Abstract
Heterogeneous results have been obtained in the relationship between serum uric acid (SUA) and target organ damage (TOD) in patients with hypertension. Clinic blood pressure, SUA, and cardiac, arterial (carotid and aortic), and renal TOD were assessed in 762 consecutive patients with hypertension. Hyperuricemia was defined as an SUA >7.0 in men and >6.0 mg/dL in women. Men with hyperuricemia compared with those with normal SUA showed lower estimated glomerular filtration rates and E/A ratios and a higher prevalence of carotid plaques. Women with hyperuricemia showed lower estimated glomerular filtration rates and E/A ratios and a higher intima‐media thickness. Except for pulse wave velocity, all TODs significantly correlated with SUA. However, at multivariate analysis, only estimated glomerular filtration rate was significantly determined by SUA. Our data provide evidence on the role of SUA in the development of TOD only in the case of renal alteration. It is likely that SUA may indirectly act on the other TODs through the increase in blood pressure and the decrease in glomerular filtration rate.
Keywords: albuminuria, arterial hypertension, arterial stiffness, carotid plaque, kidney damage, left ventricular mass index, pulse wave velocity, target organ damage, uric acid
1. INTRODUCTION
Hyperuricemia is frequently detected in patients with essential hypertension, in which serum uric acid (SUA) levels have been related to the development of elevated blood pressure (BP) values as well as to the occurrence of cardiovascular and renal events.1, 2, 3 SUA has also been identified as a potential factor concurring with others in the development and/or progression of preclinical target organ damage (TOD) associated with the hypertensive state throughout a variety of direct and indirect mechanisms. However, the studies aimed at investigating the association between SUA and TOD have frequently provided heterogeneous results. Among the factors responsible for the heterogeneity of the findings, two appear to be relevant. First, the studies published so far have been based, with few exceptions,4, 5 on the evaluation of a single or, at most, two measures of TOD, thereby failing to provide systematic information on different organs in the same population. Second, data were frequently analyzed without taking into account the factor of sex, which may significantly affect the results.6, 7, 8, 9, 10
In the present study, we sought to assess the role of SUA in determining multiple TOD in patients with treated hypertension by overcoming the limitations of the previously published studies. We thus conducted the analysis of SUA data related to the vascular, endothelial, metabolic, cardiac, and renal targets in a population of patients with treated hypertension as a whole, and as classified in subgroups according to sex.
2. METHODS
2.1. Study population
We enrolled 762 consecutive outpatients between the ages of 18 and 80 years, who had essential hypertension and were followed by the hypertension unit of the San Gerardo Hospital (Monza, Italy). Exclusion criteria included age younger than 18 years, pregnancy, secondary hypertension (investigated by appropriate biochemical and instrumental assessment), chronic kidney and pulmonary disease, substance abuse, history of cancer, and cardiovascular events in the 12 months preceding the study (myocardial infarction, angina pectoris, heart failure, stroke, transient cerebral ischemic attacks, and claudication). We collected, in all patients, a comprehensive medical history and performed a complete physical examination. With the patient in the sitting position for at least 5 minutes and with the arm placed at heart level, two clinic BP measurements were taken by a trained physician via a semiautomated device (OMRON Healthcare).11 The average of the two measurements was used for statistics. Hypertension was defined as a systolic BP (SBP) of ≥140 mm Hg and/or a diastolic BP of ≥90 mm Hg or as the reported use of antihypertensive drugs.
Laboratory analyses were performed on an automatic analyzer Modular Analytics Serum Work Area (Roche Diagnostics) with the following methods: enzymatic colorimetric (glucose oxidase–phenol+aminophenazone method) for glucose, enzymatic colorimetric (cholesterol oxidase–phenol+aminophenazone method) for total cholesterol, enzymatic colorimetric without pretreatment (third generation) for high‐density lipoprotein cholesterol, and enzymatic colorimetric (glycerol‐3‐phosphate oxidase–phenol+aminophenazone method) for triglycerides. Low‐density lipoprotein cholesterol was estimated according to the Friedewald equation, creatinine was measured by the Jaffe Kinetic colorimetric test with rate blanking and compensation, albuminuria was evaluated by the immunoturbidimetric test, and estimated glomerular filtration rate was estimated (eGFR) by the Modification of Diet in Renal Disease equation.12 Hyperuricemia was defined as an SUA level >6.0 mg/dL in women and >7.0 mg/dL in men, as previously reported.13, 14 Waist circumference was assessed halfway between the lower ribs and the iliac crest. The study protocol complied with the Declaration of Helsinki and was approved by the ethics committees of the institutions involved. All participants provided informed written consent after being informed of its nature and purpose.
