Distinct gut microbiota signatures and metabolic dysregulation in individuals with type 1 diabetes: insights into a microbiome-metabolite axis
- Open Access
- 06.12.2025
- Research
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Abstract
Introduction
Type 1 Diabetes (T1D) is a complex autoimmune disease characterized by the immune-mediated destruction of insulin-producing pancreatic β-cells, leading to absolute insulin deficiency and lifelong dependency on exogenous insulin therapy [3]. Although genetic predisposition, particularly human leukocyte antigen (HLA) class II alleles plays a fundamental role in T1D susceptibility, it is increasingly clear that genetic risk alone is insufficient to explain the disease’s rising global incidence, especially among children in low-risk populations [24]. This epidemiological trend points towards environmental and lifestyle factors as critical contributors of the disease pathogenesis. Among these, the gut microbiota has garnered substantial interest due to its close interaction with the host immune system. The human gastrointestinal tract harbors trillions of microorganisms, including bacteria, viruses, and fungi, which form a dynamic ecosystem involved in nutrient processing, energy metabolism, and immune regulation [4]. Disruption of this finely balanced microbial ecosystem referred to as gut dysbiosis can lead to immune dysregulation and has been implicated in the pathophysiology of several autoimmune diseases, including T1D [31].
Numerous studies have reported that individuals with T1D or those at high risk for developing the disease exhibit distinctive alterations in gut microbiota composition and diversity. These changes include reduced microbial richness and evenness (alpha diversity) and distinct shifts in community structure (beta diversity) [11, 23]. For instance, longitudinal studies such as the TEDDY (The Environmental Determinants of Diabetes in the Young) and DIABIMMUNE cohorts have shown that changes in microbial divergence, including the loss of beneficial taxa likeBifidobacterium, Akkermansia, and Faecalibacterium, often precede the seroconversion to islet autoimmunity and the clinical onset of T1D [18, 30]. Concurrently, an increase in pro-inflammatory or pathobiont taxa such as Escherichia-Shigella, Bacteroides, and the members of the Proteobacteria phylum has been associated with immune activation and compromised gut epithelial integrity. In addition to these taxonomic shifts, the functional capacity of the microbiota particularly its ability to produce metabolites that modulate immune and metabolic pathways, is increasingly recognized as a key determinant of host health. Commensal-derived metabolites such as short-chain fatty acids (SCFAs), bile acids, and aromatic amino acid derivatives influence immune tolerance, intestinal barrier function, and insulin sensitivity [2, 28]. SCFAs, including acetate, propionate, and butyrate, promote the expansion of regulatory T (Treg) cells, enhance mucosal immunity, and inhibit pro-inflammatory cytokine production. Conversely, a reduction in SCFA-producing microbes may tilt the host immune environment towards chronic inflammation, which are pathological hallmarks of T1D [21].
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In the light of the emerging role of the gut microbiota in autoimmune diseases, Fecal Microbiota Transplantation (FMT) has gained attention as a potential therapeutic strategy for restoring microbial balance in T1D. FMT involves the transfer of gut microbial communities from healthy donors to recipients to re-establish a diverse and functional microbiota. Although primarily established for recurrent Clostridioides difficile infections, FMT is increasingly being explored in metabolic and immune-mediated conditions, including diabetes [1, 17]. Recent preclinical studies in non-obese diabetic (NOD) mouse models have shown that FMT can delay the onset of T1D by enhancing Treg cell populations and improving gut barrier integrity [35]. A pilot clinical study further demonstrated that FMT from healthy donors to newly diagnosed T1D patients altered gut microbial composition and was associated with preserved β-cell function over time [12]. These findings support the notion that modulating the gut microbiota through FMT may offer a novel immunomodulatory approach for delaying or preventing T1D onset, particularly during early disease stages or in at-risk individuals.
Despite this growing understanding, relatively few studies have employed a multi-omics approach to investigate both taxonomic and functional dysbiosis in T1D. Most existing studies have focused solely on microbiota composition or have explored metabolomic signatures in isolation. Given the dynamic and reciprocal interactions between gut microbes and host-derived or microbial-derived metabolites, it is critical to analyze these layers in an integrated framework to fully capture the biological complexity of T1D.
