 (8.7) |
| Brush teeth (no) n (%) | 189 (5.2) | 106 (5.1) | 83 (5.3) |
Values are reported as mean (95% confidence interval) for continuous variables and as number (percentage) for categorical variables
n number, CI confidence interval, % percentage
Table 2.
Mean difference of anthropometric and oral health characteristics in tertile of energy adjusted Food Dietary Inflammatory Index (E-FDII) in Iranian adults, n = 3630, Bandare-Kong Non-Communicable Disease (BKNCD) cohort, 2016–2018
| Variables | Energy adjusted Food Dietary Inflammatory Index (E-FDII) | ||||
|---|---|---|---|---|---|
| T1 E-DII < -0.7 Mean (95% CI) |
T2 -0.7 ≥ E-DII ≤ 1.3 Mean (95% CI) |
T3 E-DII > 1.3 Mean (95% CI) |
P value* | P-trend | |
| Subject | 1210 | 1210 | 1210 | ||
| Age (year) | 48.8 (48.0–49.5) | 47.7 (47.2–48.2) | 46.4 (45.8–47.1) | 0.001 | < 0.001 |
| BMI (kg/m2) | 27.1 (26.8–27.3) | 26.4 (26.2–27.0) | 26.7 (25.9–26.5) | 0.04 b | 0.01 |
| Weight (kg) | 71.0 (70.7–71.2) | 70.4 (70.1–70.7) | 70.4 (70.1–70.7) | 0.03 a | 0.05 |
| Oral health status | |||||
| Teeth number (n) | 28.6 (25.8–31.0) | 27.8 (24.4–30.2) | 27.7 (25.8–30.0) | 0.03 a | 0.01 |
| Decayed teeth number (n) | 2.9 (2.6–3.2) | 3.2 (3.0–3.6) | 3.2 (2.9–3.6) | 0.001 | 0.05 |
| Missing teeth number (n) | 3.5 (3.0–3.7) | 3.8 (3.5–4.2) | 3.8 (3.5–4.1) | 0.04 | 0.07 |
| Filled teeth number (n) | 2.1 (1.8–2.2) | 1.8 (1.5–1.9) | 1.8 (1.6–2.0) | 0.01 | 0.06 |
| DMFT | 8.0 (7.7–8.4) | 8.8 (8.0–9.2) | 8.7 (7.9–9.1) | < 0.001 a | 0.009 |
Values are mean ± standard deviation (SD). Analyses were adjusted for gender, age, BMI, wealth status index (WSI), total energy intake (TEI), alcohol use, drug use, and smoking status using analysis of covariance (ANCOVA), except when age or BMI were the outcomes
T tertile, BMI body mass index, kg kilogram, m2meter square, cm centimetre, DMFT decayed missing and filled teeth
aSignificant differences were observed between T1 and T2, and between T1 and T3 (p < 0.05), bSignificant difference was observed only between T1 and T3 (p < 0.05)
*ANCOVA with Bonferroni post hoc test was used to compare differences between E-DII tertiles
p < 0.05 was considered significant and are bolded
Dietary characteristics
Participants with the most anti-inflammatory diets (T1) consumed significantly higher amounts of whole grains, fruits, vegetables, legumes, nuts, fiber, and several key micronutrients (e.g., calcium, magnesium, potassium, iron, vitamins A, C, and K). In contrast, those in the most pro-inflammatory tertile (T3) showed suggestive associations with higher intakes of refined grains, hydrogenated fats, French fries, pizza, soft drinks, and saturated fatty acids, alongside lower fiber and antioxidant-rich food consumption. Additionally, fluoride intake was substantially lower in T3. Although processed meat, fish and seafood, red meat, potatoes, sweets and desserts, protein intake, fat intake, PUFA, and vitamin D did not show significant associations with E-FDII, other dietary components demonstrated clear relationships with the dietary inflammatory potential. Most trends were statistically significant, highlighting distinct dietary patterns between anti- and pro-inflammatory diet groups. However, Analyses were adjusted for sociodemographic and lifestyle confounders, all p < 0.05. Table 3 illustrates food and nutrient intake across tertiles of E-FDII.
Table 3.
