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<p>Interest in aging ‘omic’ biomarkers has grown due to their ability to quantify biological age. Most of these biomarkers have been derived in blood and fall into many diverse categories, yet relatively little is known about their correlative patterns, especially between biomarkers from different categories. Here we present the <i>OmniAge</i> R and Python package, a collection of 413 aging omic biomarkers representing 12 different categories, including traditional epigenetic clocks, epigenetic mitotic clocks, DNA methylation-based proxies for clonal hematopoiesis and inflammaging, causal clocks, cell-type specific epigenetic clocks and single-cell transcriptomic clocks. By studying their inter-class correlations across large blood datasets, we reveal associations of mitotic age with clonal hematopoiesis subtypes and causal clocks, which are predictive of cancer risk. Using proxies of serum protein levels, we further dissect associations with mitotic clocks, clonal hematopoiesis and causal clocks into distinct biological processes mapping to key aging pathways. Applying <i>OmniAge</i> to multi-modal data of sorted immune cell-types reveals that age-acceleration derived from transcriptomic and epigenetic clocks correlate, but that this is driven by underlying cell-type heterogeneity. In summary, the <i>OmniAge</i> package is an exploratory tool for evaluating large numbers of aging omic biomarkers, and to aid discovery and generate new hypotheses.</p>

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The OmniAge compendium of aging omic biomarkers links mitotic clocks to clonal hematopoiesis and causality

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Zhaozhen Du,
  • Yunchao Ling,
  • Huige Tong,
  • Xiaolong Guo,
  • Andrew E. Teschendorff

摘要

Interest in aging ‘omic’ biomarkers has grown due to their ability to quantify biological age. Most of these biomarkers have been derived in blood and fall into many diverse categories, yet relatively little is known about their correlative patterns, especially between biomarkers from different categories. Here we present the OmniAge R and Python package, a collection of 413 aging omic biomarkers representing 12 different categories, including traditional epigenetic clocks, epigenetic mitotic clocks, DNA methylation-based proxies for clonal hematopoiesis and inflammaging, causal clocks, cell-type specific epigenetic clocks and single-cell transcriptomic clocks. By studying their inter-class correlations across large blood datasets, we reveal associations of mitotic age with clonal hematopoiesis subtypes and causal clocks, which are predictive of cancer risk. Using proxies of serum protein levels, we further dissect associations with mitotic clocks, clonal hematopoiesis and causal clocks into distinct biological processes mapping to key aging pathways. Applying OmniAge to multi-modal data of sorted immune cell-types reveals that age-acceleration derived from transcriptomic and epigenetic clocks correlate, but that this is driven by underlying cell-type heterogeneity. In summary, the OmniAge package is an exploratory tool for evaluating large numbers of aging omic biomarkers, and to aid discovery and generate new hypotheses.