2.2. Pulse wave velocity
Aortic stiffness was evaluated by pulse wave velocity (PWV) between the carotid and femoral arteries of the same side with the patient in the supine position. The pressure pulse waveforms were simultaneously obtained at the two arterial sites on the right side using an automatic device (Complior, Colson; Alam Medical) and their distance was calculated by taking the distance between the hip and neck via a rigid ruler. Measurements were corrected by a 0.8 factor accordingly to the PWV measurement methods consensus documents, which indicate the use of the subtraction methods instead of the direct one when assessing the distance between the two measurement points.15 Two measurements were obtained in each patient and the mean was used for the analysis. In our laboratory, the intrasession within‐ and between‐operator variability of PWV amounted to a coefficient of variation of the mean value of 2% and 4%, respectively. The corresponding value for the intersession between‐operator variability was 4%. Arterial stiffness was defined as a PWV measurement >10 m/s according to current guidelines.15
2.3. Cardiac ultrasonography
Two‐dimensional echocardiograms were performed by an experienced cardiologist using a SONOS 5500 ultrasound machine (Philips Healthcare; with an ultrasound transducer of 2.5 MHz) in each patient. Two‐dimensional high frame rate gray‐scale loops of four‐, two‐, and three‐chamber views with an average frame rate of 90 frames per second were used to measure left ventricular end‐diastolic diameter, interventricular septum, posterior wall thickness, and ejection fraction by the Simpson method. Left ventricular mass (LVM) was calculated using the Devereux formula16: LVM (g)=0.8 × 1.04 × {[left ventricular end‐diastolic diameter (cm) + interventricular septum + posterior wall thickness (cm)]3 − left ventricular end‐diastolic diameter 3 (cm)} + 0.6. LVM values were normalized for body surface area (BSA) to obtain the LVM index (LVMI). We calculated BSA using the DuBois and DuBois formula: BSA(m2)=0.007184 × height (cm)0.725 × weigh (kg) × 0.425. The intraoperator variability in terms of coefficient of variation of the mean of two measurements is <3% in our laboratory. Left ventricular hypertrophy was diagnosed by the detection of an LVMI of at least 115 g/m2 for men and at least 95 g/m2 for women.17
Furthermore, pulsed Doppler was placed on the mitral annulus and the transmitral flow was evaluated to measure the diastolic function parameters (E wave peak systolic velocity, A wave peak systolic velocity, deceleration time, and E/A ratio).18
2.4. Carotid ultrasonography
With the patient in the supine position and the neck in partial extension, we scanned the right carotid artery through an ultrasonography device (Philips Sonos 5500).19 The transducer was manually oriented perpendicularly to the longitudinal axis of the vessel under B‐mode guidance, and common carotid intima‐media thickness (IMT) was measured at a posterior wall site located 2 cm below bifurcation as the difference between the inner hypoechogenic and the middle anechogenic layers. Measurements were made by two operators blinded to the patient's clinical status. Two measurements were obtained in each patient and the mean was used for the analysis. In our laboratory, the intrasession within‐ and between‐operator variability of IMT amounts to a coefficient of variation of the mean value of 2.5% and 2%, respectively. The corresponding value for the intersession between‐operator variability was 3.9%. Carotid plaque was defined as the presence of IMT >1.2 mm in the common carotid, bulb, or internal carotid artery.
