Glycative Stress Research
Online edition : ISSN 2188-3610
Print edition : ISSN 2188-3602
Received : October 12, 2020
Accepted : November 4, 2020
Published online : December 31, 2020
doi:10.24659/gsr.7. 4_268

Original article

Examination of postprandial blood glucose prediction model using foodnutrition component values
Shiori Uenaka 1), Mari Ogura 1, 2), Masayuki Yagi 1), Wakako Takabe 1, 3), Yoshikazu Yonei 1)
1) Anti-Aging Medical Research Center/Glycative Stress Research Center, Graduate School of Life and Medical Sciences,
Doshisha University, Kyoto, Japan
2) Department of Food and Nutrition, Kyoto Bunkyo Junior College3) Department of Materials and Life Science, Faculty of Science and Technology, Shizuoka Institute of Science and Technology,
Shizuoka, Japan

Abstract

Objective: One of the methods for reducing glycative stress is to suppress postprandial hyperglycemia (PPHG). The purposeof this study is to establish a non-invasive and easy- to-implement means for suppressing PPHG. Based on the results of thepast intake tests of various foods, a model formula for predicting the degree of PPHG from food contents was created.
Methods: A model formula was created to predict the indices for PPHG, i.e. iAUC (incremental area under the curve),
ΔCmax (maximum blood glucose concentration), based on iAUC (mg/dL · min) or ΔCmax when ingested a standard food (i.e.,
cocked rice, udon, and bread) and the nutritional component of the test food. The past results of the model food intake test
in our laboratory were used to create the predictive model formula. We applied 18 kinds of food to the formula and verified
the degree of coincidence with the actual postprandial glucose change. Then, the mean absolute relative difference (MARD)
between the predicted value and the measured value was calculated for each food (n = 18) and for each subject (n = 159) inthe 18 tests. In a subclass analysis, subjects were divided into three groups: top 25 % (n = 42, iAUC; 7,379.9 ± 146. 5), middle(n = 75, iAUC; 5,302.7 ± 73.5), and bottom 25 % (n = 42, iAUC; 3,243.9 ± 61.5), based on iAUC at standard food intake.
Pearson's correlation analysis was used to test the correlation between predicted and measured values, and Turkey's HSD test
was used to analyze MARD.
Results: In the simulation of the food intake test (18 types), a highly positive correlation of r = 0.7 was observed between thepredicted and measured value, and the average MARD was less than 15%. A subclass analysis showed the MARD in the top
25 % group were lower than those in the bottom 25 % group ( p < 0.05).
Conclusion: A high correlation was found between the predicted value from the model formula and the measured value.
Among them, the accuracy of prediction tended to be higher as the data of the subjects whose blood glucose was more likely
to rise.

KEY WORDS: postprandial hyperglycemia, protein, fat, acetic acid, citric acid, dietary fiber

Introduction

The phenomenon caused by the non-enzymatic bindingof reducing sugars, i.e., glucose and fructose, to proteinsin vivo and the production and accumulation of advanced
glycation end products (AGEs), which are the causative agentsof age-related degeneration, is called “glycative stress ”.
Glycative stress is one of the risk factors for aging and plays
a role in the development of diseases such as skin aging
and diabetic complications 1, 2). Reduction of glycative stressincludes suppression of rapid postprandial hyperglycemia

Contact Address: Professor Masayuki Yagi
Anti-Aging Medical Research Center, Graduate School of Life andMedical Sciences, Doshisha University
1-3, Tatara Miyakodani, Kyotanabe-shi, Kyoto, 610-0321 Japan
TEL/FAX: +81-774-65-6394 e-mail: myagi@mail.doshisha.ac.jp
Co-authors: Uenaka S, ctud2025@mail4.doshisha.ac.jp;
Mari O, m-ogura@po.kbu.ac.jp; Takabe W, takabe.wakako@sist.ac.jp;
Yoshikazu Y, yyonei@mail.doshisha.ac.jp