Materials and methods
The 16S rRNA gene sequencing data and untargeted fecal metabolomics profiles analyzed in this study were obtained from the open-access dataset published by Yuan and co-workers [34] under the NCBI Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra) accession [PRJNA664632] and corresponding MetaboLights accession [MTBLS2330] (Supplementary Table S1). The data were downloaded and reprocessed according to the objectives of the present study. This reuse complies with the Creative Commons license of the original publication, and the dataset is cited throughout this manuscript. The analytical workflow for systematic evaluation of gut microbiota and metabolite alterations associated with T1D included standardized preprocessing, taxonomic profiling, diversity and differential abundance analysis, metabolite quantification, and correlation analysis between microbial taxa and metabolites. A schematic overview of the complete data processing and analysis pipeline is illustrated in Supplementary Figure S1.
Data retrieval
The dataset used in this study included fecal samples from children with newly diagnosed T1D and healthy non-diabetic controls, obtained from a previously published cohort study [34]. The dataset (n = 141), the T1D group (n = 64) had a mean age of 7.5 years, and the control group (n = 77) had a mean age of 7.9 years. The proportion of female participants was 40.6% in the T1D group and 41.6% in the control group (Supplementary Table S2). The dataset was selected based on the following inclusion criteria: (A) all individuals in the T1D group had a confirmed clinical diagnosis of the condition; (B) the study included a control group consisting of healthy individuals without T1D; and (C) the dataset contained detailed statistical information on differential metabolite profiles between T1D patients and healthy controls. This dataset enabled a comprehensive comparison of gut microbial composition and associated metabolic alterations between the two groups.
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16S rRNA Microbiome analysis and bioinformatics statistics
The raw sequence data were processed and analyzed using QIIME 2 (Quantitative Insights into Microbial Ecology) version 2022.8 [6, 8] along with the MicrobiomeAnalyst platform [10]. Initially, paired-end reads (forward and reverse) were merged for each sample, followed by de-multiplexing and quality filtering at a Phred quality score threshold of Q25 to ensure high sequence fidelity. High-quality amplicon sequence variants (ASVs) were generated using the DADA2 algorithm [25], which models and corrects Illumina-sequenced amplicon errors without the need for clustering.
Taxonomic assignment was performed using the Greengenes2 2024.09 reference database [9]. Despite the stringent steps, a subset of sequences could not be classified at deeper taxonomic levels, reflecting the inherent limitations of 16S rRNA resolution and reference database coverage. The results were expressed as relative abundances at multiple taxonomic levels phylum, class, order, family, genus, and species. Subsequently, open-reference operational taxonomic units (OTUs) were picked at 97% sequence similarity from non-chimeric sequences, and the most abundant read within each OTU was selected as its representative. OTUs were then assigned based on the closest match in the Greengenes2 2024.09 reference database. To assess differential abundance between the groups, OTUs present with fewer than 10 total counts across all samples were filtered out. The remaining OTUs were transformed into relative abundance. Statistical significance between groups was evaluated using the Wilcoxon signed-rank test.
Biodiversity analysis
Analysis of alpha (within-sample) and beta (between-sample) diversity was conducted to evaluate microbial diversity and community structure across groups [19, 26, 33]. Alpha diversity was assessed using multiple ecological indices, including Observed species, Chao1 and Fisher’s alpha, providing a comprehensive understanding of species richness and evenness within individual samples. Taxonomic abundance data at the genus level were used for these calculations. To assess beta diversity, Bray-Curtis dissimilarity and Jensen-Shannon divergence distance matrices were computed [20]. These distances were visualized using Principal Coordinates Analysis (PCoA) to identify clustering patterns and community-level differences between the T1D and control groups. Statistical significance of alpha diversity measures was evaluated using non-parametric Mann-Whitney U tests and Kruskal-Wallis tests, while permutational multivariate analysis of variance (PERMANOVA) was employed to assess differences in beta diversity between groups.
