Urinary Angiotensinogen Excretion Level Is Associated With Elevated Blood Pressure in the Normotensive General Population

Urinary Angiotensinogen Excretion Level Is Associated With Elevated Blood Pressure in the... Abstract BACKGROUND Inflammation, intrarenal renin–angiotensin system (RAS) activation, oxidative stress, and carbonyl stress have been postulated to play a fundamental role in controlling blood pressure. However, little is known about the association among renal RAS activation, carbonyl stress, and blood pressure elevation. METHODS We evaluated the relationship between blood pressure elevation and either renal RAS activity or carbonyl stress in the general population (N = 355) in Japan. To minimize the effect of antihypertensive drug therapy, we divided participants into 3 groups (normotensive, hypertensive-with-non-medication, and hypertensive-with-medication). Intrarenal RAS activity and carbonyl stress were indicated by the urinary angiotensinogen (AGT) and carbonyl compound excretion levels, respectively. RESULTS The urinary AGT and carbonyl compound excretion levels were significantly associated with blood pressure. Using a stepwise multiple regression analysis, we found that the urinary AGT excretion levels were strongly associated with blood pressure elevation, compared with inflammation, oxidative stress, and carbonyl stress markers, in all groups. Urinary carbonyl compound excretion was significantly associated with blood pressure in only the hypertensive-without-medication group. Furthermore, blood pressure was significantly increased in these participants, and both the urinary AGT and carbonyl compound levels were high. The urinary AGT excretion levels were strongly associated with elevated blood pressure in normotensive people, and inappropriate renal RAS activity and carbonyl stress independently contributed to the development of hypertension. CONCLUSIONS These findings suggest that RAS activation, particularly renal RAS activation exert a fundamental role in the pathogenesis of hypertension in the general population. angiotensinogen, blood pressure, carbonyl stress, hypertension, intrarenal renal–angiotensin system, methylglyoxal That activation of the renin–angiotensin system (RAS), inflammation, and oxidative stress are involved in the pathogenesis of hypertension is well-known. RAS-mediated renal production of reactive oxygen species is vital in blood pressure regulation;1,2 RAS-induced reactive oxygen species and inflammation are associated with the pathogenesis of hypertensive diabetic nephropathy.3 It is demonstrated that molecules related to reactive oxygen species and inflammation are upregulated in rat kidneys exposed to high-perfusion pressure for 2 weeks.1 Angiotensinogen (AGT) is a substrate for renin and yields angiotensin peptides such as angiotensin I and angiotensin II upon reaction with renin. Although circulating AGT is produced and secreted by the liver, it cannot be filtrated easily across the glomerular membrane; thus, proximal tubules produce AGT and secrete it in tubular fluid.4 Two studies have demonstrated that some AGT derived from the liver may be filtrated and significantly contribute to renal–angiotensin production.5,6 Among the multiple independent factors that contribute to intrarenal RAS regulation, the urinary AGT excretion rate has recently been shown to be a marker of the intrarenal RAS status in patients with hypertension7,8 and chronic kidney disease.9,10 Our previous data also suggested that the urinary AGT excretion rate is associated with high blood pressure in young adults with obesity.11 Carbonyl stress is the abnormal accumulation of α-oxoaldehyde metabolites, leading to increased reaction to an eventual modification of protein, nucleotides, and basic phospholipids. This reaction contributes to cell and tissue dysfunction in elderly people and those with disease. Carbonyl compounds such as methylglyoxal (MG) and glyoxal (GO) are strongly associated with chronic kidney disease pathology and are increased in patients with chronic kidney disease per disease stage.12 Our previous studies reported that MG is pathologically involved in the vascular damage of endothelial cells and the development of hypertension, insulin resistance, cardiovascular fibrosis, and renal injury.12–15 MG-induced hypertension, cardio-renal injury, renal carbonyl/oxidative stress, and renal inflammation in Dahl salt-sensitive rats are inhibited by angiotensin-II receptor blockers (ARBs).15 Although carbonyl stress and inappropriate RAS activation are involved in blood pressure elevation, the associations among carbonyl stress, RAS activation, and blood pressure have not yet been examined. We focused on the general population because they include many normotensive people; therefore, we could evaluate the factors associated with blood pressure without requiring to consider the effects of concomitant diseases and drugs. Therefore, we examined the relationship between carbonyl stress, and intrarenal RAS and blood pressure, in a community health examination. We also evaluated the relationship between urinary AGT excretion level and serum AGT level in the same cohort. METHODS Participants A total of 1,715 adults residing in the town of Kawasaki in Miyagi prefecture, Japan, aged >20 years and who underwent a community health examination in 2011 were enrolled. A total of 529 adults underwent the community health examination over 2 days. Four hundred thirty-four adults provided informed consent for study participation. The serum/urine samples of 79 adults were inadequate for analysis. Morning urine and serum samples of 355 adults (men: 162, women: 193) were collected and analyzed. The adults were divided into 3 groups (normotensive, hypertensive-non-medication, and hypertensive-with-medication) according to their blood pressure and antihypertensive medication status. Clinical, demographic, and anthropometric measurements Body height, body weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), albumin–creatinine ratio (ACR), and hemoglobinA1c (HbA1c), triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol level were measured during the examination. All health checkups parameters were collected per the guidelines of Specific Health Checkups and Specific Health Guidance from the Japanese Ministry of Health, Labour and Welfare (http://www.mhlw.go.jp). Blood pressure was measured once. Information regarding participants’ medication profiles regarding blood pressure, blood glucose, and lipid-lowering therapies were extracted using a questionnaire. Medication type was not identified because of limited information in the questionnaire. The urine sodium (Na) and creatinine levels were determined using an automated analyzer, Unicel Dx800 (Beckman Coulter, Sacramento, CA). The daily urinary parameters were corrected using the creatinine level. The 24-hour Na excretion (mmol/day) was predicted using the following equation: 24-hour urinary Na (mmol) = 21.98 × [spot urinary Na (mmol/l)/spot urinary creatinine (mg/dl) × the predicted 24-hour urinary creatinine level (mg/day)]0.392, where the predicted 24-hour urinary creatinine level was calculated as −2.04 × age + 14.89 × weight (kg) + 16.14 × height (cm) − 2244.45.16 Creatinine clearance was predicted using the Cockcroft–Gault equation: Creatinine clearance (ml/min) = {((140–age) × weight (kg))/(72 × serum creatinine (mg/dl))} × 0.85 (if female). Diabetes mellitus or abnormal blood glucose control was defined as the use of insulin or oral hypoglycemic agents or an HbA1c level ≥6.5%. Hyperlipidemia or abnormal blood lipid control was defined as the use of an oral hypolipidemic agent, triglyceride level ≥150 mg/dl, high-density lipoprotein cholesterol level <40 mg/dl, or low-density lipoprotein cholesterol level ≥140 mg/dl. Hypertension was defined as the use of an oral hypotensive agent, SBP ≥140 mm Hg, or DBP ≥90 mm Hg. Smoking was defined as smoking cigarettes habitually (for the past month, a total of >100, or >6 months.) Measurement of circulating and urine carbonyl compounds, AGT, MCP-1, and thiobarbituric acid reactive substances Blood samples were obtained after fasting, with the patient seated. The samples were centrifuged and stored at −80°C until analysis. Urine samples were obtained from the morning urine and stored at −80°C until analysis. Serum carbonyl compounds, such as MG, GO, and 3-deoxyglucose were determined using a previously described method.17 The serum and urinary AGT levels were determined using a human total AGT assay kit (Immuno-Biological Laboratories, Gunma, Japan). The serum and urinary monocyte chemoattractant protein-1 (MCP-1) levels were determined using a human MCP-1 immunoassay kit (Invitrogen, Waltham, MA). The excretion level of urinary thiobarbituric acid reactive substances (TBARS) was determined using a previously described method.11,14,18 Data analysis First, the subjects were divided into 2 groups based on blood pressure. Then, because antihypertensive medication affects AGT levels, the hypertensive group was further subdivided into 2 groups based on medication status. Multiple groups were compared statistically with an analysis of variance with the Tukey–Kramer test. A multiple linear regression analysis was used to examine the relationship between urinary or serum markers and blood pressure. The multivariables were adjusted for age, sex, body mass index (BMI), smoking status, prevalence of cardiovascular disease, prevalence of diabetes, antihypertensive medication, and prevalence of hyperlipidemia. A stepwise multiple regression analysis (with age and sex factored), with SBP as the dependent variable, was performed to determine which independent variable had a strong association with blood pressure in each group. Stepwise multiple regression analysis (with age and sex factored), with urinary AGT excretion level as the dependent variable, was performed to determine which independent variable had a strong association with urinary AGT in each group. P values needed to be <0.05 for a variable to be entered and kept in the model. Interaction term analysis with SBP was performed to determine if carbonyl stress modified the effect of urinary AGT on blood pressure. The multivariables were adjusted for age, sex, BMI, smoking status, cardiovascular disease, diabetes, antihypertensive medication, and hyperlipidemia. Interaction effect term was also added. The urinary ACR, AGT, MCP-1, TBARS, MG, GO, and 3-deoxyglucosone levels, and serum AGT, MCP-1, MG, and GO levels were not normally distributed. In the data analysis, they were expressed as natural log-transformed. For database management and statistical analysis, JMP Pro software, version 12. 2. 