Dataset Information

Bi-stability in type 2 diabetes mellitus multi-organ signalling network.

ABSTRACT: Type 2 diabetes mellitus (T2DM) is believed to be irreversible although no component of the pathophysiology is irreversible. We show here with a network model that the apparent irreversibility is contributed by the structure of the network of inter-organ signalling. A network model comprising all known inter-organ signals in T2DM showed bi-stability with one insulin sensitive and one insulin resistant attractor. The bi-stability was made robust by multiple positive feedback loops suggesting an evolved allostatic system rather than a homeostatic system. In the absence of the complete network, impaired insulin signalling alone failed to give a stable insulin resistant or hyperglycemic state. The model made a number of correlational predictions many of which were validated by empirical data. The current treatment practice targeting obesity, insulin resistance, beta cell function and normalization of plasma glucose failed to reverse T2DM in the model. However certain behavioural and neuro-endocrine interventions ensured a reversal. These results suggest novel prevention and treatment approaches which need to be tested empirically.

SUBMITTER: Kulkarni S

PROVIDER: S-EPMC5540287 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bi-stability in type 2 diabetes mellitus multi-organ signalling network.

Kulkarni Shubhankar S Sharda Sakshi S Watve Milind M

PloS one 20170802 8

Type 2 diabetes mellitus (T2DM) is believed to be irreversible although no component of the pathophysiology is irreversible. We show here with a network model that the apparent irreversibility is contributed by the structure of the network of inter-organ signalling. A network model comprising all known inter-organ signals in T2DM showed bi-stability with one insulin sensitive and one insulin resistant attractor. The bi-stability was made robust by multiple positive feedback loops suggesting an e ...[more]

PMID: 28767672

Similar Datasets

Project description:BACKGROUND:Recent analyses in a registry of hypertensive patients suggested that preceding left ventricular (LV) hypertrophy (LVH) and/or carotid atherosclerosis are associated with incident type 2 diabetes, independent of confounders. We assess the relation between prevalent cardio-renal target organ damage (TOD) and subsequent incident type 2 diabetes in a population-based study with high prevalence of obesity. METHODS:We selected 2887 non-diabetic participants from two cohorts of the Strong Heart Study (SHS). Clinical exam, laboratory tests and echocardiograms were performed. Adjudicated TODs were LVH, left atrium (LA) dilatation, and high urine albumin/creatinine ratio (UACR). Multivariable logistic regression models were used to identify variables responsible for the association between initial TODs and incident diabetes at 4-year follow-up (FU). RESULTS:After 4 years, 297 new cases of diabetes (10%) were identified, 216 of whom exhibited baseline impaired fasting glucose (IFG, 73%, p < 0.0001). Participants developing type 2 diabetes exhibited higher inflammatory markers, fat-free mass and adipose mass and higher prevalence of initial LVH and LA dilatation than those without (both p < 0.04). In multivariable logistic regression, controlling for age, sex, family relatedness, presence of arterial hypertension and IFG, all three indicators of TOD predicted incident diabetes (all p < 0.01). However, the effects of TOD was offset when body fat and inflammatory markers were introduced into the model. CONCLUSIONS:In this population-based study with high prevalence of obesity, TOD precedes clinical appearance of type 2 diabetes and is related to the preceding metabolic status, body composition and inflammatory status. Trial registration Trial registration number: NCT00005134, Name of registry: Strong Heart Study, URL of registry: https://clinicaltrials.gov/ct2/show/NCT00005134, Date of registration: May 25, 2000, Date of enrolment of the first participant to the trial: September 1988.

Project description:BackgroundThe relationship between dyslipidemia, inflammation and CV organ damage in type 2 diabetes mellitus (T2DM) is complex. Insulin resistance and inflammatory cytokines interleukins (ILs) increase plasma triglycerides (TG). ILs also up-regulate expression of matrix-metalloproteinases (MMPs) that, together with TG, decrease high density lipoprotein cholesterol (HDL) levels. High TG, low HDL, increased ILs and MMPs trigger structural and functional changes in different parts of cardiovascular (CV) system. To understand better the role of lipids and inflammation in CV organ damage, the present study investigated the inter-relationships between lipids, ILs and MMPs, as well as the associations of lipids, ILs and MMPs with various CV measures, both in diabetic and non-diabetic population (nonT2DM).MethodsIn T2DM patients (N = 191) and nonT2DM subjects (N = 94) were assessed carotid intima-media thickness (cIMT) and inter-adventitial diameter (IADiam), carotid wave speed (ccaWS), carotid-femoral pulse wave velocity (cfPWV), left ventricular (LV) mass, LV systolic (s') and early diastolic (e') longitudinal velocities of mitral annulus, together with glycemic control, lipid profile, IL-6, IL-18 and MMP-12.ResultsT2DM patients, as compared to nonT2DM subjects, had significantly higher plasma levels of IL-6, IL-18, MMP-12 and lower HDL (P < 0.05-0.0001). They had also higher cIMT, IADiam, ccaWS, cfPWV and LV mass, and lower e' velocity (P < 0.005-0.0001). Both in T2DM patients and nonT2DM subjects, MMP-12 increased with IL-6 (r = 0.43 and 0.39; P < 0.0001) and IL-18 (r = 0.32 and 0.42; P < 0.0001), and HDL decreased with MMP-12 (r = - 0.29 and - 0.42; P < 0.0001). In both populations, MMP-12 was directly associated with IADiam, ccaWS, cfPWV and LV mass (r = 0.42, 0.32, 0.26 and 0.29; P < 0.0001 in T2DM patients, and r = 0.39, 0.28, 0.32 and 0.27; P < 0.01-0.0001 in nonT2DM subjects). In multivariate analysis, MMP-12 remained independently related to IADiam, ccaWS, cfPWV and LV mass in T2DM patients, and to IADiam only in nonT2DM subjects.ConclusionsThis cross-sectional study demonstrated a direct association between ILs and MMP-12, as well as an inverse association between MMP-12 and HDL, both in T2DM patients and in nonT2DM subjects. In T2DM patients, who had higher levels of ILs and MMP-12, the latter was independently related to several structural and functional markers of preclinical CV organ damage.

Project description:BackgroundNumerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided.MethodsThis study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance.ResultsThe results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.

Dataset Information

Bi-stability in type 2 diabetes mellitus multi-organ signalling network.

Publications

Bi-stability in type 2 diabetes mellitus multi-organ signalling network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets