- Research
- Open access
- Published:
Microbial and proteomic signatures of type 2 diabetes in an Arab population
Journal of Translational Medicine volume 22, Article number: 1132 (2024)
Abstract
Background
The rising prevalence of Type 2 diabetes mellitus (T2D) in the Qatari population presents a significant public health challenge, highlighting the need for innovative approaches to early detection and management. While most efforts are centered on using blood samples for biomarker discovery, the use of saliva remains underexplored.
Methods
Using noninvasive saliva samples from 2974 Qatari subjects, we analyzed the microbial communities from diabetic, pre-diabetic, and non-diabetic participants based on their HbA1C levels. The salivary microbiota was assessed in all subjects by sequencing the V1–V3 regions of 16S rRNA gene. For the proteomics profiling, we randomly selected 50 gender and age-matched non-diabetic and diabetic subjects and compared their proteome with SOMAscan. Microbiota and proteome profiles were then integrated to reveal candidate biomarkers for T2D.
Results
Our results indicate that the salivary microbiota of pre-diabetic and diabetic individuals differs significantly from that of non-diabetic subjects. Specifically, a significant increase in the abundance of Campylobacter, Dorea, and Bacteroidales was observed in the diabetic subjects compared to their non-diabetic controls. Metabolic pathway prediction analysis for these bacteria revealed a significant overrepresentation of genes associated with fatty acid and lipid biosynthesis, as well as aromatic amino acid metabolism in the diabetic group. Additionally, we observed distinct differences in salivary proteomic profiles between diabetic and non-diabetic subjects. Notably, levels of Haptoglobin, Plexin-C1, and MCL-1 were elevated, while Osteopontin (SPP1), Histone1H3A (HIST3H2A), and Histone H1.2 were reduced in diabetic individuals. Furthermore, integrated correlation analysis of salivary proteome and microbiota data demonstrated a strong positive correlation between HIST1H3A and HIST3H2A with Porphyromonas sp., all of which were decreased in the diabetic group.
Conclusion
This is the first study to assess the salivary microbiota in T2D patients from a large cohort of the Qatari population. We found significant differences in the salivary microbiota of pre-diabetic and diabetic individuals compared to non-diabetic controls. Our study is also the first to assess the salivary proteome using SOMAScan in diabetic and non-diabetic subjects. Integration of the microbiota and proteome profiles revealed a unique signature for T2D that can be used as potential T2D biomarkers.
Introduction
Diabetes is a global health problem, with 529 million cases reported in 2021 [1]. According to the International Diabetes Federation, this number is expected to reach 783.2 million by 2045 [2]. Diabetes significantly elevates the risk of cardiovascular diseases and leads to several complications, such as retinopathy, diabetic kidney disease, and peripheral neuropathy [3]. The majority of diabetes cases are attributed to type 2 diabetes (T2D), which accounts for 96% of all diabetes cases in the world [1]. T2D is a chronic metabolic disorder characterized by dysregulated blood glucose levels, impairment in the β-cells function, insulin resistance, and systemic inflammation [4].
Qatar has the highest prevalence of diabetes among the elderly, with 76.1% of individuals aged 75–79 affected, compared to 24.4% globally in the same age group [1]. The country is facing a significant social and healthcare burden due to diabetes, with prevalence among adults aged 20–79 years projected to rise from 17.8% in 2023 to around 30% by 2050 [5, 6]. This increasing trend in T2D is largely attributed to several factors, including unhealthy diet, sedentary lifestyle [1, 7], as well as obesity that accounts for 57.5% of the T2D cases [6]. Diabetes expenditure is expected to reach nearly one-third of national health expenditure by 2050 [8, 9]. Currently, T2D diagnosis relies on invasive blood tests, including hemoglobin A1c (HbA1c) and the oral glucose tolerance test [10]. Biomarkers have the potential to enhance disease management, prognosis, and the prediction of diabetes-related complications [11]. Early detection and proper management can prevent and, in some cases, reverse T2D, underscoring the urgent need for rapid, non-invasive biomarkers for early prediction and detection [1].
The rapid advancement of Omics technologies has significantly accelerated the discovery of new biomarkers and enhanced our understanding of disease complexity [12]. T2D is characterized by a heterogeneous and complex etiology, influencing its progression, complications, and treatment [13, 14]. T2D is caused by a complex interplay between genetics and environmental factors [10]. Among the environmental factors, the human microbiome has gained more focus in T2D research [15]. The microbiome has been implicated in the development of insulin resistance and the pathogenesis of T2D [15]. Comprising thousands of different taxa that colonize various body sites, the human microbiome is dynamic and plays a crucial role in physiological processes and interactions with the host immune system [16,17,18]. On the other hand, the immune system governs the interplay between the host and the microbiome [18]. Change in the host microenvironment can alter the microbiome composition [19], potentially leading to systemic inflammation [15]. Most microbiome studies use 16S rRNA sequencing, to assess the microbiome composition and diversity in a given site [20,21,22]. These studies have revolutionized our knowledge of the human microbiome and its role in health disease [23]. Most studies examining the link between the microbiome and T2D focused on the gut microbiome [24,25,26]. However, there is limited research on the salivary microbiome and its association with T2D [27]. In healthy subjects, the core salivary microbiome includes genera such as Streptococcus, Veillonella, Neisseria, and Actinomyces [28, 29]. A large-scale Japanese study identified dominant genera in the salivary microbiome such as Streptococcus, Neisseria, Rothia, Prevotella, Actinomyces, Granulicatella, Haemophilus and Porphyromonas [30]. In our previous study aiming to characterize the salivary microbiome composition in the Qatari population [31, 32], we showed that Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria were the common phyla isolated from saliva samples, with Bacteroidetes being the most predominant phylum [31]. Dysbiosis in the salivary microbiome has been linked to oral diseases [33,34,35] as well as systemic diseases like obesity, cardiovascular diseases and diabetes [36,37,38]. Compared to healthy individuals, those with T2D exhibit significant differences in salivary microbiome biodiversity, including increased levels of Streptococcus and Lactobacillus [39]. In a large cohort study comparing the salivary microbiomes of diabetic and non-diabetic adults with periodontal disease, the diabetic group showed a higher abundance of Olsenella, Mitsukella, Peptidiphaga, Bifidobacterium, Abiotrophia, Firmicutes, Veillonellaceae, Neisseriaceae, Actinobacterium, and Corynebacterium [40]. Another study aiming to explore the shift in the salivary microbiome during the onset and post-treatment of T2D, revealed that Blautia wexlerae, Lactobacillus fermentum, Nocardia coeliaca and Selenomonas artemidis levels decreased in treated T2D patients compared to the non-treated patients [41].
In addition to its microbial content, saliva comprises minerals, buffers, electrolytes, and proteins derived from local cells and the bloodstream [42]. While dysbiosis in the salivary microbiome has been linked to T2D, understanding the changes in the salivary proteome can provide further insights into the disease’s pathology. The salivary proteome includes a diverse array of proteins such as immunoglobulins, glycoproteins, enzymes, and hormones, that play crucial roles in maintaining oral homeostasis [43, 44]. Given its rich and varied composition, saliva presents a promising source for biomarker discovery. It is also a non-invasive and easily collectible sample, enhancing its potential for diagnostic applications [42, 45]. Despite these advantages, research on salivary biomarkers for specific diseases remains limited compared to the extensive use of blood samples for protein biomarker discovery. Blood is favored due to its high concentration of molecules, whereas saliva, though less examined, may offer unique advantages. Proteins found in saliva are often biologically active at the cellular level, potentially making the saliva proteome more dynamic and clinically relevant [10, 46, 47]. Although there is a clear link between T2D and the salivary microbiome, its interaction with other factors, such as the salivary proteome, remains largely unexplored. Integrating salivary proteomics with microbiota analysis could provide a more comprehensive understanding of T2D, offering new tools for identifying predictive biomarkers panels, and novel ways for disease diagnosis and management [10, 20, 43, 48]. In this study, we combined 16S rRNA sequencing with both saliva and plasma proteomics to explore the interactions between the salivary microbiota and proteomic profiles in a large cohort of Qatari individuals with T2D. This study represents the first comprehensive assessment of the salivary microbiota in a large T2D cohort and the first to integrate both salivary microbiota and proteomic profiles in this context. Our findings identified distinctive microbial and protein signatures associated with T2D, which could potentially be utilized for noninvasive detection and monitoring of the disease. This study offers new insights into the relationship between host responses and the salivary microbiota in T2D, paving the way for improved early diagnostic and management strategies.