2.5. Statistical analysis
Data obtained in each patient were averaged, and individual data were summed and expressed as mean (±standard deviation). The study population was subdivided into normal and hyperuricemic groups according to baseline SUA levels differently in men and women. Between‐group differences were assessed by Student t test, Mann‐Whitney test, and χ2 tests (or Fisher exact test when needed) for normally distributed, non‐normally distributed, and categorical variables, respectively. Pearson or Spearman correlation coefficients were used, as appropriate, to test the association between variables. We performed linear regression using the additive model and adjusting for covariates determined by stepwise regression. We used PWV, LVMI, E/A ratio, IMT, eGFR, albuminuria, and number of TODs as the dependent variables, with SUA, age, sex, SBP, waist circumference, total cholesterol, glucose, and creatinine as covariates. SPSS version 13.0 (SPSS Inc) was used for statistical analyses and a P value <.05 was taken as the level of statistical significance.
3. RESULTS
3.1. Population characteristics
Table 1 reports the main demographic and clinical characteristics of the population as a whole, and of the sex‐related subgroups. Patients had a mean age of 53.7 ± 13 years, with a prevalence of men (57.1%). SBP/diastolic BP values were 140.9 ± 18/85.1 ± 13 mm Hg, while glucose, triglycerides, and total and fractioned cholesterol levels showed controlled values. The mean SUA level was 5.2 ± 1.4 mg/dL. Table 1 shows the mean values of TOD measurements and the proportion of patients in which the values exceeded the cutoff for TOD. Only a small proportion of patients displayed two (17.1%) or ≥3 TODs (7.1%).
Table 1.
Demographic, clinical, and TOD characteristics of the whole population divided by sex
| All patients | Men | Women | P value (men vs women) | |
|---|---|---|---|---|
| No. | 762 | 435 | 327 | – |
| Men, % | 57.1 | – | – | – |
| Age, y | 53.7 ± 13.6 | 53.3 ± 13.4 | 54.2 ± 13.8 | .363 |
| SBP, mm Hg | 140.9 ± 18.6 | 141.8 ± 17.8 | 139.7 ± 19.6 | .115 |
| DBP, mm Hg | 85.1 ± 13.2 | 86.1 ± 13.1 | 83.8 ± 13.3 | .023 |
| Heart rate, beats per min | 66.6 ± 11.3 | 65.6 ± 11.3 | 67.8 ± 11.3 | .025 |
| Waist circumference, cm | 93.3 ± 12.5 | 97.5 ± 10.1 | 87.7 ± 13.2 | <.001 |
| BMI, kg/m2 | 26.7 ± 4.1 | 27.1 ± 3.5 | 26.1 ± 4.8 | .003 |
| Smoking, % | 39.1 | 49.3 | 26.7 | <.001 |
| Diabetes mellitus, % | 9.1 | 10.3 | 7.3 | .163 |
| Dyslipidemia, % | 45.8 | 60.3 | 39.6 | .082 |
| Glucose, mg/dL | 91.1 ± 23.6 | 93.6 ± 26.1 | 87.7 ± 19.7 | .001 |
| Triglycerides, mg/dL | 120.2 ± 79.8 | 129.5 ± 83.7 | 108.2 ± 72.7 | <.001 |
| Total cholesterol, mg/dL | 197.4 ± 36.4 | 194.1 ± 36.3 | 201.5 ± 36.2 | .009 |
| HDL‐C, mg/dL | 53.6 ± 13.9 | 49.1 ± 12.2 | 59.5 ± 13.8 | <.001 |
| LDL‐C, mg/dL | 119.7 ± 31.9 | 119.6 ± 31.5 | 119.8 ± 32.5 | .923 |
| Uric acid, mg/dL | 5.2 ± 1.4 | 5.7 ± 1.3 | 4.5 ± 1.2 | <.001 |
