ArticleBMC bioinformatics2019
A correlation-based network for biomarker discovery in obesity with metabolic syndrome.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 26 citations in OpenAlex.
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- A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.Bioengineered · 2021Article
- Confounders in Identification and Analysis of Inflammatory Biomarkers in Cardiovascular Diseases.Biomolecules · 2021Review
- The Redox-Metabolic Couple of T Lymphocytes: Potential Consequences for Hypertension.Antioxidants & redox signaling · 2021Review
- CANTARE: finding and visualizing network-based multi-omic predictive models.BMC bioinformatics · 2021Article
- Elevated Plasma Angiopoietinlike Protein 5 (ANGPTL5) Is More Positively Associated with Glucose Metabolism Disorders in Patients with Metabolic Syndrome.Medical science monitor : international medical journal of experimental and clinical research · 2021Article
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- Toward computational modelling on immune system function.BMC bioinformatics · 2019Article
- Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObesity is associated with chronic activation of the immune system and an altered gut microbiome, leading to increased risk of chronic disease development. As yet, no biomarker profile has been found to distinguish individuals at greater risk of obesity-related disease. The aim of this study was to explore a correlation-based network approach to identify existing patterns of immune-microbiome interactions in obesity.
resultsThe current study performed correlation-based network analysis on five different datasets obtained from 11 obese with metabolic syndrome (MetS) and 12 healthy weight men. These datasets included: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentration, and gut microbial composition. The obese with MetS group had a denser network (total number of edges, n = 369) compared to the healthy network (n = 299). Within the obese with MetS network, biomarkers from the immune cell abundance group was found to be correlated to biomarkers from all four other datasets. Conversely in the healthy network, immune cell abundance was only correlated with serum cytokine concentration and gut microbial composition. These observations suggest high involvement of immune cells in obese with MetS individuals. There were also three key hubs found among immune cells in the obese with MetS networks involving regulatory T cells, neutrophil and cytotoxic cell abundance. No hubs were present in the healthy network.
conclusionThese results suggest a more complex interaction of inflammatory markers in obesity, with high connectivity of immune cells in the obese with MetS network compared to the healthy network. Three key hubs were identified in the obese with MetS network, involving Treg, neutrophils and cytotoxic cell abundance. Compared to a t-test, the network approach offered more meaningful results when comparing obese with MetS and healthy weight individuals, demonstrating its superiority in exploratory analysis.
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