Open Access

Comparative effect of angiotensin II type I receptor blockers and calcium channel blockers on laboratory parameters in hypertensive patients with type 2 diabetes

  • Yayoi Nishida1, 2,
  • Yasuo Takahashi1, 2Email author,
  • Tomohiro Nakayama4 and
  • Satoshi Asai2, 3
Cardiovascular Diabetology201211:53

https://doi.org/10.1186/1475-2840-11-53

Received: 11 February 2012

Accepted: 7 April 2012

Published: 17 May 2012

Abstract

Background

Both angiotensin II type I receptor blockers (ARBs) and calcium channel blockers (CCBs) are widely used antihypertensive drugs. Many clinical studies have demonstrated and compared the organ-protection effects and adverse events of these drugs. However, few large-scale studies have focused on the effect of these drugs as monotherapy on laboratory parameters. We evaluated and compared the effects of ARB and CCB monotherapy on clinical laboratory parameters in patients with concomitant hypertension and type 2 diabetes mellitus.

Methods

We used data from the Clinical Data Warehouse of Nihon University School of Medicine obtained between Nov 1, 2004 and July 31, 2011, to identify cohorts of new ARB users (n = 601) and propensity-score matched new CCB users (n = 601), with concomitant mild to moderate hypertension and type 2 diabetes mellitus. We used a multivariate-adjusted regression model to adjust for differences between ARB and CCB users, and compared laboratory parameters including serum levels of triglyceride (TG), total cholesterol (TC), non-fasting blood glucose, hemoglobin A1c (HbA1c), sodium, potassium, creatinine, alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), hemoglobin and hematocrit, and white blood cell (WBC), red blood cell (RBC) and platelet (PLT) counts up to 12 months after the start of ARB or CCB monotherapy.

Results

We found a significant reduction of serum TC, HbA1c, hemoglobin and hematocrit and RBC count and a significant increase of serum potassium in ARB users, and a reduction of serum TC and hemoglobin in CCB users, from the baseline period to the exposure period. The reductions of RBC count, hemoglobin and hematocrit in ARB users were significantly greater than those in CCB users. The increase of serum potassium in ARB users was significantly greater than that in CCB users.

Conclusions

Our study suggested that hematological adverse effects and electrolyte imbalance are greater with ARB monotherapy than with CCB monotherapy.

Keywords

Angiotensin II receptor blocker (ARB) Calcium channel blocker (CCB) Hematological parameter Retrospective observational study

Introduction

Angiotensin II type I receptor blockers (ARBs) are well established antihypertensive drugs that are frequently used as the first-line drug for hypertension. Recently, there has been a focus on the beneficial effects of ARBs other than their antihypertensive effect, such as reduction of proteinuria [1] and decreased heart failure risk in patients with chronic heart failure [2]. Calcium channel blockers (CCBs) are also widely used first-line antihypertensive drugs. CCBs are known to decrease the risk of coronary heart disease and non-fatal stroke in patients with hypertension [3], and to decrease proteinuria in patients with chronic renal disease [4]. While ARBs and CCBs have a favorable effect on blood pressure and decrease the risk of several complications, these drugs have some adverse effects. Renin-angiotensin system inhibitors including ARBs are known to cause hyperkalemia [5] and anemia [6, 7]. CCBs are known to cause edema [8].

Hypertension and type 2 diabetes mellitus are conditions that frequently coexist [9], both of which carry an increased risk of cardiovascular and renal disease. Hypertension significantly hastens the progression of diabetic nephropathy and increases the risk of cardiovascular events or death in patients with diabetes. On the contrary, lowering blood pressure decreases albuminuria in type 2 diabetes [10, 11]. On the other hand, ARBs have a beneficial effect that prevents the new-onset of diabetes [12], and there has been a recent focus on the effect of ARBs on glucose metabolism. We demonstrated a favorable effect of ARB monotherapy on glucose metabolism in non-diabetic hypertensive patients [13]. Whether ARBs have a favorable effect on laboratory parameters, including parameters of glucose metabolism in diabetic hypertensive patients, may be of clinical significance.