Identification of biomarker Microbiome
Linear Discriminant Analysis Effect Size (LEfSe) was employed to identify bacterial taxa whose relative abundance differed significantly between phenotypic groups. The LEfSe algorithm was run using a Benjamini-Hochberg False Discovery Rate (FDR) adjusted p-value threshold of 0.05 and a logarithmic LDA score cut-off of 2 to ensure robust detection of differentially abundant features. LEfSe bar plots were generated using the MicrobiomeAnalyst platform [10]. All p-values were corrected for multiple testing using FDR to reduce the likelihood of false positives.
Correlation analysis
To explore potential functional interactions between gut microbial composition and fecal metabolites, we performed correlation analysis between differentially abundant microbial genera and significantly altered metabolites. The analysis was conducted using Spearman’s rank correlation coefficient, which is suitable for detecting monotonic relationships in non-parametric data. Prior to correlation, microbiome data (genus-level relative abundances) and metabolite concentration values were log-transformed and normalized to ensure comparability.
We used the MicrobiomeAnalystR package (v0.1.0) to compute pairwise Spearman correlations between selected microbial taxa and metabolites. The significance of each correlation was assessed using two-sided hypothesis testing, and p-values were adjusted for multiple comparisons using the Benjamini-Hochberg FDR method. Only correlations with FDR-adjusted p-value < 0.05 were considered statistically significant. Results were visualized as a heatmap displaying correlation coefficient (r) values. These associations offer insight into potential microbe-metabolite axes relevant to metabolic dysregulation in T1D.
Results
Microbial dysbiosis in T1D: evidence from alpha & beta diversity analyses
To investigate microbial dysbiosis associated with T1D, we compared alpha and beta diversity metrics between patients and healthy controls (Fig. 1). Alpha diversity was assessed using the Chao1 and Fisher indices. We observed a trend towards reduced alpha diversity in T1D subjects compared to controls (Fig. 1A-B), with median Chao1 values indicating a lower number of estimated species. Similarly, the Fisher index, which accounts for species abundance, also showed diminished diversity in the T1D group.
Beta diversity was analyzed using Principal Coordinates Analysis (PCoA) based on Bray-Curtis dissimilarity (Fig. 1C) and Jensen-Shannon divergence (Fig. 1D) plots. The ordination plots reveal a partial but notable separation between the microbiome profiles of control and T1D groups, with Axis 1 and Axis 2 explaining 16.8% and ~ 13.3–13.4% of the variation, respectively. The distribution of samples and the 95% confidence ellipses indicate that T1D-associated microbiota exhibit distinct clustering patterns, implying significant alterations in the overall microbial structure. These findings echo previous reports indicating that T1D is associated with microbial shifts toward pro-inflammatory or less diverse communities, potentially contributing to immune dysregulation and metabolic imbalance. Together, these results highlight both quantitative (alpha diversity) and qualitative (beta diversity) changes in the gut microbiome of T1D individuals, reinforcing the hypothesis that microbial dysbiosis may play a key role in the pathogenesis or progression of autoimmune diabetes.
