Journal of Obesity & Metabolic Syndrome



J Obes Metab Syndr 2024; 33(1): 64-75

Published online March 30, 2024

Copyright © Korean Society for the Study of Obesity.

Gut Microbiome and Metabolic and Immune Indices in Males with or without Evidence of Metabolic Dysregulation

Kyle M. Hatton-Jones1, Nicholas P. West1,2, Mike W.C. Thang3, Pin-Yen Chen1, Peter Davoren4, Allan W. Cripps2,5, Amanda J. Cox1,2,*

1School of Pharmacy and Medical Science, 2Menzies Health Institute Queensland, Griffith University, Southport; 3QCIF Facility for Advanced Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia; 4Diabetes and Endocrinology, Gold Coast University Hospital, Southport; 5School of Medicine, Griffith University, Southport, Australia

Correspondence to:
Amanda J. Cox
School of Pharmacy and Medical Science, Griffith University, Parklands Drive, Southport, QLD 4215, Australia
Tel: +61-7-56780898
Fax: +61-7-56780795

Received: May 31, 2023; Reviewed : June 23, 2023; Accepted: November 30, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: The contributions of the gut microbiota to obesity and metabolic disease represent a potentially modifiable factor that may explain variation in risk between individuals. This study aimed to explore relationships among microbial composition and imputed functional attributes, a range of soluble metabolic and immune indices, and gene expression markers in males with or without evidence of metabolic dysregulation (MetDys).
Methods: This case-control study included healthy males (n=15; 41.9±11.7 years; body mass index [BMI], 22.9±1.2 kg/m2) and males with evidence of MetDys (n=14; 46.6±10.0 years; BMI, 35.1±3.3 kg/m2) who provided blood and faecal samples for assessment of a range of metabolic and immune markers and microbial composition using 16S rRNA gene sequencing. Metagenomic functions were imputed from microbial sequence data for analysis.
Results: In addition to elevated values in a range of traditional metabolic, adipokine and inflammatory indices in the MetDys group, 23 immunomodulatory genes were significantly altered in the MetDys group. Overall microbial diversity did not differ between groups; however, a trend for a higher relative abundance of the Bacteroidetes (P=0.06) and a lower relative abundance of the Verrucomicrobia (P=0.09) phyla was noted in the MetDys group. Using both family- and genera-level classifications, a partial least square discriminant analysis revealed unique microbial signatures between the groups.
Conclusion: These findings confirm the need for ongoing investigations in human clinical cohorts to further resolve the relationships between the gut microbiota and metabolic and immune markers and risk for metabolic disease.

Keywords: Microbiota, Metabolic diseases, Obesity, Inflammation

The contributions of the gut microbiota to obesity and metabolic disease are incompletely understood in humans. Given the significant microbial mass within the gut and the considerable metabolic activities of these resident bacteria, understanding the potential contributions of the types of bacteria and species-related variations in microbial metabolite profile to host metabolic and inflammatory signalling may be relevant when considering why some individuals with excess body weight are at greater risk for metabolic disease than others. Inherent limitations in animal models including coprophagia, faster intestinal transit times and differences in the composition of the gut microbiota1 mean that findings do not fully translate to humans, and the potential utility of microbial and associated indices in assessing risk for obesity-associated disease is unrealised. For this reason, additional human studies in well-characterised cohorts and maximising use of available data are needed to continue to support efforts to further clarify the contributions of gut microbiota to obesity and metabolic disease.

The potential for the gut microbiota to contribute to excess body mass via energy harvest from the diet was initially gleaned from animal models.2 The transfer of faecal preparations from obese or lean mice to germ-free recipients resulted in the development of the corresponding phenotype in the recipient animals.2 Understandably, similar demonstrations are challenging in humans, and studies have traditionally relied on comparison of the composition of the intestinal microbiota between obese and non-obese individuals to support relationships between microbial composition and body mass. Although reductions in overall bacterial diversity in obese individuals have been replicated,3 observations of phyla-level differences remain mixed.4 In addition, faecal microbial transplant studies involving transplantation from lean donors to obese individuals have largely explored metabolic outcomes in the short term (<6 months) but not changes in body mass over longer periods.5

Extending beyond obesity, the potential for aspects of microbial composition to reliably demarcate states of cardiometabolic disease remains a key area of research interest. However, as was noted for studies of microbial composition in obesity, differences in abundance of key bacterial taxa in individuals with and without type 2 diabetes mellitus are not always replicated.6 Of particular interest is a recent report by Michels et al.7 that collated observations from published systematic reviews/meta analyses assessing microbial composition in metabolic disorders (including obesity, type 2 diabetes mellitus, metabolic syndrome [MetS], and non-alcoholic fatty liver disease); among >5,500 publications, they did not identify a single systematic review of observational studies in MetS. This outcome supports the need for additional studies focused on this early disease phenotype to ascertain whether differences in microbial composition may be evident before the onset of overt disease. However, it must also be acknowledged that publications focused on MetS may be limited due to difficulties in clear phenotypic criteria for case inclusion or lack of compelling statistically significant differences in microbial composition in the absence of overt disease and subsequent absence from the published literature.

