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.

BackgroundPrincipal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to determine whether individuals can be clustered into two classification groups based on pre-determined criteria: control and disease group. However, a combination of other components may exist which better distinguish diseased individuals from healthy controls. Genetic algorithms (GAs) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and a genetic algorithm (GA) for identifying sets of bacterial species associated with obesity and metabolic syndrome (Mets).ResultsThe prediction models built using the combination of principal components (PCs) selected by GA were compared to the models built using the top PCs that explained the most variance in the sample and to models built with selected original variables. The advantages of combining PCA with GA were demonstrated.ConclusionsThe proposed algorithm overcomes the limitation of PCA for data analysis. It offers a new way to build prediction models that may improve the prediction accuracy. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.

No abstract

No abstract

Background: Obesity 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 biomarkers have been found to distinguish individuals at greater risk of obesity-related disease. The aim of this study was to explore a correlationbased network approach to find existing patterns of immunemicrobiome interactions in obesity.Results: The current study performed correlation-based network analysis on five different datasets obtained from 11 obese and 12 healthy weight men: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentration, and gut microbial composition. The obese cohort had a denser network (total number of edges, n = 237) compared to the healthy network (n = 190). Within the obese network, immune cell abundance was found to be correlated to biomarkers from all four other datasets while in the healthy network, immune cell abundance was only correlated with serum cytokine concentration and gut microbial composition. Neutrophils within the obese immune cell abundance group were correlated with the most number of other biomarkers. Two different types of neutrophil measurements were taken, an abundance measure from immune cell gene expression and a whole blood count, with correlations to 10 and 6 other biomarkers, respectively. From the combined number of 16 biomarkers, 4 biomarkers were correlated between the two measurements: Anaerostipes abundance, Blautia abundance, Escherichia/Shigella abundance and Flavonifractor abundance. Conclusion: The obesity-related dysregulation of immune biomarkers was suggested by the high connectivity of immune cells in the obese network compared to the healthy network.Our study also revealed the importance of integrated analysis to uncover immune-microbiome interactions in obesity that are likely to have been missed in univariate analysis.