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<p>Chronic kidney disease (CKD) is a complex condition where the kidneys are damaged and progressively lose their ability to filter blood, 10% of the world population have the disease that often goes undetected until it is too late for intervention. Using the UK Biobank (UKBB) we constructed a CKD cohort of patients (<i>n</i> = 46,986) with genomic, clinical and demographic data available, a subset (<i>n</i> = 2,151) having also whole body Magnetic Resonance Imaging (MRI) scans. We used this multimodal cohort to successfully predict, from initially healthy patients, their 5-year outcomes for End-Stage Renal Disease (ESRD, <i>n</i> = 210, AUC = 0.804 <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\pm\)</EquationSource></InlineEquation> 0.03 with 5 fold cross-validation) and the larger cohort for validation to predict time-to ESRD and perform Genome-wide association studies (GWAS). Extracting important clinical, phenotypic and genetic features from the models, we were able to stratify the cohorts based on a novel set of significant previously unreported SNPs related to mitochondria/cell death, kidney development and function. In particular, we show that the risk allele of SNP rs1383063 present in 30% of the population irrespective of ancestry and putatively regulating <i>MAGI-1</i>, a gene expressed in the podocyte slit diaphragm, is a strong predictor of ESRD and stratifies male populations of older age.</p>

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Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Simona Rabinovici-Cohen,
  • Daniel E. Platt,
  • Toshiya Iwamori,
  • Itai Guez,
  • Sanjoy Dey,
  • Aritra Bose,
  • Michiharu Kudo,
  • Laura Cosmai,
  • Camillo Porta,
  • Akira Koseki,
  • Pablo Meyer

摘要

Chronic kidney disease (CKD) is a complex condition where the kidneys are damaged and progressively lose their ability to filter blood, 10% of the world population have the disease that often goes undetected until it is too late for intervention. Using the UK Biobank (UKBB) we constructed a CKD cohort of patients (n = 46,986) with genomic, clinical and demographic data available, a subset (n = 2,151) having also whole body Magnetic Resonance Imaging (MRI) scans. We used this multimodal cohort to successfully predict, from initially healthy patients, their 5-year outcomes for End-Stage Renal Disease (ESRD, n = 210, AUC = 0.804 \(\pm\) 0.03 with 5 fold cross-validation) and the larger cohort for validation to predict time-to ESRD and perform Genome-wide association studies (GWAS). Extracting important clinical, phenotypic and genetic features from the models, we were able to stratify the cohorts based on a novel set of significant previously unreported SNPs related to mitochondria/cell death, kidney development and function. In particular, we show that the risk allele of SNP rs1383063 present in 30% of the population irrespective of ancestry and putatively regulating MAGI-1, a gene expressed in the podocyte slit diaphragm, is a strong predictor of ESRD and stratifies male populations of older age.