Many systems of the body have been reported in the literature as being dysregulated in obesity and subsequently increasing the risk of chronic disease development. Due to the complexity of the human body, integrated networks are necessary to better understand the intricate interactions between biomarkers involved in obesity-related diseases. CNA was performed on various datasets obtained from 11 obese men with MetS and 12 healthy weight men. Datasets included were: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentrations, and gut microbial composition. Until recently, functional studies in obesity have had conflicting outcomes due to the issue of redundancy and functional interdependencies between biomarkers across different body systems. The aim of this study was to compare the networks constructed for the two studied groups and identify key biomarker interactions that may characterise obesity and related diseases.
When comparing the networks constructed for each group, the obese with MetS group had a denser overall network than the healthy weight group. The differences in the number of correlations suggest the obese with MetS network displayed a more complex connectivity compared to the healthy weight group. The concept of a more complex network confirms the paradigm that obesity is associated with an alteration of multiple parameters across a broad range of biological systems. The interconnected nature of different body systems calls for the need to utilise integrated analytical approaches to deconstruct the complexity of the biological dysregulation in obesity. Through this approach, biomarkers that may be central for investigation in future studies may be identified. The correlation network analysis used in this study supports the use of cluster-based analysis to better understand obesity-related diseases.
In the obese with MetS network, biomarkers of each individual variable group were found to be correlated with other biomarkers from their own group as well as other variable groups. On the other hand, immune cell biomarkers in the healthy weight network were not shown to be correlated with biomarkers from two other variable groups: anthropometric measures and metabolic measures. The contrast between correlations in the obese with MetS and healthy weight networks suggest immune cells to be heavily perturbed in obesity. Both human and animal studies have reported on obesity-related changes in the immune cell abundance and activity which were linked with the development of chronic diseases [13,14,15,16]. The similarity in findings between the current study and previous literature suggests CNA to be a reliable analytical method which can be used in studies looking at diseases with complex aetiology.
Further comparisons between the two networks in relation to immune cell abundance revealed more correlations in the obese with MetS network compared to the healthy weight network, with 11 and 7 correlations, respectively. Within the correlations in the obese with MetS network, there were three biomarkers with high betweenness centrality scores. Betweenness centrality is a measure of the number of shortest paths between two other biomarkers that passes through the biomarker in question. A high betweenness centrality score would therefore suggest the biomarker to be the centre of a key hub within the network. The three central biomarkers were: Treg cell abundance, neutrophil abundance and cytotoxic T cell abundance. The correlations found in our study that constitute these hubs have shown positive correlations between pro-inflammatory biomarkers, such as between neutrophils and macrophages, and negative correlations between pro-inflammatory and anti-inflammatory biomarkers, including Treg cells and cytotoxic cells. These correlations are consistent with the findings from earlier studies which have reported a dysregulation in the immune system of obese individuals, resulting in a high pro-inflammatory-to-anti-inflammatory biomarker ratio . All biomarkers have connections with a number of other biomarkers and therefore the recognition of key hubs is crucial in identifying biomarker profiles that characterise obesity-related diseases.
While correlation networks are particularly useful in discovering correlations between biomarkers and key hubs of a system, unpaired t-tests reveal very little in comparison. Performed on the same immune cell abundance data, an unpaired t-test between the obese with MetS and healthy weight group only observed significant differences in mast cell and T-helper cell abundances. Both mast cell and T-helper cell abundances were higher in the healthy weight group. In a study by Liu et al., mast cells contributed to obesity by producing pro-inflammatory cytokines . Therefore, mast cell abundance is expected to be higher in the obese with MetS group, inconsistent with the findings from the current study. Additionally, neither mast cell nor T-helper cell abundance were present in any of the three key hubs found in the obese with MetS network, suggesting the findings from the t-test to be uninformative. The clear distinction between the results of the correlation network and t-test is attributable to the inability of linear causality models to account for the complexity of human body systems.
Using correlation networks, the current study also found many interesting relationships, such as a positive correlation between pro-inflammatory neutrophils and anti-inflammatory Tregs. As obese individuals typically have a high pro-inflammatory-to-anti-inflammatory ratio, this finding was unexpected. A possible explanation for this relationship is suggested in a study by Mishalian et al. who observed the ability of neutrophils to recruit Tregs, exacerbating the impairment of the immune system in disease . Without the use of CNA, a finding that is pertinent in better understanding this multi-factorial disease would be missed in a simple t-test. Relationships between biomarkers, such as neutrophils and Tregs, are important in intervention research which may consider targeting both biomarkers for an exacerbated effect.
Both Treg and neutrophil abundances were also correlated with biomarkers outside of the immune cell abundance variable group. Treg cell abundance was positively correlated with serum MIP-1β concentration, consistent with the findings of Patterson et al., whereby stimulated Tregs produced MIP-1β to assist with T cell migration . Our study also found neutrophils to be associated with a number of gut microbes which is also consistent with earlier studies [19,20,21,22]. Neutrophil abundance was positively correlated with gut microbes belonging to neutrophil-associated microbiomes : Firmicutes (Anaerostipes, Blautia, Flavonifractor and Holdemania) and Proteobacteria (Escherichia/Shigella) phyla. The correlations between biomarkers from different variable groups demonstrates the complexity of interactions between physiological systems and the importance of utilising multi-analyte networks when analysing diseases with complex aetiology.
The differences in results obtained in univariate and multivariate analysis highlights the biggest advantage to using CNA in high-throughput studies. Multivariate analysis allows researchers to consider underlying connections between biomarkers, both within the same or across different variable groups. A simple comparison of biomarker levels between groups does not have the ability to recognise key hubs within a network which may be targeted for future intervention studies. Multivariate analysis has the means to overcome the limitation of redundancy among biomarkers which has limited the ability of functional research to identify key biomarkers in obesity-related disease. Other advantages to using multivariate CNA includes its ease of use and interpretability. The use of correlation networks should therefore be considered for exploratory analysis, rather than unpaired t-test, prior to the use of more complex analytical tools.
The limitations of this study have also been recognised, in particular the small sample size that was used. As a pilot study, the current work was exploratory and utilised high correlation coefficient cut-offs rather than p-values to define important results. Another limitation is the small number of molecular markers included in the analysis. While many obesity studies examined markers within adipose tissue, the current study performed analysis on peripheral blood to examine systemic rather than peripheral immune dysregulation. Additionally, the current study did not consider the effects of participant ethnicity in genetic analysis which may result in false positive findings. However, from the known participant ethnicities, 70% were Caucasian, 0.04% were Hispanic and the remaining were unknown. Despite these limitations, the study was still able to gather a multitude of results that supports further research with larger sample sizes and datasets.