Browse
Submit Data
Databases
API
Help

Dataset Information

12 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Deciphering the Plasma Proteome of Type 2 Diabetes.

ABSTRACT: With an estimated prevalence of 463 million affected, type 2 diabetes represents a major challenge to health care systems worldwide. Analyzing the plasma proteomes of individuals with type 2 diabetes may illuminate hitherto unknown functional mechanisms underlying disease pathology. We assessed the associations between type 2 diabetes and >1,000 plasma proteins in the Cooperative Health Research in the Region of Augsburg (KORA) F4 cohort (n = 993, 110 cases), with subsequent replication in the third wave of the Nord-Trøndelag Health Study (HUNT3) cohort (n = 940, 149 cases). We computed logistic regression models adjusted for age, sex, BMI, smoking status, and hypertension. Additionally, we investigated associations with incident type 2 diabetes and performed two-sample bidirectional Mendelian randomization (MR) analysis to prioritize our results. Association analysis of prevalent type 2 diabetes revealed 24 replicated proteins, of which 8 are novel. Proteins showing association with incident type 2 diabetes were aminoacylase-1, growth hormone receptor, and insulin-like growth factor-binding protein 2. Aminoacylase-1 was associated with both prevalent and incident type 2 diabetes. MR analysis yielded nominally significant causal effects of type 2 diabetes on cathepsin Z and rennin, both known to have roles in the pathophysiological pathways of cardiovascular disease, and of sex hormone-binding globulin on type 2 diabetes. In conclusion, our high-throughput proteomics study replicated previously reported type 2 diabetes-protein associations and identified new candidate proteins possibly involved in the pathogenesis of type 2 diabetes.

SUBMITTER: Elhadad MA

PROVIDER: S-EPMC7679779 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Plasma proteome analysis of patients with type 1 diabetes with diabetic nephropathy.

Project description:BackgroundAs part of a clinical proteomics program focused on diabetes and its complications we are looking for new and better protein biomarkers for diabetic nephropathy. The search for new and better biomarkers for diabetic nephropathy has, with a few exceptions, previously focused on either hypothesis-driven studies or urinary based investigations. To date only two studies have investigated the proteome of blood in search for new biomarkers, and these studies were conducted in sera from patients with type 2 diabetes. This is the first reported in depth proteomic study where plasma from type 1 diabetic patients was investigated with the goal of finding improved candidate biomarkers to predict diabetic nephropathy. In order to reach lower concentration proteins in plasma a pre-fractionation step, either hexapeptide bead-based libraries or anion exchange chromatography, was performed prior to surface enhanced laser desorption/ionization time-of-flight mass spectrometry analysis.ResultsProteomic analysis of plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric, gave rise to 290 peaks clusters of which 16 were selected as the most promising biomarker candidates based on statistical performance, including independent component analysis. Four of the peaks that were discovered have been identified as transthyretin, apolipoprotein A1, apolipoprotein C1 and cystatin C. Several yet unidentified proteins discovered by this novel approach appear to have more potential as biomarkers for diabetic nephropathy.ConclusionThese results demonstrate the capacity of proteomic analysis of plasma, by confirming the presence of known biomarkers as well as revealing new biomarkers for diabetic nephropathy in plasma in type 1 diabetic patients.

| S-EPMC2827395 | biostudies-literature

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes.

Project description:Purpose of the reviewProteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D).Recent findingsTechnological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.

| S-EPMC7543966 | biostudies-literature

Deciphering the dark proteome of Chikungunya virus.

Project description:Chikungunya virus (CHIKV) is a mosquito-borne alphavirus. The outbreak of CHIKV infection has been seen in many tropical and subtropical regions of the biosphere. Current reports evidenced that after outbreaks in 2005-06, the fitness of this virus propagating in Aedes albopictus enhanced due to the epistatic mutational changes in its envelope protein. In our study, we evaluated the prevalence of intrinsically disordered proteins (IDPs) and IDP regions (IDPRs) in CHIKV proteome. IDPs/IDPRs are known as members of a 'Dark Proteome' that defined as a set of polypeptide segments or whole protein without unique three-dimensional structure within the cellular milieu but with significant biological functions, such as cell cycle regulation, control of signaling pathways, and maintenance of viral proteomes. However, the intrinsically disordered aspects of CHIKV proteome and roles of IDPs/IDPRs in the pathogenic mechanism of this important virus have not been evaluated as of yet. There are no existing reports on the analysis of intrinsic disorder status of CHIKV. To fulfil this goal, we have analyzed the abundance and functionality of IDPs/IDPRs in CHIKV proteins, involved in the replication and maturation. It is likely that these IDPs/IDPRs can serve as novel targets for disorder based drug design.

| S-EPMC5895634 | biostudies-literature

Retinal proteome alterations in a mouse model of type 2 diabetes.

