What this is
- This research examines food combinations and their relation to diet quality in Japanese adults using a food combination questionnaire (FCQ).
- The study involved 2233 participants aged 19-80 years across Japan, assessing their dietary habits through the FCQ and other dietary questionnaires.
- Key findings reveal that certain food combinations are positively associated with diet quality, particularly at breakfast, while associations vary by meal type.
Essence
- Common food combinations in Japanese meals, particularly 'rice, total vegetables, and tea and coffee', are positively linked to diet quality, especially at breakfast. However, these associations differ across meals.
Key takeaways
- The most frequently consumed food combinations included 'rice, total vegetables, and tea and coffee' for breakfast, lunch, and dinner. These combinations were consistently positively associated with diet quality for breakfast.
- For lunch, the associations between these food combinations and diet quality were weaker compared to breakfast, while for dinner, the associations were inconsistent, showing inverse relationships with the Healthy Eating Index (HEI-2015).
- Snacks often included 'confectioneries and tea and coffee', which showed weak associations with diet quality, indicating a need for better dietary choices in snacking.
Caveats
- The sample may not be representative of the entire Japanese population, as participants were volunteers and potentially more health-conscious. Further research is needed in a broader sample.
- Self-reported dietary data may introduce measurement errors, and the validity of the FCQ in estimating food combinations requires further rigorous assessment.
- Seasonal variations in food intake were not accounted for, which could affect the generalizability of the findings regarding average food combinations.
Definitions
- Healthy Eating Index 2015 (HEI-2015): A 100-point scale assessing diet quality based on compliance with dietary guidelines, with higher scores indicating better diet quality.
- Nutrient-Rich Food Index 9.3 (NRF9.3): A composite measure of nutrient density in the diet, calculated from the percentage of daily values for key nutrients minus disqualifying nutrients.
AI simplified
1. Introduction
Efforts to overcome the limitations of evaluating single nutrients and foods in isolation have led to a gradual shift in nutrition research to the evaluation of dietary patterns [1,2]. Although the investigation of dietary patterns is generally performed in terms of the daily intake of individual foods or food groups [3,4,5,6], an increasing number of studies have focused on dietary intake at the level of each eating occasion (i.e., breakfast, lunch, dinner, and snack) or meal patterns [7,8,9]. Studying dietary patterns at this level rather than overall dietary patterns might be more relevant considering synergies and interactions during digestion and metabolism [10]. Recent studies also suggest that not only the amount and content of food intake but also the circadian timing of food intake need to be considered [11,12,13]. Understanding food combinations at meals could be useful for the development of public health nutrition policies and recommendations.
However, little is known about the combinations of foods consumed simultaneously during specific eating occasions [14,15,16,17,18,19,20,21,22,23], mainly because of a lack of practical assessment tools. There exist a near-infinite number of feasible food combinations, resulting in an unmanageable number of individual meals. Thus, a practical examination of meal patterns or food combinations requires the development of unique codes for meals [15], and then the development of inexpensive and practical assessment tools (e.g., questionnaires) [9,22,23]. Such a meal coding system will also be essential in the development of Internet-delivered personalized dietary analysis [24] as well as Internet-based self-administered dietary assessment tools [25,26].
We recently applied the ‘frequent item sets’ data-mining method [27] to data from 16 day weighed dietary records obtained from 242 Japanese adults to characterize combinations of foods served as a meal, and then developed a meal coding system [22]. Analysis of a total of 14,734 meals identified 80 generic meals. As one example, a meal code for breakfast was built on the combination of vegetables, tea and coffee, rice, pulses, fruit, and dairy products. Interrogation of these 80 meal codes by principal components analysis identified 11 interpretable meal patterns; examples include patterns characterized by three main meals consisting of the combination of rice and vegetables. Taken together, this meal coding system may be useful for developing a practical assessment tool for combinations of foods consumed simultaneously during specific eating occasions.
In the present study, we developed a self-administered dietary assessment questionnaire, namely a food combination questionnaire (FCQ), to characterize food combinations in each meal (breakfast, lunch, dinner, and snacks) on the basis of our meal coding system (food combination database). We then examined food combinations in relation to the quality of the overall diet and each meal in a nationwide sample of Japanese adults.
2. Materials and Methods
2.1. Study Procedure and Participants
This cross-sectional analysis was based on data obtained from a nationwide survey conducted between October and December 2018. The target population consisted of apparently healthy Japanese aged 18–80 years living in private households in Japan. Initially, 32 (of 47) prefectures, which account for >85% of the total population of Japan, were selected on the basis of geographical diversity and feasibility of the survey, particularly the recruitment of research dietitians as collaborators. After being recruited in person or by email, a total of 475 research dietitians agreed to support the study by collecting data. Although we did not provide them with any specific training for this study, all of them had experience in dietary data collection. They then conducted the recruitment of participants from local communities.
Based on feasibility and human and financial resources, we decided to include 474 individuals (237 for each sex) for each of six age groups: 18–29, 30–39, 40–49, 50–59, 60–69, and 70–80 years (n = 2844 in total). The non-random sampling procedure was performed to reflect the proportion of the overall Japanese population in each region: Hokkaido 4%, Tohoku 7%, Kanto I 28%, Kanto II 8%, Hokuriku 4%, Tokai 12%, Kinki I 13%, Kinki II 3%, Chugoku 6%, Shikoku 3%, Kita-kyushu 7%, and Minami-kyushu 5% [28]. Inclusion criteria consisted of willingness to participate and community-dwelling (free-living) individuals. Exclusion criteria were dietitians, individuals living together with a dietitian, those working together with a research dietitian, those who had experienced dietary counseling from a doctor or dietitian, those taking insulin treatment for diabetes, those receiving dialysis treatment, and pregnant or lactating women. Participation of only one person per household was permitted. Consequently, a total of 2248 individuals participated in this study (response rate 79%).
Participants were asked to answer two questionnaires sequentially on dietary habits, namely the FCQ and a brief diet history questionnaire (BDHQ). Responses to both questionnaires were thoroughly checked by research dietitians and then by the first author (K.M.) at the study center. If any missing or erroneous responses were given, the participant was asked to complete the questions again in person or by telephone. For analysis, we excluded participants who did not answer both questionnaires (n = 3) and those aged outside the 18–80 year age range (n = 12), leaving 2233 participants aged 19–80 years. A flow diagram of participants included in the present analysis is shown in Figure S1.
The study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Ethics Committee of the University of Tokyo Faculty of Medicine (number 12031). Written informed consent was obtained from each participant, and from a parent or guardian for participants aged < 20 years.
2.2. BDHQ
2.2.1. General Description
Details of the BDHQ’s structure and calculation method of dietary intake have been published elsewhere [29,30]. In brief, the BDHQ is a four page self-administered questionnaire on dietary habits during the preceding month which generally takes 15 min to answer. It consists of structured questions asking about the consumption frequency of selected foods commonly consumed in Japan, as well as general dietary behavior and usual cooking methods. Estimates of daily intake of foods (58 items in total), energy, and selected nutrients were calculated using an ad hoc computer algorithm for the BDHQ, which incorporates the sex-specific portion size, determined mainly based on recipe books for Japanese dishes [29], and nutrient composition of each food item derived from the Standard Tables of Food Composition in Japan [31]. It should be noted that only overall dietary intake was calculated from the BDHQ.