Food intake according to tertile categories of energy adjusted Food Dietary Inflammatory Index (E-FDII), n = 3630, Bandare-Kong Non-Communicable Disease (BKNCD) cohort, 2016–2018
| Variables | Energy adjusted Food Dietary Inflammatory Index (E-FDII) | |||
|---|---|---|---|---|
| T1 E-DII < -0.7 Mean (95% CI) |
T2 -0.7 ≥ E-DII ≤ 1.3 Mean (95% CI) |
T3 E-DII > 1.3 Mean (95% CI) |
P-trend * | |
| Subject | 1210 | 1210 | 1210 | |
| Total energy intake (TEI) (kcal/day) | 2759.5 (2715.6–280.5) | 2779.3 (2737.1–2821.5) | 2862.0 (2818.6–2905.4) | 0.003 |
| Food groups | ||||
| Whole grains (g/d) | 5.3 (4.2–6.4) | 4.54 (3.51–5.57) | 2.46 (1.40–3.52) | 0.001 |
| Refined grain (g/d) | 279.4 (272.2–286.5) | 337.2 (330.3–344.1) | 417.8 (410.3–424.8) | < 0.001 |
| Nuts (g/d) | 7.6 (7.2–8.0) | 6.2 (5.8–6.6) | 4.7 (4.3–5.2) | < 0.001 |
| Legumes (g/d) | 67.3 (64.8–69.8) | 62.4 (59.9–64.8) | 57.0 (54.5–59.5) | < 0.001 |
| Vegetables(g/d) | 290.1 (283.7–296.4) | 212.4 (206.4–218.5) | 162.4 (156.2–168.7) | < 0.001 |
| Fruits (g/d) | 508.4 (498.5–518.3) | 356.1 (346.6–365.6) | 239.9 (230.2–249.8) | < 0.001 |
| Eggs (g/d) | 32.8 (31.4–34.3) | 33.9 (32.5–35.2) | 35.62 (34.2–37.0) | 0.02 |
| Dairy (ml/d) | 250.1 (240.9–259.3) | 242.2 (233.4–251.0) | 264.8 (255.1–273.2) | 0.003 |
| Processed meat (g/d) | 5.4 (5.1–5.8) | 5.4 (5.1–5.9) | 5.8 (5.4–6.1) | 0.2 |
| Fish and seafood (g/d) | 10.6 (10.0–10.3) | 9.2 (8.7–9.8) | 8.8 (8.2–9.4) | 0.1 |
| Red meat (g/d) | 14.4 (13.6–15.4) | 14.6 (13.8–15.4) | 14.9 (13.9–15.6) | 0.6 |
| poultry (g/d) | 29.2 (28.5–32.0) | 32.1 (31.2–33.6) | 33.0 (31.5–32.9) | 0.04 |
| Vegetable oils | 6.9 (6.5–7.2) | 6.3 (5.9–6.6) | 5.7 (5.4–6.1) | < 0.001 |
| Hydrogenated fats (g/d) | 10.2 (8.4–9.9) | 12.4 (11.6–13.1) | 13.3 (12.5–14.5) | 0.001 |
| Butter (g/d) | 0.7 (0.6–0.9) | 0.9 (0.7–1.2) | 1.0 (0.9–1.4) | 0.009 |
| Potatoes (g/d) | 38.5 (36.7–40.3) | 37.9 (36.2–39.7) | 37.5 (36.0–39.4) | 0.5 |
| French fries (g/d) | 4.4 (4.0–4.7) | 5.3 (5.0–5.7) | 6.8 (5.5–7.1) | 0.005 |
| Pizza (g/d) | 8.5 (7.9–9.1) | 9.31 (8.7–10.8) | 11.2 (10.2–12.8) | < 0.001 |
| Sweets and desserts (g/d) | 23.9 (22.9–25.9) | 25.1 (24.0–26.1) | 25.2 (24.1–26.2) | 0.11 |
| Soft drinks (ml/d) | 57.0 (51.8–62.3) | 67.3 (62.2–72.3) | 84.4 (79.2–89.6) | < 0.001 |
| Coffee (ml/d) | 30.3 (27.0–33.6) | 37.1 (34.0–40.3) | 48.1 (44.8–51.3) | < 0.001 |
| Tea (ml/d) | 585.5 (566.9–604.0) | 370.7 (352.8–388.5) | 225.8 (207.4–244.1) | < 0.001 |
| Macronutrients | ||||
| Carbohydrate (% of TEI) | 65.0 (64.7–65.3) | 64.4 (64.1–64.7) | 63.5 (63.2–63.8) | < 0.001 |
| Protein intake (% TEI) | 12.7 (12.6–12.9) | 12.8 (12.73–12.9) | 12.8 (12.8–12.9) | 0.71 |
| Fat intake (% of TEI) | 24.0 (23.8–24.5) | 24.4 (24.2–24.7) | 24.6 (24.3–24.9) | 0.15 |
| Fiber (gr/1000 kcal) | 12.2 (12.1–12.3) | 10.1 (9.9–10.2) | 8.5 (8.4–8.6) | < 0.001 |
| PUFA (g/d) | 12.9 (12.7–13.0) | 12.4 (12.2–12.5) | 12.8 (12.7–13.1) | 0.12 |
| SAF (g/d) | 26.9 (26.4–27.4) | 27.9 (27.4–28.4) | 28.8 (28.3–29.3) | < 0.001 |
| Cholesterol (g/d) | 290.4 (283.8–297.0) | 294.1 (287.7–300.4) | 302.8 (296.2–309.3) | 0.03 |
| Micronutrient | ||||
| Calcium (mg/day) | 776.4 (763.4–788.9) | 723.6 (711.6–735.6) | 724.5 (712.1–736.9) | < 0.001 |
| Magnesium (mg/day) | 410.5 (404.2–416.7) | 388.3 (382.3–394.3) | 383.1 (377.7–390.0) | < 0.001 |