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Fig. 1
Microbial diversity analysis in control and T1D groups: (A) Alpha diversity was measured using the Chao1 richness index (B) Alpha diversity was measured using the Fisher index. Boxplots show median, interquartile range, and individual data points for each group. T1D samples exhibit a trend toward reduced alpha diversity compared to controls. The differences in Chao1 and Fisher indices were not statistically significant (p > 0.05), despite a trend toward reduced diversity in the T1D group. (C) Beta diversity assessed using Principal Coordinates Analysis (PCoA) based on Bray-Curtis distance. (D) Beta diversity assessed using Principal Coordinates Analysis (PCoA) based on Jensen-Shannon divergence. Plots show partial clustering between Control (red) and T1D (blue) groups, suggesting distinct microbial community structures. Ellipses represent 95% confidence intervals for each group (Permutational multivariate analysis of variance; (PERMANOVA), F = 3.22, R-squared = 0.023; p < 0.002)
Taxonomic distribution and differential abundance of gut microbiota in T1D vs. control samples
The overall taxonomic composition of the gut microbiota in T1D and control groups was compared at multiple taxonomic levels. At the phylum level, both groups were dominated by Bacteroidota, followed by Firmicutes, Actinobacteriota, and Proteobacteria (Fig. 2A-B). The relative abundances of these phyla were broadly similar between groups. However, T1D samples exhibited a modest but notable enrichment of Proteobacteria, a phylum often associated with dysbiosis, inflammation, and compromised gut integrity. Concurrently, a relative reduction in Actinobacteriota was observed in T1D, potentially indicating a loss of beneficial microbes such as Bifidobacterium, which play roles in maintaining mucosal immunity and regulating host metabolism. These findings echo previous reports suggesting that an imbalance in the Firmicutes/Bacteroidota ratio and an expansion of Proteobacteriamay reflect a pro-inflammatory gut environment conducive to T1D pathogenesis [7, 31]. At the genus level, Bacteroides remained the most abundant genus in both groups, but stark contrasts emerged in other genera (Fig. 2C). In T1D patients, we observed an increased abundance of Escherichia-Shigella, Veillonella, and Streptococcus, genera known to include pathobionts that stimulate inflammation and impair immune tolerance. Notably, Escherichia-Shigella has been implicated in epithelial disruption and endotoxin production, both of which are detrimental in autoimmune settings. Zooming into the species level, Bacteroides vulgatus was found to be the most abundant classified species across both groups, but its elevated levels in T1D are noteworthy given its potential to induce pro-inflammatory responses under certain conditions (Fig. 2D). In addition, species such as Escherichia coli, Alistipes putredinis, Bacteroides thetaiotaomicron, and Bacteroides uniformis showed varied abundance between groups. Interestingly, Alistipes putredinis and Bacteroides caccae both associated with health-promoting functions were comparatively enriched in the control group. The high proportion of “Not_Assigned” species also points to limitations in current microbial reference databases and suggests that important uncharacterized taxa may still be playing key roles in T1D-associated dysbiosis.
Fig. 2
Rarefied Relative Abundance of Gut Microbiota in T1D and Control Individuals. (A) Stacked bar plots showing the relative abundances of bacterial phyla across individual samples in the control and T1D groups, revealing compositional shifts in the microbiota profile. (B) Mean relative abundance of the major bacterial phyla across the two study groups. highlighting the predominance of Bacteroidota and Firmicutes and their variation in T1D. (C) Genus-level average relative abundance of the top 20 taxa identified in the control and T1D groups. (D) Species-level relative abundance of the top 20 taxa.
To statistically identify group-specific microbial signatures, we applied LEfSe, generating a cladogram that highlights differentially abundant taxa between the two groups (Fig. 3A). The cladogram visually demonstrates a T1D-associated enrichment in taxa such as Escherichia-Shigella, Haemophilus, Pasteurellaceae, Veillonella, and members of the Negativicutes and Erysipelotrichaceae, groups previously linked with mucosal inflammation and gut barrier disruption [15, 31]. In contrast, healthy controls exhibited enrichment in Bifidobacterium, Bacteroides caccae, Alistipes putredinis, Actinobacteria, and Firmicutes lineages, reflecting a more balanced and metabolically beneficial microbial ecology. These differential signatures reinforce the hypothesis that gut microbial imbalance in T1D is characterized by both the loss of protective commensals and the expansion of potentially pathogenic taxa.
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Taken together, these findings support the growing body of evidence that T1D is associated with profound gut microbial alterations across multiple taxonomic levels. These changes are likely to influence host immune responses, gut permeability, and metabolic signaling, thereby contributing to disease development and progression. Our results highlight the importance of microbial biomarkers for early risk assessment and potential therapeutic targeting in T1D.
