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© 2017 Jin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Defining and characterizing the critical transition state prior to the type 2 diabetes disease
Author
Jin, Bo; Liu, Rui; Hao, Shiying; Li, Zhen; Zhu, Chunqing; Zhou, Xin; Chen, Pei; Fu, Tianyun; Hu, Zhongkai; Wu, Qian; Liu, Wei; Liu, Daowei; Yu, Yunxian; Zhang, Yan; McElhinney, Doff B; Yu-Ming, Li; Culver, Devore S; Alfreds, Shaun T; Stearns, Frank; Sylvester, Karl G; Widen, Eric; Ling, Xuefeng B
First page
e0180937
Section
Research Article
Publication year
2017
Publication date
Jul 2017
Publisher
Public Library of Science
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1916991473
Copyright
© 2017 Jin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.