| Title: | Proteomic risk score for early prediction of kidney disease progression in individuals with APOL1 high-risk genotypes |
| Journal: | Nature Medicine |
| Published: | 15 Apr 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41986737/ |
| DOI: | https://doi.org/10.1038/s41591-026-04337-2 |
Publication 17996
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Abstract
Individuals of African ancestry carrying APOL1 (apolipoprotein L1) high-risk genotypes face a markedly increased risk of kidney failure, yet tools to identify those individuals likely to progress to chronic kidney disease are lacking. Here we profiled plasma proteomes of 851 Penn Medicine BioBank participants of African ancestry (285 males and 566 females) with APOL1 high-risk genotypes and preserved estimated glomerular filtration rate (eGFR) (≥60 ml min−1 1.73 m−2). Using elastic net Cox regression adjusted for age, sex, eGFR and albuminuria, we derived a nine-protein APOL1 Proteomic Risk Score (APRS) that predicts a composite outcome of ≥40% eGFR decline, kidney failure or death. APRS achieved a time-dependent area under the receiver operating characteristic curve (tAUC) of 86.5%, outperforming the Kidney Failure Risk Equation (66.1%) and polygenic risk scores, with 10-year event rates of 62.5% versus 3.3% across risk quintiles. External validation in Atherosclerosis Risk in Communities and UK Biobank cohorts confirmed robust accuracy (tAUC 82-85%) and consistent performance across demographic and clinical subgroups. Plasma levels of APRS component proteins correlated with kidney tissue fibrosis and tubular injury pathways, indicating strong biological plausibility. By enabling early and accurate prediction of disease progression in APOL1 high-risk individuals, APRS bridges the gap between genetic susceptibility and clinical translation. This scalable and biologically informed approach provides a precision medicine framework for early intervention and may accelerate development of APOL1-targeted therapies to reduce kidney disease disparities.</p>
24 Authors
- Chenyu Li
- Shola M. Richards
- Ghazal Quinn
- Amin Abedini
- Minyan Zhu
- Tanya Verma
- Samer Mohandes
- Rebecca Pitts
- Vesna Barros
- Xiazi Qiu
- Taehwan Shin
- Joseph J. Loureiro
- Nancy Finkel
- Aditya Surapaneni
- Josef Coresh
- Morgan E. Grams
- Anil Karihaloo
- Hongzhe Li
- Anurag Verma
- Marylyn Ritchie
- Daniel J. Rader
- William F. Dietrich
- Lori L. Jennings
- Katalin Susztak
1 Application
| Application ID | Title |
|---|---|
| 273810 | Discovery and validation of blood biomarkers from proteomics as predictors of kidney disease progression |
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