Biomarkers in clinical epidemiology studies

Abstract

This paper discusses the use of biomarkers in clinical practice and biomedical research. Biomarkers are measurable characteristics that can be used to indicate the presence or absence of a disease or to track the progression of a disease. They can also be used to predict how a patient will respond to a particular treatment. Biomarkers have enriched clinical practice and disease prognosis by providing measurable characteristics that indicate biological processes. They offer valuable insights into disease susceptibility, progression, and treatment response, aiding drug development and personalized medicine. However, developing and implementing biomarkers come with challenges that must be addressed. Rigorous testing, standardization of assays, and consideration of ethical factors are crucial in ensuring the reliability and validity of biomarkers. Reliability is vital in biomarker research. It ensures accurate measurements by preventing biases and facilitating robust correlations with outcomes. Conversely, validation examines which and how many biomarkers correspond to theoretical constructs and external criteria, establishing their predictive value. Multiple biomarkers are sometimes necessary to represent the complex relationship between exposure and disease outcomes accurately. Susceptibility factors are pivotal in disease states' complex interaction among genetic and environmental factors. Gaining a comprehensive understanding of these factors is essential for effectively interpreting biomarker data and maximizing their clinical usefulness. Using well-validated biomarkers can improve diagnoses, more effective treatment evaluations, and enhanced disease prediction. This, in turn, will contribute to better patient outcomes and drive progress in medicine.

Keywords: CKD; biomarkers; cardiovascular; epidemiology; inflammation.

Figure 1:

Possibilities of application of biomarkers in research across the exposure–prognosis series. Internal dose (the quantity of the toxic exposure, be it an endogenous factor such as hyperglycaemia, or external factors such as air pollutants) may predict the clinical disease and prognosis. Each element of the exposure–prognosis pathway can be analysed in the relationship to the other elements. In all, there are 21 possibilities.

Figure 2:

The risk of disease for genetic markers depends on the intensity of exposure to environmental factors, the strength of genotype–environment interactions, and the nature of the environmental effect in relation to the genotype. Reference risk is the risk in the undiseased population (the broken line). Six patterns of genetic and environmental interactions can influence this relationship. The first pattern involves the combined effect of genotype and exposure causing excess risk (e.g. phenylalanine and phenylketonuria leading to mental retardation). The second pattern is when an innocuous genotype is affected by an environmental trigger (e.g. xeroderma pigmentosa and sunlight-induced skin cancer). The third pattern shows a genotype associated with risk (G6PD deficiency), while the environmental exposure alone, i.e. eating fava beans carries no excess risk. The fourth pattern involves both genotype and environment contributing to disease risk (e.g. α-1 antitrypsin deficiency and smoking in pulmonary emphysema). The fifth pattern shows how the genotype's effect changes with the presence or absence of an environmental factor (e.g. sickle cell trait being protective against malaria but harmful in its absence).