The validity of the BDHQ was examined in 92 women and 92 men using a 16 day weighed dietary record as reference [29,30]. In brief, the median of Spearman correlation coefficients for food groups was 0.44 (range 0.14–0.82) in women and 0.48 (range 0.22–0.83) in men [29], while the median of Pearson correlation coefficients for nutrients was 0.54 (range 0.27–0.84) in women and 0.56 (range 0.19–0.81) in men [30].
2.2.2. Calculation of Diet Quality Scores
In this study, we used the Healthy Eating Index 2015 (HEI-2015) [32,33,34] and Nutrient-Rich Food Index 9.3 (NRF9.3) [35,36,37,38] as measures of diet quality. The HEI-2015 is a 100 point scale to assess compliance with the 2015–2020 Dietary Guidelines for Americans [39], with a higher score indicating a better quality of overall diet. The HEI-2015 consists of nine adequacy components (total fruits, whole fruits, total vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, and fatty acids as the ratio of the sum of polyunsaturated and monounsaturated fatty acids to saturated fatty acids) and four moderation components (refined grains, sodium, added sugars, and saturated fats). As described elsewhere [40], we calculated the HEI-2015 component and total scores based on energy-adjusted values of overall dietary intake, namely amount per 1000 kcal of energy or percentage of energy, except for fatty acids.
The NRF9.3 is a composite measure of the nutrient density of the total diet, calculated as the sum of the percentage of reference daily values (RDVs) for nine qualifying nutrients, namely protein, dietary fiber, vitamin A, vitamin C, vitamin D, calcium, iron, potassium, and magnesium, minus the sum of the percentage of RDVs for three disqualifying nutrients, namely added sugars, saturated fats, and sodium. RDVs were determined for sex and age categories, based on the Dietary Reference Intakes (DRIs) for Japanese, 2015 [41], except for added sugars, for which the conditional recommendation advocated by the World Health Organization (i.e., upper limit of 5% of energy) [42] was used because of the lack of a recommended value for added sugars in Japan, as well as their low intake levels [43]. As described elsewhere [40], we calculated the NRF9.3 component and total scores based on the overall daily intake of each nutrient for each participant, which was adjusted for energy intake by the density method and then normalized for the sex- and age-specific Estimated Energy Requirement for a moderate level of physical activity (from DRIs) and expressed as a percentage of the RDV. Higher NRF9.3 scores indicated a better quality of the overall diet.
The validity of the BDHQ in terms of HEI-2015 and NRF9.3 has been previously investigated against a 16 day weighed dietary record as reference [40]. Briefly, the Pearson correlation coefficient for the former was 0.52 in women (n = 121) and 0.43 in men (n = 121), while that for the latter was 0.61 in women and 0.37 in men.
2.3. FCQ
2.3.1. Food Combination Database
The basis of the FCQ is a food combination database (meal coding system) which was recently developed by our research group using four day weighed dietary record data collected in each season over a one year period (16 days in total) from 242 Japanese adults aged 31–81 years; the food combination database used in this study is shown in Table S1. A detailed description of the development of the database has been published elsewhere [22], and Figure S2 shows a flow diagram of the development process. Briefly, for all meal types, namely breakfast (n = 3788), lunch (n = 3823), dinner (n = 3856), and snacks (n = 3267), we categorized the most commonly consumed combinations of 17 selected food groups according to the ‘frequent item sets’ data-mining method [27]. An example of this procedure is shown in Figure S3. We estimated the nutrient content of each coded generic meal using the aggregation of the nutrient composition of individual meals assigned to that code. For each meal for each individual, we calculated the total amount (g), weight of each food group, and content of each nutrient using the Standard Tables of Food Composition in Japan [44]. We then calculated mean values of these variables based on all meals classified for each meal code. A compilation of these mean values thus comprised the food combination database. In this study, all the food combinations (meal codes) except for “all other combinations” for each meal were used (n = 76).
2.3.2. Development of FCQ
Our priority in developing the FCQ was to collect information which is sufficient to distinguish food combinations using as few questions as possible. Careful scrutiny of the food combination database showed that there was no meal code in which staple foods for Japanese (i.e., rice, bread, and noodles) appeared in combination, whereas many meal codes included at least one of these staple foods. We thus considered that a format consisting of questions on staple foods followed by questions on accompanying foods is the best for FCQ. Figure 1 shows the structure of the FCQ. Based on the food combination database (meal codes), staple foods in the FCQ were defined as follows: rice and bread for breakfast; rice, bread, and noodles for lunch; rice for dinner; and no staple food for snacks. For each staple food for each meal type, accompanying foods were then defined as food groups which had contributed to the determination of meal codes in the development of food combination database [22], so that we could collect minimum information for distinguishing food combinations.
In the FCQ, we asked about consumption frequency as the number of days the food was consumed per week for each staple food for each meal type; for snacks, consumption frequency was asked about in a similar way without specifying any staple foods. The reference time period was defined as the preceding month, which was in accordance with the BDHQ. For accompanying foods, we asked about relative consumption frequency, namely how often the food was consumed with the staple food, with possible answers of ‘always’, ‘sometimes’, and ‘never’. The developed FCQ was a four page, self-administered questionnaire, which generally took 5 min to answer according to a pretest conducted among 19 individuals.
Structure of the food combination questionnaire (FCQ). In the FCQ, consumption frequency of each staple food in each meal type was enquired about in terms of the number of days with consumption per week during the preceding month; for snacks, consumption frequency was similarly enquired about without specifying any staple foods. For accompanying foods for each staple food, relative consumption frequency was enquired about, namely how often the food was consumed with the staple food, with the possible answers of ‘always’, ‘sometimes’, and ‘never’. The food group ‘fish’ includes shellfish; the food group ‘pulses’ includes nuts. ALB, alcoholic beverages; CON, confectioneries; DRP, dairy products; NNB, tea and coffee (i.e., nonalcoholic and noncaloric beverages); SDK, soft drinks; TVG, total vegetables.
2.3.3. Development of an Algorithm for Determining Food Combinations
We developed an ad hoc computer algorithm for determining the food combinations consumed by each participant based on the information collected from the FCQ. An example of the calculation of the daily consumption frequency of food combinations is described in. First, based on the possible answers for accompanying foods, namely ‘always’, ‘sometimes’, and ‘never’, in the FCQ, each of the food combinations (meal codes) was characterized in the same manner. For this, foods labeled as ‘always’ were those always included in the meal code, foods labeled as ‘never’ were those not included (always excluded) in the meal code, and foods labeled as ‘sometimes’ were all other foods. All the answers were then coded based on the coding rule. A coefficient value was then calculated for each meal code for each staple food for each meal. If the coefficient value was negative, a value of zero was assigned. Finally, the consumption frequency of each meal code was calculated based on the consumption frequency of the staple food weighted by the coefficient value as a percentage of the sum of coefficient values with the same staple food in each meal. Figure S4
2.3.4. Calculation of Dietary Intakes
Generally speaking, estimates of daily intakes of food groups, energy, and nutrients from each meal code were calculated as the daily consumption frequency of each food combination (meal code) multiplied by the composition of each meal code. However, a different calculation procedure was used for accompanying foods, as well as the nutrients and energy derived from these foods, for each staple food for each meal. As described in Figure S5, the daily consumption frequency of accompanying foods was calculated as the daily consumption frequency of the corresponding staple foods multiplied by a factor determined based on the answer for relative consumption frequency. Estimates of daily intakes of accompanying foods were then calculated as the consumption frequency multiplied by the average composition, which was weighted by the number of appearances in the original meal composition database [22], of all meal codes included in the respective staple food (for example, meal codes 1101–1108, 1301, 1302, and 1401 for rice for breakfast). Daily intakes as well as intakes from each meal were calculated by summing all of the estimates calculated as described above.
