ABSTRACT
Background
Several studies have examined the impact of tooth loss on nutrient intake, yielding mixed results.
Objectives
This cross‐sectional study aimed to investigate the association among the number of teeth present, nutrient intake and food group consumption in Japanese adults with no history of the four major diseases (diabetes, cancer, heart disease and stroke).
Methods
Data were obtained from the baseline survey of the Japan Multi‐Institutional Collaborative Cohort study conducted in the Shizuoka and Daiko areas. Participants completed a self‐administered questionnaire on their number of teeth, medical history (diabetes, cancer, heart disease and stroke), education level, employment status and lifestyle factors, such as diet, smoking history and exercise habits. The number of teeth was classified into four groups: 0 teeth, 1–19 teeth, 20–27 teeth and 28–32 teeth.
Results
A total of 8912 individuals included in the analysis had a mean age ± SD of 51.7 ± 9.5 years, with women accounting for 53.6% of the total. In adult men, the number of teeth was negatively associated with the intake of vitamin D, n‐3 highly unsaturated fatty acid intake and seafood, while in women, bread consumption was positively associated with the number of teeth.
Conclusions
These findings suggest that the number of teeth is independently associated with poor nutritional status in Japanese adults with no history of the four major diseases.
Keywords: cross‐sectional studies, food, Japan, middle‐aged, nutrition, teeth
1. Introduction
Tooth loss reduces masticatory ability [1], which can limit the variety of foods a person eats. This, in turn, can affect nutrient intake and nutritional status [2]. Although numerous studies have reported on teeth and nutrient intake or food consumption [3, 4, 5, 6, 7, 8, 9, 10, 11, 12], the dental care system and food culture have changed dramatically over time, leading to changes in the relationship between teeth and nutrient intake. Until the 1990s, many studies had demonstrated an association between poor dental health and inadequate energy intake [13]. However, more recent studies have revealed that poor dental health is associated with a diet that is high in energy and fat, leading to obesity [14].
Several factors, including age, gender and national food culture, influence dietary habits. Notably, most previous studies have been conducted with older people [4, 8, 10, 11, 12], with few reports on middle‐aged people [6]. Some studies included young or middle‐aged adults in their study samples; however, they were conducted in specific populations such as veterans [3], health professionals [5], nurses [7] or dentists [9]. Therefore, a survey of middle‐aged Japanese adults is needed.
Furthermore, diseases such as cancer and diabetes have been linked to dietary habits [15]. However, no studies have yet focused on these associations. In the case of diabetes, nutrition therapy is often recommended [16], and people with a history of the disease are likely to pay ongoing attention to their diet. This suggests possible differences in nutritional intake between those with and without a history of such diseases, and this type of bias needs to be eliminated. Therefore, in the present analysis, only those with no history of diabetes, cancer, heart disease or stroke (four major diseases in Japan) were included in the study. Cancer, heart disease and stroke have long been the leading causes of death in Japan, and for more than 30 years, policies for these diseases have been created under the National Health Promotion Plan [17]. The mortality rates of diabetes mellitus are not as high as those of cancer or cardiovascular diseases, but the disease burden is of national concern. For these reasons, we felt that excluding those with a history of cancer, heart disease, stroke or diabetes would allow us to assess more clearly the association between diet and oral health.
This cross‐sectional study aimed to clarify the associations among tooth loss, nutrient intake and food consumption in a population of middle‐aged Japanese adults with no history of the four major diseases.
2. Materials and Methods
2.1. Study Participants
The study used data from the Japan Multi‐Institutional Collaborative Cohort (J‐MICC) study, a large cohort study in Japan launched in 2005 to identify the interactions between genetic and lifestyle factors for lifestyle‐related diseases, including cancer. The purpose and outline of the J‐MICC study have been previously described [18, 19]. Participants aged 35–69 years were enrolled in health check‐up examinations commissioned by local governments, local health check‐up centres or cancer hospitals.
For the current study, data from the baseline survey of the J‐MICC study in the Shizuoka and Daiko areas were analysed. The inclusion criteria for the Shizuoka study were residence in the west‐central area of Shizuoka Prefecture, Japan and attendance of health check‐ups at the Seirei Preventive Health Care Center in Hamamatsu City, Shizuoka Prefecture, Japan, between January 2006 and December 2007. The Daiko study recruited participants mainly through a citywide mailbox distribution of leaflets, personal communications and community information (e.g., posters in public or commercial facilities). The inclusion criteria for the study were visits to the Daiko Medical Center of Nagoya University in Nagoya, Japan, between June 2008 and May 2010. Notably, those who requested the withdrawal of consent after the survey were excluded. The final dataset was finalised on July 27, 2024.
2.2. Questionnaire
Participants completed a self‐administered questionnaire on the number of teeth, medical history (diabetes, cancer, heart disease and stroke), education level, employment and lifestyle factors, such as diet, smoking history and exercise habits.
A validated short Food Frequency Questionnaire (FFQ) was used for the dietary evaluation [20, 21, 22]. Participants were asked about the frequency of consumption of 46 foods and beverages over the past year. Specifically, the consumption of rice, bread and noodles at breakfast, lunch and dinner was divided into six categories: rarely, 1–3 times/month, 1–2 times/week, 3–4 times/week, 5–6 times/week and daily. For other food items, including coffee and green tea, the frequency was divided into eight categories: rarely, 1–3 times/month, 1–2 times/week, 3–4 times/week, 5–6 times/week, once a day, 2 times/day and ≥ 3 times/day. Information on portion size was collected only for staple foods (rice, bread and noodles). Furthermore, the intake of total energy, 25 nutrients (protein, fat, carbohydrate, sodium, potassium, calcium, iron, carotene, retinol, vitamins D, E, B1, B2 and C, folate, saturated fatty acid, monounsaturated fatty acid, polyunsaturated fatty acid, cholesterol, soluble dietary fibre, insoluble dietary fibre, total dietary fibre, n‐3 polyunsaturated fatty acid, n‐6 polyunsaturated fatty acid and n‐3 highly unsaturated fatty acid [HUFA]) and 19 food groups (rice, bread, noodles, potatoes, confectioneries, oils and fats, pulses, seafood, meat, eggs, milk and dairy products, green‐yellow vegetables, other vegetables, fruits, mushrooms, seaweed, alcoholic beverages, coffee and green tea) was computed using a program developed by the Department of Public Health, Nagoya City University School of Medicine. Notably, the FFQ demonstrated high one‐year interval reproducibility for the consumption of foods and nutrients [23].
