Characteristics of glycometabolism in individuals without diabetes and a model to assess their glucometabolic category
Tomoki Uchida1, 2, Takeshi Kanamori2, Takanori Teramoto2, Hiroyuki Kato3, Yuji Nonaka4, Makoto Suematsu5, Hiroki Tanaka1, Satoshi Nakamura1, 6, Norihito Murayama2
1, Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan. 2, Suntory Global Innovation Center Limited, Research Institute, Japan. 3, Development & Design Department, Japan Business Division, Suntory Beverage & Food Limited, Japan.
4, Research Planning Division, Suntory Holdings Limited, Japan. 5, Department of Biochemistry, Keio University School of Medicine, Japan. 6, Data Science Center, Nara Institute of Science and Technology, Japan

Introduction

Methods
Data collection:• Participants: 977 Japanese adults aged 20-64 without diabetes• OGTT: Blood glucose and insulin levels at 0, 30, 60, 90, and 120 minutes• Lifestyle questionnaire (309 questions): Topics are exercise and sleep
habits, diet, family history, constitution, and physical condition.
120 mPG < 126 mg/dL 120 mPG ≥ 126 mg/dL
and Mi > 4.97
or Mi ≤ 4.97
Category 2030100

Plasma insulin(μU/mL)(b)

20015010050060 90 120
Time (min)Figure 3. Mean blood glucose (a)and insulin (b) levels of participantsin each glycometabolic category inthe oral glucose tolerance test

7550250030 60 90 120
Time (min)Yellow line: Category 1
Green line: Category 2
Blue line: Category 3
Red line:
Category 4

Performances of the models:• Random forest provided the best performance among the models.
• Another random forest model was developed using top 10 important variables in theprevious random forest model. Its area under the receiver operating characteristic curves(AUCs) for classifying [category 1, 2, 3, 4] and the others were [0.68, 0.66, 0.61, 0.70].
Sensitivity todetect categories
2, 3, and 4

Specificity todetect category 10.68 (0.62-0.75)

0.70

0.41

Training the models
Assessing the performances of the models
Figure 2. Flow chart of the models development

Sensitivity
0.4
0.6

0.8

5-fold cross validation
for parameters tuning

0.66 (0.58-0.73)

0.61 (0.51-0.70)

Variable

Category 1 vs others
Category 2 vs others
Category 3 vs others
Category 4 vs others

0.230%
TestingdataAUC for classifying AUC for classifying AUC for classifying
category 2 andcategory 3 andcategory 4 andthe others
the others
the others
0.70 (0.62-0.77)

Table 2. The variables and these importances of the model

0.0

Original data70%
Training
data

AUC for classifying
category 1 andthe others

1.0

30 mPG ≥ 157 mg/dL
Category 3
Category 4
Figure 1. the classification conditions for the glycometabolic categories.
Abbreviations: x mPG, x-min post-load plasma glucose level; Mi, Matsuda index

Development of the models:Explanatory variables: questionnaire answers
Objective variables: glycometabolic categories
Considered models: decision tree, support
vector machine, random forest, and XGBoost(a)

Table 1. Performance of the random forest model using ten variables (95% confidence interval)

Classification of the glycometabolic category:Category 1

Characteristics of the glycometabolic categories:
Compared with category 1 (the best glucose metabolism group, 46% of the participants),
Category 2: low insulin sensitivity and high 120-min blood glucose levels (21%)
Category 3: low insulin-secreting capacity and rapid rise in blood glucose levels (13%)
Category 4: combination of categories 2 and 3 (20%)
Plasma glucose(mg/dL)

Backgrounds:
Early detection of prediabetes and improvement of lifestyle are essential
for diabetes prevention. The oral glucose tolerance test (OGTT) is astandard method to evaluate the state of glucose metabolism. However, it
is rarely performed in non-diabetics. The models also have beendeveloped to detect prediabetes using medical checkup values. However,there is no easy tool to assess the glycometabolic profiles of non-diabetics.
Aims:• To classify glycometabolic profiles of non-diabetics into different
categories based on the OGTT results.
• To develop a model that predict the glycometabolic category using alifestyle questionnaire.

30 mPG < 157 mg/dL

Conclusion

Results

0.0

0.2

0.4
0.6
0.8
1 - Specificity

1.0

Figure 4. The ROC curves of the model

Body mass index
AgeHeight
Wake up in the middle of the night
Which do you usually eat, rice or bread?
Frequency of tea intake at lunch
Wake up late on non-working day
Frequency of mobile phone and tablet
computer use at bedtime
Frequency of soup intake
Frequency of toothbrush replacement

The mean decrease
in Gini coefficient
10.3
8.1
3.3
3.1
2.5
2.1
1.9
1.4

1.4
0.8

• Japanese adults without diabetes
are classified into four categories
with each different insulin sensitivity
and insulin secretion.
• The random forest model wasdeveloped for assessing theglycometabolic category in nondiabetics. It needed only 10 lifestylequestions.
• Some of the selected factors were notreported clear association withglucose metabolism. Future studies
may clarify the association withdiabetes risk.

References
1. World Health Organization & International DiabetesFederation. Definition and diagnosis of diabetes mellitus
and intermediate hyperglycaemia: report of a WHO/IDF
consultation. World Health Organization; 2006 [cited Aug2021].
2. Phillips DI, Clark PM, Hales CN et al. Understanding oral
glucose tolerance: comparison of glucose or insulin
measurements during the oral glucose tolerance test withspecific measurements of insulin resistance and insulin
secretion. Diabet Med 1994; 11(3): 286-292.
3. Paulweber B, Valensi P, Lindström J, et al. A European
evidence-based guideline for the prevention of type 2diabetes. Horm Metab Res 2010; 42: S3-S36.
4. Barber SR, Davies MJ, Khunti K, et al. Risk assessment
tools for detecting those with pre-diabetes: a systematicreview. Diabetes Res Clin Pract. 2014; 105: 1-13.
5. Khan FA, Zeb K, Rakhami AM, et al. Detection andPrediction of Diabetes Using Data Mining: A
Comprehensive Review. IEEE 2021; 9: 43711-43735.
6. Hirakawa Y, Hata J, Yoshinari M, et al. 30-minute postload
plasma glucose levels during an oral glucose tolerance
test predict the risk of future type 2 diabetes: theHisayama Study. BMJ Open Diabetes Res Care 202; 8:e001156.
7. Yoshinari M, Hirakawa Y, Hata J, et al. Comparison of thecontributions of impaired beta cell function and insulin
resistance to the development of type 2 diabetes in aJapanese community: the Hisayama Study. Diabetologia.
2021;64(8):1775-1784.
Conflict of interests:
This study was funded by the Suntory Global Innovation
Center Limited.