| Phosphorus (mg/day) | 1232.8 (1220.0–1245.0) | 1186.1 (1174.1–1198.0) | 1195.8 (11.9–1205.8) | 0.09 |
| Potassium (mg/day) | 4659.8 (4586.6–4733.5) | 4171.0 (4100.4–4241.8) | 3943.1 (3875.3–4015.8) | 0.002 |
| Zinc (mg/day) | 8.7 (8.6–8.87) | 8.4 (8.3–8.5) | 8.4 (8.3–8.5) | 0.009 |
| Iron (mg/day) | 17.7 (17.6–17.9) | 17.0 (16.9–17.1) | 16.1 (16.0–16.3) | 0.001 |
| Fluoride (mg/day) | 2261.5 (2192.4–2323.6) | 1457.9 (1390.6–1524.2) | 928.1 (858.8–997.4) | < 0.001 |
| Selenium (mg/day) | 77.9 (75.9–79.5) | 76.6 (75.4–77.0) | 75.2 (73.2–77.93) | 0.04 |
| Vitamin A (IU/day) | 1584.1 (1532.0–1632.8) | 1183.5 (1139.6–1227.7) | 912.5 (866.4–958.8) | < 0.001 |
| Vitamin C (mg/day) | 179.3 (174.8–182.4) | 136.2 (132.9–139.5) | 104.9 (101.6–108.3) | < 0.001 |
| Vitamin K (mg/day) | 228.3 (222.4–234.3) | 181.3 (175.6–187.1) | 147.6 (141.7–153.5) | < 0.001 |
| Vitamin D (mg/day) | 214.6 (207.9–221.6) | 212.4 (205.9–218.8) | 210.9 (204.3–217.6) | 0.6 |
Values are mean ± standard deviation (SD), p < 0.05 was considered significant and are bolded
T tertile, CI confidence interval, g gram, IU international unit, ml millilitre
*Analyses were adjusted for gender, age, BMI, wealth status index (WSI), total energy intake (TEI), alcohol use, and smoking status using analysis of covariance (ANCOVA), except when TEI was outcome
Main findings in the total population
In the total population, higher adherence to a pro-inflammatory diet, as indicated by increasing E-FDII tertiles, was significantly associated with poorer dental health outcomes. Compared to the lowest tertile (T1), individuals in the second tertile (T2) were linked to fewer teeth (β = − 0.9; 95% CI: − 1.2 to − 0.6) and those in the highest tertile (T3) were related to even fewer (β = − 1.0; 95% CI: − 1.5 to − 0.8). Similarly, for decayed teeth, T2 vs. T1 showed an increase (β = 0.3; 95% CI: 0.1 to 0.5), and T3 vs. T1 showed a further increase (β = 0.4; 95% CI: 0.2 to 0.8). The number of missing teeth was also related to greater E-FDII across tertiles, T2 vs. T1: (β = 0.9; 95% CI: 0.6 to 1.3) and T3 vs. T1: (β = 1.1; 95% CI: 0.8 to 1.4). The DMFT index was significantly linked to higher in T2 vs. T1 (β = 1.1; 95% CI: 0.6 to 1.5) and further in T3 vs. T1 (β = 1.5; 95% CI: 1.0 to 1.9), P-trend = 0.001). While the number of filled teeth was not significantly related. Significant interactions were observed for age and sex in relation to dental health, suggesting that the potential effect of dietary inflammation on oral health may vary across subgroups (Table 4).
Table 4.
Associations between energy adjusted Food Dietary Inflammatory Index (E-FDII) in tertiles and dental health, Bandare-Kong Non-Communicable Disease (BKNCD) cohort data, n = 3630, 2016–2018
| Variables | Model | Energy adjusted Food Dietary Inflammatory Index (E-FDII) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Total population | Interactions | |||||||||
| T1E-DII< -0.7 | T2 -0.75 ≥ E-DII ≤ 1.3 β (95% CI) |
P value* | T3 E-DII > 1.3 β (95% CI) |
P value* | P-trend ** | P-Interaction age*a | P-Interaction sex*b | P-Interaction obesity*c | ||
| Teeth number | Model 1 | Ref. | -1.0 (-1.4 to -0.7) | < 0.001 | -1.2 (-1.6 to -0.9) | 0.004 | 0.001 | |||
