Abstract

   Type 2 diabetes mellitus (T2DM) is increasingly recognized as a
   condition influenced by gut microbiota composition and associated
   metabolic pathways. This study investigated the differences in gut
   microbial diversity, composition, and metabolomic profiles between
   diabetic and control individuals. Using 16 S rRNA gene sequencing and
   metabolomic analyses, we observed significantly higher microbial
   diversity and evenness in the diabetic group, with distinct clustering
   patterns as revealed by Principal Coordinate Analysis (PCoA). Taxonomic
   profiling demonstrated an increased relative abundance of
   Bacteroidaceae and Lachnospiraceae in the diabetic group, while
   Streptococcaceae was more prevalent in the control group. LEfSe
   analysis identified key microbial taxa such as Bacteroides, Blautia,
   and Lachnospiraceae_FCS020_group enriched in diabetic individuals,
   suggesting a role in metabolic dysregulation. Metabolomic pathway
   enrichment analysis revealed significant differences in pathways
   related to fatty acid metabolism, glucose homeostasis, bile acid
   metabolism, and amino acid biosynthesis in diabetic individuals.
   Enriching fatty acid elongation and β-oxidation pathways, alongside
   disrupted glucose metabolism, indicate profound metabolic changes
   associated with diabetes. Bile acid metabolism and branched-chain amino
   acid (BCAA) pathways were also elevated, linking these metabolites to
   the observed gut microbiota shifts. These findings suggest that
   diabetes is associated with significant alterations in the gut
   microbiome’s composition and function, leading to disruptions in
   critical metabolic pathways. This study provides insights into
   potential microbial biomarkers and therapeutic targets for improving
   metabolic health in diabetic patients.

   Keywords: Gut microbiota, Diabetes, Microbial diversity, Metabolic
   pathways, LEfSe analysis, Metabolomics

   Subject terms: Computational biology and bioinformatics, Microbiology,
   Endocrine system and metabolic diseases

Introduction

   Diabetes Mellitus (DM) is a chronic metabolic characterized by elevated
   blood glucose levels (hyperglycemia)^[28]1,[29]2. It represents a major
   global health challenge, with a global prevalence of 10.5% in 2021,
   projected to increase to 12.2% by 2045^[30]2–[31]4. The condition
   affects both sexes equally, but it is more common among the elderly and
   in urban populations^[32]4. DM is classified into Type 1 Diabetes
   Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM). T1DM is caused by
   the autoimmune destruction of pancreatic ß-islet cells, while T2DM
   results from insulin resistance and relative insulin deficiency,
   primarily in adults^[33]1,[34]2,[35]5.

   Recent advances have highlighted the role of the gut microbiota in
   diabetes^[36]6–[37]8. This diverse microbial community in the
   gastrointestinal tract, primarily comprised of Bacteroides and
   Firmicutes is essential for metabolic and immune
   functions^[38]6,[39]7,[40]9–[41]11. Alterations in gut microbial
   balance have been linked to metabolic disorders, including
   diabetes^[42]6–[43]8. This has led to global research endeavors, as
   outlined by Ursell et al. (2012), to decode these symbiotic
   relationships between the gut microbiota and health outcomes,
   particularly in diabetic conditions^[44]12,[45]13. Studies like Gowd et
   al. (2019) have shown associations between microbial dysbiosis and DM
   onset^[46]14,[47]15, emphasizing its role in metabolic health.

   The gut microbiome may influence diabetes through multiple mechanisms,
   including the production of short-chain fatty acids, modulation of
   immune response, impacting gut barrier integrity, and modulating host
   genetic expression^[48]8–[49]10,[50]14. These pathways interact in
   complex ways to affect glucose metabolism and insulin
   sensitivity^[51]6,[52]7,[53]14,[54]15.

   In this context, our study investigates gut microbiota differences in
   diabetic versus non-diabetic individuals using 16 S rRNA sequencing. By
   comparing stool samples, we aim to identify the microbiota signatures
   that may contribute to DM pathogenesis. Our findings may support the
   development of microbiota-targeted therapies for diabetes management.