Glycative Stress Research 2020; 7 ( 4 ) : 268- 277(c) Society for Glycative Stress Research_ 268 _Glycative Stress Research(PPHG), suppression of glycation reaction, and promotion of
decomposition and excretion of AGEs. Of these, suppressionof PPHG is a measure that can be easily incorporated into
the daily diet.
The Glycemic Index (GI) was proposed by Jenkins DJ
et al. 3) in 1981 as an index showing the degree of PPHG infoods. GI is an index developed to “qualitatively” evaluate
the in vivo function of a certain amount of carbohydrates,
focusing on the difference in the physiological functions ofcarbohydrates contained in foods. In Japan, Sugiyama et al. 4)
are trying to find GI in combination with a variety of foods
based on cooked rice, and to incorporate the PPHG reactionin Japanese foods into nutrition education. It is concluded
that these nutrition education methods are useful for disease
prevention and health promotion. Furthermore, it has beenreported that the GI can be lowered by combining cookedrice as a staple food with vegetable salad, vinegar, soybeans,
and milk and dairy products 5, 6). Therefore, the introductionof foods that reduce GI by co-ingestion with carbohydrates
can be also effective in suppressing PPHG. However,Japanese food is characterized by ingesting many types offood in combination, for example, a “combination food”
with a staple food, or a “composite food” with a staple food,main dish, and side dish, thus it is often difficult to apply the
GI evaluated for each food. It is required to evaluate GI byingesting multiple foods at once in a “food form”.
The GI measurement is usually started to perform on
the subjects from 7 to 9 a.m. without breakfast. Since theblood glucose level is measured about 7 times every 15 to 30
minutes from the start of ingestion of the test food, there areproblems of multiple invasions and restraint time. Therefore,
a method that does not require blood sampling is required toquickly evaluate the GI of various foods.
We have reported model foods (salad chicken, olive oil,grain vinegar, cabbage, and lemon juice), that are rich innutritional components (protein, lipids, acetic acid, dietaryfiber, and citric acid) that have been reported to reduce PPHG,
was examined for its effect of suppressing the elevation inblood glucose when each food with different amounts wasingested before cooked rice 7, 8). As a result, it was shown that
the PPHG inhibitory effect of each nutritional component
becomes stronger as the intake is increased. Additionally, whena complex food containing a part or all of each nutritional
component was ingested before ingestion of cooked rice,strong suppression of PPHG was observed as compared withthat of cooked rice alone. It was considered that this action
was due to the fact that each nutritional component containedin the complex food worked synergistically and contributed
to the PPHG suppressing effect.
In this study, we created a model formula for predicting
the degree of PPHG from dietary content based on the resultsof intake tests of various model foods conducted in the past.
Furthermore, the model formula created was applied to 18
types of food that had already been verified in the test, andthe coincidence with the actual postprandial blood glucosechange was analyzed, thus attempting a postprandial blood
glucose simulation.

Methods

Creation of prediction model formula for
postprandial blood glucose change

A model formula was created to predict indices for
PPHG (iAUC, ΔCmax) when a test food was ingested byusing the data of the incremental area under the curve (iAUC[mg/dL · min]) or maximum blood glucose concentration(ΔCmax [mg/dL]) and the nutritional component valueof the test food. The results of the model food intake
studies conducted in our laboratory were used to create thepredictive model formula. Furthermore, simulation of PPHG
was performed by substituting the results of the dietaryintake tests of 18 cases conducted in our laboratory into thecreated prediction model formula.

