377

ORIGINAL
Validation of a dietary balance score in middle-aged andolder community-dwelling Japanese
Mariko Nakamoto1, 2, Tsuzuri Nagashima1, Yukiko Tanaka1, Satomi Ono1, Yuki Iwasaki1, Akiko Nakamoto1, Shu Zhang2,
Kaori Kinoshita3, Kanae Furuya2, Tomoko Imai4, Rei Otsuka2, and Tohru Sakai1
Department of Public Health and Applied Nutrition, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima,
Japan, 2Department of Epidemiology of Aging, National Center for Geriatrics and Gerontology, Aichi, Japan, 3Department of Frailty Research,Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Aichi, Japan, 4Faculty of Human Life and Science,
Doshisha Women’s College of Liberal Arts, Kyoto, Japan
1

Abstract : This study assessed the validity of dietary balance scores (DBSs) by investigating the association
between DBSs and nutrient adequacy (NA) in two Japanese populations. The participants were 65 community-dwelling Japanese from Tokushima Prefecture and 2,330 community-dwelling Japanese from Aichi Prefecture. Based on food frequency questionnaires or 3-day dietary records, we obtained 18 food groups. The NA score
integrates nine beneficial nutrients and two nutrients that should be limited. We calculated four different DBSs:
DBS1 consisted of five food groups (score range : 0–20), DBS2 consisted of nine food groups (score range : 0–36),
DBS3 consisted of eight food groups (score range : 0–32), and DBS4 consisted of 10 food groups (score range : 0–40).
Both the Spearman rank correlation coefficient with NA and the area under the receiver operating characteristic curve (AUC-ROC) for the nine beneficial nutrients were then estimated to test the performance of each DBS
in predicting nutrient intake. The results showed that DBS1 and DBS4 were positively correlated with NA, while
the AUC-ROC showed that DBS4 could moderately discriminate individuals with adequate intake levels of allnine nutrients. These findings suggest DBSs (especially DBS4) are useful in assessing dietary balance in middle-aged and older community-dwelling Japanese. J. Med. Invest. 70 : 377-387, August, 2023
Keywords : dietary balance, nutritional balance, middle-aged and older community-dwelling Japanese

INTRODUCTION
Populations are aging rapidly worldwide (1, 2). In Japan, people aged ≥ 65 years account for 28.9% of the total population, andthis percentage is estimated to increase to 38.4% by 2065 (3).
Environmental factors, including dietary intake, are thought toplay an important role in the longevity of Japanese. The Dietary
Reference Intakes (DRI) for Japanese (2020) is a comprehensive
guideline for diet and nutrition that aims to extend healthy life
expectancy by preventing malnutrition and frailty in Japanesepeople (4). In the new DRI for Japanese (2020), the nutrient
requirements for middle-aged and older people are defined according to new age groups (50–64 years, 65–74 years, and ≥ 75
years), and the importance of dietary intake is highlighted.
Several assessment scales, including the Mediterranean Diet
Index (5), Healthy Eating Index (6), Japanese Diet Index (7),and Quantitative Index for Dietary Diversity (8), have been used
to evaluate the quality of dietary intake. However, calculatingthese scores over a long period in a survey targeting older people
is difficult because data on dietary intake from dietary records
and food frequency surveys that contain many food items are required. The dietary variety score (DVS) developed by Kumagai
et al. (9) is a score obtained from a short-form questionnaire
about the frequencies of intake per week of 10 food items, and istherefore easy to use as an assessment tool for dietary diversity
Abbreviations :3DR, 3-day dietary record ; AUC-ROC, area under the receiver operating characteristic curve ; BMI, body mass index ; DBS, dietarybalance score ; DRI, Dietary Reference Intakes ; DVS, dietary variety
score ; FFQ, food frequency questionnaire ; MMSE, Mini-Mental
State Examination ; NA, nutrient adequacy ; NILS-LSA, National
Institute for Longevity Sciences-Longitudinal Study of Aging ; SD,
standard deviation.

in older populations. It has been reported that higher DVS
values are not only associated with lower risks of higher-level
functional capacity decline (10), falls (11), and sarcopenia (12),but also moderately correlated with higher intakes of various
nutrients (13) and quantitative diversity (14). However, although
this score indicates the variety of food intake, whether dietaryintakes that meet the DRI for major nutrients in Japanese can beevaluated by this score remains unclear. Consequently, a simple
and easy-to-use score to assess whether the nutrient intake of
middle-aged and older Japanese people meets the DRI has yet
to be established.
In the present study, we developed a dietary balance score(DBS) with the aim of determining the association between
DBS and satisfaction of nutrients based on the DRI in community-dwelling older adults. We used information on the frequency
and / or amount of food consumption to establish different DBSs.
The satisfaction of DRI was defined by a nutrient adequacy (NA)score integrated with nine beneficial nutrients and two nutrientsthat should be limited according to the Japanese DRI. First,
we examined the internal validity of the DBSs by investigating
their correlations with NA scores in a community-dwelling older
population. Next, we evaluated the external validity of the DBSs
by assessing their associations with NA scores in another community-dwelling population that included both middle-aged andolder individuals.
Received for publication February 2, 2023 ; accepted May 1, 2023.
Address correspondence and reprint requests to Mariko Nakamoto,MSc, PhD, Department of Public Health and Applied Nutrition, Institute of Biomedical Sciences, Tokushima University Graduate School,
3-18-15 Kuramoto-cho, Tokushima City, Tokushima 770-8503, Japanand Fax : +81-886-33-9427. E-mail : nakamoto@tokushima-u.ac.jp

The Journal of Medical Investigation Vol. 70 2023

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M. Nakamoto, et al. Validation of a dietary balance score

MATERIALS AND METHODS
Study subjects

This study involved two different populations of middle-aged
and older community-dwelling Japanese. The first population
comprised 65 community-dwelling volunteers aged 60–79 years
who participated in a survey conducted in Tokushima Prefecture
in 2015. Of these 65 participants, we excluded those with any
missing data from 3-day dietary records (3DRs) (n = 6), leaving
59 (4 men and 55 women) for the analysis. Written informed
consent was obtained from all participants. The study protocol
was approved by the institutional review board of Tokushima
University Hospital (ethics approval No. : 2556).
The second population comprised 2,330 randomly selected
Japanese community-dwelling men and women aged 40–79
years who participated in the 7th wave survey (2010–2012)of the National Institute for Longevity Sciences-Longitudinal
Study of Aging (NILS-LSA). We also excluded those with any
missing data from 3DRs (n = 185), leaving 2,145 (1,069 men and
1,076 women) for the analysis. Details of the NILS-LSA study
have been reported elsewhere (15). Written informed consent
was obtained from all participants. The study protocol was reviewed and approved by the Human Research Ethics Committeeof the National Center for Geriatrics and Gerontology, Japan(Approval No. : 899-6).

been established in the DRI for Japanese 2020 (4), we excluded
sugar from the index and developed an NA score containing 11
nutrients (nine beneficial nutrients plus saturated fatty acids
and sodium). The NA ratio of each nutrient was calculatedusing the following equation : NA ratio = [(daily nutrient intake
value) / (age- and sex-specific reference value)] × 100 (%). Then,each NA ratio was further standardized by the age- and sex-specific recommended daily energy intakes (except for saturatedfat). Both the daily reference value for each nutrient and thedaily recommended energy intake were based on the DRIs forJapanese 2020 (Supplemental table 1). Therefore, the NA score
was obtained as follows : NA score = Σ9 beneficial NA ratios – Σ2limit NA ratios. A higher NA score means better overall nutrient
intake.