Physical activity was assessed using a self‐administered questionnaire similar to the short format of the International Physical Activity Questionnaire [24]. Education level was classified into elementary school/junior high school, high school, professional school, junior college/technical college, university/college, graduate school and others.
2.3. Statistical Analysis
Based on previous studies [8, 9] and to further ensure statistical power, participants were categorised into four groups according to the number of teeth present: 0, 1–19, 20–27 and 28–32 teeth. Analyses were conducted using sex. Moreover, participants' background characteristics were analysed using the Kruskal–Wallis test for continuous variables and the chi‐square test for categorical data.
We calculated the nutrient intake and food group consumption per 1000 kcal of total energy intake. The association between the number of teeth and nutrient intake, as well as food group consumption, was evaluated using the analysis of covariance and tests of trend, adjusting for age, total energy intake, area (Shizuoka or Daiko), smoking history, body mass index (BMI) (weight [kg]/height [m2]), physical activity in daily life and leisure time, employment status (whether currently employed) and education level. Participants with missing covariate data and those with medical histories (diabetes, cancer, heart disease and stroke) were excluded from the analysis. In addition, a multiple regression analysis was used to examine the association between the number of teeth and nutritional intake, a continuous variable.
All statistical analyses were performed using IBM SPSS Statistics for Windows, version 28 (IBM Japan Ltd., Tokyo, Japan).
3. Results
A total of 10 157 participants were included in the study, comprising 5005 and 5152 from Shizuoka and Daiko, respectively. Importantly, 119 of these participants were excluded due to either missing data on the number of teeth present or reports of having 33 or more teeth. In addition, 1126 patients with medical histories of the four major diseases (diabetes, cancer, heart disease and stroke) were excluded. Finally, 8912 participants were included in the analysis: 4139 men and 4773 women aged 52.2 ± 9.1 and 51.2 ± 9.8 years, respectively, with both peaking in their 50s.
The characteristics of the study participants, stratified by sex and number of teeth present, are shown in Table 1. In men and women, the proportion of individuals with 20–27 teeth was the highest, while that of edentulous participants was the lowest. Moreover, the mean age decreased as the number of remaining teeth increased. Current smokers and individuals with a high school education or below had fewer teeth, while employed individuals had more teeth remaining.
TABLE 1.
Background characteristics of study subjects by sex and number of teeth present.
| Number of teeth present | p | ||||
|---|---|---|---|---|---|
| 0 | 1–19 | 20–27 | 28–32 | ||
| Men | |||||
| No. of participants (N) | 30 | 452 | 2091 | 1566 | |
| Age (Years, mean ± SD) a | 56.4 ± 9.9 | 56.5 ± 8.1 | 53.4 ± 8.6 | 49.2 ± 9.3 | < 0.001 |
| Area (Daiko) (N, %) b | 7, 23.3 | 121, 26.8 | 598, 28.6 | 506, 32.3 | 0.033 |
| Current smokers (N, %) b | 16, 53.3 | 159, 35.2 | 498, 23.9 | 319, 20.4 | < 0.001 |
| Total energy intake (kcal/day, mean ± SD) a | 1901.6 ± 398.3 | 1922.5 ± 363.7 | 1903.8 ± 344.0 | 1891.4 ± 344.6 | 0.624 |
| Body mass index (kg/m2, mean ± SD) a | 23.0 ± 3.3 | 23.4 ± 3.0 | 23.3 ± 2.8 | 23.4 ± 2.9 | 0.876 |
| Education level c (N, %) b | 20, 66.7 | 251, 55.9 | 900, 43.2 | 530, 34.0 | < 0.001 |
| Employed (N, %) b | 26, 86.7 | 369, 81.6 | 1842, 88.1 | 1430, 91.5 | < 0.001 |
| Daily life activity (MET‐h/day, mean ± SD) a | 10.1 ± 12.1 | 11.5 ± 12.7 | 9.8 ± 10.3 | 8.8 ± 9.7 | < 0.001 |
| Leisure‐time exercise (MET‐h/day, mean ± SD) a | 1.7 ± 1.8 | 1.6 ± 2.0 | 1.4 ± 1.8 | 1.3 ± 1.6 | 0.069 |
| Women | |||||
| No. of participants (N) | 20 | 360 | 2323 | 2070 | |
| Age (Years, mean ± SD) a | 61.0 ± 9.7 | 59.0 ± 7.5 | 53.1 ± 9.4 | 47.8 ± 9.2 | < 0.001 |
| Area (Daiko, N, %) b | 13, 65.0 | 216, 60.0 | 1605, 69.1 | 1487, 71.8 | < 0.001 |
| Current smokers (N, %) b | 3, 15.0 | 42, 11.7 | 164, 7.1 | 98, 4.7 | < 0.001 |
| Total energy intake (kcal/day, mean ± SD) a | 1610.2 ± 250.6 | 1537.8 ± 298.5 | 1525.0 ± 239.1 | 1535.0 ± 244.6 | 0.365 |
| Body mass index (kg/m2, mean ± SD) a | 22.1 ± 3.1 | 22.2 ± 3.0 | 21.4 ± 3.0 | 21.1 ± 2.9 | < 0.001 |
| Education level c (N, %) b | 17, 85.0 | 225, 62.8 | 1062, 46.0 | 731, 35.4 | < 0.001 |
| Employed (N, %) b | 8, 40.0 | 194, 53.9 | 1416, 61.0 | 1388, 67.1 | < 0.001 |
| Daily life activity (MET‐h/day, mean ± SD) a | 16.1 ± 13.4 | 13.2 ± 10.3 | 12.4 ± 8.8 | 12.1 ± 8.9 | 0.313 |
| Leisure‐time exercise (MET‐h/day, mean ± SD) a | 1.9 ± 2.1 | 1.5 ± 2.3 | 1.5 ± 2.0 | 1.2 ± 1.8 | < 0.001 |
Kruskal–Wallis test.
Chi‐square test.
High school graduate and below.
The association between the number of teeth and nutrient intake is shown in Table 2. In men, vitamin D and n‐3 HUFA intakes were negatively associated with the number of teeth (p = 0.026, 0.021). In women, no significant differences were observed; however, a similar trend was apparent. Multiple regression analysis revealed significant positive associations with protein, fat, potassium, calcium, carotene, vitamin B2 and saturated fatty acids (SFAs) in relation to the number of teeth in women (Table S1).
TABLE 2.
