# Polygenic Risk Scores Predicting Estimated GFR Validated With Iohexol Clearance

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

PMC · DOI: 10.1016/j.ekir.2025.10.016 · Kidney International Reports · 2025-10-29

## TL;DR

This study compares genetic risk scores for kidney function using measured and estimated GFR methods, finding that genetic effects are stronger with measured GFR.

## Contribution

Validates polygenic risk scores using measured GFR instead of estimated GFR, revealing stronger genetic associations.

## Key findings

- Polygenic risk scores predicted eGFRcr better than other methods (P < 0.05).
- Genetic effects were 11-46% stronger for measured GFR than estimated GFR in most comparisons.
- Measured GFR showed higher heritability (0.47) than all estimated GFR methods.

## Abstract

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.

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 (h2) and the mean effect of SNVs on mGFR, eGFRcr, cystatin C–based eGFR (eGFRcys) and eGFRcr-cys.

The performance of the PGS differed for mGFR and the 3 eGFRs, with best performance for prediction of eGFRcr (P < 0.05). However, when the beta coefficients of the SNVs in the 3 PGS were estimated in the RENIS-cohort, their magnitude was 11% to 46% greater for mGFR than for the 3 eGFR methods in 8 of 9 comparisons (P < 0.05). mGFR had higher h2 (0.47) than eGFRcr (0.21), eGFRcys (0.37), and eGFRcr-cys (0.42).

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.

## Linked entities

- **Diseases:** kidney disease (MONDO:0001343), cardiovascular disease (MONDO:0004995), diabetes (MONDO:0005015)

## Full-text entities

- **Genes:** CST3 (cystatin C) [NCBI Gene 1471] {aka ADLDWA, ARMD11, HEL-S-2}
- **Diseases:** kidney disease (MESH:D007674), diabetes (MESH:D003920), cardiovascular disease (MESH:D002318)
- **Chemicals:** creatinine (MESH:D003404), Iohexol (MESH:D007472), Renal Iohexol (-)

## Full text

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## Figures

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## References

64 references — full list in the complete paper: https://tomesphere.com/paper/PMC12799510/full.md

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Source: https://tomesphere.com/paper/PMC12799510