Materials and methods
Study cohort
The study was approved by the Institutional Review Board (IRB) of Sidra Medicine (protocol #1510001907) and Qatar Biobank (QBB) (protocol #E/2018/QBB-RES-ACC-0063/0022). All participants provided informed consent before sample collection. All experiments were conducted in accordance with approved guidelines and the Declaration of Helsinki. A collaboration agreement between QBB and Sidra Medicine facilitated the collection of the coded saliva samples, along with associated phenotypic and clinical data from a total of 2974 Qatari participants that were selected randomly. All participants were 18 years old and above. No exclusion criteria were applied in this studied cohort. The cohort consisted of 1430 males and 1544 females (Table 1). All participants answered the baseline questionnaire to describe their medical history and dietary habits. Based on their HbA1C levels and treatment details, all participants were categorized into non-diabetic (HbA1C < 5.7%), pre-diabetic (HbA1C between 5.7–6.5%), and diabetic (HbA1C > 6.5%).
Total salivary DNA extraction
Saliva samples were collected by QBB according to a standard technique as described previously [31]. In brief, a total of 5 mL of spontaneous, whole, unstimulated saliva was collected into a 50 mL sterile DNA-free Falcon tube from each participant. The samples were divided into 0.4 mL aliquots and stored at − 80 °C until further analysis. The aliquots were received from QBB for total salivary microbial DNA extraction, as shown in Fig. 1. Total salivary microbial DNA was extracted using an automated QIAsymphony protocol (Qiagen, Hilden, Germany) following the manufacturer’s instructions (Fig. 1) [49]. DNA purity was evaluated by the A260/A280 ratio using a NanoDrop 7000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
Summary of the experimental workflow used
16S rRNA gene sequencing and data analysis
The V1–V3 regions of the 16S rRNA gene were amplified using Illumina Nextera XT library preparation Kit (FC-131-1002, Illumina Inc., San Diego, CA, USA) (Fig. 1). In brief, PCR was performed in triplicate in a 50 μL reaction mixture containing 10 ng of template DNA and 2× Phusion HotStart Ready Mix (Thermo Scientific™). The following thermal cycling conditions were used: 5 min of initial denaturation at 94 °C; 25 cycles of denaturation at 94 °C for 30 s, annealing at 62 °C for 30 s, extension at 72 °C for 30 s; and a final extension at 72 °C for 10 min. The amplified PCR products were then purified using the Illumina 16S metagenomic sequencing library preparation protocol #15044223B as per the instructions using Agencourt Magnetic beads [50]. The dual Index PCR was performed using the same protocol and purified as per the instructions [50]. Equimolar concentrations of each purified libraries were pooled, and the concentration and size of the libraries were measured using Qubit 3.0 Fluorometer and Agilent Bioanalyzer, respectively. High throughput sequencing was performed on an Illumina MiSeq2×300 platform in accordance with the manufacturer’s instructions. Image analysis and base calling were carried out directly on the MiSeq. The sequenced data were demultiplexed using the MiSeq Control Software (MCS), processed, and analyzed as described in the previous work using QIIME v1.9.0 pipeline [31, 51]. Operational taxonomic units (OTUs) were generated by aligning against the Greengenes database (Version 13_8) with a confidence threshold of 97% [52]. After profiling the salivary microbiota, all participants from the non-diabetic group were age and gender-matched for further analysis.
Salivary microbiota diversity and functional prediction analysis
Alpha diversity measures, including Chao1, Observed, Shannon, and Simpson indices, were calculated with R software, using the phyloseq package [53]. Beta diversity was measured using phylogenetic beta diversity metrics and non-phylogenetic beta diversity metrics. Differences in the beta diversity were presented as principle coordinate analysis using QIIME [52]. The dysbiosis score has been calculated based on median community level variation using dysbiosisR package [54, 55].
The metagenome KEGG orthologs (KOs) [56] of the analyzed samples were predicted with the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) tool [57], against the OTUs present in the Greengenes database [52]. The detected KOs were then collapsed to the pathway level (KEGG level 3) using PICRUSt. The obtained profiles of functional pathways were further analyzed with Kruskal Wallis and Tukey–Kramer post hoc analysis. These were then corrected for multiple testing with the Bonferroni method using the software package statistical analysis of functional profiles (STAMP) [58].
SOMAscan analysis
The salivary proteome was characterized using the SOMAscan platform as has been described previously [59,60,61] (Fig. 1). Briefly, each SOMAmer® reagent binds a target protein (in total 1317 proteins). The SOMAscan assay utilizes different dilution bins for SOMAmers depending on the sample matrix, ensuring that analyte concentrations remain within the assay’s linear range. In conventional matrices like plasma and serum, SOMAmers are distributed across 0.05%, 1%, and 40% dilution bins. However, for non-conventional matrices such as saliva, established dilution bins are not available; thus, samples are typically analyzed at a single dilution. To determine the optimal saliva dilution, we conducted a SOMAscan assay with all SOMAmers in pooled and individual saliva samples serially diluted from 40 to 0.3125%. Our goal was to identify a dilution that placed assay values in the middle of the dynamic range for each SOMAmer. We found that a 10% saliva dilution in assay buffer was optimal. The SOMAscan assay was performed following the manufacturer’s cell and tissue protocol instructions. To correct for systematic effects introduced during hybridization, relative fluorescence unit values were normalized against a hybridization control, calculated by pooling samples from different plates. Median normalization was applied across all samples within the arrays, and all samples met the signal intensity variance criteria based on the hybridization controls.
To characterize the salivary proteome using SOMAScan, we randomly selected 50 diabetic subjects and matched them by age and gender with 50 non-diabetic subjects, excluding those in the pre-diabetic category. The raw fluorescence data of 1317 proteins were first normalized via quantile normalization using the “normalizeBetweenArrays” from the limma package (v3.56.2) [62]. UMAP analysis indicated that BMI and age strongly influenced sample segregation. Consequently, we performed a differential expression analysis with limma, incorporating age and BMI into the design matrix. The ‘lmFit’ function was used for multiple linear regression, followed by the ‘eBayes’ function with the parameter ‘robust = TRUE’ to compute moderated t-statistics, F-statistics, and log-odds ratios. Proteins with p-values < 0.05 and at least a 50%-fold change between diabetic and non-diabetic groups in either plasma or saliva were selected. Additionally, we identified differential proteins that showed consistent expression trends between the two tissues and significant statistical changes in both as initial biomarker candidates. For visualization, we first regressed out the effects of age and BMI. Samples were then clustered into two groups based on UMAP representations of low and high age/BMI values. Cluster IDs were used to regress out the effects of age and BMI using the ‘removeBatchEffect’ function from the limma package.
The enrichGO function from the R/Bioconductor clusterProfiler package (v4.8.3) [63] was employed to conduct Gene Ontology (GO) and pathway enrichment analysis focusing on Biological Process ontologies. Only GO terms exhibiting an adjusted p-value < 0.05 were included in the analysis. Subsequently, GO enrichment plots were generated utilizing the ggplot2 package.
Estimation of protein biomarkers importance using machine learning models
The following machine learning models: Random Forest (RF) [64], Elastic-net (eNet) [65], Partial least squares via mixOmics (pls) [66], XGBoost [67], and Radial Basis Function (RBF) kernel SVM [68] were respectively used to estimate the predictive importance of each marker in classifying diabetic and non-diabetic groups. We used the tidymodels R package to train the different models [69]. Each model was trained using repeated cross validation [4, 9]. To avoid label unbalancing during training, the different cross-validation subsets were generated in a stratified manner. Hyper parameter tuning was done using a grid search algorithm. The RF, eNet, and pls models had the highest performance in all tissues (plasma, saliva) and were selected to calculate the mean importance of each marker in the three models. The variable importance of each model was scaled to be within [0,1].
Statistical analysis and visualization
The demographic and clinical data of the study cohort were analyzed using GraphPad Prism (10.1.2). Mann–Whitney U tests were utilized to compare variables, including age, BMI, systolic and diastolic blood pressure, glucose level, HbA1C, lipid profile, total protein, albumin, urea, and creatinine. Additionally, the Chi-square test was employed to compare the impact of smoking and sex between the diabetic and non-diabetic groups. Statistical significance was set at p-values less than 0.05.
Linear Discriminant Analysis Effect Size (LEfSe) [70] was used to find differentially abundant taxa between the studied categories, with per-sample normalization to 1 million, an alpha cut-off value of 0.05 for the Kruskal–Wallis factorial test, and a threshold for discriminative features at a logarithmic LDA score > 2. P-values less than 0.05 were considered statistically significant. Analysis of similarities software (Anosim) was used to calculate the distance matrix difference between the categories included in this study (non-diabetic, pre-diabetic, and diabetic) using unweighted beta diversity parameters [51].
All statistical analysis were conducted using R version 4.3.1, with the limma package (version 3.56.2) [62]. The visualization of the results was carried out using ggplot2 and ComplexHeatmap R packages [71].