0 was used. Data are shown as mean ± SE, unless otherwise specified. A P value <0.05 was considered statistically significant. Ethical approval The study protocol was approved by the Institutional Ethical Review Board of the Tohoku University School of Medicine (2015-1-825) and was conducted per the principles of the Declaration of Helsinki. Informed consent was obtained from all subjects. RESULTS Characteristics of the participants Table 1 shows the study sample characteristics and concentrations of the urinary and serum markers. In this study, data from 355 adults out of 1,715 source adults were collected. The characteristics of the source population are shown in Supplementary Table 1. There was no major difference in characteristics between the study and the source adults. The study sample had no major differences in BMI, abdominal circumference, SBP, DBP, triglycerides, high-density lipoprotein, and low-density lipoprotein, excluding HbA1c compared to the general population of Miyagi and Japan (Supplementary Table 2). Table 1. Characteristics of the subjects and concentration of urinary and serum markers categorized by blood pressure Hypertensive ANOVA Total Normotensive Nonmedication Medication P N 355 193 39 123 Age (years) 66.6 ± 14.4 62.3 ± 15.9 67.2 ± 13.0 73.1 ± 8.9**,† <0.0001 Sex (male) (%) 45.6 46.6 48.7 43.1 0.7 Body mass index (kg/m2) 23.5 ± 3.2 22.9 ± 3.2 23.9 ± 3.4 24.2 ± 3.0** 0.0008 Smoking status (%) 15.8 19.7 18 8.9 0.03 Cardiovascular disease (%) 9.6 7.3 0 16.3 0.0007 Hyperlipidemia (%) 53.2 47.7 56.4 61 0.06 Diabetes (%) 10.4 8.3 10.3 13.8 0.3 HbA1c (%) 5.6 ± 0.6 5.4 ± 0.6 5.6 ± 0.7 5.7 ± 0.7** 0.001 Estimated glomerular filtration rate (ml/ min/1.73 m2) 74.6 ± 14.4 77.4 ± 13.5 70.5 ± 15.7 69.9 ± 14.6** 0.0005 Creatinine clearance (ml/min) (n = 245) 84.0 ± 1.7 87.3 ± 2.1 80.5 ± 5.1 78.0 ± 3.2* 0.04 Systolic blood pressure (mm Hg) 127.0 ± 17.8 118.7 ± 11.4 152.4 ± 14.4** 132.0 ± 17.5**,‡ <0.0001 Diastolic blood pressure (mm Hg) 73.5 ± 11.7 69.4 ± 8.9 86.5 ± 11.3** 75.8 ± 12.1**,‡ <0.0001 Urinary albumin–creatinine ratio (n = 246) 6.8 (4.8–12.1) 6.2 (4.5–9.7) 9.2 (5.5–18.2)** 8.1 (5.6–17.7)** <0.0001 Estimated 24-hour urinary Na excretion (mmol/predict day) (n = 348) 406.2 ± 91.5 397.2 ± 87.3 422.0 ± 87.9 415.3 ± 98.0 0.1 Urinary markers  AGT (ng/creatinine mg) 13.8 10.6 19.8** 15.3** <0.0001 (6.7–28.5) (5.9–20.4) (11.8–44.9) (7.9–35.7)  MCP-1 (pg/creatinine mg) 258.4 238 292.9 270.6 0.3 (151.9–505.3) (145.1–460.7) (139.1–693.6) (172.0–581.8)  TBARS (nM/creatinine mg) 6 5.8 6.5 6.3 0.08 (4.3–8.5) (4.3–8.1) (4.5–9.3) (4.5–9.6)  MG (ng/creatinine mg) 2.3 2 2.5 2.8** <0.0001 (1.6–3.6) (1.4–3.0) (1.9–3.9) (1.9–4.1)  GO (µmol/creatinine mg) 3.7 3.3 4.1 4.6** 0.003 (2.0–5.9) (1.8–5.0) (2.7–6.5) (2.4–6.8)  3-DG (µmol/creatinine mg) 1.6 1.5 1.8 1.8** 0.001 (1.0–2.3) (0.9–2.1) (1.3–2.3) (1.2–2.7) Serum markers  AGT (ng/ml) 46 44.7 47.2 46.7 0.8 (36.6–56.6) (35.4–56.7) (38.0–55.4) (37.0–56.3)  MCP-1 (pg/ml) 330.9 304.3 321.5 328.4 0.7 (212.8–571.9) (200.9–552.0) (217.4–604.6) (206.6–544.5)  MG (nmol/ml) 0.1 0.09 0.1 0.1 0.7 (0.07–0.28) (0.07–0.28) (0.07–0.45) (0.07–0.27)  GO (nmol/ml) 0.26 0.24 0.29 0.27 0.8 (0.20–0.33) (0.20–0.31) (0.22–0.33) (0.22–0.35)  3-DG (nmol/ml) 0.29 0.28 0.29 0.33* 0.04 (0.19–0.49) (0.14–0.45) (0.23–0.48) (0.22–0.57) Hypertensive ANOVA Total Normotensive Nonmedication Medication P N 355 193 39 123 Age (years) 66.6 ± 14.4 62.3 ± 15.9 67.2 ± 13.0 73.1 ± 8.9**,† <0.0001 Sex (male) (%) 45.6 46.6 48.7 43.1 0.7 Body mass index (kg/m2) 23.5 ± 3.2 22.9 ± 3.2 23.9 ± 3.4 24.2 ± 3.0** 0.0008 Smoking status (%) 15.8 19.7 18 8.9 0.03 Cardiovascular disease (%) 9.6 7.3 0 16.3 0.0007 Hyperlipidemia (%) 53.2 47.7 56.4 61 0.06 Diabetes (%) 10.4 8.3 10.3 13.8 0.3 HbA1c (%) 5.6 ± 0.6 5.4 ± 0.6 5.6 ± 0.7 5.7 ± 0.7** 0.001 Estimated glomerular filtration rate (ml/ min/1.73 m2) 74.6 ± 14.4 77.4 ± 13.5 70.5 ± 15.7 69.9 ± 14.6** 0.0005 Creatinine clearance (ml/min) (n = 245) 84.0 ± 1.7 87.3 ± 2.1 80.5 ± 5.1 78.0 ± 3.2* 0.04 Systolic blood pressure (mm Hg) 127.0 ± 17.8 118.7 ± 11.4 152.4 ± 14.4** 132.0 ± 17.5**,‡ <0.0001 Diastolic blood pressure (mm Hg) 73.5 ± 11.7 69.4 ± 8.9 86.5 ± 11.3** 75.8 ± 12.1**,‡ <0.0001 Urinary albumin–creatinine ratio (n = 246) 6.8 (4.8–12.1) 6.2 (4.5–9.7) 9.2 (5.5–18.2)** 8.1 (5.6–17.7)** <0.0001 Estimated 24-hour urinary Na excretion (mmol/predict day) (n = 348) 406.2 ± 91.5 397.2 ± 87.3 422.0 ± 87.9 415.3 ± 98.0 0.1 Urinary markers  AGT (ng/creatinine mg) 13.8 10.6 19.8** 15.3** <0.0001 (6.7–28.5) (5.9–20.4) (11.8–44.9) (7.9–35.7)  MCP-1 (pg/creatinine mg) 258.4 238 292.9 270.6 0.3 (151.9–505.3) (145.1–460.7) (139.1–693.6) (172.0–581.8)  TBARS (nM/creatinine mg) 6 5.8 6.5 6.3 0.08 (4.3–8.5) (4.3–8.1) (4.5–9.3) (4.5–9.6)  MG (ng/creatinine mg) 2.3 2 2.5 2.8** <0.0001 (1.6–3.6) (1.4–3.0) (1.9–3.9) (1.9–4.1)  GO (µmol/creatinine mg) 3.7 3.3 4.1 4.6** 0.003 (2.0–5.9) (1.8–5.0) (2.7–6.5) (2.4–6.8)  3-DG (µmol/creatinine mg) 1.6 1.5 1.8 1.8** 0.001 (1.0–2.3) (0.9–2.1) (1.3–2.3) (1.2–2.7) Serum markers  AGT (ng/ml) 46 44.7 47.2 46.7 0.8 (36.6–56.6) (35.4–56.7) (38.0–55.4) (37.0–56.3)  MCP-1 (pg/ml) 330.9 304.3 321.5 328.4 0.7 (212.8–571.9) (200.9–552.0) (217.4–604.6) (206.6–544.5)  MG (nmol/ml) 0.1 0.09 0.1 0.1 0.7 (0.07–0.28) (0.07–0.28) (0.07–0.45) (0.07–0.27)  GO (nmol/ml) 0.26 0.24 0.29 0.27 0.8 (0.20–0.33) (0.20–0.31) (0.22–0.33) (0.22–0.35)  3-DG (nmol/ml) 0.29 0.28 0.29 0.33* 0.04 (0.19–0.49) (0.14–0.45) (0.23–0.48) (0.22–0.57) Tukey–Kramer’s test, **P < 0.01 vs. normotensive; *P < 0.05 vs. normotensive; †P < 0.01 vs. hypertensive-non-medication; ‡P < 0.05 vs. hypertensive-non-medication. Abbreviations: AGT, angiotensinogen; ANOVA, analysis of variance; 3-DG, 3-deoxyglucosone; GO, glyoxal; HbA1c, hemoglobin A1c level; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; Na, sodium; TBARS, thiobarbituric acid reaction substances. View Large Table 1. Characteristics of the subjects and concentration of urinary and serum markers categorized by blood pressure Hypertensive ANOVA Total Normotensive Nonmedication Medication P N 355 193 39 123 Age (years) 66.6 ± 14.4 62.3 ± 15.9 67.2 ± 13.0 73.1 ± 8.9**,† <0.0001 Sex (male) (%) 45.6 46.6 48.7 43.1 0.7 Body mass index (kg/m2) 23.5 ± 3.2 22.9 ± 3.2 23.9 ± 3.4 24.2 ± 3.0** 0.0008 Smoking status (%) 15.8 19.7 18 8.9 0.03 Cardiovascular disease (%) 9.6 7.3 0 16.3 0.0007 Hyperlipidemia (%) 53.2 47.7 56.4 61 0.06 Diabetes (%) 10.4 8.3 10.3 13.8 0.3 HbA1c (%) 5.6 ± 0.6 5.4 ± 0.6 5.6 ± 0.7 5.7 ± 0.7** 0.001 Estimated glomerular filtration rate (ml/ min/1.73 m2) 74.6 ± 14.4 77.4 ± 13.5 70.5 ± 15.7 69.9 ± 14.6** 0.0005 Creatinine clearance (ml/min) (n = 245) 84.0 ± 1.7 87.3 ± 2.1 80.5 ± 5.1 78.0 ± 3.2* 0.04 Systolic blood pressure (mm Hg) 127.0 ± 17.8 118.7 ± 11.4 152.4 ± 14.4** 132.0 ± 17.5**,‡ <0.0001 Diastolic blood pressure (mm Hg) 73.5 ± 11.7 69.4 ± 8.9 86.5 ± 11.3** 75.8 ± 12.1**,‡ <0.0001 Urinary albumin–creatinine ratio (n = 246) 6.8 (4.8–12.1) 6.2 (4.5–9.7) 9.2 (5.5–18.2)** 8.1 (5.6–17.7)** <0.0001 Estimated 24-hour urinary Na excretion (mmol/predict day) (n = 348) 406.2 ± 91.5 397.2 ± 87.3 422.0 ± 87.9 415.3 ± 98.0 0.1 Urinary markers  AGT (ng/creatinine mg) 13.8 10.6 19.8** 15.3** <0.0001 (6.7–28.5) (5.9–20.4) (11.8–44.9) (7.9–35.7)  MCP-1 (pg/creatinine mg) 258.4 238 292.9 270.6 0.3 (151.9–505.3) (145.1–460.7) (139.1–693.6) (172.0–581.8)  TBARS (nM/creatinine mg) 6 5.8 6.5 6.3 0.08 (4.3–8.5) (4.3–8.1) (4.5–9.3) (4.5–9.6)  MG (ng/creatinine mg) 2.3 2 2.5 2.8** <0.0001 (1.6–3.6) (1.4–3.0) (1.9–3.9) (1.9–4.1)  GO (µmol/creatinine mg) 3.7 3.3 4.1 4.6** 0.003 (2.0–5.9) (1.8–5.0) (2.7–6.5) (2.4–6.8)  3-DG (µmol/creatinine mg) 1.6 1.5 1.8 1.8** 0.001 (1.0–2.3) (0.9–2.1) (1.3–2.3) (1.2–2.7) Serum markers  AGT (ng/ml) 46 44.7 47.2 46.7 0.8 (36.6–56.6) (35.4–56.7) (38.0–55.4) (37.0–56.3)  MCP-1 (pg/ml) 330.9 304.3 321.5 328.4 0.7 (212.8–571.9) (200.9–552.0) (217.4–604.6) (206.6–544.5)  MG (nmol/ml) 0.1 0.09 0.1 0.1 0.7 (0.07–0.28) (0.07–0.28) (0.07–0.45) (0.07–0.27)  GO (nmol/ml) 0.26 0.24 0.29 0.27 0.8 (0.20–0.33) (0.20–0.31) (0.22–0.33) (0.22–0.35)  3-DG (nmol/ml) 0.29 0.28 0.29 0.33* 0.04 (0.19–0.49) (0.14–0.45) (0.23–0.48) (0.22–0.57) Hypertensive ANOVA Total Normotensive Nonmedication Medication P N 355 193 39 123 Age (years) 66.6 ± 14.4 62.3 ± 15.9 67.2 ± 13.0 73.1 ± 8.9**,† <0.0001 Sex (male) (%) 45.6 46.6 48.7 43.1 0.7 Body mass index (kg/m2) 23.5 ± 3.2 22.9 ± 3.2 23.9 ± 3.4 24.2 ± 3.0** 0.0008 Smoking status (%) 15.8 19.7 18 8.9 0.03 Cardiovascular disease (%) 9.6 7.3 0 16.3 0.0007 Hyperlipidemia (%) 53.2 47.7 56.4 61 0.06 Diabetes (%) 10.4 8.3 10.3 13.8 0.3 HbA1c (%) 5.6 ± 0.6 5.4 ± 0.6 5.6 ± 0.7 5.7 ± 0.7** 0.001 Estimated glomerular filtration rate (ml/ min/1.73 m2) 74.6 ± 14.4 77.4 ± 13.5 70.5 ± 15.7 69.9 ± 14.6** 0.0005 Creatinine clearance (ml/min) (n = 245) 84.0 ± 1.7 87.3 ± 2.1 80.5 ± 5.1 78.0 ± 3.2* 0.04 Systolic blood pressure (mm Hg) 127.0 ± 17.8 118.7 ± 11.4 152.4 ± 14.4** 132.0 ± 17.5**,‡ <0.0001 Diastolic blood pressure (mm Hg) 73.5 ± 11.7 69.4 ± 8.9 86.5 ± 11.3** 75.8 ± 12.1**,‡ <0.0001 Urinary albumin–creatinine ratio (n = 246) 6.8 (4.8–12.1) 6.2 (4.5–9.7) 9.2 (5.5–18.2)** 8.1 (5.6–17.7)** <0.0001 Estimated 24-hour urinary Na excretion (mmol/predict day) (n = 348) 406.2 ± 91.5 397.2 ± 87.3 422.0 ± 87.9 415.3 ± 98.0 0.1 Urinary markers  AGT (ng/creatinine mg) 13.8 10.6 19.8** 15.3** <0.0001 (6.7–28.5) (5.9–20.4) (11.8–44.9) (7.9–35.7)  MCP-1 (pg/creatinine mg) 258.4 238 292.9 270.6 0.3 (151.9–505.3) (145.1–460.7) (139.1–693.6) (172.0–581.8)  TBARS (nM/creatinine mg) 6 5.8 6.5 6.3 0.08 (4.3–8.5) (4.3–8.1) (4.5–9.3) (4.5–9.6)  MG (ng/creatinine mg) 2.3 2 2.5 2.8** <0.0001 (1.6–3.6) (1.4–3.0) (1.9–3.9) (1.9–4.1)  GO (µmol/creatinine mg) 3.7 3.3 4.1 4.6** 0.003 (2.0–5.9) (1.8–5.0) (2.7–6.5) (2.4–6.8)  3-DG (µmol/creatinine mg) 1.6 1.5 1.8 1.8** 0.001 (1.0–2.3) (0.9–2.1) (1.3–2.3) (1.2–2.7) Serum markers  AGT (ng/ml) 46 44.7 47.2 46.7 0.8 (36.6–56.6) (35.4–56.7) (38.0–55.4) (37.0–56.3)  MCP-1 (pg/ml) 330.9 304.3 321.5 328.4 0.7 (212.8–571.9) (200.9–552.0) (217.4–604.6) (206.6–544.5)  MG (nmol/ml) 0.1 0.09 0.1 0.1 0.7 (0.07–0.28) (0.07–0.28) (0.07–0.45) (0.07–0.27)  GO (nmol/ml) 0.26 0.24 0.29 0.27 0.8 (0.20–0.33) (0.20–0.31) (0.22–0.33) (0.22–0.35)  3-DG (nmol/ml) 0.29 0.28 0.29 0.33* 0.04 (0.19–0.49) (0.14–0.45) (0.23–0.48) (0.22–0.57) Tukey–Kramer’s test, **P < 0.01 vs. normotensive; *P < 0.05 vs. normotensive; †P < 0.01 vs. hypertensive-non-medication; ‡P < 0.05 vs. hypertensive-non-medication. Abbreviations: AGT, angiotensinogen; ANOVA, analysis of variance; 3-DG, 3-deoxyglucosone; GO, glyoxal; HbA1c, hemoglobin A1c level; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; Na, sodium; TBARS, thiobarbituric acid reaction substances. View Large The difference in the blood pressure, and urinary ACR was significant between the normotensive and hypertensive patients not taking medication. The difference in age, BMI, HbA1c, estimated glomerular filtration rate, blood pressure, and urinary ACR was significant between the normotensive and hypertensive patients on medication. In the hypertensive-non-medication and hypertensive-with-medication groups, the urinary AGT excretion level was significantly higher than that of the normotensive group. Only in the hypertensive-medication group, the carbonyl compound excretion level and circulating 3-deoxyglucosone level were significantly high than those of the normotensive group. The circulating AGT, MCP-1, and urinary TBARS were not significantly different among groups. Factors associated with blood pressure Next, we performed a multiple linear regression analysis to examine the relationship between urinary or serum markers and blood pressure (Table 2). Based on the multiple regression analysis, the urinary AGT excretion level was significantly associated with SBP and DBP (β = 5.5 and 2.8, respectively), along with urinary carbonyl stress markers (indicated by the MG level, β = 3.5 and 1.8 and GO level, β = 3 and 2.2, respectively) and serum AGT (β = 2.4 and 1.6, respectively) (Table 2). However, blood pressure was not associated with the urinary MCP-1, TBARS, or circulating carbonyl stress markers. Table 2. Relationship between urinary and serum markers and blood pressure in the multiple regression analyses Dependent variable: SBP (mm Hg) DBP (mm Hg) Independent variables (every 1 SD increase) β SE P β SE P Urinary markers  log AGT 5.5 0.9 <0.0001 2.8 0.6 <0.0001  log MCP-1 0.0007 0.0006 0.2 0.000008 0.0004 1  log TBARS 0.6 0.9 0.5 0.4 0.6 0.5  log MG 3.5 1 0.0008 1.8 0.7 0.009  log GO 3 1 0.003 2.2 0.7 0.0008  log 3-DG 0.8 1 0.5 1.5 0.7 0.03 Serum markers  log AGT 2.4 0.9 0.01 1.6 0.6 0.01  log MCP-1 -0.02 0.9 1 0.3 0.6 0.6  log MG 0.2 0.2 0.2 0.06 0.1 0.6  log GO 0.5 0.4 0.2 0.1 0.2 0.6  log 3-DG -0.1 0.3 0.6 -0.2 0.2 0.2 Dependent variable: SBP (mm Hg) DBP (mm Hg) Independent variables (every 1 SD increase) β SE P β SE P Urinary markers  log AGT 5.5 0.9 <0.0001 2.8 0.6 <0.0001  log MCP-1 0.0007 0.0006 0.2 0.000008 0.0004 1  log TBARS 0.6 0.9 0.5 0.4 0.6 0.5  log MG 3.5 1 0.0008 1.8 0.7 0.009  log GO 3 1 0.003 2.2 0.7 0.0008  log 3-DG 0.8 1 0.5 1.5 0.7 0.03 Serum markers  log AGT 2.4 0.9 0.01 1.6 0.6 0.01  log MCP-1 -0.02 0.9 1 0.3 0.6 0.6  log MG 0.2 0.2 0.2 0.06 0.1 0.6  log GO 0.5 0.4 0.2 0.1 0.2 0.6  log 3-DG -0.1 0.3 0.6 -0.2 0.2 0.2 Multivariable-adjusted for age, sex, body mass index, smoking status, prevalence of cardiovascular disease, prevalence of diabetes, use of antihypertensive medication, and prevalence of hyperlipidemia. Abbreviations: AGT, angiotensinogen; DBP, diastolic blood pressure; 3-DG, 3-deoxyglucosone; GO, glyoxal; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; SBP, systolic blood pressure; TBARS, thiobarbituric acid reaction substances. View Large Table 2. Relationship between urinary and serum markers and blood pressure in the multiple regression analyses Dependent variable: SBP (mm Hg) DBP (mm Hg) Independent variables (every 1 SD increase) β SE P β SE P Urinary markers  log AGT 5.5 0.9 <0.0001 2.8 0.6 <0.0001  log MCP-1 0.0007 0.0006 0.2 0.000008 0.0004 1  log TBARS 0.6 0.9 0.5 0.4 0.6 0.5  log MG 3.5 1 0.0008 1.8 0.7 0.009  log GO 3 1 0.003 2.2 0.7 0.0008  log 3-DG 0.8 1 0.5 1.5 0.7 0.03 Serum markers  log AGT 2.4 0.9 0.01 1.6 0.6 0.01  log MCP-1 -0.02 0.9 1 0.3 0.6 0.6  log MG 0.2 0.2 0.2 0.06 0.1 0.6  log GO 0.5 0.4 0.2 0.1 0.2 0.6  log 3-DG -0.1 0.3 0.6 -0.2 0.2 0.2 Dependent variable: SBP (mm Hg) DBP (mm Hg) Independent variables (every 1 SD increase) β SE P β SE P Urinary markers  log AGT 5.5 0.9 <0.0001 2.8 0.6 <0.0001  log MCP-1 0.0007 0.0006 0.2 0.000008 0.0004 1  log TBARS 0.6 0.9 0.5 0.4 0.6 0.5  log MG 3.5 1 0.0008 1.8 0.7 0.009  log GO 3 1 0.003 2.2 0.7 0.0008  log 3-DG 0.8 1 0.5 1.5 0.7 0.03 Serum markers  log AGT 2.4 0.9 0.01 1.6 0.6 0.01  log MCP-1 -0.02 0.9 1 0.3 0.6 0.6  log MG 0.2 0.2 0.2 0.06 0.1 0.6  log GO 0.5 0.4 0.2 0.1 0.2 0.6  log 3-DG -0.1 0.3 0.6 -0.2 0.2 0.2 Multivariable-adjusted for age, sex, body mass index, smoking status, prevalence of cardiovascular disease, prevalence of diabetes, use of antihypertensive medication, and prevalence of hyperlipidemia. Abbreviations: AGT, angiotensinogen; DBP, diastolic blood pressure; 3-DG, 3-deoxyglucosone; GO, glyoxal; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; SBP, systolic blood pressure; TBARS, thiobarbituric acid reaction substances. View Large Per the results of the stepwise multiple regression analysis with SBP (Table 3), the urinary AGT excretion level (R2 = 0.06) and BMI (R2 = 0.05) were selected as variables in the normotensive group. In the hypertensive-non-medication group, the urinary AGT excretion level (R2 = 0.2) and urinary MG excretion level (R2 = 0.1) were selected. Age (R2 = 0.02) and the urinary AGT excretion level (R2 = 0.03) were selected in the hypertensive-with-medication group. The urinary AGT excretion level was selected as a variable in all groups; the urinary MG excretion level was selected only in the hypertensive-non-medication group using a stepwise multiple regression analysis with SBP. The serum AGT and urinary MG excretion levels were not selected as variables in the normotensive and hypertensive-with-medication groups with a stepwise multiple regression analysis, regardless of their significant association with SBP and DBP on multiple regression analysis. Furthermore, we performed a multiple linear regression analysis to examine the relationship between the urinary AGT or MG excretion level and blood pressure in each group. In the normotensive group, the urinary AGT excretion level was significantly related to SBP (β = 3.0, P = 0.001) but the urinary MG excretion level was not (β = 1.0, P = 0.3). In the hypertensive-non-medication group, both the urinary AGT and MG excretion levels were significantly correlated with SBP (β = 2.8, P = 0.02 and β = 2.7, P = 0.008, respectively). In the hypertensive-with-medication group, the urinary AGT level was significantly related to SBP (β = 4.5, P = 0.004), but not the urinary MG excretion level (β = 3.0, P = 0.1). Supplementary Figure 1 shows the relationship between urinary AGT excretion level and SBP in all participants. Urinary AGT excretion level correlated significantly with SBP in all groups. Table 3. Relationship between urinary and serum markers and blood pressure in the stepwise multiple regression analyses Dependent variable: SBP Normotensive (n = 193) Hypertensive-non-medication (n = 39) Hypertensive-medication (n = 123) β Partial R2 P β Partial R2 P β Partial R2 P Age 0.03 0.001 0.7 -0.2 0.02 0.4 −0.4 0.02 0.04 Sex 0.4 0.001 0.5 -3 0.01 0.1 −0.8 0.02 0.3 BMI 0.8 0.05 0.002 Diabetes Hyperlipidemia Smoking status CVD Antihypertensive medication  Log urinary AGT 2.6 0.06 0.001 5.7 0.2 0.004 4.3 0.03 0.005  Log serum AGT  Log urinary MG 7.8 0.1 0.02  Log urinary GO  Log urinary 3-DG Dependent variable: SBP Normotensive (n = 193) Hypertensive-non-medication (n = 39) Hypertensive-medication (n = 123) β Partial R2 P β Partial R2 P β Partial R2 P Age 0.03 0.001 0.7 -0.2 0.02 0.4 −0.4 0.02 0.04 Sex 0.4 0.001 0.5 -3 0.01 0.1 −0.8 0.02 0.3 BMI 0.8 0.05 0.002 Diabetes Hyperlipidemia Smoking status CVD Antihypertensive medication  Log urinary AGT 2.6 0.06 0.001 5.7 0.2 0.004 4.3 0.03 0.005  Log serum AGT  Log urinary MG 7.8 0.1 0.02  Log urinary GO  Log urinary 3-DG A stepwise multiple regression analysis (with age and sex forced in), with SBP as the dependent variable and all variables listed in the table as objective variables, was performed. A P value <0.05 was necessary for a variable to be entered and kept in the model. Abbreviations: AGT, angiotensinogen; BMI, body mass index; CVD, cardiovascular disease; 3-DG, 3-deoxyglucosone; GO, glyoxal; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; SBP, systolic blood pressure. View Large Table 3. Relationship between urinary and serum markers and blood pressure in the stepwise multiple regression analyses Dependent variable: SBP Normotensive (n = 193) Hypertensive-non-medication (n = 39) Hypertensive-medication (n = 123) β Partial R2 P β Partial R2 P β Partial R2 P Age 0.03 0.001 0.7 -0.2 0.02 0.4 −0.4 0.02 0.04 Sex 0.4 0.001 0.5 -3 0.01 0.1 −0.8 0.02 0.3 BMI 0.8 0.05 0.002 Diabetes Hyperlipidemia Smoking status CVD Antihypertensive medication  Log urinary AGT 2.6 0.06 0.001 5.7 0.2 0.004 4.3 0.03 0.005  Log serum AGT  Log urinary MG 7.8 0.1 0.02  Log urinary GO  Log urinary 3-DG Dependent variable: SBP Normotensive (n = 193) Hypertensive-non-medication (n = 39) Hypertensive-medication (n = 123) β Partial R2 P β Partial R2 P β Partial R2 P Age 0.03 0.001 0.7 -0.2 0.02 0.4 −0.4 0.02 0.04 Sex 0.4 0.001 0.5 -3 0.01 0.1 −0.8 0.02 0.3 BMI 0.8 0.05 0.002 Diabetes Hyperlipidemia Smoking status CVD Antihypertensive medication  Log urinary AGT 2.6 0.06 0.001 5.7 0.2 0.004 4.3 0.03 0.005  Log serum AGT  Log urinary MG 7.8 0.1 0.02  Log urinary GO  Log urinary 3-DG A stepwise multiple regression analysis (with age and sex forced in), with SBP as the dependent variable and all variables listed in the table as objective variables, was performed. A P value <0.05 was necessary for a variable to be entered and kept in the model. Abbreviations: AGT, angiotensinogen; BMI, body mass index; CVD, cardiovascular disease; 3-DG, 3-deoxyglucosone; GO, glyoxal; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; SBP, systolic blood pressure. View Large Relationship between the urinary AGT and MG excretion level Figure 1 shows the relationship between the urinary AGT and MG excretion levels and SBP. The participants were divided into 4 groups based on the urinary AGT and MG excretion levels. A low AGT or MG meant that the urinary AGT or MG excretion level was under the median. A high AGT or MG meant that the urinary AGT or MG excretion level was above the median. SBP was significantly increased in participants with high AGT and MG excretion levels than those with low excretion levels (Figure 1, P < 0.0001). Furthermore, SBP was significantly increased in participants with high AGT and MG excretion levels compared to those with high MG excretion levels and low AGT excretion levels (P < 0.05). In all populations, multiple regression analysis showed that urinary AGT and MG excretion levels were independently and significantly associated with SBP (β = 5.2, P < 0.0001 and β = 2.8, P = 0.005, respectively). The interactions among urinary AGT excretion level and urinary MG excretion level on blood pressure was not significant in all study populations (P > 0.2), the normotensive (P > 0.7), hypertensive-non-medication (P > 0.8), and hypertensive-with-medication (P > 0.05) groups. Figure 1. View largeDownload slide Comparisons of systolic blood pressure. High AGT: urinary AGT excretion levels ≥13.7 (ng/mg Cre). High MG: urinary MG excretion levels ≥2.3 (nmol/mg Cre). Low