Simulation of PPHG
Regarding the test food (test food + carbohydrate) anddata of iAUC and ΔCmax used in the simulation, we usedthe verification results of 18 tests conducted in our laboratoryfrom 2014 to 2020 as follows ( Table 1 ) 7, 9-13);_ 2 69 _· Beef bowl A (standard food: rice 230 g): (test food: beefbowl [gyudon] fixings 135 g) + rice 230 g 9). Here, ricemeans cooked rice. The fixing consists of beef meat,
onion, and sauce for gyudon.
· Beef bowl B (standard food: rice 230 g): (test food: beefbowl fixings 135 g + ginger 15 g) + rice 230 g 9).
· Beef bowl C (standard food: rice 200 g): (test food: beefbowl fixings 125 g) + rice 200 g 10).
· Beef bowl D (standard food: rice 200 g): (test food: beefmeat 65 g) + rice 200 g 10).
· Beef bowl E (standard food: rice 200 g): (test food: beefmeat 65 g) + rice 200 g 10).
· Breakfast A (standard food: rice 200 g): (test food: beefbowl fixings 135 g) + rice 200 g 11).
· Breakfast B (standard food: rice 200 g): (test food: riceball [onigiri] 113 g + bread 90 g) 11).
· Breakfast C (standard food: rice 200 g): (test food: 60 g ofeggs, 3 wieners, 5 g of salad oil, 15 g of hashed potatoes,
and 12 g of ketchup) + rice 200 g 11).
· Vinegar rice (standard food: rice 200 g): (test food: sushi
vinegar 21 g) + rice 177 g 7).
· Fried chicken A (standard food: rice 200 g): (test food:fried chicken 135 g) + rice 149 g 7).
· Fried chicken B (standard food: rice 200 g): (test food:fried chicken 135 g and lemon juice 15 g) + rice 145 g 7).
· Gyoza (standard food: rice 200 g): (test food: gyoza 138 g
and ponzu soy sauce 15 g) + rice 129 g 7). Gyoza are fried
dumplings with vegetable and meat ingredients in a flour
wrapper.
· Grapefruit (GF) smoothie (standard food: bread 170 g):(test food: GF smoothie 230 g) + bread 132 g 12).
· Citric acid water (standard food: bread 170 g): (test food:citric acid solution 200 g) + bread 170 g 12).
· Mapo eggplant bowl (standard food: 200 g of rice): (test
food: eggplant with rice [mabo-don] *) + rice 180 g 13).
· Mapo eggplant udon (standard diet: udon with dietaryfiber 250 g): (test diet: eggplant with dietary fiber *) +

Examination of Postprandial Blood Glucose Prediction Model

Table 1. The test food and subjects for the PPHG simulation.
Test food

Number of subjects

Age

Beef bowl A

4 males, 4 females

23.3 ± 1.3

Beef bowl C

3 males, 3 females

22.2 ± 1.0

3 males, 3 females

22.2 ± 1.0

Beef bowl B

4 males, 2 females

Beef bowl D

3 males, 3 females

Beef bowl E
Breakfast A

6 males, 8 females

Breakfast B

6 males, 8 females

Breakfast C

5 males, 7 females

Vinegar rice

2 males, 9 females

Fried chicken A
Fried chicken B
GF smoothie

Udon with a soft boiled egg
Total 18

23.1 ± 1.3

23.0 ± 1.3
23.1 ± 1.2

23.2 ± 1.1

22.9 ± 1.2

2 males, 3 females

22.3 ± 1.2

2 males, 3 females

23.6 ± 0.9

1 male, 2 females

Salad udon

22.0 ± 0.4

23.0 ± 1.3

4 males, 4 females

Mapo eggplant udon

22.2 ± 0.9

5 males, 9 females
2 males, 3 females

Mapo eggplant bowl

22.2 ± 0.9

23.0 ± 1.3

3 males, 5 females

Citric acid water

22.2 ± 1.0

5 males, 9 females
5 males, 9 females

Gyoza + ponzu soy sauce

23.0 ± 1.3

23.3 ± 1.2

159 (65 males, 94 females)

