Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis

Abstract

Type 2 diabetes mellitus (T2DM) is a metabolic disease caused by multiple etiologies, the development of which can be divided into three states: normal state, critical state/pre-disease state, and disease state. To avoid irreversible development, it is important to detect the early warning signals before the onset of T2DM. However, detecting critical states of complex diseases based on high-throughput and strongly noisy data remains a challenging task. In this study, we developed a new method, i.e., degree matrix network entropy (DMNE), to detect the critical states of T2DM based on a sample-specific network (SSN). By applying the method to the datasets of three different tissues for experiments involving T2DM in rats, the critical states were detected, and the dynamic network biomarkers (DNBs) were successfully identified. Specifically, for liver and muscle, the critical transitions occur at 4 and 16 weeks. For adipose, the critical transition is at 8 weeks. In addition, we found some “dark genes” that did not exhibit differential expression but displayed sensitivity in terms of their DMNE score, which is closely related to the progression of T2DM. The information uncovered in our study not only provides further evidence regarding the molecular mechanisms of T2DM but may also assist in the development of strategies to prevent this disease.

Specific network information gain for detecting the critical state of colorectal cancer based on gut microbiome

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New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

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“…Mouse hematopoietic stem cells (mHSCs) scRNA-seq of mHSCs [31] Embryonic stem cell differentiation Human embryonic stem cells (hESCs) scRNA-seq of hESCs [32] Immune cell differentiation T cells from DO11.10 TCR mice Raman imaging [33] Metabolic syndrome Metabolic syndrome model mouse (TSOD mice) Microarray of the adipose tissues [34,35] Type 2 diabetes Diabetes model rat (GK rats) Microarray of the adipose tissues [36] Understanding cell fate transitions and controlling them is critically important, not only in the field of developmental biology, but also in aging. Emerging technologies such as single-cell RNA sequencing (scRNA-seq) combined with bioinformatics approaches have shown great progress.…”

mNFE: microbiome network flow entropy for detecting pre-disease states of type 1 diabetes

“… 22 , 23 The degree matrix network entropy method can detect the critical states of type 2 diabetes mellitus based on a sample-specific network. 24 However, these methods are mainly based on gene expression data and suffer from unsatisfactory effectiveness and robustness due to highly noisy omics data, especially on the basis of a single sample. Unlike gene expression data, gut microbiome sequencing data can be noninvasively collected from stool samples, so they are more easily accessible.…”