Comparison of the serum whole molecular composition with the serum metabolome to acquire the pathophysiological state

Omics Sciences serve as an essential tool to advance precision medicine. Since conventional omics sciences rely on laborious, complex and time-consuming analytical processes, this study evaluated whether the serum molecular fingerprint, captured by FTIR spectroscopy, could predict mortality risk in critically ill patients. Both the whole serum and the serum metabolome (i.e., serum after removal of macromolecules) were analyzed. PCA-LDA models demonstrated strong performance in predicting patients’ pathophysiological state. A significantly more accurate model for predicting the patients’ pathophysiological state was achieved using the serum metabolome (94%) compared to the whole serum (81%). This is consistent with metabolomics, which provides a more direct view of the systems’ functionality. These promising results highlight the importance of FTIR spectroscopy analysis of the serum metabolome, offering a rapid, cost-effective, and high-throughput method for assessing patients' pathophysiological state.