Characterizing the Impact of Diet on Glycemic Variability in Type 1 and Type 2 Diabetes: A Hidden Markov Model Approach

Abstract

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.