Integrative data analysis between proteins and salivary microbiota
The integration of matched salivary microbiota and SOMAscan proteomics data was conducted using multi-block sparse partial least squares discriminant analysis (sPLS-DA), also known as DIABLO [66, 72]. For this analysis, the normalized proteomics values and the centered log-ratio (CLR) normalized 16S relative abundance values at the genus level were used. Furthermore, the transformed data were used with the “tune.block.splsda” function with twofold repeated (n = 5) cross-validation under the “centroids.dist” metric to determine the number of feature needed for each dimension (three dimensions were used). Subsequently, the “block.splsda” function was used to get the integration results. To identify the most strongly associated omics pairs, the association matrices were calculated using mixOmic [66] and kept all the protein-bacteria pairs with absolute association values exceeding 75% percentile. The selected pairs were used to build the association network.
Estimation of the predictive power of each omic
To assess the predictive power of each omic, we calculated the Area Under the Curve of the Receiver Operating Characteristics curves (AUC-ROC). For single omics, the AUC-ROC was calculated using DIABLO component 1 weights using mixOmics::auroc function, with parameters roc.comp = 1 and roc.block = 1 or 2, depending on the omic. For the combined features, we used DIABLO weighted predictions to calculate the AUC-ROC score. An AUC-ROC closer to 1 indicates good performance, and AUC-ROC of 0.5 indicates random prediction behavior.
Results
Demographic and clinical parameters of the study population
The study population comprised 2974 Qatari participants from the Qatar Genome Project [49]. Based on their HbA1C levels and treatment history, participants were classified into three groups: non-diabetic, pre-diabetic, and diabetic. Of these, 2166 were non-diabetic, 349 were pre-diabetic, and 459 were diabetic (Table 1). The average age of participants in the diabetic group (49.42 ± 0.504 years) was significantly higher than those in the pre-diabetic (44.84 ± 0.529 years) and non-diabetic (34.40 ± 0.124 years) groups (Table 1). The body mass index (BMI), systolic and diastolic blood pressure were significantly lower in the non-diabetic group compared to the pre-diabetic and diabetic groups (Table 1).
Among the blood parameters tested, bicarbonate, calcium, dehydroxyvitamin D, fibrinogen, folate, thyroxine, glucose, HbA1C, insulin, magnesium, potassium, triglycerides, and urea were significantly elevated in the diabetic group compared to the other two groups (Table 1). Conversely, albumin, bilirubin, chloride, HDL-cholesterol, and sodium were significantly higher in the non-diabetic subjects, while alkaline phosphatase, ALT, AST, cholesterol, C-peptide, creatinine, hemoglobin, LDL-cholesterol, magnesium, total iron binding capacity and total proteins were significantly higher in the pre-diabetic group (Table 1).
The salivary microbiota composition reveals signatures and biomarkers for T2D
The sequencing of the 16S rRNA amplicons resulted in approximately 123 million reads [122] across 2974 samples with a median read count of 44,914 per sample. After filtering and alignment, an average of 41,452 reads per sample were assigned to 4813 operational taxonomic units (OTUs). Analysis of the 16S microbial sequence data generated from all the participants revealed 22 bacterial phyla, 46 classes, 87 orders, 173 families, and 390 genera.
Participants were categorized into three groups based on HbA1C levels and treatment methods: non-diabetic, pre-diabetic, and diabetic. In the saliva samples from Qatari subjects, the most abundant phyla were Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria, which together accounted for approximately 90% of the total microbial abundance (Fig. 2A). Similar to our previous findings, our data showed that Streptococcus, Prevotella, Porphyromonas, Granulicatella, Veillonella were identified among the core genera of the salivary microbiota (Fig. 2B). Alpha diversity parameters including Observed, Chao1, Shannon, and Simpson indices were measured. Both observed (p = 0.034) and Chao1 (p = 0.044) indices were significantly lower in the pre-diabetic group compared to the non-diabetic group (Fig. 2C). However, Shannon, and Simpson indices did not show any significant differences (Fig. 2C). Beta diversity analysis using the Bray–Curtis distance matrix revealed no significant difference among the non-diabetic, pre-diabetic, and diabetic groups (Fig. 2D). The dysbiosis score of median microbial community level variation—CLV showed that diabetic group reflects a significantly higher dysbiosis score compared to the pre-diabetic (p < 0.01) and non-diabetic (p < 0.01) groups (Fig. 2E). We further divided the diabetic group into treated (n = 334) and non-treated (n = 125) subgroups based on their medication or treatment history (Figure S2). There was no significant difference observed in both alpha and beta diversity analysis among the study groups (Figure S2C and S2D). But CLV-based dysbiosis score showed a significant increase in dysbiosis in both treated and non-treated diabetic subjects compared to pre-diabetic and non-diabetic subjects (Figure S2E).
Salivary microbiota signature in non-diabetic, pre-diabetic and diabetic groups. A Salivary microbiota composition at the phylum level. B At Genus level. Y-axis shows % of relative abundance of the microbiota; X-axis indicates the non-diabetic, pre-diabetic and diabetic groups. C Alpha diversity measures of salivary microbiota of the study groups. D Principal Coordinates Analysis (PCoA) based on Bray–Curtis distances of salivary microbiota. Axes were scaled to the amount of variation explained. The Wilcoxon test was used to compare the two groups. p-value < 0.05 was considered significant. E Salivary microbiota dysbiotic scores using median CLV approach to the non-diabetic, pre-diabetic and diabetic groups. The Wilcoxon test was used to compare the two groups. p-value < 0.05 was considered significant. F Graphs of linear discriminant analysis (LDA) scores for differentially abundant bacterial genera between non-diabetic (green) and pre-diabetic (yellow) groups. G Graphs of linear discriminant analysis (LDA) scores for differentially abundant bacterial genera between non-diabetic (green) and diabetic (red) groups. H Graphs of linear discriminant analysis (LDA) scores for differentially abundant bacterial genera between non-diabetic (green) and diabetic (red) groups. I Graphs of linear discriminant analysis (LDA) scores for differentially abundant bacterial genera between pre-diabetic (yellow) and diabetic (red) groups. Features with LDA scores ≥ 2 are presented
LEfSe was performed to identify the differentially abundant microbial markers among the three groups (Fig. 2). At the phylum level, no significant differences were observed in the abundances of Firmicutes (p = 0.923) and Bacteroidetes (p = 0.509) between the non-diabetic and diabetic groups using the Mann–Whitney test (data not shown). However, at the genus level, Akkermansia, Dialister, and Camphylobacter were significantly enriched in the non-diabetic group compared to the pre-diabetic group (Fig. 2F). Conversely, Treponema, Tannerella, Dorea and Mycoplasma were significantly elevated in prediabetic group (Fig. 2F). Bulleidia, Porphyromonas, Oribacterium, and members of Clostridiaceae and Weekellaceae were significantly more abundant in the non-diabetic subjects than diabetic subjects (p < 0.0001), whereas Dorea, Campylobacter, and unclassified Bacteroidales were significantly abundant in the diabetic group (p < 0.0001) (Fig. 2G). In the comparison between the pre-diabetic and diabetic groups, Oribacterium, Tannerella, members of Clostridiaceae, and Rikenellaceae were significantly more dominant in the pre-diabetic group (p < 0.0001) according to Lefse analysis (Fig. 2H). Although no significant differences were observed in the diversity parameters, notable variations were identified in the abundances of specific bacterial taxa between diabetic treated and non-treated groups. Specifically, Microbacterium, Coprococcus and Sutterella were significantly more abundant in the non-treated group while Leptotrichia, Butyrivibrio and Aggregatibacter were significantly abundant members of the diabetic treated groups (Figure S2K).
Given the observed differences in microbial profiles among non-diabetic, pre-diabetic, and diabetic subjects, we explored the potential functional roles of these microbes in diabetes pathogenesis using PICRUSt for functional predictions. Our analysis revealed significant differences in the estimated functional capabilities of the salivary microbiota across the three groups (Supplementary Figure S1A–C). PICRUSt analysis indicated a notable increase in microbes associated with betalain biosynthesis and melanogenesis pathways in the non-diabetic group compared to the pre-diabetic group (Figure S1A). In contrast, the diabetic group exhibited elevated microbial contributions to fatty acid and lipid biosynthesis, as well as retinol and sulfur metabolism, relative to the non-diabetic group (Figure S1B). Additionally, microbial sequences related to melanogenesis, betalain biosynthesis, and Notch signaling pathways were significantly higher in the pre-diabetic group compared to the diabetic group (Figure S1C).
Plasma and saliva proteome in diabetic subjects
To investigate the host proteome in diabetes, we employed a high-throughput SOMAscan platform to analyze the proteomic profiles in the plasma and saliva of diabetic and non-diabetic subjects. Among the 1317 proteins examined, 187 plasma proteins and 134 saliva proteins exhibited significant differences (p-value < 0.05) between diabetic and non-diabetic groups. We focused on proteins with at least a 50% fold change and visualized these differences using heatmaps (Fig. 3).
