Evidence map›Paper›PMID 41541770›Full record

ArticleKidney international reports2026

Polygenic Risk Scores Predicting Estimated GFR Validated With Iohexol Clearance.

Bjørn O Eriksen, Matthis Kretzler, Viji Nair, Inger T T Enoksen, Stein Hallan, Jon V N Porserud, Ludvig Rinde, Toralf Melsom

Abstract read
In one paragraph

Article in Kidney international reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Bjørn O EriksenMetabolic and Renal Research Group, UiT The Arctic University of Norway, Tromsø, Norway.
Matthis KretzlerDivision of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Viji NairDivision of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Inger T T EnoksenMetabolic and Renal Research Group, UiT The Arctic University of Norway, Tromsø, Norway.
Stein HallanDepartment of Nephrology, St Olav's Hospital, Trondheim, Norway.
Jon V N PorserudMetabolic and Renal Research Group, UiT The Arctic University of Norway, Tromsø, Norway.
Ludvig RindeMetabolic and Renal Research Group, UiT The Arctic University of Norway, Tromsø, Norway.
Toralf MelsomMetabolic and Renal Research Group, UiT The Arctic University of Norway, Tromsø, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Genome-wide association studies (GWAS) have identified hundreds of single nucleotide variants (SNVs) associated with estimated glomerular filtration rate (eGFR). eGFR has been used as a proxy phenotype because of the complexity and cost of measured GFR (mGFR) in large studies. Because eGFR is influenced by non-GFR factors, these GWAS results may be biased compared with a hypothetical study using mGFR. We aimed to investigate this by comparing aggregate measures of genetic effects on mGFR and eGFR. Methods: We studied 1492 persons from the Renal Iohexol Clearance Survey (RENIS) cohort, a representative sample of the general population in Northern Norway without preexisting cardiovascular disease, kidney disease, or diabetes. We measured iohexol-clearance, and genotyping was performed with a microarray chip enriched for GFR-related SNVs. We compared the performance of 3 published polygenic risk scores (PGS) developed for creatinine-based eGFR (eGFRcr), narrow-sense heritability (h Results: The performance of the PGS differed for mGFR and the 3 eGFRs, with best performance for prediction of eGFRcr ( Conclusions: SNVs with non-GFR effects on creatinine and cystatin-C influence GWAS results. The results of GWAS using eGFR should be validated using experimental and other more precise methods.

Indexed as

chronic kidney diseaseestimated glomerular filtration rategenetic expressionglomerular filtration ratemeasured glomerular filtration rate

Identifiers

PMID41541770
PMCPMC12799510

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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.