Materials and methods

Study population and sample collection

   Twenty participants were recruited from the Susad Kafafi Memorial
   Hospital in Egypt, including 10 patients with Diabetes Mellitus (DM)
   and 10 non-diabetic individuals. Ethical approval for collecting and
   analyzing patient stool samples was obtained under reference number
   2024/0047 from the Institutional Review Board at Misr University for
   Science and Technology (MUST-IRB). All participants provided written
   and signed informed consent prior to sample collection. The stool
   samples were promptly frozen and stored at − 80 °C until processing.
   All methods were performed in accordance with the relevant guidelines
   and regulations, including those outlined by the Declaration of
   Helsinki and the ethical standards of the responsible institutional
   committee.

DNA extraction

   Microbial DNA was extracted from 200 mg of each homogenized fecal
   sample. The DNA extraction was performed according to the
   manufacturer’s instructions for the QIAamp Fast DNA Stool Mini Kit
   (Qiagen, Germany). After extraction, the concentration and quality of
   the DNA were checked using a NanoDrop spectrophotometer. The
   concentration and purity of the extracted DNA were measured with a
   NanoDrop spectrophotometer evaluating both concentration (at 260 nm
   absorbance) and purity (260/280 nm ratio). Subsequently, all the
   extracted DNA specimens were stored at − 80 °C until the next step.

16 S rRNA gene sequencing

   Illumina Nextera barcoded, two-step PCR libraries using 16 S V3–V4
   marker region primers pairs; 341 F (5′-CCTAYGGGRBGCASCAG-3′) and 806R
   (5′-GGACTACNNGGGTATCTAAT-3′) to construct the library, and Illumina
   MiSeq, v2, 2 × 250 bp was utilized for sequencing at Macrogen
   (Macrogen, 2020). We analyzed 16 S rRNA using the QIIME2
   pipeline^[55]16. Following data quality checks, denoising with DADA2
   was conducted to merge paired reads, generating amplicon sequence
   variants (ASVs)^[56]17. An alpha rarefaction module ensured sufficient
   depth to capture most features. We then calculated alpha and beta
   diversity using the core-metrics-phylogenetic module. The Shannon
   Diversity Index was employed for alpha analysis to estimate microbial
   species richness, and Pielou matrices were used for evenness. To
   quantify community dissimilarity, we utilized Bray-Curtis and UniFrac
   distance matrices in beta diversity. Principal coordinate analysis
   (PCoA) was performed to enhance the visualization of beta diversity.
   Taxonomy was assigned to ASVs using the q2-feature-classifier
   classify-sklearn naïve Bayes taxonomy classifier against the
   pre-trained Naïve Bayes silva-132-99-nb-classifier, which was trained
   on Silva (release 138) full-length sequences. Biomarkers with
   statistically significant differences between groups were identified
   through a linear discriminant analysis effect size (LEfSe) analysis
   based on LEfSe software with default settings, aiming to identify taxa
   that are differentially abundant among diabetic patients and healthy
   controls.

Functional metabolic pathway prediction

   To predict metagenome functions and pathway abundance based on 16 S
   amplicon sequences, we used the PICRUSt2 algorithm^[57]18. In short,
   genus and pathway abundance were prepared upon sample aggregation (DM
   and Healthy). Correlation was run between genus and pathways within
   each condition, followed by filtering the significant correlation with
   a value less than 0.001 and a correlation value more or less than 0.7.
   Since this might lead to multiple correlations (one genus could
   correlate with more than one pathway), Sankey plots were generated
   using (networkD3) package and plotted separately for data visualization
   and interpretation for each condition.

Metabolomics analysis

   Each stool sample, weighing approximately 100 mg, was placed in a 1.5
   mL Eppendorf tube. A reconstitution buffer was prepared by mixing
   acetonitrile and methanol in a 3:1 ratio (v/v). For the initial
   extraction, 400 µL of this buffer was added to each sample, followed by
   vortexing for 2 min and ultrasonication for 10 min. The extract was
   dried and resuspended in water: methanol: acetonitrile (2:1:1) before
   LC-MS injection. Chromatographic separation utilizing a Waters XSelect
   HSS T3 column, and mass detection was done in negative ion mode. Data
   were analyzed using MS-DIAL version 4.9 with HMDB for annotation,, and
   feature extraction was performed using MasterView with a
   signal-to-noise threshold > 10.