| Teeth number | Model 2 | Ref. | -0.9 (-1.2 to -0.6) | 0.001 | -1.0 (-1.5 to -0.8) | 0.001 | 0.004 | 0.001 | 0.04 | 0.14 |
| Decayed teeth number | Model 1 | Ref. | 0.4 (0.3 to 0.9) | 0.03 | 0.6 (0.4 to 0.8) | 0.01 | 0.07 | |||
| Decayed teeth number | Model 2 | Ref. | 0.3 (0.1 to 0.5) | 0.06 | 0.43 (0.2 to 0.8) | 0.06 | 0.06 | 0.004 | 0.04 | 0.37 |
| Missing teeth number | Model 1 | Ref. | 1.0 (0.8 to 1.3) | 0.003 | 1.4 (0.9 to 1.8) | 0.001 | 0.01 | |||
| Missing teeth number | Model 2 | Ref. | 0.9 (0.6 to 1.3) | 0.001 | 1.1 (0.8 to 1.4) | 0.001 | 0.02 | 0.007 | 0.03 | 0.15 |
| Filled teeth number | Model 1 | Ref. | -0.4 (-0.6 to 0.2) | 0.07 | -0.32 (-0.7 to 0.0) | 0.11 | 0.07 | |||
| Filled teeth number | Model 2 | Ref. | -0.2 (-0.4 to 0.0) | 0.08 | 0.0 (-0.3 to 0.2) | 0.62 | 0.06 | 0.002 | 0.81 | 027 |
| DMFT | Model 1 | Ref. | 0.7 (0.2 to 1.2) | 0.005 | 0.8 (0.3 to 1.3) | 0.002 | 0.001 | |||
| DMFT | Model 2 | Ref. | 1.1 (0.6 to1.5) | < 0.001 | 1.5 (1.0 to 1.9) | < 0.001 | 0.001 | 0.001 | 0.01 | 0.08 |
Values are presented as β (95% CI) in total population. Analyses including interaction terms were adjusted for all Model 2 covariates except the variable involved in the interaction (i.e., age, sex, or obesity). Model 1: Adjusted for age, sex, and total energy intake (TEI). Model 2: Additionally adjusted for BMI (body mass index), wealth status index (WSI), alcohol use, drug use, tooth brushing frequency, and smoking status
T tertile, Ref references, CI confidence interval, DMFT decayed missing and filled teeth
aAge group: ≤ 46 years (n = 1838) vs. ≥ 47 years (n = 1792),bSex: Women (n = 2069) vs. Men (n = 1561), cBMI group: < 25 kg/m² (n = 1227) vs. ≥ 25 kg/m² (n = 2303)
*Generalized linear regression was used, **Linear regression analysis was used
p < 0.05 was considered significant and are bolded
Sensitivity analyzing
Stratified by gender
Higher E-FDII scores were significantly associated with poorer dental health in both women and men. In women, T2 of E-FDII was significantly related to fewer teeth (β = − 1.1; 95% CI: − 1.6 to − 0.7) and more decayed (β = 0.5; 95% CI: 0.2 to 0.8) and missing teeth (β = 1.1; 95% CI: 0.7 to 1.5) compared to T1. Similarly, women in T3 also had fewer teeth (β = − 1.2; 95% CI: − 1.6 to − 0.7), more decayed (β = 0.5; 95% CI: 0.2 to 0.9) and missing teeth (β = 1.2; 95% CI: 0.7 to 1.6), and significantly higher DMFT scores (β = 1.2; 95% CI: 0.5 to 1.9) than T1. Among men, higher E-FDII scores showed suggestive trends toward poorer dental health, with participants in T2 and T3 exhibiting fewer teeth (T2: β = − 0.6; 95% CI: − 1.0 to − 0.2; T3: β = − 0.9; 95% CI: − 1.4 to − 0.5), more missing teeth in T3 (β = 0.9; 95% CI: 0.5 to 1.3), and higher DMFT scores in both T2 (β = 1.4; 95% CI: 0.8 to 2.0) and T3 (β = 1.6; 95% CI: 0.9 to 2.2) compared with T1. We did not find significant relationships between filled teeth and dietary inflammatory potential in either sex. Additionally, decayed teeth in men were not significantly associated with E-FDII. These findings, in this cross-sectional look, suggest a consistent gradient where higher dietary inflammatory potential is linked to worse dental health outcomes in both sexes, though more pronounced in women (Table 5).
Table 5.
