ArticleBMC medical genomics2013
Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers.
Article in BMC medical genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed, 100 citations in OpenAlex.
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- Revealing the critical state and identifying individualized dynamic network biomarker for type 2 diabetes through advanced analysis methods on individual basis.Scientific reports · 2025Article
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- Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis.International journal of molecular sciences · 2024Article
- Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE).Research (Washington, D.C.) · 2024Article
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- Artificial neural network applied to fragile X-associated tremor/ataxia syndrome stage diagnosis based on peripheral mitochondrial bioenergetics and brain imaging outcomes.Scientific reports · 2022Article
- Application of the Dynamical Network Biomarker Theory to Raman Spectra.Biomolecules · 2022Article
- Identifying the critical states and dynamic network biomarkers of cancers based on network entropy.Journal of translational medicine · 2022Article
- Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development.Computational and structural biotechnology journal · 2022Review
- Identifying critical transitions in complex diseases.Journal of biosciences · 2022Review
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 1 diabetes (T1D) is a complex disease and harmful to human health, and most of the existing biomarkers are mainly to measure the disease phenotype after the disease onset (or drastic deterioration). Until now, there is no effective biomarker which can predict the upcoming disease (or pre-disease state) before disease onset or disease deterioration. Further, the detail molecular mechanism for such deterioration of the disease, e.g., driver genes or causal network of the disease, is still unclear.
methodsIn this study, we detected early-warning signals of T1D and its leading biomolecular networks based on serial gene expression profiles of NOD (non-obese diabetic) mice by identifying a new type of biomarker, i.e., dynamical network biomarker (DNB) which forms a specific module for marking the time period just before the drastic deterioration of T1D.
resultsTwo dynamical network biomarkers were obtained to signal the emergence of two critical deteriorations for the disease, and could be used to predict the upcoming sudden changes during the disease progression. We found that the two critical transitions led to peri-insulitis and hyperglycemia in NOD mice, which are consistent with other independent experimental results from literature.
conclusionsThe identified dynamical network biomarkers can be used to detect the early-warning signals of T1D and predict upcoming disease onset before the drastic deterioration. In addition, we also demonstrated that the leading biomolecular networks are causally related to the initiation and progression of T1D, and provided the biological insight into the molecular mechanism of T1D. Experimental data from literature and functional analysis on DNBs validated the computational results.
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