Statistical analysis

   Statistical analyses were performed in R (version 4.4.3). A pairwise
   comparison was conducted to compare the relative abundance of taxa
   between the two conditions (DM and Healthy). The Wilcoxon test was
   used, and the P-value was corrected for multiple comparisons using the
   Holm–Bonferroni method. Linear discriminant analysis effect size
   (LEFSe) and the linear discriminant analysis (LDA) score were obtained.
   Additionally, a pairwise PERMANOVA of beta diversity was performed to
   assess differences in overall community composition between the two
   groups using the “adonis” function in the “vegan” package in R. A
   p-value of less than 0.05 was considered significant. The correlation
   between the considerable genera and enriched pathways was conducted
   using the corrr package in R and the Sankey plot. The MetaboAnalystR
   package version 3.0.3 was used for metabolomics analysis. The
   normalized intensities by the median for each sample were mean-centered
   and divided by the standard deviation of each variable of all annotated
   features. A heatmap visually presented hierarchical clustering using
   the Euclidean method for distance calculation and Ward’s linkage for
   clustering. Metabolite set enrichment analysis (MSEA) was carried out
   against the “main" data set, and a generalized linear model was used
   during the quantitative enrichment analysis. Spearman correlations
   between genera were calculated using the psych package, with p-values
   adjusted via the Benjamini–Hochberg method. Correlations with |r| > 0.5
   were retained. The number of significant associations per genus was
   summarized and visualized by phylum. The directionality of correlations
   (positive vs. negative) was displayed using a donut chart, which is the
   plot style used, adapted from PMID: 40,027,484^[58]19.

Results

Microbial diversity and compositional shifts in diabetic versus control
groups

   The analysis revealed a higher microbial diversity in the diabetic
   group compared to the control group, as indicated by the Shannon
   diversity index (Fig. [59]1A). Additionally, the diabetic group
   exhibited a more even distribution of species, with a higher Pielou’s
   evenness index (Fig. [60]1B). Conversely, the control group showed a
   less uniform distribution of species. The Principal Coordinate Analysis
   (PCoA) based on Bray-Curtis dissimilarity demonstrated distinct
   separation between the diabetic and control groups, with 9.9% of the
   total variation (Fig. [61]1C). Furthermore, the phylogenetic
   composition of the communities displayed noticeable differences, as
   evidenced by the PCoA plot based on unweighted UniFrac distances
   (Fig. [62]1D). The distinct clustering observed along Axis 1, which
   accounts for 16.3% of the variation, highlights that the microbial
   communities in diabetic individuals are compositionally different and
   phylogenetically distinct from those in the control group. This
   underscores the impact of diabetes on the gut microbiota’s structure
   and evolutionary relationships. Furthermore, individuals with diabetes
   seem to possess a richer and more diverse gut microbiota, suggesting a
   more balanced microbial ecosystem. These differences reflect
   significant variations in the relative abundance of microbial species
   between the two groups.

Fig. 1.

   [63]Fig. 1
   [64]Open in a new tab

   Microbial diversity and compositional differences between control and
   diabetic groups. (A) Boxplot illustrating the Shannon diversity index,
   reflecting overall microbial richness within the control and diabetic
   groups. The diabetic group exhibited significantly higher microbial
   diversity than the control group. (B) Boxplot of Pielou’s evenness,
   which measures the uniformity of species distribution. A higher
   evenness is observed in the diabetic group, indicating a more
   homogeneous distribution of microbial taxa relative to the control
   group. (C) Principal Coordinate Analysis (PCoA) plot based on
   Bray-Curtis dissimilarity, displaying the distinct clustering of
   microbial communities between control and diabetic groups along Axis 1,
   which explains 9.9% of the total variation. (D) PCoA plot based on
   unweighted UniFrac distances, further highlighting the separation of
   microbial communities between control and diabetic samples. Axis 1
   accounts for 16.3% of the variation, demonstrating significant
   differences in phylogenetic composition. Alpha and beta diversity were
   assessed using the Wilcoxon rank-sum test and PERMANOVA, respectively.

Distinct shifts in gut microbial composition and abundance at family and
fenus levels in diabetic versus control groups

   To explore microbial diversity, a comprehensive taxonomic analysis was
   performed focusing on differences at both the family and genus
   taxonomic levels within the microbiome. The study revealed significant
   differences in the relative abundance of several microbial families
   between the diabetic and control groups (Fig. [65]2A). Notably, the
   diabetic group had a higher relative abundance of Bacteroidaceae and
   Lachnospiraceae, whereas Streptococcaceae was more abundant in the
   control group. These findings highlight distinct group-associated
   microbial patterns rather than temporal changes between the two groups.

Fig. 2.

