Abstract
This review article is based upon the findings of the role of different biomarkers in the progression of the diseases. These findings were from past studies and meta-analysis of the biomarker in cardiovascular and chronic kidney diseases (CKDs). It mainly focuses on the organ crosstalk of diseases because of different biomarkers such as proteomics, genomics, metabolomics, and bioinformatics. In this reviewing process, we analyzed some biomarkers responsible for various cardiovascular and CKDs, out of which we found few biomarkers which are responsible for indication and diagnosis of disease relating to both the systems. It was found that the MiR-21, a type of genomic biomarker C-reactive protein, N-terminal pro-B-type natriuretic peptide, high-sensitivity cardiac troponin T, and estimated glomerular filtration rate, types of proteomics biomarkers has shown role in the prediction of diseases. As well as we also reviewed research work from the past to analyze the relation between the disease and its clinical outcomes using machine learning, which can be used to create few models to predict the clinical outcomes. Out of all the algorithms used, they showing most promising results was XGBoost method. In the past, there were studies supporting the statement - algorithms created by the scientists gives variables relating to the disease. These variables can be used to predict the disease based on the levels of biomarkers present in the sample of the patients.