Endocrinology Vol. 144, No. 6 2184-2190
Copyright © 2003 by The Endocrine Society
Minireview: Computer Simulations of Blood Pressure Regulation by the Renin-Angiotensin System
Nobuyuki Takahashi,
John R. Hagaman,
Hyung-Suk Kim and
Oliver Smithies
Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7525
Address all correspondence and requests for reprints to: Nobuyuki Takahashi, M.D., Ph.D., Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, 701 Brinkhous-Bullitt Building, Chapel Hill, North Carolina 27599-7525. E-mail: ntakaha{at}med.unc.edu.
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Abstract
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Gene targeting experiments in mice have been used by us and others to test whether quantitative changes in gene expression in the renin-angiotensin system affect blood pressure. Surprisingly, these studies showed that blood pressure does not change with mild quantitative changes in the expression of the angiotensin converting enzyme (ACE). Yet, ACE inhibitors are widely used for the treatment of hypertension. This apparent paradox motivated us to develop a simple computer simulation, which qualitatively reconciled the paradox. We have now improved the simulation by including blood pressure as an explicit variable and by adding the kallikrein-kinin system and feedback control of plasma renin via plasma angiotensin II levels. The new simulation now matches quantitative aspects of the experimental data and suggests that a decrease in bradykinin plays an important role in the increased risk of diabetic nephropathy associated with genetically determined higher levels of ACE activity. This emphasizes that the value of these types of simulation lies in the thoughts that they provoke rather than in their ability to replicate experimental data.
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Introduction
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HYPERTENSION IS A MAJOR risk factor for stroke, myocardial infarction, congestive heart failure, and end-stage renal disease (1). Importantly, "the higher the blood pressure the worse the prognosis" (1). But, much of its pathogenesis remains unknown. Lifton and colleagues (2, 3) have made great progress in understanding severe hypertension or hypotension caused by mutations in single genes. However, hypertension inherited in a simple Mendelian fashion is rare, and in most cases hypertension in the general population is not as severe as in these conditions. Because milder hypertension bears the major burden of excess deaths due to this condition (4), it is important to better understand the etiology of the common forms of essential hypertension. Approximately two thirds of the familial aggregation of blood pressure (BP) is explained by genetic factors and the remainder by cultural and environmental factors (5). A likely hypothesis is that hypertension is caused by combinations of small quantitative changes in the expression of genes combined with environmental factors. Some success has been achieved in studies directed toward identifying genes that influence the incidence of hypertension. One of the most successful is that described by Jeunemaitre et al. (6), who carried out linkage analysis of variants of the angiotensinogen gene (AGT) in relation to hypertension. One variant, 235T, was more frequent than its alternative, 235M, in hypertensive vs. normotensive individuals. In addition, a higher plasma AGT concentration was observed in the 235T-bearing individuals than in the individuals with 235M. However, these correlations and the linkage analysis do not prove that the changes in either the sequence and/or the expression of the AGT gene cause hypertension, because the observed association might be caused by another genetic difference linked to the AGT gene. Studies of this type cannot determine whether the differences in gene expression cause the increase in BP or are caused by it.
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Gene Titration
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Gene-targeting approaches in mice have been used to test the consequences of changes in the expression of genes of interest. Previous observations have shown that the expression of a protein is usually close to 50% of normal in individuals who are heterozygous for a loss-of-function mutation (7), whereas gene expression in trisomic individuals is close to 150% normal (8). Using these observations as a starting point, we developed a way to test whether a quantitative decrease or increase in the expression of a candidate gene alters BP. To do this, we generated mice that have one copy (heterozygotes for a gene disruption), two copies (wild type), three copies (heterozygotes for a gene duplication), and four copies (homozygotes for a gene duplication) of the gene of interest (9, 10, 11). We then tested whether these quantitative changes cause hypo- or hypertension. We call this procedure gene titration. The targeting constructs for producing gene disruption and gene duplication differ only in the topological assembly of the DNA pieces: gene disruption occurs as a consequence of a simple gene replacement following homologous recombination; gene duplication occurs by gap repair accompanying homologous recombination as illustrated in Fig. 1
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Figure 1. Two models of gene targeting. A, Gene disruption (knockout); B, gene duplication. The top lines represent the target gene, ENDOCRINOLOGY; the next lower lines, the targeting construct; the bottom lines, the targeted locus. Neo is the selectable marker gene. Note that the same DNA fragments are used to make targeting constructs for both gene disruption and duplication, but they are assembled differently.