Some randomized clinical studies have compared the adverse effects of ARBs and CCBs. [1416]. However, those studies usually focused on the adverse events of antihypertensive drugs, and there are few large-scale studies focused on the effects of the drugs on laboratory parameters. In addition, few studies have targeted ARB and CCB monotherapy using a clinical database reflecting 'real-world' data. Therefore, in this study, we evaluated and compared the effects of ARB and CCB monotherapy on laboratory parameters, including parameters of lipid metabolism, glucose metabolism, renal function, hepatic function and hematological analysis in patients with concomitant hypertension and type 2 diabetes mellitus, using a clinical database.

Materials and methods

Data source

This was a retrospective database study using the Nihon University School of Medicine (NUSM) Clinical Data Warehouse (CDW). NUSM's CDW is a centralized data repository that integrates separate databases, including an order entry database and a laboratory results database, from the hospital information systems at three hospitals affiliated to NUSM. The prescribing data of over 0.5 million patients are linked longitudinally to detailed clinical information such as patient demographics, diagnosis, and laboratory results data. The schema of NUSM's CDW has been reported by Takahashi et al. [17].

Study population

For this study, we identified type 2 diabetes mellitus patients with mild to moderate hypertension aged over 20 years, who had been newly treated with ARB monotherapy (n = 922) or dihydropyridine CCB monotherapy (n = 731) for at least two months between Nov 1, 2004 and July 31, 2011. The antihypertensive drugs used in the ARB and CCB monotherapy groups are listed in Table 1. We compared new users of ARBs (n = 601) with propensity-score matched samples of new CCB users (n = 601). We excluded patients who had been treated with other antihypertensive drugs (ARB combination drug, angiotensin-converting enzyme inhibitor (ACEI), diuretic, alpha-blocker, beta-blocker, alpha and beta-blocker, alpha-agonist, reserpine, vasodilator, or renin inhibitor) during the study period. The experimental protocol was approved by the Ethical Committee of Nihon University School of Medicine.
Table 1

Antihypertensive drugs

Category

Generic name

Trade name

No. of cases of monothrapy

   

Before PS matching

After PS matching

ARBs

 

candesartan cilexetil

Blopress

289

200

losartan potassium

Nu-lotan

154

87

olmesartan medoxomil

Olmetec

177

113

telmisartan

Micardis

141

91

valsartan

Diovan

161

110

CCBs

 

amlodipine besilate

Norvasc, Amlodin

355

277

 

azelnidipine

Calblock

46

38

 

benidipine hydrochloride

Coniel

82

66

 

cilnidipine

Atelec, Cinalong

41

36

 

manidipine hydrochloride

Calslot

25

21

 

nicardipine hydrochloride

Perdipine

17

13

 

nifedipine

Adalat, Herlat, Sepamit

133

110

 

nilvadipine

Nivadil

30

24

 

others (barnidipine hydrochloride, efonidipine hydrochloride ethanolate, felodipine, nitrendipine and nisoldipine)

Hypoca, Landel, Munobal, Baylotensin, Baymycard

22

16

PS: propensity score.

Exposure and measurements

The baseline measurement period (non-exposure period) was defined as within 12 months before the start of ARB or CCB monotherapy. The exposure period (outcome measurement period) was defined as between 2 and 12 months after the start of ARB or CCB monotherapy. The mean exposure of ARB users and CCB users was 243.2 days and 242.1 days, respectively. Laboratory data, including serum levels of triglyceride (TG), total cholesterol (TC), non-fasting blood glucose, hemoglobin A1c (HbA1c), creatinine, sodium, potassium, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyltransferase (GGT), hemoglobin, hematocrit, and white blood cell (WBC), red blood cell (RBC) and platelet (PLT) counts, were collected for each individual at the date nearest the start of ARB or CCB monotherapy in the baseline period, and at the date nearest 12 months after the start of ARB or CCB monotherapy in the exposure period.

