Unveiling the connection between gut microbiome and metabolic health in individuals with chronic spinal cord injury

Abstract

Accumulating evidence has revealed that alterations in the gut microbiome following spinal cord injury (SCI) exhibit similarities to those observed in metabolic syndrome. Considering the causal role of gut dysbiosis in metabolic syndrome development, SCI-induced gut dysbiosis may be a previously unidentified contributor to the increased risk of cardiometabolic diseases, which has garnered attention. With a cross-sectional design, we evaluated the correlation between gut microbiome composition and functional potential with indicators of metabolic health among 46 individuals with chronic SCI. Gut microbiome communities were profiled using next-generation sequencing techniques. Indices of metabolic health, including fasting lipid profile, glucose tolerance, insulin resistance, and inflammatory markers, were assessed through fasting blood tests and an oral glucose tolerance test. We used multivariate statistical techniques (i.e., regularized canonical correlation analysis) to identify correlations between gut bacterial communities, functional pathways, and metabolic health indicators. Our findings spotlight bacterial species and functional pathways associated with complex carbohydrate degradation and maintenance of gut barrier integrity as potential contributors to improved metabolic health. Conversely, those correlated with detrimental microbial metabolites and gut inflammatory pathways demonstrated associations with poorer metabolic health outcomes. This cross-sectional investigation represents a pivotal initial step toward comprehending the intricate interplay between the gut microbiome and metabolic health in SCI. Furthermore, our results identified potential targets for future research endeavors to elucidate the role of the gut microbiome in metabolic syndrome in this population.NEW & NOTEWORTHY Spinal cord injury (SCI) is accompanied by gut dysbiosis and the impact of this on the development of metabolic syndrome in this population remains to be investigated. Our study used next-generation sequencing and multivariate statistical analyses to explore the correlations between gut microbiome composition, function, and metabolic health indices in individuals with chronic SCI. Our results point to potential gut microbial species and functional pathways that may be implicated in the development of metabolic syndrome.

Keywords: gut microbiome; insulin resistance; metabolic health; next-generation sequencing; spinal cord injury.

Graphical abstract

Figure 1.

Spearman’s correlations between indices of gut microbiome alpha diversity and metabolic health in individuals with chronic spinal cord injury. CAUC, c-peptide area under the curve during an OGTT; Cpep0, fasting c-peptide; Cpep120, c-peptide at 2 h during an OGTT; CRP, C-reactive proteins; DI, disposition index; GAUC, OGTT glucose area under the curve; Glu0, fasting glucose; Glu120, glucose concentration at 2 h during an oral glucose tolerance test (OGTT); GSIS, glucose-stimulated insulin secretion; HDL, high-density lipoprotein cholesterol; HIE, hepatic insulin extraction; HOMA, Homeostatic Model Assessment for Insulin Resistance; iCAUC, incremental area under the curve during an OGTT; IFN-g, interferon-γ; iGAUC, OGTT incremental area under the curve; IL, interleukin; Ins0, fasting insulin; Ins120, insulin concentration at 2 h during an OGTT; LDL, low-density lipoprotein cholesterol, MI, Matsuda Index; TNF-a, tumor necrosis factor-α. *P < 0.05, #P < 0.1.

Figure 2.

Nonmetric multidimensional scaling (NMDS) plot of the Bray–Curtis dissimilarities with metabolic health variables in individuals with chronic spinal cord injury. Each black dot represents each participant. The blue arrow shows the direction of the (increasing) value of each of metabolic health indicators, and the length of the line is proportional to the correlation between the variable and the NMDS score. A longer arrow indicates a stronger association. Only the insulin areas under the curve (AUC, total and incremental) are significantly correlated with the NMDS score (P < 0.05). CAUC, c-peptide area under the curve during an OGTT; Cpep0, fasting c-peptide; Cpep120, c-peptide at 2 hour during an OGTT; CRP, C-reactive proteins; DI, disposition index; GAUC, OGTT glucose area under the curve; Glu0, fasting glucose; Glu120, glucose concentration at 2 h during an oral glucose tolerance test (OGTT); GSIS, glucose-stimulated insulin secretion; HDL, high-density lipoprotein cholesterol; HIE, hepatic insulin extraction; HOMA, Homeostatic Model Assessment for Insulin Resistance; iCAUC, incremental area under the curve during an OGTT; IFN-g, interferon-γ; iGAUC, OGTT incremental area under the curve; IL, interleukin; Ins0, fasting insulin; Ins120, insulin concentration at 2 h during an OGTT; LDL, low-density lipoprotein cholesterol; MI, Matsuda Index; TNF-a, tumor necrosis factor-α.

Figure 3.

Heatmap showing correlations between gut microbiome function (A) or composition (B) and metabolic health using the regularized canonical correlation analysis method. An arbitrary cut-off of |r| = 0.2 was applied to limit potentially negligible correlations and make the figures more legible and interpretable. CAUC, c-peptide area under the curve during an OGTT; Cpep0, fasting c-peptide; Cpep120, c-peptide at 2 h during an OGTT; CRP, C-reactive proteins; DI, disposition index; Glu0, fasting glucose; Glu120, glucose concentration at 2 h during an oral glucose tolerance test (OGTT); GSIS, glucose-stimulated insulin secretion; HDL, high-density lipoprotein cholesterol; HIE, hepatic insulin extraction; HOMA, Homeostatic Model Assessment for Insulin Resistance; LDL, low-density lipoprotein cholesterol; iCAUC, incremental area under the curve during an OGTT; IFN-g, interferon-γ; iGAUC, incremental area under the curve during an OGTT; IL, interleukin; Ins0, fasting insulin; Ins120, insulin concentration at 2 h during an OGTT; MI, Matsuda Index.