Nutrient contributions of the 18 food groups
Among older adults in Tokushima Prefecture, we calculated
the contribution of each food group to the daily DRI for 11 nutrients included in the NA scores. Using 3DRs, we calculated themean daily intake of protein, potassium, calcium, magnesium,iron, vitamin A, vitamin E, vitamin C, fiber, saturated fatty
acids, and sodium for each participant in Tokushima Prefecture
for 3 days, as well as the mean daily intake of each of the above
nutrients contained in each of the 18 food groups, thereby obtaining the contribution of each food group to each nutrient.

Dietary assessments

Dietary balance score

The dietary intake of two study participants was assessedusing a 3DR and dietary weight records for 3 consecutive days
(2 weekdays and 1 weekend day) (16). The raw food materials
were weighed separately on a scale (UH-3303 2-kg digital homescale ; A&D Company, Ltd., Tokyo, Japan) before being cooked,
or the portion size was estimated. We asked the participants
to photograph their plates before and after eating by using adisposable camera (27 shots ; Fuji Film, Tokyo, Japan), a digital
camera, or the camera function of their mobile phone. These
photos were then used by dietitians to fill in any missing data.
When necessary, the subjects were called to resolve any discrepancies or obtain further information. Then, the daily averageintakes and daily nutrient and energy intakes of 18 food groups(cereals ; potatoes and starches ; sugars and sweeteners ; pulses ; nuts and seeds ; vegetables [dark-green and deep-yellow vegetables ; and other vegetables] ; fruits ; mushrooms ; algae ; fish,
mollusks, and crustaceans ; meat ; eggs ; milk and milk products ; fats and oils ; confectionaries ; beverages ; seasonings andspices ; and prepared foods) were calculated according to the2015 Standard Food Composition Tables in Japan (17).
In addition, we obtained information on the frequency of
consumption of the following 10 food groups among the subjects
in Tokushima Prefecture using a food frequency questionnaire(FFQ) : fish, mollusks, and crustaceans ; meat ; eggs ; milk andmilk products ; pulses ; dark-green and deep-yellow vegetables ; algae ; potatoes and starches ; fruits ; and fats and oils. The
participants answered the frequency of consumption of the 10
food groups per week, with frequency options including “almost
every day”, “once every 2 days”, “once or twice a week”, and “almost never”.

We developed four different DBSs (DBS1 to DBS4) (Supplemental table 2). Based on the nutrient contributions of 18 foodgroups, DBS1 and DBS2 consisted of food groups (except cereals)
that contributed more than 15% and 10% of daily dietary intake
for the 11 nutrients in the NA scores, respectively. DBS3 consisted of food groups (except cereals and fats and oils) that contributed more than 10% of daily dietary intake for the 11 nutrients.
DBS4 consisted of food groups summarized by the Standard
Tables of Food Composition in Japan 2015 (17) (except cereals,sugar and sweeteners, potatoes and starches, seasonings andspices, fats and oils, and beverages). Sugar and sweeteners,
potatoes and starches, seasonings and spices, fats and oils, andbeverages were excluded from the DBS calculations because theintake of these food groups is not recommended, and / or becausesuch intake contributes little to nutrients such as vitamins andminerals (22, 23). Cereals were excluded from all DBS calculations because only small variation was seen in the frequency of
cereal consumption among all participants (all participants inTokushima Prefecture consumed cereals three times a day).
Two scoring patterns were used to calculate DBSs according
to the dietary assessment method. Pattern 1 was set based on
the quartiles of daily consumption of the food groups derived
from the 3DRs : “Quantile 4 (the highest quartile group)” as 4
points, “Quantile 3” as 2 points, “Quantile 2” as 1 point, and“Quantile 1 (the lowest quartile group)” as 0 points. Pattern 2
was set based on the frequency of food groups obtained by the
FFQs : 4 points for “almost every day”, 2 points for “once every 2days”, 1 point for “once or twice a week” and 0 points for “almost
never”. When scoring food groups for which no information on
consumption frequency was available (i.e., other vegetables andmushrooms), the scoring rules of Pattern 1 were used. These
points (0 to 4) were set in order to simplify the calculation inconsideration of the frequency of data based on the method used
in a previous study (14).
DBS1 was defined by five food groups (fish, mollusks, andcrustaceans ; milk and milk products ; dark-green and deep-yellow vegetables ; other vegetables ; and meat) and ranged from0 to 20 points. DBS2 was defined by nine food groups (fish,
mollusks, and crustaceans ; milk and milk products ; dark-green

Nutrient adequacy score
To assess the participants’ nutritional balance based on the
DRI, we modified the NRF9.3 index (a nutrient density indicator
composed of 12 nutrients) (18-21) as an NA score. The NRF9.3
index consists of nine beneficial nutrients (protein, potassium,
calcium, magnesium, iron, vitamin A, vitamin E, vitamin C, andfiber) and three nutrients that should be limited (saturated fatty
acids, sodium, and sugar). As no reference value for sugar has

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The Journal of Medical Investigation Vol. 70 August 2023
and deep-yellow vegetables ; other vegetables ; meat ; pulses ; eggs ; fruits ; and fats and oils) and ranged from 0 to 36 points.
DBS3 was defined by eight food groups (fish, mollusks, andcrustaceans ; milk and milk products ; dark-green and deep-yellow vegetables ; other vegetables ; meat ; pulses ; and eggs andfruits) and ranged from 0 to 32 points. DBS4 was defined by
10 food groups (fish, mollusks, and crustaceans ; milk and milk
products ; dark-green and deep-yellow vegetables ; other vegetables ; meat ; pulses ; eggs ; fruits ; mushrooms ; and algae) andranged from 0 to 40 points.

Other measurements
Other measurements were assessed among older adults inTokushima Prefecture using the following methods. The heightof each participant was measured to the nearest 0.1 cm witheach participant standing upright without shoes, and bodyweight was measured to the nearest 0.1 kg with each subject
wearing lightweight clothing. Body mass index (BMI) was
calculated as weight (kg) / height (m)2 . Cognitive function wasassessed by the Mini-Mental State Examination (MMSE) onlyin those aged ≥ 60 years. The MMSE was conducted during faceto-face interviews with graduate students who had received intensive training. Data regarding education level (categorical : ≤ 9years, 9–12 years, or > 12 years), annual household income(categorical : ≤ 1.49 million yen, 1.50–4.49 million yen, or ≥ 4 .50
million yen), current and previous histories of hypertension,
dyslipidemia, diabetes, heart disease, and stroke (binary : yesor no), current physical activity (binary : yes or no), drinking
habit (categorical : current, former, or never) and smoking habit(categorical : current, former, or never) were collected using aself-administered questionnaire.
The same measurements, except for current physical activity
and alcohol intake, were also assessed in the NILS-LSA using
similar methods. Current physical activity (metabolic equivalent of task [METs]-min / year) was assessed using the METs (amultiple of the resting metabolic rate) score, which was obtained
through participant interviews with trained interviewers using
a semi-quantitative method to assess the participants’ level of
habitual physical activity during leisure time and on the job,
as well as their sleeping hours (24). Alcohol intake (g / day) was
assessed using the 3DRs (16).