| Creatinine, mg/dL | 0.86 ± 0.21 | 0.96 ± 0.21 | 0.74 ± 0.14 | <.001 |
| GFR, mL/min | 92.2 ± 20.8 | 92.8 ± 21.2 | 91.4 ± 20.4 | .377 |
| Albuminuria, mg/L | 11.2 ± 18,8 | 13.5 ± 21.9 | 8.4 ± 11.5 | .537 |
| GFR <60 mL/min, % | 5.2 | 6.0 | 4.1 | .294 |
| Microalbuminuria >20 mg/L, % | 17.1 | 20.7 | 12.5 | .012 |
| Drugs, % | ||||
| ACEIs | 31.0 | 35.6 | 24.7 | .002 |
| ARBs | 28.6 | 28.5 | 28.7 | 1.000 |
| β‐Blockers | 22.8 | 20.0 | 26.6 | .036 |
| CCBs | 29.7 | 33.5 | 24.4 | .007 |
| α‐Blockers | 11.2 | 11.9 | 10.0 | .486 |
| Diuretics | 28.2 | 28.0 | 28.4 | .935 |
| Statins | 12.6 | 13.7 | 11.0 | .271 |
| PWV, m/s | 8.6 ± 2.2 | 8.9 ± 2.2 | 8.2 ± 2.1 | .001 |
| PWV >10 m/s, % | 18.5 | 21.6 | 14.3 | .011 |
| IMT, cm | 0.72 ± 0.19 | 0.73 ± 0.20 | 0.70 ± 0.19 | .017 |
| IMT >0.9 cm, % | 15.4 | 16.9 | 13.3 | .209 |
| Carotid plaque, % | 26.2 | 29.8 | 21.4 | .010 |
| LVMI, g/m2 | 110.4 ± 32.3 | 119.2 ± 32.4 | 99.1 ± 28.6 | <.001 |
| LVH, % | 49.3 | 48.6 | 50.1 | .705 |
| E/A ratio | 1.05 ± 0.33 | 1.04 ± 0.33 | 1.06 ± 0.33 | .293 |
| No. of TODs, % | ||||
| 1 | 37.3 | 36.3 | 38.5 | .107 |
| 2 | 17.1 | 17.9 | 15.9 | |
| 3 | 7.1 | 8.9 | 4.8 | |
Data are shown as means±standard deviation or percentage. Bold values indicate significance.
Abbreviations: ACEIs, angiotensin‐converting enzyme inhibitors; ARBs, angiotensin receptor blockers; BMI, body mass index; CCBs, calcium channel blockers; DBP, diastolic blood pressure; GFR, glomerular filtration rate; HDL‐C, high‐density lipoprotein cholesterol; IMT, intima‐media thickness; LDL‐C, low‐density lipoprotein cholesterol; LVH, left ventricular hypertrophy; LVMI, left ventricular mass index; PWV, pulse wave velocity; SBP, systolic blood pressure; TOD, target organ damage.
When patients were subdivided according to sex, men showed a slightly greater burden of cardiovascular risk factors in comparison with women. Indeed, they showed higher diastolic BP, waist circumference, glucose, triglycerides, total cholesterol, creatinine, and uric acid (5.7 ± 1.3 vs 4.5 ± 1.2 mg/dL, P < .001) values. Men showed greater PWV, IMT, LVMI, and carotid plaque presence, while E/A ratio and eGFR were superimposable to the values detected in women.
3.2. Men with normouricemia vs hyperuricemia
When men were classified according to their SUA levels in normal and hyperuricemic individuals, we found that the latter group displayed greater waist circumference, glucose, triglycerides, and creatinine values and a lower low‐density lipoprotein cholesterol (Table 2). Patients with hyperuricemia, when compared with individuals with normouricemia, showed significantly lower eGFR (84.6 ± 25 vs 94.3 ± 20 mL/min, P = .002; patients with eGFR <60 mL/min: 16.6% vs 4.4%, P = .004) and E/A ratio (0.90 ± 0.24 vs 1.06 ± 0.33, P < .001), with higher prevalence of carotid plaque (50.0% vs 26.9%, P = .001) (Figure 1). No significant difference was detected in PWV, IMT, LVMI, or number of TODs.
Table 2.