Associations between energy adjusted Food Dietary Inflammatory Index (E-FDII) in tertiles and dental health stratified by gender [Women (n = 2069) vs. Men (n = 1561)], Bandare-Kong Non-Communicable Disease (BKNCD) cohort data, n = 3630, 2016–2018
| Variables | Model | Energy Adjusted Dietary Inflammatory Index (E-FDII) - stratified by sex | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Women (n = 2069) |
Men (n = 1561) |
||||||||||
| T1E-DII <-0.7 | T2 -0.7 ≤ E-DII ≥ 1.3 β (95% CI) |
FDR | T3 E-DII > 1.3 β (95% CI) |
FDR | T1 E-DII < -0.7 |
T2 -0.7 ≥ E-DII ≤ 1.3 β (95% CI) |
FDR | T3E-DII > 1.3β (95% CI) | FDR | ||
| Subject | 683 | 706 | 680 | 565 | 449 | 547 | |||||
| Teeth number | Adjusted | Ref. | -1.1 (-1.6 to -0.7) | 0.003 | -1.2 (-1.6 to -0.7) | 0.003 | Ref. | -0.6 (-1.0 to -0.2) | 0.01 | -0.9 (-1.4 to -0.5) | 0.003 |
| Decayed teeth number | Adjusted | Ref. | 0.5 (0.2 to 0.8) | 0.003 | 0.5 (0.2 to 0.9) | 0.003 | Ref. | 0.0 (-0.3 to 0.4) | 0.83 | 0.2 (-0.1 to 0.6) | 0.17 |
| Missing teeth number | Adjusted | Ref. | 1.1 (0.7–1.5) | 0.003 | 1.2 (0.7 to 1.6) | 0.003 | Ref. | 0.6 (0.2 to 1.0) | 0.002 | 0.9 (0.5 to 1.3) | 0.002 |
| Filled teeth number | Adjusted | Ref. | -0.2 (-0.04 to 0.1) | 0.14 | -0.1 (-0.4 to 0.1) | 0.33 | Ref. | -0.2 (-0.5 to 0.0) | 0.07 | 0.0 (-0.2 to 0.3) | 0.73 |
| DMFT | Adjusted | Ref. | 0.4 (-0.3 to 1.1) | 0.19 | 1.2 (0.5 to 1.8) | 0.003 | Ref. | 1.4 (0.8 to 2.0) | 0.0003 | 1.6 (0.9 to 2.2) | 0.003 |
Values are presented as β (95% CI) in total population. Adjusted for age, sex, and total energy intake (TEI). BMI (body mass index), wealth status index (WSI), alcohol use, drug use, tooth brushing frequency, and smoking status
T tertile, Ref references, CI confidence interval, FDR false discovery rate, DMFT decayed missing and filled teeth
p < 0.05 was considered significant and are bolded
Stratified by age
In the age-stratified analysis (Table 6), a higher E-FDII was associated with poorer dental health outcomes in both younger (≤ 46 years) and older (≥ 47 years) participants. Among younger adults, compared to T1, those in T2 were associated with fewer teeth (β = − 0.6; 95% CI: − 0.9 to − 0.3) and more missing teeth (β = 0.5; 95% CI: 0.2 to 0.8), while those in T3 were linked to even fewer teeth (β = − 0.8; 95% CI: − 1.2 to − 0.5), more missing teeth (β = 0.7; 95% CI: 0.4 to 1.0), and higher DMFT scores (β = 0.8; 95% CI: 0.2 to 1.4). Among older adults, stronger associations were observed: individuals in T2 were related to fewer teeth (β = − 1.2; 95% CI: − 1.6 to − 0.6), more decayed (β = 0.4; 95% CI: 0.0 to 0.8) and missing teeth (β = 1.2; 95% CI: 0.7 to 1.7), and higher DMFT (β = 1.5; 95% CI: 0.8 to 2.2) compared to T1. Those in T3 were linked to the most adverse dental outcomes: fewer teeth (β = − 1.3; 95% CI: − 1.9 to − 0.7), more decayed teeth (β = 0.7; 95% CI: 0.2 to 1.1), more missing teeth (β = 1.3; 95% CI: 0.8 to 1.9), and higher DMFT scores (β = 1.8; 95% CI: 1.0 to 2.1). No significant associations were observed between filled teeth and E-FDII in either younger or older adults. Furthermore, decayed teeth in the younger group showed no significant relationship with E-FDII. These results highlight an age-related gradient, with older individuals showing associations between pro-inflammatory diets and poorer dental health, although the observed effect sizes are small in some associations.
Table 6.
Associations between energy adjusted dietary inflammatory index (E-DII) in tertiles and dental health stratified by age [≤ 46 years (n = 1838) vs. ≥ 47 years (n = 1792)], Bandare-Kong Non-Communicable Disease (BKNCD) cohort data, n = 3630, 2016–2018
| Variables | Model | Energy Adjusted Dietary Inflammatory Index (E-DII) - stratified by age | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Younger (n = 1838) |
older (n = 1792) |
||||||||||
| T1E-DII <-0.76 | T2-0.75 ≤ E-DII ≥ 1.34β (95% CI) | FDR | T3 E-DII > 1.35 β (95% CI) |