2.3.5. Calculation of Diet Quality Scores
Before development of the food combination database, we merged the original database, which consisted of individual food items compiled in the Standard Tables of Food Composition in Japan [44], with a cup and ounce equivalent database needed to estimate the HEI-2015 [40]. Thus, all of the estimates needed to calculate the HEI-2015 were obtained in the same way as the calculation of intakes of food groups, energy, and nutrients, described in the previous section. For the overall diet as well as for each meal, both the HEI-2015 and NRF9.3 were calculated based on the information derived from the FCQ, using the same procedure used in the BDHQ.
We compared overall diet quality scores and overall dietary intakes assessed by the FCQ and those assessed by the BDHQ. As shown in, the Spearman correlation coefficients for total scores of HEI-2015 and NRF9.3 were 0.49 and 0.48, respectively, while the median value of Spearman correlation coefficients for 19 food groups was 0.42 (range 0.07 to 0.82). These results suggest that the FCQ potentially has sufficient ability to estimate intakes, for the overall diet at least. Table S2
2.4. Statistical Analysis
All statistical analyses were performed using SAS statistical software (version 9.4, SAS Institute Inc., Cary, NC, USA). Data are presented as means ± standard deviations for diet quality scores and as medians and 25th and 75th percentiles for food group intakes and food combinations for each meal. Spearman correlation coefficients were calculated among diet quality scores. Associations between food group intakes in each meal and diet quality scores for each meal were examined using the Spearman correlation coefficients. Associations between food combinations in each meal and diet quality scores for each meal were also examined using the Spearman correlation coefficients. Finally, associations between food combinations in each meal and overall diet quality scores were examined using the Spearman correlation coefficients.
3. Results
The present analysis included 2233 Japanese adults (1070 men and 1163 women aged 19–80 years) with a mean age of 50 years (Table 1). The mean HEI-2015 for overall diet was 53.3 (standard deviation 2.7) while the mean NRF9.3 for overall diet was 709 (standard deviation 56).
| Variable | Total (= 2233)n | Men (= 1070)n | Women (= 1163)n |
|---|---|---|---|
| Age (years) | 50.1 ± 17.3 | 50.3 ± 17.2 | 50.0 ± 17.5 |
| Body height (cm)b | 162.6 ± 8.9 | 169.4 ± 6.3 | 156.3 ± 5.9 |
| Body weight (kg)b | 60.9 ± 12.1 | 68.0 ± 10.9 | 54.4 ± 9.0 |
| Body mass index (kg/m)2c | 22.9 ± 3.5 | 23.7 ± 3.3 | 22.3 ± 3.5 |
3.1. Quality of Breakfast, Lunch, Dinner, and Snacks
In this population, dinner was, on average, the top contributor to total energy intake, followed, in order, by lunch, breakfast, and snacks (Table 2). Diet quality as assessed by the HEI-2015 and NRF9.3 was also highest for dinner, followed by lunch, breakfast, and snacks. As theoretically expected, the quality of each meal was positively correlated with that of the overall diet (Spearman r: 0.29–0.71 for HEI-2015 and 0.33–0.71 for NRF9.3) (Table S3). Nevertheless, the correlation among meals was relatively weak (Spearman r: 0.11–0.21 for HEI-2015 and 0.16–0.38 for NRF9.3). The correlation between HEI-2015 and NRF9.3 was considerably high for total diet, breakfast, lunch, and snacks, but not for dinner (Spearman r: 0.67, 0.82, 0.75, 0.77, and 0.17, respectively).
| Variable | Maximum Score | Breakfast | Lunch | Dinner | Snacks |
|---|---|---|---|---|---|
| Energy (kcal/day) | --- | 382 ± 111 | 551 ± 100 | 621 ± 69 | 101 ± 54 |
| Percentage of total energy | --- | 22.8 ± 5.8 | 33.2 ± 5.3 | 37.9 ± 5.4 | 6.1 ± 3.1 |
| HEI-2015b | 100 | 46.8 ± 7.7 | 52.2 ± 2.9 | 55.3 ± 4.2 | 46.4 ± 6.6 |
| Total fruits | 5 | 1.7 ± 1.4 | 1.9 ± 0.5 | 1.1 ± 0.6 | 3.0 ± 1.9 |
| Whole fruits | 5 | 2.6 ± 2.0 | 3.6 ± 0.7 | 2.0 ± 1.1 | 3.3 ± 2.3 |
| Total vegetables | 5 | 3.5 ± 1.7 | 4.0 ± 1.2 | 4.9 ± 0.6 | 2.1 ± 0.7 |
| Greens and beans | 5 | 2.9 ± 1.6 | 2.6 ± 1.0 | 4.4 ± 0.9 | 2.1 ± 0.5 |
| Whole grains | 10 | 0.5 ± 0.3 | 1.4 ± 0.6 | 0.7 ± 0.1 | 0.3 ± 0.2 |
| Dairy | 10 | 4.3 ± 3.2 | 1.6 ± 1.5 | 1.0 ± 0.2 | 3.3 ± 2.5 |
| Total protein foods | 5 | 3.6 ± 1.0 | 3.7 ± 0.7 | 5.0 ± 0.2 | 2.7 ± 0.8 |
| Seafood and plant proteins | 5 | 4.1 ± 1.2 | 4.7 ± 0.9 | 5.0 ± 0.3 | 4.8 ± 1.0 |
| Fatty acids | 10 | 4.9 ± 3.2 | 8.7 ± 1.9 | 9.8 ± 0.5 | 1.7 ± 2.3 |
| Refined grains | 10 | 0.4 ± 1.7 | 0.0 ± 0.7 | 1.6 ± 2.8 | 3.9 ± 2.5 |
| Sodium | 10 | 1.1 ± 2.1 | 0.1 ± 0.8 | 0.0 ± 0.3 | 9.9 ± 0.4 |
| Added sugars | 10 | 9.3 ± 2.1 | 10.0 ± 0.2 | 10.0 ± 0.0 | 1.1 ± 2.1 |
| Saturated fats | 10 | 8.0 ± 2.7 | 9.8 ± 0.8 | 9.8 ± 0.6 | 8.1 ± 1.7 |
| NRF9.3c | 900 | 610 ± 277 | 623 ± 78 | 733 ± 61 | 281 ± 107 |
| Protein | 100 | 99 ± 10 | 100 ± 4 | 100 ± 3 | 91 ± 20 |
| Dietary fiber | 100 | 78 ± 18 | 74 ± 13 | 86 ± 15 | 68 ± 18 |
| Vitamin A | 100 | 59 ± 22 | 64 ± 16 | 96 ± 9 | 53 ± 14 |
| Vitamin C | 100 | 81 ± 25 | 87 ± 15 | 95 ± 12 | 92 ± 22 |
| Vitamin D | 100 | 81 ± 24 | 81 ± 22 | 98 ± 10 | 46 ± 14 |
| Calcium | 100 | 87 ± 20 | 66 ± 15 | 75 ± 13 | 83 ± 23 |
| Iron | 100 | 88 ± 18 | 90 ± 16 | 96 ± 9 | 89 ± 22 |
| Potassium | 100 | 89 ± 15 | 78 ± 11 | 96 ± 8 | 94 ± 21 |
| Magnesium | 100 | 90 ± 13 | 81 ± 8 | 98 ± 6 | 91 ± 20 |
| Added sugars | ---d | 51 ± 200 | 3 ± 11 | 1 ± 4 | 393 ± 100 |
| Saturated fats | ---d | 30 ± 36 | 4 ± 13 | 6 ± 11 | 33 ± 22 |
| Sodium | ---d | 61 ± 32 | 90 ± 29 | 101 ± 26 | 0 ± 2 |
3.2. Food Group Intake in Breakfast, Lunch, Dinner, and Snacks
For breakfast and lunch (Table 3) as well as snacks (Table S4), the median of tea and coffee consumption was the largest among the 20 food groups considered in this study. However, the next most commonly consumed food groups (i.e., >50 g/1000 kcal) differed considerably, namely rice, dairy products, seasonings, total vegetables, and bread for breakfast; rice, seasonings, total vegetables, and noodles for lunch; and confectioneries, dairy products, soft drinks, and fruit for snacks. For dinner (Table 3), total vegetables was the most commonly consumed food group, followed by rice, tea and coffee, and seasonings.