Demographic, clinical, and TOD characteristics of men with normouricemia vs hyperuricemia
| Normal SUA | Hyperuricemia | P value | |
|---|---|---|---|
| No. | 379 | 56 | – |
| Age, y | 52.9 ± 13.2 | 55.6 ± 14.3 | .136 |
| SBP, mm Hg | 141.4 ± 17.8 | 144.4 ± 18.1 | .230 |
| DBP, mm Hg | 85.9 ± 13.3 | 87.1 ± 11.5 | .499 |
| Heart rate, beats per min | 65.3 ± 11.1 | 68.1 ± 12.5 | .101 |
| Waist circumference, cm | 96.9 ± 9.9 | 101.1 ± 9.7 | .002 |
| Smoking, % | 48.6 | 55.3 | .282 |
| Diabetes mellitus, % | 9.7 | 14.2 | .344 |
| Dyslipidemia, % | 30,0 | 37,7 | .303 |
| Glucose, mg/dL | 92.5 ± 23.5 | 100.2 ± 36.4 | .040 |
| Triglycerides, mg/dL | 119.3 ± 70.2 | 186.1 ± 122.3 | <.001 |
| Total cholesterol, mg/dL | 193.7 ± 36.4 | 196.5 ± 36.2 | .592 |
| HDL‐C, mg/dL | 49.9 ± 12.2 | 44.3 ± 10.8 | .002 |
| LDL‐C, mg/dL | 120.2 ± 31.5 | 116.1 ± 31.4 | .381 |
| Uric acid, mg/dL | 5.4 ± 0.9 | 8.01 ± 0.9 | <.001 |
| Creatinine, mg/dL | 0.94 ± 0.21 | 1.06 ± 0.22 | <.001 |
| GFR, mL/min | 94.3 ± 20.0 | 84.6 ± 25.1 | .002 |
| Albuminuria, mg/L | 13.6 ± 22.1 | 11.6 ± 19.8 | .083 |
| GFR <60 mL/min, % | 4.4 | 16.6 | .004 |
| Microalbuminuria >20 mg/L, % | 20.3 | 23.5 | .656 |
| Drugs, % | |||
| ACEIs | 35.6 | 35.7 | 1.000 |
| ARBs | 29.0 | 25.0 | .635 |
| β‐Blockers | 20.5 | 16.0 | .480 |
| CCBs | 34.8 | 25.0 | .173 |
| α‐Blockers | 10.2 | 23.2 | .013 |
| Diuretics | 26.3 | 39.2 | .055 |
| Statins | 12.4 | 23.2 | .037 |
| PWV, m/s | 8.4 ± 2.2 | 8.4 ± 2.7 | .932 |
| PWV >10 m/s, % | 21.6 | 21.4 | 1.000 |
| IMT, cm | 0.73 ± 0.22 | 0.76 ± 0.13 | .312 |
| IMT >0.9 cm, % | 16.6 | 19.2 | .692 |
| Carotid plaque, % | 26.9 | 50.0 | .001 |
| LVMI, g/m2 | 118.6 ± 31.9 | 122.7 ± 34.8 | .375 |
| LVH, % | 48.5 | 49.0 | 1.000 |
| E/A ratio | 1.06 ± 0.33 | 0.90 ± 0.24 | <.001 |
| No. of TODs, % | |||
| 1 | 37.4 | 28.5 | .454 |
| 2 | 17.4 | 21.4 | |
| 3 | 8.7 | 10.7 | |
Data are shown as means±standard deviation or percentage. Bold values indicate significance.
Abbreviations: ACEIs, angiotensin‐converting enzyme inhibitors; ARBs, angiotensin receptor blockers; CCBs, calcium channel blockers; DBP, diastolic blood pressure; GFR, glomerular filtration rate; HDL‐C, high‐density lipoprotein cholesterol; IMT, intima‐media thickness; LDL, low‐density lipoprotein cholesterol; LVH, left ventricular hypertrophy; LVMI, left ventricular mass index; PWV, pulse wave velocity; SBP, systolic blood pressure; SUA, serum uric acid; TOD, target organ damage.
Figure 1.
Target organ damage in patients with normouricemia vs hyperuricemia divided according to sex. Estimated glomerular filtration rate (eGFR; panel A), pulse wave velocity (PWV; panel B), E/A ratio (panel C), intima‐media thickness (IMT; panel D), left ventricular mass (LVM) index (panel E), and albuminuria (panel F). SUA indicates serum uric acid. Vertical lines show the fifth and 95th percentiles, boxes show the 25th and 75th percentiles, and horizontal lines show the 50th percentile.
Figure 2.
Correlation between uric acid and estimated glomerular filtration rate (eGFR) in the whole study population (panel A) and separately in men (panel B) and women (panel C).