Beyond profiling of gut microbial composition, consideration of microbial metabolic activity and potential interplay with aspects of host physiology may provide additional insights for studies exploring contributions of the gut microbiota to early metabolic disease. For example, reports of enrichment of carbohydrate, fatty acid and lipid biosynthetic pathways among patients with MetS in a Chinese cohort, in the absence of differences in the relative abundance of key bacteria phyla,8 provide insights into potential mechanisms via which the gut microbiota may impact host physiology that cannot be ascertained by compositional data alone. Development of computational prediction tools to impute microbial functional activity9 offers the opportunity to support further interrogation of 16S compositional data in the absence of full metagenomic analyses, which may remain out of reach for some studies. A number of studies involving modestly-sized cohorts have utilised such approaches to extend available microbiome data and to explore potential metabolite signatures in various metabolic disease states.10,11

The purpose of this study was to simultaneously assess faecal microbial composition with imputed functional indices; a range of soluble metabolic and inflammatory indices; and circulating gene expression markers in males with or without evidence of metabolic dysregulation (MetDys) to consider the potential value of multidimensional analysis approaches in exploring risk for early metabolic disease states. Given the recognised sex differences in both gut microbial composition12 and aspects of MetS,13 males only were assessed in this investigation to limit potential confounding of any outcomes by sex.

Study design and participants

This study involved two groups of community-dwelling males from Queensland, Australia, aged 20 to 65 years: (1) males with healthy body mass and without MetS (healthy; n=15) and (2) males with obesity and either MetS or impaired fasting glucose (subsequently referred to as MetDys, n=14). Given the modest sample, males only were recruited to minimise potential heterogeneity in the cohort. Healthy body mass was considered as body mass index (BMI) <25 kg/m2 and obesity as BMI >30 kg/m2, as defined by World Health Organisation (WHO) criteria. MetDys was determined based on the presence of three or more of the following, according to the Adult Treatment Panel III criteria: high waist circumference (>102 cm), high blood pressure (systolic ≥130, diastolic ≥85 mmHg, or treatment with blood pressure lowering medications), high triglycerides (>1.7 mmol/L or treatment with lipid lowering medications), reduced high-density lipoprotein (HDL) cholesterol (<1.04 mmol/L), and high fasting plasma glucose (≥6.1 mmol/L). Impaired fasting glucose was defined as fasting plasma glucose >5.6 mmol/L based on the revised American Diabetes Association criteria. Individuals consuming fish oil, probiotics, prebiotics or whey protein supplements; with a history of liver, kidney, thyroid, or gastrointestinal disease; or using anti-inflammatory or immune-modulating medications were excluded from the study. The Griffith University Human Research Ethics Committee (approval: 2015/229) and the Gold Coast Hospital and Health Service Human Research Ethics Committee (approval: HREC/15/QGC/148) provided ethical approval for this study. All study procedures were carried out in accordance with the Declaration of Helsinki, and all subjects provided written informed consent prior to participation.

Anthropometric measurements

Participants presented to the laboratory between the hours of 6:00 AM and 9:00 AM, following an overnight fast, for measurement and sample collection. Height was determined to the nearest half centimetre using a wall-mounted stadiometer (Surgical & Medical Products). Body mass was determined using a digital body composition scale (model HBF-202; Omron Australia). Waist and hip circumference were assessed in accordance with the WHO STEPwise approach to surveillance protocols using a graduated anthropometric measuring tape (Seca). Blood pressure and pulse rate were determined using an automatic blood pressure monitor (model HEM-7121; Omron Australia) with individuals in a seated position.

Sample collection and metabolic profiling

Participants collected a faecal sample in the 12 hours prior to attending the research laboratory. Collected faecal samples were stored at –80 °C from delivery to the laboratory until analysis. Fasting blood samples were collected from all participants using standard venepuncture techniques for routine haematology (including a full blood count with white cell differential and glycosylated hemoglobin), clinical chemistry analyses (including cholesterol, triglycerides, HDL cholesterol, glucose, and C-reactive protein [CRP]), erythrocyte sedimentation rate, and whole-blood transcriptomics (using PAXgene Blood RNA tubes; BD Biosciences).

