Dataset Information

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

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.

SUBMITTER: Jin B

PROVIDER: S-EPMC5501620 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

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

Jin Bo B Liu Rui R Hao Shiying S Li Zhen Z Zhu Chunqing C Zhou Xin X Chen Pei P Fu Tianyun T Hu Zhongkai Z Wu Qian Q Liu Wei W Liu Daowei D Yu Yunxian Y Zhang Yan Y McElhinney Doff B DB Li Yu-Ming YM Culver Devore S DS Alfreds Shaun T ST Stearns Frank F Sylvester Karl G KG Widen Eric E Ling Xuefeng B XB

PloS one 20170707 7

<h4>Background</h4>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.<h4>Method</h4>We applied the transition-based network entropy methodology which previously identified a dynamic driver network (DDN) underlying the critical T2DM transition at the t ...[more]

PMID: 28686739

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Dataset Information

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

Background

Method

Results

Conclusions

Publications

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

Similar Datasets

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