3.3. Women with normouricemia vs hyperuricemia
When women were classified according to their SUA levels in normal and hyperuricemic individuals, we found the latter group to be older (61.1 ± 11 vs 53.0 ± 13.9, P < .001) and displayed greater waist circumference, glucose, triglycerides, total cholesterol, and creatinine values, with lower low‐density lipoprotein cholesterol (Table 3). Patients with hyperuricemia, when compared with patients with normouricemia, showed significantly lower eGFR (79.5 ± 18.5 vs 93.6 ± 20 mL/min, P < .001; patients with GFR <60 mL/min: 13.0% vs 2.4%, P = .005) and E/A ratio (0.92 ± 0.30 vs 1.09 ± 0.33, P = .003), with higher IMT (0.78 ± 0.22 vs 0.68 ± 018 cm, P = .001; patients with IMT >0.9 cm: 26.8% vs 11.2%, P = .01) (Figure 1). No significant difference was detected for PWV and carotid plaque presence. Finally, patients with hyperuricemia showed a greater prevalence of multiple TOD (24.0%vs 14.4% for 2 and 8.0% vs 4.3% for ≥3 TODs). Figure 1 summarizes the TOD findings. The only two TODs significantly different between normal SUA and hyperuricemia in both sexes were eGFR and E/A ratio. The other TODs were significantly greater only in women (IMT and LVMI) or similar in both sexes (PWV and albuminuria).
Table 3.
Demographic, clinical, and TOD characteristics of women with normouricemia vs hyperuricemia
| Normal SUA | Hyperuricemia | P value | |
|---|---|---|---|
| No. | 277 | 50 | – |
| Age, y | 53.0 ± 13.9 | 61.1 ± 11.1 | <.001 |
| SBP, mm Hg | 138.9 ± 18.7 | 143.9 ± 23.3 | .102 |
| DBP, mm Hg | 84.6 ± 12.5 | 79.7 ± 16.4 | .018 |
| Heart rate, beats per min | 67.8 ± 11.0 | 67.7 ± 13.1 | .972 |
| Waist circumference, cm | 85.8 ± 12.6 | 97.6 ± 11.8 | <.001 |
| Smoking, % | 28.3 | 18.0 | .457 |
| Diabetes mellitus, % | 6.3 | 13.9 | .107 |
| Dyslipidemia, % | 37.6 | 66.6 | .001 |
| Glucose, mg/dL | 86.4 ± 19.3 | 94.2 ± 20.4 | .014 |
| Triglycerides, mg/dL | 97.7 ± 45.0 | 169.6 ± 142.5 | .002 |
| Total cholesterol, mg/dL | 199.3 ± 35.4 | 214.5 ± 38.4 | .010 |
| HDL‐C, mg/dL | 60.4 ± 13.6 | 53.9 ± 13.4 | .004 |
| LDL‐C, mg/dL | 118.8 ± 32.9 | 125.2 ± 29.8 | .254 |
| Uric acid, mg/dL | 4.1 ± 0.8 | 6.7 ± 0.7 | <.001 |
| Creatinine, mg/dL | 0.72 ± 0.12 | 0.82 ± 0.11 | <.001 |
| GFR, mL/min | 93.63 ± 20.0 | 79.53 ± 18.5 | <.001 |
| Albuminuria, mg/L | 8.4 ± 11.7 | 8.4 ± 10.4 | .152 |
| GFR <60 mL/min, % | 2.4 | 13.0 | .005 |
| Microalbuminuria >20 mg/L, % | 11.1 | 21.2 | .150 |
| Drugs, % | |||
| ACEIs | 24.6 | 25.5 | .852 |
| ARBs | 26.4 | 44.1 | .020 |
| β‐Blockers | 25.0 | 37.2 | .098 |
| CCBs | 24.6 | 23.2 | <.001 |
| α‐Blockers | 10.2 | 9.3 | 1.000 |
| Diuretics | 23.9 | 58.1 | <.001 |
| Statins | 10.9 | 11.6 | .798 |
| PWV, m/s | 8.0 ± 2.1 | 8.4 ± 1.8 | .104 |
| PWV >10 m/s, % | 13.7 | 18.6 | .361 |
| IMT, cm | 0.68 ± 0.18 | 0.78 ± 0.22 | .001 |
| IMT >0.9 cm, % | 11.2 | 26.8 | .012 |
| Carotid plaque, % | 20.0 | 30.2 | .161 |
| LVMI, g/m2 | 96.7 ± 26.7 | 113.5 ± 35.1 | <.001 |
| LVH, % | 48.5 | 62.1 | .160 |
| E/A ratio | 1.09 ± 0.33 | 0.92 ± 0.30 | .003 |
| No. of TODs, % | |||
| 1 | 36.4 | 50.0 | .004 |
| 2 | 14.4 | 24.0 | |
| 3 | 4.3 | 8.0 | |
Data are shown as mean ± standard deviation or percentage. Bold values indicate significance.