Adipokine and intestinal permeability markers

A commercially available multiplex suspension array system (Bio-Rad Laboratories) was used to quantify circulating concentrations of insulin, C-peptide, glucagon, leptin, resistin, visfatin, ghrelin, glucagon-like peptide 1, gastric inhibitory polypeptide, and plasminogen activator inhibitor-1. Plasma adiponectin concentrations were determined using a commercially available enzyme-linked immunosorbent assay (ELISA) kit (AdipoGen LifeSciences). Circulating concentrations of lipopolysaccharide binding protein (LBP; Biometec) and intestinal fatty acid-binding protein (iFABP; Ray- Biotech) were determined using commercially available ELISA kits. All assays were completed according to manufacturers’ instructions, and all samples were analysed in duplicate.

Whole-blood transcriptomics

Whole-blood stored in PAXgene Blood RNA (BD Biosciences) tubes was thawed and RNA was isolated using the automated Maxwell RSC Instrument (Promega) and the RSC simplyRNA tissue kit (Promega). Isolated RNA was analysed using both the commercially available nCounter PanCancer Immune Profiling Panel consisting of 770 genes (Nanostring Technologies) and a custom panel targeting 48 metabolic and immune genes (Nanostring Technologies). A complete list of the genes assessed in the custom panel can be found in Supplementary Table 1. Assays were completed according to manufacturers’ instructions. Samples were processed using the automated nCounter Prep Station prior to direct digital counting on the nCounter Digital Analyser platform (Nanostring Technologies) at a field of view of 555. Transcript counts were normalized according to the geomean of the housekeepers for each panel. Genes with average counts lower than the background (approximately 20) were removed. Genes that exhibited significant differences between groups on Student’s t-test (P<0.05) or that exhibited a 1.5-fold change difference were retained for downstream between-group analysis.

Faecal microbial composition

DNA was isolated from faecal samples as described previously.14 Briefly, thawed faecal samples were homogenised by repeated bead beating and DNA was extracted using a commercially available kit (Qiagen). The V3–V4 region of the microbial 16S rRNA marker gene was amplified using universal primers (F:5´-CCTACGGGN GGCWGCAG-3´; R:5´-GACTACHVGGGTATCTAATCC-3´) and polymerase chain reaction products were sequenced on an Illumina MiSeq system (Illumina) by a commercial provider (Macrogen).

A total of 34 Illumina paired-end reads was processed with the R package DADA2 (v1.18; R Foundation for Statistical Computing). The DADA2 package performs four key functions: (1) detect and filter out low-quality reads; (2) create amplicon sequence variants (ASVs) by merging the high-quality forward and reverse end reads; (3) identify and remove the chimeric sequences; and (4) align the high-quality ASVs to a taxonomic reference database SILVA (v132). The ASVs that survived the DADA2 pipeline were used as input in the R package Phyloseq (v1.40.0). A total of 9,300 ASVs was produced in the DADA2 pipeline. The Phyloseq package was used to remove the ASVs without phylum assignment for downstream analysis. A total of 809 uncharacterized ASVs of 9,300 was filtered out, leaving 8,491 ASVs. Subsequently, we removed the taxa (ASVs) from samples with low prevalence using a 0.05 prevalence threshold. A total of 2,542 ASVs remained after prevalence filtering. The remaining 2,542 ASVs were used as input in the Phylogenetic Investigation of Communities by Reconstructed Unobserved Stats (PICRUSt2) analysis.

Faecal metagenomic functional inference

The package PICRUSt2 v2.4.115 and the default Kyoto Encyclopedia of Genes and Genomes (KEGG) DB provided by PICRUSt2 were used to predict metagenome function from the 16S rRNA data. Functional gene/pathways that were detected in more than 50% of samples or that exhibited significant differences (P<0.05) or >3-fold difference between groups were retained for downstream between-group analysis.

Statistical analysis

Data distributions for continuous variables were assessed using a Shapiro-Wilk test for normality. Differences in anthropometric, metabolic, intestinal, and genomic variables, along with microbial diversity metrics and taxa abundance, were compared between healthy and MetDys groups using Student’s t-test for unpaired samples. Where variables did not conform to a normal distribution, differences between groups were assessed using the non-parametric Mann-Whitney test. Partial least square discriminant analysis (PLS-DA) was performed initially using prevalent bacterial family and genera-level data (detected in >50% of all samples) to reveal whether broad differences in composition were evident between the two groups (healthy vs. MetDys). Relationships between demographic/ phenotypic, microbial composition and gene expression outcomes were assessed using a Pearson’s correlation. Analyses were completed using GraphPad Prism version 8 (GraphPad Software), except the PLS-DA analyses were performed using R. All measures are presented as mean±standard deviation. Statistical significance was accepted at P<0.05.

