Defining and characterizing the critical transition state prior to the type 2 diabetes disease

Abstract

BackgroundType 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed through the longitudinal electronic medical record (EMR) analysis.MethodWe applied the transition-based network entropy methodology which previously identified a dynamic driver network (DDN) underlying the critical T2DM transition at the tissue molecular biological level. To profile pre-disease phenotypical changes that indicated a critical transition state, a cohort of 7,334 patients was assembled from the Maine State Health Information Exchange (HIE). These patients all had their first confirmative diagnosis of T2DM between January 1, 2013 and June 30, 2013. The cohort’s EMRs from the 24 months preceding their date of first T2DM diagnosis were extracted.ResultsAnalysis of these patients’ pre-disease clinical history identified a dynamic driver network (DDN) and an associated critical transition state six months prior to their first confirmative T2DM state.ConclusionsThis 6-month window before the disease state provides an early warning of the impending T2DM, warranting an opportunity to apply proactive interventions to prevent or delay the new onset of T2DM.

Dysglycemia-Based Chronic Disease: An American Association of Clinical Endocrinologists Position Statement

“…The proposed evidence-based 4-stage model outlined below emphasizes the context of prediabetes as a component in the progression of DBCD, with opportunities for various prevention modalities to reduce evolution to T2D, CVD, or both ( Fig. 1 (118). Along these lines, Khera et al (119) found that in patients with a high genetic (molecular) risk for coronary heart disease, a favorable (healthy) lifestyle was associated with a nearly 50% reduction in relative risk.…”

“…Those with insulin resistance are at higher risk of progressing along the DBCD continuum and merit primary disease prevention: structured lifestyle interventions that reduce cardiometabolic risk factors and later DBCD stage likelihoods, respectively. A molecular roadmap for this process was recently presented by Jin et al(118), who identified a dynamic driver network governing the key predisease-T2D transition mechanisms. It is postulated that appropriate interventions (such as structured lifestyle change) at these critical time points could prevent or delay progression of disease…”

Lipidomics of human adipose tissue reveals diversity between body areas

“…Adipocytes are cells that form and store lipids in adipose tissue, they play a major role in energy homeostasis [1,2]. Obesity, which is characterized by increased storage of lipids in adipose tissue and modification of the metabolic functions of adipocytes, is a risk factor for several metabolic diseases, including atherosclerosis, cardiovascular ischemic disease, hypertension, hyperglycemia and insulin resistance in type 2 diabetes [1,3,4,5,6,7]. The molecular composition of adipose tissue may reveal its functionality and links to metabolic disease [8,2].…”

A modified Ising model of Barabási–Albert network with gene-type spins

“…In this regard, the analogy between phase transitions occurring in living systems (such as normal to diseased state transition) and physical systems (such as condensation of water) has been well-motivated (Davies et al 2011;Holstein et al 2013;Trefois et al 2015;Smith 2010). The normal state to cancer state transition has been described as a process similar to the first-order irreversible discontinuous phase transition occurring in physical systems (Facciotti 2013;Jin et al 2017;Liu et al 2013;Mojtahedi et al 2016;Torquato 2010). The central idea is that living systems are open systems in quasisteady state type equilibrium in continuous exchange with their environment wherein cells behave like a network in heat bath under external perturbations (Pastor-Satorras et al 2015;Scheffer et al 2012).…”