Association of number of teeth present with nutrient intake by sex.
| Nutrients a | Number of teeth present | p for trend b | |||
|---|---|---|---|---|---|
| 0 | 1–19 | 20–27 | 28–32 | ||
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | ||
| a. Men | |||||
| Protein (g) | 30.5 (5.9) | 29.2 (4.3) | 29.4 (4.0) | 29.8 (4.1) | 0.212 |
| Fat (g) | 22.5 (6.5) | 22.0 (5.4) | 22.9 (5.6) | 23.7 (6.0) | 0.858 |
| Carbohydrate (g) | 141.3 (22.2) | 144.0 (16.3) | 143.4 (15.8) | 143.3 (15.2) | 0.547 |
| Sodium (mg) | 1000.3 (248.0) | 995.8 (270.0) | 969.8 (264.3) | 973.1 (257.0) | 0.592 |
| Potassium (mg) | 1140.6 (265.0) | 1099.8 (247.8) | 1100.7 (227.1) | 1113.3 (218.4) | 0.491 |
| Calcium (mg) | 267.5 (93.7) | 266.7 (81.1) | 267.5 (77.4) | 270.1 (78.2) | 0.947 |
| Iron (mg) | 3.9 (1.4) | 3.8 (1.1) | 3.8 (1.0) | 3.8 (0.9) | 0.955 |
| Carotene (μg) | 1601.1 (689.6) | 1490.4 (708.1) | 1507.0 (622.1) | 1538.9 (571.5) | 0.644 |
| Retinol (μg) | 507.2 (209.4) | 508.1 (306.8) | 497.5 (219.7) | 499.0 (199.8) | 0.773 |
| Vitamin D (μg) | 4.9 (3.1) | 4.0 (1.8) | 3.9 (1.8) | 3.9 (1.6) | 0.026 |
| Vitamin E (mg) | 4.3 (1.3) | 4.1 (1.1) | 4.2 (1.1) | 4.4 (1.1) | 0.855 |
| Vitamin B1 (mg) | 0.4 (0.1) | 0.3 (0.1) | 0.3 (0.1) | 0.4 (0.1) | 0.780 |
| Vitamin B2 (mg) | 0.6 (0.2) | 0.6 (0.2) | 0.6 (0.1) | 0.6 (0.1) | 0.512 |
| Folate (μg) | 177.8 (69.9) | 174.3 (56.3) | 174.2 (53.2) | 173.2 (48.3) | 0.782 |
| Vitamin C (mg) | 50.0 (26.4) | 48.7 (17.3) | 48.9 (17.4) | 48.0 (15.3) | 0.763 |
| SFA (g) | 5.7 (1.4) | 5.7 (1.4) | 5.9 (1.4) | 6.0 (1.5) | 0.755 |
| MUFA (g) | 8.5 (2.5) | 8.2 (2.0) | 8.6 (2.2) | 8.9 (2.3) | 0.686 |
| PUFA (g) | 7.1 (2.2) | 6.8 (1.7) | 6.9 (1.9) | 7.1 (1.8) | 0.890 |
| n‐3 PUFA (mg) | 1287.7 (434.6) | 1179.3 (299.9) | 1209.4 (319.8) | 1231.5 (329.6) | 0.415 |
| n‐6 PUFA (mg) | 5776.1 (1689.2) | 5706.1 (1449.7) | 5838.5 (1650.6) | 6020.5 (1600.0) | 0.809 |
| n‐3 HUFA (mg) | 491.6 (313.5) | 393.8 (175.3) | 392.2 (180.0) | 384.5 (161.4) | 0.021 |
| Cholesterol (mg) | 128.4 (44.9) | 126.2 (42.4) | 124.2 (35.6) | 128.8 (38.4) | 0.566 |
| SDF (g) | 1.0 (0.3) | 1.0 (0.3) | 1.0 (0.3) | 1.0 (0.3) | 0.889 |
| IDF (g) | 3.9 (1.2) | 3.8 (1.1) | 3.9 (1.1) | 3.9 (1.0) | 0.906 |
| TDF (g) | 5.6 (1.7) | 5.4 (1.5) | 5.4 (1.5) | 5.5 (1.4) | 0.915 |
| b. Women | |||||
| Protein (g) | 33.2 (4.6) | 32.7 (4.9) | 32.9 (4.4) | 32.9 (4.0) | 0.967 |
| Fat (g) | 27.7 (4.5) | 28.2 (6.2) | 29.4 (6.0) | 30.0 (6.0) | 0.506 |
| Carbohydrate (g) | 140.2 (15.0) | 139.1 (14.4) | 136.2 (13.8) | 135.3 (13.0) | 0.937 |
| Sodium (mg) | 1078.5 (242.5) | 1154.1 (292.6) | 1137.6 (265.2) | 1132.1 (262.0) | 0.146 |
| Potassium (mg) | 1445.9 (331.1) | 1389.6 (276.3) | 1405.3 (303.9) | 1384.9 (286.5) | 0.568 |
| Calcium (mg) | 359.5 (102.9) | 347.3 (92.7) | 357.1 (97.1) | 352.8 (90.8) | 0.943 |
| Iron (mg) | 5.1 (1.2) | 4.9 (1.3) | 4.7 (1.3) | 4.6 (1.2) | 0.359 |
| Carotene (μg) | 2122.0 (663.6) | 2279.0 (943.0) | 2311.9 (988.4) | 2220.8 (911.1) | 0.248 |
| Retinol (μg) | 623.4 (189.1) | 652.1 (273.3) | 650.9 (291.6) | 631.7 (270.9) | 0.684 |
| Vitamin D (μg) | 4.5 (1.6) | 4.9 (2.1) | 4.6 (1.9) | 4.3 (1.6) | 0.183 |
| Vitamin E (mg) | 5.2 (1.1) | 5.5 (1.2) | 5.5 (1.3) | 5.5 (1.3) | 0.421 |
| Vitamin B1 (mg) | 0.4 (0.1) | 0.4 (0.1) | 0.4 (0.1) | 0.4 (0.1) | 0.806 |
| Vitamin B2 (mg) | 0.8 (0.2) | 0.7 (0.2) | 0.7 (0.2) | 0.7 (0.2) | 0.362 |
| Folate (μg) | 253.9 (96.0) | 241.0 (72.4) | 236.3 (77.2) | 226.3 (73.1) | 0.489 |
| Vitamin C (mg) | 80.3 (40.3) | 76.1 (28.1) | 71.4 (26.0) | 66.7 (24.1) | 0.414 |
| SFA (g) | 7.7 (1.6) | 7.4 (1.6) | 7.7 (1.7) | 7.8 (1.6) | 0.418 |
| MUFA (g) | 10.1 (1.6) | 10.5 (2.4) | 10.7 (2.4) | 10.8 (2.5) | 0.605 |
| PUFA (g) | 8.0 (1.3) | 8.5 (2.0) | 8.5 (2.0) | 8.5 (2.0) | 0.407 |
| n‐3 PUFA (mg) | 1373.8 (264.7) | 1463.8 (341.4) | 1458.9 (338.3) | 1444.3 (326.8) | 0.333 |
| n‐6 PUFA (mg) | 6672.9 (1078.2) | 7132.0 (1747.0) | 7197.6 (1735.9) | 7300.0 (1797.4) | 0.376 |
| n‐3 HUFA (mg) | 442.2 (173.1) | 477.4 (200.9) | 452.1 (186.0) | 423.6 (156.8) | 0.230 |
| Cholesterol (mg) | 163.4 (41.5) | 157.5 (48.6) | 159.9 (42.5) | 160.9 (41.4) | 0.183 |
| SDF (g) | 1.4 (0.5) | 1.4 (0.4) | 1.4 (0.4) | 1.4 (0.4) | 0.637 |
| IDF (g) | 5.8 (2.0) | 5.7 (1.4) | 5.6 (1.5) | 5.5 (1.3) | 0.894 |
| TDF (g) | 7.8 (2.4) | 7.7 (2.0) | 7.7 (2.0) | 7.5 (1.9) | 0.832 |
Abbreviations: HUFA, highly unsaturated fatty acids; IDF, insoluble dietary fibre; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; SDF, soluble dietary fibre; SFA, saturated fatty acids; TDF, total dietary fibre.