Metabolic, inflammatory and intestinal permeability markers

Key demographic, metabolic, inflammatory and permeability measures for the two groups are shown in Table 1. As anticipated, traditional cardiometabolic risk factors were significantly different between the two groups. The MetDys group exhibited higher average BMI and waist circumference, blood triglycerides, total cholesterol and fasting plasma blood glucose (Table 1). Similarly, fasting plasma insulin concentrations were approximately 2-fold higher (P<0.002) in the MetDys group than the healthy group. Lactate dehydrogenase concentrations were also 10% higher in the MetDys group (P=0.006). Circulating leptin was the most significantly different adipokine between groups, with the MetDys group presenting with concentrations approximately 6-fold higher (P<0.0001) than the control group. Adipsin concentrations also were significantly elevated in the MetDys group (approximately 28% higher; P=0.01) compared to the control group. C-peptide (P=0.0001), CRP (P=0.02), and LBP (P<0.0001) concentrations were all approximately 2-fold higher in the MetDys group and ghrelin was approximately 0.6-fold lower (P=0.07) than in the control group.

Whole-blood transcriptomics

Whole-blood RNA assessed using the PanCancer Immune Profiling panel revealed several genes that exhibited significantly different expression levels between the metabolically distinct groups when assessed using Student’s t-test or Mann-Whitney test (Fig. 1A and B). The genes cathelicidin antimicrobial peptide (CAMP), lipocalin 2 (LCN2), lactotransferrin 2 (LTF), and C-X-C motif chemokine receptor 6 (CXCR6) were significantly different between groups in both panels (Fig. 1A and B). Using data from the PanCancer panel, the MetDys group exhibited altered expression of 23 genes; most notable was the elevated expression of CAMP (approximately 2-fold higher; P=0.002), LTF (approximately 2.5-fold higher; P=0.0009), and LCN2 (approximately 2-fold higher; P=0.002) and reduced expression of CXCR6 (approximately 45% lower; P=0.001) (Fig. 1C-F). Interestingly, the immune-inflammatory receptor Fc epsilon receptor Ia (FCER1A) was moderately lower in the MetDys group (approximately 45% lower; P=0.04), while interleukin 15 receptor subunit alpha (IL15RA) was significantly higher in the MetDys group (approximately 30% higher; P=0.03). Additional genes that exhibited marked differences between groups (2-fold higher in the MetS group) included BCL2 like 1 (BCL2L1; P=0.009) and galectin 3 (LGALS3; P=0.004).

A Pearson’s correlation test was performed to explore the relationships between the high expressing transcripts (Fig. 2A). The genes BCL2L1, LGALS3, LTF, CAMP, LCN2, IL15RA, and CEA cell adhesion molecule 8 (CAECAM8) were all higher in the MetDys group, positively correlated with each other and exhibited similar correlation patterns. Interestingly, CAMP (r(24)=0.66; P=0.0004), LTF (r(24)=0.63; P=0.001), and LCN2 (r(24)=0.64; P=0.001) were positively correlated to circulating level of LBP (Fig. 2B-D).

Faecal microbial composition

A summary of faecal microbial composition is presented in Table 2. Overall, microbial diversity based on both ASVs (584± 148 vs. 586±152, P=0.28) and Shannon’s diversity index (6.60± 1.47 vs. 6.33±1.29, P=0.61) was not significantly different between the healthy and MetDys groups. Of the six bacterial phyla identified among the samples, only five were considered prevalent (detected in >50% of all samples). The Tenericutes phylum was identified in two samples from the healthy group only. For the five key phyla, Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Verrucomicrobia, the relative abundance values were not significantly different between the groups; however, a trend for a higher relative abundance of Bacteroidetes (P=0.06) and lower relative abundance of Verrucomicrobia (P=0.09) among the MetDys group was noted.