AGT: urinary AGT excretion levels <13.7 (ng/mg Cre). Low MG: urinary MG excretion levels <2.3 (nmol/mg Cre). The data are expressed as the mean. The differences between multiple groups were detected using Tukey–Kramer’s test. ***P < 0.0001 and *P < 0.05 vs. low AGT and low MG. †P < 0.05 vs. low AGT and high MG. Abbreviations: AGT, angiotensinogen; ANOVA, analysis of variance; MG, methylglyoxal; SBP, systolic blood pressure. Figure 1. View largeDownload slide Comparisons of systolic blood pressure. High AGT: urinary AGT excretion levels ≥13.7 (ng/mg Cre). High MG: urinary MG excretion levels ≥2.3 (nmol/mg Cre). Low AGT: urinary AGT excretion levels <13.7 (ng/mg Cre). Low MG: urinary MG excretion levels <2.3 (nmol/mg Cre). The data are expressed as the mean. The differences between multiple groups were detected using Tukey–Kramer’s test. ***P < 0.0001 and *P < 0.05 vs. low AGT and low MG. †P < 0.05 vs. low AGT and high MG. Abbreviations: AGT, angiotensinogen; ANOVA, analysis of variance; MG, methylglyoxal; SBP, systolic blood pressure. Association between urinary AGT and serum AGT Figure 2 shows the relationship between urinary AGT excretion level and urinary ACR in all participants. Urinary ACR was significantly correlated with urinary AGT excretion level in all groups. We examined the association between urinary AGT and serum AGT using stepwise multiple regression analysis (Supplementary Table 3). Per the results of the stepwise multiple regression analysis with urinary AGT excretion level, urinary ACR level was selected as a variable in all groups (normotensive, R2 = 0.2; hypertensive-non-medication, R2 = 0.7; hypertensive-medication, R2 = 0.3). Although the serum AGT level was selected as a variable in hypertensive-with-medication, the contribution of serum AGT to urinary AGT is small because the partial R2 is 0.16. Figure 2. View largeDownload slide Relationship between the urinary angiotensinogen excretion level and albumin–creatinine ratio (ACR). Single linear regression analysis between urinary angiotensinogen (AGT) excretion level and ACR. Pearson’s correlation coefficient. Figure 2. View largeDownload slide Relationship between the urinary angiotensinogen excretion level and albumin–creatinine ratio (ACR). Single linear regression analysis between urinary angiotensinogen (AGT) excretion level and ACR. Pearson’s correlation coefficient. DISCUSSION In this study, we examined the associations among carbonyl stress, RAS activation, and blood pressure in the general population. We found that (i) although circulating AGT was not significantly associated with blood pressure, urinary AGT excretion level was associated with blood pressure in the normotensive and hypertensive groups; and (ii) urinary AGT and urinary MG excretion level were independently related to elevated blood pressure. Urinary AGT excretion was associated with blood pressure, independent of circulating RAS, in a group of people with African ancestry.19 Another human study showed that urinary AGT levels were higher in hypertensive patients than in control subjects, and treatment with ARBs or angiotensin-converting enzyme inhibitors normalized the elevated urinary AGT levels.7 Furthermore, increased urinary AGT excretion levels are reported in patients with nonhypertensive diseases, such as diabetes20 and chronic kidney disease.9 However, the association between urinary AGT excretion levels and increased blood pressure in the normotensive general population is not reported yet. In this study, we examined the associations among carbonyl stress, RAS activation, and blood pressure in the normotensive, hypertensive-non-medication, and hypertensive-with-medication groups to minimize the antihypertensive drug effects. In our study, urinary AGT excretion level was selected, and the serum AGT level was not selected as a variable in all groups for stepwise multiple regression analysis. Our results showed that renal activity is associated with increased blood pressure, independent of circulating AGT, in both normotensive and hypertensive Japanese people. However, 2 studies suggested that some AGT derived from the liver may be filtrated to the kidney.5,6 Urinary AGT is likely derived from circulating AGT and contributes to blood pressure elevation. Therefore, we examined the association between urinary AGT and serum AGT with a stepwise multiple regression analysis (Supplementary Table 3), the results of which showed that urinary AGT excretion level was significantly associated with urinary ACR but not serum AGT level. It is suggested that urinary AGT excretion levels are associated with renal injury indicated by urinary ACR than circulating AGT. This strong correlation between urinary AGT excretion and ACR correlated with previous study results.7,9,21,22 Although we could not exclude the possibility that circulating AGT increases the blood pressure, present results indicate that urinary AGT is also responsible for blood pressure, independent of circulating AGT. Inflammation, RAS-induced oxidative stress, and carbonyl stress likely play a fundamental role in blood pressure control.12–14,23 However, little is known about the relationships among carbonyl stress, renal RAS activity, and blood pressure. We found that although the urinary excretion levels of carbonyl compounds such as MG and GO are significantly associated with blood pressure, the urinary AGT excretion levels are strongly associated with blood pressure in both normotensive and hypertensive people. These results suggest that inappropriate renal RAS activity is a major contributor to hypertension development. We have previously demonstrated that RAS causes carbonyl stress-induced hypertension and cardio-renal injury.14,15,24 In this study, the urinary MG excretion level was significantly associated with SBP, but only in the hypertensive-with-non-medication group (Table 3). Our results suggest that inappropriate intrarenal activity and carbonyl stress were independently related to elevated blood pressure. Our regression analysis indicated no effect of the interaction between urinary AGT excretion level and urinary MG excretion level on blood pressure. These results suggest an additive, not synergetic, effect among inappropriate intrarenal activity and carbonyl stress on blood pressure. In this study, the urinary MG excretion level was not significantly associated with SBP in the hypertensive-with-medication group. This might be due to antihypertensive drugs, which suppress the downstream of inappropriate renal RAS activity. Our study has several limitations. This study was cross-sectional with a small sample. Therefore, further large-scale, prospective studies are needed to verify the association between the urinary AGT excretion levels and blood pressure elevation in normotensive people. Because this study is based on a health check-up examination, we could not collect full drug information. Previous studies have shown that ARB administration reduces the urinary AGT.25,26 ARBs are frequently used for patients with hypertension in Japan, and hypertensive subjects are more likely to be administered ARBs. Despite the high prevalence of ARB administration, the present study demonstrated a high urinary AGT level. Therefore, we conclude that a high concentration of urinary AGT is due to high renal RAS activity. The study lacks data on plasma renin activity and aldosterone level, because we had only serum samples. Therefore, further examinations are needed to discuss the associations among circulating RAS activity, urinary activity, and blood pressure. The present study predominantly focused on a middle-aged and older Asian population. This limits the generalizability of our findings. However, the study population might be representative of Miyagi prefecture since the population is like that of Miyagi prefecture (Supplementary Table 2). In conclusion, inappropriately activated intrarenal RAS activities contributes significantly to hypertension development, and urinary AGT excretion levels could be a marker of blood pressure elevation in normotensive people. Although carbonyl stress is known to be involved in cardiovascular and renal injury, no existing drug specifically targets this pathway. We determined whether carbonyl stress and RAS are involved in the pathogenesis of the development of hypertension in the general population. Carbonyl stress markers and RAS markers could be biomarkers to predict future hypertension and surrogate markers for investigating novel drugs for treating cardiovascular renal injury. SUPPLEMENTARY DATA Supplementary data are available at American Journal of Hypertension online. DISCLOSURE The authors declared no conflict of interest. This manuscript was sent to Guest Editor, Theodore A. Kotchen, MD, for editorial handling and final disposition. ACKNOWLEDGMENTS The authors are grateful to the Biomedical Research Unit of Tohoku University Hospital for the use of their equipment. We thank Yoshimi Nakamichi, Hiroko Ito, Mayuko Ishikawa, Yuika Aizawa, Mutsuko Fujiwara, and the staff of the Division of Nephrology, Endocrinology, and Vascular Medicine, Tohoku University Graduate School of Medicine, for their technical assistance; and the Health and Welfare Center, Kawasaki-machi government office. English language editing was conducted by Editage (www.editage.jp). Research funded by Japan Society for the promotion of Science [Grant-in-Aid for Scientific Research (B) (23300243), Grant-in-Aid for Young Scientist (B) (24790832), and Grant-in-Aid for Scientific Research (B) (15H04834)]. REFERENCES 1. Mori T , Cowley AW Jr . Role of pressure in angiotensin II-induced renal injury: chronic servo-control of renal perfusion pressure in rats . Hypertension 2004 ; 43 : 752 – 759 . Google Scholar CrossRef Search ADS PubMed 2. Mori T , O’Connor PM , Abe M , Cowley AW Jr . Enhanced superoxide production in renal outer medulla of Dahl salt-sensitive rats reduces nitric oxide tubular-vascular cross-talk . Hypertension 2007 ; 49 : 1336 – 1341 . Google Scholar CrossRef Search ADS PubMed 3. Ogawa S , Mori T , Nako K , Kato T , Takeuchi K , Ito S . Angiotensin II type 1 receptor blockers reduce urinary oxidative stress markers in hypertensive diabetic nephropathy . Hypertension 2006 ; 47 : 699 – 705 . Google Scholar CrossRef Search ADS PubMed 4. 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Google Scholar CrossRef Search ADS PubMed © American Journal of Hypertension, Ltd 2018. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Hypertension Oxford University Press

Urinary Angiotensinogen Excretion Level Is Associated With Elevated Blood Pressure in the Normotensive General Population

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Oxford University Press