   [66]Fig. 2
   [67]Open in a new tab

   Differential microbial composition and abundance between control and
   diabetic groups. (A) Circos plot illustrating the differential
   microbial family-level composition between control and diabetic groups.
   Each segment represents a microbial family, with the connections
   between the groups showing the relative abundance of each family in the
   two conditions. Diabetic patients show a higher abundance of families
   such as Bacteroidaceae and Lachnospiraceae, while families such as
   Streptococcaceae are more prevalent in the control group. (B) Heatmap
   showing the genus-level relative abundance of bacterial taxa across
   control (blue) and diabetic (yellow) groups. The intensity of the
   colors represents the relative abundance of each genus, with red
   indicating higher abundance and blue indicating lower abundance.
   Notably, genera such as Bacteroides, Blautia, and Streptococcus are
   more abundant in diabetic patients, whereas Lachnospiraceae and
   Ruminococcaceae show higher abundance in the control group. This
   analysis highlights significant differences assessed by Wilcoxon test
   and p-value were corrected using FDR (p < 0.05) in the microbial
   composition between the two conditions, suggesting a potential role of
   specific bacterial taxa in the pathophysiology of diabetes.

   Further differences were observed at the genus level (Fig. [68]2B). The
   genera Bacteroides, Blautia, and Streptococcus were significantly more
   abundant in the diabetic group. Conversely, genera linked to
   Lachnospiraceae and Ruminococcaceae, such as Faecalibacterium and
   Roseburia, were more prevalent in the control group. These distinctions
   at the genus level further emphasize the divergent microbial profiles
   between the two conditions. Additionally, other notable genera, such as
   Bifidobacterium, Methanobrevibacter, and Christensenellaceae, were
   distributed differently between the two groups. The diabetic group
   showed a higher relative abundance of Bifidobacterium, while
   Christensenellaceae was predominantly found in the control group.

LEfSe highlights core microbial taxa underpinning gut microbiome divergence
in diabetic and control groups

   LEfSe analysis identified significant differences in bacterial taxa
   between the control and diabetic groups. Taxa from the families
   Bacteroidaceae and Erysipelotrichaceae were predominantly enriched in
   the diabetic group. In contrast, Streptococcaceae was more abundant in
   the control group, as illustrated by the phylogenetic cladogram
   (Fig. [69]3A). Supporting these observations, the LDA scores further
   delineated the microbial differences between the groups. Taxa with
   positive LDA scores, including Bacteroides and members of the
   Lachnospiraceae family, were predominantly associated with the diabetic
   group. Conversely, negative LDA scores pointed to taxa like
   Streptococcus and Leuconostoc, which were more abundant in the control
   group (Fig. [70]3B). Additionally, boxplots displaying the relative
   abundance of key bacterial genera provided a clear illustration of the
   divergences between the groups (Fig. [71]3C). Genera such as
   Bacteroides, Lachnospiraceae_FCS020_group, and Angelakisella were
   significantly enriched in the diabetic group, suggesting their
   potential involvement in disease-related microbial shifts. On the other
   hand, the genera Leuconostoc and Streptococcus were more prevalent in
   the control group, reinforcing the distinct microbial profiles of
   healthy individuals. The statistical significance of these findings
   further highlights the robustness of the observed differences in
   bacterial communities between the diabetic and control groups.

Fig. 3.

   [72]Fig. 3
   [73]Open in a new tab

   LEfSe analysis of bacterial taxa distinguishing control and diabetic
   groups.

   (A) Cladogram showing the phylogenetic distribution of bacterial taxa
   enriched in control (blue) and diabetic (yellow) groups based on Linear
   Discriminant Analysis Effect Size (LEfSe). Taxa enriched in the
   diabetic group are primarily from the families Bacteroidaceae and
   Erysipelotrichaceae, while Streptococcaceae is more abundant in the
   control group. (B) Linear Discriminant Analysis (LDA) scores
   highlighting bacterial taxa with significant differential abundance
   between the control and diabetic groups. Positive LDA scores (yellow
   bars) represent taxa enriched in the diabetic group, while negative LDA
   scores (blue bars) represent taxa enriched in the control group. (C)
   Boxplots of the relative abundance of key bacterial genera identified
   by LEfSe analysis. Genera such as Bacteroides,
   Lachnospiraceae_FCS020_group, and Angelakisella are significantly more
   abundant in diabetic patients, while Leuconostoc and Streptococcus are
   more prevalent in the control group. Statistical significance is
   indicated with adjusted p-values (p < 0.05) using Wilcoxon test for
   each comparison.