The associations between intakes of these major food groups and diet quality also differed considerably for each meal. In breakfast, total vegetables and rice as well as seasonings were positively associated with diet quality, while bread showed an inverse association (with less clear inverse associations for tea and coffee or dairy products). For lunch, only total vegetables showed a positive association with diet quality. For dinner, there was an inverse association of rice with HEI-2015 (but not NRF9.3) and of seasonings with NRF9.3 (but not HEI-2015), while total vegetables showed positive associations with both HEI-2015 and NRF9.3. For snacks, there were positive associations for tea and coffee and fruits as well as dairy products, while confectioneries and soft drinks showed inverse associations.
| Breakfast | Lunch | Dinner | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Food | Intake (g of wet weight per 1000 kcal of energy from breakfast) | r with HEI- | r with | Intake (g of wet weight per 1000 kcal of energy from lunch) | r with HEI- | r with | Intake (g of wet weight per 1000 kcal of energy from dinner) | r with HEI- | r with | ||||||
| Group | Median | P25 | P75 | 2015 | NRF9.3 | Median | P25 | P75 | 2015 | NRF9.3 | Median | P25 | P75 | 2015 | NRF9.3 |
| Rice | 180.7 | 3.1 | 284.8 | 0.52 | 0.56 | 231.8 | 182.3 | 270.9 | 0.06 | 0.16 | 215.7 | 158.2 | 245.4 | −0.72 | 0.11 |
| Bread | 53.1 | 3.3 | 141.2 | −0.59 | −0.37 | 3.2 | 1.7 | 24.4 | −0.10 | −0.13 | 2.2 | 1.2 | 4.3 | 0.63 | −0.37 |
| Noodles | 3.4 | 1.3 | 4.4 | 0.36 | 0.11 | 74 | 64.4 | 122.8 | −0.05 | −0.34 | 18.5 | 15.6 | 33.6 | 0.17 | −0.22 |
| Pulsesb | 22.6 | 9.9 | 30.1 | 0.69 | 0.57 | 15.2 | 14 | 16.7 | 0.19 | 0.06 | 31.3 | 25.9 | 43.4 | 0.46 | 0.03 |
| Total vegetables | 87.6 | 44.6 | 139.7 | 0.84 | 0.84 | 88.9 | 73.9 | 145.1 | 0.76 | 0.75 | 221.1 | 131.7 | 241.3 | 0.35 | 0.56 |
| Fruit | 34.6 | 0 | 51.9 | 0.41 | 0.25 | 41.8 | 38 | 46.9 | −0.12 | −0.19 | 23.5 | 19.7 | 33.3 | 0.85 | −0.07 |
| Fishc | 10.6 | 4.8 | 17.8 | 0.57 | 0.59 | 25 | 20.8 | 27.7 | 0.32 | 0.23 | 43.6 | 40.7 | 46.3 | 0.03 | −0.11 |
| Meat | 16.3 | 14.7 | 18.1 | −0.58 | −0.65 | 21.4 | 19.1 | 23.7 | 0.1 | 0.24 | 37.2 | 34.3 | 46.2 | 0.3 | −0.30 |
| Dairy products | 120.9 | 62.8 | 190.8 | −0.17 | −0.10 | 19.7 | 17.5 | 56 | −0.17 | −0.13 | 23.7 | 21.7 | 26.9 | 0.49 | −0.44 |
| Alcoholic beverages | 0.5 | 0.2 | 0.6 | 0.72 | 0.65 | 13.4 | 11.1 | 15.4 | 0.33 | 0.33 | 29 | 0 | 140.7 | 0.31 | 0.04 |
| Tea and coffeed | 404.5 | 363.2 | 457.8 | −0.17 | −0.29 | 301.5 | 239.1 | 329.3 | 0.05 | −0.06 | 209.5 | 138.5 | 230.8 | −0.22 | −0.07 |
| Seasonings | 108 | 44.3 | 152.8 | 0.59 | 0.57 | 99.5 | 90.9 | 108.5 | 0.02 | −0.24 | 115.5 | 109 | 120.6 | 0.12 | −0.44 |
3.3. Food Combinations in Breakfast, Lunch, Dinner, and Snacks
For breakfast, the most commonly consumed food combinations consisted of ‘rice, total vegetables, and tea and coffee’ (meal codes 1101–1108) which, in total, contributed to 7.0% of total energy intake (Table 4). These meal codes, as well as those consisting of rice accompanied by total vegetables, tea and coffee, or both (meal codes 1301, 1302, and 1401), were consistently positively associated with the quality of the breakfast. On the other hand, the meal codes consisting of bread, which were frequently accompanied by total vegetables, dairy products, and tea and coffee (meal codes 1109–1111, 1201–1204, and 1501), were generally inversely associated with the quality of the breakfast.For lunch (Table S5) and dinner (Table S6), the most commonly consumed food combinations again consisted of ‘rice, total vegetables, and tea and coffee’ (meal codes 2101–2110 for lunch and 3101–3111 for dinner), which, in total, contributed to 14.6% and 18.5% of total energy intake, respectively. Nevertheless, the associations between these meal codes and diet quality were weak in the case of lunch, compared with the case of breakfast. For dinner, the associations were inconsistent, namely the meal codes showing inverse associations with HEI-2015 but generally weak positive associations with NRF9.3. For snacks, the most commonly consumed food combinations consisted of “confectioneries and tea and coffee” (meal codes 4101–4103), but these showed rather weak associations with diet quality (Table S7). On the other hand, the combinations of ‘dairy products and tea and coffee’ and ‘fruit and tea and coffee’ (meal codes 4201 and 4301, respectively) showed positive associations with diet quality. These associations, observed in each meal, were generally consistent with those between food combinations and the quality of the overall diet (Table 5).
| Food Group Included | Intake (% of total energy) | ||||||
|---|---|---|---|---|---|---|---|
| Meal Code | Staple Food | Accompanying Foodb | Median | P25 | P75 | r with HEI-2015 | r with NRF9.3 |
| 1101 | Rice | Total vegetables, tea and coffee, pulses, fruit, dairy products | 0.86 | 0 | 2.25 | 0.77 | 0.82 |
| 1102 | Rice | Total vegetables, tea and coffee, pulses, fruit | 0.88 | 0 | 2.01 | 0.75 | 0.75 |
| 1103 | Rice | Total vegetables, tea and coffee, pulses, eggs | 1.09 | 0 | 2.35 | 0.63 | 0.71 |
| 1104 | Rice | Total vegetables, tea and coffee, pulses, fish | 0.73 | 0 | 1.78 | 0.64 | 0.72 |
| 1105 | Rice | Total vegetables, tea and coffee, pulses | 0.87 | 0 | 1.67 | 0.52 | 0.58 |
| 1106 | Rice | Total vegetables, tea and coffee, eggs | 1.15 | 0 | 2.12 | 0.59 | 0.67 |
| 1107 | Rice | Total vegetables, tea and coffee, fish | 0.72 | 0 | 1.55 | 0.59 | 0.67 |
| 1108 | Rice | Total vegetables, tea and coffee | 0.81 | 0 | 1.53 | 0.46 | 0.5 |
| 1109 | Bread | Total vegetables, tea and coffee, dairy products, eggs | 0.62 | 0 | 2.07 | −0.18 | 0.03 |
| 1110 | Bread | Total vegetables, tea and coffee, dairy products | 0.58 | 0 | 1.9 | −0.36 | −0.14 |
| 1111 | Bread | Total vegetables, tea and coffee | 0.5 | 0 | 1.46 | −0.34 | −0.16 |
| 1112 | No staple food | Total vegetables, tea and coffee | 0 | 0 | 0 | −0.09 | −0.33 |
| 1201 | Bread | Dairy products, tea and coffee, fruit | 0.53 | 0 | 1.79 | −0.35 | −0.16 |
| 1202 | Bread | Dairy products, tea and coffee | 0.58 | 0 | 1.72 | −0.62 | −0.38 |
| 1203 | Bread | Dairy products, total vegetables | 0.33 | 0 | 1.05 | −0.18 | 0 |
| 1204 | Bread | Dairy products | 0.29 | 0 | 0.98 | −0.43 | −0.25 |
| 1301 | Rice | Total vegetables, dairy products | 0.81 | 0 | 1.72 | 0.63 | 0.71 |
| 1302 | Rice | Total vegetables | 0.68 | 0 | 1.32 | 0.55 | 0.56 |
| 1401 | Rice | Tea and coffee | 0.8 | 0 | 1.29 | 0.46 | 0.48 |
| 1501 | Bread | Tea and coffee | 0.58 | 0 | 1.75 | −0.51 | −034 |
| 1601 | No staple food | Dairy products, tea and coffee | 0 | 0 | 0 | −0.15 | −0.41 |
| 1701 | No staple food | Tea and coffee | 0 | 0 | 0.05 | −0.23 | −0.48 |
| Meal | Food Group Included | r with | r with | ||
|---|---|---|---|---|---|
| Code | Meal Type | Staple Food | Accompanying Food | HEI-2015 | NRF9.3 |
| 1101 | Breakfast | Rice | Total vegetables, tea and coffee, pulses, fruit, dairy products | 0.46 | 0.44 |
| 1102 | Breakfast | Rice | Total vegetables, tea and coffee, pulses, fruit | 0.44 | 0.38 |
| 1103 | Breakfast | Rice | Total vegetables, tea and coffee, pulses, eggs | 0.31 | 0.35 |
| 1104 | Breakfast | Rice | Total vegetables, tea and coffee, pulses, fish | 0.32 | 0.34 |
| 1105 | Breakfast | Rice | Total vegetables, tea and coffee, pulses | 0.22 | 0.26 |
| 1106 | Breakfast | Rice | Total vegetables, tea and coffee, eggs | 0.3 | 0.31 |
| 1107 | Breakfast | Rice | Total vegetables, tea and coffee, fish | 0.31 | 0.3 |
| 1112 | Breakfast | No staple food | Total vegetables, tea and coffee | −0.02 | −0.21 |
| 1201 | Breakfast | Bread | Dairy products, tea and coffee, fruit | −0.22 | −0.12 |
| 1202 | Breakfast | Bread | Dairy products, tea and coffee | −0.50 | −0.29 |
| 1204 | Breakfast | Bread | Dairy products | −0.37 | −0.22 |
| 1301 | Breakfast | Rice | Total vegetables, dairy products | 0.33 | 0.34 |
| 1302 | Breakfast | Rice | Total vegetables | 0.25 | 0.23 |
| 1501 | Breakfast | Bread | Tea and coffee | −0.41 | −0.30 |
| 1601 | Breakfast | No staple food | Dairy products, tea and coffee | −0.05 | −0.24 |
| 1701 | Breakfast | No staple food | Tea and coffee | −0.09 | −0.28 |
| 2101 | Lunch | Rice | Total vegetables, tea and coffee, fish, meat, eggs | 0.08 | 0.23 |
| 2102 | Lunch | Rice | Total vegetables, tea and coffee, fish, meat | 0.06 | 0.21 |
| 2103 | Lunch | Rice | Total vegetables, tea and coffee, fish, eggs | 0.12 | 0.21 |
| 2104 | Lunch | Rice | Total vegetables, tea and coffee, fish, pulses | 0.18 | 0.24 |
| 2111 | Lunch | Rice | Total vegetables, meat, fish | 0.08 | 0.2 |
| 2203 | Lunch | Noodles | Tea and coffee | −0.23 | −0.30 |
| 2401 | Lunch | Rice | Tea and coffee | −0.25 | −0.21 |
| 3102 | Dinner | Rice | Total vegetables, tea and coffee, fish, pulses, fruit | 0.13 | 0.24 |
| 3105 | Dinner | Rice | Total vegetables, tea and coffee, fish, meat | −0.22 | 0.03 |
| 3107 | Dinner | Rice | Total vegetables, tea and coffee, fish | −0.29 | −0.06 |
| 3110 | Dinner | Rice | Total vegetables, tea and coffee, meat | −0.28 | −0.05 |
| 3115 | Dinner | Rice | Total vegetables, fish | −0.21 | −0.07 |
| 3116 | Dinner | Rice | Total vegetables, meat, potatoes | −0.20 | 0.04 |
| 3117 | Dinner | Rice | Total vegetables, meat | −0.28 | −0.04 |
| 3118 | Dinner | Rice | Total vegetables | −0.20 | −0.04 |
| 3401 | Dinner | Rice | Tea and coffee | −0.38 | −0.24 |
4. Discussion
Using the FCQ based on a meal coding system, we found that the most commonly consumed food combinations consisted of ‘rice, total vegetables, and tea and coffee’ for all main meals in Japanese adults. For breakfast, these food combinations were consistently positively associated with diet quality, as assessed by the HEI-2015 and NRF9.3. The associations were similar in direction for lunch but rather weak. For dinner, however, the associations were inconsistent: there were inverse associations with HEI-2015 but weak positive associations with NRF9.3. For snacks, the most commonly consumed food combinations consisted of ‘confectioneries and tea and coffee’, but these showed rather weak associations with diet quality. Thus, associations between the common food combinations and diet quality differed among meals. These diverse associations were confirmed when associations with the quality of the overall diet, rather than each meal, were investigated. To our knowledge, this is the first epidemiologic study to comprehensively investigate food combinations in each meal in relation to diet quality.
Japanese dietary habits have long attracted interest from other countries, primarily because of their possible contribution to a low prevalence of coronary artery disease and long life expectancy [45,46]. According to a secondary analysis of one day dietary record data obtained from 15,618 Japanese adults in the 2012 National Health and Nutrition Survey, breakfast was characterized by a high (>30 g/d) intake of tea and coffee, rice, vegetables, dairy products, and fruit; lunch by a high intake of tea and coffee, rice, vegetables, and noodles; dinner by a high intake of vegetables, rice, alcoholic beverages, tea and coffee, fish, meat, seasonings, potatoes, and pulses; and snacks by a high intake of tea and coffee and fruit [47]. In the same study, breakfast, lunch, dinner, and snacks on average contributed to 23%, 30%, 40%, and 8% of total energy intake, respectively [47]. Additionally, the most frequently identified food combination for all three main meals was ‘rice and vegetables’, whereas ‘confectioneries and tea and coffee’ was the most prevalent combination for snacks [23]. With regard to the HEI-2015 and NRF9.3, mean values derived from a 16 day weighed dietary record were 55.4 and 704 in 121 women and 54.3 and 728 in 121 men, respectively [40]. These are generally consistent with the present findings, suggesting the utility of the FCQ and the meal coding system for characterizing dietary intake and meal patterns in Japanese as well as the robustness of the present findings.