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© American Journal of Hypertension, Ltd 2018. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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0895-7061
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1941-7225
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10.1093/ajh/hpy020
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Abstract

Abstract BACKGROUND Inflammation, intrarenal renin–angiotensin system (RAS) activation, oxidative stress, and carbonyl stress have been postulated to play a fundamental role in controlling blood pressure. However, little is known about the association among renal RAS activation, carbonyl stress, and blood pressure elevation. METHODS We evaluated the relationship between blood pressure elevation and either renal RAS activity or carbonyl stress in the general population (N = 355) in Japan. To minimize the effect of antihypertensive drug therapy, we divided participants into 3 groups (normotensive, hypertensive-with-non-medication, and hypertensive-with-medication). Intrarenal RAS activity and carbonyl stress were indicated by the urinary angiotensinogen (AGT) and carbonyl compound excretion levels, respectively. RESULTS The urinary AGT and carbonyl compound excretion levels were significantly associated with blood pressure. Using a stepwise multiple regression analysis, we found that the urinary AGT excretion levels were strongly associated with blood pressure elevation, compared with inflammation, oxidative stress, and carbonyl stress markers, in all groups. Urinary carbonyl compound excretion was significantly associated with blood pressure in only the hypertensive-without-medication group. Furthermore, blood pressure was significantly increased in these participants, and both the urinary AGT and carbonyl compound levels were high. The urinary AGT excretion levels were strongly associated with elevated blood pressure in normotensive people, and inappropriate renal RAS activity and carbonyl stress independently contributed to the development of hypertension. CONCLUSIONS These findings suggest that RAS activation, particularly renal RAS activation exert a fundamental role in the pathogenesis of hypertension in the general population. angiotensinogen, blood pressure, carbonyl stress, hypertension, intrarenal renal–angiotensin system, methylglyoxal That activation of the renin–angiotensin system (RAS), inflammation, and oxidative stress are involved in the pathogenesis of hypertension is well-known. RAS-mediated renal production of reactive oxygen species is vital in blood pressure regulation;1,2 RAS-induced reactive oxygen species and inflammation are associated with the pathogenesis of hypertensive diabetic nephropathy.3 It is demonstrated that molecules related to reactive oxygen species and inflammation are upregulated in rat kidneys exposed to high-perfusion pressure for 2 weeks.1 Angiotensinogen (AGT) is a substrate for renin and yields angiotensin peptides such as angiotensin I and angiotensin II upon reaction with renin. Although circulating AGT is produced and secreted by the liver, it cannot be filtrated easily across the glomerular membrane; thus, proximal tubules produce AGT and secrete it in tubular fluid.4 Two studies have demonstrated that some AGT derived from the liver may be filtrated and significantly contribute to renal–angiotensin production.5,6 Among the multiple independent factors that contribute to intrarenal RAS regulation, the urinary AGT excretion rate has recently been shown to be a marker of the intrarenal RAS status in patients with hypertension7,8 and chronic kidney disease.9,10 Our previous data also suggested that the urinary AGT excretion rate is associated with high blood pressure in young adults with obesity.11 Carbonyl stress is the abnormal accumulation of α-oxoaldehyde metabolites, leading to increased reaction to an eventual modification of protein, nucleotides, and basic phospholipids. This reaction contributes to cell and tissue dysfunction in elderly people and those with disease. Carbonyl compounds such as methylglyoxal (MG) and glyoxal (GO) are strongly associated with chronic kidney disease pathology and are increased in patients with chronic kidney disease per disease stage.12 Our previous studies reported that MG is pathologically involved in the vascular damage of endothelial cells and the development of hypertension, insulin resistance, cardiovascular fibrosis, and renal injury.12–15 MG-induced hypertension, cardio-renal injury, renal carbonyl/oxidative stress, and renal inflammation in Dahl salt-sensitive rats are inhibited by angiotensin-II receptor blockers (ARBs).15 Although carbonyl stress and inappropriate RAS activation are involved in blood pressure elevation, the associations among carbonyl stress, RAS activation, and blood pressure have not yet been examined. We focused on the general population because they include many normotensive people; therefore, we could evaluate the factors associated with blood pressure without requiring to consider the effects of concomitant diseases and drugs. Therefore, we examined the relationship between carbonyl stress, and intrarenal RAS and blood pressure, in a community health examination. We also evaluated the relationship between urinary AGT excretion level and serum AGT level in the same cohort. METHODS Participants A total of 1,715 adults residing in the town of Kawasaki in Miyagi prefecture, Japan, aged >20 years and who underwent a community health examination in 2011 were enrolled. A total of 529 adults underwent the community health examination over 2 days. Four hundred thirty-four adults provided informed consent for study participation. The serum/urine samples of 79 adults were inadequate for analysis. Morning urine and serum samples of 355 adults (men: 162, women: 193) were collected and analyzed. The adults were divided into 3 groups (normotensive, hypertensive-non-medication, and hypertensive-with-medication) according to their blood pressure and antihypertensive medication status. Clinical, demographic, and anthropometric measurements Body height, body weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), albumin–creatinine ratio (ACR), and hemoglobinA1c (HbA1c), triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol level were measured during the examination. All health checkups parameters were collected per the guidelines of Specific Health Checkups and Specific Health Guidance from the Japanese Ministry of Health, Labour and Welfare (http://www.mhlw.go.jp). Blood pressure was measured once. Information regarding participants’ medication profiles regarding blood pressure, blood glucose, and lipid-lowering therapies were extracted using a questionnaire. Medication type was not identified because of limited information in the questionnaire. The urine sodium (Na) and creatinine levels were determined using an automated analyzer, Unicel Dx800 (Beckman Coulter, Sacramento, CA). The daily urinary parameters were corrected using the creatinine level. The 24-hour Na excretion (mmol/day) was predicted using the following equation: 24-hour urinary Na (mmol) = 21.98 × [spot urinary Na (mmol/l)/spot urinary creatinine (mg/dl) × the predicted 24-hour urinary creatinine level (mg/day)]0.392, where the predicted 24-hour urinary creatinine level was calculated as −2.04 × age + 14.89 × weight (kg) + 16.14 × height (cm) − 2244.45.16 Creatinine clearance was predicted using the Cockcroft–Gault equation: Creatinine clearance (ml/min) = {((140–age) × weight (kg))/(72 × serum creatinine (mg/dl))} × 0.85 (if female). Diabetes mellitus or abnormal blood glucose control was defined as the use of insulin or oral hypoglycemic agents or an HbA1c level ≥6.5%. Hyperlipidemia or abnormal blood lipid control was defined as the use of an oral hypolipidemic agent, triglyceride level ≥150 mg/dl, high-density lipoprotein cholesterol level <40 mg/dl, or low-density lipoprotein cholesterol level ≥140 mg/dl. Hypertension was defined as the use of an oral hypotensive agent, SBP ≥140 mm Hg, or DBP ≥90 mm Hg. Smoking was defined as smoking cigarettes habitually (for the past month, a total of >100, or >6 months.) Measurement of circulating and urine carbonyl compounds, AGT, MCP-1, and thiobarbituric acid reactive substances Blood samples were obtained after fasting, with the patient seated. The samples were centrifuged and stored at −80°C until analysis. Urine samples were obtained from the morning urine and stored at −80°C until analysis. Serum carbonyl compounds, such as MG, GO, and 3-deoxyglucose were determined using a previously described method.17 The serum and urinary AGT levels were determined using a human total AGT assay kit (Immuno-Biological Laboratories, Gunma, Japan). The serum and urinary monocyte chemoattractant protein-1 (MCP-1) levels were determined using a human MCP-1 immunoassay kit (Invitrogen, Waltham, MA). The excretion level of urinary thiobarbituric acid reactive substances (TBARS) was determined using a previously described method.11,14,18 Data analysis First, the subjects were divided into 2 groups based on blood pressure. Then, because antihypertensive medication affects AGT levels, the hypertensive group was further subdivided into 2 groups based on medication status. Multiple groups were compared statistically with an analysis of variance with the Tukey–Kramer test. A multiple linear regression analysis was used to examine the relationship between urinary or serum markers and blood pressure. The multivariables were adjusted for age, sex, body mass index (BMI), smoking status, prevalence of cardiovascular disease, prevalence of diabetes, antihypertensive medication, and prevalence of hyperlipidemia. A stepwise multiple regression analysis (with age and sex factored), with SBP as the dependent variable, was performed to determine which independent variable had a strong association with blood pressure in each group. Stepwise multiple regression analysis (with age and sex factored), with urinary AGT excretion level as the dependent variable, was performed to determine which independent variable had a strong association with urinary AGT in each group. P values needed to be <0.05 for a variable to be entered and kept in the model. Interaction term analysis with SBP was performed to determine if carbonyl stress modified the effect of urinary AGT on blood pressure. The multivariables were adjusted for age, sex, BMI, smoking status, cardiovascular disease, diabetes, antihypertensive medication, and hyperlipidemia. Interaction effect term was also added. The urinary ACR, AGT, MCP-1, TBARS, MG, GO, and 3-deoxyglucosone levels, and serum AGT, MCP-1, MG, and GO levels were not normally distributed. In the data analysis, they were expressed as natural log-transformed. For database management and statistical analysis, JMP Pro software, version 12. 