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<p>Precision medicine requires biomarkers that stratify patients and improve clinical outcomes. Although longitudinal multi-omic analyses provide insights into pathological states, their utility in stratifying healthy individuals remains underexplored. We performed a cross-sectional integrative study of three omic layers, including genomics, urine metabolomics, and serum metabolomics/lipoproteomics, on a cohort of 162 individuals without pathological manifestations. We studied each omic layer separately and after integration, concluding that multi-omic integration provides optimal stratification capacity. We identified four subgroups and, for a subset of 61 individuals, longitudinal data for two additional time-points allowed us to evaluate the temporal stability of the molecular profiles of each identified subgroup. Additional functional annotation uncovered accumulation of risk factors associated with dyslipoproteinemias in one subgroup, suggesting targeted monitoring could reduce future cardiovascular risks. Overall, our methodology uncovers the potential of multi-omic profiling to serve as a framework for precision medicine aimed at early prevention strategies.</p>

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Multi-omic integration sets the path for early prevention strategies on healthy individuals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月3日

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  • Dimitrios Kioroglou,
  • Rubén Gil-Redondo,
  • Nieves Embade,
  • Maider Bizkarguenaga,
  • Ricardo Conde,
  • Oscar Millet,
  • José M. Mato,
  • Urko M. Marigorta

摘要

Precision medicine requires biomarkers that stratify patients and improve clinical outcomes. Although longitudinal multi-omic analyses provide insights into pathological states, their utility in stratifying healthy individuals remains underexplored. We performed a cross-sectional integrative study of three omic layers, including genomics, urine metabolomics, and serum metabolomics/lipoproteomics, on a cohort of 162 individuals without pathological manifestations. We studied each omic layer separately and after integration, concluding that multi-omic integration provides optimal stratification capacity. We identified four subgroups and, for a subset of 61 individuals, longitudinal data for two additional time-points allowed us to evaluate the temporal stability of the molecular profiles of each identified subgroup. Additional functional annotation uncovered accumulation of risk factors associated with dyslipoproteinemias in one subgroup, suggesting targeted monitoring could reduce future cardiovascular risks. Overall, our methodology uncovers the potential of multi-omic profiling to serve as a framework for precision medicine aimed at early prevention strategies.