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

• Published in: PloS one 2017-07, Vol.12 (7), p.e0180937-e0180937
• Main Authors: 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, Li, Yu-Ming, Culver, Devore S, Alfreds, Shaun T, Stearns, Frank, Sylvester, Karl G, Widen, Eric, Ling, Xuefeng B
• Format: Article
• Language: English
• Subjects: Adult; Analysis; Antibiotics; Beta blockers; Children & youth; Complications; Computer engineering; Datasets as Topic; Diabetes; Diabetes mellitus; Diabetes Mellitus, Type 2 - blood; Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - genetics; Diabetes Mellitus, Type 2 - physiopathology; Diagnosis; Disease prevention; Electric power; Electronic Health Records - statistics & numerical data; Electronic medical records; Emergency communications systems; Entropy; Exercise; Feasibility studies; Female; Gene Expression Profiling; Gene Expression Regulation; Health aspects; Health Information Exchange; Heart; Hospitals; Humans; Hypotheses; Insulin; Insulin Resistance; Intervention; Laboratories; Lifestyles; Maine; Male; Markov Chains; Medical electronics; Medical records; Medicine and Health Sciences; Patients; People and places; Physical Sciences; Population; Prediabetic state; Prediabetic State - blood; Prediabetic State - diagnosis; Prediabetic State - genetics; Prediabetic State - physiopathology; Support Vector Machine; Type 2 diabetes; Warning
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0180937

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. 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. 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. 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.