Proteomic signature in the diabetic and non-diabetic groups. Hierarchical clustering heatmaps of proteins that are differentially expressed between the diabetic and non-diabetic groups in plasma (A) and saliva (B). The cohort age, BMI, systolic blood pressure (BP), diastolic blood pressure (BP), smoking, and gender are shown. Samples are clustered using ward. D2 hierarchical clustering method. Key: red = up-regulated, blue = down-regulated. Proteins with p-values < 0.05 and at least 50%-fold change are shown. Pathways enrichment analysis for the differential proteins in the diabetic and non-diabetic groups. The 10 most enriched Gene Ontology (GO) terms in plasma (C) and saliva (D) are listed. The circle size represents the ratio of proteins/genes. The enrichment level is indicated by the color bar (blue to red) representing the p-value (0.050 to 0.000). E Expression level of proteins with correlated enrichment in both plasma and saliva of diabetic and non-diabetic groups. The Wilcoxon test was used to compare the two groups. p-value < 0.05 was considered significant
In the plasma, 28 proteins were differentially expressed, with 20 proteins showing increased levels and 8 proteins showing decreased levels in diabetic subjects (Fig. 3A). In saliva, 47 proteins were differentially expressed, with 40 proteins increased and 7 proteins decreased in diabetic subjects (Fig. 3B).
We identified 8 shared protein biomarkers between saliva and plasma (Fig. 3E). Notably, Interleukin-19 (IL-19), Mcl-1 (Induced myeloid leukemia cell differentiation protein), Haptoglobin (HP), and Plexin-C1 (PLXNC1) were significantly elevated in diabetic subjects. Conversely, Osteopontin (SPP1), Histone H3.1 (HIST1H3A), and Histone H1.2 (HIST1H1C) were significantly decreased in the diabetic group (Fig. 3E).
Pathway enrichment analysis was conducted using the enrichGO function from the R/Bioconductor clusterProfiler to elucidate the pathways associated with differentially expressed proteins. This analysis revealed distinct pathways for plasma and saliva proteins, with 10 unique pathways identified for each (Fig. 3C, D). The only two pathways shared between the plasma and saliva proteins in diabetic subjects were the “Reactive oxygen species metabolic process” and “Regulation of reactive oxygen species metabolic process” pathways (Fig. 3C, D). Interestingly, the “Insulin receptor signaling pathway” was uniquely enriched in the saliva proteins of diabetic subjects.
Integration of salivary microbial and protein biomarkers for T2D detection
An integrative analysis combining salivary microbiota, plasma proteome, and salivary proteome was conducted using mixOmics to identify potential biomarkers for T2D. The sparse Partial Least Squares Discriminant Analysis (sPLS-DA) revealed key associations between the microbiota and proteome markers.
In non-diabetic subjects, the top microbiota markers included Porphyromonas Clostridiaceae, Enterobacteriaceae, and Weeksellaceae, while the top plasma protein markers were PRSS1, SPP1, HIST2H2BE, and EDA2R (Fig. 4A). In contrast, for diabetic subjects, Kingella, Shuttleworthia, and Aerococcaceae were prominent microbiota markers, with PLXNB2, LPO, CD36, HP, ENPP7, RASA1, CRP, LEPR, MYC, ASAH2, EDA2R, and FUT3 as the top plasma protein markers (Fig. 4A). The association cut-off ranged from ± 0.17 to ± 0.78.
Integration between plasma and salivary protein markers with salivary microbiota. A Top associated plasma protein markers of diabetic group (left panel) and top associated microbiota markers (right panel). B Network analysis of top associated plasma protein markers with salivary microbiota markers. Green edges indicate positive association, red edges indicate negative association. C Receiver operating characteristic (ROC) curve for the model of salivary microbiota, proteome and both markers. Orange color indicates salivary microbiota markers, blue color indicates plasma proteome markers, and black color indicates the model of both proteome and microbiota markers together. The x-axis and y-axis represent specificity and sensitivity, respectively, for the tested markers. D Top associated salivary protein markers of diabetic group (left panel) and top associated microbiota markers (right panel). E Network analysis of top associated salivary protein markers with salivary microbial markers. Green edges indicate positive association, red edges indicate negative association. F Receiver operating characteristic (ROC) curve for the model of salivary microbiota, proteome, and both markers. Orange color indicates salivary microbial markers, blue color indicates salivary proteome markers, and black color indicates the model of both proteome and microbial markers together. The x-axis and y-axis represent specificity and sensitivity respectively, for the tested markers
Among the significant findings, Porphyromonas was negatively correlated with CRP, LPO, HP, and ASAH2 but positively associated with PRSS1 and SPP1 proteins (Fig. 4B). Unclassified members of Clostridiaceae showed a negative association with LEPR and HP, but a positive association with SPP1 (Fig. 4B). The Area Under the Receiver Operating Characteristic curve (AUROC) analysis demonstrated that the integration of both proteome and microbiota biomarkers achieved a higher AUC of 0.9588 compared to microbiota alone (AUC = 0.7264) and proteome alone (AUC = 0.924) (Fig. 4C).
Further analysis of salivary proteome and microbiota integration identified HIST3H2A and SFN as top-associated protein markers, with Porphyromonas, Enterobacteriaceae, Clostridiaceae, Gemellaceae, and Weeksellaceae as the key microbiota markers in non-diabetic groups. For diabetic subjects, the prominent protein markers were NME2, ALDOA, TYMS, PLXNC1, CASP3, LUM, AKR7A2, and UBB, while the top microbiota markers were Kingella, Sharpea, and Aerococcaceae (Fig. 4D). Network analysis revealed that Porphyromonas was negatively associated with CASP3, AKR7A2, and NME2, but positively associated with HIST1H3A and HIST3H2A. HP was positively associated with Shuttleworthia and negatively associated with Corynebacterium (Fig. 4E).
The AUROC analysis for salivary proteome and microbiota integration indicated that their combined use also yielded a higher AUC of 0.8152 compared to microbiota alone (AUC = 0.7432) and proteome alone (AUC = 0.7648) (Fig. 4F).
Discussion
The search for non-invasive tools to predict and prevent type 2 diabetes (T2D) has intensified, driven by the need for patient-friendly diagnostic methods. Saliva, with its advantages over blood samples—such as lower cost, reduced infection risk, and improved patient compliance—emerges as a promising medium for biomarker discovery. The primary aim of this study was to explore potential microbial and protein biomarkers associated with T2D using saliva samples [73,74,75]. We assessed the salivary microbiota and proteome in Qatari subjects with or without T2D, and we found promising candidate microbes and proteins that warrant future research to be tested as biomarkers of T2D. While some studies have looked at the changes in the salivary microbiota in patients with diabetes [40, 76, 77], none of these studies had come to a conclusion for defining a microbial signature associated with pre-diabetes or diabetes in a large population-based study using high throughput sequencing technology.
Our analysis of the salivary microbiota in a large cohort of 2974 Qatari participants revealed distinct microbial signatures associated with T2D. The dominant phyla identified—Firmicutes and Bacteroidetes—and genera such as Streptococcus, Prevotella, and Porphyromonas align with previous studies conducted in other populations [31, 32, 78,79,80,81,82]. We observed a higher species richness in the non-diabetic group compared to the diabetic and pre-diabetic groups, consistent with reports of dysbiosis in diabetes [83]. Significant changes in the salivary microbiota were observed in the prediabetic group. Differential abundance analysis revealed that Dorea and unclassified Bacteroidales (Fig. 2F, G) were significantly more abundant in both pre-diabetic and diabetic subjects compared to controls. Dorea, a saccharolytic anaerobe, has been positively linked to insulin-resistant carbohydrate metabolism in a study of host-microbe interactions in Japanese individuals with insulin resistance [84]. Notably, Dorea has also been implicated in inflammation, and its increased abundance in the gut microbiome of T2D patients has been negatively correlated with butyrate-producing bacteria [85]. In line with our findings, Dorea was significantly elevated in Chinese diabetic patients relative to controls and showed a negative correlation with butyrate-reducing bacteria such as Akkermansia and Bifidobacterium [85]. Additionally, a Mendelian randomization study found that members of the Bacteroidales order were positively associated with diabetic hypoglycemia [86]. Collectively, these findings suggest that both Dorea and unclassified Bacteroidales are involved early in the prediabetic stage and persist through the progression of T2D.
Furthermore, notable differences in the salivary microbiota were observed between treated and non-treated participants, particularly in the abundance of specific bacterial taxa. This aligns with findings from other studies that have reported shifts in the salivary microbiota of T2D patients following treatment interventions [87, 88]. These results underscore the modulating effect of medication on the salivary microbiota, reinforcing its potential as a tool for monitoring treatment efficacy and supporting its role in diagnostic applications.
