Dataset Information

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease.

ABSTRACT: The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice.

SUBMITTER: Pena MJ

PROVIDER: S-EPMC4969331 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease.

Pena Michelle J MJ Mischak Harald H Heerspink Hiddo J L HJ

Diabetologia 20160625 9

The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many ...[more]

PMID: 27344310

Similar Datasets

Project description:Background and objectivesProtein carbamylation is a consequence of uremia and carbamylated lipoproteins contribute to atherogenesis in CKD. Proteins can also be carbamylated by a urea-independent mechanism, and whether carbamylated lipoproteins contribute to the progression of CKD has not been investigated.Design, setting, participants, & measurementsA case-control study was performed to determine whether there were changes in plasma levels of carbamylated lipoproteins in individuals with type 2 diabetes with eGFR >60 ml/min per 1.73 m2 compared with a group of age- and sex-matched healthy controls. A cohort of 1320 patients with type 2 diabetes with baseline eGFR ≥30 ml/min per 1.73 m2 was longitudinally followed up to evaluate the association between carbamylated lipoproteins and progression of CKD. The primary kidney outcome was defined as doubling of serum creatinine and/or initiation of KRT during follow-up. Plasma carbamylated LDLs and HDLs was measured by ELISA.ResultsIn individuals with diabetes with eGFR >60 ml/min per 1.73 m2, both plasma carbamylated LDL and HDL levels were higher compared with healthy controls (P<0.001). After a mean follow-up of 9 years of the diabetic cohort, individuals in the top quartile of carbamylated LDL (hazard ratio, 2.21; 95% confidence interval, 1.42 to 3.46; P<0.001) and carbamylated HDL (hazard ratio, 4.53; 95% confidence interval, 2.87 to 7.13; P<0.001) had higher risk of deterioration of kidney function compared with those in the lowest quartile. On multivariable Cox regression analysis, plasma carbamylated LDL was no longer associated with kidney outcome after adjusting for baseline eGFR and potential confounding factors. However, the association between plasma carbamylated HDL and kidney outcome remained significant and was independent of HDL cholesterol.ConclusionsPlasma carbamylated HDL but not carbamylated LDL was independently associated with progression of CKD in patients with type 2 diabetes.

Project description:BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717-0.817), AUC of 0.780(95%CI: 0.721-0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.

Dataset Information

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease.

Publications

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets