Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

The homeostatic dynamics of feeding behaviour identify novel mechanisms of anorectic agents

Better understanding of feeding behaviour will be vital in reducing obesity and metabolic syndrome, but we lack a standard model that captures the complexity of feeding behaviour. We construct an accurate stochastic model of rodent feeding at the bout level in order to perform quantitative behavioural analysis. Analysing the different effects on feeding behaviour of peptide YY3-36 (PYY3-36), lithium chloride, glucagon-like peptide 1 (GLP-1), and leptin shows the precise behavioural changes caused by each anorectic agent. Our analysis demonstrates that the changes in feeding behaviour evoked by the anorectic agents investigated do not mimic the behaviour of well-fed animals and that the intermeal interval is influenced by fullness. We show how robust homeostatic control of feeding thwarts attempts to reduce food intake and how this might be overcome. In silico experiments suggest that introducing a minimum intermeal interval or modulating upper gut emptying can be as effective as anorectic drug administration.

Unraveling the Inhibition of Intestinal Glucose Transport by Dietary Phenolics: A Review

Background:

Glucose transport across the intestinal brush border membrane plays a key role in metabolic regulation. Depending on the luminal glucose concentration, glucose is mainly transported by the sodium- dependent glucose transporter (SGLT1) and the facilitated-transporter glucose transporter (GLUT2). SGLT1 is apical membrane-constitutive and it is active at a low luminal glucose concentration, while at concentrations higher than 50 mM, glucose is mainly transported by GLUT2 (recruited from the basolateral membrane). Dietary phenolic compounds can modulate glucose homeostasis by decreasing the postprandial glucose response through the inhibition of SGLT1 and GLUT2.

Methods:

Phenolic inhibition of intestinal glucose transport has been examined using brush border membrane vesicles from rats, pigs or rabbits, Xenopus oocytes and more recently Caco-2 cells, which are the most promising for harmonizing in vitro experiments.

Results:

Phenolic concentrations above 100 µM has been proved to successfully inhibit the glucose transport. Generally, the aglycones quercetin, myricetin, fisetin or apigenin have been reported to strongly inhibit GLUT2, while quercetin-3-O-glycoside has been demonstrated to be more effective in SGLT1. Additionally, epigallocatechin as well as epicatechin and epigallocatechin gallates were observed to be inhibited on both SGLT1 and GLUT2.

Conclusion:

Although, valuable information regarding the phenolic glucose transport inhibition is known, however, there are some disagreements about which flavonoid glycosides and aglycones exert significant inhibition, and also the inhibition of phenolic acids remains unclear. This review aims to collect, compare and discuss the available information and controversies about the phenolic inhibition of glucose transporters. A detailed discussion on the physicochemical mechanisms involved in phenolics-glucose transporters interactions is also included.

Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM.

OBJECTIVE

The objective of this work was to dynamically forecast daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before.

METHODS

We used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory-based recurrent neural networks to forecast the next-day glucose levels in individual patients. The neural network used several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbohydrates; exercise; and weight) were progressing from one day to another from noisy data.

RESULTS

The model was validated based on a data set of 10 patients who had been monitored daily for over 6 months. The proposed deep learning model demonstrated considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values.

CONCLUSIONS

Using machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to better predict future glucose levels for T2DM management.

Dynamical systems approaches to personalized medicine

The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.

Labile haemoglobin as a glycaemic biomarker for patient-specific monitoring of diabetes: mathematical modelling approach

Diabetes mellitus constitutes a major health problem and its clinical presentation and progression may vary considerably. A number of standardized diagnostic and monitoring tests are currently used for diabetes. They are based on measuring either plasma glucose, glycated haemoglobin or both. Their main goal is to assess the average blood glucose concentration. There are several sources of interference that can lead to discordances between measured plasma glucose and glycated haemoglobin levels. These include haemoglobinopathies, conditions associated with increased red blood cell turnover or the administration of some therapies, to name a few. Therefore, there is a need to provide new diagnostic tools for diabetes that employ clinically accessible biomarkers which, at the same time, can offer additional information allowing us to detect possible conflicting cases and to yield more reliable evaluations of the average blood glucose level concentration. We put forward a biomathematical model to describe the kinetics of two patient-specific glycaemic biomarkers to track the emergence and evolution of diabetes: glycated haemoglobin and its labile fraction. Our method incorporates erythrocyte age distribution and utilizes a large cohort of clinical data from blood tests to support its usefulness for diabetes monitoring.