Ages are expressed as mean ± standard deviation. Beef bowl A, beef bowl fixings 135 g + rice 230 g; Beef bowl B, beef bowl
fixings 135 g + ginger 15 g + rice 230 g; Beef bowl C, beef bowl fixings 135 g + rice 200 g; Beef bowl D, beef bowl meat
only + rice 200 g; Beef bowl E, beef bowl onion only + rice 200 g; Breakfast A, beef bowl fixings 135 g+ rice 200 g;
Breakfast B, cooked rice balls 113 g + bread 90 g; Breakfast C, egg 60 g + three wieners + salad oil 5 g + hash browns 15 g +ketchup 12 g + rice 200 g; Vinegar rice, sushi vinegar 21 g + rice 177 g; Fried chicken A, fried chicken 135 g + rice 14 9 g;
Fried chicken B, fried chicken 135 g + lemon juice 15 g + rice 14 5 g; Gyoza + ponzu soy sauce, gyoza 138 g + ponzu soy
sauce 15 g + rice 129 g; Grapefruit (GF) smoothie, bread 132 g + GF smoothie 230 g; Citric acid, bread 170 g + citric acidwater 200 g; Mapo eggplant bowl, mapo eggplant * + rice 180 g; Mapo eggplant udon, mapo eggplant + *udon with dietaryfiber 230 g; Udon with a soft boiled egg, soft boiled egg * + handmade udon 210 g; Salad udon, vegetable salad * + sesame
dressing * + handmade udon 210 g. Rice means cooked rice. * Intake of mapo eggplant, hot spring egg, vegetable salad, andsesame dressing is unknown.

udon with dietary fiber 230 g 13). Udon is a thick white
noodle made of wheat flour.
· Udon with a soft boiled egg (standard food: kake udon
270 g): (test food: a soft boiled egg *) + udon 210 g 13).
· Salad udon (standard food: kake udon 270 g): (test food:vegetable salad * + sesame dressing *) + udon 210 g 13).
* Intake of eggplant with rice, eggplant with dietary fiber,
soft boiled eggs, dressing for vegetable salad + sesame isunknown.
The effective analysts adapted to this simulation werea total of 159 young men and women aged between 20
and 30 years at the time of obtaining consent to participate
in the study. The measured values used for analysis were
the average value for 18 kinds of food and the individual
value for 159 subjects. The mean absolute relative difference(MARD) between the predicted value and the measured
value was calculated according to the following formula 14).

MARD was calculated for the mean value (n = 18) andindividual value (n = 159) of the intake group evaluated ineach test. In a subclass analysis, subjects were divided into
three groups: top 25 % (n = 42, iAUC; 7,379.9 ± 146.5)
where blood glucose is likely to rise, middle (n = 75, iAUC;
5, 302.7 ± 73.5), and bottom 25 % (n = 42, iAUC; 3, 243.9± 61.5) where blood glucose does not likely rise. Then, theMARD of each group was calculated.

Statistical analysis
IMB SPSS Statics 26 (IMB Japan, Minato-ku, Tokyo,
Japan) was used for statistical analysis. A two-sided test
determined that there was a significant difference when therisk rate was less than 5%, and the results are expressed asmean ± standard error (SE). Pearson's correlation analysis
was used to test the correlation. When comparing MARDs,
the Turkey's HSD test was used.

MARD (%) = 100 × | (measured value) − (predicted value) | /predicted value_ 270 _Glycative Stress Research

Ethical standards
All 18 dietary intake tests evaluated in this study compliedwith the Declaration of Helsinki (revised at the 2013 WMA
Fortaleza General Assembly) and the ethical guidelines forhuman medical research (Ministry of Education, Culture,
Sports, Science and Technology, Ministry of Health, Labourand Welfare), and carried out in response to the submissionof a voluntary consent form. These tests were conducted withthe approval of the Ethics Review Committee on “Studies in
Humans” 7, 9-13).