Data elements

For each patient, we collected information of patient demographics (age and sex), medical history, use of medication, and laboratory results. Medical history included cerebrovascular disease (ICD-10 code, I60-I69), ischemic heart disease (I20-I25), other heart disease (I30-I52), liver disease (K70-K77), kidney disease (N00-N19), gout (M10), thyroid gland disorder (E00-E07), rheumatoid arthritis (M05-M06), hyperlipidemia (E78.0-E78.5), or proteinuria diagnosed in the 365 days preceding the first date of prescription of ARB or CCB. Drugs used during the 60 days before the start of ARB or CCB monotherapy included hypoglycemic drugs (including insulin and oral hypoglycemic drugs), lipid-lowering drugs (including statins, fibrates and other lipid-lowering drugs), diuretics, immunosuppressive drugs, gout drugs, potassium preparations, antipsychotics, chemotherapeutic drugs, steroids, non-steroidal anti-inflammatory drugs (NSAIDs), proton pump inhibitors, histamine H2 receptor blockers and thyroid drugs.

Statistical analysis

The ARB user group and CCB user group were matched by propensity score using a 5-digit, greedy 1:1 matching algorithm [1820]. This method is the standard method to reduce bias by balancing covariates between settings, and has been used in many reports. To generate the propensity score, we used covariates including age, sex, medical history (cerebrovascular disease, ischemic heart disease, other heart disease, liver disease, kidney disease, gout, thyroid gland disorder, rheumatoid arthritis, hyperlipidemia and proteinuria) and previous drugs (hypoglycemic drugs including insulin and oral hypoglycemic drugs, lipid-lowering drugs including statins, fibrates and other lipid-lowering drugs, diuretics, immunosuppressive drugs, gout drugs, potassium preparations, antipsychotics, chemotherapeutic drugs, steroids, NSAIDs, proton pump inhibitors, histamine H2 receptor blockers and thyroid drugs), as listed in Table 2. We compared the prevalence of all baseline covariates before and after propensity score matching using t-test for continuous variables and chi-squared test for categorical data. After propensity score matching, covariance-adjusted and unadjusted generalized linear models (Dunnett-Hsu post-hoc analysis) were fitted to compare the mean values of laboratory parameters at baseline and during the exposure period in ARB users and CCB users, and were used to compare the mean change from the baseline value to the exposure value in ARB users and CCB users. The covariates that were used in the adjusted model included age, sex, medical history and previous medication, as listed in Table 2. All reported P values of less than 0.05 were considered to indicate statistical significance. All statistical analyses were performed with SAS software, version 9.1.3 (SAS Institute Inc., Cary, NC).
Table 2

Baseline characteristics before and after propensity score matching

Characteristics

Before matching

After macthing

ARB users (n = 922)

CCB users (n = 731)

p value

ARB users (n = 601)

CCB users p value

(n = 601)

Age (mean, SE)

61.7 ± 0.4

66.8 ± 0.35

<.0001 *

65.5 ± 0.4

65.6 ± 0.39

0.8268

Age over 75 years

130 (14.1%)

158 (21.6%)

<.0001 *

110 (18.3%)

113 (18.8%)

0.8238

Women

316 (34.3%)

281 (38.4%)

0.0798

224 (37.3%)

225 (37.4%)

0.9525

Medical history

 Cerebrovascular disease

254 (27.5%)

208 (28.5%)

0.6837

170 (28.3%)

185 (30.8%)

0.3429

 Ischemic heart disease

317 (34.4%)

297 (40.6%)

0.009 *

228 (37.9%)

233 (38.8%)

0.7668

 Other heart disease

208 (22.6%)

193 (26.4%)

0.0703

156 (26.0%)

149 (34.8%)

0.6427

 Gout

28 (3.0%)

43 (5.9%)

0.0046 *

25 (4.2%)

25 (4.2%)

1

 Thyroid disorder

266 (28.9%)

155 (21.2%)

0.0004 *

141 (23.5%)

142 (23.6%)

0.9458

 Rheumatoid arthritis

85 (9.2%)

68 (9.3%)

0.9538

48 (8.0%)

51 (8.5%)

0.753

 Liver disease

481 (52.2%)

404 (55.3%)

0.2098

327 (54.4%)

316 (52.6%)

0.5247

 Kidney disease

688 (74.6%)

483 (66.1%)

0.0001 *

407 (67.7%)

411 (68.4%)

0.8046

 Hyperlipidemia

860 (93.3%)

678 (92.7%)

0.6764

559 (93.0%)

561 (93.3%)

0.819

 Proteinuria

463 (50.2%)

298 (40.8%)

0.0001 *

255 (42.4%)

256 (42.6%)

0.9535

Previous drugs

 Hypoglycemic drugs

226 (24.5%)

