Evidence map›Paper›PMID 31222504›Full record

ArticleDiabetologia2019

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

Marco Colombo, Erkka Valo, Stuart J McGurnaghan, Niina Sandholm, Luke A K Blackbourn, R Neil Dalton, David Dunger, Per-Henrik Groop, Paul M McKeigue, Carol Forsblom and 2 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetologia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 54 citations in OpenAlex.

  1. Urinary biomarkers of diabetic kidney disease.World journal of diabetes · 2026
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  13. Up-Date on Diabetic Nephropathy.Life (Basel, Switzerland) · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 3 countries.

Marco ColomboUsher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Erkka ValoFolkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Stuart J McGurnaghanMRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK.
Niina SandholmFolkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Luke A K BlackbournMRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK.
R Neil DaltonWellChild Laboratory, Evelina London Children's Hospital, Guy's and St Thomas' National Health Service Foundation Trust, London, UK.
David DungerDepartment of Paediatrics, University of Cambridge, Cambridge, UK.
Per-Henrik GroopFolkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Paul M McKeigueUsher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Carol ForsblomFolkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Helen M ColhounMRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK. helen.colhoun@igmm.ed.ac.uk.ORCID 0000-0002-8345-3288
FinnDiane Study Group and the Scottish Diabetes Research Network (SDRN) Type 1 Bioresource Collaboration
University of Helsinki · FIWestern General Hospital · GBUniversity of Edinburgh · GBNational Health Service · GBUniversity of Cambridge · GB

Funding

Chief Scientist Office ETM/47Diabetes UK 10/0004010Medical Research Council G0600717
6 · The paper itself

Abstract

aims/hypothesisWe aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes.

methodsWe 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

resultsFor final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO, and nine proteins and five metabolites or tryptic peptides in FinnDiane, beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p < 10 CONCLUSIONS/

interpretationAmong a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories.

Indexed as

AdultBayes TheoremBiomarkersChromatography, LiquidDiabetes Mellitus, Type 1Diabetic NephropathiesDisease ProgressionFemaleGlomerular Filtration RateHumansLogistic ModelsMaleMiddle AgedTandem Mass SpectrometryBiomarkersClinical scienceEpidemiologyMetabolomicsNephropathyProteomics

Identifiers

PMID31222504
PMCPMC6677704
OpenAlexW2947792589

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.