Microbial signatures associated with T1D: a differential abundance analysis reveals gut dysbiosis
To explore the gut microbial shifts associated with T1D, we conducted a taxonomic breakdown of the microbiome composition at phylum, genus, and species levels and used an LEfSe-based cladogram to identify taxa that are differentially abundant between T1D patients and healthy controls (Fig. 3A-B). A complete list of differentially abundant microbial taxa identified by LEfSe, along with their corresponding LDA scores and FDR-adjusted p-values, is provided in Supplementary Tables S3 and S4. The analysis identified several taxa with significantly higher relative abundance in T1D (positive LDA scores, shown in blue). Among these, Escherichia-Shigella emerged as the most enriched genus in T1D, a finding consistent with its known association with gut inflammation, increased intestinal permeability, and immune activation in autoimmune diseases. Other T1D-enriched taxa included Bacteroides massiliensis, Veillonella atypica, and members of the Erysipelotrichaceae family such as Erysipeloclostridium ramosum. These microbes have previously been implicated in pro-inflammatory or pathobiont-like behavior and have been observed in higher abundance in patients with various metabolic and autoimmune conditions. In contrast, control samples were enriched with Bifidobacterium, Parabacteroides, and Phascolarctobacterium, genera typically associated with gut homeostasis, anti-inflammatory effects, and SCFA production. The observed depletion of these beneficial microbes in T1D supports previous studies that have shown their protective roles in gut-immune cross-talk [5, 18].
A larger number of taxa were found to be significantly depleted in the T1D group (negative LDA scores, shown in red), suggesting a loss of protective or beneficial microbes. These included Parabacteroides distasonis and Bacteroides plebeius, both known for their immunomodulatory properties and roles in maintaining gut barrier function. Other depleted taxa included Bacteroides caccae, Bacteroides coprococa, Alistipes putredinis, and Bifidobacterium spp. microbes that have been associated with anti-inflammatory effects and SCFA production. The reduction of Bifidobacterium in particular is noteworthy, as this genus is involved in early immune training and maintenance of gut homeostasis.
Taken together, the LEfSe analysis highlights a distinct microbial signature in T1D, characterized by an enrichment of potentially pathogenic and/or pro-inflammatory taxa and a depletion of beneficial, SCFA-producing, and immunoregulatory microbes. These compositional changes in the gut microbiota may contribute to the initiation or progression of autoimmunity in T1D and suggest potential microbial biomarkers for early diagnosis or therapeutic intervention.
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Fig. 3
Differentially abundant gut microbiota between T1D and control groups identified by LEfSe analysis. (A) Cladogram generated via LEfSe analysis identifying taxa differentially abundant between the groups. Colors indicate group enrichment (yellow: T1D; green: Control). Node size corresponds to effect size (log2 fold change), and branch structure reflects phylogenetic relationships. (B) Bar chart represents taxa with significant differences in relative abundance between groups, ranked by LDA (Linear Discriminant Analysis) scores. Green bars indicate microbial taxa that are significantly more abundant in T1D, including Escherichia-Shigella, Veillonella atypica, and Erysipeloclostridium ramosum. Red bars represent taxa depleted in T1D, including beneficial genera and species such as Bifidobacterium, Parabacteroides distasonis, Bacteroides plebeius, and Alistipes putredinis. These patterns suggest distinct microbial dysbiosis associated with T1D
Integrative analysis of gut microbiota and metabolite profiles reveals functional dysbiosis in T1D
To investigate the functional consequences of taxonomic shifts in the gut microbiota of individuals with T1D, we performed a correlation analysis between key microbial taxa and gut-associated metabolites (Fig. 4). The resulting heatmap reveals distinct microbe-metabolite interaction patterns, providing insights into how microbial dysbiosis may influence host metabolic pathways. Notably, beneficial microbes depleted in T1D, such as Alistipes putredinis, Bacteroides plebeius, and Bacteroides caccae, showed positive correlations with several anti-inflammatory and bioenergetic metabolites including thioglycolic acid, D-gluconic acid, L-lactic acid, and pyruvate. These associations suggest that the loss of the taxa may impair SCFA production and central carbon metabolism, both vital for maintaining gut barrier integrity and immunomodulation. In contrast, microbes enriched in T1D, such as Erysipeloclostridium ramosum and unclassified members of Erysipelotrichaceae, exhibited distinct and potentially unfavorable correlations, including a strong positive correlation with 7,8-dihydro-L-biopterin and negative associations with hydroxybutyrate and phosphatidylcholines, key metabolites involved in energy homeostasis and lipid metabolism. These findings are consistent with prior studies linking Erysipelotrichaceaeto inflammation, insulin resistance, and lipid dysregulation. Of particular interest, several microbial species, such as Bacteroides coprococa and Odoribacter splanchnicus, demonstrated selective correlations with specific metabolites, including bile acids, benzylic alcohols, and flavonoid derivatives.
