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.

Fig 1. The schematic of dynamic driver network (DDN) based model integrated to the Maine Health Information Exchange workflow to characterize the critical transition state prior to the type 2 diabetes mellitus (T2DM) disease.

(A) Based on clinical records of 1.3 million people from Maine State, USA, we carried out a population study and extracted a sub-cohort with 7,334 patients with the first T2DM confirmative diagnosis during the study period. (B) The progression of T2DM can be divided into three stages, i.e., the normal state with relatively low entropy, the transition state right before the critical transition with relatively high entropy, the disease state with relatively low entropy. The sharp increase of entropy is expected to characterize the transition state before getting into the disease state. (C) With a transition-based network entropy, the features can be classified into three layers, and the DDN can be obtained. Based on the dynamical characteristics (such as comprehensive clinic history, or time-course information) the cluster analysis suffices to separate the network into a few functional modules. Further analysis via network entropy aggregates these modules and identifies the DDN. (D) Employing the transition-based network entropy method, we succeed in presenting the existence of a transition state (orange) between a normal state (green) and a disease state (red). The network structure of features can be divided into two parts, the DDN, and other downstream features. The DDN provides the indicative warning signals to the sudden deterioration of diabetes. The map in the figure was created by Adobe Photoshop CS6 (https://helpx.adobe.com/x-productkb/policy-pricing/cs6-product-downloads.html).

Fig 2. The comparison of dynamical evolution for the dynamic driver network (DDN) and the traditional biomarkers.

(A) The trending of BMI illustrates no obvious increase tendency even when the patient is near the critical transition into type 2 diabetes mellitus (T2DM). (B) The glucose index remains a consistent value (less than 126 mg/dl) before the confirmative diagnosis (time point 0) with no indicative signal even when the patient is near the critical transition into T2DM. (C) The A1C index remains less than 6.5% before the confirmative diagnosis (time point 0) and does not change significantly even when the patient is near the critical transition into T2DM. (D) The evolution of DDN shows an early-warning signal can be detected 6 months before the diagnosis of T2DM.

Fig 3. Modular structure of the dynamic driver network (DDN) features.

The selected features of the DDN network can be classified into 16 subgroups of demographics, primary diagnosis, procedure, etc. Most features were found to be directly involving to the utilization of medicine to manage chronic diseases such as cardiovascular diseases and metabolic disease, which indicates the derived DDN is clinically reasonable for the critical transition identification of type 2 diabetes mellitus.

Fig 4. Trending of T2DM related chronic disease counts from 24 months prior to the diagnosis of T2DM to 6 months after the diagnosis of T2DM.

The counts of diabetes mellitus without complication, the essential hypertension, and the disorders of lipid metabolism abruptly increase after the diagnosis was confirmed.

Fig 5. Trending of the total cost of utilization from 24 months prior to the diagnosis of type 2 diabetes mellitus (T2DM) to 6 months after the diagnosis of T2DM.

The total cost reaches the peak rapidly after the confirmative diagnosis.

Fig 6. Trending of the total emergency department (ED) visit utilization and total inpatient admission utilization from 24 months prior to the diagnosis of type 2 diabetes mellitus (T2DM) to 6 months after the diagnosis of T2DM.

The two curves both rise abruptly after confirmative diagnosis.

Fig 7. Trending of unique abnormal lab test volume and total abnormal lab test volume from 24 months prior to the diagnosis of type 2 diabetes mellitus (T2DM) to 6 months after the diagnosis of T2DM.

Both volumes increase sharply after the confirmative diagnosis.

Fig 8. The flow chart of the cohort construction.

Based on EMR episode data, a cohort of 8,098 patients was screened out with confirmatory diagnosis as T2DM. 764 patients who had the obviously abnormal results before the confirmative diagnosis in laboratory tests (positive twice) such as fasting glucose test, glucose tolerance test, A1C test, etc., were excluded for analysis, since these subjects might already suffer from T2DM before the confirmative diagnosis. A final cohort of 7,334 patients was constructed. EMR: electronic medical record; HIE: Health information exchange.