Results

1. A model formula for prediction of PPHG

(1) Prediction formula for PPHG after intake of eachnutrition
The prediction of iAUC and ΔCmax by ingestion of eachnutrient alone is based on the result of the PPHG inhibitory
effect when the subjects ate together cocked rice (200 g)
and a model food having nutritional components (protein,lipids, acetic acid, dietary fiber, and citric acid), which havebeen reported to have an effect of alleviating PPHG 7, 8), as
follows: salad chicken (A), olive oil (B), grain vinegar (C),cabbage (D), and lemon juice (E). In this model, we decided
to predict iAUC and ΔCmax by focusing on the relationshipbetween the collected data (iAUC, ΔCmax) and nutritional
intake. Table 2-a shows an example of the relationship of
the protein amount of the test food to the mean value ofiAUC for 120 minutes and ΔCmax. From the mean value ofiAUC and ΔCmax after ingestion of the test food with low
or high protein, we calculated the reduction value and rate ofiAUC and ΔCmax when compared with the intake of only
the standard food. Similar calculations were performed forlipids, acetic acid, dietary fiber, and citric acid (Table 2-b, c,d, e).
Next, a simple regression analysis was performed
with the reduction rate in iAUC and ΔCmax after the test
food ingestion as the objective variable and the amount of
nutritional components contained in each model food as the
explanatory variable ( Table 3). By substituting x (nutrient
component amount) into the obtained regression coefficient,y (iAUC or ΔCmax reduction rate) was obtained when eachnutrition component was ingested with cooked rice alone.
(2) Prediction formula for PPHG depending on the amountof carbohydrates
We created a prediction formula for how much iAUC
and ΔCmax increase (calculation of elevated iAUC and
ΔCmax) from the carbohydrate amount in the test food andiAUC and ΔCmax when ingested the standard food.

Elevated iAUC = (carbohydrate content of test food) × (iAUC
at standard food intake/carbohydrate content of standard
food)
Elevated ΔCmax = (carbohydrate amount of test food) ×(ΔCmax at standard food intake/carbohydrate amount ofstandard food)Table 2. Predicted values of iAUC, ΔCmax, and reduction rate after ingesting the model food.

a)Protein(g)Standard food
AL

AH

0

iAUC
(mg/dL ･min)
0.0

iAUC reduction rate
(%)
0.0

ΔCmax
(mg/dL)
62.7

11.5

20.9

20.9
35.7

46. 2

Lipids( g)

iAUC(mg/dL ･ min)iAUC reduction rate
(%)

ΔCmax
(mg/dL)04,689.8

0.0

62.7

23.0

35.7

54. 2

Cmax reduction rate
(%)
0.0

13.6

26.3

b)

Standard foodBL

BH

Cmax reduction rate
(%)
0.0

- 0.2

14

4,085.3

12.9

62.8

Acetic acid
( g)

iAUC
(mg/dL ･min)iAUC reduction rate
(%)

ΔCmax
(mg/dL)

Cmax reduction rate(%)

04,689.8

0.0

62.7

0.0

28

3,876.7

17.3

55.9

10.9

c)

Standard food
CL

CH

0.6
1.3

3,780.0

3,279.8

19.4

54.8

Fiber
( g)

iAUC
(mg/dL ･ min)

rate
_iAUC
_
271 reduction
(%)

ΔCmax
(mg/dL)

Cmax reduction rate(%)

04,689.8

0.0

62.7

0.0

30.1

41.2

12.6

34.3

d)

Standard foodc)Acetic acid( g)Standard food
CL

CH

iAUC
(mg/dL ･min)iAUC reduction rate(%)

Cmax reduction rate
(%)

ΔCmax
(mg/dL)of Postprandial Blood 0.0
Glucose Prediction Model
0 Examination
4,689.8
62.7

0.6

0.0

1.3

3,780.0

3,279.8

19.4

54.8

Fiber
( g)

iAUC
(mg/dL ･ min)iAUC reduction rate
(%)

ΔCmax
(mg/dL)

Cmax reduction rate(%)

04,689.8

0.0

62.7

0.0

30.1

12.6

41.2

34.3

d)

Standard food
DL

DH

1. 8

4,755.0

4,478.3

- 1.4

63.6

Citric acid
( g)

iAUC
(mg/dL ･min)iAUC reduction rate
(%)

ΔCmax
(mg/dL)

Cmax reduction rate(%)