At the family level, 21 unique families were detected, with 17 considered prevalent (detected in >50% of all samples); the four non-prevalent families were Eggerthellaceae, Clostridiaceae, Veillonellaceae, and Pasteurellaceae. The MetDys group exhibited significant relative abundance differences in the Bacteroidaceae (approximately 50% higher) and Prevotellaceae families (approximately 50% lower) compared to the healthy group (Table 2). A PLS-DA using the prevalent families indicated unique microbial composition between the two groups, with 32.7% of the variance between the groups explained by the first two components (Supplementary Fig. 1A). A similar pattern was observed when all bacterial families were used (Supplementary Fig. 1B). Likewise, separation between healthy and MetDys groups was noted when the PLS-DA analysis was repeated using genera-level data (Supplementary Fig. 1C and D).

Faecal microbiome functional inference

The package PICRUSt2 v2.4.1 was used to predict metagenome functions from the 16S rRNA data. A total of 5,433 pathways was identified; after filtering, 19 functional gene pathways were significantly different between groups, with a >3-fold difference based on Student’s t-test (Fig. 3A). Interestingly, 17 of the 19 genes were significantly lower in the MetDys group and were related to metabolism of sulphur, nitrogen and proteins or to cell morphology, survival and growth; one gene was related to the synthesis of vitamin K2. Upregulated genes included thiol-activated cytolysin and phospholipase C (Fig. 3B and C).

Excess body weight is frequently accompanied by a broad range of physiological changes that increase the risk of morbidity and mortality. Most notable are the robust changes to metabolic and inflammatory processes that increase the risk for cardiometabolic disease, gastrointestinal functional disorders, cancer and reduced life expectancy. The last few decades of research have identified a contributing role of the gut microbiome and the host-microbe interface in the progression of obesity-related diseases; however, the exact modalities remain poorly characterised. This study characterised faecal microbial composition, imputed functional features and simultaneously assessed a range of gene expression markers and soluble metabolic and immune indices in Australian males with or without evidence of MetDys. The study found that males with evidence of MetDys exhibited higher levels of the acute phase inflammatory proteins CRP and LBP, higher levels of the adipokines leptin and adipsin and altered circulating expression of 23 immunomodulatory genes, but only minimal changes to the faecal microbiome composition. These findings confirm the need for ongoing investigations in human clinical cohorts to further resolve the relationships among obesity, the gut microbiota, faecal metabolites and the risk for cardiometabolic diseases.

Existing literature supports that obesity accompanied by MetDys is associated with low-grade chronic inflammation and perturbed metabolic hormones and adipokines.16 The current study found that the MetDys group exhibited elevated levels of known cardiometabolic disease biomarkers such as CRP, insulin, and lactate dehydrogenase. The current study also noted significant elevation in circulating levels of leptin in the MetDys group, supporting the general paradigm of obesity-induced hyperleptinemia.17 Interestingly, adipsin was the only other adipokine that exhibited significant differences between the groups. Adipsin is an adipokine secreted by adipocytes, monocytes and macrophages with a diverse range of functions.18 Notably, adipsin, also known as complement factor D, plays an integral role in the complement system; however, it also has received considerable attention in metabolic conditions as an insulin secretagogue and regulator of adipose and pancreatic β-cell function.19 Elevated circulating adipsin concentrations have been reported in humans with MetS20 and are thought to be a compensatory response to normalise lipid and glucose metabolism in obesity. Based on this purported mechanism, the higher concentrations noted in our MetDys group further support the potential for early compromise of glycaemic control in this group.

Whole-blood transcriptomic analysis revealed 23 immunomodulatory genes with significantly altered expression in the MetDys group. Most notable was the robust elevation of the expression of LTF, CAMP, and LCN2 genes, all encoding antimicrobial peptides, in the MetDys group. While LCN2 expression has been reported to increase with obesity and metabolic disease, CAMP and LTF expression changes in obesity and metabolic disease are less clear.21,22 While both CAMP and LCN2 are secreted by adipocytes,23,24 elevated expression in the current study occurred concurrently with increased concentrations of LBP. Considering reports of Toll-like receptor 4 (TLR4) regulation of CAMP expression and lipopolysaccharide (LPS) promotion of LCN2 expression,25,26 the observed elevated expression in the MetDys group could be due to increased translocation of bacterial by-products from the gastrointestinal tract. Regardless of the trigger, CAMP has been reported to be beneficial to islet cell function and to reduce lipid accumulation,27,28 suggesting a possible compensatory response to regulate metabolic pathways in response to bacterial stimulation of inflammatory pathways. Similarly, the reported ability of LTF to interfere with components of the CD14-LPS complex lends further support to the contribution of altered LTF expression to regulation of metabolic pathways in states of inflammation.29 Last, despite being commonly associated with the pro-inflammatory outcomes in obesity,30 recent reports highlight a more complex and potentially protective role of LCN2 in response to LPS stimulation and systemic inflammation.31 The complex immunomodulatory effects of these antimicrobial peptides and their reported relationship with LPS highlight the need for further consideration of microbiome factors in MetDys.