Calculated the nutrient intake per 1000 kcal of total energy intake.
Adjusted for age, area, total energy intake, smoking history, education level, employment status, BMI, daily life activity and leisure‐time exercise.
The association between the number of teeth and food group consumption is shown in Table 3. In men, seafood consumption was negatively associated with the number of teeth (p = 0.041), while in women, bread consumption was positively associated with the number of teeth (p = 0.048). The multiple regression analysis showed a significant positive association between the number of teeth and green tea in men and dairy products, green‐yellow vegetables, mushrooms and seaweed in women (Table S2).
TABLE 3.
Association of number of teeth present with food group consumption by sex.
| Food group a | Number of teeth present | p for trend b | |||
|---|---|---|---|---|---|
| 0 | 1–19 | 20–27 | 28–32 | ||
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | ||
| a. Men | |||||
| Rice (g) | 226.9 (71.6) | 230.6 (68.1) | 229.5 (68.0) | 227.2 (67.2) | 0.599 |
| Bread (g) | 22.3 (20.9) | 22.9 (27.0) | 24.4 (25.5) | 24.8 (25.3) | 0.634 |
| Noodles (g) | 23.3 (25.0) | 23.4 (25.0) | 22.8 (23.6) | 24.2 (23.2) | 0.998 |
| Potatoes (g) | 8.5 (9.1) | 7.9 (6.6) | 7.8 (6.9) | 7.6 (5.4) | 0.615 |
| Confectioneries (g) | 7.9 (6.9) | 8.8 (8.4) | 8.2 (7.2) | 8.3 (6.7) | 0.562 |
| Oils and fats (g) | 7.6 (4.9) | 7.3 (4.0) | 7.9 (4.4) | 8.2 (4.3) | 0.730 |
| Pulses (g) | 31.7 (17.8) | 30.4 (17.1) | 29.7 (16.1) | 28.9 (14.9) | 0.777 |
| Seafood (g) | 35.2 (24.7) | 27.6 (14.9) | 27.5 (15.2) | 26.9 (13.3) | 0.041 |
| Meats (g) | 16.8 (9.2) | 17.2 (10.2) | 18.8 (10.6) | 20.4 (11.2) | 0.378 |
| Eggs (g) | 9.4 (7.5) | 10.3 (9.0) | 9.6 (7.1) | 10.6 (7.8) | 0.839 |
| Milk and dairy products (g) | 47.2 (43.5) | 55.1 (55.7) | 57.0 (51.9) | 58.8 (55.0) | 0.544 |
| Green‐yellow vegetables (g) | 34.5 (22.0) | 30.6 (24.1) | 31.3 (21.3) | 32.1 (19.3) | 0.638 |
| Other vegetables (g) | 30.6 (18.2) | 29.3 (18.4) | 29.5 (18.5) | 30.0 (16.8) | 0.739 |
| Fruit (g) | 28.1 (41.1) | 27.5 (27.0) | 26.2 (24.8) | 24.9 (22.7) | 0.873 |
| Mushrooms (g) | 4.2 (4.5) | 3.1 (2.8) | 3.2 (2.8) | 3.3 (2.8) | 0.113 |
| Seaweed (g) | 1.0 (1.2) | 0.9 (0.8) | 0.8 (0.7) | 0.8 (0.7) | 0.186 |
| Alcoholic beverage (g) | 0.6 (1.1) | 0.5 (0.6) | 0.5 (0.6) | 0.5 (0.5) | 0.667 |
| Coffee (g) | 67.7 (50.9) | 60.1 (57.4) | 61.3 (57.7) | 64.9 (60.1) | 0.372 |
| Green tea (g) | 193.8 (154.7) | 200.6 (137.7) | 201.2 (135.3) | 190.7 (132.6) | 0.245 |
| b. Women | |||||
| Rice (g) | 189.7 (57.1) | 177.1 (62.2) | 168.3 (60.8) | 165.6 (56.9) | 0.389 |
| Bread (g) | 22.1 (19.0) | 31.1 (25.2) | 34.4 (24.9) | 36.5 (24.6) | 0.048 |
| Noodles (g) | 23.9 (24.7) | 25.7 (23.8) | 25.8 (21.5) | 27.0 (20.7) | 0.892 |
| Potatoes (g) | 16.7 (11.2) | 14.1 (11.4) | 13.1 (9.3) | 12.1 (9.3) | 0.314 |
| Confectioneries (g) | 18.1 (17.1) | 14.4 (11.6) | 14.7 (11.4) | 14.4 (11.1) | 0.206 |
| Oils and fats (g) | 9.0 (3.9) | 9.8 (5.6) | 10.0 (5.0) | 10.3 (5.3) | 0.398 |
| Pulses (g) | 41.9 (23.3) | 38.8 (21.6) | 35.5 (19.8) | 33.4 (18.0) | 0.440 |
| Seafood (g) | 29.5 (12.9) | 33.2 (16.7) | 31.1 (15.5) | 28.7 (13.5) | 0.104 |
| Meats (g) | 29.7 (14.8) | 23.1 (13.7) | 24.9 (13.5) | 26.2 (13.3) | 0.058 |
| Eggs (g) | 15.9 (10.1) | 12.7 (10.0) | 13.2 (8.9) | 13.7 (8.6) | 0.086 |
| Milk and dairy products (g) | 93.0 (79.3) | 80.2 (63.2) | 92.3 (67.6) | 93.5 (67.0) | 0.795 |
| Green‐yellow vegetables (g) | 55.2 (37.0) | 50.8 (29.5) | 52.7 (33.4) | 50.0 (31.1) | 0.943 |
| Other vegetables (g) | 57.3 (35.5) | 49.9 (28.6) | 50.2 (27.9) | 48.6 (26.7) | 0.425 |
| Fruit (g) | 56.4 (50.0) | 57.3 (48.7) | 50.6 (41.8) | 43.9 (38.7) | 0.832 |
| Mushrooms (g) | 6.2 (5.6) | 6.2 (4.8) | 6.5 (5.0) | 6.3 (4.6) | 0.449 |
| Seaweed (g) | 1.5 (1.0) | 1.4 (1.2) | 1.4 (1.2) | 1.3 (1.1) | 0.837 |
| Alcoholic beverage (g) | 0.1 (0.4) | 0.2 (0.4) | 0.1 (0.3) | 0.1 (0.3) | 0.644 |
| Coffee (g) | 89.1 (69.6) | 78.2 (69.9) | 81.5 (69.2) | 85.4 (69.1) | 0.255 |
| Green tea (g) | 295.7 (123.5) | 260.7 (151.3) | 225.0 (158.5) | 204.2 (160.1) | 0.276 |
Calculated the food group consumption per 1000 kcal of total energy intake.