RESULTS

Characteristics of the participants
Table 1 shows the characteristics of the participants inTokushima Prefecture. The mean ± SD age and BMI of the participants were 67.6 ± 4.7 years and 24.0 ± 3.9 kg / m2 , respectively.
The proportion of participants with more than 9 years of education was 93.3%, and the mean ± SD MMSE score was 28.1 ± 1.9.
There were no current smokers.
Supplemental table 3 shows the characteristics of the participants in the NILS-LSA. The mean ± SD age and BMI of theparticipants were 61.1 ± 12.5 years and 22.7 ± 3.2 kg / m2 , respectively. The proportion of participants with 9 years of education
was 83.9%, and the mean ± SD MMSE score was 27.8 ± 1.9. The
proportion of current smokers was 11.3%.

Table 1. Characteristics of the participants from Tokushima Prefecture

Total participants (n = 59)
67.6

±

4.7

Men‡

4

(

6.8

2 †

Body mass index (kg / m )

24.0

±

3.9

Current physical activity (+)†

36

(

61.0

Mini-Mental State Examination score†

28.1

±

1.9

)
)

Education‡≤ 9 years

4

(

6.8

)

9–12 years

28

(

47.5

)

> 12 years

27

(

45.8

)

Annual household income‡≤ 1.49 million yen

29

(

49.2

)

1.50–4.49 million yen

19

(

32.2

)

≥ 4.50 million yen

11

(

18.6

)

0.0

)

‡

Smoking habit
Current0(Former / never59( 100.0 )Current

17

(

28.8

)

Former / never

42

(

71.2

)

Hypertension (+)

18

(

30.5

)

Drinking habit‡

Statistical analysis
Continuous variables are expressed as the mean ± standard
deviation (SD), and categorical variables as numbers and proportions (%). First, the association between each DBS and NA score
was investigated by Spearman’s rank correlation coefficient test.
Next, the area under the receiver operating characteristic curve(AUC-ROC) and 95% confidence interval were used to test theaccuracy of each DBS in distinguishing between individuals
with sufficient (met the DRI criteria) and insufficient intakes of
all nine beneficial nutrients. The AUC-ROCs were also used todetermine the accuracy of each DBS in discriminating individuals whose intake levels of all nine nutrients exceeded the DRI
criteria. The AUC-ROCs ranged from 0.5 to 1.0, with 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.
The discriminatory accuracy of each DBS was thus considered“low” if the AUC-ROC was ≥ 0.5 and < 0.7, “moderate” if ≥ 0.7 and< 0.9, and “high” if ≥ 0.9.
All statistical tests were based on two-sided probabilities andperformed using SPSS version 28.0 Japanese for Windows (IBMJapan, Tokyo, Japan) and / or SAS version 9.3 (SAS Institute,Cary, NC, USA). All p values < 0.05 were considered statisticallysignificant.

Age (years)†‡

Medical history

Dyslipidemia (+)

13

(

22.0

)

Diabetes (+)

3

(

5.1

)

Heart disease (+)

0

(

0.0

)

Stroke (+)

0

(

0.0

)

†

Mean ± SD ; ‡ Number (%)||
Abbreviations : SD, standard deviation.

Contribution rates of food groups
Table 2 shows the contribution rates of food groups for theintake of protein, potassium, calcium, magnesium, iron, vitaminA, vitamin E, vitamin C, fiber, saturated fatty acids, and sodium(> 10%) among older adults in Tokushima Prefecture. The highest protein intake was from fish and shellfish (19.1%), followed bycereals (16.6%) and meat (13.5%). The highest potassium intake
was from other vegetables (14.4%), followed by dark-green anddeep-yellow vegetables (13.5%). The highest calcium intake was380

M. Nakamoto, et al. Validation of a dietary balance scorefrom milk and milk products (23.6%), followed by pulses (13.5%).
The highest magnesium (12.7%) and iron (14.0%) intakes werefrom pulses. The highest vitamin A, C, and E intakes were fromdark-green and deep-yellow vegetables. The highest vitamin A
intake was from dark-green and deep-yellow vegetables (42.5%),
followed by eggs (11.4%). The highest vitamin E intake was fromdark-green and deep-yellow vegetables (24.0%), followed by fats
and oils (11.2%) and fish, mollusks, and crustaceans (10.3%). The
highest vitamin C intake was from dark-green and deep-yellow
vegetables (29.5%), followed by other vegetables (23.5%) andfruits (14.3%). The food groups with a high contribution rate
for dietary fiber were other vegetables (22.1%), dark-green anddeep-yellow vegetables (17.4%), and cereals (16.1%). Meat contributed the most to saturated fatty acids (22.5%), followed bymilk and milk products (19.5%). The food group with the highest
contribution rate for sodium was seasonings and spices (53.7%).
Based on these results, we defined the DBSs based on foodgroups with high contribution rates for the nutrients amongolder adults in Tokushima Prefecture (Supplemental table 2).

and NA scores : r = 0.312 (p = 0.016) for DBS1 and r = 0.280(p = 0.032) for DBS4.

Table 3. Correlations between dietary balance score and nutrient
adequacy scores in older adults from Tokushima Prefecture†, ‡r

95% CI)p

0.053 − 0.532

)

0.016

0.196

( –0.071 − 0.436

)

0.138

0.238

( –0.027 − 0.471

)

0.070

0.280

(

0.018 − 0.506

)

0.032

0.512

(

0.288 − 0.683)< 0.001

DBS1

0.312

DBS2
DBS3
DBS4

(

−

Based on 3DRs
DBS1

Correlations between dietary balance and nutrient adequacy scores
Table 3 shows the correlations between each DBS basedon frequency of intake and NA scores among older adults inTokushima Prefecture.
First, we confirmed the correlation between each DBS calculated using the consumption frequency of food groups and NAscores. Using Spearman’s rank correlation coefficient test, statistically significant correlations were found between DBS1, DBS4,(

Based on the FFQ

DBS2

0.492

(

0.263 − 0.669)<  0.001

DBS3

0.529

(

0.308 − 0.695)<  0.001

DBS4

0.468

(

0.234 − 0.651)<  0.001†

Correlation coefficients among older adults in Tokushima Prefecture. 							‡Correlations between each dietary balance score and nutrient adequacy score were estimated using Spearman’s rank correlation coefficient test.
||
Abbreviations : CI, confidence interval ; DBS, dietary balance
score ; DR, dietary record ; FFQ, food frequency questionnaire.		
			