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The gene titration experiments with the Agt gene were successful and proved that genetically determined increases or decreases in the expression of the gene cause increases or decreases in BP (9). Subsequently, Inoue et al. (12) demonstrated that the 235 polymorphism in the human AGT gene is tightly linked with a single polymorphic difference in the promoter of the gene (G at -6 with M at 235, and A at -6 with T at 235), and that the promoter with A at -6 is a stronger promoter than that with G at -6. Thus, the chain of causation in the human situation is essentially complete—the stronger promoter leads to a greater steady-state concentration of AGT, which in turn increases BP.
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Computer Simulations
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The gene ACE coding for the angiotensin converting enzyme (ACE) is another important candidate for affecting BP. Although ACE inhibitors are effective antihypertensive drugs, the BP of the animals in our gene titration experiment with the Ace gene was not affected by a nearly 3-fold difference in their plasma ACE activities (10). We previously approached this apparent paradox using a commercially available program (STELLA; High Performance Systems Inc., Hannover, NH) for simulating the behavior of complex interacting systems. Our simplest model equated angiotensin II levels with BP, and the model had no feedback regulation of renin production; nor did it include the kallikrein-kinin system (11). Additionally, because precise values for many of the variables and kinetic constants were not known, their relative values had to be estimated so that the simulation was largely qualitative rather than quantitative. Despite these shortcomings, the simulation increased our understanding of the renin-angiotensin system and particularly helped reconcile the apparent paradox between the effects on BP of genetic and drug-induced changes in ACE activity, which we discuss in more detail below.
One of the most powerful characteristics of developing computer simulations is that their sophistication can be increased progressively with experience and that they compel the investigator to think about how the components in the system interact with each other. Our current simulations now make BP an explicit variable under the control of three factors (Fig. 2). The first, as in the simple simulation, is the positive effect of angiotensin II. The second is the negative effect of bradykinin formed from kininogen by kallikrein and inactivated by ACE (13). The third factor is BP stat, which represents other physiological systems that maintain BP at some target value in the absence of angiotensin II and bradykinin. The relative magnitude of these three effects can be controlled in the simulation, and these effects are accessible to experimental determination. For example, the BP of a knockout mouse unable to synthesize AGT gives a numerical value of the pressure due to bradykinin and the other biological systems that are represented by BP stat.
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Figure 2. An improved computer simulation of BP. This simulation models the effects on BP of changes in the renin-angiotensin system and the kallikrein-kinin system and the actions of ACE inhibitors. These two systems have at least two intersections, via the phenotype BP and via the enzyme ACE, which acts on angiotensin I in the renin-angiotensin system and on bradykinin in the kallikrein-kinin system. The model has negative feedback from angiotensin II to renin production. Blue emphasizes gene products. Red emphasizes intermediates. Black emphasizes control circuits.
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An important feedback element is now included in our simulations, namely the negative feedback from angiotensin II to renin production (14). Our past experiments (15) have shown, in agreement with observations by others on the effects of chronic and severe inhibition of ACE by drugs (16), that renin synthesis is homeostatically adjusted largely by changes in the number of cells committed to its synthesis. Accordingly, a change in the number of cells synthesizing renin is one mechanism for changing renin production. Up- or down-regulation of renin production in cells already active in renin synthesis is a second mechanism. The signaling pathways that mediate these two mechanisms have not been fully identified but could include endocrine stimuli (for example, by angiotensin II acting directly on juxtaglomerular and macula densa cells) or neurological stimuli (for example, by cardiac and arterial baroreceptors that regulate sympathetic nerve activity) or stretch receptors in the wall of afferent arterioles (14, 17, 18). We therefore tested the effects on our simulation of incorporating feedback loops for the control of renin production that respond to angiotensin II levels and/or BP. The results of these tests showed that the effects of feedback regulation of renin by BP are minimal compared with the effects of feedback from angiotensin II levels. This is because when plasma AGT levels change, the percent change of BP is much smaller than that of angiotensin II. Indeed, as we discuss below, feedback regulation of renin by angiotensin II alone is sufficient to replicate our experimental observation without including feedback regulation of renin by BP.
