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

PLoS One. 2017 Jul 7;12(7):e0180937. doi: 10.1371/journal.pone.0180937. eCollection 2017.

Abstract

Background: Type 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.

Method: We 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.

Results: Analysis 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.

Conclusions: This 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.

MeSH terms

  • Adult
  • Datasets as Topic
  • Diabetes Mellitus, Type 2 / blood
  • Diabetes Mellitus, Type 2 / diagnosis*
  • Diabetes Mellitus, Type 2 / genetics
  • Diabetes Mellitus, Type 2 / physiopathology
  • Electronic Health Records / statistics & numerical data*
  • Female
  • Gene Expression Profiling
  • Gene Expression Regulation
  • Health Information Exchange
  • Humans
  • Insulin Resistance*
  • Maine
  • Male
  • Markov Chains
  • Prediabetic State / blood
  • Prediabetic State / diagnosis*
  • Prediabetic State / genetics
  • Prediabetic State / physiopathology
  • Support Vector Machine*

Grants and funding

The authors received no public funding for this work. HBI Solutions, Inc. (HBI) is a private commercial company, and several authors are employed by HBI. HBI provided funding in the form of salaries to the authors employed by HBI: BJ, TF, CZ, FS and EW, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.