Table 2. Food groups that contribute to each nutrient intake based on 3-day dietary records in older adults from Tokushima Prefecture†, ‡Nutrients to encourage (9 items)†

Nutrients to limit (2 items)
Fiber

Saturatedfatty acids

Sodium

0.0

16.1

5.6

7.1

1.8

6.3

4.2

0.1

0.0

0.0

0.0

0.0

0.0

0.0

5.3

0.0

8.2

7.1

0.6

0.2

1.4

1.2

0.0

29.5

17.4

0.2

0.4

23.5

22.1

0.6

3.9

5.9

14.3

7.7

0.2

2.4

0.0

0.0

0.5

2.9

0.0

0.0

2.3

0.3

0.7

3.1

0.0

2.1

8.4

5.3

10.3

0.6

0.0

8.4

9.4

4.0

5.8

2.4

2.8

3.4

0.0

22.5

3.1

3.8

1.5

8.2

11.4

5.1

0.0

0.0

7.5

1.9

23.6

5.3

0.5

9.3

2.6

1.1

0.0

19.5

2.7

0.0

0.0

0.0

1.1

11.2

0.0

0.0

9.2

0.3

1.6

2.7

1.9

3.0

1.8

2.6

0.3

3.0

4.8

1.7

6.4

3.8

5.5

5.7

1.8

1.6

9.6

1.5

0.2

0.1

3.9

5.3

3.8

7.1

8.1

0.6

5.4

0.2

3.1

3.1

53.7

0.9

0.7

0.6

0.0

0.9

0.5

0.1

0.3

0.0

0.4

1.1

Food groups

Protein

Potassium

Calcium Magnesium

Iron

Cereals

16.6

Potatoes and starches
Sugars and sweeteners

5.0

5.4

11.8

9.6

0.0

3.7

0.7

4.4

1.2

2.2

2.0

0.0

0.1

0.1

0.1

0.0

0.4

0.0

Pulses

9.2

5.8

13.5

12.7

14.0

0.0

Nuts and seeds

0.6

0.5

1.9

1.8

1.4

0.0

0.8

Dark-green and deep-yellow vegetables

2.3

13.5

6.2

7.5

7.2

42.5

24.0

Other vegetables

3.9

14.4

9.9

10.9

9.6

5.9

7.4

Fruits

1.3

8.1

1.9

5.5

3.4

5.9

Mushrooms

0.5

1.2

0.0

0.5

0.7

Algae

0.4

1.7

2.1

2.8

2.0

Fish, mollusks, and crustaceans

19.1

8.1

9.6

9.7

Meat

13.5

5.5

0.6

Eggs

6.6

1.9

Milk and milk products

7.1

6.8

Fats and oils

0.1

0.0

Confectionaries

2.6

Beverages

1.3

Seasonings and spices
Prepared foods

Food groups are shown based on the Standard Tables of Food Composition in Japan.
‡
Contribution rates of food groups (%).

Vitamin A Vitamin E Vitamin C

381

The Journal of Medical Investigation Vol. 70 August 2023
Second, we confirmed the correlation between each DBS calculated using quartiles of daily food group consumption volumes
and NA scores. Using Spearman’s rank correlation coefficienttest, statistically significant moderate correlations were foundbetween all DBSs and NA scores : r = 0.512 (p < 0.001) for DBS1,
r = 0.492 (p < 0.001) for DBS2, r = 0.529 (p < 0.001) for DBS3 andr = 0.468 (p < 0.001) for DBS4.
Next, to assess the external validity of the DBSs, we confirmed
the correlations between DBSs calculated using quartiles ofdaily food group consumption volumes and NA scores according
to gender and age groups in the NILS-LSA (Supplemental table4). All DBSs had similar and moderate correlations with both
NA scores using Spearman’s rank correlation coefficient test.
Additionally, the results of all correlations for the participantsin the NILS-LSA were similar to those for the participants inTokushima using the 3DRs.

Area under the receiver operating characteristic curve for dietarybalance score
Figures 1 and 2 and Table 4 show the AUC-ROC for each DBS
for discriminated individuals with sufficient intake (met the DRI
criteria) of all nine beneficial nutrients among older adults inTokushima Prefecture.
Using the consumption frequency of food groups (Figure 1
and Table 4), DBS4 had the highest ability to discriminate
nutrient sufficiency. The results of the AUC-ROCs for the DBSs
were : AUC-ROC = 0.730 for DBS1, 0.699 for DBS2, 0.728 for
DBS3, and 0.742 for DBS4. Using 3DRs (Figure 2 and Table 4),
DBS3 had the highest ability to discriminate nutrient sufficiency ; however, all DBSs had moderate accuracy to discriminate
nutrient sufficiency : AUC-ROC = 0.788 for DBS1, 0.753 for
DBS2, 0.815 for DBS3, and 0.763 for DBS4.
Next, to assess the external validity of the DBSs, we confirmed
the AUC-ROCs for each DBS for discriminated individuals with

Figure 1. AUC-ROCs showing whether four DBSs based on
FFQ-discriminated individuals with sufficient intake (met the
DRI criteria) of all nine beneficial nutrients among older adults inTokushima Prefecture. □ , △ , ○ , and × show the AUC-ROCs of
DBS1, 2, 3, and 4, respectively. Although the results for all AUCROCs were similar, DBS4 had the highest ability to discriminate
nutrient sufficiency, and its ability to discriminate nutrients showed
moderate accuracy.

sufficient intake (met the DRI criteria) of all nine beneficial nutrients in the NILS-LSA by gender (Supplemental figures 1 and
2). Although the results for all AUC-ROCs were similar, DBS2
had the highest ability to discriminate nutrient sufficiency inboth men (AUC-ROC = 0.860 for DBS1, 0.865 for DBS2, 0.826
for DBS3, and 0.820 for DBS4) and women (AUC-ROC = 0.891
for DBS1, 0.892 for DBS2, 0.874 for DBS3, and 0.864 for DBS4).

Table 4. Area under the receiver operating characteristic curve forthe dietary balance score in older adults from Tokushima Prefecture†
AUCROC(

95% CI)

SE

Based on the FFQ
DBS1

0.730

(

0.581 ― 0.879

)

0.076

DBS2

0.699

(

0.557 ― 0.840

)

0.072

DBS3

0.728

(

0.592 ― 0.864

)

0.069

DBS4

0.742

(

0.614 ― 0.871

)

0.065

Based on 3DRs
DBS1

0.788

(

0.666 ― 0.910

)

0.062

DBS2

0.753

(

0.628 ― 0.878

)

0.064

DBS3

0.815

(

0.701 ― 0.929

)

0.058

DBS4

0.763

(

0.636 ― 0.891

)

0.065

†
AUC-ROCs and 95% confidence intervals were used to evaluate theaccuracy of each formula for DBSs for discriminating individuals
whose intake levels of all nine nutrients exceed the levels in the Japanese Dietary Reference Intake criteria.
‡
Abbreviations : AUC-ROC, area under the receiver operating
characteristic curve ; CI, confidence interval ; DBS, dietary balance
score ; DR, dietary record ; FFQ, food frequency questionnaire ; SE,standard error.					Figure 2. AUC-ROCs showing whether four DBSs based on
3DR-discriminated individuals with sufficient intake (met the
DRI criteria) of all nine beneficial nutrients among older adults inTokushima Prefecture. □ , △ , ○ , and × show the AUC-ROCs of
DBS1, 2, 3, and 4, respectively. Although the results for all AUCROCs were similar, DBS3 had the highest ability to discriminate
nutrient sufficiency, and its ability to discriminate nutrients showed
moderate accuracy.