FDR | T1 E-DII < -0.75 |
T2 -0.75 ≥ E-DII ≤ 1.34 β (95% CI) |
FDR | T3 E-DII > 1.35 β (95% CI) |
FDR | ||
| Subject | 621 | 595 | 622 | 615 | 596 | 581 | |||||
| Teeth number | Adjusted | Ref. | -0.6 (-0.9 to -0.3) | 0.003 | -0.8 (-1.2 to -0.5) | 0.003 | Ref. | -1.2 (-1.6 to -0.6) | 0.004 | -1.3 (-1.9 to -0.7) | 0.0001 |
| Decayed teeth number | Adjusted | Ref. | 0.1 (-0.2 to 0.4) | 0.46 | 0.1 (-0.2 to 0.4) | 0.38 | Ref. | 0.4 (0.1 to 0.8) | 0.01 | 0.7 (0.2 to 1.1) | 0.002 |
| Missing teeth number | Adjusted | Ref. | 0.5 (0.2 to 0.8) | 0.001 | 0.7 (0.4 to 1.0) | 0.003 | Ref. | 1.2 (0.7 to 1.7) | 0.002 | 1.3 (0.8 to 1.9) | 0.003 |
| Filled teeth number | Adjusted | Ref. | -0.3 (-0.6 to 0.0) | 0.10 | -0.0 (-0.3 to 0.3) | 0.38 | Ref. | -0.1 (-0.4 to 0.1) | 0.15 | -0.2 (-0.52 to 0.0) | 0.33 |
| DMFT | Adjusted | Ref. | 0.3 (-0.2 to 0.9) | 0.20 | 0.8 (0.2 to 1.4) | 0.003 | Ref. | 1.5 (0.8 to 2.2) | 0.003 | 1.8 (1.0 to 2.2) | 0.003 |
Values are presented as β (95% CI) in total population. Adjusted for age, sex, and total energy intake (TEI). BMI (body mass index), wealth status index (WSI), alcohol use, drug use, tooth brushing frequency, and smoking status
T tertile, Ref references, CI confidence interval, FDR false discovery rate, DMFT decayed missing and filled teeth
p < 0.05 was considered significant and are bolded
Additional sensitivity analysis
Comparisons between middle-aged (45–60 years) and elderly (60–75 years) participants, after excluding younger adults, showed similar patterns. In the elderly, T2 was related to fewer teeth (β = − 1.6; 95% CI: − 2.8, − 0.4; p = 0.01), more decayed teeth (β = 0.9; 95% CI: 0.1, 1.8; p = 0.02), more missing teeth (β = 1.7; 95% CI: 0.5, 2.9; p = 0.005), fewer filled teeth (β = − 0.5; 95% CI: − 0.9, − 0.0; p = 0.04), and higher DMFT scores (β = 2.2; 95% CI: 0.8, 3.6; p = 0.002) compared to T1. In T3, participants had more decayed teeth (β = 1.5; 95% CI: 0.5, 2.5; p = 0.004) and higher DMFT (β = 1.5; 95% CI: 0.2, 3.4; p = 0.03).
In middle-aged adults, T2 was associated with fewer teeth (β = − 1.2; 95% CI: − 1.8, − 0.8; p < 0.001), more decayed teeth (β = 0.4; 95% CI: 0.0, 0.8; p = 0.04), more missing teeth (β = 1.2; 95% CI: 0.6, 1.7; p < 0.001), and higher DMFT scores (β = 1.5; 95% CI: 0.8, 2.3; p < 0.001) compared to T1. In T3, these relationships were slightly stronger: fewer teeth (β = − 1.7; 95% CI: − 2.3, − 1.2; p < 0.001), more decayed teeth (β = 0.4; 95% CI: 0.0, 0.9; p = 0.04), more missing teeth (β = 1.8; 95% CI: 1.2, 2.3; p < 0.001), and higher DMFT (β = 2.0; 95% CI: 1.2, 2.8; p < 0.001) (Table S2).
Reanalysis adjusting for added sugar intake (% of total calories) showed largely consistent relationships between E-FDII and dental health, suggesting these associations are independent of sugar consumption (Table S3).
Figure 1 presents the associations between energy-adjusted Dietary Inflammatory Index (E-FDII) tertiles and dental health outcomes in the total population (n = 3,630), and stratified by age (≤ 46 years, n = 1,838; ≥47 years, n = 1,792) and gender (Women, n = 2,069; Men, n = 1,561). Only statistically significant associations are shown. No significant relationship was observed between filled teeth and E-FDII.
Fig. 1.
Forest plot of significant associations between tertiles of the energy-adjusted Dietary Inflammatory Index (E-DII) and dental health in the total population and stratified by age [≤ 46 years (younger; n = 1,838) vs. ≥ 47 years (older; n = 1,792)] and gender [woman (n = 2,069) vs. men (n = 1,561)], based on data from the Bandare-Kong Non-Communicable Disease (BKNCD) cohort (2016–2018; total population n = 3,630). Only statistically significant associations are shown
Effect sizes were small in some associations, and the cross-sectional design limits causal inference. Overall, these findings suggest relationships between higher dietary inflammatory potential and poorer dental health in this sample of Iranian adults.