Adjusted for age, area, total energy intake, smoking history, education level, employment status, BMI, daily life activity and leisure‐time exercise.
4. Discussion
This study showed that the number of teeth was negatively associated with the intake of vitamin D, n‐3 HUFA and seafood in adult men with no history of the four major diseases, while in women, only bread consumption was positively associated with the number of teeth. In the analysis with the number of teeth as a continuous variable, green tea had a significant positive correlation with the number of teeth in men. Moreover, several nutrients and foods were positively correlated with the number of teeth in women.
The subjects in this study showed significant differences in employment rates between men and women (Table 1). Given that men who work are more likely to eat lunch out and women who do not work are more likely to eat at home, differences are inevitably observed in the content of their meals. Sam et al. reported that employment status is associated with eating habits [25]. In this case, differences in employment status could be one reason for the differences in the results between men and women. In addition, women are more knowledgeable about food and nutrition than men and may be able to improve their eating habits, for example, by devising new cooking methods, even after they lose their teeth. Therefore, it may be that there was not much difference in the amount of food intake, whether women had teeth or not, making it difficult to find a significant difference.
Vitamin D predicted a positive correlation due to its importance in tooth formation [26], but the opposite was observed in this study. Since fish and other animal products are the main sources of vitamin D, this study likely reflects seafood intake. While n‐3 HUFA indicates eicosapentaenoic acid and docosahexaenoic acid (DHA), these are abundant in fatty fish, such as sardines and mackerel, and their role has been clarified since around 1970, attracting attention [27]. In relation to oral health, one study reported that individuals who consume more DHA‐containing foods have less periodontitis [28], which contrasts with our results. According to the National Health and Nutrition Examination Survey in Japan, younger age groups, especially those in their 40s and younger, have significantly lower seafood intake than those in their 50s and older [29, 30]. Therefore, we examined the age group of men without teeth and found that 80% of them were in their 50s or older. Thus, it is possible that the age group and fish intake had a greater influence on the results of this study than did the number of teeth related to oral health.
Regarding bread consumption, edentulous individuals reported consuming bread less frequently than those with natural teeth. Fewer teeth may reduce the efficiency of mastication and decrease saliva secretion. Oral dryness is also likely to occur due to functional decline with age. While bread is generally soft and easy to chew, its water content is relatively low and requires saliva secretion during chewing. This makes chewing difficult for those with xerostomia. In addition, French bread and toast tend to be even more difficult to chew because of their low moisture content and hardness. Sheiham et al. noted that toast is one of the foods that are difficult for people with dental problems to eat, with the exception of sliced bread and crusty bread [4]. In other words, the type of bread and the method of preparation affect the ease of eating it. Recent advances in bread‐making technology have led to the sale of various types of bread, including high‐quality bread made from domestic wheat, healthy bread made from whole grains and bread for nursing care meals. Further studies comparing different types of bread and cooking methods are required.
A multiple regression analysis using the number of teeth as a continuous variable revealed that, while green tea showed a significant positive correlation with the number of teeth in men, a variety of nutrients and foods were positively correlated with the number of teeth in women, including protein, fat, potassium, calcium, carotene, vitamin B2, SFA, bread, sweets, dairy products, green and yellow vegetables, mushrooms and seaweeds. Men tend to cook less frequently, have poorer cooking skills and show lower motivation to change their eating habits compared to women [31]. This suggests that older men seldom modify their diet, weakening the association between dental status and food diversity in males. Additionally, tooth loss and poor chewing ability, linked with depression [32], a risk factor for decreased dietary diversity [33] and more prevalent in women than in men [34], likely make men less psychologically affected by tooth loss, not significantly impacting their food choices and nutrient intake.
Our findings indicate that consuming green‐yellow vegetables, mushrooms and seaweed correlates with higher intakes of key micronutrients including potassium, calcium, carotene and vitamin B2. Similarly, an association between tooth loss and milk and dairy product consumption may link to protein, fat and saturated fatty acid intake, as these are principal nutrients in these foods. These correlations align with how dietary patterns affect nutrient intake and health outcomes. The association between the number of teeth and vegetable consumption has been reported in the study of Japanese subjects [9, 11]. Vegetables, being fibrous, require mastication, primarily using the molars. The number of teeth, along with the contact and position of the teeth, should be carefully evaluated. Dairy products, rich in calcium essential for tooth structure, have been linked to dental health. Wakai et al. [9] observed a correlation between reduced dairy consumption and fewer teeth. Additionally, Shimazaki et al. [35] noted that increased consumption of yogurt and lactic acid beverages significantly lowered periodontal disease prevalence, unlike milk, which showed no effect. This distinction may arise from the lactic acid bacteria in yogurt and beverages rather than calcium. As the study's questionnaire covered milk and yogurt, it likely captured effects attributable to calcium and lactic acid bacteria.