382

M. Nakamoto, et al. Validation of a dietary balance score

DISCUSSION
In this study, we developed and assessed the internal validity of DBSs using food group consumption based on FFQs and3DRs among older community-dwelling Japanese in Tokushima
Prefecture. We then assessed the external validity of DBSs bycomparing the association between DBSs and NA scores according to gender and age groups among other community-dwellingJapanese in the NILS-LSA. When the FFQ-based food groupconsumption data obtained among older adults in Tokushima
Prefecture were used for analysis, DBS1 and DBS4 were statistically significantly correlated with NA scores. Analysis of thesame data showed that although the results were similar for all
AUC-ROCs, DBS4 had the highest discriminatory power for NA
and moderate discriminatory power. Whereas, when the 3DRbased food group consumption data from two different Japanese
populations were used for analysis, all DBSs were statisticallysignificantly correlated with NA scores and had similar andmoderate discriminatory power for nutrient adequacy.
Among older adults in Tokushima Prefecture, all DBSs basedon 3DRs were statistically significantly correlated with NAscores and showed better discriminatory power for nutrient
adequacy compared with all DBSs based on FFQs. In participants in the NILS-LSA, similar results using DBSs based on
3DRs were obtained, which suggests adequate internal andexternal validity. Because dietary intake as assessed by FFQs
means ranking the amount of dietary intake in a given study
population, it cannot assess the actual amount of dietary intake.
Conversely, because 3DRs, which directly measure the amountof food intake, minimize reliance on a subject’s memory (25),they are considered to be the closest to the true value among thecurrent dietary assessment methods (26). Therefore, the results
using all DBSs based on 3DRs may have exhibited stronger
correlations between each DBS and NA score and better discriminatory power for nutrient adequacy than those using all
DBSs based on FFQs. In addition, two scoring patterns wereused in this study to calculate DBSs according to the dietaryassessment method (3DRs or FFQs). The differences in scoring
patterns according to the dietary assessment method might also
have resulted in differences in the correlations between DBSs
and NA scores.
Our results from the FFQ-based food group consumption
data obtained from among older adults in Tokushima Prefecture revealed that DBS4 had a significant correlation with theNA score and the highest discriminatory power for nutrient
adequacy. It is important to evaluate NA with more food groups
because older adults consume nutrients from a larger variety offood groups, such as fruits, fish, beans, and vegetables comparedwith middle-aged adults (27). On the other hand, because theability to understand and memorize new things declines withage (28, 29), the simplest possible dietary survey method is preferable. Therefore, DBS4 based on a simple FFQ composed of 10
food groups, which showed the highest discriminatory power for
NA among older adults in Tokushima Prefecture, might be themost useful from among the four FFQ-based DBSs. However,caution is required when using the FFQ-based DBS4 to assess
NA. Because dietary intake information obtained from FFQs
depends on self-reports, it is highly likely to be different fromthe actual amount. Previous studies have reported that thepossibility of errors in estimating food consumption may differ
depending on food groups (30). Especially among older adults,
this difference may occur more easily between self-reports anddietary records in the case of food groups that are difficult torecognize when consumed. In fact, significant correlations
were found between FFQ- and 3DR-based consumption of milk
and milk products and dark-green and deep-yellow vegetables,

which correspond to the five food groups (milk and milk products, r = 0.72, p < 0.001 ; dark-green and deep-yellow vegetables,r = 0.30, p = 0.022 ; data not shown), whereas these correlations
were small for eggs and pulses (eggs, r = –0.023, p = 0.861 ; pulses, r = 0.007, p = 0.958 ; data not shown).
The results of the AUC-ROCs using FFQ-based DBSs amongolder adults in Tokushima Prefecture revealed that DBS4
(consisting of 10 food groups) had the highest ability to discriminate NA. To utilize the FFQ-based simple DBS4 in the future,
we estimated the optimal cutoff value of DBS4 according to
the Youden index. We found that the optimal cutoff value of
DBS4 was 26 points (data not shown). We then compared NAscores between two groups (subjects with < 26 and ≥ 26 points)
using this cutoff value among older adults in Tokushima Prefecture. As a result, the NA score was significantly higher(p = 0.045) in older subjects with ≥ 26 points (n = 26 ; mean ± SD
NA score = 915.3 ± 249.2 points) than in older subjects with< 26 points (n = 33 ; 792.1 ± 211.9 points ; data not shown). In
addition, we similarly compared NA scores between two groups
using this cutoff value among older adults in the NILS-LSA.
We also found a similar significant difference between the twogroups. In men in the NILS-LSA, the mean ± SD NA score
was 967.3 ± 238.9 points in subjects with ≥ 26 points (n = 101),
and 742.7 ± 228.5 points in those with < 26 points (n = 968) (p< 0.0001, data not shown). In women in the NILS-LSA, themean ± SD NA score was 955.1 ± 232.1 points in subjects ≥ 26
points (n = 102), and 758.5 ± 235.3 points in those with < 26
points (n = 974) (p < 0.0001, data not shown). A higher NA scoreindicates good overall dietary quality. Thus, the optimal cutoff
value of DBS4 based on the FFQ (26 points) could be useful for
judging nutritional adequacy.
Our study has several limitations. First, the sample size ofTokushima participants was small (n = 59). Second, almost
all Tokushima participants (n = 55, 93.2%) were healthy older
women. As the number of male participants was very small(n = 4, 6.8%), it was not possible to confirm the presence of any
gender difference in the correlation coefficient. However, thecorrelation coefficient between the DBS based on 3DRs and NAscores in the NILS-LSA was estimated to be higher in men thanin women. Therefore, the correlation coefficient between the
DBS based on the FFQ and NA scores will need to be confirmedin both sexes in the future. Although our results can be applied
to healthy older women, it might not be possible to generalize
our results to older people with impaired physical functions,such as those admitted to hospitals. Third, the proportion of
overweight subjects in this study (33.9%) was higher than the
proportion of underweight older adults in Tokushima Prefecture.
When Spearman’s correlation coefficients between DBS1 andNA scores were calculated by stratifying the subjects according
to body type (BMI < 25 kg / m2 or BMI ≥ 25 kg / m2), almost thesame correlation was shown, regardless of body type, although
there were no significant differences between non-overweightsubjects (r = 0.306, p = 0.058) and overweight subjects (r = 0.268,
p = 0.254) (data not shown). As our study population consistedof women who were physically and cognitively healthy, theparticipants might have provided accurate answers to the FFQ.
However, as overweight individuals are known to be likely to underreport dietary intake (31-33), it is necessary to consider thesepoints fully when interpreting our results. Fourth, for some foodgroups, such as other vegetables, dietary intake based on the
3DRs was divided into four groups in quartiles and scored because we could not obtain information on the frequency of intake.
Therefore, because of possible misclassification between actual
frequency responses and classification based on the 3DRs, it isnecessary to consider these points fully when interpreting our results. Finally, a previous study showed that the ratios of intra- toThe Journal of Medical Investigation Vol. 70 August 2023
interindividual variance tended to be smaller for macronutrientsand larger for minerals and vitamins (34). In the present study,
while all of the DBSs were evaluated by intake frequency in 1
week (7 days), nutrient intake was evaluated by 3DRs. Becausea large number of subjects and a long survey period would be
required to consider the daily variance in micronutrient intake,the use of a 3DR as an assessment tool might not be sufficient.
In addition, regarding food groups such as vegetables and fruits,
it has been reported that intake may vary depending on theseason of the survey (35). Both the dietary surveys conductedin Tokushima Prefecture and NILS-LSA did not have a specificsurvey period (i.e., each participant could be surveyed during
any season of the year). As the frequency or quantity of seasonal
food groups, such as vegetables and fruits, was used in calculating the DBS, the estimated correlation between DBS and NAscores and the ability to discriminate nutrient sufficiency could
be higher if a long-term dietary survey considering the season of
the survey were carried out.
In conclusion, our results indicate the possibility of moderate
relationships between DBSs (especially DBS4) and NA scoresand their possible usefulness in assessing dietary balance in
middle-aged and older community-dwelling Japanese. Although
we assessed the validity of DBSs using the Food Composition
Tables 2015 and DRI 2020 in two different Japanese populationsin the present study, further studies are needed to improve thevalidity of revised DBSs according to the revised Food Composition Tables and DRI in the future.

