Clinical Value of Plasma Renin Estimation in the Management of Hypertension
Author(s) -
Morris J. Brown
Publication year - 2014
Publication title -
american journal of hypertension
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.009
H-Index - 136
eISSN - 1941-7225
pISSN - 0895-7061
DOI - 10.1093/ajh/hpu113
Subject(s) - medicine , plasma renin activity , cardiology , renin–angiotensin system , estimation , blood pressure , value (mathematics) , statistics , mathematics , management , economics
In physics, pressure is defined as force/area (Figure 1). From this, a number of further elegant equations can follow. John Laragh’s acuity was to recognize the intrinsically binary nature of blood pressure. In medicine today, our ability to discover individual molecules contributing to disease has had the opposite impact to what was perhaps anticipated: the sheer number discovered by techniques such as genomewide association studies has rendered common diseases even more complex than anticipated.1 But however complex and continuous the spectrum, the rainbow reminds us that there are just 2 ends to a spectrum and some remarkably distinct patterns in between. John’s elucidation of the volume/vasoconstriction dichotomy was a direct descendant from Poiseuille’s physics, followed by a lifetime—indeed 2 lifetimes by ordinary career-length standards!—of experiment and teaching.2 My own rainbow vision was a more Damascene moment, following a study that started with a completely different hypothesis. In 1996, the prevailing view was that essential hypertension might be due to allelic differences in about 5 main genes, with angiotensinogen recently reported as one of these. I wished to test first whether there is true variability between patients in their responsiveness to different antihypertensive drug classes and, if so, whether there might be 4 patterns of response corresponding to the mechanism of action of the 4 main drug classes. To detect and measure variability, we designed what I came to call rotation studies—crossover comparison of multiple drugs.
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