Abbreviations: ACEIs, angiotensin‐converting enzyme inhibitors; ARBs, angiotensin receptor blockers; CCBs, calcium channel blockers; DBP, diastolic blood pressure; GFR, glomerular filtration rate; HDL‐C, high‐density lipoprotein cholesterol; IMT, intima‐media thickness; LDL‐C, low‐density lipoprotein cholesterol; LVH, left ventricular hypertrophy; LVMI, left ventricular mass index; PWV, pulse wave velocity; SBP, systolic blood pressure; SUA, serum uric acid; TOD, target organ damage.
3.4. Correlation, regression, and multivariate analysis
Table 4 shows the significant correlation observed in the population as a whole between TODs and classic cardiovascular risk factors. With the exception of PWV, all of the other TODs significantly correlated with SUA. Linear regression analysis showed that none of the evaluated TODs, except for eGFR, displayed SUA as a significant covariate. More specifically, with a total r 2 of .56, sex (β = −0.23, P = .01), age (β = −0.71, P ≤ .001), triglycerides (β = 0.004, P = .01), waist circumference (β = 0.14, P = .03), and SUA (β = −0.29, P < .001) were significant determinants of eGFR.
Table 4.
Correlation analysis in the whole population
| PWV | IMT | LVMI | E/A ratio | GFR | Albuminuria | No. of TODs | |
|---|---|---|---|---|---|---|---|
| Sex | r = −.11, P < .01 | r = −.08, P = .01 | r = −.30, P < .01 | – | – | – | r = −.08, P = .02 |
| Age | r = .40, P < .01 | r = .53, P < .01 | r = .33, P < .01 | r = −.57, P < .01 | r = −.45, P < .01 | – | r = .46, P < .01 |
| Waist circumference | r = .08, P = .01 | r = .18, P < .01 | r = .30, P < .01 | r = −.19, P < .01 | – | – | r = .21, P < .01 |
| Total cholesterol | – | r = .16, P < .01 | – | r = −.18, P < .01 | r = −.10, P < .01 | – | – |
| HDL‐C | – | r = −.10, P = .01 | r = −.14, P < .01 | – | – | – | – |
| LDL‐C | – | r = .13, P < .01 | – | r = −.13, P < .01 | – | – | – |
| Triglycerides | – | r = .18, P < .01 | r = .16, P < .01 | r = −.17, P < .01 | – | r = .13, P < .01 | r = .13, P < .01 |
| Glucose | r = .21, P < .01 | r = .17, P < .01 | r = .24, P < .01 | r = −.24, P < .01 | – | r = .09, P = .04 | r = .26, P < .01 |
| Creatinine | r = .15, P < .01 | r = .13, P < .01 | r = .24, P < .01 | r = −.14, P < .01 | r = −.75, P < .01 | r = .16, P < .01 | r = .15, P < .01 |
| Uric acid | – | r = .13, P < .01 | r = .23, P < .01 | r = −.17, P < .01 | r = −.27, P < .01 | r = .13, P < .01 | r = .11, P < .01 |
Abbreviations: GFR, glomerular filtration rate; HDL‐C, high‐density lipoprotein cholesterol; IMT, intima‐media thickness; LDL‐C, low‐density lipoprotein cholesterol; LVMI, left ventricular mass index; PWV, pulse wave velocity; TODs, target organ damages.