CONFLICT OF INTEREST
All authors state that they have no conflicts of interest.

ACKNOWLEDGMENTS
This research was supported in part by grants from the
Fuji Foundation for Protein Research, Research Funding forLongevity Sciences from the National Center for Geriatrics andGerontology, Japan [grant No. 21-18], and JSPS KAKENHI
Nos. JP15K15228 and JP20K0592. The funders / sponsors had
no role in the design, conduct, or reporting of the study, or in thedecision to submit the manuscript for publication. The authors
wish to thank the study participants and our colleagues for theirassistance with the survey.

CONTRIBUTIONSMN designed the research and was the principal investigator ; YT, SO, YI, and MN conducted the survey ; TN and MN
analyzed the data ; MN drafted the manuscript ; AN, RO, andTS supported the analyses and drafting of the manuscript ; TSprovided study oversight ; all authors interpreted the data, provided critical input, and read and approved the final manuscript.
MN had primary responsibility for the final content.

REFERENCES
1.

2.

Christensen K, Delhomme G, Rau R, Vaupel JW : Ageing
populations : the challenges ahead. Lancet 374 : 1196-1208,
2009 doi : 10.1016/S0140-6736(09)61460-4.
Nagy CL, Bernard MA, Hodes RJ : National Institute on Agingat middle age--its past, present, and future. J Am Geriatr Soc
60 : 1165-1169, 2012 doi : 10.1111/j.1532-5415.2012.03994.x.

3.

383

Cabinet Office, Government of Japan. White paper on Aged
Society. Cabinet Office, 2022 Available at : https://www8.
cao.go.jp/kourei/whitepaper/w-2022/zenbun/pdf/1s1s_01.
pdf. (in Japanese) (accessed September 29 2022)
4. Ministry of Health, Labor and Welfare. The Dietary Reference Intakes for Japanese (2020). Available at : https://www.
mhlw.go.jp/content/10904750/000586553.pdf. (in Japanese)(accessed September 29 2022)
5. Trichopoulou A, Kouris-Blazos A, Wahlqvist ML, GnardellisC, Lagiou P, Polychronopoulos E, Vassilakou T, Lipworth L,
Trichopoulos D : Diet and overall survival in older people.
BMJ 311 : 1457-1460, 1995 doi : 10.1136/bmj.311.7018.1457.
6. National Cancer Institute, Devision of Cancer Control
and Population Sciences. Evaluating the Healthy Eating
Index. Available at : https://epi.grants.cancer.gov/hei/
evaluation-validation.html (accessed September 29 2022)
7. Tomata Y, Watanabe T, Sugawara Y, Chou WT, Kakizaki
M, Tsuji I : Dietary patterns and incident functional disability in older Japanese : the Osaki Cohort 2006 study. J
Gerontol A Biol Sci Med Sci 69 : 843-851, 2014 doi : 10.1093/
gerona/glt182.
8. Katanoda K, Kim HS, Matsumura Y : New QuantitativeIndex for Dietary Diversity (QUANTIDD) and its annualchanges in the Japanese. Nutrition 22 : 283-287, 2006
doi : 10.1016/j.nut.2005.06.014.
9. Kumagai S, Watanabe S, Shibata H, Amano H, Fijiwara Y,
Shinkai S, Yoshida H, Suzuki T, Yukawa H, Yasumura S,
Haga H : Effects of dietary variety on declines in high-level
functional capacity in older people living in a community.
Jpn J Public Health 50 : 1117-1124, 2003 (in Japanese)
10. Yamaguchi K, Akishita M, Yamada S, Hama T, Kameyama
Y, Toba K, Ohuchi Y : Relationship between higher-level
functional capacity, mood and food intake frequency in community-dwelling older. Jpn J Molecular Psychiatry 7 : 173176, 2007 (in Japanese)
11. Yamaguchi K, Akishita M : Nutritional factors for falls. J
Clinical and Experimental Medicine 227 : 181-184, 2008 (inJapanese)
12. Tanimoto Y, Watanabe M, Sugiura Y, Hayashida I, Kusabiraki
T, Kono K : Factors related to sarcopenia in community-dwelling elderly subjects in Japan. Jpn J Public Health
60 : 683-690, 2013 doi : https://doi.org/10.11236/jph.60.11_683 (in Japanese)
13. Narita M, Kitamura A, Takemi Y, Yokoyama Y, Morita
A, Shinkai S : Food diversity and its relationship withnutrient intakes and meal days involving staple foods,
main dishes, and side dishes in community-dwelling older
adults. Jpn J Public Health 67 : 171-182, 2020 (in Japanese)doi : 10.11236/jph.67.3_171.
14. Nakamoto M, Kanmura M, Yoshida M, Tanaka Y, Ono S,
Iwasaki Y, Nakamoto A, Sakai T : Validity of dietary diversity assessed using short-form questionnaire among older
Japanese community dwellers. J Med Invest 69 : 31-37,
2022 doi : 10.2152/jmi.69.31.
15. Shimokata H, Ando F, Niino N : A new comprehensive study
on aging--the National Institute for Longevity Sciences,
Longitudinal Study of Aging (NILS-LSA). J Epidemiol 10(1Suppl) : S1-9, 2000 doi : 10.2188/jea.10.1sup_1.
16. Imai T, Sakai S, Mori K, Ando F, Niino N, Shimokata
H : Nutritional assessments of 3-day dietary records in National Institute for Longevity Sciences--Longitudinal Studyof Aging (NILS-LSA). J Epidemiol 10(1 Suppl) : S70-76,
2000 doi : 10.2188/jea.10.1sup_70.
17. Ministry of Education, Culture, Sports, Science and Technology. The Standard Tables of Food Composition in Japan(2015). Available at : https://www.mext.go.jp/a_menu/