188 (25.7%)

0.574

148 (24.6%)

156 (26.0%)

0.5955

  Insulin

79 (8.6%)

44 (6.0%)

0.0498 *

36 (6.0%)

42 (7.0%)

0.4823

  Oral hypoglycemic drugs

171 (18.5%)

160 (21.9%)

0.0918

126 (21.0%)

129 (21.5%)

0.8324

 Lipid-lowering drugs

339 (36.8%)

237 (32.4%)

0.0655

201 (33.4%)

200 (33.3%)

0.9512

  Statin

286 (31.0%)

200 (27.4%)

0.1048

171 (28.5%)

169 (28.1%)

0.8981

  Fibrate

36 (3.9%)

29 (4.0%)

0.9481

21 (3.5%)

23 (3.8%)

0.7587

  Other lipid-lowering drugs

30 (3.3%)

23 (3.1%)

0.902

18 (3.0%)

18 (3.0%)

1

 Diuretics

7 (0.8%)

1 (0.1%)

0.0701

1 (0.2%)

1 (0.2%)

1

 Immunosuppressive drugs

14 (1.5%)

10 (1.4%)

0.7995

5 (0.8%)

8 (1.3%)

0.4028

 Gout drugs

59 (6.4%)

86 (11.8%)

0.0001 *

48 (8.0%)

51 (8.5%)

0.753

 Potassium preparations

2 (0.2%)

4 (0.5%)

0.2674

1 (0.2%)

0 (0%)

0.3171

 Antipsychotics

38 (4.1%)

19 (2.6%)

0.0921

18 (3.0%)

17 (2.8%)

0.8638

 Chemotherapeutic drugs

14 (1.5%)

18 (2.5%)

0.1666

11 (1.8%)

9 (1.5%)

0.652

 Steroids

57 (6.2%)

43 (5.9%)

0.7995

32 (5.3%)

33 (5.5%)

0.8985

 NSAIDs

284 (30.8%)

236 (32.3%)

0.5193

181 (30.1%)

190 (31.6%)

0.5741

 Proton pump inhibitors

126 (13.7%)

82 (11.2%)

0.136

77 (12.8%)

75 (12.5%)

0.8622

 H2 blockers

120 (13.0%)

144 (19.7%)

0.0002 *

93 (15.5%)

100 (16.6%)

0.5824

 Thyroid drugs

12 (1.3%)

13 (1.8%)

0.4301

10 (1.7%)

9 (1.5%)

0.8171

Data are numbers of individuals (%) unless otherwise stated. Abbreviations: ARB; angiotensin II type I receptor blocker, CCB; calcium channel blocker, NSAID; nonsteroidal anti-inflammatory drug. *: p<0.05 (ARB vs CCB).

Results

Table 2 shows the characteristics of the patients who had been treated with ARB monotherapy or CCB monotherapy, before and after propensity score matching. Before propensity score matching, ARB users were more likely to have thyroid disease, kidney disease, proteinuria and use insulin, and less likely to have ischemic heart disease, gout, use gout drugs and use H2 blockers than CCB users. After propensity score matching, the mean age was 65.5 and 65.6 years, and 37.3% and 37.4% of ARB users and CCB users were women, respectively.

Table 3 shows laboratory parameters at baseline and during the exposure period. In ARB users, the mean values of TC, HbA1c, hematocrit and hemoglobin and RBC count significantly decreased during the exposure period compared with those during the baseline period, after adjustment for age, sex, medical history and previous medication. The adjusted mean value of potassium significantly increased during the exposure period compared with that in the baseline period in ARB users. The adjusted mean values of TG, glucose, creatinine, sodium, ALT, AST, GGT and WBC and PLT counts were not significantly different during the exposure period compared with those in the baseline period in ARB users. In CCB users, the adjusted mean values of TC and hemoglobin significantly decreased during the exposure period compared with those in the baseline period. The adjusted mean values of TG, glucose, HbA1c, sodium, creatinine, potassium, ALT, AST, GGT, hematocrit, and WBC, RBC and PLT counts were not significantly different during the exposure period compared with those in the baseline period in CCB users.
Table 3

Unadjusted and adjusted mean (95% CI) laboratory test values according to ARB or CCB use after propensity score matching

