Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population.
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ABSTRACT:
SUBMITTER: Zaghlool SB
PROVIDER: S-EPMC9675829 | biostudies-literature | 2022 Nov
REPOSITORIES: biostudies-literature
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Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population.
Zaghlool Shaza B SB Halama Anna A Stephan Nisha N Gudmundsdottir Valborg V Gudnason Vilmundur V Jennings Lori L LL Thangam Manonanthini M Ahlqvist Emma E Malik Rayaz A RA Albagha Omar M E OME Abou-Samra Abdul Badi AB Suhre Karsten K
Nature communications 20221119 1
Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of ...[more]
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Project description:BackgroundThe 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.MethodsUsing 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.ResultsOur 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.ConclusionThis 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.
| S-EPMC11662572 | biostudies-literature
Project description:Type 2 diabetes (T2D) is a heterogeneous disease influenced by both genetic and environmental factors. Recent studies suggest that T2D subtypes may exhibit distinct gene expression profiles. In this study, we aimed to identify T2D cluster-specific miRNA expression signatures for the previously reported five clinical subtypes that characterize the underlying pathophysiology of long-standing T2D: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and mild early-onset diabetes (MEOD). We analyzed the circulating microRNAs (miRNAs) in 45 subjects representing the five T2D clusters and 7 non-T2D healthy controls by single-end small RNA sequencing. Bioinformatic analyses identified a total of 430 known circulating miRNAs and 13 previously unreported novel miRNAs. Of these, 71 were upregulated and 37 were downregulated in either controls or individual clusters. Each T2D subtype was associated with a specific dysregulated miRNA profile, distinct from that of healthy controls. Specifically, 3 upregulated miRNAs were unique to SIRD, 1 to MARD, 9 to MOD, and 18 to MEOD. Among the downregulated miRNAs, 11 were specific to SIRD, 9 to SIDD, 2 to MARD, and 1 to MEOD. Our study confirms the heterogeneity of T2D, represented by distinguishable subtypes both clinically and epigenetically and highlights the potential of miRNAs as markers for distinguishing the pathophysiology of T2D subtypes.
| S-EPMC11766090 | biostudies-literature
Project description:Aims/introductionIn the development of type 1 diabetes, metabolites are significantly altered and might be involved in β-cell destruction and protection. We aimed to identify new metabolic markers of β-cell destruction in type 1 diabetes patients.Materials and methodsA total of 33 participants were recruited for this cross-sectional observational study: 23 with type 1 diabetes, seven with type 2 diabetes and three healthy controls. Those with type 1 diabetes were further subdivided into three groups: new-onset, microsecretors and complete lack of endogenous insulin in type 1 diabetes.ResultsMetabolomic analysis identified a total of 737 peaks, and partial least square analysis was successful in discriminating between the three groups of type 1 diabetes. Among the factor loadings discriminating type 1 diabetes, 3-phenylpropionic acid (r = 0.80, P = 4.7E-6 ) and hypotaurine (r = -0.484, P = 1.9E-2 ) strongly contributed to identifying new-onset type 1 diabetes, and 5-methylcytosine to identifying complete-lack type 1 diabetes (r = 0.586, P = 6.5E-3 ). Reporter operating characteristics analysis, including all type 1 diabetes, type 2 diabetes and healthy controls, showed that high 3-phenylpropionic acid (Pc <0.0001) and low hypotaurine (Pc <0.0001) were useful for identifying new-onset type 1 diabetes, and high 5-methylcytosine (Pc = 0.002) for the complete-lack type 1 diabetes.ConclusionsIn the present study, metabolic signatures were shown to be useful in identifying type 1 diabetes at different clinical stages, and 3-phenylpropionic acid and hypotaurine are novel biomarkers for identifying new-onset type 1 diabetes, suggesting the involvement of the gut bacterial environment, anti-oxidant mechanisms through the hypotaurine-taurine pathway and methylated deoxyribonucleic acid fragmentation in the process of β-cell destruction.
| S-EPMC9807153 | biostudies-literature
Project description:Emerging evidence reveals a complex association between type 2 diabetes (T2D) and coronary heart disease (CHD), which share common risk factors and biological pathways. This study aims to identify the shared proteomic signatures of T2D and CHD, as well as whether the shared proteins predict incident CHD in T2D patients, and to develop predictive models. Utilizing data from 53,014 UK Biobank participants and 2923 plasma proteins, we identified 488 proteins associated with T2D, of which 125 proteins were also associated with CHD. Among the shared proteins, we determine nine proteins showing causal associations with CHD, including PCSK9, NRP1, and CD27. Mediation analyses suggest that the nine proteins mediate the association between T2D and CHD. By integrating these proteins into our predictive model, we achieved a desirable prediction (AUC = 0.819) for future CHD onset in T2D patients. Additionally, druggability evaluation show 32 potential therapeutic agents, including established antihypertensives and nine novel compounds, suggesting avenues for dual-targeted treatment strategies. Collectively, our findings unveil the proteomic signatures associated with both T2D and CHD, providing implications for screening and predicting future CHD onset in T2D patients.
| S-EPMC11909814 | biostudies-literature
Project description:Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites--metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.