Project description:Aims/hypothesisDiabetic retinopathy is a major complication of type 2 diabetes and the leading cause of blindness in adults of working age. Neuronal defects are known to occur early in disease, but the source of this dysfunction is unknown. The aim of this study was to examine differences in the retinal membrane proteome among non-diabetic mice and mouse models of diabetes either with or without metformin treatment.MethodsAlterations in the retinal membrane proteome of 10-week-old diabetic db/db mice, diabetic db/db mice orally treated with the anti-hyperglycaemic metformin, and congenic wild-type littermates were examined using label-free mass spectrometry. Pathway enrichment analysis was completed with Genomatix and Ingenuity. Alterations in Slc17a7 mRNA and vesicular glutamate transporter 1 (VGLUT1) protein expression were evaluated using real-time quantitative PCR and IMMUNOFLUORESCENCE.ResultsA total of 98 proteins were significantly differentially abundant between db/db and wild-type animals. Pathway enrichment analysis indicated decreases in levels of proteins related to synaptic transmission and cell signalling. Metformin treatment produced 63 differentially abundant proteins compared with untreated db/db mice, of which only 43 proteins were found to occur in both datasets, suggesting that treatment only partially normalises the alterations induced by diabetes. VGLUT1, which is responsible for loading glutamate into synaptic vesicles, was found to be differentially abundant in db/db mice and was not normalised by metformin. The decrease in Slc17a7/VGLUT1 was confirmed by transcriptomic and immunocytochemical analysis.Conclusions/interpretationThese findings expand the knowledge of the protein changes in diabetic retinopathy and suggest that membrane-associated signalling proteins are susceptible to changes that are partially ameliorated by treatment

| S-EPMC3855476 | biostudies-literature

Human Plasma Proteome During Normal Pregnancy

Project description:This study used SomaScan v 4.1 to profile>7000 proteins in maternal plasma and asses changes with gestational age and maternal characteristics. The study proposed models to estimate the expected protein abundance so that future datasets can be normalized agains these expected value to obtain Multiples of the Mean values.

2022-09-26 | GSE206454 | GEO

Localized-statistical quantification of human serum proteome associated with type 2 diabetes.

Project description:BACKGROUND: Recent advances in proteomics have shed light to discover serum proteins or peptides as biomarkers for tracking the progression of diabetes as well as understanding molecular mechanisms of the disease. RESULTS: In this work, human serum of non-diabetic and diabetic cohorts was analyzed by proteomic approach. To analyze total 1377 high-confident serum-proteins, we developed a computing strategy called localized statistics of protein abundance distribution (LSPAD) to calculate a significant bias of a particular protein-abundance between these two cohorts. As a result, 68 proteins were found significantly over-represented in the diabetic serum (p<0.01). In addition, a pathway-associated analysis was developed to obtain the overall pathway bias associated with type 2 diabetes, from which the significant over-representation of complement system associated with type 2 diabetes was uncovered. Moreover, an up-stream activator of complement pathway, ficolin-3, was observed over-represented in the serum of type 2 diabetic patients, which was further validated with statistic significance (p = 0.012) with more clinical samples. CONCLUSIONS: The developed LSPAD approach is well fit for analyzing proteomic data derived from biological complex systems such as plasma proteome. With LSPAD, we disclosed the comprehensive distribution of the proteins associated with diabetes in different abundance levels and the involvement of ficolin-related complement activation in diabetes.

| S-EPMC2529402 | biostudies-literature

Deciphering protective mechanism against human type 2 diabetes through in vitro β cell differentiation

Project description:To systemically investigate the role of ZnT8 in β cell maturation, we performed single cell RNA-seq in both WT and KO β cells at both S6 (immature) and S7 (mature) stages. Both WT and KO β cells were obtained from FACS as positive for both INS and NKX6.1. Single cell RNA-seq results revealed that SLC30A8 is mainly involved in β cell maturation process, and further showed that SLC30A8 LOF accelerates β cell maturation and upregulates insulin secretion pathway in mature β cells.

