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Nutrients
Volume 17
Issue 24
10.3390/nu17243832
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Prediction of Postprandial Blood Glucose Variability Using Machine Learning in Frequent Insulin Injection Therapy with a Simplified Carbohydrate Counting Model

by
Hiroyuki Tominaga
1,
Masahide Hamaguchi
1,*,
Youji Hamaguchi
2,
Ren Yashiki
1,
Aki Yamaguchi
3,
Tadaharu Arai
3,
Masahiro Yamazaki
4,
Noriyuki Kitagawa
1,5,
Yoshitaka Hashimoto
6,
Hiroshi Okada
1 and
Michiaki Fukui
1
1
Department of Endocrinology and Metabolism, Graduate School of Medicine, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
2
S.I.P., Tokyo 141-0021, Japan
3
Persol Avc Technology Co., Ltd., Osaka 569-1194, Japan
4
Department of Metabolism and Endocrinology, Japanese Red Cross Society Kyoto Daini Hospital, Kyoto 602-8026, Japan
5
Department of Diabetes, Kameoka Municipal Hospital, Kameoka 621-8585, Japan
6
Department of Diabetes and Endocrinology, Matsushita Memorial Hospital, Osaka 570-8540, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(24), 3832; https://doi.org/10.3390/nu17243832
Submission received: 13 October 2025 / Revised: 27 November 2025 / Accepted: 4 December 2025 / Published: 7 December 2025
(This article belongs to the Special Issue Enhancing Dietary Assessments and Nutritional Monitoring Through Digital Health Monitoring and Intervention)
Versions Notes

Abstract

Background/Objectives: Postprandial glucose variability is a key challenge in diabetes management for patients receiving multiple daily insulin injections (MDI). This study evaluated transformer-based machine-learning models for predicting post-prandial glucose peaks and nadirs using pre-meal glucose, insulin dose, and nutritional input. Methods: In this observational study, 58 adults with diabetes provided dietary records, insulin logs, and continuous glucose monitoring data. After preprocessing and participant-level splitting (64:16:20), model-ready datasets comprised 6155/1449/1805 (train/validation/test) meal events for the Full-Nutrition model and 6299/1484/1849 for the Carbohydrate and Available-Carbohydrate models. We evaluated three transformer-based models and assessed performance using MAE, R2, and the Clarke error grid. Results: The Full Nutrition Model achieved MAEs of 32.2 mg/dL (peak) and 21.8 mg/dL (nadir) with R2 values of 0.58 for both. Carbohydrate-based models showed similar accuracy. Most predictions fell within Clarke error grid Zones A and B. Conclusions: Transformer-based machine-learning models can accurately predict postprandial glucose variability in MDI-treated patients. Carbohydrate-only inputs performed comparably to full-nutrient data, supporting the feasibility of simplified dietary inputs in clinical applications.
Keywords: diabetes; frequent insulin injection therapy; machine learning; blood glucose variability; carbohydrate counting method; transformer model diabetes; frequent insulin injection therapy; machine learning; blood glucose variability; carbohydrate counting method; transformer model

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MDPI and ACS Style

Tominaga, H.; Hamaguchi, M.; Hamaguchi, Y.; Yashiki, R.; Yamaguchi, A.; Arai, T.; Yamazaki, M.; Kitagawa, N.; Hashimoto, Y.; Okada, H.; et al. Prediction of Postprandial Blood Glucose Variability Using Machine Learning in Frequent Insulin Injection Therapy with a Simplified Carbohydrate Counting Model. Nutrients 2025, 17, 3832. https://doi.org/10.3390/nu17243832

AMA Style

Tominaga H, Hamaguchi M, Hamaguchi Y, Yashiki R, Yamaguchi A, Arai T, Yamazaki M, Kitagawa N, Hashimoto Y, Okada H, et al. Prediction of Postprandial Blood Glucose Variability Using Machine Learning in Frequent Insulin Injection Therapy with a Simplified Carbohydrate Counting Model. Nutrients. 2025; 17(24):3832. https://doi.org/10.3390/nu17243832

Chicago/Turabian Style

Tominaga, Hiroyuki, Masahide Hamaguchi, Youji Hamaguchi, Ren Yashiki, Aki Yamaguchi, Tadaharu Arai, Masahiro Yamazaki, Noriyuki Kitagawa, Yoshitaka Hashimoto, Hiroshi Okada, and et al. 2025. "Prediction of Postprandial Blood Glucose Variability Using Machine Learning in Frequent Insulin Injection Therapy with a Simplified Carbohydrate Counting Model" Nutrients 17, no. 24: 3832. https://doi.org/10.3390/nu17243832

APA Style

Tominaga, H., Hamaguchi, M., Hamaguchi, Y., Yashiki, R., Yamaguchi, A., Arai, T., Yamazaki, M., Kitagawa, N., Hashimoto, Y., Okada, H., & Fukui, M. (2025). Prediction of Postprandial Blood Glucose Variability Using Machine Learning in Frequent Insulin Injection Therapy with a Simplified Carbohydrate Counting Model. Nutrients, 17(24), 3832. https://doi.org/10.3390/nu17243832

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