Defining and characterizing the critical transition state prior to the type 2 diabetes disease
| Autor: | Shiying Hao, Yu-Ming Li, Xuefeng B. Ling, Bo Jin, Chunqing Zhu, Shaun T. Alfreds, Rui Liu, Zhen Li, Yan Zhang, Wei Liu, Eric Widen, Yunxian Yu, Pei Chen, Xin Zhou, Daowei Liu, Doff B. McElhinney, Frank Stearns, Zhongkai Hu, Tianyun Fu, Devore S Culver, Karl G. Sylvester, Qian Wu |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Male Pediatrics Health Information Exchange Support Vector Machine endocrine system diseases Entropy Psychological intervention Datasets as Topic lcsh:Medicine Type 2 diabetes Disease Markov Processes Physical Chemistry Geographical locations 0302 clinical medicine Endocrinology Medicine and Health Sciences Electronic Health Records Public and Occupational Health 030212 general & internal medicine lcsh:Science Multidisciplinary Physics Health information exchange Markov Chains Type 2 Diabetes Chemistry Reaction Dynamics Cohort Physical Sciences Thermodynamics Female Statistics (Mathematics) Research Article Adult medicine.medical_specialty Endocrine Disorders Cardiology Prediabetic State 03 medical and health sciences Diabetes mellitus medicine Diabetes Mellitus Humans Maine Preventive healthcare business.industry Gene Expression Profiling lcsh:R Type 2 Diabetes Mellitus nutritional and metabolic diseases Transition State medicine.disease Probability Theory United States Surgery 030104 developmental biology Diabetes Mellitus Type 2 Gene Expression Regulation Metabolic Disorders North America lcsh:Q Preventive Medicine People and places Insulin Resistance business Mathematics |
| Zdroj: | PLoS ONE, Vol 12, Iss 7, p e0180937 (2017) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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