Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Biomarker panels associated with progression of renal disease in type 1 diabetes

20 Jun 2019

Abstract excerpt

We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes. We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Finnish Diabetic Nephropathy (FinnDiane) study. All had an entry eGFR between 30 and 75 ml min −1 [1.73 m] −2 , with those from FinnDiane being...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Chronic Kidney Disease and Diabetes
Share to Diabetes and associated disorders
Share to GDF15 and Related Biomarkers

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 31222504
DOI: 10.1007/s00125-019-4915-0

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Biomarker panels associated with progression of renal disease in type 1 diabetesPMID 31222504Read ↗

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney diseaseSemantic proximity Read ↗

Results of untargeted analysis using the SOMAscan proteomics platform indicates novel associations of circulating proteins with risk of progression to kidney failure in diabetesSemantic proximity Read ↗

Urine Proteomics Identifies Biomarkers for Diabetic Kidney Disease at Different StagesSemantic proximity Read ↗

Trajectories of kidney function in diabetes: a clinicopathological update.Semantic proximity Read ↗

Prediction of rapid kidney function decline using machine learning combining blood biomarkers and electronic health record dataSemantic proximity Read ↗

Prediagnostic Plasma Proteomic Signatures and Risk Stratification for Incident Chronic Kidney Disease in Type 2 DiabetesSemantic proximity Read ↗

Proteonano™: a novel deep proteomics platform with 1000-plex profiling capacity and picogram sensitivity and its application in diabetic kidney diseaseSemantic proximity Read ↗

Insights into the Novel Biomarkers Expressed in Diabetic Nephropathy: Potential Clinical ApplicationsSemantic proximity Read ↗

The "Picasso faces" of diabetic kidney disease - how the art of phenotyping and molecular biomarkers is transforming clinical nephrology: an observational study in patients with type 2 diabetes.Semantic proximity Read ↗

Association of Multiple Plasma Biomarker Concentrations with Progression of Prevalent Diabetic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.Semantic proximity Read ↗

Omics research in diabetic kidney disease: new biomarker dimensions and new understandings?Semantic proximity Read ↗

Prediction of diabetic kidney disease risk using machine learning models: a population-based cohort study of Asian adultsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.