Distinct microbial pathways and taxa associations characterize metabolic
alterations in diabetic gut microbiomes

   Of the 375 predicted metabolic pathways, 233 showed significant
   differences between the control and diabetic groups. To explore the
   association of these pathways with specific bacterial genera altered in
   the diabetic group, participants were initially divided into control
   and diabetic groups. A correlation analysis was subsequently conducted.
   A strict threshold was implemented to ensure robust and meaningful
   associations: only pathways with a p-value below 0.0001 were included
   in the final correlation analysis. This process resulted in 159
   pathways correlated with 59 altered bacterial genera. The analysis was
   further refined by applying a correlation cutoff of 0.7, along with a
   p-value threshold of less than 0.005. This aided in identifying the
   strongest associations between metabolic pathways and bacterial genera.
   These results demonstrated the differences between bacterial genera and
   metabolic pathways under the two conditions. While Bacteroides and
   Alistipes were positively correlated with seven different pathways in
   the control group, Fusicatenibacter was negatively correlated with
   three distinct pathways (Fig. [74]4A). Conversely, in the diabetic
   group, we found that Christensenellaceae, Eubacterium coprostanoligenes
   group, and UCG-005 positively contributed to most of the altered
   pathways (Fig. [75]4B).

Fig. 4.

   [76]Fig. 4
   [77]Open in a new tab

   Functional pathway predictions and microbial taxa associations in
   control and diabetic groups. (A) Control group. (B) Diabetic group.
   Each Sankey plot illustrates significant correlations (adjusted
   p < 0.05) between differentially abundant microbial genera and
   functionally enriched metabolic pathways. Correlations were calculated
   using Spearman’s method, with a threshold of |r| ≥ 0.7. Dark blue
   ribbons indicate strong positive correlations (r > 0.7), while light
   blue ribbons represent strong negative correlations (r < − 0.7). Only
   taxa and pathways with both statistical significance and functional
   relevance are displayed, highlighting distinct microbe–function
   associations in each group.

Metabolomic profiles in diabetic versus control groups

   Metabolomic analysis revealed differences in key metabolic pathways
   between diabetic and control groups. Pathway enrichment analysis
   identified fatty acid metabolism, glucose regulation, bile acid
   metabolism, and amino acid biosynthesis as significantly enriched in
   the diabetic group (Fig. [78]5A). Pathways related to fatty acid
   metabolism, particularly fatty acid elongation and β-oxidation, were
   more pronounced in diabetic individuals. These pathways displayed a
   high enrichment ratio, indicating changes in lipid metabolism in the
   diabetic group compared to controls (Fig. [79]5B). Additionally,
   pathways related to glucose homeostasis, such as insulin signaling and
   hexose metabolism, were enriched in the diabetic group, reflecting
   differences in glucose metabolic processes. Bile acid metabolism
   pathways were notably enriched in the diabetic group. Specifically,
   primary bile acid synthesis showed a significant enrichment ratio,
   pointing to potential changes in bile acid processing in diabetic
   individuals. Amino acid biosynthesis and transport pathways, including
   those for branched-chain amino acids (BCAAs) and aromatic amino acids,
   were also significantly enriched, suggesting alterations in amino acid
   metabolism in the diabetic group. The heatmap of metabolite abundances
   (Fig. [80]5C) revealed distinct clustering between the diabetic and
   control groups. Diabetic individuals displayed higher metabolites
   associated with the enriched pathways, including metabolites linked to
   fatty acid metabolism, glucose regulation, and bile acid synthesis.
   This clustering pattern supports the significant differences in
   metabolite profiles between the groups.

Fig. 5.

   [81]Fig. 5
   [82]Open in a new tab

   Metabolite Pathway Enrichment and Abundance Differences between
   Diabetic and Control Groups. (A) Network plot illustrating enriched
   metabolic pathways in diabetic versus control groups. Nodes represent
   pathways, with node size corresponding to the enrichment ratio and node
   color indicating significance (p-value). Pathways related to fatty acid
   metabolism, glucose homeostasis, and amino acid biosynthesis
   significantly enrich the diabetic group. (B) Bar plot showing the top
   enriched metabolic pathways based on enrichment ratios and p-values.
   Pathways such as bile acid metabolism, glucose homeostasis, and amino
   acid transport are highly enriched in diabetic samples. (C) Heatmap
   representing the relative abundance of key metabolites across control
   and diabetic groups. Red indicates higher abundance, and blue
   represents lower abundance. Clustering shows distinct metabolite
   profiles between control and diabetic individuals, highlighting
   potential biomarkers and metabolic shifts associated with diabetes.