The literature generally suggests that reductions in overall microbial diversity and phylum level shifts are evident in obesity32,33 and type 2 diabetes mellitus.34 However, we were unable to replicate such a relationship in the current study, perhaps due to the modest sample size and not inconsistent with the mixed observations in the field. That said, consideration of the global compositional profile using a PLS-DA approach did reveal some separation of the groups. With regard to key bacterial phyla, our findings indicate a trend towards an increased abundance of Bacteroidetes and a reduced abundance of Verrucomicrobia in MetDys. This is in contrast to earlier studies reporting reductions in the Bacteroidetes phyla in human obesity.33,35 At the family level, only two species showed significant abundance differences in the MetDys group. The lower relative abundance of the Prevotellaceae family and higher relative abundance of the Bacteroidaceae family in the MetDys group contrasts with prior reports of individuals with obesity.36 The inconsistencies noted in the microbiome composition of individuals with obesity highlight that a taxonomic signature alone may provide only limited insights into the microbiome changes associated with metabolic and inflammatory disorders in obesity.

Imputation of functional pathways from faecal microbial composition data did not reveal significant differences in large numbers of pathways considered relevant to the development of MetDys. While most pathways with the greatest difference in abundance between the groups, including those related to bacterial metabolism, morphology and growth, were reduced in the MetDys group, the poreforming toxin thiol-activated cytolysin and the phospholipid cleaving enzyme phospholipase C demonstrated marked increases in abundance. Interestingly, while there are few reports detailing the role of microbial phospholipase C in health and disease, phospholipase A2 has received attention as an immune modulator and promotor of inflammatory pathways.37 Moreover, work performed by Kolmeder et al.38 suggests that there may be a disconnect between microbiome composition and its functional output in conditions like obesity, so further consideration of the utility of metagenomic data in exploring potential underpinnings of obesity-associated disease is required.

This study was able to demonstrate significant changes in the gene expression of antimicrobial peptides and circulating soluble acute phase proteins and metabolic hormones in males with MetDys, independent of microbiome compositional changes. However, the need to verify these findings in a larger cohort, including both males and females, is recognised. Other aspects of the study design also warrant consideration in the context of the design of any future investigations. The MetDys group was comprised of individuals with evidence of MetS or impaired glycaemic control based on the lowered American Diabetes Association threshold for fasting blood glucose; recruitment based on a more severe disease phenotype may have revealed a greater demarcation in microbial and/or metabolite measures between the groups. In addition, dietary intake was not assessed based on recognised inconsistences of self-reported and retrospective recording;39 however, with alterations in dietary intake known to influence microbial composition in both the short40 and longer term,41 alternate approaches to retrospective selfreporting should be considered.

In conclusion, this simultaneous assessment of faecal microbial composition, circulating transcriptomics and soluble metabolic and inflammatory markers in males with evidence of MetDys provides sufficient evidence to support ongoing investigations in a larger cohort to further resolve the relationships among the gut microbiota, global immunomodulatory changes and metabolism in obesity.

The authors would like to gratefully acknowledge the valuable contributions of the study participants. This work was supported, in part, by a grant from the Gold Coast Hospital Foundation and Griffith University. The authors declare that they have no competing interests relevant to this work.

Study concept and design: NPW, PD, AWC, and AJC; acquisition of data: KMHJ, NPW, PYC, and AJC; analysis and interpretation of data: KMHJ, NPW, MWCT, PYC, PD, AWC, and AJC; drafting of the manuscript: KMHJ and AJC; critical revision of the manuscript: KMHJ, NPW, MWCT, PYC, PD, AWC, and AJC; obtained funding: NPW, PD, AWC, and AJC; administrative, technical, or material support: KMHJ, MWCT, and PYC; and study supervision: NPW and AJC.

