Methods

We have obtained informed consent from each subject, and this study was approved by the Institutional Review Board at the University of Campinas. We included 169 patients followed up at the Hypertension Unit of the University of Campinas and 179 healthy controls. Clinical data were obtained by physical examination, routine analytical tests, and medical history. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or current antihypertensive medication use. Subjects with the following features were excluded from our study: age <18 years and evidence of moderate or severe cardiac valve disease, previous myocardial infarction, neoplastic disease, or suspected secondary hypertension. Blood pressure records were obtained using a validated digital oscillometric device (Omron HEM-705CP; Omron Healthcare, Kyoto, Japan) with appropriate cuff sizes. Body mass index was calculated as body weight divided by height squared (expressed in kilogram per meter squared).

Echocardiography was performed on each subject at rest in the left lateral decubitus position using a Vivid 3 Pro (General Electric, Milwaukee, Wisconsin) apparatus equipped with a 2.5-MHz transducer, as previously described. LV end-diastolic and end-systolic diameters, interventricular septum thickness, posterior wall thickness, and LV mass were measured in accordance with the American Society of Echocardiography guidelines. Relative wall thickness was computed as twice the posterior wall thickness divided by LV end-diastolic diameter. LV mass index (LVMI) was considered as LV mass/height ^2.7 , and LV hypertrophy was defined using a cut-off point >51 g/m ^2.7 . All the recordings were made by the same physician, who was unaware of other data regarding the subjects. The reproducibility of both acquiring and measuring LV mass was determined in recordings obtained from 10 subjects. Intraobserver LV mass variability was <8%.

Venous blood samples were collected and genomic DNA was extracted from the cellular component of 1 ml of whole blood and stored at −20°C until analyzed. Genotypes for the g.-2578C>A, g.-1154G>A, and g.-634G>C polymorphisms were determined by TaqMan Allele Discrimination assays (assay IDs: C_8311602-10, C_1647379-10, and C_8311614-10, respectively; Applied Biosystems, Carlsbad, California). TaqMan polymerase chain reaction was performed in a total volume of 10 μl (5 ng of DNA, 1 × TaqMan master mix, 1 × assay mix) placed in 96-well polymerase chain reaction plates. Fluorescence from polymerase chain reaction amplification was detected using StepOnePlus equipment (Applied Biosystems, Carlsbad, California) and analyzed with manufacturer’s software.

Clinical features and biochemical parameters of the studied groups were compared by unpaired t test (parametric data), Mann-Whitney test (nonparametric data), or chi-square test (categorical variables). Allele and genotype distributions were compared by chi-square tests. Deviations from Hardy-Weinberg equilibrium were assessed by chi-square test. The Haplo.stats package (version 1.4.4; http://www.r-project.org ) was used to estimate the haplotype frequencies. The function haplo.em computes maximum likelihood estimates of haplotype probabilities using the progressive insertion algorithm, which progressively inserts batches of loci into haplotypes of growing lengths. The function haplo.score was used to compute haplotype-specific score statistics to test for association, with the value of p <0.05 considered statistically significant. Only the haplotypes with frequencies >1% were taken into consideration in the haplotype-specific score analysis. The function haplo.cc was also performed to calculate odds ratio and 95% confidence intervals for each haplotype. The possible haplotypes including the 3 VEGFA gene polymorphisms (g.-2578C>A, g.-1154G>A, and g.-634G>C) were CGG, CGC, CAG, CAC, AGG, AGC, AAG, and AAC. Linear regression analysis and nonlinear fitting routines were performed to assess univariate relations between variables (software JMP 5.0.1a; SAS Institute, Cary, North Carolina). In addition, a bivariate analysis was also used to assess for the potential confounding influence of each covariate on the relation between VEGFA genotypes or haplotypes and LVMI. The variables of clinical importance, as identified by the bivariate approach, were then included in the final multiple linear regression models. Septal thickness, posterior wall thickness, LVMI, relative wall thickness, end-diastolic diameter, and ejection fraction were considered as dependent variables. VEGFA haplotypes or genotypes, gender, body mass index, age, pharmacologic treatment, and mean arterial pressure were considered as independent variables. In the VEGFA genotype multivariate model, we compared wild-type versus grouped heterozygous and variant homozygous to increase statistical power because of the limited frequency of the variant homozygotes in our sample.

Results

Clinical features of the studied voluntaries are listed in Table 1 . All clinical features were similar in healthy controls and hypertensive patients, except for body mass index, systolic and diastolic blood pressures, and triglycerides, which were increased in hypertensive patients ( Table 1 ). Allele and genotype distributions are listed in Table 2 . No deviations from Hardy-Weinberg equilibrium regarding the genotype distributions were found. No associations between the studied alleles or genotypes of VEGFA with hypertension were found ( Table 2 ). However, when VEGFA gene variants were analyzed together as haplotypes, a borderline association of the CAG haplotype with the healthy group was found ( Table 3 ).

We next addressed the question whether VEGFA genotypes and haplotypes are associated with changes in echocardiographic parameters in hypertensive patients by performing multivariate regression analyses ( Table 4 and Table 5 , respectively). Although the variant genotypes of the g.-634G>C (GC + CC) were associated with reduced LVMI ( Table 4 ), the variant genotypes for the g.-1154G>A (GA + AA) were associated with reduced ejection fraction ( Table 4 ). Haplotypes of VEGFA were associated with altered ejection fraction ( Table 5 ). Specifically, although the AAG haplotype was associated with reduced ejection fraction, the AGG haplotype was associated with increased ejection fraction.

Table 5

Effects of vascular endothelial growth factor haplotypes on echocardiographic parameters in hypertensive patients, after adjusting for selected variables

Source	Septal Thickness (mm)		Posterior Wall Thickness (mm)		LVMI (g/m 2.7 )		Relative Wall Thickness		End-Diastolic Diameter (mm)		Ejection Fraction
	R 2	Root Mean Square Error	R 2	Root Mean Square Error	R 2	Root Mean square Error	R 2	Root Mean Square Error	R 2	Root Mean Square Error	R 2	Root Mean Square Error
Model	0.174	1.460	0.191	1.485	0.236	0.273	0.076	0.170	0.286	0.105	0.111	0.234

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