Co-abundance correlation analysis reveals restructured microbial interactions
in diabetes

   To elucidate potential alterations in microbial ecological structure
   associated with type 2 diabetes, we performed a co-abundance
   correlation analysis based on Spearman’s rank correlations with
   FDR-adjusted p-values. This analysis revealed a substantial shift in
   microbial interaction dynamics, with the most significant correlations
   being positive (73.1%) and a smaller fraction negative (26.9%)
   (Fig. [83]6C). The predominance of positive correlations suggests
   enhanced co-occurrence and potential cooperative behavior among
   microbial taxa in the diabetic gut environment. Taxa belonging to the
   phyla Firmicutes and Bacteroidota were among the most interconnected,
   exhibiting the highest number of statistically significant associations
   (Fig. [84]6A). Furthermore, degree distribution analysis indicated that
   several taxa serve as highly connected nodes, or potential hubs, within
   the microbial network (Fig. [85]6B), implying their key role in
   structuring microbial communities in diabetes. These findings indicate
   that diabetes is not only characterized by taxonomic and functional
   shifts but also by a reorganization of microbial interaction networks -
   hallmarks of ecological dysbiosis that may influence host metabolic
   outcomes.

Fig. 6.

   [86]Fig. 6
   [87]Open in a new tab

   Co-abundance correlation analysis of microbial taxa in diabetic
   subjects. (A) Bar plot showing the number of statistically significant
   pairwise correlations per taxon, colored by phylum. The height of each
   bar represents the number of significant associations that the taxon
   forms with others (Spearman correlation, FDR-adjusted p < 0.05). (B)
   Histogram depicting the distribution of taxa by degree (number of
   connections), stratified by phylum. (C) Donut chart illustrating the
   directionality of significant correlations. Positive correlations
   (blue) accounted for 73.1%, while negative correlations (red)
   represented 26.9%, indicating that most microbial interactions in the
   diabetic group were co-occurring rather than mutually exclusive.

Discussion

   The study investigated the gut microbiota composition and metabolomic
   profiles of stool samples collected from 10 individuals diagnosed with
   Diabetes Mellitus (DM) and 10 non-diabetic controls in Egypt.
   Significant differences in microbial and metabolic characteristics
   between the two groups were identified, supporting the increasing
   evidence that changes in gut microbiota are linked to metabolic
   disorders, such as DM. The notable variations in microbial diversity,
   composition, and metabolite profiles between the diabetic and control
   groups emphasize the intricate relationship between gut microbiota and
   host metabolism, particularly in the context of diabetes. The microbial
   diversity analysis showed a significantly higher Shannon diversity
   index and evenness in the diabetic group, indicating a more diverse and
   evenly distributed microbial community. Our finding of increased
   Shannon diversity and evenness in the diabetic group contrasts with
   some Western studies but is consistent with reports from Egypt, Saudi
   Arabia, and the UAE, where higher alpha diversity has also been
   observed in diabetic individuals^[88]20–[89]23. This variation likely
   reflects population-specific factors such as diet, medication use, and
   baseline microbiota composition. Moreover, a recent meta-analysis
   reported no consistent trend in alpha diversity across diabetes
   studies, highlighting methodological and regional heterogeneity^[90]24.
   These results suggest that elevated diversity in diabetes may reflect
   dysbiosis rather than microbial health.

   The observed clustering in the Principal Coordinate Analysis (PCoA)
   plots, regarding both Bray-Curtis dissimilarity and unweighted UniFrac
   distances, highlights significant differences in the overall
   composition and phylogenetic structure of the gut microbiota between
   diabetic and control individuals Taxonomic profiling further supports
   this compositional shift, which showed the enrichment of families such
   as Bacteroidaceae and Lachnospiraceae in the diabetic group and
   Streptococcaceae in the control group. This finding aligns with
   previous studies that link metabolic disorders, such as diabetes, to
   shifts in gut microbial diversity^[91]25,[92]26. Conversely, we
   observed an enrichment of Intestinibacter and Lachnospiraceae NK4A136
   group in the gut microbiota of DM patients. Intestinibacter has been
   previously linked to metabolic dysfunction and inflammation, with some
   studies suggesting its association with reduced SCFA production and
   increased gut permeability, both of which are implicated in diabetes
   progression^[93]27. These results indicate that diabetes is associated
   with profound changes in gut microbial ecosystems, particularly in taxa
   that are known to influence metabolic health and inflammation.