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Simulation of Agt Gene Titration
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Figure 3 presents the results of applying the improved model to the same three experiments that we analyzed previously (11). Figure 3A illustrates the currently simulated changes that result from varying the mRNA expression of genes coding for Agt. The simulation assumed that mice heterozygous for the Agt gene disruption have liver Agt mRNA 50% of wild type as an approximation to the real situation. Mice homozygous for the Agt gene duplication proved, in actual experiments, to have liver Agt mRNA 125% of wild type, rather than the expected 200% (19); the simulation now assumes that homozygous gene duplication mice have the equivalent of 125% of the Agt gene. The results of the simulation are presented as plots of the effects of varying Agt mRNA expression on BP and on the plasma levels of peptides and proteins relative to wild type as 100%. We compared the expectations of the simulations (smooth lines) with observations from experiments with mice (colored dots with error bars). The experimental data are from previously described gene titrations of the Agt gene (9, 15) and of the Ace gene (10, 20). The experimental values for BP were obtained using computer-assisted tail-cuff procedure with unanesthetized mice (21). This procedure yields values that are reproducible and can typically discriminate between groups of animals with BP differences of 3–4 mmHg; the values approximate roughly to the mean BP (22, 23). The new simulation improves on our earlier efforts in several ways. It now includes BP, AGT, and renin as explicit variables in addition to angiotensin I and angiotensin II, and the matches between the simulations and the animal data are now quantitative. Particularly noticeable in the current simulation is its replication of an asymmetry in the renin responses: renin production increases much more when BP decreases than it decreases when BP increases. The simulation also now recapitulates the decrease in AGT to 35% wild type, which is less than the 50% level observed in most one-copy animals of other genes; this is the result of the feedback increase in renin. The simulation also emphasizes that, because of the renin feedback, the increase in BP when the Agt expression increases is smaller than the decrease in BP when the Agt expression decreases.
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Figure 3. Simulations of Agt, Ace gene titration and progressive ACE inhibition. A, Agt gene titration. The plasma AGT level becomes less than 50% in Agt one-copy, and when Agt gene expression increases the BP does not increase in proportional to the plasma AGT level because of the inhibition of renin production caused by negative feedback. BK, Bradykinin. The dots and error bars represent the mean and SE values of each parameter measured, as previously described (15 21 ), from gene titration experiments in mice expressed as percent of wild-type values (n  5). The primary sources of data are Refs. 9 and 15 . The levels expected in the human M235T polymorphism are shown. B, Ace gene titration. Mild quantitative changes in the expression of ACE do not affect plasma angiotensin II level or BP because of the concomitant changes in the level of angiotensin I. However, plasma BK level changes with changes in ACE expression. The levels expected in association with the human I/D polymorphism are shown. The dots and error bars represent the mean and SE values of each parameter measured, as previously described (15 21 ), from gene titration experiments in mice expressed as percent of wild-type values (n 5). The primary sources of data are Refs. 10 and 20 . C, Effects of ACE inhibition. When inhibition is virtually complete, the simulation shows that: 1) angiotensin I levels are high and plateaued; 2) angiotensin II levels are low; 3) renin production is markedly increased; 4) AGT levels are decreased by the increase in renin; 5) BK levels are increased by the virtual absence of ACE; and 6) BP is substantially lowered. The precise numerical values of the variables should not be interpreted literally, because they are sensitive to numerical assumptions incorporated in the simulations; trends in the effects of extensive ACE inhibition are, however, not affected by these assumptions.
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Simulation of Ace Gene Titration
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Figure 3B
illustrates the outcome of simulating the effects of varying the Ace mRNA expression from 50% (Ace one-copy) to 150% (Ace three-copy) of wild type (Ace two-copy) using the same program and numerical constants as in simulating the Agt gene titration. Again, in agreement with the quantitative experimental data from mice (10), shown as dots in Fig. 3B, and the previous simple qualitative simulation (11), the chief response is a progressive decrease in angiotensin I levels as Ace expression increases. When ACE decreases, plasma angiotensin I increases; as a consequence, the effects of the decrease in ACE activity are offset, so that angiotensin II production and BP are virtually unchanged. It is important to note, however, that this effect is solely a result of the steady-state kinetics of the system; no biological feedback is involved. Indeed, the introduction of renin feedback in the current simulation did not affect the response of angiotensin I and II to the changes in Ace gene expression compared with that observed in the previous simulation without renin feedback. Our observation of an absence of differences in BP between Ace one-copy and wild-type mice (10) is in agreement with data from Ace disruption mice generated by Esther et al. (Ref. 24 ; Ace +/+, 110 mm Hg; Ace+/-, 110 mm Hg; Ace-/- 73, mm Hg).
