Dataset Information

Proteomic analysis to identify candidate biomarkers associated with type 1 diabetes.

ABSTRACT:

Purpose

Type 1 diabetes mellitus (DM1) is one of the most common chronic diseases observed during childhood. The incidence of DM1 is increasing worldwide, and there is currently no way to prevent or delay the onset or to cure the disease. Most diseases, including diabetes, stem from abnormalities in the functioning of proteins, and some studies have reported the expression of protein variation to be involved in the development of DM1. Thus, the aim of this study was to investigate the differential expression of serum proteins in patients with DM1.

Materials and methods

Serum of patients with DM1 (n=30) and healthy controls (n=30) was collected. A proteomic approach was used with depletion of albumin and immunoglobulin G chromatography on serum samples followed by data-independent, label-free mass spectrometric analysis.

Results

A total of eight serum proteins were identified as being differentially expressed and involved in the immune system, lipid metabolism, and pathways of coagulation. DM1 was associated with the upregulation of six proteins: alpha-2-macroglobulin, apolipoprotein A-II, β2 glycoprotein I, Ig alpha-2 chain C region, alpha-1-microglobulin, and prothrombin. A total of two proteins were downregulated, including pregnancy zone protein and complement C4.

Conclusion

To the best of our knowledge, these findings show differential expression of proteins revealing new proteins that may be involved in the development and progression of diabetes.

SUBMITTER: do Nascimento de Oliveira V

PROVIDER: S-EPMC6005324 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Proteomic analysis to identify candidate biomarkers associated with type 1 diabetes.

do Nascimento de Oliveira Valzimeire V Lima-Neto Abelardo Barbosa Moreira ABM van Tilburg Maurício Fraga MF de Oliveira Monteiro-Moreira Ana Cristina AC Duarte Pinto Lobo Marina M Rondina Davide D Fernandes Virgínia Oliveira VO Montenegro Ana Paula Dias Rangel APDR Montenegro Renan Magalhães RM Guedes Maria Izabel Florindo MIF

Diabetes, metabolic syndrome and obesity : targets and therapy 20180613

<h4>Purpose</h4>Type 1 diabetes mellitus (DM1) is one of the most common chronic diseases observed during childhood. The incidence of DM1 is increasing worldwide, and there is currently no way to prevent or delay the onset or to cure the disease. Most diseases, including diabetes, stem from abnormalities in the functioning of proteins, and some studies have reported the expression of protein variation to be involved in the development of DM1. Thus, the aim of this study was to investigate the di ...[more]

PMID: 29942143

Similar Datasets

Project description:BackgroundIndividuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict.MethodsWe designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which "omics" studies will be built. Cases (n = 535) and controls (n = 895) were defined as having an annual eGFR decline of ≥3 and <1 mL/min/1.73 m2, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques.ResultsAt baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m2, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was -5.7 and 0.6 mL/min/1.73 m2, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68-0.79; p < 0.001).ConclusionKnown risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.

Dataset Information

Proteomic analysis to identify candidate biomarkers associated with type 1 diabetes.

Purpose

Materials and methods

Results

Conclusion

Publications

Proteomic analysis to identify candidate biomarkers associated with type 1 diabetes.

Similar Datasets

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