This highlights the potential role of microbial metabolism in shaping xenobiotic and secondary bile acid pathways, processes increasingly implicated in autoimmune regulation and glucose metabolism. Among the differentially abundant metabolites identified, a few exogenous compounds such as Aspirin were detected. These annotations are based on spectral matches in the original untargeted metabolomics dataset and may reflect residual medication use or diet-derived substances, rather than endogenous metabolic activity. Overall, this integrative analysis underscores that the microbial dysbiosis observed in T1D is not only taxonomic but functionally significant, potentially disrupting metabolic signaling networks that influence immune function, energy balance, and inflammation. The findings support the concept of a functional microbial-metabolite axis in T1D pathogenesis and may be used for identification of candidate microbial markers for diagnostic and therapeutic purposes .
Fig. 4
Heatmap shows Spearman correlation between differentially abundant gut microbial taxa and fecal metabolites in T1D and control groups. Rows represent microbial taxa (at species or genus level), and columns represent significantly altered metabolites. The color gradient reflects the strength and direction of Spearman correlation coefficients (ranging from − 1 to + 1), with blue, red, yellow, and white boxes representing negative, positive, weak, and no correlation. Only statistically significant correlations (FDR-adjusted p < 0.05) are marked with an asterisk (*)
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Discussion
In this study, we conducted an integrated metagenomic and metabolomic analysis to characterize gut microbial dysbiosis and functional metabolic disruptions in individuals with T1D. Our results reveal significant alterations in microbial diversity, community structure, taxonomic composition, and microbe-metabolite correlations, all of which collectively support the hypothesis that the gut microbiota plays a pivotal role in the pathogenesis and progression of T1D. Reduced alpha diversity observed in T1D patients suggests a decline in microbial richness and evenness, which is frequently associated with disease states, including autoimmune conditions. Indeed, lower diversity is known to compromise microbial resilience and the ability to maintain intestinal and systemic homeostasis [23]. Previous studies in pediatric cohorts at high genetic risk for T1D have similarly demonstrated that children who progress to autoimmunity-associated issues such as T1D often display lower gut microbial diversity [18]. Our beta diversity analysis further revealed distinct clustering patterns between T1D and control microbiomes, indicating a significant shift in the microbial community composition. These findings are consistent with those of Vatanen et al., who observed that early-life microbial divergence often precedes the appearance of islet autoantibodies [31]. Such structural shifts in microbial ecosystems may influence host metabolism and immune activation through altered antigen presentation, microbial metabolite production, and mucosal signaling pathways.
Taxonomic profiling across phylum, genus, and species levels revealed hallmark features of gut dysbiosis in T1D. An increased abundance of Proteobacteria and Escherichia-Shigella, both associated with inflammation, endotoxin release, and intestinal barrier dysfunction, was notable in T1D samples. These findings align with prior reports showing elevated Proteobacteriain both T1D and Type 2 Diabetes (T2D), reflecting shared inflammatory gut phenotypes [7, 11]. In contrast, key beneficial genera including Bifidobacterium, Parabacteroides, and Phascolarctobacterium were significantly depleted in T1D individuals. Bifidobacterium plays an essential role in maintaining gut epithelial integrity and producing acetate and lactate, which support mucosal health and Treg cell function [29]. Likewise, Parabacteroides distasonis has been shown to modulate host inflammatory and metabolic pathways in murine models of obesity and diabetes [32]. The consistent depletion of these genera reinforces the idea that T1D is associated with a breakdown in symbiotic host-microbe interactions.