In this study, the associations of diet quality were rather weak among meals, irrespective of diet quality measure. The reason is unknown, but may be due to the low consistency of food intake among meals, as well as the somewhat independent nature of each meal, at least in terms of quality rather than quantity. This is not inconsistent with a within-person comparison of daily dietary intake with and without breakfast, in which there were no differences in the quality of foods selected, as reflected in energy density and energy-adjusted intakes of energy-providing nutrients and dietary fiber [48]. The present findings in turn suggest the importance of accumulating evidence at the meal level to develop effective meal-level dietary guidelines.
Interestingly, we found that while the common food combinations were ‘rice, total vegetables, and tea and coffee’ in all main meals, the associations between these food combinations and diet quality were rather different among these meals. This may be due to differences in the associations between food group intake and diet quality. For example, the association between rice and diet quality was positive for breakfast, almost null for lunch, and inverse (for HEI-2015) or almost null (for NRF9.3) for dinner. Additionally, the positive association between total vegetables and diet quality was quite strong for breakfast and lunch but moderate for dinner. Another reason may be that, while there were multiple options for staple foods in breakfast (rice and bread) and lunch (rice, bread, and noodles), only rice was available as a staple food in dinner. Because of this, while the quality of the common food combinations mainly comprising rice was generally assessed by comparison with the quality of food combinations comprising other staple foods (which were generally inversely associated with diet quality) in breakfast and lunch, the quality of the common food combinations was assessed within the same staple food, namely rice, in dinner, which may cause an inverse association between rice and HEI-2015. In any case, these observations highlight the complex nature of food combinations, which, in turn, suggests the importance of this kind of research.
In this study, the mean quality of breakfast was lower than that of lunch and dinner. This may be mainly due to high intakes of rice and bread—major sources of energy—as well as dairy products—a major source of saturated fatty acids—in breakfast, making the energy-adjusted values of other important foods and nutrients decrease. The present finding is consistent with recent analyses of national dietary survey datasets, in which the nutrient density of breakfast per se was, on average, low in Japan [49] compared with Western countries [50]. On the other hand, the variation (standard deviation) in diet quality was higher in breakfast than in lunch and dinner. Because of this, food combinations in breakfast were more strongly associated with the quality of the overall diet compared with those in lunch and dinner, as well as those in snacks, whose contribution to overall diet was rather small. Consequently, improvement in the quality of breakfast may be not only the most important strategy for improving overall diet quality in Japanese adults, but also the most feasible. On the other hand, a considerably different picture was observed in German adults, in whom dinner was the greatest contributor to the formation of the four overall dietary patterns identified [18]. The reason for this discrepancy is unknown but may include differences in the study populations, dietary intakes and habits, and dietary assessment methods. Nevertheless, both studies clearly showed that overall dietary intakes or patterns originate, to some extent, at the meal level, which could, in turn, lead to a better understanding of how dietary patterns, meal patterns, and food combinations arise. This kind of basic information should be accumulated from various countries.
Recently, chrono-nutrition has been emphasized in the field of nutritional epidemiology because of the potential importance of the timing, in addition to composition, of dietary intake [11,12,13]. For example, in a British cohort, increasing energy intake from carbohydrates at the expense of a similar amount of energy from fat at breakfast and at mid-morning at the age of 43 years was associated with decreased prevalence of metabolic syndrome 10 years later [51]. Another six year prospective cohort study in Italy has shown that a higher intake of energy at dinner was associated with higher incidence of obesity, metabolic syndrome, and non-alcoholic fatty liver disease [52]. Further, a traditional wheat-based breakfast identified by factor analysis was associated with a decreased risk of hyperglycemia in a six year prospective cohort study in Chinese adults, whereas a rice-based traditional lunch and dinner was associated with an increased risk [53]. Because the FCQ can provide information on dietary intake at each meal, it might be a promising tool for this kind of research, as well as in investigating the associations between food combinations and various health outcomes in large-scale epidemiologic studies.
The strengths of the present study include its use of the FCQ and a meal coding system, which were empirically developed based on detailed information on actual food combinations over a one year period with a large number of recording days (16 days) in 242 Japanese [22]. However, there are also several limitations. First, although sampling was conducted to reflect regional differences in population proportion, the present population is not a nationally representative sample of general Japanese, but rather volunteers. In particular, our participants may be biased toward greater health consciousness. Further research in a more representative sample is needed.
Second, the nature and extent of the measurement error of self-reported information on food combinations obtained by the FCQ are largely unknown. The present results should therefore be interpreted with caution in this respect. Nevertheless, comparison of the FCQ with the BDHQ, a widely used and well-validated dietary assessment questionnaire [29,30], suggests that the FCQ may have sufficient ability to estimate overall dietary intakes and diet quality scores. Additionally, all the dietary variables used in this study were energy-adjusted to minimize the influence of measurement error in self-reported dietary intake [54]. In any case, more rigorous validation assessment of the FCQ is needed, particularly for characterizing dietary intake and food combinations in each meal, notwithstanding the lack of objective markers of food combinations [48] and the fact that even more extensive dietary assessment methods, such as dietary record and 24 hour recall, not only rely on self-reporting but are themselves also subject to both random and systematic measurement errors [54,55,56].
Third, because the survey was conducted within a three month period (from October to December 2018) and the FCQ assessed dietary habits during the preceding month, any seasonal variation in food combinations was not considered. Given that several previous studies have observed seasonal differences in intakes of at least some nutrients and food groups in Japanese adults [57,58,59], this might have produced some bias in assessing average food combinations over the year. Additionally, because of the design of the study, we do not know how much intra-person variability in food combinations and meal intake there is; there would be some compensation within individuals across meals and across days. Further studies in these respects are needed.
Finally, we used the HEI-2015 and NRF9.3 in this Japanese study, even though both scores were primarily developed to assess the overall diet quality of Americans. Thus, these diet quality measures are not optimal for assessing the overall quality of Japanese diet, but rather the best available [40]. The use of other diet quality scores, such as the Dietary Inflammatory Index [1], which is not culture bound, would be of interest in future studies. Nevertheless, in our recent systematic review of Japanese studies which obtained dietary patterns using principal component analysis, we found that those food groups which contributed to dietary patterns termed healthy (fruits, vegetables, potatoes, mushrooms, seaweeds, and pulses) are at least partly similar to those often observed in Western countries (fruits, vegetables including mushrooms, poultry, fish, low-fat dairy, legumes, and whole grains) [60]. It should also be stressed that our recent analysis supports the efficacy of these measures in assessing the overall diet quality of Japanese: a higher total score in the HEI-2015 and NRF9.3 was associated with favorable patterns of overall diet, including higher intakes of dietary fiber and key vitamins and minerals and lower intakes of saturated fats [40].
5. Conclusions
In this study, the most commonly consumed food combinations consisted of ‘rice, total vegetables, and tea and coffee’ for all main meals, including breakfast, lunch, and dinner, but associations between these common food combinations and diet quality differed among meals. The FCQ and meal coding system used here may be useful in capturing the complex nature of food combinations in Japanese adults. Further methodological studies on whether similar methods for characterizing food combinations within meals may be applicable to other populations would also be of interest.
