Abstract
Objective
The honeymoon phase in type 1 diabetes (T1D) represents a temporary improvement in glycemic control but may complicate insulin management. The aim was to develop and validate a machine learning (ML)-driven method for accurately detecting this phase to optimize insulin therapy and prevent adverse outcomes.
Methods
Data from pediatric T1D patients aged 6-17 years, including continuous glucose monitoring data, glucose management indicator (GMI) reports, hemoglobin A1c (HbA1c) values, and patient medical history, were used to train ML models including long short-term memory (LSTM) networks, transformer models, random forest, and gradient boosting machines (GBMs). These were designed to analyze glucose trends and identify the honeymoon phase in T1D patients.
Results
The transformer model achieved the highest accuracy at 91%, followed by GBMs at 89%, LSTM at 88%, and random forest at 87%. Key features, such as glucose variability, insulin adjustments, GMI values, and HbA1c levels were critical to model performance. Accurate identification of the honeymoon phase enabled optimized insulin adjustments, enhancing glucose control and reducing hypoglycemia risk.
Conclusion
The ML-driven approach provides a robust method for detecting the honeymoon phase in T1D patients, demonstrating potential for improved personalized insulin management. The findings suggest significant benefits in patient outcomes, with future research focused on further validation and clinical integration.
Keywords: Honeymoon phase, insulin management, machine learning, type 1 diabetes
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References
James EA, Joglekar AV, Linnemann AK, Russ HA, Kent SC. The beta cell-immune cell interface in type 1 diabetes (T1D). Mol Metab. 2023;78:101809. Epub 2023 Sep 20.
Al-Worafi YM. Type 1 diabetes management in developing countries. In: Al-Worafi YM. (eds) Handbook of Medical and Health Sciences in Developing Countries. Springer, Cham. 2024.
Giannella AD, Cavaiola TS, Kulasa K. Inpatient type 1 diabetes. In: Schulman-Rosenbaum, R.C. (eds). Diabetes management in hospitalized patients. Contemporary Endocrinology. Springer, Cham. 2023. Available from: https://link.springer.com/chapter/10.1007/978-3-031-44648-1_4
Fox D, Zhang Q, Islam N, Chen L, Leung J, Bone J, Amed S. Defining a childhood type 1 diabetes cohort, clinical practice measures, and outcomes within administrative data in British Columbia. Can J Diabetes. 2024;48:26-35.e1. Epub 2023 Aug 29.
Minasian V, Nazari M. The association between type 1 diabetes and exercise/physical activity and prolongation of the honeymoon phase in patients. Life Sci. 2023;332:122114. Epub 2023 Sep 20.
Shah P. Prolonged honeymoon phase in adult-onset type 1 diabetes mellitus: a case study. J Diabetol. 2024;15:238-40.
Aghanouri Z, Siavash M, Mombeini H, Monfared M, Mojahedi M, Ilkhani R. Extended honeymoon period in a type 1 diabetic child by Iranian Traditional Medicine treatments, a case report. Prim Care Diabetes. 2017;11:583-585. Epub 2017 Sep 29.
Omieljanowicz M, Ignatowicz T, Omieljanowicz A. Predicting of hypo- and hyperglycemia in patients with type 1 diabetes with limited data. In: Chaki R, Chaki N, Cortesi A, Saeed K. (eds). Applied computing for software and smart systems. ACSS 2023. Lecture Notes in Networks and Systems, vol 781. Springer, Singapore. 2023.
Janež A, Guja C, Mitrakou A, Lalic N, Tankova T, Czupryniak L, Tabák AG, Prazny M, Martinka E, Smircic-Duvnjak L. Insulin therapy in adults with type 1 diabetes mellitus: a narrative review. Diabetes Ther. 2020;11:387-409. Epub 2020 Jan 4.
Thomas NJ, Jones AG. The challenges of identifying and studying type 1 diabetes in adults. Diabetologia. 2023;66:2200-2212.