Fig. 1. (A) PanCancer and (B) a custom panel were used to assess whole-blood gene expression. Gene counts above background are represented in logFC2 volcano plots with expression relative to the healthy control. Genes with significant differential expression between groups are represented by red dots, all other genes are represented by black dots. Four genes showed similar expression differences in the two panels and are labelled with the gene name: (C) C-X-C motif chemokine receptor 6 (CXCR6), (D) cathelicidin antimicrobial peptide (CAMP), (E) lipocalin 2 (LCN2), and (F) lactotransferrin (LTF). Circles and triangles are used to differentiate the two groups. Data are expressed as mean± standard deviation. Significant difference based on *Student’s t-test (P<0.001); Mann-Whitney test (P<0.001); Mann-Whitney test (P<0.0001). FC, fold change; MetDys, metabolic dysregulation.
Fig. 2. (A) Pearson’s correlation matrix of significantly highly expressed genes and (B-D) correlation of lipopolysaccharide binding protein (LBP) concentration with normalized count data for cathelicidin antimicrobial peptide (CAMP), lactotransferrin (LTF), and lipocalin 2 (LCN2). CDH1, cadherin 1; ANP32B, acidic nuclear phosphoprotein 32 family member B; LAIR2, leukocyte associated immunoglobulin like receptor 2; NUP107, nucleoporin 107; BCL2L1, BCL2 like 1; CEACAM8, carcinoembyronic antigen-related cell adhesion molecule 8; ECSIT, ECSIT signalling regulator; PSMD7, proteasome 26S subunit, non-ATPase 7; CD44, cluster of differentiation marker 44; PVR, PVR cell adhesion molecule; LGALS3, galectin 3; IL15RA, interleukin 15 receptor subunit alpha; TNFSF8, TNF superfamily member 8; FCER1A, Fc epsilon receptor Ia; CCR5, C-C motif chemokine receptor 5; REL, REL proto-oncogene NF-κβ subunit; RELA, RELA proto-oncogene NF-κβ subunit; IL7R, interleukin 7 receptor; CXCR6, C-X-C motif chemokine receptor 6; MAPK1, mitogen-activated protein kinase 1; MAP2K2, mitogen-activated protein kinase kinase 2; ARG1, arginase 1.
Fig. 3. (A) Genes that exhibited the most significant fold change difference between the metabolic dysregulation (MetDys) and healthy groups from functional pathways imputed from 16S sequence data. Two functional imputed pathways upregulated in the MetDys group were (B) the toxin cytolysin and (C) the membrane-enzyme phospholipase C. Circles and triangles are used to differentiate the two groups. Data are expressed as mean± standard deviation. *Significant difference based on Student’s t-test (P<0.05). FC, fold change.

Demographic characteristics, standard laboratory measures, markers of intestinal permeability and metabolic hormones in males with MetDys or without healthy evidence of metabolic dysregulation

Characteristic Healthy (n = 15) MetDys (n = 14) P
Age (yr) 41.4 ± 11.5 46.6 ± 10.0 0.20*
BMI (kg/m2) 23.0 ± 1.2 35.1 ± 3.3 < 0.01
Waist (cm) 83.4 ± 5.4 117.4 ± 9.7 < 0.01
Systolic BP (mmHg) 123.0 ± 5.6 144.4 ± 11.8 < 0.01
Diastolic BP (mmHg) 78.3 ± 5.6 96.4 ± 9.0 < 0.01*
Clinical chemistry
Triglycerides (mmol/L) 1.2 ± 0.7 2.0 ± 0.6 < 0.01
Cholesterol (mmol/L) 5.1 ± 1.3 5.6 ± 0.9 0.31*
HDL (mmol/L) 1.5 ± 0.4 1.2 ± 0.2 < 0.01
LDL (mmol/L) 3.1 ± 1.0 3.5 ± 0.8 0.14*
HbA1c (%) 5.2 ± 0.2 5.3 ± 0.4 0.34*
Glucose (mmol/L) 5.2 ± 0.4 5.7 ± 0.6 0.02
Insulin (mIU/L) 7.6 ± 3.7 15.6 ± 9.7 < 0.01
HOMA-IR 1.6 ± 0.6 4.1 ± 2.9 < 0.01
LDH (U/L) 162.7 ± 19.8 183.4 ± 12.5 < 0.01
Non-specific inflammatory markers
ESR (mm/hr) 6.0 ± 6.3 5.8 ± 4.1 0.63*
CRP (mg/L) 1.1 ± 0.8 1.8 ± 1.1 0.02
Intestinal permeability
LBP (ng/mL) 18.8 ± 6.6 38.5 ± 6.5 < 0.01*
FABP2 (ng/mL) 2.8 ± 2.2 4.3 ± 4.3 0.31
Metabolic hormones
C-peptide (ng/mL) 0.9 ± 0.4 1.6 ± 0.5 < 0.01*
Glucagon (pg/mL) 107.9 ± 108.8 80.4 ± 25.5 0.32
GLP-1 (pg/mL) 234.8 ± 28.6 246.8 ± 60.5 0.73
GIP (pg/mL) 164.7 ± 45.4 253.8 ± 230.3 0.69
Ghrelin (ng/mL) 2.3 ± 1.6 1.3 ± 0.6 0.07
Leptin (ng/mL) 1.5 ± 1.2 9.7 ± 4.7 < 0.01
Resistin (ng/mL) 3.1 ± 0.6 2.6 ± 0.9 0.24*
Visfatin (ng/mL) 21.1 ± 10.3 16.2 ± 5.6 0.80
Adipsin (μg/mL) 5.5 ± 1.3 7.0 ± 1.6 0.01*
Adiponectin (μg/mL) 20.2 ± 10.4 16.2 ± 5.6 0.43
PAI-1 (ng/mL) 59.3 ± 8.6 57.7 ± 24.2 0.80