| S-EPMC2848542 | biostudies-literature
Project description:Gastric cancer is a leading cause of death from cancer globally. Gastric cancer is classified into intestinal, diffuse and indeterminate subtypes based on histology according to the Laurén classification. The intestinal and diffuse subtypes, although different in histology, demographics and outcomes, are still treated in the same fashion. This study was designed to discover proteomic signatures of diffuse and intestinal subtypes. Mass spectrometry-based proteomics using tandem mass tags (TMT)-based multiplexed analysis was used to identify proteins in tumor tissues from patients with diffuse or intestinal gastric cancer with adjacent normal tissue control. A total of 7448 or 4846 proteins were identified from intestinal or diffuse subtype, respectively. This quantitative mass spectrometric analysis defined a proteomic signature of differential expression across the two subtypes, which included gremlin1 (GREM1), bcl-2-associated athanogene 2 (BAG2), olfactomedin 4 (OLFM4), thyroid hormone receptor interacting protein 6 (TRIP6) and melanoma-associated antigen 9 (MAGE-A9) proteins. Although GREM1, BAG2, OLFM4, TRIP6 and MAGE-A9 have all been previously implicated in tumor progression and metastasis, they have not been linked to intestinal or diffuse subtypes of gastric cancer. Using immunohistochemical labelling of a tissue microarray comprising of 124 cases of gastric cancer, we validated the proteomic signature obtained by mass spectrometry in the discovery cohort. Our findings should help investigate the pathogenesis of these gastric cancer subtypes and potentially lead to strategies for early diagnosis and treatment.
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Project description:BackgroundThe rs7903146 and rs12255372 variants of TCF7L2 have been strongly associated with type 2 diabetes (T2D) risk in most populations studied to date. Meta-analysis of 27 different studies has resulted in a global OR of 1.46 [1.42-1.51] (rs7903146 variant). Thus far, despite a high incidence of T2D, the role of this variant in Arabs has not been established.MethodsWe performed a case-control association study using 522 Saudi T2D patients (WHO criteria), and 346 controls (age > 60; fasting plasma glucose < 7 mmol/L). Genotyping was performed by pyrosequencing. Statistical analyses were performed using SPSS version 13.0 for Windows (SPSS, Chicago, IL, USA).ResultsFor rs7903146, the T allele frequency of the cases (0.415) was not different from that observed in the controls (0.405). The crude odds ratio (OR) was 1.04 with a 95% CI of 0.86-1.27 (P = 0.675). For rs12255372, the T allele frequency of the cases (0.368) was not different from that observed in the controls (0.355). Retrospective power calculations based upon an OR of 1.46 reported in a comprehensive meta-analysis of TCF7L2 risk, indicated this study was sufficiently powered (96.92%; alpha = 0.05) to detect an effect of similar magnitude to that reported for rs7903146.ConclusionOur study is consistent with weak or no association of T2D in Arabs with the two TCF7L2 variants, however it cannot rule out an effect of other SNPs in this gene. Future studies in this population are required to confirm our findings and may indicate the presence of yet to be defined genetic risk factors for T2D.
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Project description:Obesity plays an important role in the development of insulin resistance and diabetes, but the molecular mechanism that links obesity and diabetes is still not completely understood. Here, we used 146 targeted metabolomic profiles from the German KORA FF4 cohort consisting of 1715 participants and associated them with obesity and type 2 diabetes. In the basic model, 83 and 51 metabolites were significantly associated with body mass index (BMI) and T2D, respectively. Those metabolites are branched-chain amino acids, acylcarnitines, lysophospholipids, or phosphatidylcholines. In the full model, 42 and 3 metabolites were significantly associated with BMI and T2D, respectively, and replicate findings in the previous studies. Sobel mediation testing suggests that the effect of BMI on T2D might be mediated via lipids such as sphingomyelin (SM) C16:1, SM C18:1 and diacylphosphatidylcholine (PC aa) C38:3. Moreover, mendelian randomization suggests a causal relationship that BMI causes the change of SM C16:1 and PC aa C38:3, and the change of SM C16:1, SM C18:1, and PC aa C38:3 contribute to T2D incident. Biological pathway analysis in combination with genetics and mice experiments indicate that downregulation of sphingolipid or upregulation of phosphatidylcholine metabolism is a causal factor in early-stage T2D pathophysiology. Our findings indicate that metabolites like SM C16:1, SM C18:1, and PC aa C38:3 mediate the effect of BMI on T2D and elucidate their role in obesity related T2D pathologies.
| S-EPMC9965667 | biostudies-literature
Project description:Pancreatic islets are essential for maintaining physiological blood glucose levels, and declining islet function is a hallmark of type 2 diabetes. We employ mass spectrometry-based proteomics to systematically analyze islets from 9 genetic or diet-induced mouse models representing a broad cross-section of metabolic health. Quantifying the islet proteome to a depth of >11,500 proteins, this study represents the most detailed analysis of mouse islet proteins to date. Our data highlight that the majority of islet proteins are expressed in all strains and diets, but more than half of the proteins vary in expression levels, principally due to genetics. Associating these varied protein expression levels on an individual animal basis with individual phenotypic measures reveals islet mitochondrial function as a major positive indicator of metabolic health regardless of strain. This compendium of strain-specific and dietary changes to mouse islet proteomes represents a comprehensive resource for basic and translational islet cell biology.
| S-EPMC8479695 | biostudies-literature
Project description:Aims/hypothesisThe plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits.MethodsClinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO2max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R2) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Δ) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM.ResultsAcross all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R2 of truncal fat and VO2max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Δ AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO2max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM.Conclusions/interpretationPlasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM.
| S-EPMC11419213 | biostudies-literature