Bacteria and their microbial products can influence the development of T2D through various interconnected and tightly regulated mechanisms controlling gut permeability, inflammatory regulation, and glucose metabolism [89]. In prediabetic subjects, our predictive functional analysis revealed a significant reduction in betalain biosynthesis and melanogenesis pathways compared to non-diabetic groups (Figure S1). Melanin plays an important role in reducing inflammation and oxidative stress by scavenging reactive oxygen species (ROS) [90, 91] which helps mitigate the harmful effects of oxidative damage. It has also been shown that glucose can inhibit melanogenesis in melanocytes [92]. Low melanin content was linked to diabetic retinopathy and neuropathy [92]. Betalains, known for their antioxidant properties, also have a hypoglycemic effect [93] which may help explain the observed reduction in betalain biosynthesis in both prediabetic and diabetic groups compared to non-diabetic individuals (Figure S1). This suggests that alterations in these metabolic pathways could be linked to the progression of T2D.
The differential microbes identified in T2D patients encode for proteins involved in fatty acid and lipid biosynthesis, as well as in the metabolism of phenylalanine, tyrosine, tryptophan, and sulfur. Previous research has established that fatty acids play a crucial role in the development of insulin resistance and T2D [94,95,96]. Elevated levels of fatty acids and triglycerides can interfere with the PI3K (Phosphatidylinositol 3-kinase) pathway by phosphorylating serine residues on IRS-1 (insulin receptor substrate), disrupting the balance between β-cell function and insulin resistance [97]. Aromatic amino acids such as phenylalanine, tyrosine, and tryptophan have been linked to the pathogenesis of diabetic nephropathy in other studies [98, 99]. Additionally, hydrogen sulfide (H2S), a gasotransmitter, impairs insulin secretion from β-cells by activating potassium channels and inhibiting calcium channels [100].
Current research on T2D protein biomarkers using high-throughput aptamer technology has primarily focused on blood-based markers [14, 101,102,103,104,105,106,107,108,109,110,111,112], with limited attention to saliva-based biomarkers and only a single study investigating urine-based biomarkers [113]. In this study, we identified 187 plasma proteins, and 134 saliva protein biomarkers associated with T2D in the Qatari population. Notably, four proteins—Haptoglobin (HP), Plexin-C1 (PLXNC1), Interleukin-19 (IL-19), and MCL-1—were elevated in both saliva and plasma samples from T2D patients. Similar to our finding, IL-19 and Plexin-C1 were significantly increased in T2D compared to controls in a proteome-wide examination for the T2D blood protein markers in a mixed cohort from Arab, Philippine, and South Asian populations [112]. Haptoglobin (HP), an acute-phase protein, increases in response to inflammation, oxidative stress, and high levels of free hemoglobin [114]. Elevated urinary haptoglobin levels in Asian and European subjects have been linked to a higher risk of mortality in T2D, reflecting significant inflammation and systemic oxidative stress [114]. Haptoglobin 2 precursor (Zonulin) has been associated with chronic inflammatory diseases, including diabetes, and is considered an indicator gut permeability [115]. Additionally, haptoglobin genotype is considered a risk factor for coronary artery disease in prediabetes [116] and could serve as a therapeutic target [117]. Under high glucose conditions, the glycated haptoglobin-hemoglobin complex acts as a weak antioxidant, leading to increased oxidative activity and the formation of reactive oxygen species that contribute to oxidized cholesterol [117]. Plexin-C1 (PLXNC1), a receptor for semaphorins, including Semaphorin 7A which increases with high glucose levels and is linked to retinal damage in diabetic mouse models [118]. Elevated PLXNC1 gene expression was observed in liver tissues collected from diabetic mice [119]. MCL-1 gene expression was notably different in T2D and identified as a potential biomarker for the disease using public gene databases [120]. Moreover, the MCL-1/miR-25 axis has been implicated in pancreatic β-cell apoptosis, a key feature of T2D pathogenesis [121].
The shared T2D protein biomarkers identified in this study are strongly associated with inflammation and oxidative stress, both of which are critical factors in the development and progression of diabetes. T2D is often described as a redox disease due to the elevated production of oxidants observed in diabetic conditions [122]. Increased oxidative stress contributes significantly to insulin resistance [123] and is also linked to obesity [84]. High levels of oxidative stress markers have been associated with a greater risk of cardiovascular and renal complications in T2D patients [123,124,125]. Therefore, identifying protein biomarkers related to oxidative stress is essential for understanding diabetes [123].
To investigate the association between the salivary microbiota, and the host plasma and saliva proteome in T2D, mixOmics was used to calculate the association values and generate the association networks. Data integration showed that Porphyromonas was among the top associated bacteria with both plasma proteins and saliva proteins (Fig. 4), noting that in the diabetic group, plasma and saliva proteins overlap in the oxidative stress pathway (Fig. 3C, D). Porphyromonas is a gram-negative anaerobe colonizing the oral cavity of healthy adults [126]. Our previous study on profiling the salivary microbiome across a large cohort of the Qatari population showed that Porphyromonas was among the top genera enriched [31]. The current study in T2D revealed an interesting shift in Porphyromonas abundance as it increased in the non-diabetic compared to the diabetic group (Fig. 2G). Oxidative stress induced by excess reactive oxygen species (ROS) in T2D can damage host and microbiome nucleic acids, lipids, and proteins [127]. Coupled with hyperglycemia in T2D, which causes glycosylation of host proteins and, as a result, promotes biofilm formation by bacteria [128]. Altogether, leading to an alteration in the proteome and salivary microbiome and indicates the influence of T2D and its pathophysiological changes on Porphyromonas in particular and the salivary microbiome.
In this study, we evaluated the predictive power of biomarkers from the salivary microbiota and host proteins (plasma and saliva) by calculating the AUC for each omic separately, as well as for their combined profiles. Our results demonstrated that the combined AUC value for integrating salivary microbiota and host protein biomarkers (plasma or saliva) surpassed the predictive power of each omic individually. This highlights the advantage of an integrative approach in biomarker discovery for complex diseases like T2D [20].
The enhanced predictive performance of the combined biomarkers can be attributed to the higher AUC observed for plasma protein markers (AUC = 0.924) compared to saliva protein markers (AUC = 0.7648). Plasma is widely used in protein biomarker research due to its comprehensive representation of body-wide protein expression, as it is a key component of various body fluids such as urine, cerebrospinal fluid, and amniotic fluid [10, 47]. Conversely, saliva proteins primarily originate from local salivary glands [91], which may limit their scope compared to plasma proteins [129].
Our data integration and predictive power analysis indicated that while all three omics-salivary microbiota, plasma proteome, and saliva proteome were effective in distinguishing between T2D and non-diabetic groups, the combination of salivary proteome and plasma proteome offered the highest predictive power. This finding underscores the value of incorporating multiple omic layers to improve the accuracy of T2D diagnostics.
The main limitation of the current study is its cross-sectional design, which did not allow for the tracking of T2D progression over time in the same subjects. Future longitudinal studies are needed to validate the observed changes in T2D biomarkers as the disease progresses and to further explore the impact of diet on salivary T2D biomarkers. Despite this limitation, the large cohort used in this study provided a greater number of subjects in each group compared to previous research [27, 40, 83] enabling a more comprehensive analysis. This large sample size allowed for a detailed examination of salivary microbiota across the three groups and demonstrated how salivary T2D biomarkers can reflect changes associated with different stages of the disease. The integrated multi-omics approach also revealed distinct salivary microbiota and proteomic signatures in individuals with diabetes, compared to controls, highlighting different pathways for T2D-related protein markers in both plasma and saliva samples. Importantly, this study identified significant alterations in the salivary microbiota as early as the pre-diabetic stage, with consistent changes observed as the disease progressed. These findings suggest the potential for developing an early, non-invasive, and easily accessible indicator for T2D progression, which could have important implications for early detection and monitoring of the disease.
Availability of data and materials
The sequencing data generated in this project can be accessed using NCBI-Bioproject accession no. PRJNA781451.
References
Collaborators GD. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global burden of disease study 2021. Lancet. 2023;402(10397):203–34.
Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183: 109119.
Tomic D, Shaw JE, Magliano DJ. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol. 2022;18(9):525–39.
Sanches JM, Zhao LN, Salehi A, Wollheim CB, Kaldis P. Pathophysiology of type 2 diabetes and the impact of altered metabolic interorgan crosstalk. FEBS J. 2023;290(3):620–48.
Awad SF, Toumi AA, Al-Mutawaa KA, Alyafei SA, Ijaz MA, Khalifa SA, et al. Type 2 diabetes epidemic and key risk factors in Qatar: a mathematical modeling analysis. BMJ Open Diabetes Res Care. 2022;10(2): e002704.
Awad SF, Dargham SR, Toumi AA, Dumit EM, El-Nahas KG, Al-Hamaq AO, et al. A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes. Sci Rep. 2021;11(1):1811.
Namazi N, Moghaddam SS, Esmaeili S, Peimani M, Tehrani YS, Bandarian F, et al. Burden of type 2 diabetes mellitus and its risk factors in North Africa and the Middle East, 1990–2019: findings from the global burden of disease study 2019. BMC Public Health. 2024;24(1):98.