2022-05-25 | GSE135076 | GEO

Longitudinal plasma protein profiling of newly diagnosed type 2 diabetes.

Project description:Background: Comprehensive proteomics profiling may offer new insights into the dysregulated metabolic milieu of type 2 diabetes, and in the future, serve as a useful tool for personalized medicine. This calls for a better understanding of circulating protein patterns at the early stage of type 2 diabetes as well as the dynamics of protein patterns during changes in metabolic status.Methods: To elucidate the systemic alterations in early-stage diabetes and to investigate the effects on the proteome during metabolic improvement, we measured 974 circulating proteins in 52 newly diagnosed, treatment-naïve type 2 diabetes subjects at baseline and after 1 and 3 months of guideline-based diabetes treatment, while comparing their protein profiles to that of 94 subjects without diabetes.Findings: Early stage type 2 diabetes was associated with distinct protein patterns, reflecting key metabolic syndrome features including insulin resistance, adiposity, hyperglycemia and liver steatosis. The protein profiles at baseline were attenuated during guideline-based diabetes treatment and several plasma proteins associated with metformin medication independently of metabolic variables, such as circulating EPCAM.Interpretation: The results advance our knowledge about the biochemical manifestations of type 2 diabetes and suggest that comprehensive protein profiling may serve as a useful tool for metabolic phenotyping and for elucidating the biological effects of diabetes treatments.Funding: This work was supported by the Swedish Heart and Lung Foundation, the Swedish Research Council, the Erling Persson Foundation, the Knut and Alice Wallenberg Foundation, and the Swedish state under the agreement between the Swedish government and the county councils (ALF-agreement).

| S-EPMC7718461 | biostudies-literature

A unique plasma microRNA profile defines type 2 diabetes progression.

Project description:A major unmet medical need to better manage Type 2 Diabetes (T2D) is the accurate disease prediction in subjects who show glucose dysmetabolism, but are not yet diagnosed as diabetic. We investigated the possibility to predict/monitor the progression to T2D in these subjects by retrospectively quantifying blood circulating microRNAs in plasma of subjects with i) normal glucose tolerance (NGT, n = 9); ii) impaired glucose tolerance (IGT, n = 9), divided into non-progressors (NP, n = 5) and progressors (P, n = 4) based on subsequent diabetes occurrence, and iii) newly diagnosed T2D (n = 9). We found that impaired glucose tolerance associated with a global increase of plasma circulating microRNAs. While miR-148 and miR-222 were specifically modulated in diabetic subjects and correlated with parameters of glucose tolerance, the most accentuated microRNA dysregulation was found in NP IGT subjects, with increased level of miR-122, miR-99 and decreased level of let-7d, miR-18a, miR-18b, miR-23a, miR-27a, miR-28 and miR-30d in comparison with either NGT or T2D. Interestingly, several of these microRNAs significantly correlated with parameters of cholesterol metabolism. In conclusion, we observed the major perturbation of plasma circulating microRNA in NP pre-diabetic subjects and identified a unique microRNA profile that may become helpful in predicting diabetic development.

| S-EPMC5714331 | biostudies-literature

Normal fasting plasma glucose and risk of type 2 diabetes.

Project description:ObjectiveTo investigate the association of normal fasting plasma glucose (FPG) and the risk for type 2 diabetes.Research design and methodsData concerning 13,845 subjects, aged 40-69 years, who had their FPG measured at least three times between 1992 and 2008 were extracted from a database. Three FPG groups were defined (51-82, 83-90, and 91-99 mg/dL). A Cox proportional hazards analysis was applied to estimate the risk of incident diabetes adjusted for other risk factors.ResultsDuring 108,061 person-years of follow-up (8,110 women and 5,735 men), 307 incident cases of type 2 diabetes were found. The final model demonstrated a hazard ratio of 2.03 (95% CI 1.18-3.50) for 91-99 mg/dL and 1.42 (0.42-4.74) for 83-90 mg/dL.ConclusionsOur data suggest that FPG between 91 and 99 mg/dL is a strong independent predictor of type 2 diabetes and should be used to identify people to be further investigated and aided with preventive measures.

| S-EPMC3114342 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data