Values are presented as mean± standard deviation.

*P-values were determined using Student’s t-test; P-value is based on an unpaired ttest using non-parametric Mann-Whitney test.

MetDys, metabolic dysregulation; BMI, body mass index; BP, blood pressure; HDL, highdensity lipoprotein; LDL, low-density lipoprotein; HbA1c, glycosylated hemoglobin; HOMA-IR, homeostatic model assessment for insulin resistance; LDH, lactate dehydrogenase; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; LBP, lipopolysaccharide binding protein; FABP2, fatty acid-binding protein 2; GLP-1, glucagon-like peptide 1; GIP, gastric inhibitory polypeptide; PAI-1, plasminogen activator inhibitor-1.

Microbial diversity metrics and relative abundance data for bacterial phyla and family level taxa

Variable Healthy (n = 15) MetDys (n = 14) P
Diversity metrics
ASV count 584 ± 148 586 ± 152 0.97*
Shannon index 6.60 ± 1.47 6.33 ± 1.29 0.61*
Bacterial phyla: relative abundance (%)
Actinobacteria 2.08 ± 1.58 1.99 ± 1.34 0.99
Bacteroidetes 26.49 ± 7.64 32.59 ± 8.67 0.06*
Firmicutes 61.58 ± 7.26 62.28 ± 8.48 0.81*
Proteobacteria 2.91 ± 3.94 1.66 ± 1.52 0.27
Tenericutes 0.08 ± 0.20 0.00 ± 0.00 -
Verrucomicrobia 6.87 ± 11.32 1.46 ± 2.00 0.09*
Bacterial families: relative abundance (%)
A_Bifidobacteriaceae 1.00 ± 1.13 0.97 ± 1.06 0.84
A_Coriobacteriaceae 0.82 ± 0.62 0.97 ± 0.79 0.60*
A_Eggerthellaceae 0.20 ± 0.26 0.04 ± 0.08 0.13
B_Bacteroidaceae 14.06 ± 8.04 22.59 ± 9.75 0.01*
B_Barnesillaceae 0.65 ± 0.67 0.77 ± 0.83 0.73
B_Prevotellaceae 5.51 ± 7.35 2.23 ± 5.32 0.02
B_Rikenellaceae 3.10 ± 2.19 3.59 ± 2.34 0.56*
B_Tannerellaceae 2.68 ± 2.52 2.79 ± 2.13 0.78
F_Streptococcaceae 0.53 ± 0.73 0.62 ± 0.58 0.44
F_Christensenellaceae 1.03 ± 1.19 0.61 ± 0.70 0.53
F_Clostridiaceae 1.00 ± 3.76 0.31 ± 0.48 0.36
F_Lachnospiraceae 22.65 ± 7.59 24.01 ± 8.32 0.65*
F_Ruminococcaceae 27.27 ± 7.22 28.52 ± 9.99 0.70*
F_Erysipelotrichaceae 1.88 ± 1.36 1.64 ± 1.11 0.60*
F_Acidaminococcaceae 0.79 ± 1.01 1.10 ± 1.08 0.39
F_Veillonellaceae 2.76 ± 5.28 1.21 ± 2.22 0.67
P_Desulfovibrionaceae 0.46 ± 0.52 0.18 ± 0.23 0.11
P_Burkholderiaceae 0.65 ± 0.91 0.43 ± 0.37 0.94
P_Enterobacteriaceae 1.72 ± 4.07 0.97 ± 1.43 0.54
P_Pasteurellaceae 0.05 ± 0.11 0.06 ± 0.17 0.38
V_Akkermansiaceae 6.87 ± 11.32 1.46 ± 2.00 0.42

Values are presented as mean± standard deviation.

*P-values determined using Student’s t-test; P-value is based on an unpaired t-test using non-parametric Mann-Whitney test; Detected in less than 50% of all samples.

MetDys, metabolic dysregulation; ASV, amplicon sequence variant.

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