Tucker ME. Only 20% with type 1 diabetes are meeting hba1c goals. MedScape. Available from: https://www.medscape.com/viewarticle/908314
Goldenberg RM, Aroda VR, Billings LK, Donatsky AM, Frederiksen M, Klonoff DC, Kalyanam B, Bergenstal RM. Correlation between time in range and HbA1c in people with type 2 diabetes on basal insulin: post hoc analysis of the SWITCH PRO study. Diabetes Ther. 2023;14:915-924. Epub 2023 Mar 11.
Badawy M, Ramadan N, Hefny HA. Healthcare predictive analytics using machine learning and deep learning techniques: a survey. Journal of Electrical Systems and Inf Technol. 2023;10:40.
Goyal S, Batra N, Chhabra K. Diabetes disease diagnosis using machine learning approach. In: Gupta D, Khanna A, Bhattacharyya S, Hassanien AE, Anand S, Jaiswal A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems. 2022;473:229-237. Available from: https://doi.org/10.1007/978-981-19-2821-5_19
Mishra M, Dubey V, Hackett TA, Kashyap MK. Artificial intelligence and machine learning in clinical research and patient remediation. In: Yadav DK, Gulati A (eds). Artificial intelligence and machine learning in healthcare. Springer, Singapore. 2023.
Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: advancements, opportunities, and challenges. Cell Rep Med. 2023;4:101213. Epub 2023 Oct 2.
Patil AR, Schug J, Liu C, Lahori D, Descamps HC; Human Pancreas Analysis Consortium; Naji A, Kaestner KH, Faryabi RB, Vahedi G. Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets. Cell Rep Med. 2024;5:101535. Epub 2024 Apr 26.
Liu K, Li L, Ma Y, Jiang J, Liu Z, Ye Z, Liu S, Pu C, Chen C, Wan Y. Machine learning models for blood glucose level prediction in patients with diabetes mellitus: systematic review and network meta-analysis. JMIR Med Inform. 2023;11:e47833.
Zale A, Mathioudakis N. Machine learning models for inpatient glucose prediction. Curr Diab Rep 2022;22:353-364.
Eghbali-Zarch M, Masoud S. Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review. Artif Intell Med. 2024;151:102868. Epub 2024 Apr 4.
Bian Q, As’arry A, Cong X, Rezali KABM, Raja Ahmad RMKB. A hybrid transformer-LSTM model apply to glucose prediction. PLoS One. 2024;19:e0310084.
Jaloli M, Cescon M. long-term prediction of blood glucose levels in type 1 diabetes using a CNN-LSTM-based deep neural network. J Diabetes Sci Technol. 2023;17:1590-1601. Epub 2022 Apr 25.
van Doorn WPTM, Foreman YD, Schaper NC, Savelberg HHCM, Koster A, van der Kallen CJH, Wesselius A, Schram MT, Henry RMA, Dagnelie PC, de Galan BE, Bekers O, Stehouwer CDA, Meex SJR, Brouwers MCGJ. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: the Maastricht study. PLoS One. 2021;16:e0253125.
Ying LP, Yin OX, Quan OW, Jain N, Mayuren J, Pandey M, Gorain B, Candasamy M. Continuous glucose monitoring data for artificial intelligence-based predictive glycemic event: A potential aspect for diabetic care. Int J Diabetes Dev Ctries. 2024.
Acuna E, Aparicio R, Palomino V. Analyzing the performance of transformers for the prediction of the blood glucose level considering imputation and smoothing. Big Data and Cognitive Computing. 2023;7:41.
Gündoğdu, S. Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique. Multimed Tools Appl. 2023;82:34163-34181.
Talukder MA, Islam MM, Uddin MA, Kazi M, Khalid M, Akhter A, Ali Moni M. Toward reliable diabetes prediction: İnnovations in data engineering and machine learning applications. Digit Health. 2024;10:20552076241271867. Erratum in: Digit Health. 2025;11:20552076251313644.