LEfSe-based differential abundance analysis further validated the case-control dichotomy by identifying specific taxa enriched in T1D including Veillonella atypica and Erysipeloclostridium ramosum, and those depleted, such as Alistipes putredinis, Bacteroides plebeius, and Bacteroides caccae. The expansion of Erysipelotrichaceae, in particular, has been previously linked with high-fat diet-induced inflammation and impaired gut permeability [14‐16, 27], suggesting its potential role in perpetuating the autoimmune cascade. While taxonomic shifts provide a structural view of dysbiosis, integrating metabolomic data sheds light on the functional implications of altered microbial landscapes in T1D. Our correlation heatmap revealed strong associations between health-associated taxa and key metabolites involved in energy metabolism and immune regulation. For instance, Alistipes putredinis and Bacteroides plebeius positively correlated with D-gluconic acid, L-lactic acid, and pyruvate metabolites involved in central carbon metabolism, redox balance, and intestinal barrier support. Conversely, T1D-enriched microbes like Erysipeloclostridium ramosum showed negative correlations with SCFAs and phosphatidylcholines-lipid-derived molecules with anti-inflammatory functions. Instead, they were positively associated with metabolites like 7,8-dihydro-L-biopterin, which is linked to oxidative stress and endothelial dysfunction. These shifts may reflect a metabolic milieu favoring immune activation, oxidative damage, and β-cell vulnerability. Interestingly, Bacteroides coprococa and Odoribacter splanchnicus, although not universally depleted in T1D, displayed selective correlations with bile acids and flavonoid derivatives. These pathways have been increasingly implicated in modulating host glucose metabolism, intestinal inflammation, and systemic immunity [22]. This highlights the potential role of microbial functional diversity in influencing disease susceptibility, even beyond taxonomic abundance. The detection of exogenous compounds like Aspirin in the fecal metabolome highlights a limitation of untargeted metabolomics, where spectral matches may include dietary or pharmaceutical residues. While these may represent real host-environment interactions, their interpretation should be made cautiously, especially when clinical medication history is unavailable.
FMT as a potential therapeutic approach based on our findings
The distinct alterations in microbial diversity, taxonomic composition, and metabolite associations observed in our study underscore the potential of microbiota-based interventions, such as FMT, to restore microbial and metabolic balance in individuals with T1D. Our findings of reduced alpha diversity and depletion of beneficial taxa such as Bifidobacterium, Parabacteroides distasonis, and Alistipes putredinis, along with enrichment of pro-inflammatory microbes like Escherichia-Shigella and Erysipeloclostridium ramosum, are consistent with a dysbiotic gut environment that may promote immune activation and metabolic dysfunction. These microbiota profiles have previously been implicated in increased gut permeability, loss of immune tolerance, and progression toward autoimmunity in T1D [11, 31]. Importantly, FMT has emerged as a promising strategy to correct such dysbiosis, with several studies demonstrating its potential to modulate immune responses and preserve β-cell function. For example, it has been shown that FMT from healthy donors into non-obese diabetic (NOD) mice delayed diabetes onset by restoring microbial diversity and enhancing gut immune homeostasis [35]. More recently, a pilot clinical study by de Groot et al. [12] reported that FMT in newly diagnosed T1D patients resulted in favorable microbial shifts and partial preservation of C-peptide levels over 12 months. These results suggest that reconstituting the gut microbiota with eubiotic communities may delay disease progression or improve metabolic outcomes in T1D. A recent study by [13] further supports the therapeutic potential of FMT in T1D, demonstrating that it led to temporary modulation of gut microbiota composition and was associated with transient preservation of β-cell function in newly diagnosed patients [13]. These findings complement earlier studies and strengthen the rationale for exploring microbiota-targeted interventions in the early stages of autoimmune diabetes. The microbial and metabolic signatures identified in our analysis, including associations between beneficial microbes and SCFA-related anti-inflammatory metabolites (e.g., D-gluconic acid, lactic acid, pyruvate) further support the concept of a functional gut microbiota-metabolite axis that can be therapeutically modulated. FMT, particularly when combined with dietary prebiotics to support colonization, may help restore this axis. Our study thus provides a rationale for exploring targeted FMT or synthetic microbial consortia as next-generation microbiome therapies for early-stage T1D or high-risk individuals.