Protein [8, 12], fat [36], potassium [7, 12], carotene [9] and dairy [9] intakes were consistent with those of previous studies. Although vitamin B2 has not been directly associated with tooth count, Ariizumi et al. [37] reported that oral health behaviours, such as interdental cleaning and tongue brushing, correlate with a higher dietary intake of vitamin B2. Additionally, Marcel Hrubša et al. [38] reported that milk and other dairy products contributed the most to overall riboflavin (vitamin B2) uptake, followed by meat, cereals and vegetables. Eggs, legumes, nuts, mushrooms and organ meat (liver) are also important sources of vitamin B2. In other words, the results may reflect the impact of several food groups. In this study, SFA was positively correlated with the number of teeth, while previous studies showed a negative correlation [7]. Because dairy products are major contributors to dietary SFA [39], this study may reflect their intake. A survey of Japanese schoolchildren indicated that the primary dietary sources of SFA were meat and dairy products, followed by confectioneries [40], suggesting that the present study also reflects the amount of confectionery intake.
Mushrooms and seaweed have not been reported to show a direct association with the number of teeth present. Mushrooms are high in fibre and should be chewed thoroughly. Additionally, the calcium and phosphorus found in natural mushrooms have been shown to strengthen teeth and bones [41]. Seaweeds are rich in calcium [42], and Hudiyati et al. [43] showed that fucoidan, produced by the cell walls of brown seaweed, had various therapeutic potentials in the field of dentistry, including anti‐cancer, anti‐inflammatory, radioprotection, protection of dental pulp tissue and bone regeneration.
This study's strengths lie in its large, diverse sample of middle‐aged adults and rigorous control for confounders such as BMI and smoking history. It specifically examines the link between diet and disease, excluding those with prior illnesses. Given the distinct backgrounds of the male and female participants, the analysis was appropriately stratified by sex and adjusted for variables including medical and educational history.
The study's limitations include its cross‐sectional design, which prevents determining causality between tooth count and dietary intake, as it is unclear whether tooth loss leads to decreased food consumption or vice versa. Additionally, the lack of data on dentures, bridges or implants, common in individuals with fewer teeth, is a significant oversight since these can markedly enhance chewing ability and food intake, particularly affecting the correlation in those with 0 to 19 teeth. Further limitations are the absence of detailed oral examinations, like periodontal assessments and potential discrepancies between self‐reported and actual tooth counts, though prior research has confirmed the reliability of self‐reported data in Japanese adults [44]. Fifth, the fixed food list in the FFQ may not capture all consumed foods, potentially biasing results if it fails to reflect regional or new dietary options. Sixth, nutrient intake might be under‐ or overestimated due to errors in reporting specific food groups. Seventh, the study excludes diseases beyond the four major ones, although other diet‐related diseases may exist; some apparently healthy individuals may have undiagnosed conditions [45]. Finally, the bias arising from the high proportion of individuals with 28 teeth should be considered.
5. Conclusion
In conclusion, this study provides valuable insights into the association between the number of teeth and poor nutritional status in Japanese adults. Overall, these findings underscore the importance of good oral health, regular dental check‐ups and proper oral hygiene practices.
Author Contributions
Mayuka Asaeda: contributed to conception, design, data analysis and interpretation and drafted the manuscript. Rumi Nishimura, Nishiki Arimoto and Tomoko Maehara: contributed to the interpretation of data and critically revised the manuscript. Shino Suma, Mineko Tsukamoto, Yuka Kadomatsu, Yoko Kubo, Rieko Okada, Mako Nagayoshi, Takashi Tamura, Asahi Hishida, Kenji Takeuchi, Chiho Goto, and Nahomi Imaeda: contributed to data acquisition and critically revised the manuscript. Kenji Wakai and Mariko Naito: contributed to conception, design and data acquisition and critically revised the manuscript. All the authors gave their final approval and agreed to be accountable for all aspects of the work.
Ethics Statement
This study was approved by the Ethics Committee of Nagoya University School of Medicine (approval numbers were 2008‐0618‐16 253 and 2011‐1248‐15) and Hiroshima University (approval numbers were E2020‐9253 and E2020‐9254). Written informed consent was obtained from all participants, and they were informed about the voluntary nature of participation, their right to withdraw at any point and the confidentiality of their data. This study was conducted in accordance with the principles of the World Medical Association's Declaration of Helsinki.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1 Association of number of teeth present with nutrient intake by sex: Analysis using multiple regression analysis.
Table S2. Association of number of teeth present with food group consumption by sex: Analysis using multiple regression analysis.
Acknowledgements
We are grateful to all participants of the baseline survey of the J‐MICC Study for their cooperation. This study was supported by Grant‐in‐Aid for Scientific Research for Priority Areas of Cancer (No. 17015018) and Innovative Areas (No. 221S0001) and by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant (No. 16H06277 and 22H04923 [CoBiA]) from the Japanese Ministry of Education, Culture, Sports, Science and Technology.
Funding: This work was supported by Japan Society for the Promotion of Science (JSPS) [10.13039/501100001691]; Grant‐in‐Aid for Scientific Research for Priority Areas of Cancer (No. 17015018) and Innovative Areas (No. 221S0001).
Data Availability Statement
The datasets used and analysed in the current study are available from the corresponding author upon reasonable request and approval of the ethics committee.
