Proteomic-Biostatistic Integrated Approach for Finding the Underlying Molecular Determinants of Hypertension in Human Plasma

Abstract

Despite advancements in lowering blood pressure, the best approach to lower it remains controversial because of the lack of information on the molecular basis of hypertension. We, therefore, performed plasma proteomics of plasma from patients with hypertension to identify molecular determinants detectable in these subjects but not in controls and vice versa. Plasma samples from hypertensive subjects (cases; n=118) and controls (n=85) from the InGenious HyperCare cohort were used for this study and performed mass spectrometric analysis. Using biostatistical methods, plasma peptides specific for hypertension were identified, and a model was developed using least absolute shrinkage and selection operator logistic regression. The underlying peptides were identified and sequenced off-line using matrix-assisted laser desorption ionization orbitrap mass spectrometry. By comparison of the molecular composition of the plasma samples, 27 molecular determinants were identified differently expressed in cases from controls. Seventy percent of the molecular determinants selected were found to occur less likely in hypertensive patients. In cross-validation, the overall R 2 was 0.434, and the area under the curve was 0.891 with 95% confidence interval 0.8482 to 0.9349, P <0.0001. The mean values of the cross-validated proteomic score of normotensive and hypertensive patients were found to be −2.007±0.3568 and 3.383±0.2643, respectively, P <0.0001. The molecular determinants were successfully identified, and the proteomic model developed shows an excellent discriminatory ability between hypertensives and normotensives. The identified molecular determinants may be the starting point for further studies to clarify the molecular causes of hypertension.