Olawsky E, Zhang Y, Eberly LE, Helgeson ES, Chow LS. A new analysis tool for continuous glucose monitor data. J Diabetes Sci Technol. 2022;16:1496-1504. Epub 2021 Jul 20
Nicolau J, Romano A, Rodríguez I, Sanchís P, Puga M, Masmiquel L. Influence of obesity on blood glucose control using continuous glucose monitoring data among patients with type 1 diabetes. Endocrinol Diabetes Nutr (Engl Ed). 2024;71:202-207.
Mittal M, Porchezhian P, Kapoor N. Honeymoon phase in type 1 diabetes mellitus: a window of opportunity for diabetes reversal? World J Clin Cases. 2024;12:9-14.
Ghimire S, Celik T, Gerdes M, Omlin CW. Deep learning for blood glucose level prediction: how well do models generalize across different data sets? PLoS One. 2024;19:e0310801.
Rastogi R, Bansal M. Diabetes prediction model using data mining techniques. Measurement: Sensors. 2023;25:100605.
Pranjali S. Prolonged honeymoon phase in adult-onset type 1 diabetes mellitus: a case study. Journal of Diabetology. 2024;15:238-240.
Asaduzzaman S, Masud FA, Bhuiyan T, Ahmed K, Paul BK, Rahman SAMM. Dataset on significant risk factors for type 1 diabetes: a Bangladeshi perspective. Data Brief. 2018;21:700-708.
Yoo JH, Kim JH. Advances in continuous glucose monitoring and integrated devices for management of diabetes with insulin-based therapy: improvement in glycemic control. Diabetes Metab J. 2023;47:27-41. Epub 2023 Jan 12.
Arora R, Kaur G, Gulati P. Feature selection and hyperparameter tuning in diabetes mellitus prediction. In: Dave M, Garg R, Dua M, Hussien J (eds). Proceedings of the international conference on paradigms of computing, communication and data sciences. Algorithms for Intelligent Systems. Springer, Singapore. (2021). Available from: https://doi.org/10.1007/978-981-15-7533-4_74
Gordon J, Danne T, Beresford-Hulme L, Bennet H, Tank A, Edmonds C, Thorén F, Scheerer MF, McEwan P. Adverse changes in HbA1c, body weight and insulin use in people with type 1 diabetes mellitus following dapagliflozin discontinuation in the DEPICT clinical trial programme. Diabetes Ther. 2020;11:1135-1146. Epub 2020 Apr 9.
Nilsson C, Dereke J. Cystatin C as an adjunct to HbA1c may prove useful in predicting the development of diabetic complications in children and adolescents with type 1 diabetes. J Diabetes Metab Disord. 2024;23:1251-1257.
Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr. 2022;14:196.
Choi SR, Lee M. Transformer architecture and attention mechanisms in genome data analysis: a comprehensive review. Biology (Basel). 2023;12:1033.
Bidari I, Chickerur S, Kulkarni A, Mahajan A, Nikkam A, THM A. Deploying machine learning inference on diabetic retinopathy in binary and multi-class classification. IEEE. 2021:1-6.
Khan AA, Chaudhari O, Chandra R. A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Systems with Applications. 2024;244:122778.
Carvalho CF, Liang Z. Glucose prediction with long short-term memory (LSTM) models in three distinct populations. Engineering Proceedings. 2024;82:87.
Li W, Peng Y, Peng K. Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm. PLoS One. 2024;19:e0311222.
Sai MJ, Chettri P, Panigrahi R, Garg A, Bhoi AK, Barsocchi P. An ensemble of light gradient boosting machine and adaptive boosting for prediction of type-2 diabetes. Int J Comput Intell Syst. 2023;16:14.
Qin Y, Wu J, Xiao W, Wang K, Huang A, Liu B, Yu J, Li C, Yu F, Ren Z. Machine learning models for data-driven prediction of diabetes by lifestyle type. Int J Environ Res Public Health. 2022;19:15027.
