How biomarker patterns can be utilized to identify individuals with a high disease burden: a bioinformatics approach towards predictive, preventive, and personalized (3P) medicine

Nina Bertele, Alexander Karabatsiakis*, Claudia Buss, Anat Talmon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Prevalences of non-communicable diseases such as depression and a range of somatic diseases are continuously increasing requiring simple and inexpensive ways to identify high-risk individuals to target with predictive and preventive approaches. Using k-mean cluster analytics, in study 1, we identified biochemical clusters (based on C-reactive protein, interleukin-6, fibrinogen, cortisol, and creatinine) and examined their link to diseases. Analyses were conducted in a US American sample (from the Midlife in the US study, N = 1234) and validated in a Japanese sample (from the Midlife in Japan study, N = 378). In study 2, we investigated the link of the biochemical clusters from study 1 to childhood maltreatment (CM). The three identified biochemical clusters included one cluster (with high inflammatory signaling and low cortisol and creatinine concentrations) indicating the highest disease burden. This high-risk cluster also reported the highest CM exposure. The current study demonstrates how biomarkers can be utilized to identify individuals with a high disease burden and thus, may help to target these high-risk individuals with tailored prevention/intervention, towards personalized medicine. Furthermore, our findings raise the question whether the found biochemical clusters have predictive character, as a tool to identify high-risk individuals enabling targeted prevention. The finding that CM was mostly prevalent in the high-risk cluster provides first hints that the clusters could indeed have predictive character and highlight CM as a central disease susceptibility factor and possibly as a leverage point for disease prevention/intervention.

Original languageAmerican English
Pages (from-to)507-516
Number of pages10
JournalEPMA Journal
Volume12
Issue number4
DOIs
StatePublished - Dec 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • Biomarker patterns
  • Childhood maltreatment
  • Patient stratification
  • Personalized medicine (PPPM/3PM)
  • Psychiatric disorders
  • Risk assessment

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