384

M. Nakamoto, et al. Validation of a dietary balance scoresyokuhinseibun/1365419.htm. (in Japanese) (accessed
December 29 2022)
18. Zhou Z, Hu W, Li M, Xu R, Shi L, Huang C : Development
and validation of a new model of desirable dietary pattern (N-DDP) score for Chinese diets. Public Health Nutr
17 : 519-528,2014 doi : 10.1017/S1368980012005629.
19. Drewnowski A : The Nutrient Rich Foods Index helps
to identify healthy, affordable foods. Am J Clin Nutr
91 : 1095.1095S-1101S, 2010 doi : 10.3945/ajcn.2010.28450D.
20. Drewnowski A, Fulgoni V L 3rd : Nutrient density : principles and evaluation tools. Am J Clin Nutr 99(5Suppl) : 1223S-1228S, 2014 doi : 10.3945/ajcn.113.073395.
21. Fulgoni VL III, Keast DR, Drewnowski A : Development
and validation of the nutrient-rich foods index : a tool tomeasure nutritional quality of foods. J Nutr 139(8) : 15491554, 2009 doi : 10.3945/jn.108.101360.
22. Yokoyama Y, Yoshizaki T, Kotemori A, Nofuji Y, Seino S,
Nishi M, Amano H, Narita M, Abe T, Shinkai S, Kitamura
A, Fujiwara Y : Development and evaluation of a modified
version of the dietary variety score for community-dwellingolder adults. Jpn J Public Health 69 : 665-675, 2022 (inJapanese)
23. Imai T, Tsuji T, Yamamoto H, Fukuwatari T, Shibata K :Comparing the Main Regular Dietary Mineral Sourceamon Elementary Students, College Students, and theOlder Based on Weighing Dietary Records. Jpn J Nutr Diet
72(2) : 51-66, 2014 (in Japanese)
24. Kozakai R, Ando F, Kim H, Rantanen T, Shimokata
H : Regular exercise history as a predictor of exercise incommunity-dwelling older Japanese people. J Phys Fitness
Sports Med 1(1) : 167-174, 2012 doi : 10.23736/S0022-4707.
20.10998-8.
25. Shim JS, Oh K, Kim HC : Dietary assessment methods inepidemiologic studies. Epidemiol Health 36 : e2014009, 2014
doi : 10.4178/epih/e2014009.
26. Willett W (editor) (1998) Nutritional epidemiology second
edition, pp50-73. New York, USA : Oxford University Press.
27. Otsuka R, Yatsuya H, Tamakoshi K : Descriptive epidemiological study of food intake among Japanese adults : analyses by age, time and birth cohort model. BMC Public Health
14 : 328, 2014 doi : 10.1186/1471-2458-14-328.

28. Hu C, Yu D, Sun X, Zhang M, Wang L, Qin H : Theprevalence and progression of mild cognitive impairment
among clinic and community populations : a systematic
review and meta-analysis. Int Psychogeriatr 29(10) : 15951608, 2017 doi : 10.1017/S1041610217000473.
29. GBD 2019 Dementia Forecasting Collaborators : Estimationof the global prevalence of dementia in 2019 and forecasted
prevalence in 2050 : an analysis for the Global Burden of
Disease Study 2019. Lancet Public Health 7(2) : e105-e125,2022 doi : 10.1016/S2468-2667(21)00249-8.
30. Errors in estimating food consumption from the menu withdescription of approximate quantity of consumed food. J
Jpn Soc Food Nutr 29(4) : 229-234, 1976 (in Japanese)doi : https://doi.org/10.4327/jsnfs1949.29.229.
31. Govindaraju T, McCaffrey TA, McNeil JJ, Reid CM, Smith
BJ, Campbell DJ, Owen AJ : Mis-reporting of energy intake
among older Australian adults : Prevalence, characteristics,
and associations with quality of life. Nutrition 90 : 111259,
2021 doi : 10.1016/j.nut.2021.111259.
32. Meng X, Kerr DA, Zhu K, Devine A, Solah VA, Wright J,
Binns CW, Prince RL : Under-reporting of energy intake
in older Australian women is associated with a higher
body mass index. J Nutr Health Aging 17(2) : 112-118, 2013
doi : 10.1007/s12603-012-0382-8.
33. Murakami K, Sasaki S, Okubo H, Freshmen in Dietetic
Courses Study II Group : Characteristics of under- andover-reporters of energy intake among young Japanesewomen. J Nutr Sci Vitaminol (Tokyo) 58(4) : 253-262,2012
doi : https://doi.org/10.3177/jnsv.58.253
34. Ogawa K, Tsubono Y, Nishino Y, Watanabe Y, Ohkubo T,
Watanabe T, Nakatsuka H, Takahashi N, Kawamura M,
Tsuji I, Hisamichi S : Inter- and intra-individual variationof food and nutrient consumption in a rural Japanese population. Eur J Clin Nutr 53 : 781-785, 1999 doi : 10.1038/
sj.ejcn.1600845.
35. Sasaki S, Takahashi T, Iitoi Y, Iwase Y, Kobayashi M,
Ishihara J, Akabane M, Tsugane S, JPHC : Food and nutrient intakes assessed with dietary records for the validation
study of a self-administered food frequency questionnairein JPHC Study Cohort I. J Epidemiol 13(1 Suppl):S23-50,
2003 doi : 10.2188/jea.13.1sup_23.

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The Journal of Medical Investigation Vol. 70 August 2023
Supplemental table 1. Japanese Dietary Reference Intake values for the calculation of the overall nutrient adequacy score†
RDA‡, ¶

Nutrients
Men

Women

40–49 years 50–64 years 65–74 years
Energy (kcal)

2700

2600

≥ 75 years

2400

2100

40–49 years 50–64 years 65–74 years
2050

1950

1850

≥ 75 years
1650

Nutrients to encourage (9 items)
Protein (g)

65

65

60

60

50

50

50

50

Potassium (mg)

2500

2500

2500

2500

2000

2000

2000

2000

Calcium (mg)

750

750

750

700

650

650

650

600
260

Magnesium (mg)

370

370

350

320

290

290

280

Iron (mg)

7.5

7.5

7.5

7.0

6.5

6.5

6.0

6.0

Vitamin A (μgRAE)

900

900

850

800

700

700

700

650

Vitamin E (mg)

6.0

7.0

7.0

6.5

5.5

6.0

6.5

6.5

Vitamin C (mg)

100

100

100

100

100

100

100

100

Fiber (g)

21

21

20

20

18

18

17

17

Saturated fatty acids (%) ||

≤7

≤7

≤7

≤7

≤7

≤7

≤7

≤7

§

< 3000< 3000< 3000< 3000< 2600< 2600< 2600< 2600

Nutrients to limit (2 items)
Sodium (mg)†Guided by the Dietary Reference Intakes for Japanese, 2020‡
As nutrients for which RDA values have not been published, we adopted the Tentative Dietary Goal value (fiber, saturated fat, sodium) or
Adequate Intake value (potassium, vitamin E).
||% energy intake§
Daily reference values for sodium intake are < 7.5 g for men and < 6.5 g for women.
Thus, we converted salt to sodium : sodium (mg) = daily reference value for salt intake (g) × [600 (mg) / 1.5 (g)]¶Abbreviations : RDA, recommended dietary allowance.

