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

doi:10.1007/s13167-021-00255-0

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

EPMA J. 2021 Sep 29;12(4):507-516. doi: 10.1007/s13167-021-00255-0. eCollection 2021 Dec.

Authors

Nina Bertele^{1

2}, Alexander Karabatsiakis³, Claudia Buss^{2

4}, Anat Talmon¹

Affiliations

¹ Psychology Department, Stanford University, Stanford, CA USA.
² Institute of Medical Psychology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
³ Institute of Psychology, Department of Clinical Psychology-II, University of Innsbruck, Innsbruck, Austria.
⁴ Development, Health and Disease Research Program, Department of Pediatrics, University of California Irvine, Irvine, CA USA.

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-021-00255-0.

Keywords: Biomarker patterns; Childhood maltreatment; Patient stratification; Personalized medicine (PPPM/3PM); Psychiatric disorders; Risk assessment.