Discussion
This cross-sectional study of Iranian adults suggests a potential association between the E-FDII, which evaluates the inflammatory potential of the diet, and poorer dental health. Higher DII scores, indicating greater relationship to a pro-inflammatory diet, were generally associated with fewer teeth, more decayed and missing teeth, and higher DMFT (decayed, missing, and filled teeth) scores, whereas no clear association was observed for filled teeth. Stratified analyses by gender and age indicated that higher E-FDII scores may be linked to poorer dental health in both men and women, with somewhat stronger suggestive trends in participants aged ≥ 47 years. Comparisons between middle-aged (45–60 years) and older adults (60–75 years) showed similar potential patterns, although associations were slightly weaker among the elderly. Women in higher E-FDII tertiles suggestively tended to have fewer teeth, more decayed and missing teeth, and higher DMFT scores, while men showed similar but slightly less pronounced trends. Younger participants in higher tertiles appeared to link fewer teeth and more missing teeth, whereas these effects were more noticeable in older adults, who also showed more decayed teeth and higher DMFT scores. Previous studies have examined the relationship between dietary inflammatory load and dental health; [8, 27] however, findings remain inconclusive [28]. Most research has focused on the effects of the DII on periodontitis in Western populations using the National Health and Nutrition Examination Survey (NHANES) datasets, whereas our study employed the DMFT index to evaluate oral health [29, 30]. Only one recent small-scale study in an Asian population assessed energy-adjusted FDII in relation to dental health and reported no significant association with DMFT, highlighting the gap addressed by our research [27]. Dental caries are highly prevalent [31], with significant psychosocial and socioeconomic consequences [32]. Diet plays a critical role in both inflammatory imbalance and the etiology of dental problems [33, 34]. Regarding the above findings and a noticeable increase in consumption of unhealthy foods due to behavioral habits, environmental and commercial effects on food choices [35–38] might explain greater attention to the inflammation load of foods, investigations with health status, and dental caries. A cross-sectional study in the United States, using datasets from the NHANES, revealed a significant association between DII-high tertile and severe tooth loss among adults [29]. Similarly, two other US-based cross-sectional studies reported that pro-inflammatory diets adversely affected periodontal measures [7, 30]. Likewise, Kotsakis et al. demonstrated that individuals in the highest dietary inflammatory index quartile lost an average of 0.84 more teeth than those in the lowest quartile [8]. These findings further support the adverse impact of pro-inflammatory dietary patterns on oral and dental health. Inconsistently, individuals consuming anti-inflammatory diets had fewer missing teeth [8]. In Japan, elderly adults adhering to diets with greater anti-inflammatory potential (lower DII) exhibited a higher number of functional teeth [39]. However, some studies have reported no significant associations: one study among Iranian participants found no link between E-DII and DMFT [19], and another adult cohort reported no strong relationship between DII quartiles and DMFT scores [40]. Notably, analyses from the NHANES have indicated a link between higher dietary inflammatory indices and elevated prevalence or severity of periodontal disease [29, 30]. Various effect sizes observed across studies might reflect differences in oral health outcomes (e.g., caries experiences evaluated by DMFT or clinical measures of periodontitis), dietary assessment tools (food frequency questionnaires or 24-hour recalls), and population characteristics. Despite different methodologies, these studies align with our results, enlightening the role of dietary inflammation in shaping oral health outcomes at the population level.
Emerging evidence suggests that inflammatory dietary patterns may influence dental health through interconnected local and systemic pathways. Cross-sectional studies report that higher dietary inflammatory potential is associated with alterations in the periodontal microenvironment, including elevated gingival crevicular inflammatory mediators (e.g., MCP-1, IL-33) and changes in subgingival microbial composition [41]. At the systemic level, diet-induced inflammation also appears relevant: analyses indicate that the DII can account for a substantial proportion of various circulating inflammatory biomarkers [42]. As a matter of fact, pro-inflammatory diets rich in refined carbohydrates, processed foods, and saturated fats may enhance circulating cytokines such as CRP and IL-6 [42, 43], which have been implicated in the initiation and progression of periodontitis and tooth loss [44, 45]. Diets high in sugar and processed foods can adhere to tooth surfaces, promoting decay, whereas anti-inflammatory dietary patterns emphasizing fruits, vegetables, legumes, whole grains, and unsaturated fats have been shown to reduce inflammatory cytokine production, support healthier oral immune responses, and provide essential nutrients and antioxidants for oral tissue integrity and maintenance [7, 16, 39]. These findings support the hypothesis that pro-inflammatory diets may contribute to oral health problems both by modulating local immune responses and by intensifying systemic inflammatory activity.
Fiber intake may also mechanically cleanse teeth and support healthy salivary microbiota, further contributing to dental health [46]. Adherence to Mediterranean or anti-inflammatory dietary patterns has been linked to reduced pathogenic bacteria in saliva, although no effect has been observed with vegan diets [47, 48]. Added sugar intake remains a well-established determinant of dental caries, as frequent consumption of free sugars increases acid production by cariogenic bacteria, leading to enamel demineralization [4]. Importantly, after adjusting for added sugar intake, we found that the association between higher FDII scores and dental caries persisted, suggesting that this relationship is not solely driven by sugar consumption and may reflect broader dietary inflammatory effects. These findings highlight the potential role of anti-inflammatory, nutrient-rich dietary patterns in promoting overall oral health.