The simulation now includes the kallikrein-kinin system and matches observations in humans that the plasma level of bradykinin is inversely related to the mild changes in the expression of ACE that are seen in the insertion (I)/deletion (D) polymorphism (25). D/D homozygotes have a 25% increase, and I/I homozygotes have a 25% decrease in plasma ACE activity relative to the I/D heterozygotes (26). The simulation also replicates well our experimental observations that the Ace one-copy mice have a plasma bradykinin level 120% of wild type (11). We note that the absence of differences in BP between Ace one-copy and wild-type mice is strong presumptive evidence that systemic BP is not normally sensitive to bradykinin levels, and our simulations assume that this is the case. This assumption may require modification as more relevant experimental data are acquired.
An important distinction, which the simulation readily illustrates, is the contrasting effects of genetic variation in ACE activity on the plasma levels of angiotensin II compared with its effects on plasma bradykinin. Thus, as ACE activity increases, the plasma concentration of angiotensin II scarcely changes, but the concentration of bradykinin decreases substantially. This is because modest changes in ACE activity affect the steady-state plasma concentrations of its substrates but not of its products; and bradykinin is a substrate, whereas angiotensin II is a product of the enzyme. The distinction between these two effects has important consequences in relation to diabetic nephropathy, as is discussed below.
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Simulation of ACE Inhibition
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We return now to the apparent paradox that ACE inhibitors lower BP, whereas the genetically determined 50% decrease in ACE activity does not affect BP. The resolution of this contradiction is illustrated by the simulation presented in Fig. 3C using the same program and numerical constants as in simulating the Agt and Ace gene titration. (Note that the horizontal axis for ACE inhibition is logarithmic in the figure so that a wide range of ACE inhibition can be considered. Likewise, the levels of angiotensin I and renin are plotted as logarithmic to the base 10.) The simulation showed, in agreement with the experimental data, that mild inhibition of ACE, as in the Ace one-copy mice (equivalent to 50% ACE inhibition), causes an increase in angiotensin I, which offsets the decrease in ACE so that plasma angiotensin II and BP stay normal. However, if ACE is extensively inhibited, plasma angiotensin I levels plateau. The consequence is that further decreases in ACE activity are no longer offset and angiotensin II and BP therefore decrease. The reason why plasma angiotensin I eventually plateaus with further inhibition of ACE is because the conversion of angiotensin I to II becomes insignificant compared with its concentration-dependent renal clearance and its degradation by means other than conversion to angiotensin II. Mild changes in the expression of ACE, such as those seen in the human polymorphism and in the mouse gene titration experiments, are not sufficient to move the system away from the offset region to where ACE differences affect angiotensin II levels and BP. ACE inhibitors, on the other hand, accomplish this, as has already been well documented by Campbell et al. (27) in rats using the ACE inhibitor perindopril.
Our improved simulations are informative with respect to the different effects of homeostatic increases in renin synthesis caused by changes in Agt gene function compared with those caused by inhibition of ACE. Thus, an increase in renin synthesis of about 2-fold is induced in the Agt one-copy mice by the decrease in angiotensin II consequent to the genetically induced decrease in AGT (to 35% normal). This increase in renin causes an increase in the rate of angiotensin I formation, but this is partly abrogated by the decrease in the steady-state level of plasma AGT caused by the increased renin. The result is that BP is only partially corrected. The much more substantial increase (>10-fold) in renin synthesis induced by BP-lowering doses of ACE inhibitors likewise causes an increase in the rate of angiotensin I formation but now at the expense of a substantial decrease in AGT.
The current simulations include the feedback regulation of renin expression by circulating angiotensin II. In the future, we expect to incorporate other systems that regulate renin production, such as the effects of the NaCl concentration in luminal fluid on the macula densa. Additional systems that regulate salt and water reabsorption in the kidney to maintain body fluid, such as the aldosterone and natriuretic peptide systems, must also be considered, as should the tissue renin-angiotensin system, which may be an important determinant of BP (28), particularly in the kidney, in which tubular concentration of angiotensin II can be 1000 times that in plasma (29). As relevant data from mice become available, we plan to include them for more precise simulations of BP regulation.