2. 0 was used. Data are shown as mean ± SE, unless otherwise specified. A P value <0.05 was considered statistically significant. Ethical approval The study protocol was approved by the Institutional Ethical Review Board of the Tohoku University School of Medicine (2015-1-825) and was conducted per the principles of the Declaration of Helsinki. Informed consent was obtained from all subjects. RESULTS Characteristics of the participants Table 1 shows the study sample characteristics and concentrations of the urinary and serum markers. In this study, data from 355 adults out of 1,715 source adults were collected. The characteristics of the source population are shown in Supplementary Table 1. There was no major difference in characteristics between the study and the source adults. The study sample had no major differences in BMI, abdominal circumference, SBP, DBP, triglycerides, high-density lipoprotein, and low-density lipoprotein, excluding HbA1c compared to the general population of Miyagi and Japan (Supplementary Table 2). Table 1. Characteristics of the subjects and concentration of urinary and serum markers categorized by blood pressure Hypertensive ANOVA Total Normotensive Nonmedication Medication P N 355 193 39 123 Age (years) 66.6 ± 14.4 62.3 ± 15.9 67.2 ± 13.0 73.1 ± 8.9**,† <0.0001 Sex (male) (%) 45.6 46.6 48.7 43.1 0.7 Body mass index (kg/m2) 23.5 ± 3.2 22.9 ± 3.2 23.9 ± 3.4 24.2 ± 3.0** 0.0008 Smoking status (%) 15.8 19.7 18 8.9 0.03 Cardiovascular disease (%) 9.6 7.3 0 16.3 0.0007 Hyperlipidemia (%) 53.2 47.7 56.4 61 0.06 Diabetes (%) 10.4 8.3 10.3 13.8 0.3 HbA1c (%) 5.6 ± 0.6 5.4 ± 0.6 5.6 ± 0.7 5.7 ± 0.7** 0.001 Estimated glomerular filtration rate (ml/ min/1.73 m2) 74.6 ± 14.4 77.4 ± 13.5 70.5 ± 15.7 69.9 ± 14.6** 0.0005 Creatinine clearance (ml/min) (n = 245) 84.0 ± 1.7 87.3 ± 2.1 80.5 ± 5.1 78.0 ± 3.2* 0.04 Systolic blood pressure (mm Hg) 127.0 ± 17.8 118.7 ± 11.4 152.4 ± 14.4** 132.0 ± 17.5**,‡ <0.0001 Diastolic blood pressure (mm Hg) 73.5 ± 11.7 69.4 ± 8.9 86.5 ± 11.3** 75.8 ± 12.1**,‡ <0.0001 Urinary albumin–creatinine ratio (n = 246) 6.8 (4.8–12.1) 6.2 (4.5–9.7) 9.2 (5.5–18.2)** 8.1 (5.6–17.7)** <0.0001 Estimated 24-hour urinary Na excretion (mmol/predict day) (n = 348) 406.2 ± 91.5 397.2 ± 87.3 422.0 ± 87.9 415.3 ± 98.0 0.1 Urinary markers  AGT (ng/creatinine mg) 13.8 10.6 19.8** 15.3** <0.0001 (6.7–28.5) (5.9–20.4) (11.8–44.9) (7.9–35.7)  MCP-1 (pg/creatinine mg) 258.4 238 292.9 270.6 0.3 (151.9–505.3) (145.1–460.7) (139.1–693.6) (172.0–581.8)  TBARS (nM/creatinine mg) 6 5.8 6.5 6.3 0.08 (4.3–8.5) (4.3–8.1) (4.5–9.3) (4.5–9.6)  MG (ng/creatinine mg) 2.3 2 2.5 2.8** <0.0001 (1.6–3.6) (1.4–3.0) (1.9–3.9) (1.9–4.1)  GO (µmol/creatinine mg) 3.7 3.3 4.1 4.6** 0.003 (2.0–5.9) (1.8–5.0) (2.7–6.5) (2.4–6.8)  3-DG (µmol/creatinine mg) 1.6 1.5 1.8 1.8** 0.001 (1.0–2.3) (0.9–2.1) (1.3–2.3) (1.2–2.7) Serum markers  AGT (ng/ml) 46 44.7 47.2 46.7 0.8 (36.6–56.6) (35.4–56.7) (38.0–55.4) (37.0–56.3)  MCP-1 (pg/ml) 330.9 304.3 321.5 328.4 0.7 (212.8–571.9) (200.9–552.0) (217.4–604.6) (206.6–544.5)  MG (nmol/ml) 0.1 0.09 0.1 0.1 0.7 (0.07–0.28) (0.07–0.28) (0.07–0.45) (0.07–0.27)  GO (nmol/ml) 0.26 0.24 0.29 0.27 0.8 (0.20–0.33) (0.20–0.31) (0.22–0.33) (0.22–0.35)  3-DG (nmol/ml) 0.29 0.28 0.29 0.33* 0.04 (0.19–0.49) (0.14–0.45) (0.23–0.48) (0.22–0.57) Hypertensive ANOVA Total Normotensive Nonmedication Medication P N 355 193 39 123 Age (years) 66.6 ± 14.4 62.3 ± 15.9 67.2 ± 13.0 73.1 ± 8.9**,† <0.0001 Sex (male) (%) 45.6 46.6 48.7 43.1 0.7 Body mass index (kg/m2) 23.5 ± 3.2 22.9 ± 3.2 23.9 ± 3.4 24.2 ± 3.0** 0.0008 Smoking status (%) 15.8 19.7 18 8.9 0.03 Cardiovascular disease (%) 9.6 7.3 0 16.3 0.0007 Hyperlipidemia (%) 53.2 47.7 56.4 61 0.06 Diabetes (%) 10.4 8.3 10.3 13.8 0.3 HbA1c (%) 5.6 ± 0.6 5.4 ± 0.6 5.6 ± 0.7 5.7 ± 0.7** 0.001 Estimated glomerular filtration rate (ml/ min/1.73 m2) 74.6 ± 14.4 77.4 ± 13.5 70.5 ± 15.7 69.9 ± 14.6** 0.0005 Creatinine clearance (ml/min) (n = 245) 84.0 ± 1.7 87.3 ± 2.1 80.5 ± 5.1 78.0 ± 3.2* 0.04 Systolic blood pressure (mm Hg) 127.0 ± 17.8 118.7 ± 11.4 152.4 ± 14.4** 132.0 ± 17.5**,‡ <0.0001 Diastolic blood pressure (mm Hg) 73.5 ± 11.7 69.4 ± 8.9 86.5 ± 11.3** 75.8 ± 12.1**,‡ <0.0001 Urinary albumin–creatinine ratio (n = 246) 6.8 (4.8–12.1) 6.2 (4.5–9.7) 9.2 (5.5–18.2)** 8.1 (5.6–17.7)** <0.0001 Estimated 24-hour urinary Na excretion (mmol/predict day) (n = 348) 406.2 ± 91.5 397.2 ± 87.3 422.0 ± 87.9 415.3 ± 98.0 0.1 Urinary markers  AGT (ng/creatinine mg) 13.8 10.6 19.8** 15.3** <0.0001 (6.7–28.5) (5.9–20.4) (11.8–44.9) (7.9–35.7)  MCP-1 (pg/creatinine mg) 258.4 238 292.9 270.6 0.3 (151.9–505.3) (145.1–460.7) (139.1–693.6) (172.0–581.8)  TBARS (nM/creatinine mg) 6 5.8 6.5 6.3 0.08 (4.3–8.5) (4.3–8.1) (4.5–9.3) (4.5–9.6)  MG (ng/creatinine mg) 2.3 2 2.5 2.8** <0.0001 (1.6–3.6) (1.4–3.0) (1.9–3.9) (1.9–4.1)  GO (µmol/creatinine mg) 3.7 3.3 4.1 4.6** 0.003 (2.0–5.9) (1.8–5.0) (2.7–6.5) (2.4–6.8)  3-DG (µmol/creatinine mg) 1.6 1.5 1.8 1.8** 0.001 (1.0–2.3) (0.9–2.1) (1.3–2.3) (1.2–2.7) Serum markers  AGT (ng/ml) 46 44.7 47.2 46.7 0.8 (36.6–56.6) (35.4–56.7) (38.0–55.4) (37.0–56.3)  MCP-1 (pg/ml) 330.9 304.3 321.5 328.4 0.7 (212.8–571.9) (200.9–552.0) (217.4–604.6) (206.6–544.5)  MG (nmol/ml) 0.1 0.09 0.1 0.1 0.7 (0.07–0.28) (0.07–0.28) (0.07–0.45) (0.07–0.27)  GO (nmol/ml) 0.26 0.24 0.29 0.27 0.8 (0.20–0.33) (0.20–0.31) (0.22–0.33) (0.22–0.35)  3-DG (nmol/ml) 0.29 0.28 0.29 0.33* 0.04 (0.19–0.49) (0.14–0.45) (0.23–0.48) (0.22–0.57) Tukey–Kramer’s test, **P < 0.01 vs. normotensive; *P < 0.05 vs. normotensive; †P < 0.01 vs. hypertensive-non-medication; ‡P < 0.05 vs. hypertensive-non-medication. Abbreviations: AGT, angiotensinogen; ANOVA, analysis of variance; 3-DG, 3-deoxyglucosone; GO, glyoxal; HbA1c, hemoglobin A1c level; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; Na, sodium; TBARS, thiobarbituric acid reaction substances. View Large Table 1. Characteristics of the subjects and concentration of urinary and serum markers categorized by blood pressure Hypertensive ANOVA Total Normotensive Nonmedication Medication P N 355 193 39 123 Age (years) 66.6 ± 14.4 62.3 ± 15.9 67.2 ± 13.0 73.1 ± 8.9**,† <0.0001 Sex (male) (%) 45.6 46.6 48.7 43.1 0.7 Body mass index (kg/m2) 23.5 ± 3.2 22.9 ± 3.2 23.9 ± 3.4 24.2 ± 3.0** 0.0008 Smoking status (%) 15.8 19.7 18 8.9 0.03 Cardiovascular disease (%) 9.6 7.3 0 16.3 0.0007 Hyperlipidemia (%) 53.2 47.7 56.4 61 0.06 Diabetes (%) 10.4 8.3 10.3 13.8 0.3 HbA1c (%) 5.6 ± 0.6 5.4 ± 0.6 5.6 ± 0.7 5.7 ± 0.7** 0.001 Estimated glomerular filtration rate (ml/ min/1.73 m2) 74.6 ± 14.4 77.4 ± 13.5 70.5 ± 15.7 69.9 ± 14.6** 0.0005 Creatinine clearance (ml/min) (n = 245) 84.0 ± 1.7 87.3 ± 2.1 80.5 ± 5.1 78.0 ± 3.2* 0.04 Systolic blood pressure (mm Hg) 127.0 ± 17.8 118.7 ± 11.4 152.4 ± 14.4** 132.0 ± 17.5**,‡ <0.0001 Diastolic blood pressure (mm Hg) 73.5 ± 11.7 69.4 ± 8.9 86.5 ± 11.3** 75.8 ± 12.1**,‡ <0.0001 Urinary albumin–creatinine ratio (n = 246) 6.8 (4.8–12.1) 6.2 (4.5–9.7) 9.2 (5.5–18.2)** 8.1 (5.6–17.7)** <0.0001 Estimated 24-hour urinary Na excretion (mmol/predict day) (n = 348) 406.2 ± 91.5 397.2 ± 87.3 422.0 ± 87.9 415.3 ± 98.0 0.1 Urinary markers  AGT (ng/creatinine mg) 13.8 10.6 19.8** 15.3** <0.0001 (6.7–28.5) (5.9–20.4) (11.8–44.9) (7.9–35.7)  MCP-1 (pg/creatinine mg) 258.4 238 292.9 270.6 0.3 (151.9–505.3) (145.1–460.7) (139.1–693.6) (172.0–581.8)  TBARS (nM/creatinine mg) 6 5.8 6.5 6.3 0.08 (4.3–8.5) (4.3–8.1) (4.5–9.3) (4.5–9.6)  MG (ng/creatinine mg) 2.3 2 2.5 2.8** <0.0001 (1.6–3.6) (1.4–3.0) (1.9–3.9) (1.9–4.1)  GO (µmol/creatinine mg) 3.7 3.3 4.1 4.6** 0.003 (2.0–5.9) (1.8–5.0) (2.7–6.5) (2.4–6.8)  3-DG (µmol/creatinine mg) 1.6 1.5 1.8 1.8** 0.001 (1.0–2.3) (0.9–2.1) (1.3–2.3) (1.2–2.7) Serum markers  AGT (ng/ml) 46 44.7 47.2 46.7 0.8 (36.6–56.6) (35.4–56.7) (38.0–55.4) (37.0–56.3)  MCP-1 (pg/ml) 330.9 304.3 321.5 328.4 0.7 (212.8–571.9) (200.9–552.0) (217.4–604.6) (206.6–544.5)  MG (nmol/ml) 0.1 0.09 0.1 0.1 0.7 (0.07–0.28) (0.07–0.28) (0.07–0.45) (0.07–0.27)  GO (nmol/ml) 0.26 0.24 0.29 0.27 0.8 (0.20–0.33) (0.20–0.31) (0.22–0.33) (0.22–0.35)  3-DG (nmol/ml) 0.29 0.28 0.29 0.33* 0.04 (0.19–0.49) (0.14–0.45) (0.23–0.48) (0.22–0.57) Hypertensive ANOVA Total Normotensive Nonmedication Medication P N 355 193 39 123 Age (years) 66.6 ± 14.4 62.3 ± 15.9 67.2 ± 13.0 73.1 ± 8.9**,† <0.0001 Sex (male) (%) 45.6 46.6 48.7 43.1 0.7 Body mass index (kg/m2) 23.5 ± 3.2 22.9 ± 3.2 23.9 ± 3.4 24.2 ± 3.0** 0.0008 Smoking status (%) 15.8 19.7 18 8.9 0.03 Cardiovascular disease (%) 9.6 7.3 0 16.3 0.0007 Hyperlipidemia (%) 53.2 47.7 56.4 61 0.06 Diabetes (%) 10.4 8.3 10.3 13.8 0.3 HbA1c (%) 5.6 ± 0.6 5.4 ± 0.6 5.6 ± 0.7 5.7 ± 0.7** 0.001 Estimated glomerular filtration rate (ml/ min/1.73 m2) 74.6 ± 14.4 77.4 ± 13.5 70.5 ± 15.7 69.9 ± 14.6** 0.0005 Creatinine clearance (ml/min) (n = 245) 84.0 ± 1.7 87.3 ± 2.1 80.5 ± 5.1 78.0 ± 3.2* 0.04 Systolic blood pressure (mm Hg) 127.0 ± 17.8 118.7 ± 11.4 152.4 ± 14.4** 132.0 ± 17.5**,‡ <0.0001 Diastolic blood pressure (mm Hg) 73.5 ± 11.7 69.4 ± 8.9 86.5 ± 11.3** 75.8 ± 12.1**,‡ <0.0001 Urinary albumin–creatinine ratio (n = 246) 6.8 (4.8–12.1) 6.2 (4.5–9.7) 9.2 (5.5–18.2)** 8.1 (5.6–17.7)** <0.0001 Estimated 24-hour urinary Na excretion (mmol/predict day) (n = 348) 406.2 ± 91.5 397.2 ± 87.3 422.0 ± 87.9 415.3 ± 98.0 0.1 Urinary markers  AGT (ng/creatinine mg) 13.8 10.6 19.8** 15.3** <0.0001 (6.7–28.5) (5.9–20.4) (11.8–44.9) (7.9–35.7)  MCP-1 (pg/creatinine mg) 258.4 238 292.9 270.6 0.3 (151.9–505.3) (145.1–460.7) (139.1–693.6) (172.0–581.8)  TBARS (nM/creatinine mg) 6 5.8 6.5 6.3 0.08 (4.3–8.5) (4.3–8.1) (4.5–9.3) (4.5–9.6)  MG (ng/creatinine mg) 2.3 2 2.5 2.8** <0.0001 (1.6–3.6) (1.4–3.0) (1.9–3.9) (1.9–4.1)  GO (µmol/creatinine mg) 3.7 3.3 4.1 4.6** 0.003 (2.0–5.9) (1.8–5.0) (2.7–6.5) (2.4–6.8)  3-DG (µmol/creatinine mg) 1.6 1.5 1.8 1.8** 0.001 (1.0–2.3) (0.9–2.1) (1.3–2.3) (1.2–2.7) Serum markers  AGT (ng/ml) 46 44.7 47.2 46.7 0.8 (36.6–56.6) (35.4–56.7) (38.0–55.4) (37.0–56.3)  MCP-1 (pg/ml) 330.9 304.3 321.5 328.4 0.7 (212.8–571.9) (200.9–552.0) (217.4–604.6) (206.6–544.5)  MG (nmol/ml) 0.1 0.09 0.1 0.1 0.7 (0.07–0.28) (0.07–0.28) (0.07–0.45) (0.07–0.27)  GO (nmol/ml) 0.26 0.24 0.29 0.27 0.8 (0.20–0.33) (0.20–0.31) (0.22–0.33) (0.22–0.35)  3-DG (nmol/ml) 0.29 0.28 0.29 0.33* 0.04 (0.19–0.49) (0.14–0.45) (0.23–0.48) (0.22–0.57) Tukey–Kramer’s test, **P < 0.01 vs. normotensive; *P < 0.05 vs. normotensive; †P < 0.01 vs. hypertensive-non-medication; ‡P < 0.05 vs. hypertensive-non-medication. Abbreviations: AGT, angiotensinogen; ANOVA, analysis of variance; 3-DG, 3-deoxyglucosone; GO, glyoxal; HbA1c, hemoglobin A1c level; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; Na, sodium; TBARS, thiobarbituric acid reaction substances. View Large The difference in the blood pressure, and urinary ACR was significant between the normotensive and