The current study could illuminate the association between diet-borne inflammation and worse dental outcomes. The use of energy-adjusted DII as a comprehensive tool to assess dietary inflammatory load, a relatively large sample size of adults, and the adjustment of many potential confounders deserve consideration. More importantly, dental health assessment was performed by a trained specialist, which increased the accuracy and precision of our findings. The inflammatory dietary burden in our study was evaluated using an FDII, developed and validated within an Iranian population [11, 13]. Compared to the standard literature-based dietary inflammatory index, the FDII was computed from 28 food groups with inflammatory weights derived from their relationships with circulating concentrations of hs-CRP, IL-6, and TNF- α. This locally derived FDII, in turn, elevates cultural relevance and reflects habitual dietary patterns specific to the Iranian context. Nevertheless, utilizing the locally adapted approach may constrain direct comparability with previous studies that applied the validated DII. Various food groupings, weighting methodologies, and dietary assessment tools could result in variability in effect sizes across populations. The mentioned factors deserve consideration when interpreting the results or generalizing them to other dietary settings. Several limitations are also worth discussing. The cross-sectional survey design restricts our ability to present a cause-and-effect relationship. Additionally, it is important to consider the possibility of reverse causation, as pre-existing poor dental status, such as tooth loss, oral pain, and chewing difficulties, may modify individuals’ dietary choices [49, 50]. Longitudinal analyses, therefore, using future waves of the PERSIAN cohort to determine the temporal association between dietary inflammatory potential and oral health outcomes would be essential. Another limitation is relying on food frequency questionnaires to evaluate diet, which is subject to reporting bias. We employed the DII score with 28 dietary items (out of 45), and it can be viewed as a shortcoming. Aside from that, we included only adults aged 35 to 75, which might impact the generalizability of the results. Furthermore, only 95 participants out of 3,630 (≈ 2.6%) reported fluoride use, and information on dental care frequency for systemic conditions such as diabetes was not available. These limitations should be considered as a potential source of residual confounding, as unmeasured factors may obscure or attenuate some associations. Also, oral examinations were conducted by trained examiners; however, inter- and intra-examiner reliability data were not available, which should be regarded as a potential limitation of the study. Additionally, although several associations reached statistical significance, the observed differences in DMFT scores across FDII tertiles were modest (approximately 1–2 teeth). Such effect sizes may have limited clinical relevance at the individual level but could remain meaningful at the population level, regarding the high prevalence of dental caries and the cumulative public health effects of small shifts in risk across large populations. Finally, it should be noted that the DMFT index does not differentiate between active and treated lesions and is influenced by participants’ access to dental care. This may affect the interpretation of associations between dietary inflammatory potential and dental health outcomes. Future studies with various age groups are needed to clarify this topic.
Conclusion
Dietary inflammatory potential may be linked to dental health, with higher DII scores associated with fewer teeth, more decayed and missing teeth, and higher DMFT scores in this adult population. Although these findings are biologically plausible, as pro-inflammatory diets may exacerbate periodontal inflammation locally and systemically. Promoting anti-inflammatory, nutrient-rich diets—rich in fruits, vegetables, whole grains, and nuts—could support oral health. Public health strategies incorporating dietary counseling alongside standard oral hygiene practices may help reduce dental caries, but further longitudinal studies are needed to confirm these associations.
Supplementary Information
Acknowledgements
We sincerely thank all participants for their valuable contributions to this study. We are also grateful to Hormozgan University of Medical Science for their generous funding and support, which made this research possible.
Abbreviations
- BKNCD
Bandare-Kong Non-Communicable Disease
- FDII
Food-based Dietary Inflammatory Index
- GLR
Generalized linear regression models
- CRP
C-reactive protein
- MCA
Multiple Correspondence Analysis
- WSI
Wealth Score Index
- FFQ
Food Frequency Questionnaire
- hs-CRP
High-sensitivity C-Reactive Protein
- IL-6
Interleukin-6
- TNF-α
Tumor Necrosis Factor-alpha
- Cis
Confidence Intervals
- ANCOVA
Analysis of Covariance
- E-FDII
Energy-adjusted Food-Based Dietary Inflammatory Index
- DAG
Directed Acyclic Graph
- BMI
Body Mass Index
- FDR
False Discovery Rate
- DII
Dietary Inflammatory Index
- NHANES
National Health and Nutrition Examination Survey
Authors' contributions
FGh and AL contributed to the concept and design and wrote the manuscript. AL contributed to data analysis, statistical analysis and manuscript preparation. MS, FD, NR, TT and NP contributed to the manuscript preparation, manuscript editing, and manuscript review. AN acted as the cohort leader, providing guidance, oversight, and coordination throughout the study. NSh supervised the study and provided guidance on study design. FGh and AL assumed equal responsibility for the integrity and accuracy of the work. All authors reviewed and approved the final version of manuscript.
Funding
This project was funded by a research grant from the Hormozgan University of Medical Sciences (Grant number: 4030299). The funding body (HUMS) had no role in the design of the study, the collection, analysis, and interpretation of the data, or the writing of the manuscript.
Data availability
The datasets used and analyzed in this study can be obtained from the corresponding author upon reasonable request.
Declarations
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Ethical Approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Hormozgan University of Medical Sciences (Ethics code: IR.HUMS.REC.1403.246). All participants provided written informed consent before taking part in the study.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Azadeh Lesani, Email: a_lesani@alumnus.tums.ac.ir.
Fatemeh Gholami, Email: Gholami_fghlm67@yahoo.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and analyzed in this study can be obtained from the corresponding author upon reasonable request.