Acknowledgments
The authors would like to thank the research dietitians who conducted the data collection: Tamotsu Noshiro, Ikuko Kato, Yoshie Awa, Erika Takayama, Mari Sakurai, Mihoko Yanase, Masae Kato, Mihoko Furukawa, Yuna Nodera, Kazue Fukushi, Miwako Onodera, Yoshie Sato, Megumi Yoshida, Masako Shimooka, Kaori Takahashi, and Fuki Kudo (Hokkaido); Yumiko Sato, Yutaka Shojiguchi, Kazunori Kimoto, Saori Kikuchi, Megumi Maeta, Mayumi Sugawara, Shinogu Muraoka, Kanako Takahashi, Noriko Suzuki, Yoko Fujihira, and Megumi Onodera (Iwate); Yukiko Takahashi, Kaoru Honda, Chie Yamada, Miki Sato, Katsue Watanabe, Akemi Konno, and Reina Kato (Miyagi); Akiko Sato, Hiromi Kawaguchi, Miyuki Awano, Chigusa Miyake, Ayako Konno, Ayumi Goto, Shizuko Taira, Yuka Takeda, Akiko Matsunaga, Nao Konta, Yumi Miura, Satoshi Numazawa, Chiemi Ito, Sachie Yokosawa, Manami Endo, and Hiromi Seki (Yamagata); Yoko Tsukada, Tomoko Oga, Satoko Fujita, Hitomi Sonobe, Hanayo Kadoi, Toshie Nakayama, Hiromi Takasawa, Yoko Ichikawa, Yuko Takano, Junko Hanzawa, Kiyomi Seki, Emi Kamoshita, Yuri Kawakami, and Hisako Watahiki (Ibaraki); Reiko Ishii, Yoshiyuki Tatsuki, Daisuke Mogi, Akiko Nakamura, Suguru Yagi, Yumiko Furushima, Noriko Ogiwara, Kiyomi Kimura, Kinue Takahashi, Kikue Tomaru, Kana Tsukagoshi, Fumiko Fujiu, Kyoko Maehara, and Yuki Kobayashi (Gunma); Kaoru Goto, Yuka Inaba, Michiko Koresawa, Tomoko Tsuchida, Naoko Sakakibara, Fumika Shimoyama, Akiko Kato, Miki Hori, Rika Kurosaki, Hiroko Yamada, Hitomi Sasaki, Keiko Arai, Yuka Arai, Manami Honda, Akiko Utsumi, Asako Hamada, Keiko Sekine, Akiko Yamada, Mami Ono, Satoko Maruyama, Emiko Kajiwara, Taeko Takahashi, Hitomi Kawata, Satoru Arai, Ryoko Hirose, Madoka Ono, and Mihoko Ainai (Saitama); Masako Shinohara, Noriko Nakamura, Mitsuko Ito, Yuka Takahara, Minako Fukuda, Masae Ito, Yayoi Sueyoshi, Hiroko Shigeno, Tomohiro Murakami, Masako Kametani, Kyoko Wada, Mika Ueda, Jun Kouno, Hiroyo Yamaguchi, Mariko Oya, Junko Suegane, Yumiko Asai, Miyuki Ono, Mitsuko Uekusa, Chieko Sunada, Yumi Tanada, Mariko Shibata, Emi Tsukii, Kae Terayama, Hiroko Iwasaki, Keiko Yokoyama, Haruna Kayano, Kazuyo Shimota, Keiko Ifuku, and Keiko Honma (Chiba); Yoshiko Saito, Megumi Suzuki, Eiko Kobayashi, Yoshiko Katayama, Sonoko Yamaguchi, Tomoko Kita, Naoko Yuasa, Hitomi Okahashi, Shinobu Matsui, Yurina Arai, Sanae Togo, Eiko Horiguchi, Juri Sato, Takehiro Komatsu, Yumi Matsuura, Junko Higashi, Ayaka Nakashita, Takako Sakanashi, Yoko Kono, Naomi Nakazawa, and Yukiko Shibata (Tokyo); Machiko Tanaka, Ikue Sahara, Yasue Watanabe, Kanako Yoshijima, Yuko Harashima, Yoshiko Iba, Haruko Irisawa, Junko Inose, Reiko Okui, Taeko Endo, Mayuko Sakitsu, Ikuko Endo, Haruko Terada, Chiaki Nishikawa, Ai Yasudomi, Suzuyo Takeda, Kaori Shimizu, Mari Ikeda, Yuko Okamoto, Keiko Yamada, Fumiko Nemoto, Shinobu Katayama, Yuki Takakuwa, Michiru Goukon, Megumi Koike, Masae Kamiya, Takako Okada, Yayoi Hayashi, Etsuko Abe, Akiko Hamamoto, Kumiko Ono, Kazumi Takagi, Sachiko Ito, Yuki Kumagai, Noriko Ozaki, Haruka Sato, Hisae Takahashi, Masuko Komazaki, Akiko Nako, Tatehiro Inamoto, and Kimiyo Matsumoto (Kanagawa); Masako Koike, Reiko Kunimatsu, Keiko Kuribayashi, Hiroko Adachi, Yuri Shikama, Yurika Seida, Ryouko Ito, Satoko Kimura, Yoko Sato, Michiyo Nakamura, Hisako Kaneko, Hatsuyo Ikarashi, Mamiko Karo, Keiko Hirayama, Ikumi Torigoe, and Fumiko Gumizawa (Niigata); Natsuko Mizuguchi, Aki Sakai, Hisako Noguchi, Chie Tanabe, Yokako Osaki, Ikiko Kawasaki, Nobuko Yoshii, Yumiko Nishihara, Izumi Takahara, Mika Minato, Yuuki Okamoto, Yukiyo Sakai, Shitomi Nakamura, Kanako Kobayashi, Emi Hano, and Megumi Emori (Toyama); Kazumi Horiguchi, Michiyo Kubota, Naoya Mochizuki, Miyuki Yokokoji, Kazuko Koizumi, Megumi Ariizumi, Hozumi Kakishima, Mayumi Kawakubo, Chisato Nakajima, Yasuko Ishii, Yukie Shiogami, Yukiko Uchida, Ikuko Kayanuma, and Kikuyo Moriya (Yamanashi); Noriko Sumi, Noriko Takahashi, Kuniko Watanabe, Yoko Ido, Akiko Adachi, and Manami Tauchi (Gifu); Naoya Terada, Chisato Suwa, Toshihiro Tamori, Natsuko Osakabe, Toshiyuki Serizawa, Akiko Seki, Izumi Mochizuki, Nagako Matsui, Eiko Watanabe, Kyoko Yui, Yuki Murakami, Tomomi Iwasaki, and Tomoko Sugiyama (Shizuoka); Keiko Kawasumi, Masako Tanaka, Kayoko Ishida, Megumi Yamatani, Shihoko Yama, Miyuki Otono, Mie Kojima, Tamaki Kobayashi, Hiroe Komaki, Miki Yanagida, Yumiko Fukaya, Syoko Sawaki, Tomomi Ota, Yasuko Kito, Mei Tobinaga, Takashi Yasue, Kuniko Hatamoto, Toru Ono, Takako Minami, Akemi Kumazawa, Masami Kato, Miyuki Kondo, Kyoko Shimizu, Sayoko Tanaka, and Shizue Masuda (Aichi); Hiromi Ashida, Shintaro Hinaga, Yoshiko Shoji, Ryusuke Yamaguchi, Hiroka Morita, and Atsuko Nakabayashi (Mie); Erika Shioi, Sawa Mizukawa, Miwako Ohashi, Eriko Taniguchi, Yuri Mitsushima, Mariko Teraya, Kazuko Ogawa, Yoko Minami, Megumi Ito, Yasuhiro Morimoto, Shizuka Kurokawa, and Manami Hayashi (Kyoto); Yumiko Noutomi, Yoshiko Iwamoto, Junko Ikukawa, Shinobu Fujiwara, Tami Irei, Keiko Takata, Yasuka Tabuchi, Naoko Murayama, Kaori Maruyama, Hiromi Tashiro, Miki Tanaka, Miho Nomura, Shizuyo Umezawa, Minori Shintani, Ikuyo Maruishi, Atsuko Toyokawa, Rumi Kitada, Yuka Takashima, Eriko Nakatani, Wakana Tsujimoto, Yumi Koori, Emi Iwamoto, and Masumi Yamada (Osaka); Atsuko Konishi, Yoshiko Nakamori, Yumi Ikawa, Junko Shimizu, Mie Atsumi, Atsuko Fukuzaki, Akemi Yamamoto, Yasuko Inoue, Miyuki