hypertensive patients not taking medication. The difference in age, BMI, HbA1c, estimated glomerular filtration rate, blood pressure, and urinary ACR was significant between the normotensive and hypertensive patients on medication. In the hypertensive-non-medication and hypertensive-with-medication groups, the urinary AGT excretion level was significantly higher than that of the normotensive group. Only in the hypertensive-medication group, the carbonyl compound excretion level and circulating 3-deoxyglucosone level were significantly high than those of the normotensive group. The circulating AGT, MCP-1, and urinary TBARS were not significantly different among groups. Factors associated with blood pressure Next, we performed a multiple linear regression analysis to examine the relationship between urinary or serum markers and blood pressure (Table 2). Based on the multiple regression analysis, the urinary AGT excretion level was significantly associated with SBP and DBP (β = 5.5 and 2.8, respectively), along with urinary carbonyl stress markers (indicated by the MG level, β = 3.5 and 1.8 and GO level, β = 3 and 2.2, respectively) and serum AGT (β = 2.4 and 1.6, respectively) (Table 2). However, blood pressure was not associated with the urinary MCP-1, TBARS, or circulating carbonyl stress markers. Table 2. Relationship between urinary and serum markers and blood pressure in the multiple regression analyses Dependent variable: SBP (mm Hg) DBP (mm Hg) Independent variables (every 1 SD increase) β SE P β SE P Urinary markers  log AGT 5.5 0.9 <0.0001 2.8 0.6 <0.0001  log MCP-1 0.0007 0.0006 0.2 0.000008 0.0004 1  log TBARS 0.6 0.9 0.5 0.4 0.6 0.5  log MG 3.5 1 0.0008 1.8 0.7 0.009  log GO 3 1 0.003 2.2 0.7 0.0008  log 3-DG 0.8 1 0.5 1.5 0.7 0.03 Serum markers  log AGT 2.4 0.9 0.01 1.6 0.6 0.01  log MCP-1 -0.02 0.9 1 0.3 0.6 0.6  log MG 0.2 0.2 0.2 0.06 0.1 0.6  log GO 0.5 0.4 0.2 0.1 0.2 0.6  log 3-DG -0.1 0.3 0.6 -0.2 0.2 0.2 Dependent variable: SBP (mm Hg) DBP (mm Hg) Independent variables (every 1 SD increase) β SE P β SE P Urinary markers  log AGT 5.5 0.9 <0.0001 2.8 0.6 <0.0001  log MCP-1 0.0007 0.0006 0.2 0.000008 0.0004 1  log TBARS 0.6 0.9 0.5 0.4 0.6 0.5  log MG 3.5 1 0.0008 1.8 0.7 0.009  log GO 3 1 0.003 2.2 0.7 0.0008  log 3-DG 0.8 1 0.5 1.5 0.7 0.03 Serum markers  log AGT 2.4 0.9 0.01 1.6 0.6 0.01  log MCP-1 -0.02 0.9 1 0.3 0.6 0.6  log MG 0.2 0.2 0.2 0.06 0.1 0.6  log GO 0.5 0.4 0.2 0.1 0.2 0.6  log 3-DG -0.1 0.3 0.6 -0.2 0.2 0.2 Multivariable-adjusted for age, sex, body mass index, smoking status, prevalence of cardiovascular disease, prevalence of diabetes, use of antihypertensive medication, and prevalence of hyperlipidemia. Abbreviations: AGT, angiotensinogen; DBP, diastolic blood pressure; 3-DG, 3-deoxyglucosone; GO, glyoxal; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; SBP, systolic blood pressure; TBARS, thiobarbituric acid reaction substances. View Large Table 2. Relationship between urinary and serum markers and blood pressure in the multiple regression analyses Dependent variable: SBP (mm Hg) DBP (mm Hg) Independent variables (every 1 SD increase) β SE P β SE P Urinary markers  log AGT 5.5 0.9 <0.0001 2.8 0.6 <0.0001  log MCP-1 0.0007 0.0006 0.2 0.000008 0.0004 1  log TBARS 0.6 0.9 0.5 0.4 0.6 0.5  log MG 3.5 1 0.0008 1.8 0.7 0.009  log GO 3 1 0.003 2.2 0.7 0.0008  log 3-DG 0.8 1 0.5 1.5 0.7 0.03 Serum markers  log AGT 2.4 0.9 0.01 1.6 0.6 0.01  log MCP-1 -0.02 0.9 1 0.3 0.6 0.6  log MG 0.2 0.2 0.2 0.06 0.1 0.6  log GO 0.5 0.4 0.2 0.1 0.2 0.6  log 3-DG -0.1 0.3 0.6 -0.2 0.2 0.2 Dependent variable: SBP (mm Hg) DBP (mm Hg) Independent variables (every 1 SD increase) β SE P β SE P Urinary markers  log AGT 5.5 0.9 <0.0001 2.8 0.6 <0.0001  log MCP-1 0.0007 0.0006 0.2 0.000008 0.0004 1  log TBARS 0.6 0.9 0.5 0.4 0.6 0.5  log MG 3.5 1 0.0008 1.8 0.7 0.009  log GO 3 1 0.003 2.2 0.7 0.0008  log 3-DG 0.8 1 0.5 1.5 0.7 0.03 Serum markers  log AGT 2.4 0.9 0.01 1.6 0.6 0.01  log MCP-1 -0.02 0.9 1 0.3 0.6 0.6  log MG 0.2 0.2 0.2 0.06 0.1 0.6  log GO 0.5 0.4 0.2 0.1 0.2 0.6  log 3-DG -0.1 0.3 0.6 -0.2 0.2 0.2 Multivariable-adjusted for age, sex, body mass index, smoking status, prevalence of cardiovascular disease, prevalence of diabetes, use of antihypertensive medication, and prevalence of hyperlipidemia. Abbreviations: AGT, angiotensinogen; DBP, diastolic blood pressure; 3-DG, 3-deoxyglucosone; GO, glyoxal; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; SBP, systolic blood pressure; TBARS, thiobarbituric acid reaction substances. View Large Per the results of the stepwise multiple regression analysis with SBP (Table 3), the urinary AGT excretion level (R2 = 0.06) and BMI (R2 = 0.05) were selected as variables in the normotensive group. In the hypertensive-non-medication group, the urinary AGT excretion level (R2 = 0.2) and urinary MG excretion level (R2 = 0.1) were selected. Age (R2 = 0.02) and the urinary AGT excretion level (R2 = 0.03) were selected in the hypertensive-with-medication group. The urinary AGT excretion level was selected as a variable in all groups; the urinary MG excretion level was selected only in the hypertensive-non-medication group using a stepwise multiple regression analysis with SBP. The serum AGT and urinary MG excretion levels were not selected as variables in the normotensive and hypertensive-with-medication groups with a stepwise multiple regression analysis, regardless of their significant association with SBP and DBP on multiple regression analysis. Furthermore, we performed a multiple linear regression analysis to examine the relationship between the urinary AGT or MG excretion level and blood pressure in each group. In the normotensive group, the urinary AGT excretion level was significantly related to SBP (β = 3.0, P = 0.001) but the urinary MG excretion level was not (β = 1.0, P = 0.3). In the hypertensive-non-medication group, both the urinary AGT and MG excretion levels were significantly correlated with SBP (β = 2.8, P = 0.02 and β = 2.7, P = 0.008, respectively). In the hypertensive-with-medication group, the urinary AGT level was significantly related to SBP (β = 4.5, P = 0.004), but not the urinary MG excretion level (β = 3.0, P = 0.1). Supplementary Figure 1 shows the relationship between urinary AGT excretion level and SBP in all participants. Urinary AGT excretion level correlated significantly with SBP in all groups. Table 3. Relationship between urinary and serum markers and blood pressure in the stepwise multiple regression analyses Dependent variable: SBP Normotensive (n = 193) Hypertensive-non-medication (n = 39) Hypertensive-medication (n = 123) β Partial R2 P β Partial R2 P β Partial R2 P Age 0.03 0.001 0.7 -0.2 0.02 0.4 −0.4 0.02 0.04 Sex 0.4 0.001 0.5 -3 0.01 0.1 −0.8 0.02 0.3 BMI 0.8 0.05 0.002 Diabetes Hyperlipidemia Smoking status CVD Antihypertensive medication  Log urinary AGT 2.6 0.06 0.001 5.7 0.2 0.004 4.3 0.03 0.005  Log serum AGT  Log urinary MG 7.8 0.1 0.02  Log urinary GO  Log urinary 3-DG Dependent variable: SBP Normotensive (n = 193) Hypertensive-non-medication (n = 39) Hypertensive-medication (n = 123) β Partial R2 P β Partial R2 P β Partial R2 P Age 0.03 0.001 0.7 -0.2 0.02 0.4 −0.4 0.02 0.04 Sex 0.4 0.001 0.5 -3 0.01 0.1 −0.8 0.02 0.3 BMI 0.8 0.05 0.002 Diabetes Hyperlipidemia Smoking status CVD Antihypertensive medication  Log urinary AGT 2.6 0.06 0.001 5.7 0.2 0.004 4.3 0.03 0.005  Log serum AGT  Log urinary MG 7.8 0.1 0.02  Log urinary GO  Log urinary 3-DG A stepwise multiple regression analysis (with age and sex forced in), with SBP as the dependent variable and all variables listed in the table as objective variables, was performed. A P value <0.05 was necessary for a variable to be entered and kept in the model. Abbreviations: AGT, angiotensinogen; BMI, body mass index; CVD, cardiovascular disease; 3-DG, 3-deoxyglucosone; GO, glyoxal; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; SBP, systolic blood pressure. View Large Table 3. Relationship between urinary and serum markers and blood pressure in the stepwise multiple regression analyses Dependent variable: SBP Normotensive (n = 193) Hypertensive-non-medication (n = 39) Hypertensive-medication (n = 123) β Partial R2 P β Partial R2 P β Partial R2 P Age 0.03 0.001 0.7 -0.2 0.02 0.4 −0.4 0.02 0.04 Sex 0.4 0.001 0.5 -3 0.01 0.1 −0.8 0.02 0.3 BMI 0.8 0.05 0.002 Diabetes Hyperlipidemia Smoking status CVD Antihypertensive medication  Log urinary AGT 2.6 0.06 0.001 5.7 0.2 0.004 4.3 0.03 0.005  Log serum AGT  Log urinary MG 7.8 0.1 0.02  Log urinary GO  Log urinary 3-DG Dependent variable: SBP Normotensive (n = 193) Hypertensive-non-medication (n = 39) Hypertensive-medication (n = 123) β Partial R2 P β Partial R2 P β Partial R2 P Age 0.03 0.001 0.7 -0.2 0.02 0.4 −0.4 0.02 0.04 Sex 0.4 0.001 0.5 -3 0.01 0.1 −0.8 0.02 0.3 BMI 0.8 0.05 0.002 Diabetes Hyperlipidemia Smoking status CVD Antihypertensive medication  Log urinary AGT 2.6 0.06 0.001 5.7 0.2 0.004 4.3 0.03 0.005  Log serum AGT  Log urinary MG 7.8 0.1 0.02  Log urinary GO  Log urinary 3-DG A stepwise multiple regression analysis (with age and sex forced in), with SBP as the dependent variable and all variables listed in the table as objective variables, was performed. A P value <0.05 was necessary for a variable to be entered and kept in the model. Abbreviations: AGT, angiotensinogen; BMI, body mass index; CVD, cardiovascular disease; 3-DG, 3-deoxyglucosone; GO, glyoxal; MCP-1, monocyte chemoattractant protein-1; MG, methylglyoxal; SBP, systolic blood pressure. View Large Relationship between the urinary AGT and MG excretion level Figure 1 shows the relationship between the urinary AGT and MG excretion levels and SBP. The participants were divided into 4 groups based on the urinary AGT and MG excretion levels. A low AGT or MG meant that the urinary AGT or MG excretion level was under the median. A high AGT or MG meant that the urinary AGT or MG excretion level was above the median. SBP was significantly increased in participants with high AGT and MG excretion levels than those with low excretion levels (Figure 1, P < 0.0001). Furthermore, SBP was significantly increased in participants with high AGT and MG excretion levels compared to those with high MG excretion levels and low AGT excretion levels (P < 0.05). In all populations, multiple regression analysis showed that urinary AGT and MG excretion levels were independently and significantly associated with SBP (β = 5.2, P < 0.0001 and β = 2.8, P = 0.005, respectively). The interactions among urinary AGT excretion level and urinary MG excretion level on blood pressure was not significant in all study populations (P > 0.2), the normotensive (P > 0.7), hypertensive-non-medication (P > 0.8), and hypertensive-with-medication (P > 0.05) groups. Figure 1. View largeDownload slide Comparisons of systolic blood