References
- 1. Brennan D. S., Spencer A. J., and Roberts‐Thomson K. F., “Tooth Loss, Chewing Ability and Quality of Life,” Quality of Life Research 17, no. 2 (2008): 227–235, 10.1007/s11136-007-9293-2. [DOI] [PubMed] [Google Scholar]
- 2. Furuya J., Suzuki H., Hidaka R., et al., “Oral Health Status and Its Association With Nutritional Support in Malnourished Patients Hospitalised in Acute Care,” Gerodontology 39, no. 3 (2022): 282–290, 10.1111/ger.12582. [DOI] [PubMed] [Google Scholar]
- 3. Krall E., Hayes C., and Garcia R., “How Dentition Status and Masticatory Function Affect Nutrient Intake,” Journal of the American Dental Association (1939) 129, no. 9 (1998): 1261–1269, 10.14219/jada.archive.1998.0423. [DOI] [PubMed] [Google Scholar]
- 4. Sheiham A., Steele J. G., Marcenes W., Tsakos G., Finch S., and Walls A. W. G., “Prevalence of Impacts of Dental and Oral Disorders and Their Effects on Eating Among Older People; a National Survey in Great Britain,” Community Dentistry and Oral Epidemiology 29, no. 3 (2001): 195–203, 10.1034/j.1600-0528.2001.290305.x. [DOI] [PubMed] [Google Scholar]
- 5. Hung H.‐C., Willett W., Ascherio A., Rosner B. A., Rimm E., and Joshipura K. J., “Tooth Loss and Dietary Intake,” Journal of the American Dental Association (1939) 134, no. 9 (2003): 1185–1192, 10.14219/jada.archive.2003.0353. [DOI] [PubMed] [Google Scholar]
- 6. Sahyoun N. R., Lin C.‐L., and Krall E., “Nutritional Status of the Older Adult Is Associated With Dentition Status,” Journal of the American Dietetic Association 103, no. 1 (2003): 61–66, 10.1053/jada.2003.50003. [DOI] [PubMed] [Google Scholar]
- 7. Hung H.‐C., Colditz G., and Joshipura K. J., “The Association Between Tooth Loss and the Self‐Reported Intake of Selected CVD‐Related Nutrients and Foods Among US Women,” Community Dentistry and Oral Epidemiology 33, no. 3 (2005): 167–173, 10.1111/j.1600-0528.2005.00200.x. [DOI] [PubMed] [Google Scholar]
- 8. Yoshihara A., Watanabe R., Nishimuta M., Hanada N., and Miyazaki H., “The Relationship Between Dietary Intake and the Number of Teeth in Elderly Japanese Subjects,” Gerodontology 22, no. 4 (2005): 211–218, 10.1111/j.1741-2358.2005.00083.x. [DOI] [PubMed] [Google Scholar]
- 9. Wakai K., Naito M., Naito T., et al., “Tooth Loss and Intakes of Nutrients and Foods: A Nationwide Survey of Japanese Dentists,” Community Dentistry and Oral Epidemiology 38, no. 1 (2010): 43–49, 10.1111/j.1600-0528.2009.00512.x. [DOI] [PubMed] [Google Scholar]
- 10. De Marchi R. J., Hugo F. N., Padilha D. M. P., et al., “Edentulism, Use of Dentures and Consumption of Fruit and Vegetables in South Brazilian Community‐Dwelling Elderly,” Journal of Oral Rehabilitation 38, no. 7 (2011): 533–540, 10.1111/j.1365-2842.2010.02189.x. [DOI] [PubMed] [Google Scholar]
- 11. Yoshida M., Kikutani T., Yoshikawa M., Tsuga K., Kimura M., and Akagawa Y., “Correlation Between Dental and Nutritional Status in Community‐Dwelling Elderly Japanese,” Geriatrics & Gerontology International 11, no. 3 (2011): 315–319, 10.1111/j.1447-0594.2010.00688.x. [DOI] [PubMed] [Google Scholar]
- 12. Iwasaki M., Yoshihara A., Ogawa H., et al., “Longitudinal Association of Dentition Status With Dietary Intake in Japanese Adults Aged 75 to 80 Years,” Journal of Oral Rehabilitation 43, no. 10 (2016): 737–744, 10.1111/joor.12427. [DOI] [PubMed] [Google Scholar]
- 13. Lamy M., Mojon P. H., Kalykakis G., Legrand R., and Butz‐Jorgensen E., “Oral Status and Nutrition in the Institutionalized Elderly,” Journal of Dentistry 27, no. 6 (1999): 443–448, 10.1016/S0300-5712(99)00002-0. [DOI] [PubMed] [Google Scholar]
- 14. Zhu Y. and Hollis J. H., “Associations Between the Number of Natural Teeth and Metabolic Syndrome in Adults,” Journal of Clinical Periodontology 42, no. 2 (2015): 113–120, 10.1111/jcpe.12361. [DOI] [PubMed] [Google Scholar]
- 15. Woo H. D., Park S., Oh K., et al., “Diet and Cancer Risk in the Korean Population: A Meta‐ Analysis,” Asian Pacific Journal of Cancer Prevention 15, no. 19 (2014): 8509–8519, 10.7314/apjcp.2014.15.19.8509. [DOI] [PubMed] [Google Scholar]
- 16. Iizuka K. and Yabe D., “Dietary and Nutritional Guidelines for People With Diabetes,” Nutrients 15, no. 20 (2023): 4314, 10.3390/nu15204314. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Yanagisawa N., “The Future of Occupational and General Health in Japan,” Industrial Health 54, no. 3 (2016): 191–193, 10.2486/indhealth.54-191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Hamajima N. and J‐MICC Study Group , “The Japan Multi‐Institutional Collaborative Cohort Study (J‐MICC Study) to Detect Gene‐Environment Interactions for Cancer,” Asian Pacific Journal of Cancer Prevention 8, no. 2 (2007): 317–323. [PubMed] [Google Scholar]
- 19. Takeuchi K., Naito M., Kawai S., et al., “Study Profile of the Japan Multi‐Institutional Collaborative Cohort (J‐MICC) Study,” Journal of Epidemiology 31, no. 12 (2021): 660–668, 10.2188/jea.JE20200147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Tokudome S., Goto C., Imaeda N., Tokudome Y., Ikeda M., and Maki S., “Development of a Data‐Based Short Food Frequency Questionnaire for Assessing Nutrient Intake by Middle‐Aged Japanese,” Asian Pacific Journal of Cancer Prevention 5, no. 1 (2004): 40–43. [PubMed] [Google Scholar]
- 21. Tokudome Y., Goto C., Imaeda N., et al., “Relative Validity of a Short Food Frequency Questionnaire for Assessing Nutrient Intake Versus Three‐Day Weighed Diet Records in Middle‐Aged Japanese,” Journal of Epidemiology 15, no. 4 (2005): 135–145, 10.2188/jea.15.135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Imaeda N., Goto C., Sasakabe T., et al., “Reproducibility and Validity of Food Group Intake in a Short Food Frequency Questionnaire for the Middle‐Aged Japanese Population,” Environmental Health and Preventive Medicine 26, no. 1 (2021): 28, 10.1186/s12199-021-00951-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Imaeda N., Goto C., Tokudome Y., Hirose K., Tajima K., and Tokudome S., “Reproducibility of a Short Food Frequency Questionnaire for Japanese General Population,” Journal of Epidemiology 17, no. 3 (2007): 100–107, 10.2188/jea.17.100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Craig C. L., Marshall A. L., Sjöström M., et al., “International Physical Activity Questionnaire: 12‐Country Reliability and Validity,” Medicine & Science in Sports & Exercise 35, no. 8 (2003): 1381–1395, 10.1249/01.MSS.0000078924.61453.FB. [DOI] [PubMed] [Google Scholar]
- 25. Sam L., Craig T., Horgan G. W., and Macdiarmid J. I., “Association Between Hours Worked in Paid Employment and Diet Quality, Frequency of Eating out and Consuming Takeaways in the UK,” Public Health Nutrition 22, no. 18 (2019): 3368–3376, 10.1017/S1368980019002222. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Krall E. A., Wehler C., Garcia R. I., Harris S. S., and Dawson‐Hughes B., “Calcium and Vitamin D Supplements Reduce Tooth Loss in the Elderly,” American Journal of Medicine 111, no. 6 (2001): 452–456, 10.1016/s0002-9343(01)00899-3. [DOI] [PubMed] [Google Scholar]
- 27. Dyerberg J., Bang H. O., and Hjørne N., “Fatty Acid Composition of the Plasma Lipids in Greenland Eskimos,” American Journal of Clinical Nutrition 28, no. 9 (1975): 958–966, 10.1093/ajcn/28.9.958. [DOI] [PubMed] [Google Scholar]
- 28. Iwasaki M., Yoshihara A., Moynihan P., Watanabe R., Taylor G. W., and Miyazaki H., “Longitudinal Relationship Between Dietary Omega‐3 Fatty Acids and Periodontal Disease,” Nutrition 26, no. 11–12 (2010): 1105–1109, 10.1016/j.nut.2009.09.010. [DOI] [PubMed] [Google Scholar]
- 29.“1‐1‐2(1). An Unprecedented “Shift Away From Fish,” (2006). Vol 9(10), https://warp.da.ndl.go.jp/collections/info:ndljp/pid/10368291/www.maff.go.jp/hakusyo/sui/h18/index.htm.