05,031.9

0.0

71. 3

0.0

24.8

56.8

20.3

0.9

- 1.4

61.3

4.5

2.2

e)

Standard food
EL

EH

0.95

1.90

4,368.1

13.2

3,784.4

67.4

5.5

Standard food is cooked rice 200 g in all studies. a) Comparison of protein; AL, salad chicken 55 g before cooked rice 200 g; AH, salad chicken 110 g
before cooked rice 200 g, n = 10. b) Comparison of lipids; BL, olive oil 14 g before cooked rice 200 g; BH; olive oil 28 g before cooked rice 200 g, n =10. c ) Comparison of acetic acid; CL, grain vinegar 15 g before cooked rice 200 g; CH, grain vinegar 30 g before cooked rice 200 g, n = 10. d)
Compariosn of dietary fiber; DL, cabbage 50 g before cooked rice 200 g; DH, cabbage 100 g before cooked rice 200 g, n = 10. e) Comparison of citricacid; EL, lemon juice 15 mL before cooked rice 200 g; EH; lemon juice 30 mL before cooked rice 200 g, n = 12. Results are calculated according to the
below equation: iAUC reduction rate (%) = 100 (iAUC after intake of standard food – iAUC after intake of model food)/iAUC after intake of standard
food ΔCmax reduction rate (%) = 100 × (ΔCmax after intake of standard food – ΔCmax after intake of model food)/ΔCmax after intake of standard foodiAUC, incremental area under the curve; ΔCmax, maximum blood glucose concentration.

Table 3. Regression analysis between the measured and predicted values.

a) iAUC
Regression coefficientStandard error

t -value

p-value

Protein

1.60540.073621.8260.002

Acetic acid

25.2481.9601

12.8810.006

0.224855.839< 0.001

Regression coefficientStandard error

t -value

p-value

Protein1.15110.0098117.38< 0.001

Acetic acid25.7 712.040712.629LipidsFiber

Citric acid0.67950.0853

1.6952

1.14 59

12.555

7.96791.4793

0.015

0.277

b) ΔCmax

LipidsFiber

Citric acid

0. 30810.67349.22900.11300.80201.32172.72760.83976.9827

0.112

0.006

0.49 00.020a) Results of simple regression analysis with iAUC reduction rate as the objective variable and the amount of nutrients contained in eachmodel food as the explanatory variable. b) Results of simple regression analysis with ΔCmax reduction rate as the objective variable andthe amount of nutrients contained in each model food as the explanatory variable. See Table 2 for substitution values; iAUC, incremental
area under the curve; ΔCmax, maximum blood glucose concentration.

_ 272 _Glycative Stress Research(3) Calculation of predicted iAUC and ΔCmax
By combining the evaluation methods described in (1)and (2), a predictive model formula for iAUC and ΔCmax
was created from the food content. The predicted value ofiAUC after ingestion was defined as predicted iAUC, andthe predicted value of ΔCmax after ingestion was defined as
predicted ΔCmax.

Predicted iAUC = Elevated iAUC × {1 – (1.6054 a / 100)} ×{1 − (0.6795b / 100)} × {1 − (25.248c /100)} × {1 – (1.6952d /100)} × {1 – (12.555e / 100)}			Predicted ΔCmax = Elevated ΔCmax × {1 – (1.1511a / 100)} ×{1 − (0.3081b/100)} × {1 − (25.771c / 100)} × {1 – ( 0.6734d /100)} × {1 – (9.229e / 100)}					
a: protein content, b: lipid content, c: acetic acid content,
d: dietary fiber content, and e: citric acid content of the testfood.