The independent predictors for PWV, with a total r 2 value of .54, were age (β = 0.64, P < .001), sex (β = −0.72, P < .001), and SBP (β = 0.03, P < .001). With a total r 2 value of .55, age (β = 0.63, P < .001), SBP (β = 0.41, P < .001), and waist circumference (β = 0.35, P < .001) were significant covariates of LVMI with linear regression analysis. With multivariate analysis, age (β = 0.008, P < .001) was the only independent predictor of IMT (r 2 = .58). Albuminuria showed only SBP (β = 0.32, P = .01) and creatinine (β = 0.79, P = .01) as independent predictors (r 2 = .26). Finally, E/A ratio showed age (β = −0.52, P < .001), waist circumference (β = −0.09, P = .02), total cholesterol (β = −0.07, P = .03), and triglycerides (β = −0.07, P = .04) as significant determinants (r 2 = .61). Similar figures were also found when correlation and linear regression analyses were repeated in the two sexes separately. Particularly, SUA was confirmed as a significant determinant of eGFR in both sexes.
4. DISCUSSION
In patients with essential hypertension, SUA levels were significantly related to many of the TODs (except for PWV), resulting in a significant determinant only in the case of renal damage (evaluated as eGFR). Thus, alteration in renal function appears to be the TOD more closely related than others to SUA level. The relationship between SUA and renal dysfunction could be bidirectional, representing the cause but also the consequence of renal impairment. Uric acid could determine renal damage throughout a number of mechanisms. These include the deposition of uric acid into the renal tubules as a result of hyperuricosuria and crystal formation.20 Furthermore, oxidative stress has been found to increase tubular‐interstitial inflammation, which is able to cause an afferent arteriolopathy with intimal hyperplasia, muscular hypertrophy, and hyalinosis, as well as interstitial inflammation and fibrosis.21, 22, 23
As previously mentioned, the results obtained in previous studies investigating the relationship between SUA and TOD are heterogeneous.1, 2, 3 This heterogeneity can be ascribed to a variety of factors. Indeed some studies were based on the general population while others evaluated patients with comorbidities such as never treated or newly diagnosed hypertension, high cardiovascular risk, or diabetes mellitus. Furthermore, the adjustments made for confounding factors also differ between studies. Based on our data and on a review of the literature, we can affirm that only renal TOD has been clearly linked to SUA together with the development of hypertension. In our opinion, SUA is able to act on the other TODs (arterial stiffness, left ventricular hypertrophy, carotid atherosclerosis) only indirectly through the increase in BP values and renal impairment.24
A further finding of our study deserves to be mentioned, namely the difference among sexes in the relationship between SUA and TOD. As shown in previous studies when SUA analysis was conducted according to sex, some results were confirmed only in men or in women.7, 8, 9 In our study, women showed a greater prevalence of TOD when hyperuricemia was detected. This is in line with evidence from previous longitudinal studies in patients with gout showing that women are at increased risk for cardiovascular events as compared with men.8, 9 The most likely explanation of these findings is that SUA metabolism is genetically controlled and sex differences exist in gene function.10 Another possible explanation is also the relationship with menopausal status. It has previously been reported that hyperuricemia has been associated with LVMI in postmenopausal but not in premenopausal women.25
5. STUDY LIMITATIONS AND STRENGTHS
Our study has some limitations and strengths. One limitation is that the cross‐sectional design of the study prevented us from collecting longitudinal information on the progression of TOD and its association with SUA levels. Furthermore, we examined patients with treated hypertension, and some specific classes of drugs (eg, diuretics) may have affected the study results. Finally, we did not collect data on menopausal status, leading to the impossibility to perform such an analysis. The study strengths include the large sample size of the population studied, which guarantees adequate power in identifying the association between TOD and SUA levels, and the multiorgan assessment of TOD, a study feature, which, as discussed above, is shared only by few investigations published so far.4, 5
6. CONCLUSIONS
Our findings provide evidence on the independent and direct role of SUA in the development of renal impairment. It is likely that in the case of other TODs, the role of SUA is more indirect and dependent on the increase in BP per se, which characterizes the essential hypertensive state.
CONFLICT OF INTEREST
This work was funded by the European Community Seventh Framework Programme (FP7/2007‐2013) grant agreement No. 278249. The authors report no other specific funding in relation to this research and have no conflicts of interest to disclose.
Maloberti A, Maggioni S, Occhi L, et al. Sex‐related relationships between uric acid and target organ damage in hypertension. J Clin Hypertens. 2018;20:193–200. 10.1111/jch.13136
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