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1. Towards The Development of Subject-Independent Inverse Metabolic Models

   https://doi.org/10.1109/ICASSP39728.2021.9413829  

   Sajjadi, Seyedhooman ; Das, Anurag ; Gutierrez-Osuna, Ricardo ; Chaspari, Theodora ; Paromita, Projna ; Ruebush, Laura E. ; Deutz, Nicolaas E. ; Mortazavi, Bobak J. ( June 2021 , ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP))

   null (Ed.)

   Diet monitoring is an important component of interventions in type 2 diabetes, but is time intensive and often inaccurate. To address this issue, we describe an approach to monitor diet automatically, by analyzing fluctuations in glucose after a meal is consumed. In particular, we evaluate three standardization techniques (baseline correction, feature normalization, and model personalization) that can be used to compensate for the large individual differences that exist in food metabolism. Then, we build machine learning models to predict the amounts of macronutrients in a meal from the associated glucose responses. We evaluate the approach on a dataset containing glucose responses for 15 participants who consumed 9 meals. Three techniques improve the accuracy of the models: subtracting the baseline glucose, performing z-score normalization, and scaling the amount of macronutrients by each individuals’ body mass index. 

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2. A Sparse Coding Approach to Automatic Diet Monitoring with Continuous Glucose Monitors

   https://doi.org/10.1109/ICASSP39728.2021.9414452  

   Das, Anurag ; Sajjadi, Seyedhooman ; Mortazavi, Bobak ; Chaspari, Theodora ; Paromita, Projna ; Ruebush, Laura ; Deutz, Nicolaas ; Gutierrez-Osuna, Ricardo ( June 2021 , ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP))

   null (Ed.)

   Measuring dietary intake is a major challenge in the management of chronic diseases. Current methods rely on self-report measures, which are cumbersome to obtain and often unreliable. This article presents an approach to estimate dietary intake automatically by analyzing the post-prandial glucose response (PPGR) of a meal, as measured with continuous glucose monitors. In particular, we propose a sparse-coding technique that can be used to estimate the amounts of macronutrients (carbohydrates, protein, fat) in a meal from the meal’s PPGR. We use Lasso regularization to represent the PPGR of a new meal as a sparse combination of PPGRs in a dictionary, then combine the sparse weights with the macronutrient amounts in the dictionary’s meals to estimate the macronutrients in the new meal. We evaluate the approach on a dataset containing nine standardized meals and their corresponding PPGRs, consumed by fifteen participants. The proposed technique consistently outperforms two baseline systems based on ridge regression and nearest-neighbors, in terms of correlation and normalized root mean square error of the predictions. 

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3. Optimal Meal Time after Bolusing for Type 1 Diabetes Patients under Meal Uncertainties

   Nandi, Souransu ; Singh, Tarunraj ( May 2017 , 2017 American Control Conference (ACC))

   The focus of this paper is on the characterization of the uncertainties in the evolving states of a diabetic model, to permit a study of the impact of the time interval between insulin bolusing and meal initiation on hypo- and hyperglycemic events. A polynomial chaos based approach is used to characterize the independent uncertainties in the initial condition and meal size. Galerkin projection of the resulting equations reduce the stochastic differential equations to a set of deterministic equations. This forms the framework to optimize for the post bolusing time to initiate the meal. Two cost functions are considered which correspond to the postprandial hypoand hyperglycemic excursions of the blood glucose. Numerical results from the minimal Bergman model suggest a 13 and 14 minute interval between bolusing and the initiation of the meal. 

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4. Postprandial sleep in short-sleeping Mexican cavefish

   https://doi.org/10.1101/2024.07.03.602003  

   Gallman, Kathryn ; Rastogi, Aakriti ; North, Owen ; O’Gorman, Morgan ; Hutton, Pierce ; Lloyd, Evan ; Warren, Wes ; Kowalko, Johanna E ; Duboue, Erik R ; Rohner, Nicolas ; et al ( July 2024 , bioRxiv)

   Interaction between sleep and feeding behaviors are critical for adaptive fitness. Diverse species suppress sleep when food is scarce to increase the time spent foraging. Post-prandial sleep, an increase in sleep time following a feeding event, has been documented in vertebrate and invertebrate animals. While interactions between sleep and feeding appear to be highly conserved, the evolution of postprandial sleep in response to changes in food availability remains poorly understood. Multiple populations of the Mexican cavefish,Astyanax mexicanus,have independently evolved sleep loss and increased food consumption compared to surface-dwelling fish of the same species, providing the opportunity to investigate the evolution of interactions between sleep and feeding. Here, we investigate effects of feeding on sleep in larval and adult surface fish, and two parallelly evolved cave populations ofA. mexicanus.Larval surface and cave populations ofA. mexicanusincrease sleep immediately following a meal, providing the first evidence of postprandial sleep in a fish model. The amount of sleep was not correlated to meal size and occurred independently of feeding time. In contrast to larvae, postprandial sleep was not detected in adult surface or cavefish, that can survive for months without food. Together, these findings reveal that postprandial sleep is present in multiple short-sleeping populations of cavefish, suggesting sleep-feeding interactions are retained despite the evolution of sleep loss. These findings raise the possibility that postprandial sleep is critical for energy conservation and survival in larvae that are highly sensitive to food deprivation.

    

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5. Predicting Postprandial Glycemic Responses With Limited Data in Type 1 and Type 2 Diabetes

   https://doi.org/10.1177/19322968251321508  

   Shen, Yiheng ; Choi, Euiji ; Kleinberg, Samantha ( March 2025 , Journal of Diabetes Science and Technology)

   A core challenge in managing diabetes is predicting glycemic responses to meals. Prior work identified significant interindividual variation in responses and developed personalized forecasts. However, intraindividual variation is still not well understood, and the most accurate approaches require invasive microbiome data. We aimed to investigate (1) whether postprandial glycemic responses (PPGRs) can be predicted with limited data and (2) sources of intraindividual variation.

   We used data collected from 397 people with Type 1 Diabetes (T1DEXI) and 100 people with Type 2 Diabetes (ShanghaiT2DM) who wore continuous glucose monitors (CGMs) and logged meals. Using dietary, demographic, and temporal features, we predicted 2 hours PPGR, and peak 2 hours postprandial glucose rise (Glu_max). We evaluated the contribution of food features (eg, macronutrients, food category) and use of personal training data and investigated intraindividual variability in responses.

   We achieved comparable accuracy to prior work for PPGR (T1DEXI R = 0.61, ShanghaiT2DM R = 0.72) and Glu_max(T1DEXI R = 0.64, ShanghaiT2DM R = 0.73), without using invasive data like microbiome. Including food category features led to higher accuracy than macronutrients alone. Analysis of glycemic responses to duplicate meals identified time of day (PPGR: T1DEXI P < .05 for lunch, ShanghaiT2DM P < .001 for lunch and dinner) and menstrual cycle (Glu_max: P < .05 for perimenstrual) as sources of variability.

   We demonstrate that in individuals with T1D and T2D, glycemic responses to meals can be predicted without personalized training data or invasive physiological data.

    

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