Characterizing the Impact of Diet on Glycemic Variability in Type 1 and Type 2 Diabetes: A Hidden Markov Model Approach - Arumugam P; Sornalatha M E; Uma Maheswari R (The Bioscan, 2026)
Type 1 diabetes and type 2 diabetes stem from varying causes and typically necessitate distinct management approaches. However, managing blood glucose in both cases is crucial, and diet influence in both cases is an important factor to understand for the optimal management of glycemic variability. Blood glucose management is important for individuals with both diabetes. This study employs the use of a Hidden Markov Model to analyze the intricate interplay between dietary habits and resulting glucose variability in individuals with both type 2 and type 1 diabetes conditions to prevent and reduce long-term complications. The HMM framework helps in characterizing the probabilistic relationships between these factors, providing more insight into the influence and effect of dietary choices on glucose dynamics. We aim to identify patterns and potential predictors of glucose fluctuations by examining transition probabilities between different glycemic states. A comparative analysis between type 1 and type 2 diabetes populations will demonstrate the similarities and differences in the impact of diet on glycemic control. This study contributes to a deeper understanding of the relationship between diet and glucose control. This work focuses mainly on informing the development of personalized dietary interventions for improved diabetes management, which involves a broad focus on utilization of data- driven approaches including continuous glucose monitoring and advanced statistical modeling to ultimately develop an optimal approach towards diabetes care. This study is also helpful in optimal nutritional therapy for type 1 and type 2 patients by understanding the likelihood stages in the ICU admission times.