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

ABSTRACT: Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight from these large, relatively unstructured datasets. We review both physiological models and machine learning results across a diverse range of applications in energy homeostasis, and highlight how modelling and machine learning can work together to improve predictive ability. We collect quantitative details in a comprehensive mathematical supplement. We also discuss the prospects of forecasting homeostatic behaviour and stress the importance of characterizing stochasticity within and between individuals in order to provide practical, tailored forecasts and guidance to combat the spread of obesity.

SUBMITTER: McGrath T

PROVIDER: S-EPMC5805973 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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

McGrath Thomas T Murphy Kevin G KG Jones Nick S NS

Journal of the Royal Society, Interface 20180124 138

Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight ...[more]

PMID: 29367240

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Project description:Aims/hypothesisThe brain is a major consumer of glucose as an energy source and regulates systemic glucose as well as energy balance. Although glucose transporters such as GLUT2 and sodium-glucose cotransporter 2 (SGLT2) are known to regulate glucose homeostasis and metabolism, the identity of a receptor that binds glucose to activate glucose signalling pathways in the brain is unknown. In this study, we aimed to discover a glucose receptor in the mouse hypothalamus.MethodsHere we used a high molecular mass glucose-biotin polymer to enrich glucose-bound mouse hypothalamic neurons through cell-based affinity chromatography. We then subjected the enriched neurons to proteomic analyses and identified adhesion G-protein coupled receptor 1 (ADGRL1) as a top candidate for a glucose receptor. We validated glucose-ADGRL1 interactions using CHO cells stably expressing human ADGRL1 and ligand-receptor binding assays. We generated and determined the phenotype of global Adgrl1-knockout mice and hypothalamus-specific Adgrl1-deficient mice. We measured the variables related to glucose and energy homeostasis in these mice. We also generated an Adgrl1Cre mouse model to investigate the role of ADGRL1 in sensing glucose using electrophysiology.ResultsAdgrl1 is highly expressed in the ventromedial nucleus of the hypothalamus (VMH) in mice. Lack of Adgrl1 in the VMH in mice caused fasting hyperinsulinaemia, enhanced glucose-stimulated insulin secretion and insulin resistance. In addition, the Adgrl1-deficient mice had impaired feeding responses to glucose and fasting coupled with abnormal glucose sensing and decreased physical activity before development of obesity and hyperglycaemia. In female mice, ovariectomy was necessary to reveal the contribution of ADGRL1 to energy and glucose homeostasis.Conclusions/interpretationAltogether, our findings demonstrate that ADGRL1 binds glucose and is involved in energy as well as glucose homeostasis in a sex-dependent manner. Targeting ADGRL1 may introduce a new class of drugs for the treatment of type 2 diabetes and obesity.

Dataset Information

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

Publications

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

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