Integrating personomics into precision medicine

Karolin Rose Krause; Philippe Ravaud; Viet-Thi Tran

doi:10.1016/j.jclinepi.2025.111859

Integrating personomics into precision medicine

J Clin Epidemiol. 2025 May 30:184:111859. doi: 10.1016/j.jclinepi.2025.111859. Online ahead of print.

Authors

Karolin Rose Krause¹, Philippe Ravaud², Viet-Thi Tran²

Affiliations

¹ Centre de Recherche en Epidémiologie et Statistiques (CRESS UMR 1153), Université Paris Cité, 1 Parvis Notre Dame, Paris 75004, France. Electronic address: karolin.krause@u-paris.fr.
² Centre de Recherche en Epidémiologie et Statistiques (CRESS UMR 1153), Université Paris Cité, 1 Parvis Notre Dame, Paris 75004, France.

PMID: 40451477
DOI: 10.1016/j.jclinepi.2025.111859

Abstract

Background and objective: Precision medicine has made advances in tailoring treatments to biological patient markers, but has largely overlooked patients' psychosocial and contextual factors, or "personomics." Such factors can modify the effect of therapeutic interventions by influencing how patients engage with treatment. While clinicians often intuitively consider personomic factors in clinical practice, there is a lack of data from clinical trials on how these factors relate to heterogeneity in treatment response. This viewpoint seeks to clarify the conceptual scope of "personomics," propose a concept map of personomic factors, and outline a research agenda to integrate personomic factors into therapeutic evaluations and precision medicine.

Methods: We conducted a systematic Medline search to identify articles presenting frameworks, taxonomies, or models of psychosocial or contextual patient factors relevant to tailoring treatment. We extracted and synthesized these factors and organized them into a concept map drawing on ecological systems theory and the COM-B model of behavior. We then applied this draft framework to 14 case examples of treatment adaptation drawn from essays in the American Journal of Medicine's personomics series, to identify additional factors not captured in the reviewed literature.

Results: Our search retrieved 7 eligible articles. We identified 41 personomic factors that may affect treatment outcomes by influencing how patients engage with and follow their care plans. These include relatively stable factors-such as gender, age, education, cognitive ability, or personality traits-and more dynamic ones, such as health literacy, burden of treatment, or illness perception, which may change over the course of treatment.

Conclusion: To facilitate the evidence-based consideration of personomic patient factors for personalizing treatment plans, a systematic, data-driven effort is needed to measure the relevant factors in clinical trials.

Keywords: Heterogeneity of treatment effects; Personalization; Personomics; Precision medicine; Psychosocial and contextual factors; Social determinants of health; Treatment engagement.