Biomarkers in clinical epidemiology studies

Author:

Zoccali Carmine123ORCID,Tripepi Giovanni45,Stel Vianda67,Fu Eduard L8,Mallamaci Francesca458,Dekker Friedo8,Jager Kitty J89

Affiliation:

1. Renal Research Institute , New York , USA

2. Institute of Molecular Biology and Genetics (Biogem) , Ariano Irpino , Italy

3. Associazione Ipertensione Nefrologia Trapianto Renale (IPNET), c/o Nefrologia, Grande Ospedale Metropolitano , Reggio Calabria , Italy

4. CNR-IFC, Institute of Clinical Physiology, Research Unit of Clinical Epidemio

5. logy

6. ERA Registry, Amsterdam UMC location and the University of Amsterdam, Department of Medical Informatics , Amsterdam , The Netherlands

7. Amsterdam Public Health, Quality of Care , Amsterdam , The Netherlands

8. Department of Clinical Epidemiology, Leiden University Medical Center , Leiden , The Netherlands

9. Nephrology, Dialysis and Transplantation Unit, Azienda Ospedaliera “Bianchi-Melacrino-Morelli” Grande Ospedale Metropolitano of Reggio Calabria , Italy

Abstract

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.

Publisher

Oxford University Press (OUP)

Reference16 articles.

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