Scholarlink

<p>Predictive safety risk biomarkers for drug-induced liver injury (DILI) are critically needed to enhance patient risk stratification and minimize adverse outcomes. This study aims to identify genomic markers associated with increased mortality in toxic liver disease, using the KEM® (Knowledge Extraction and Management) explainable Artificial Intelligence platform. From 225 participants diagnosed with toxic liver disease within the UK Biobank cohort, data were consolidated, including survival outcomes, clinical phenotypes, comorbidities, and 36 394 genomic single nucleotide polymorphisms (SNPs) focusing on liver-related pathways. Fifteen SNPs were found to be significantly associated with increased mortality risk, notably rs73158145 in the PRKAG2 gene. Predictive models built on these selected SNPs achieved a mean accuracy of 85%, outperforming models without pre-selection (68.9% accuracy). Further validation in independent cohorts is planned to confirm the clinical relevance of these biomarkers.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Genomic biomarkers of liver toxicity risk from UK Biobank data

  • 期刊论文

  • 发表日期：

    2025年10月28日

查看全文
  • Anna Shteto,
  • Jawad Boulahfa,
  • Adrien Etcheto,
  • Yann Zhong,
  • Martin Kindermans,
  • Frederic Parmentier,
  • Federico Goodsaid

摘要

Predictive safety risk biomarkers for drug-induced liver injury (DILI) are critically needed to enhance patient risk stratification and minimize adverse outcomes. This study aims to identify genomic markers associated with increased mortality in toxic liver disease, using the KEM® (Knowledge Extraction and Management) explainable Artificial Intelligence platform. From 225 participants diagnosed with toxic liver disease within the UK Biobank cohort, data were consolidated, including survival outcomes, clinical phenotypes, comorbidities, and 36 394 genomic single nucleotide polymorphisms (SNPs) focusing on liver-related pathways. Fifteen SNPs were found to be significantly associated with increased mortality risk, notably rs73158145 in the PRKAG2 gene. Predictive models built on these selected SNPs achieved a mean accuracy of 85%, outperforming models without pre-selection (68.9% accuracy). Further validation in independent cohorts is planned to confirm the clinical relevance of these biomarkers.