2. Simulation of PPHG

(1) PPHG simulation from measured values for each food
PPHG simulation was performed by substituting
the verification results of the past 18 tests into the above
prediction formula. The predicted iAUC was calculated bysubstituting the nutritional content and elevated iAUC of
the test food of each test into the formula. However, since
the nutritional component values of beef bowl D and beefbowl E were unknown, we used the nutritional component
values of 65 g of beef (ribs: raw with fat) and 25 g of onion
stalk (boiled) listed in Food Composition Table 15) as areference value. Figure 1-a shows the correlation between
the measured value and the predicted value. A high positive
correlation was found between them; correlation coefficient,r = 0.72; MARD, 11.7 ± 2.0 %. The food with the smallest
MARD was beef bowl D (0.8 %), while the food with thehighest MARD was beef bowl B (30.8 %).
Similarly, the amount of nutritional components andthe elevation in ΔCmax of the test food in each test wereapplied to the prediction formula, and the predicted ΔCmax
was calculated. Figure 1-b shows the correlation between
the measured value and the predicted value. A high positive
correlation was found between the measured and predictedvalue; correlation coefficient, r = 0.70, MARD, 13.7 ± 1.9 %.
The food with the smallest MARD was udon with a soft
boiled egg (0.5%), while the food with the highest MARD
was beef bowl D (27.6%).
(2) PPHG simulation from measured values for eachsubject
The predicted iAUC was calculated by substituting thenutritional component amount and elevated iAUC of thetest foods of 159 subjects in the 18 tests into the prediction
model formula. Figure 2-a shows the correlation between
the measured value and the predicted value. A positive
correlation was found between them; correlation coefficient,r = 0.53; MARD, 32.4 ± 2.0%. A subclass analysis, in which
the subjects (n = 159) was divided into three groups according
to the ease with which the blood glucose rose, showed 25.5

± 2.3% in the top 25% group (n = 42), and 31.7 ± 2.8% in
the middle group (n = 75), and 40.4 ± 5.1% in the bottom
25% group (n = 42) (Fig. 3-a). The MARD in the bottom
25% group was significantly higher than that in the top 25 %group ( p < 0.05).
Similarly, the amount of nutritional components and theelevated ΔCmax were applied to the formula to calculate thepredicted ΔCmax. Figure 2-b shows the correlation between
the measured value and the predicted value. A positive
correlation was found between the measured value x of eachtest and the predicted value; correlation coefficient, r = 0.57;
MARD, 24. 5 ± 1.5%. The subclass analysis showed 21.7
± 2.4% in the top 25 % group, 23.6 ± 2.0 % in the middle
group, and 28.7 ± 3.4% in the bottom 25% group (Fig. 3-b).
There was no significant difference between three groups.

Discussion
Focusing on the PPHG simulation results that evaluatedin the standard food group of 18 tests, the MARD between
the predicted iAUC/ΔCmax and the measured value wasapproximated within 15 % on average (Fig. 1). In contrast
in the test food group, there was a large difference in the
MARD value. This difference may be due to the fact that thenutritional component values of the test foods substituted into
the model formula were not accurate, and that the sample
size evaluated in each test was biased.
From the simulation results when the individual valuesof each test were evaluated, it was suggested that the smaller
the value of the elevated iAUC at the time of standard
food intake, the larger the deviation from the subsequent
simulation value of test food intake (Fig. 3). In the intake
tests evaluated in this study, bread and udon were used inaddition to cooked rice as the standard food. Since the GI
value of bread and udon is lower than that of cooked rice,
it is considered that there was a difference in the ease withwhich the blood glucose level rose even if the carbohydratecontent was the same. Therefore, when one individual uses
this prediction formula as a means for preventing PPHG,
it is necessary in advance to measure iAUC at the time ofingestion of the standard food, followed by verification of theelevation in blood glucose level.
Additionally in this study, we focused only on theamount of nutritional components in each test food andpredicted the association of PPHG. However, since the typesof proteins and lipids composed of foods differ greatly, it
is considered that there are differences in glucose changes
depending on the composition of foods, even with the same
nutrient ratio. For example, when grilled beef or boiled
mackerel is ingested before rice as a food consisting ofprotein and lipid, the effects of promoting GLP-1 secretionand prolonging the gastric emptying time are equivalent,
while there is a difference in GIP secretion between two
types of test foods 16). It is thought that this is because even
if the energy and nutrient ratios of both are the same, thecomposition of amino acids and lipids are significantly
different between beef and blue-backed fish, i.e. mackerel,sardine, and saury. Therefore, in order to apply this prediction_ 273 _Examination of Postprandial Blood Glucose Prediction Model

a)