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ACE Gene and Diabetic Nephropathy
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Diabetic nephropathy is the most common cause of end-stage renal disease (30). The presence of microalbuminuria is an early marker of diabetic nephropathy (31, 32), and overt proteinuria is a risk factor for cardiovascular disease (31, 32). Previous studies have shown that ACE inhibitors slow the development and progression of nephropathy in type 1 diabetic patients with macroalbuminuria, as well as those with microalbuminuria or normoalbuminuria (33, 34, 35, 36). The diabetes substudy of the Heart Outcomes Prevention Evaluation (HOPE; Ref. 37) has extended these observations by showing that, at similar BP, an ACE inhibitor results in a 24% greater decrease in the progression rate to overt nephropathy than placebo in patients with type 2 diabetes with normo- or microalbuminuria. Recent clinical studies have also proved the renoprotective effects of angiotensin II receptor blockers (ARB; Refs. 33, 38 and 39). An association between the onset and progression of diabetic nephropathy in type I diabetes and the D allele of the ACE gene has been reported (40, 41, 42, 43, 44, 45). The M235T polymorphism of the AGT gene showed no association. Proof was lacking, however, that the higher ACE level of individuals having the D allele causes the progression of diabetic nephropathy. To test this potential causative chain, Ace one-, two-, and three-copy mice were made diabetic by treatment with streptozotocin (46). In confirmation of our earlier studies (10), the BP of the untreated one-, two-, and three-copy mice did not differ. But when the mice were made diabetic, the BP of the Ace three-copy mice increased with time to 10–20 mm Hg higher than the BP of the one- and two-copy diabetic mice, or the control untreated mice of all three genotypes. The untreated nondiabetic mice of all three genotypes excreted approximately 10 µg albumin per 24 h in their urine throughout the experimental period, but, 12 wk after streptozotocin injection, the three-copy mice excreted 380 ± 48 µg albumin per 24 h, whereas the one- and two-copy mice excreted 113 ± 19 and 107 ± 16 µg albumin per 24 h. The effect of genotype was highly significant (P < 0.0001). Thus, the Ace three-copy mice show a more advanced phenotype of diabetic nephropathy than the one- and two-copy mice (46).
As discussed above, our computer simulations and animal experiments demonstrate that quantitative changes in the expression of Ace gene of similar magnitude to those seen in the I/D polymorphisms do not materially affect BP or plasma angiotensin II levels, but these changes in Ace gene expression do affect plasma bradykinin levels. These observations strongly implicate the lower bradykinin level of the Ace three-copy mice compared with the two- and one-copy mice with the progression of diabetic nephropathy. We are now testing this implication by making bradykinin B2 receptor knockout mice diabetic. If it is correct, low doses of ACE inhibitors that are able to increase bradykinin levels should be beneficial in preventing diabetic nephropathy, especially in ACE DD allele individuals. This expectation should not be interpreted as meaning that ARB will not be effective. Indeed, their renoprotective effects have been clearly demonstrated (30, 35, 36). The degree to which this is an effect of bradykinin level will require further study, because ARB also increase bradykinin production by increasing the activation of type II angiotensin II receptor (47, 48, 49).
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Importance of Heterozygotes
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Some comment is required on the paucity of published studies of heterozygous knockout animals. Clearly the study of homozygous knockout animals is attractive because of their usually more drastic phenotype, and because the phenotypes of homozygous knockouts are highly informative with respect to gene function. But the phenotypes of heterozygotes are more important for understanding the quantitative effects of genes, such as those likely to be relevant to the etiology of essential hypertension. If quantitative changes in the expression of a gene in heterozygous mice alter BP, then looking for polymorphisms affecting the expression of the same gene in humans becomes worthwhile. Examples include Agt, Agtr1a (angiotensin II receptor type 1), Bdkrb2 (bradykinin receptor type 2), Drd1 and 3 (dopamine receptors type 1 and 3), Nppa (ANP), and Npr1 (natriuretic peptide receptor 1; Ref. 11).