   LEfSe analysis revealed specific microbial taxa with differential
   abundance between two groups. Increased levels of Bacteroides and
   Lachnospiraceae in diabetic individuals are consistent with findings
   that link these taxa to inflammation and metabolic
   dysregulation^[94]26,[95]28. In contrast, the higher presence of
   Streptococcus in the control group may indicate a protective role in
   gut homeostasis. These microbial signatures offer insights into how gut
   microbiota may affect metabolic processes in diabetes.

   The functional implications of these microbial shifts were further
   explored through the predicted functional pathway analysis and
   metabolite profiling. Pathway enrichment analysis highlighted several
   critical metabolic pathways significantly enriched in diabetic
   individuals, including those involved in fatty acid metabolism, glucose
   homeostasis, bile acid metabolism, and amino acid biosynthesis. These
   findings suggest that alterations in host metabolic processes accompany
   changes in the gut microbiota. For instance, the enrichment of fatty
   acid elongation and β-oxidation pathways in diabetic individuals
   indicates disrupted lipid metabolism, a hallmark of metabolic disorders
   such as type 2 diabetes^[96]29. Similarly, the enrichment of glucose
   metabolism pathways in the diabetic group reflects the impaired glucose
   regulation associated with diabetes.

   Bile acid metabolism represents another critical pathway that is
   significantly enriched in the diabetic group. Bile acids play a vital
   role in lipid digestion and glucose metabolism, and their dysregulation
   has been associated with metabolic diseases^[97]30. The increased
   presence of bile acid-related pathways in diabetic individuals may
   contribute to the observed metabolic disturbances. Furthermore, the
   enrichment of amino acid biosynthesis and transport pathways,
   particularly for branched-chain amino acids (BCAAs), offers additional
   evidence of altered nutrient metabolism in diabetes. Elevated BCAA
   levels have been linked to insulin resistance and metabolic
   dysfunction^[98]31, indicating that these pathways may influence the
   metabolic phenotype of diabetes.

   Changes in stool metabolomic profiles were observed alongside these
   microbial alterations. The metabolite abundance heatmap reveals further
   metabolic differences between the diabetic and control groups.
   Individuals with diabetes exhibited higher levels of metabolites
   associated with enriched pathways, such as fatty acid derivatives,
   glucose-related intermediates, and bile acids. These metabolites
   strengthen the idea that diabetes involves significant metabolic
   changes, especially in lipid and glucose metabolism. The clustering of
   these metabolites in diabetic individuals indicates that distinct
   metabolite signatures may serve as potential biomarkers for diabetes
   and its progression.

   We conducted a co-abundance correlation analysis to further explore
   microbial dysbiosis, which revealed a predominance of positive
   associations among taxa in the diabetic group. This shift suggests
   altered microbial interactions and increased co-occurrence,
   particularly among Firmicutes and Bacteroidota. These findings
   highlight a reorganization of microbial networks in diabetes,
   reflecting ecological dysbiosis that may influence metabolic stability
   and host-microbiota interactions.

   Our study offers promising insights into the link between gut
   microbiota and diabetes, though it does have limitations. The small
   sample size of 10 individuals per group may limit generalizability,
   highlighting the necessity for larger, more diverse cohorts to validate
   our findings and capture individual variability. Importantly, our
   metabolomic analysis underscores the functional impacts of changes in
   gut microbiota. Further research using integrated multi-omics
   approaches is crucial to elucidate the mechanistic connections between
   microbial taxa and metabolic pathways. Tackling these aspects can
   enhance understanding and contribute to developing microbiome-based
   therapies to restore metabolic balance in diabetic patients.

   This study’s combined microbiome and metabolomic analyses illustrate a
   complex interplay between gut microbial communities and host metabolism
   in diabetes. Changes in microbial composition, alongside modifications
   in essential metabolic pathways, underscore the possible influence of
   gut microbiota on the metabolic framework of diabetes. These results
   lay the groundwork for future investigations aimed at creating
   microbiome-targeted treatments to restore metabolic balance in
   individuals with diabetes.

Acknowledgements