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While the original study by Yuan and colleagues [34] provided an integrated overview of gut microbiota and metabolic changes in children with new-onset T1D, our study offers additional insights through a focused re-analysis. Specifically, we investigated the microbe-metabolite correlation landscape in greater detail, identifying distinct microbial-metabolite axes potentially involved in immune modulation and host metabolic regulation. Notably, we highlight correlations between Alistipes putredinis, Bacteroides plebeius, and metabolites such as D-gluconic acid and lactic acid, which were not explicitly discussed in the original report. These findings contribute novel perspectives on the potential microbial drivers of metabolic dysregulation in T1D and support future hypothesis-driven therapeutic exploration.
Study limitations
Our integrative findings support the growing consensus that gut dysbiosis in T1D is not solely a compositional phenomenon but also involves profound functional impairments in microbial metabolism. These microbial and metabolite signatures could serve as non-invasive biomarkers for early risk stratification, particularly in genetically susceptible individuals. Moreover, they open avenues for microbiota-based interventions aimed at restoring metabolic balance and mucosal homeostasis. However, several limitations must be acknowledged. First, the analysis relied on publicly available 16S rRNA sequencing and metabolomics data, which may introduce technical variability due to differences in sample collection, storage, sequencing depth, and batch effects that could not be fully controlled. Second, while the use of the Greengenes2 2024.09 reference database ensured consistency with prior studies, its relatively limited taxonomic resolution compared to more recent databases such as SILVA may have constrained species-level classification accuracy. Third, the cross-sectional design of the dataset restricts causal inference regarding microbiota-metabolite associations and disease progression in T1D. Fourth, LEfSe-based differential abundance analysis, though widely used, may overestimate significance when applied to compositional data; future studies employing more robust statistical frameworks (including, ANCOM-BC or MaAsLin2) could provide additional validation. Further, the lack of experimental or longitudinal validation limits the direct mechanistic interpretation of the observed correlations between microbial taxa and metabolites. Lastly, lack of comprehensive dietary data, which may influence gut microbiota composition, is another limitation of the study. Although age, sex, and BMI were comparable between groups, unmeasured dietary differences could be a potential confounder. Despite these constraints, the integrative multi-omics approach presented here provides valuable insight into the potential microbiota-metabolite interactions underlying metabolic dysregulation in T1D and lays a foundation for future hypothesis-driven investigations.
Conclusion
This study provides a comprehensive evidence of gut microbial dysbiosis in individuals with T1D, characterized by reduced microbial diversity, compositional shifts favoring pro-inflammatory taxa, and the depletion of beneficial microbes. At multiple taxonomic levels, T1D-associated microbiota showed significant enrichment in pathobionts such as Escherichia-Shigella, Veillonella atypica, and Erysipeloclostridium ramosum, while key commensals including Bifidobacterium, Parabacteroides distasonis, and Alistipes putredinis were depleted. These alterations were accompanied by functional disruptions in host-microbiome interactions, as evidenced by distinct microbe-metabolite correlations linked to inflammation, immune modulation, and energy metabolism. The integrative approach combining microbial and metabolomic profiling underscores the potential of gut microbiota as both biomarkers and therapeutic targets in T1D. Our findings support the hypothesis that the gut microbiome plays a mechanistic role in disease onset and progression, influencing immune tolerance, mucosal integrity, and systemic metabolic pathways. Future longitudinal and interventional studies are warranted to validate these microbial signatures and assess the therapeutic efficacy of microbiota-directed strategies such as prebiotics, probiotics, or microbiota transfer, for preventing or modulating T1D. These insights may open new avenues for personalized microbiome-based diagnostics and therapies in autoimmune diabetes.
Acknowledgements
The authors would like to acknowledge their respective institutes for providing the necessary support for the work. The author SH, is grateful to Jazan University for providing the access of the Saudi Digital Library for this work.
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Competing interests
The authors declare no competing interests.
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