Identifying Biochemical Signatures in the General Population: Relations to Childhood Maltreatment and Diseases
Creators: name: Bertele, Nina
name: Karabatsiakis, Alexander
name: Talmon, Anat
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Medicine and Health Sciences

description: Clinically-used biomarkers such as C-reactive protein (CRP), interleukin 6 (IL6), fibrinogen, cortisol and creatinine are often assessed in standard diagnostics as well as for the treatment of diseases as they allow conclusions about the patient’s health on a systemic scale. In clinical routine, these biomarkers are usually evaluated in combination with other biomarkers as it is well-known that some of them contribute to similar physiological processes relevant for health and disease. Therefore, it is likely that some of these biomarkers are interrelated. However, these interactions of biomarkers and their effects have been rather underestimated in both clinical and research contexts. We aim at improving the knowledge about these biomarker interactions by employing a k-mean clustering approach based on the aforementioned clinically-relevant biomarkers CRP, IL6, Fibrinogen, Cortisol, Creatinine allowing us to account for linear and non-linear interactions among biomarker variables. In this way, individuals can be classified by distinct biochemical patterns. As robust associations of different diseases and biomarkers, that are included in the clustering process, exist in the literature, we will examine the link of the identified biochemical clusters to mental and somatic diseases in a U.S. population sample (N=1,234, retrieved from the Midlife in the United States (MIDUS) study). To do so, we will conduct z-tests comparing Odds Ratios (OR) for diseases between clusters. Furthermore, to test whether these results may be interculturally stable, we will validate the clusters as well as their relations to diseases in a sample of Japanese midlife adults (N=254, retrieved from the Midlife in Japan (MIDJA) study. In our secondary analysis, we will compare levels of self-reported childhood maltreatment (CM), which refers to any act of emotional, physical, and sexual abuse as well as emotional and physical neglect before 18 years of age (Gilbert et al., 2009), between clusters to uncover potential risk factors for certain cluster memberships and with that, for diseases. We will do so by using general linear models with the cluster variable as the predictor and controlling for sex, age, BMI, physical activity, sleep quality, alcohol and smoking habits as well as for multiple testing. Since CM has not been assessed in the MIDJA study, we will conduct these additional analyses only for the MIDUS sample. While the k-mean clustering will be an explorative approach for which we do not have a priori-hypotheses, we expect to find higher frequencies of diseases in clusters with high inflammation as compared to clusters with low inflammation. As for our secondary analyses involving CM, we hypothesize to find higher levels of CM in clusters with high inflammation as compared to clusters with low inflammation. References Gilbert, R., Widom, C. S., Browne, K., Fergusson, D., Webb, E., &amp; Janson, S. (2009). Burden and consequences of child maltreatment in high-income countries. The Lancet, 373(9657), 68–81. https://doi.org/10.1016/S0140-6736(08)61706-7