ArticlePloS one2017
Defining and characterizing the critical transition state prior to the type 2 diabetes disease.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Dynamic network entropy for pinpointing the pre-outbreak stage of infectious disease.Journal of the Royal Society, Interface · 2025Article
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- Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm.JMIR medical informatics · 2022Article
- Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.Frontiers in immunology · 2022Article
- Serological Phenotyping Analysis Uncovers a Unique Metabolomic Pattern Associated With Early Onset of Type 2 Diabetes Mellitus.Frontiers in molecular biosciences · 2022Article
- A modified Ising model of Barabási-Albert network with gene-type spins.Journal of mathematical biology · 2020Article
- Can We Reduce Mortality of COVID-19 if We do Better in Glucose Control?Medicine in drug discovery · 2020Article
- Measurement of health-related quality of life in patients with diabetes mellitus using EQ-5D-5L in Hong Kong, China.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2020Article
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Authors and funding
22 authors at 9 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 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.
methodWe 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.
resultsAnalysis 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.
conclusionsThis 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.
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