Nakahara, Reiko Fujii, Yumi Tanaka, Rika Miyachi, Mari Matsuyama, Ayana Honda, Tomoka Nakata, Miho Inagaki, Mikiyo Ueno, Mami Kamei, Kiyomi Kawachi, Yasuyo Hasegawa, and Masayo Fukumoto (Hyogo); Sachiyo Otani, Tomomi Sugimoto, Kanako Mizoguchi, Tomomi Shimada, Shima Takahashi, Yoshiko Okuno, Takahide Kijima, Masayo Ueda, Yuko Sakamoto, Hitoshi Matsuda, Yumie Shimizu, Rie Hataguchi, Junko Nohara, Yuko Sakakitani, Hatsuki Matsumoto, Sakiko Tanaka, Moe Yoshikawa, and Rena Yuki (Nara); Yurika Adachi, Akiko Notsu, Keiko Uzuki, Atsuko Umeda, Hiroko Nishio, Chikako Takeshita, and Sayuri Omoso (Tottori); Hiromi Watanabe, Nami Sakane, Nao Hino, Sakiko Sonoyama, Yukiko Katagiri, Kaori Nagami, Tsunemi Moriyama, and Yoshiko Kirihara (Shimane); Sachiko Terao, Akemi Inoue, Mieko Imanaka, Noriyuki Kubota, Sachiko Sugii, Yuri Fujiwara, and Tomoko Miyake (Okayama); Youko Fujii, Hiroko Tamura, Kimie Tanaka, Izumi Hase, Etsuko Kimura, Akiko Hamada, Tomoko Kawai, Masako Ogusu, and Emi Isomichi (Hiroshima); Kyoko Ueda, Atsuko Nakanishi, Tomoko Ishida, Nobuko Morishita, Hitomi Nakata, and Takako Yagi (Yamaguchi); Yuriko Doihara, Toyoko Kitadai, Hideyo Yamada, Mariko Nakamura, Nanako Honda, Eri Ikeuchi, Kayo Hashimoto, Azusa Onishi, and Manami Iwase (Tokushima); Machiko Ueda, Ayano Kamei, Reiko Motoie, Yoko Moriki, Nobumi Yoshida, and Kiyoko Kubo (Ehime); Kyoko Kaku, Emi Ibushi, Miho Otsuka, Kiyoko Katayama, Hisami Kumagai, Chizuru Shibata, Miki Hamachi, Yuko Hayashida, Akiko Matsuzaki, Mika Yoshioka, Yoshie Yanase, Yoshiko Yahagi, and Tomomi Ota (Fukuoka); Junko Kiyota, Hiromi Ide, Takako Fukushima, Shiho Tominaga, Satsuki Miyama, Yoko Okamura, Kayoko Kurahara, Tomomi Nagamori, Chika Shino, Akiko Taira, Yuki Kuwajima, and Miyuki Matsushita (Kumamoto); Miki Hamada, Kiyomi Aso, Toshie Eto, Mitsue Kodama, Miyoko Sato, Mutsuko Shuto, Yuko Soga, Taeko Nagami, Machiko Hirayama, Mika Moribe, Junko Yamamoto, Hideko Yoshioka, Yuko Kawano, Rika Matsuoka, and Satomi Sato (Oita); and Hisami Yamauchi, Satomi Moromi, Satoko Tomari, Kaoru Miyara, Chikako Murahama, Yukiko Furugen, Kana Awano, Hiromi Arakaki, Suzumi Uema, Yasuko Tomori, Nariko Mori, Ayako Iho, Michiru Hokama, Yasue Higa, and Chigusa Chibana (Okinawa). The authors would also like to thank the research team staff at the survey center (University of Tokyo): Tomoko Doi, Hitomi Fujihashi, Akiko Hara, Nana Kimoto, Nanako Koe, Eri Kudo, Fumie Maeda, Keika Mine, Akemi Nakahara, Hiroko Onodera, Hiroko Sato, Chifumi Shimomura, Ryoko Tajima, and Fusako Tanaka.
Supplementary Materials
The following are available online at https://www.mdpi.com/2072-6643/12/2/327/s1↗, Figure S1: Flow diagram of participants included in the present analysis, Figure S2: Flow diagram of the development process of the food combination database, Figure S3: An example of meal coding procedure, Figure S4: An example of the calculation of daily consumption frequency of food combinations (meal codes), based on an ad hoc computer algorithm for determining the food combinations each participant consumed, using information collected from the food combination questionnaire (FCQ), Figure S5: An example of the calculation of daily consumption frequency of accompanying foods, using the information collected from the food combination questionnaire (FCQ), Table S1: Food combination database used in this study, Table S2: Comparison of overall diet quality scores and overall dietary intakes assessed by FCQ and those assessed by BDHQ in 2233 Japanese adults aged 19–80 years, Table S3: Correlations among diet quality scores for each meal and overall diet quality scores in 2233 Japanese adults aged 19–80 years, Table S4: Food group intake in snacks in relation to diet quality scores for snacks in 2233 Japanese adults aged 19–80 years, Table S5: Food combinations (meal codes) in lunch in relation to diet quality scores for lunch in 2233 Japanese adults aged 19–80 years, Table S6: Food combinations (meal codes) in dinner in relation to diet quality scores for dinner in 2233 Japanese adults aged 19–80 years, Table S7: Food combinations (meal codes) in snacks in relation to diet quality scores for snacks in 2233 Japanese adults aged 19–80 years.
Author Contributions
K.M. contributed to the concept and design of the survey and data collection and management, formulated the research, developed the FCQ as well as its algorithm, analyzed and interpreted the data, prepared the first draft of the manuscript, and had primary responsibility for the final content; M.B.E.L. contributed to the formulation of the research and provided critical input into the final draft of the manuscript; N.S. contributed to the survey design and data collection and assisted in the writing of the manuscript; M.S. and A.F. contributed to data collection and assisted in the writing of the manuscript; S.M. managed the study-field establishment and recruitment and contributed to data collection; and S.S. contributed to the concept and design of the survey and the formulation of the research. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported in part by a Challenging Research (Exploratory) from the Japan Society for the Promotion of Science (to K.M.; grant number 18K19727). The Japan Society for the Promotion of Science had no role in the design, analysis or writing of this article.
Conflicts of Interest
The authors declare no conflict of interest.