Awad SF, Toumi AA, Al-Mutawaa KA, Alyafei SA, Ijaz MA, Khalifa SA, et al. Type 2 diabetes epidemic and key risk factors in Qatar: a mathematical modeling analysis. BMJ Open Diabetes Res Care. 2022;10(2): e002704.
Awad SF, O’Flaherty M, Critchley J, Abu-Raddad LJ. Forecasting the burden of type 2 diabetes mellitus in Qatar to 2050: a novel modeling approach. Diabetes Res Clin Pract. 2018;137:100–8.
Desai P, Donovan L, Janowitz E, Kim JY. The clinical utility of salivary biomarkers in the identification of type 2 diabetes risk and metabolic syndrome. Diabetes Metab Syndr Obes. 2020;13:3587–99.
Chen ZZ, Gerszten RE. Metabolomics and proteomics in type 2 diabetes. Circ Res. 2020;126(11):1613–27.
Athieniti E, Spyrou GM. A guide to multi-omics data collection and integration for translational medicine. Comput Struct Biotechnol J. 2023;21:134–49.
Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98.
Zaghlool SB, Halama A, Stephan N, Gudmundsdottir V, Gudnason V, Jennings LL, et al. Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population. Nat Commun. 2022;13(1):7121.
Overmyer KA, Rhoads TW, Merrill AE, Ye Z, Westphall MS, Acharya A, et al. Proteomics, lipidomics, metabolomics, and 16S DNA sequencing of dental plaque from patients with diabetes and periodontal disease. Mol Cell Proteom. 2021;20: 100126.
Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nat Med. 2018;24(4):392–400.
Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, et al. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe. 2024;32(4):506-26.e9.
Zheng D, Liwinski T, Elinav E. Interaction between microbiota and immunity in health and disease. Cell Res. 2020;30(6):492–506.
Takahashi N. Oral microbiome metabolism: from “who are they?” to “what are they doing?” J Dent Res. 2015;94(12):1628–37.
Lin H, He QY, Shi L, Sleeman M, Baker MS, Nice EC. Proteomics and the microbiome: pitfalls and potential. Expert Rev Proteom. 2019;16(6):501–11.
Ammer-Herrmenau C, Antweiler KL, Asendorf T, Beyer G, Buchholz SM, Cameron S, et al. Gut microbiota predicts severity and reveals novel metabolic signatures in acute pancreatitis. Gut. 2024;73(3):485–95.
Kulshrestha S, Redhu R, Dua R, Gupta R, Gupta P, Gupta S, et al. 16S rRNA female reproductive microbiome investigation reveals dalfopristin, clorgyline, and hydrazine as potential therapeutics for the treatment of bacterial vaginosis. Diagn Microbiol Infect Dis. 2024;109(3): 116349.
Starr AE, Deeke SA, Li L, Zhang X, Daoud R, Ryan J, et al. Proteomic and metaproteomic approaches to understand host–microbe interactions. Anal Chem. 2018;90(1):86–109.
Barlow GM, Mathur R. Type 2 diabetes and the microbiome. J Endocr Soc. 2022;7(2): bvac184.
Gurung M, Li Z, You H, Rodrigues R, Jump DB, Morgun A, et al. Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine. 2020;51: 102590.
Wang Z, Peters BA, Yu B, Grove ML, Wang T, Xue X, et al. Gut microbiota and blood metabolites related to fiber intake and type 2 diabetes. Circ Res. 2024;134(7):842–54.
Vieira Lima CP, Grisi DC, Guimaraes M, Salles LP, Kruly PC, Do T, et al. Enrichment of sulphate-reducers and depletion of butyrate-producers may be hyperglycaemia signatures in the diabetic oral microbiome. J Oral Microbiol. 2022;14(1):2082727.
Zaura E, Keijser BJ, Huse SM, Crielaard W. Defining the healthy “core microbiome” of oral microbial communities. BMC Microbiol. 2009;9:259.
Zaura E, Nicu EA, Krom BP, Keijser BJ. Acquiring and maintaining a normal oral microbiome: current perspective. Front Cell Infect Microbiol. 2014;4:85.
Yamashita Y, Takeshita T. The oral microbiome and human health. J Oral Sci. 2017;59(2):201–6.
Murugesan S, Al Ahmad SF, Singh P, Saadaoui M, Kumar M, Al KS. Profiling the salivary microbiome of the Qatari population. J Transl Med. 2020;18(1):127.
Murugesan S, Al KS. Salivary microbiome and hypertension in the Qatari population. J Transl Med. 2023;21(1):454.
Davis E, Bakulski KM, Goodrich JM, Peterson KE, Marazita ML, Foxman B. Low levels of salivary metals, oral microbiome composition and dental decay. Sci Rep. 2020;10(1):14640.
Mashima I, Theodorea CF, Thaweboon B, Thaweboon S, Scannapieco FA, Nakazawa F. Exploring the salivary microbiome of children stratified by the oral hygiene index. PLoS ONE. 2017;12(9): e0185274.
Gaetti-Jardim E Jr, Jardim ECG, Schweitzer CM, da Silva JCL, Oliveira MM, Masocatto DC, et al. Supragingival and subgingival microbiota from patients with poor oral hygiene submitted to radiotherapy for head and neck cancer treatment. Arch Oral Biol. 2018;90:45–52.
Wade WG. The oral microbiome in health and disease. Pharmacol Res. 2013;69(1):137–43.
Kholy KE, Genco RJ, Van Dyke TE. Oral infections and cardiovascular disease. Trends Endocrinol Metab. 2015;26(6):315–21.
Cortez RV, Taddei CR, Sparvoli LG, Angelo AGS, Padilha M, Mattar R, et al. Microbiome and its relation to gestational diabetes. Endocrine. 2019;64(2):254–64.
Casarin RC, Barbagallo A, Meulman T, Santos VR, Sallum EA, Nociti FH, et al. Subgingival biodiversity in subjects with uncontrolled type-2 diabetes and chronic periodontitis. J Periodontal Res. 2013;48(1):30–6.
Sabharwal A, Ganley K, Miecznikowski JC, Haase EM, Barnes V, Scannapieco FA. The salivary microbiome of diabetic and non-diabetic adults with periodontal disease. J Periodontol. 2019;90(1):26–34.
Yang Y, Liu S, Wang Y, Wang Z, Ding W, Sun X, et al. Changes of saliva microbiota in the onset and after the treatment of diabetes in patients with periodontitis. Aging. 2020;12(13):13090–114.
Castagnola M, Picciotti PM, Messana I, Fanali C, Fiorita A, Cabras T, et al. Potential applications of human saliva as diagnostic fluid. Acta Otorhinolaryngol Ital. 2011;31(6):347–57.
Kurgan N, Kjærgaard Larsen J, Deshmukh AS. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia. 2024;67(5):783–97.
Song M, Bai H, Zhang P, Zhou X, Ying B. Promising applications of human-derived saliva biomarker testing in clinical diagnostics. Int J Oral Sci. 2023;15(1):2.
Komarova N, Panova O, Titov A, Kuznetsov A. Aptamers targeting cardiac biomarkers as an analytical tool for the diagnostics of cardiovascular diseases: a review. Biomedicines. 2022;10(5):1085.
Monfared YK, Mirzaii-Dizgah MR, Khodabandehloo E, Sarookhani MR, Hashemipour S, Mirzaii-Dizgah I. Salivary microRNA-126 and 135a: a potentially non-invasive diagnostic biomarkers of type- 2 diabetes. J Diabetes Metab Disord. 2021;20(2):1631–8.
Huang L, Shao D, Wang Y, Cui X, Li Y, Chen Q, et al. Human body-fluid proteome: quantitative profiling and computational prediction. Brief Bioinform. 2021;22(1):315–33.
Nguyen T, Sedghi L, Ganther S, Malone E, Kamarajan P, Kapila YL. Host-microbe interactions: profiles in the transcriptome, the proteome, and the metabolome. Periodontol 2000. 2020;82(1):115–28.
Murugesan S, Elanbari M, Bangarusamy DK, Terranegra A, Al KS. Can the salivary microbiome predict cardiovascular diseases? Lessons learned from the Qatari population. Front Microbiol. 2021;12: 772736.
Illumina. 16S metagenomic sequencing library preparation. Illumina. 2013:1–28.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72(7):5069–72.
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8(4): e61217.
Shetty SA WdSPea. dysbiosisR: an R package for calculating microbiome dysbiosis measures. 2022.
Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J, Poon TW, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569(7758):655–62.
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–21.
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30(21):3123–4.
Hathout Y, Brody E, Clemens PR, Cripe L, DeLisle RK, Furlong P, et al. Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy. Proc Natl Acad Sci USA. 2015;112(23):7153–8.
Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE. 2010;5(12): e15004.
Sattlecker M, Kiddle SJ, Newhouse S, Proitsi P, Nelson S, Williams S, et al. Alzheimer’s disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimers Dement. 2014;10(6):724–34.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7): e47.
Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation. 2021;2(3): 100141.