pressure. High AGT: urinary AGT excretion levels ≥13.7 (ng/mg Cre). High MG: urinary MG excretion levels ≥2.3 (nmol/mg Cre). Low AGT: urinary AGT excretion levels <13.7 (ng/mg Cre). Low MG: urinary MG excretion levels <2.3 (nmol/mg Cre). The data are expressed as the mean. The differences between multiple groups were detected using Tukey–Kramer’s test. ***P < 0.0001 and *P < 0.05 vs. low AGT and low MG. †P < 0.05 vs. low AGT and high MG. Abbreviations: AGT, angiotensinogen; ANOVA, analysis of variance; MG, methylglyoxal; SBP, systolic blood pressure. Figure 1. View largeDownload slide Comparisons of systolic blood pressure. High AGT: urinary AGT excretion levels ≥13.7 (ng/mg Cre). High MG: urinary MG excretion levels ≥2.3 (nmol/mg Cre). Low AGT: urinary AGT excretion levels <13.7 (ng/mg Cre). Low MG: urinary MG excretion levels <2.3 (nmol/mg Cre). The data are expressed as the mean. The differences between multiple groups were detected using Tukey–Kramer’s test. ***P < 0.0001 and *P < 0.05 vs. low AGT and low MG. †P < 0.05 vs. low AGT and high MG. Abbreviations: AGT, angiotensinogen; ANOVA, analysis of variance; MG, methylglyoxal; SBP, systolic blood pressure. Association between urinary AGT and serum AGT Figure 2 shows the relationship between urinary AGT excretion level and urinary ACR in all participants. Urinary ACR was significantly correlated with urinary AGT excretion level in all groups. We examined the association between urinary AGT and serum AGT using stepwise multiple regression analysis (Supplementary Table 3). Per the results of the stepwise multiple regression analysis with urinary AGT excretion level, urinary ACR level was selected as a variable in all groups (normotensive, R2 = 0.2; hypertensive-non-medication, R2 = 0.7; hypertensive-medication, R2 = 0.3). Although the serum AGT level was selected as a variable in hypertensive-with-medication, the contribution of serum AGT to urinary AGT is small because the partial R2 is 0.16. Figure 2. View largeDownload slide Relationship between the urinary angiotensinogen excretion level and albumin–creatinine ratio (ACR). Single linear regression analysis between urinary angiotensinogen (AGT) excretion level and ACR. Pearson’s correlation coefficient. Figure 2. View largeDownload slide Relationship between the urinary angiotensinogen excretion level and albumin–creatinine ratio (ACR). Single linear regression analysis between urinary angiotensinogen (AGT) excretion level and ACR. Pearson’s correlation coefficient. DISCUSSION In this study, we examined the associations among carbonyl stress, RAS activation, and blood pressure in the general population. We found that (i) although circulating AGT was not significantly associated with blood pressure, urinary AGT excretion level was associated with blood pressure in the normotensive and hypertensive groups; and (ii) urinary AGT and urinary MG excretion level were independently related to elevated blood pressure. Urinary AGT excretion was associated with blood pressure, independent of circulating RAS, in a group of people with African ancestry.19 Another human study showed that urinary AGT levels were higher in hypertensive patients than in control subjects, and treatment with ARBs or angiotensin-converting enzyme inhibitors normalized the elevated urinary AGT levels.7 Furthermore, increased urinary AGT excretion levels are reported in patients with nonhypertensive diseases, such as diabetes20 and chronic kidney disease.9 However, the association between urinary AGT excretion levels and increased blood pressure in the normotensive general population is not reported yet. In this study, we examined the associations among carbonyl stress, RAS activation, and blood pressure in the normotensive, hypertensive-non-medication, and hypertensive-with-medication groups to minimize the antihypertensive drug effects. In our study, urinary AGT excretion level was selected, and the serum AGT level was not selected as a variable in all groups for stepwise multiple regression analysis. Our results showed that renal activity is associated with increased blood pressure, independent of circulating AGT, in both normotensive and hypertensive Japanese people. However, 2 studies suggested that some AGT derived from the liver may be filtrated to the kidney.5,6 Urinary AGT is likely derived from circulating AGT and contributes to blood pressure elevation. Therefore, we examined the association between urinary AGT and serum AGT with a stepwise multiple regression analysis (Supplementary Table 3), the results of which showed that urinary AGT excretion level was significantly associated with urinary ACR but not serum AGT level. It is suggested that urinary AGT excretion levels are associated with renal injury indicated by urinary ACR than circulating AGT. This strong correlation between urinary AGT excretion and ACR correlated with previous study results.7,9,21,22 Although we could not exclude the possibility that circulating AGT increases the blood pressure, present results indicate that urinary AGT is also responsible for blood pressure, independent of circulating AGT. Inflammation, RAS-induced oxidative stress, and carbonyl stress likely play a fundamental role in blood pressure control.12–14,23 However, little is known about the relationships among carbonyl stress, renal RAS activity, and blood pressure. We found that although the urinary excretion levels of carbonyl compounds such as MG and GO are significantly associated with blood pressure, the urinary AGT excretion levels are strongly associated with blood pressure in both normotensive and hypertensive people. These results suggest that inappropriate renal RAS activity is a major contributor to hypertension development. We have previously demonstrated that RAS causes carbonyl stress-induced hypertension and cardio-renal injury.14,15,24 In this study, the urinary MG excretion level was significantly associated with SBP, but only in the hypertensive-with-non-medication group (Table 3). Our results suggest that inappropriate intrarenal activity and carbonyl stress were independently related to elevated blood pressure. Our regression analysis indicated no effect of the interaction between urinary AGT excretion level and urinary MG excretion level on blood pressure. These results suggest an additive, not synergetic, effect among inappropriate intrarenal activity and carbonyl stress on blood pressure. In this study, the urinary MG excretion level was not significantly associated with SBP in the hypertensive-with-medication group. This might be due to antihypertensive drugs, which suppress the downstream of inappropriate renal RAS activity. Our study has several limitations. This study was cross-sectional with a small sample. Therefore, further large-scale, prospective studies are needed to verify the association between the urinary AGT excretion levels and blood pressure elevation in normotensive people. Because this study is based on a health check-up examination, we could not collect full drug information. Previous studies have shown that ARB administration reduces the urinary AGT.25,26 ARBs are frequently used for patients with hypertension in Japan, and hypertensive subjects are more likely to be administered ARBs. Despite the high prevalence of ARB administration, the present study demonstrated a high urinary AGT level. Therefore, we conclude that a high concentration of urinary AGT is due to high renal RAS activity. The study lacks data on plasma renin activity and aldosterone level, because we had only serum samples. Therefore, further examinations are needed to discuss the associations among circulating RAS activity, urinary activity, and blood pressure. The present study predominantly focused on a middle-aged and older Asian population. This limits the generalizability of our findings. However, the study population might be representative of Miyagi prefecture since the population is like that of Miyagi prefecture (Supplementary Table 2). In conclusion, inappropriately activated intrarenal RAS activities contributes significantly to hypertension development, and urinary AGT excretion levels could be a marker of blood pressure elevation in normotensive people. Although carbonyl stress is known to be involved in cardiovascular and renal injury, no existing drug specifically targets this pathway. We determined whether carbonyl stress and RAS are involved in the pathogenesis of the development of hypertension in the general population. Carbonyl stress markers and RAS markers could be biomarkers to predict future hypertension and surrogate markers for investigating novel drugs for treating cardiovascular renal injury. SUPPLEMENTARY DATA Supplementary data are available at American Journal of Hypertension online. DISCLOSURE The authors declared no conflict of interest. This manuscript was sent to Guest Editor, Theodore A. Kotchen, MD, for editorial handling and final disposition. ACKNOWLEDGMENTS The authors are grateful to the Biomedical Research Unit of Tohoku University Hospital for the use of their equipment. We thank Yoshimi Nakamichi, Hiroko Ito, Mayuko Ishikawa, Yuika Aizawa, Mutsuko Fujiwara, and the staff of the Division of Nephrology, Endocrinology, and Vascular Medicine, Tohoku University Graduate School of Medicine, for their technical assistance; and the Health and Welfare Center, Kawasaki-machi government office. English language editing was conducted by Editage (www.editage.jp). Research funded by Japan Society for the promotion of Science [Grant-in-Aid for Scientific Research (B) (23300243), Grant-in-Aid for Young Scientist (B) (24790832), and Grant-in-Aid for Scientific Research (B) (15H04834)]. REFERENCES 1. Mori T , Cowley AW Jr . Role of pressure in angiotensin II-induced renal injury: chronic servo-control of renal perfusion pressure in rats . Hypertension 2004 ; 43 : 752 – 759 . Google Scholar CrossRef Search ADS PubMed 2. Mori T , O’Connor PM , Abe M , Cowley AW Jr . Enhanced superoxide production in renal outer medulla of Dahl salt-sensitive rats reduces nitric oxide tubular-vascular cross-talk . Hypertension 2007 ; 49 : 1336 – 1341 . Google Scholar CrossRef Search ADS PubMed 3. Ogawa S , Mori T , Nako K , Kato T , Takeuchi K , Ito S . Angiotensin II type 1 receptor blockers reduce urinary oxidative stress markers in hypertensive diabetic nephropathy . Hypertension 2006 ; 47 : 699 – 705 . Google Scholar CrossRef Search ADS PubMed 4. 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Google Scholar CrossRef Search ADS PubMed © American Journal of Hypertension, Ltd 2018. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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American Journal of HypertensionOxford University Press

Published: Feb 8, 2018

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