Supplemental table 2. Number of food groups that composed the four formulas and range of the four formulas for the dietary balance score †, ‡, ¶, ††No. of food groups

Score range†

Fish, mollusks, and crustaceans, milk and milk products, dark-green and deep-yellow
vegetables, other vegetables, meat

Food groups used in the scoring formula

5

0–20

DBS2 ‡

Fish, mollusks, and crustaceans, milk and milk products, dark-green and deep-yellow
vegetables, other vegetables, meat, pulses, eggs, fruits, fats and oils

9

0–36

DBS3||

Fish, mollusks, and crustaceans, milk and milk products, dark-green and deep-yellow
vegetables, other vegetables, meat, pulses, eggs, fruits

8

0–32

DBS4 §

Fish, mollusks, and crustaceans, milk and milk products, dark-green and deep-yellow
vegetables, other vegetables, meat, pulses, eggs, fruits, mushrooms, algae

10

0–40

DBS1

†

DBS1 : The score consisted of food groups (except cereals) that contributed more than 15% of daily dietary intake for the 11 nutrients in the
NA scores.			
DBS2 : The score consisted of food groups (except cereals) that contributed more than 10% of daily dietary intake for the 11 nutrients in the
NA scores.			
||
DBS3 : The score consisted of food groups (except cereals and fats and oils) that contributed more than 10% of daily dietary intake for the 11
nutrients.			
§
DBS4 : The score consisted of food groups summarized by the Standard Tables of Food Composition in Japan (2015), except cereals, sugars
and sweeteners, potatoes and starches, seasonings and spices, fats and oils, and beverages.			
¶
Scoring pattern 1 : Pattern 1 was set based on the quartiles of daily consumption of the food groups derived from the 3DRs : “Quantile 4 (thehighest quartile group)” as 4 points, “Quantile 3” as 2 points, “Quantile 2” as 1 point, and “Quantile 1 (the lowest quartile group)” as 0 points.
††
Scoring pattern 2 : Pattern 2 was set based on the frequency of food groups obtained by FFQs : 4 points for “almost every day”, 2 points for“once every 2 days”, 1 point for “once or twice a week”, and 0 points for “almost never”. When scoring food groups for which no information on
consumption frequency was available (i.e., other vegetables and mushrooms), the scoring rules of Pattern 1 were used.			
‡‡Abbreviations : DBS, dietary balance score ; FFQ, food frequency questionnaire ; NA, nutrient adequacy.
‡

M. Nakamoto, et al. Validation of a dietary balance score

386

Supplemental table 3. Characteristics of the participants in the NILS-LSA
Total participants (n = 2,145)

Men (n = 1,069)

Women (n = 1,076)

Age (years)†

61.1

±

12.5

61.1

±

12.3

61.1

±

12.7

Body mass index (kg / m2) †

22.7

±

3.2

23.1

±

2.8

22.3

±

3.4

Current physical activity (METs-min / year)†, §701821.6 ±Mini-Mental State Examination score†, §64914.5

27.8

±

1.9

344

(

16.0

687253.6 ±71934.0

27.6

±

2.0

140

(

6.5

716335.2 ±53276.1

28.0

±

1.9

204

(

9.5

Education‡≤ 9 years)))

9–12 years

846

(

39.4

)

384

(

17.9

)

462

(

21.5

)

> 12 years

955

(

44.5

)

545

(

25.4

)

410

(

19.1

)

Annual household income‡, §≤ 1.49 million yen

71

(

3.3

)

23

(

1.1

)

48

(

2.2

)

1.50–4.49 million yen

696

(

32.4

)

328

(

15.3

)

368

(

17.2

)

≥ 4.50 million yen

1,337

(

62.3

)

707

(

33.0

)

630

(

29.4

)

Smoking habit‡
Current

242

(

11.3

)

205

(

9.6

)

37

(

1.7

)

Former / never

1,903

(

88.7

)

864

(

40.3

)

1,039

(

48.4

)

9.9

±

17.2

16.3

±

20.8

3.6

±

8.8

Alcohol intake (g / day)†
Medical history‡

Hypertension (+)

642

(

29.9

)

350

(

16.3

)

292

(

13.6

)

Dyslipidemia (+)

453

(

21.1

)

207

(

9.7

)

246

(

11.5

)

Diabetes (+)

153

(

7.1

)

91

(

4.2

)

62

(

2.9

)

Heart disease (+)

98

(

4.6

)

62

(

2.9

)

36

(

1.7

)

Stroke (+)

75

(

3.5

)

43

(

2.0

)

32

(

1.5

)

†

Mean ± SD ; ‡ Number (%)												||Abbreviations : METs, metabolic equivalents ; NILS-LSA, National Institute for Longevity Sciences-Longitudinal Study of Aging ; SD, standard deviation.
§
Current physical activity, n = 2,138 (1,067 men, 1,071 women) ; Mini-Mental State Examination score, n = 1,151 (583 men, 568 women) ; Annual household income, n = 2,104 (1,058 men, 1,046 women)

Supplemental table 4. Correlation between each dietary balance score based on 3-day dietary records and the nutrient adequacy scoreamong older community-dwelling Japanese in the NILS-LSA by gender and age groups †, ‡
Men
r

(

95% CI

Women
)

p

r

(

95% CI)p

All (n = 1,069 men, n = 1,076 women)
DBS1

0.489

( 0.442 − 0.534)<  0.001

0.388(

0.336 − 0.438)<  0.001

DBS2

0.476

( 0.428 − 0.521)<  0.001

0.382(

0.330 − 0.432)<  0.001

DBS3

0.547

( 0.503 − 0.588)<  0.001

0.452(

0.403 − 0.498)<  0.001

DBS4

0.547

( 0.503 − 0.588)<  0.001

0.472(

0.425 − 0.517)<  0.001

40–64 years (n = 637 men, n = 635 women)
DBS1

0.505

( 0.445 − 0.561)<  0.001

0.414(

0.348 − 0.477)<  0.001

DBS2

0.486

( 0.424 − 0.543)<  0.001

0.427(

0.361 − 0.489)<  0.001

DBS3

0.549

( 0.493 − 0.601)<  0.001

0.496(

0.435 − 0.553)<  0.001

DBS4

0.550

( 0.493 − 0.602)<  0.001

0.505(

0.445 − 0.561)< 0.001≥ 65 years (n = 432 men, n = 441 women)
DBS1

0.482

( 0.407 − 0.552)<  0.001

0.378(

0.296 − 0.456

)

0.001

DBS2

0.469

( 0.392 − 0.540)<  0.001

0.343(

0.258 − 0.423)<  0.001

DBS3

0.554

( 0.485 − 0.616)<  0.001

0.422(

0.342 − 0.496)<  0.001

DBS4

0.553

( 0.484 − 0.615)<  0.001

0.449(

0.371 − 0.520)<  0.001†

Correlation coefficients in the NILS-LSA. 										The correlation between each dietary balance and nutrient adequacy score was estimated using Spearman’s rank correlation coefficient test.
Abbreviations : CI, confidence interval ; DBS, dietary balance score ; NILS-LSA, National Institute for Longevity Sciences-Longitudinal
Study of Aging.													‡

||The Journal of Medical Investigation Vol. 70 August 2023

387

Supplemental figure 1. AUC-ROCs showing whether four DBSs based on 3DR-discriminated individuals with sufficient
intake (met the DRI criteria) of all nine beneficial nutrients among men in the NILS-LSA. A) AUC-ROC of DBS1, B) AUC-ROC
of DBS2, C) AUC-ROC of DBS3, and D) AUC-ROC of DBS4. Although the results for all AUC-ROCs were similar, DBS2 had
the highest ability to discriminate nutrient sufficiency, and its ability to discriminate nutrients showed moderate accuracy.

Supplemental figure 2. AUC-ROCs showing whether four DBSs based on 3DR-discriminated individuals with sufficient
intake (met the DRI criteria) of all nine beneficial nutrients among women in the NILS-LSA. A) AUC-ROC of DBS1, B) AUCROC of DBS2, C) AUC-ROC of DBS3, and D) AUC-ROC of DBS4. Although the results for all AUC-ROCs were similar, DBS2
had the highest ability to discriminate nutrient sufficiency, and its ability to discriminate nutrients showed moderate accuracy.