Wright MN, Ziegler A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw. 2017;77(1):1.
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.
Rohart F, Gautier B, Singh A, Le Cao KA. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13(11): e1005752.
Chen T, Guestrin C, editors. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining; 2016.
Karatzoglou A, Smola A, Hornik K, Zeileis A. kernlab—an S4 package for kernel methods in R. J Stat Softw. 2004;11:1–20.
Kuhn M, Wickham H. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. Boston, MA, USA; 2020
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60.
Gu Z. Complex heatmap visualization. iMeta. 2022;1(3): e43.
Singh A, Shannon CP, Gautier B, Rohart F, Vacher M, Tebbutt SJ, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35(17):3055–62.
Abraham JE, Maranian MJ, Spiteri I, Russell R, Ingle S, Luccarini C, et al. Saliva samples are a viable alternative to blood samples as a source of DNA for high throughput genotyping. BMC Med Genomics. 2012;5:19.
Nieves E, Cimino R, Krolewiecki A, Juarez M, Lanusse C, Alvarez L, et al. Albendazole metabolites excretion in human saliva as a biomarker to assess treatment compliance in mass drug administration (MDA) anthelmintic programs. Sci Rep. 2024;14(1):6271.
Debono M, Caunt S, Elder C, Fearnside J, Lewis J, Keevil B, et al. Real world evidence supports waking salivary cortisone as a screening test for adrenal insufficiency. Clin Endocrinol. 2023;99(6):517–24.
Janem WF, Scannapieco FA, Sabharwal A, Tsompana M, Berman HA, Haase EM, et al. Salivary inflammatory markers and microbiome in normoglycemic lean and obese children compared to obese children with type 2 diabetes. PLoS ONE. 2017;12(3): e0172647.
Goodson JM, Hartman ML, Shi P, Hasturk H, Yaskell T, Vargas J, et al. The salivary microbiome is altered in the presence of a high salivary glucose concentration. PLoS ONE. 2017;12(3): e0170437.
Bruserud O, Siddiqui H, Marthinussen MC, Chen T, Jonsson R, Oftedal BE, et al. Oral microbiota in autoimmune polyendocrine syndrome type 1. J Oral Microbiol. 2018;10(1):1442986.
Shaw L, Ribeiro ALR, Levine AP, Pontikos N, Balloux F, Segal AW, et al. The human salivary microbiome is shaped by shared environment rather than genetics: evidence from a large family of closely related individuals. mBio. 2017;8(5):10–128.
Fan X, Peters BA, Min D, Ahn J, Hayes RB. Comparison of the oral microbiome in mouthwash and whole saliva samples. PLoS ONE. 2018;13(4): e0194729.
Stahringer SS, Clemente JC, Corley RP, Hewitt J, Knights D, Walters WA, et al. Nurture trumps nature in a longitudinal survey of salivary bacterial communities in twins from early adolescence to early adulthood. Genome Res. 2012;22(11):2146–52.
Lloyd-Price J, Mahurkar A, Rahnavard G, Crabtree J, Orvis J, Hall AB, et al. Strains, functions and dynamics in the expanded human microbiome project. Nature. 2017;550(7674):61–6.
Li Y, Qian F, Cheng X, Wang D, Wang Y, Pan Y, et al. Dysbiosis of oral microbiota and metabolite profiles associated with type 2 diabetes mellitus. Microbiol Spectr. 2023;11(1): e0379622.
Takeuchi T, Kubota T, Nakanishi Y, Tsugawa H, Suda W, Kwon AT, et al. Gut microbial carbohydrate metabolism contributes to insulin resistance. Nature. 2023;621(7978):389–95.
Li Q, Chang Y, Zhang K, Chen H, Tao S, Zhang Z. Implication of the gut microbiome composition of type 2 diabetic patients from northern China. Sci Rep. 2020;10(1):5450.
Wang J, Teng M, Feng R, Su X, Xu K, Wang J, et al. Large-scale causal analysis of gut microbiota and six common complications of diabetes: a Mendelian randomization study. Diabetol Metab Syndr. 2024;16(1):66.
Sun X, Li M, Xia L, Fang Z, Yu S, Gao J, et al. Alteration of salivary microbiome in periodontitis with or without type-2 diabetes mellitus and metformin treatment. Sci Rep. 2020;10(1):15363.
Almeida-Santos A, Martins-Mendes D, Gaya-Vidal M, Perez-Pardal L, Beja-Pereira A. Characterization of the oral microbiome of medicated type-2 diabetes patients. Front Microbiol. 2021;12: 610370.
Diviccaro S, Falvo E, Piazza R, Cioffi L, Herian M, Brivio P, et al. Gut microbiota composition is altered in a preclinical model of type 1 diabetes mellitus: Influence on gut steroids, permeability, and cognitive abilities. Neuropharmacology. 2023;226: 109405.
Randhawa M, Huff T, Valencia JC, Younossi Z, Chandhoke V, Hearing VJ, et al. Evidence for the ectopic synthesis of melanin in human adipose tissue. FASEB J. 2009;23(3):835–43.
Page S, Chandhoke V, Baranova A. Melanin and melanogenesis in adipose tissue: possible mechanisms for abating oxidative stress and inflammation? Obes Rev. 2011;12(5):e21-31.
Lee SH, Bae I-H, Lee E-S, Kim H-J, Lee J, Lee CS. Glucose exerts an anti-melanogenic effect by indirect inactivation of tyrosinase in melanocytes and a human skin equivalent. Int J Mol Sci. 2020;21(5):1736.
Sadowska-Bartosz I, Bartosz G. Biological properties and applications of Betalains. Molecules. 2021;26(9):2520.
Muniyappa R. Vascular insulin resistance and free fatty acids: the micro-macro circulation nexus. J Clin Endocrinol Metab. 2024;109(8):e1671–2.
Son WH, Ha MS, Park TJ. Effect of physical activity on free fatty acids, insulin resistance, and blood pressure in obese older women. Phys Act Nutr. 2024;28(2):1–6.
Shiri H, Fallah H, Abolhassani M, Fooladi S, Ramezani Karim Z, Danesh B, et al. Relationship between types and levels of free fatty acids, peripheral insulin resistance, and oxidative stress in T2DM: a case-control study. PLoS ONE. 2024;19(8): e0306977.
Shetty SS, Kumari S. Fatty acids and their role in type-2 diabetes (review). Exp Ther Med. 2021;22(1):706.
Zhang S, Li X, Luo H, Fang ZZ, Ai H. Role of aromatic amino acids in pathogeneses of diabetic nephropathy in Chinese patients with type 2 diabetes. J Diabetes Complicat. 2020;34(10): 107667.
Vangipurapu J, Stancakova A, Smith U, Kuusisto J, Laakso M. Nine amino acids are associated with decreased insulin secretion and elevated glucose levels in a 7.4-year follow-up study of 5,181 Finnish men. Diabetes. 2019;68(6):1353–8.
Tang G, Zhang L, Yang G, Wu L, Wang R. Hydrogen sulfide-induced inhibition of L-type Ca2+ channels and insulin secretion in mouse pancreatic beta cells. Diabetologia. 2013;56(3):533–41.
Gudmundsdottir V, Zaghlool SB, Emilsson V, Aspelund T, Ilkov M, Gudmundsson EF, et al. Circulating protein signatures and causal candidates for type 2 diabetes. Diabetes. 2020;69(8):1843–53.
Kobayashi H, Looker HC, Satake E, Saulnier PJ, Md Dom ZI, O’Neil K, et al. Results of untargeted analysis using the SOMAscan proteomics platform indicates novel associations of circulating proteins with risk of progression to kidney failure in diabetes. Kidney Int. 2022;102(2):370–81.
Slieker RC, Donnelly LA, Fitipaldi H, Bouland GA, Giordano GN, Åkerlund M, et al. Distinct molecular signatures of clinical clusters in people with type 2 diabetes: an IMI-RHAPSODY study. Diabetes. 2021;70(11):2683–93.
Rooney MR, Chen J, Echouffo-Tcheugui JB, Walker KA, Schlosser P, Surapaneni A, et al. Proteomic predictors of incident diabetes: results from the atherosclerosis risk in communities (ARIC) study. Diabetes Care. 2023;46(4):733–41.
Steffen BT, Tang W, Lutsey PL, Demmer RT, Selvin E, Matsushita K, et al. Proteomic analysis of diabetes genetic risk scores identifies complement C2 and neuropilin-2 as predictors of type 2 diabetes: the atherosclerosis risk in communities (ARIC) study. Diabetologia. 2023;66(1):105–15.
Chen Z-Z, Gao Y, Keyes MJ, Deng S, Mi M, Farrell LA, et al. Protein markers of diabetes discovered in an African American cohort. Diabetes. 2023;72(4):532–43.
Cronjé HT, Mi MY, Austin TR, Biggs ML, Siscovick DS, Lemaitre RN, et al. Plasma proteomic risk markers of incident type 2 diabetes reflect physiologically distinct components of glucose-insulin homeostasis. Diabetes. 2023;72(5):666–73.
