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Integrating personomics into precision medicine.

Karolin Rose Krause1, Philippe Ravaud1, Viet-Thi Tran1

  • 1Centre de Recherche en Epidémiologie et Statistiques (CRESS UMR 1153), Université Paris Cité, 1 Parvis Notre Dame, Paris 75004, France.

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Summary

Precision medicine needs to incorporate "personomics"—patient psychosocial and contextual factors—to improve treatment engagement and outcomes. Research is needed to systematically measure these factors in clinical trials for personalized medicine.

Keywords:
Heterogeneity of treatment effectsPersonalizationPersonomicsPrecision medicinePsychosocial and contextual factorsSocial determinants of healthTreatment engagement

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Area of Science:

  • Health Services Research
  • Behavioral Medicine
  • Precision Medicine

Background:

  • Precision medicine advances treatments based on biological markers, but often overlooks psychosocial and contextual patient factors ('personomics').
  • These personomic factors influence patient engagement and can modify therapeutic intervention effects.
  • Current clinical practice intuitively uses personomic factors, but lacks clinical trial data on their impact on treatment response heterogeneity.

Purpose of the Study:

  • To clarify the conceptual scope of 'personomics'.
  • To propose a concept map of personomic factors.
  • To outline a research agenda for integrating personomic factors into therapeutic evaluations and precision medicine.

Main Methods:

  • Systematic Medline search for frameworks of psychosocial/contextual patient factors.
  • Synthesized extracted factors into a concept map using ecological systems theory and the COM-B model.
  • Applied the framework to case examples to identify additional factors.

Main Results:

  • Identified 41 personomic factors influencing treatment engagement and adherence.
  • Factors include stable traits (e.g., gender, personality) and dynamic elements (e.g., health literacy, illness perception).
  • These factors impact how patients engage with and follow care plans.

Conclusions:

  • A systematic, data-driven approach is necessary to measure personomic factors in clinical trials.
  • Integrating personomic data will enhance evidence-based personalization of treatment plans.
  • This integration is crucial for advancing precision medicine.