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Concluding Remarks
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In summary, homozygous gene knockouts tell us about gene function and are often predictive of useful drug targets. Heterozygotes tell us about quantitative gene effects and are often predictive of the effects of genetic polymorphisms on BP. Refinements of computer simulations of BP by introducing more precise feedback mechanisms and additional systems should allow a better understanding of the complex biological mechanisms that control BP and of the influence on this control of human genetic variations. Once we know the combinations of genetic variations of an individual, the design of diagnostic tests to detect polymorphisms that cause high BP should become possible. This in turn should eventually allow more precise choice of antihypertensive drugs, and even the adoption of ways to avoid the development of the condition or its serious sequelae. Finally, we stress that the most valuable aspect of attempting computer simulations is not the recapitulation of experiments but the stimulus to analytical thoughts that the attempts engender.
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Equations
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1) Change of [protein A] = (number of A genes*rate of producing A per gene)-(inactivation of A)
2) Change of [intermediateQ] = (rate of P to Q)-(rate of Q to R)-(clearance rate of Q)
3) Inactivation rate of A = [protein A]*inactivation constant for A
4) clearance rate of Q = [intermediateQ]*clearance constant for Q
5) AGT_conversion = (AGT*Renin*Renin_kcat)/(AGT+Renin_Km)
6) ANG_I_conversion = ANG_I*ACE_kcat*ACE/(ANG_I+ACE_Km)
7) Renin_production = renin_Genes*Renin_per_gene*renin_producing_signal
8) renin_producing_signal = GRAPH(ANG_II)
(0.00, 800), (0.6, 400), (1.20, 200), (1.80, 100), (2.40, 50.0), (3.00, 24.0), (3.60, 12.0), (4.20, 6.00), (4.80, 4.00), (5.40, 3.20), (6.00, 1.50)
9) ACE_production = Ace_Genes*ACE_per_gene/2ACEInh
10) BP_press = ANG_II*25+BP_stat-BRADYKININ*0.02
11) K’ogen_conversion = KININOGEN*Kall’n_kcat*KALLIKREIN/(KININOGEN+Kall’n_Km)+8
12) Brad’n_inact = ACE*BRADYKININ*0.05+BRADYKININ*Brad’n_inact_rate
13) BP_depress = BP*1
14) AGT(t) = AGT(t - dt) + (AGT_production - AGT_conversion - AGT_inact) * dt
15) Renin(t) = Renin(t - dt) + (Renin_production - Renin_inact) * dt
16) BP(t) = BP(t - dt) + (BP_press - BP_depress) * dt
17) ANG_I(t) = ANG_I(t - dt) + (AGT_conversion - ANG_I_conversion - ANG_I_inact) * dt
18) ACE(t) = ACE(t - dt) + (ACE_production - ACE_inact) * dt
19) ANG_II(t) = ANG_II(t - dt) + (ANG_I_conversion - ANG_II_inact) * dt
20) BRADYKININ(t) = BRADYKININ(t - dt) + (K’ogen_conversion - Brad’n_inact) * dt
21) KININOGEN(t) = KININOGEN(t - dt) + (K’ogen_production - K’ogen_conversion - K’ogen_inact) * dt
22) KALLIKREIN(t) = KALLIKREIN(t - dt) + (Kall’n_production - Kall’n_inact) * dt
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Constants
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Renin_kcat = 0.1
Renin_Km = 200
AGT_inact_rate = 0.7
Renin_inact_rate = 0.2
ACE_kcat = 20
ACE_Km = 1000
ANG_I_inact_rate = 0.05
ANG_II_inact_rate = 5
ACE_inact_rate = 2
BP_stat = 80
Brad’n_inact_rate = 0.8
Kall’n_kcat = 1
Kall’n_Km = 1
K’ogen_inact_rate = 2
Kall’n_inact_rate = 2
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Acknowledgments
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We thank Drs. Thomas M. Coffman and John H. Krege for critical reading of the manuscript and Ms. Jenny Langenbach for her excellent secretarial assistance.
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Footnotes
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Our work is supported by grants from the NIH (HL-71266-31A1 and HL-49277), W. M. Keck Foundation, Burroughs Wellcome Fund, and American Heart Association (0265464U).
Abbreviations: ACE, Angiotensin converting enzyme; AGT, angiotensinogen; ARB, angiotensin II receptor blockers; BP, blood pressure; D, deletion; I, insertion.
Received October 9, 2002.
Accepted for publication March 11, 2003.
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Abstract
Introduction