- 30.“1‐1‐2(1)Consumption Trends of Fisheries Products,” (2010), https://warp.da.ndl.go.jp/info:ndljp/pid/11287682/www.jfa.maff.go.jp/j/kikaku/wpaper/h22/pdf/h22_hakusyo6_2.pdf.
- 31. Hughes G., Bennett K. M., and Hetherington M. M., “Old and Alone: Barriers to Healthy Eating in Older Men Living on Their Own,” Appetite 43, no. 3 (2004): 269–276, 10.1016/j.appet.2004.06.002. [DOI] [PubMed] [Google Scholar]
- 32. Kimura Y., Ogawa H., Yoshihara A., et al., “Evaluation of Chewing Ability and Its Relationship With Activities of Daily Living, Depression, Cognitive Status and Food Intake in the Community‐Dwelling Elderly,” Geriatrics & Gerontology International 13, no. 3 (2013): 718–725, 10.1111/ggi.12006. [DOI] [PubMed] [Google Scholar]
- 33. Kimura Y., Wada T., Okumiya K., et al., “Eating Alone Among Community‐Dwelling Japanese Elderly: Association With Depression and Food Diversity,” Journal of Nutrition, Health & Aging 16, no. 8 (2012): 728–731, 10.1007/s12603-012-0067-3. [DOI] [PubMed] [Google Scholar]
- 34. Lopez A. D., Mathers C. D., Ezzati M., Jamison D. T., and Murray C. J. L., “Global and Regional Burden of Disease and Risk Factors, 2001: Systematic Analysis of Population Health Data,” Lancet 367, no. 9524 (2006): 1747–1757, 10.1016/S0140-6736(06)68770-9. [DOI] [PubMed] [Google Scholar]
- 35. Shimazaki Y., Shirota T., Uchida K., et al., “Intake of Dairy Products and Periodontal Disease: The Hisayama Study,” Journal of Periodontology 79, no. 1 (2008): 131–137, 10.1902/jop.2008.070202. [DOI] [PubMed] [Google Scholar]
- 36. Hamasaki T., Kitamura M., Kawashita Y., Ando Y., and Saito T., “Periodontal Disease and Percentage of Calories From Fat Using National Data,” Journal of Periodontal Research 52, no. 1 (2017): 114–121, 10.1111/jre.12375. [DOI] [PubMed] [Google Scholar]
- 37. Ariizumi M., Izumi M., and Akifusa S., “Correlation Between Dietary Intake of Vitamins and Oral Health Behaviors: A Cross‐Sectional Study,” International Journal of Environmental Research and Public Health 20, no. 7 (2023): 5243, 10.3390/ijerph20075243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Hrubša M., Siatka T., Nejmanová I., et al., “Biological Properties of Vitamins of the B‐Complex, Part 1: Vitamins B1, B2, B3, and B5,” Nutrients 14, no. 3 (2022): 484, 10.3390/nu14030484. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Givens D. I., “Saturated Fats, Dairy Foods and Health: A Curious Paradox?,” Nutrition Bulletin 42, no. 3 (2017): 274–282, 10.1111/nbu.12283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Asakura K. and Sasaki S., “SFA Intake Among Japanese Schoolchildren: Current Status and Possible Intervention to Prevent Excess Intake,” Public Health Nutrition 20, no. 18 (2017): 3247–3256, 10.1017/S1368980017002592. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Kumari J. and Jaiswal D. R. R., “Mushroom: Good Source for Better Nutrition and Health Benefits,” International Journal of Home Science 8 (2022): 312–316. [Google Scholar]
- 42. Macartain P., Gill C. I. R., Brooks M., Campbell R., and Rowland I. R., “Nutritional Value of Edible Seaweeds,” Nutrition Reviews 65, no. 12 Pt 1 (2007): 535–543, 10.1301/nr.2007.dec.535-543. [DOI] [PubMed] [Google Scholar]
- 43. Hudiyati M., Sunarintyas S., Ardhani R., and Isnansetyo A., “Therapeutic Potential of Fucoidan in Dentistry: A Review,” Journal of Herbmed Pharmacology 13, no. 2 (2024): 188–198, 10.34172/jhp.2024.48302. [DOI] [Google Scholar]
- 44. Matsui D., Yamamoto T., Nishigaki M., et al., “Validity of Self‐Reported Number of Teeth and Oral Health Variables,” BMC Oral Health 17, no. 1 (2016): 17, 10.1186/s12903-016-0248-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Jimba S., Nakagami T., Takahashi M., et al., “Prevalence of Non‐Alcoholic Fatty Liver Disease and Its Association With Impaired Glucose Metabolism in Japanese Adults,” Diabetic Medicine 22, no. 9 (2005): 1141–1145, 10.1111/j.1464-5491.2005.01582.x. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1 Association of number of teeth present with nutrient intake by sex: Analysis using multiple regression analysis.
Table S2. Association of number of teeth present with food group consumption by sex: Analysis using multiple regression analysis.
Data Availability Statement
The datasets used and analysed in the current study are available from the corresponding author upon reasonable request and approval of the ethics committee.