Type 2 Diabetes: One Disease, Many Pathways

Abstract

<jats:title>Abstract</jats:title><jats:p>Diabetes is a chronic, progressive disease that calls for longitudinal data and analysis. We introduce a longitudinal mathematical model that is capable of representing the metabolic state of an individual at any point in time during their progression from normal glucose tolerance to type 2 diabetes (T2D) over a period of years. As an application of the model, we account for the diversity of pathways typically followed, focusing on two extreme alternatives, one that goes through impaired fasting glucose (IFG) first, and one that goes through impaired glucose tolerance (IGT) first. These two pathways are widely recognized to stem from distinct metabolic abnormalities in hepatic glucose production and peripheral glucose uptake, respectively. We confirm this but go beyond to show that IFG and IGT lie on a continuum ranging from high hepatic insulin resistance and low peripheral insulin resistance to low hepatic resistance and high peripheral resistance. We show that IFG generally incurs IGT, and IGT generally incurs IFG on the way to T2D, highlighting the difference between innate and acquired defects and the need to assess patients early to determine their underlying primary impairment and appropriately target therapy. We also consider other mechanisms, showing that IFG can result from impaired insulin secretion, that non-insulin dependent glucose uptake can also mediate or interact with these pathways, and that impaired incretin signaling can accelerate T2D progression. We consider whether hyperinsulinemia can cause insulin resistance in addition to being a response to it and suggest that this is a minor effect.</jats:p>

An organ-based multi-level model for glucose homeostasis: organ distributions, timing, and impact of blood flow

“…Another longitudinal model describing glucose dynamics on both short and long-term time-scale is the one developed by Ha et al . (Ha and Sherman 2019). This model is, in contrast to the other two longitudinal models mentioned above, multi-scale in that it can look at both changes over years, including the progression towards diabetes in a semi-mechanistic fashion, as well as meal response dynamics happening in the scale of hours and minutes.…”

A simple modeling framework for prediction in the human glucose–insulin system

“…Depending on their particular purposes, researchers have developed various mathematical models ranging from extremely simple to extremely complex to describe glucose metabolism in humans. Some of these models are developed to describe a very specific system, for example, a particular biological function of the pancreas [13,34,77], whereas others have been developed to predict hypoglycemia [31,61,78], glucose control [11,14,24,30,70,89,91], or disease pathogenesis [32,35,36]. Some of these are continuous-time models in the form of ordinary differential equations (ODEs) whilst others use ML: in [13,34,77], the authors develop system of ODEs to model the phenomenon they investigate while efforts directed at inference tasks such as predicting hypoglycemia, ML approaches are more common.…”

“…In this section, we will compare the forecasting accuracy of the T2DM version of the MSG model with a well-known model developed by Ha & Sherman [36]. It is important to note that this model, the longitudinal diabetes pathogenesis (LDP) model, was designed to understand diabetes progression, not for forecasting future BG level purposes.…”

Dynamic modulation of glucose utilisation by glucocorticoid rhythms in health and disease

“…Recent work has recognised the importance of understanding the impact of chronodisruption on glucose metabolism ( 16 ), and several models ( 17-19 ) have re-examined physiological and clinical data from a dynamical perspective to shed light on the mechanisms leading to diabetes, with uncompensated insulin resistance at their core ( 20, 21 ). The work of Ha & Sherman, for example, proposes mathematical models accounting for key mechanisms mediating beta-cell responses to hyperglycaemia, namely: increased sensitivity and increased secretory response over short and intermediate timescales, and changes in beta-cell mass over longer timescales ( 19, 22 ). By systematically exploring the contribution of these mechanisms, each represented by a specific parameter in their model, the authors predict thresholds that mark the transition from pre-diabetes to diabetes, supporting some of the hypotheses already advanced by ( 21 ) and in agreement with clinical observations.…”