How biomarker patterns can be utilized to identify individuals with a high disease burden: a bioinformatics approach towards predictive, preventive, and personalized (3P) medicine
| Autor: | Nina Bertele, Claudia Buss, Anat Talmon, Alexander Karabatsiakis |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Oncology
Personalized medicine (PPPM medicine.medical_specialty Aging Patient stratification Disease susceptibility Childhood maltreatment Clinical Research Internal medicine Intervention (counseling) Personalized medicine (PPPM/3PM) Drug Discovery Low cortisol medicine Depression (differential diagnoses) Disease burden Risk assessment business.industry Health Policy Prevention Research Biochemistry (medical) 3PM) Personalized medicine Good Health and Well Being Biomarker (medicine) Biomarker patterns Disease prevention business Psychiatric disorders |
| Zdroj: | The EPMA Journal The EPMA journal, vol 12, iss 4 |
| ISSN: | 1878-5085 1878-5077 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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