1

b)

±

Fig. 1. Simulation results from measured values for each food.
a) Correlation between the measured iAUC of the simulation target (average value of each intake group) and the predicted iAUC
obtained by applying the measured value of the simulation target to the prediction model.

b) Correlation between the measured ΔCmax of the simulation target (average value of each intake group) and the predicted ΔCmax

obtained by applying the measured value of the simulation target to the prediction model. Results are expressed as mean ± SE, n = 18.
MARD, mean absolute relative difference; iAUC, incremental area under the curve; ΔCmax, maximum blood glucose concentration;
SE, standard error.

_ 274 _Glycative Stress Researcha)b)Fig. 2. Simulation results from measured values for each food.
a) Correlation between measured iAUC of simulated target (individual value of each intake group) and predicted iAUC obtained byapplying measured value of simulated target to the prediction model.

b) Correlation between measured ΔCmax of simulated target (individual value of each intake group) and predicted ΔCmax obtainedby applying measured value of simulated target to the prediction model. Results are expressed as mean ± SE, n = 159. MARD, mean
absolute relative difference; iAUC, incremental area under the curve; ΔCmax, maximum blood glucose concentration; SE, standard
error.

_ 275 _Examination of Postprandial Blood Glucose Prediction Model

a)

3

*

b)
b)

Fig. 3. Box plot of MARD (%) to the measured value and predicted value of each subject.
a) Box Plot of MARD (%) to the measured iAUC and predicted iAUC of each subject.
b) Box Plot of MARD (%) to the measured ΔCmax and predicted ΔCmax of each subject. Subjects (n = 159) were divided into threegroups by iAUC values; Group A, top 25% (n = 42); Group B, middle (n = 75); Group C, bottom 25% (n = 42). * p < 0.05, Tukey's HSD(honestly significant difference) test. MARD, Fig.
mean absolute
relative difference; iAUC, incremental area under the curve; ΔCmax,
3
maximum blood glucose concentration.

model from various angles in the future, it is necessary toconsider the influence of the amino acid composition and thetype of lipids of the food material constituting the test food.
This simulation used data from a food intake test
targeting healthy young individuals without impaired glucose
tolerance. Currently, the number of patients with lifestylerelated diseases is increasing in Japan, and the number ofdiabetic patients is increasing remarkably. According to
the National Health and Nutrition Survey in 2016 17), one infive to six people in the population are with diabetes or its
reserves. The onset of diabetes is mainly after middle age;
however, it is important to control the blood glucose from ayoung age, because it develops due to the accumulation oflifestyle habits for many years. Therefore, it is expected that
this prediction model will be further applied in the future
as a non-invasive and easy-to-implement means for young
people in selecting a food menu useful for PPHG control
from the daily eating habits.

Conclusion
In this paper, based on the results of the intake testof model foods, we investigated the prediction of iAUC
and ΔCmax as indices for PPHG in subjects with age oftwenties from the dietary content. Predictive simulation
was performed by applying the created predictive formula
to 18 tests that had been conducted in the past. As a result,a high correlation was found between the predicted and themeasured value. Among them, the accuracy of the prediction
formula tended to be higher as the data of the subjects whose
blood glucose level was more likely to rise.

Conflict of interest declaration
There are no conflicts of interest in carrying out thisresearch.

Research limitation
The subjects of this study are all university students or
post-graduate students in their 20s. Their living environment
is similar. Subjects of other ages are expected to be different
in glucose tolerance, frequencies and degrees of PPHG,
or gastric emptying time. The simulation model formula
proposed this time is mainly applied to subjects in their
twenties. The simulation model formula obtained this time
is mainly applied to subjects in their twenties. MARD may
increase further when applying subjects of other ages.

_ 276 _Glycative Stress Research

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