Slieker RC, Donnelly LA, Akalestou E, Lopez-Noriega L, Melhem R, Güneş A, et al. Identification of biomarkers for glycaemic deterioration in type 2 diabetes. Nat Commun. 2023;14(1):2533.
Noren Hooten N, Mode NA, Kowalik E, Omoniyi V, Zonderman AB, Ezike N, et al. Plasma gelsolin levels are associated with diabetes, sex, race, and poverty. J Transl Med. 2023;21(1):190.
Carrasco-Zanini J, Pietzner M, Wheeler E, Kerrison ND, Langenberg C, Wareham NJ. Multi-omic prediction of incident type 2 diabetes. Diabetologia. 2024;67(1):102–12.
Steffen BT, McDonough DJ, Pankow JS, Tang W, Rooney MR, Demmer RT, et al. Plasma neuronal growth regulator 1 may link physical activity to reduced risk of type 2 diabetes: a proteome-wide study of ARIC participants. Diabetes. 2024;73(2):318–24.
Noordam R, van Heemst D, Suhre K, Krumsiek J, Mook-Kanamori DO. Proteome-wide assessment of diabetes mellitus in Qatari identifies IGFBP-2 as a risk factor already with early glycaemic disturbances. Arch Biochem Biophys. 2020;689: 108476.
Niewczas MA, Pavkov ME, Skupien J, Smiles A, Md Dom ZI, Wilson JM, et al. A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes. Nat Med. 2019;25(5):805–13.
Liu JJ, Liu S, Saulnier PJ, Gand E, Choo RWM, Gurung RL, et al. Association of urine haptoglobin with risk of all-cause and cause-specific mortality in individuals with type 2 diabetes: a transethnic collaborative work. Diabetes Care. 2020;43(3):625–33.
Fasano A. All disease begins in the (leaky) gut: role of zonulin-mediated gut permeability in the pathogenesis of some chronic inflammatory diseases. F1000Research. 2020;9:69.
Mewborn EK, Tolley EA, Wright DB, Doneen AL, Harvey M, Stanfill AG. Haptoglobin genotype is a risk factor for coronary artery disease in prediabetes: a case-control study. Am J Prev Cardiol. 2024;17: 100625.
Carew AS, Levy AP, Ginsberg HN, Coca S, Lache O, Ransom T, et al. Haptoglobin phenotype modifies the influence of intensive glycemic control on cardiovascular outcomes. J Am Coll Cardiol. 2020;75(5):512–21.
Liu L, Jiang Y, Steinle JJ. Semaphorin 7a regulates inflammatory mediators and permeability in retinal endothelial cells. Microvasc Res. 2023;150: 104587.
Guerra-Ávila PL, Guzmán TJ, Vargas-Guerrero B, Domínguez-Rosales JA, Cervantes-Garduño AB, Salazar-Montes AM, et al. Comparative screening of the liver gene expression profiles from type 1 and type 2 diabetes rat models. Int J Mol Sci. 2024;25(8):4151.
Huang T, Nazir B, Altaf R, Zang B, Zafar H, Paiva-Santos AC, et al. A meta-analysis of genome-wide gene expression differences identifies promising targets for type 2 diabetes mellitus. Front Endocrinol. 2022;13: 985857.
Shen Z, Yu Y, Yang Y, Xiao X, Sun T, Chang X, et al. miR-25 and miR-92b regulate insulin biosynthesis and pancreatic β-cell apoptosis. Endocrine. 2022;76(3):526–35.
Sies H, Jones DP. Reactive oxygen species (ROS) as pleiotropic physiological signalling agents. Nat Rev Mol Cell Biol. 2020;21(7):363–83.
Andreadi A, Bellia A, Di Daniele N, Meloni M, Lauro R, Della-Morte D, et al. The molecular link between oxidative stress, insulin resistance, and type 2 diabetes: a target for new therapies against cardiovascular diseases. Curr Opin Pharmacol. 2022;62:85–96.
Soinio M, Marniemi J, Laakso M, Lehto S, Rönnemaa T. High-sensitivity C-reactive protein and coronary heart disease mortality in patients with type 2 diabetes: a 7-year follow-up study. Diabetes Care. 2006;29(2):329–33.
Daiber A, Hahad O, Andreadou I, Steven S, Daub S, Münzel T. Redox-related biomarkers in human cardiovascular disease—classical footprints and beyond. Redox Biol. 2021;42: 101875.
Hall MW, Singh N, Ng KF, Lam DK, Goldberg MB, Tenenbaum HC, et al. Inter-personal diversity and temporal dynamics of dental, tongue, and salivary microbiota in the healthy oral cavity. NPJ Biofilms Microbiomes. 2017;3:2.
Wang H, Zhou H, Duan X, Jotwani R, Vuddaraju H, Liang S, et al. Porphyromonas gingivalis-induced reactive oxygen species activate JAK2 and regulate production of inflammatory cytokines through c-Jun. Infect Immun. 2014;82(10):4118–26.
Śmiga M, Smalley JW, Ślęzak P, Brown JL, Siemińska K, Jenkins RE, et al. Glycation of host proteins increases pathogenic potential of Porphyromonas gingivalis. Int J Mol Sci. 2021;22(21):12084.
Zhao M, Yang Y, Guo Z, Shao C, Sun H, Zhang Y, et al. A comparative proteomics analysis of five body fluids: plasma, urine, cerebrospinal fluid, amniotic fluid, and saliva. Proteom Clin Appl. 2018;12(6): e1800008.
Acknowledgements
Data and samples used in this study were obtained from QBB (www.qatarbiobank.org.qa). Sample processing for DNA extraction was performed by members from the Omics core at Sidra Medicine.
Funding
This project was financially supported by funds from Qatar National Research Fund, project # PPM2-0216-170012, and the Qatar genome program to SAK.
Author information
Authors and Affiliations
Contributions
SAK designed the study and obtained funds for the project. SM received and processed the samples for library preparation and sequencing. GG and JCG developed the protocol for the salivary proteome and processed the samples using Somalogic. SM and MND performed the data analysis. SM and GMY wrote the first draft. SK reviewed the data and the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
The study was approved by the Institutional Review Board (IRB) of Sidra Medicine under (protocol #1510001907) and by Qatar Biobank (QBB) (protocol #E/2018/QBB-RES-ACC-0063/0022. All study participants signed an informed consent prior to sample collection. All experiments were performed in accordance with the approved guidelines.
Consent for publication
All authors reviewed the final version of the manuscript and approved it for publication.
Competing interests
The authors declare no conflict of interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
12967_2024_5928_MOESM1_ESM.pdf
Additional file 1: Figure S1. Significant metabolic functional prediction in diabetic groups.Comparison between non-diabetic and pre-diabetic groups.Pre-diabetic vs Diabetic groups.Non-Diabetic Vs Diabetic groups. The Wilcoxon test was used to compare the two groups. p-value < 0.05 was considered significant
12967_2024_5928_MOESM2_ESM.pdf
Additional file 2: Figure S2. Salivary microbiota signature in non-diabetic, pre-diabetic, diabetic non-treated and treated groups.Salivary microbiota composition at the phylum level.At Genus level. Y-axis shows % of relative abundance of the microbiota; X-axis indicates the non-diabetic, pre-diabetic, diabetic non-treated and treated groups.Alpha diversity measures of salivary microbiota of the study groups.Principal Coordinates Analysisbased on Bray–Curtis distances of salivary microbiota. Axes were scaled to the amount of variation explained. The Kruskal Wallis test was used to compare the two groups. p-value < 0.05 was considered significant.Salivary microbiota dysbiotic scores using median CLV approach to the non-diabetic, pre-diabetic, and diabetic non-treated and treated groups. The Wilcoxon test was used to compare the two groups. p-value < 0.05 was considered significant.Graphs of linear discriminant analysisscores for differentially abundant bacterial genera between non-diabeticand pre-diabeticgroups.Graphs of linear discriminant analysisscores for differentially abundant bacterial genera between non-diabeticand diabetic non-treatedgroups.Graphs of linear discriminant analysisscores for differentially abundant bacterial genera between non-diabeticand diabetic-treatedgroups.Graphs of linear discriminant analysisscores for differentially abundant bacterial genera between pre-diabeticand diabetic-non-treatedgroups.Graphs of linear discriminant analysisscores for differentially abundant bacterial genera between pre-diabeticand diabetic-treatedgroups.Graphs of linear discriminant analysisscores for differentially abundant bacterial genera between diabetic non-treatedand diabetic-treatedgroups. Features with LDA scores ≥ 2 are presented.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Murugesan, S., Yousif, G., Djekidel, M.N. et al. Microbial and proteomic signatures of type 2 diabetes in an Arab population. J Transl Med 22, 1132 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-024-05928-8
Received:
Accepted:
Published:
DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-024-05928-8