Laboratory test

ARBs (n = 601)

CCBs (n = 601)

Unadjusted

Adjusted†

Unadjusted

Adjusted†

 

Mean

(95%CI)

p-value

Mean

(95%CI)

p-value

Mean

(95%CI)

p-value

Mean

(95%CI)

p-value

TG (mmol/L)

baseline

1.65

(1.56, 1.73)

0.5113

1.65

(1.56, 1.73)

0.4846

1.63

(1.56, 1.71)

0.2961

1.63

(1.56, 1.7)

0.2604

exposure

1.60

(1.52, 1.69)

 

1.6

(1.52, 1.69)

 

1.57

(1.5, 1.65)

 

1.57

(1.5, 1.65)

 

TC (mmol/L)

baseline

5.20

(5.12, 5.28)

0.0056 *

5.2

(5.13, 5.27)

0.0018 *

5.18

(5.1, 5.26)

0.0351 *

5.18

(5.11, 5.25)

0.0206 *

exposure

5.05

(4.97, 5.12)

 

5.05

(4.98, 5.12)

 

5.06

(4.98, 5.14)

 

5.06

(4.99, 5.13)

 

Blood glucose (mmol/L)

baseline

7.88

(7.63, 8.13)

0.3744

7.88

(7.66, 8.1)

0.3133

7.96

(7.7, 8.23)

0.3809

7.96

(7.72, 8.2)

0.3257

exposure

7.72

(7.47, 7.97)

 

7.72

(7.5, 7.94)

 

7.79

(7.53, 8.06)

 

7.79

(7.55, 8.03)

 

HbA1c (%)

baseline

6.97

(6.86, 7.08)

0.0451 *

6.97

(6.88, 7.05)

0.0074 *

6.93

(6.81, 7.05)

0.2981

6.93

(6.84, 7.02)

0.1793

exposure

6.81

(6.7, 6.92)

 

6.81

(6.73, 6.89)

 

6.84

(6.73, 6.96)

 

6.84

(6.75, 6.93)

 

Creatinine (μmol/L)

baseline

72.8

(70, 75.5)

0.2045

72.8

(70.7, 74.8)

0.0934

73.8

(70, 77.7)

0.5707

73.8

(71.3, 76.4)

0.3887

exposure

75.3

(72.5, 78)

 

75.3

(73.2, 77.3)

 

75.4

(71.5, 79.2)

 

75.4

(72.9, 77.9)

 

Sodium (mmol/L)

baseline

141.4

(141.2, 141.6)

0.1841

141.4

(141.2, 141.6)

0.1725

141.9

(141.7, 142.1)`

0.1137

141.9

(141.7, 142.1)

0.0993

exposure

141.2

(141, 141.4)

 

141.2

(141, 141.4)

 

141.7

(141.5, 141.9)

 

141.7

(141.5, 141.9)

 

Potassium (mmol/L)

baseline

4.39

(4.36, 4.42)

0.0351 *

4.39

(4.36, 4.42)

0.0241 *

4.3

(4.26, 4.33)

0.8344

4.3

(4.26, 4.33)

0.8298

exposure

4.44

(4.41, 4.47)

 

4.44

(4.41, 4.47)

 

4.29

(4.26, 4.32)

 

4.29

(4.26, 4.32)

 

ALT (U/L)

baseline

27.2

(25.5, 28.8)

0.0903

27.2

(25.7, 28.6)

0.0577

28.5

(26.4, 30.7)

0.6781

28.5

(26.6, 30.5)

0.6465

exposure

25.2

(23.5, 26.8)

 

25.2

(23.7, 26.6)

 

27.9

(25.8, 30)

 

27.9

(26, 29.8)

 

AST (U/L)

baseline

27.3

(26, 28.6)

0.3521

27.3

(26.1, 28.4)

0.3041

28.2

(26.4, 30)

0.6099

28.2

(26.6, 29.9)

0.5741

exposure

26.4

(25.1, 27.7)

 

26.4

(25.2, 27.6)

 

28.9

(27.1, 30.7)

 

28.9

(27.2, 30.5)

 

GGT (U/L)

baseline

53.2

(46.8, 59.6)

0.4179

53.2

(47.1, 59.4)

0.3967

56.1

(49.3, 62.8)

0.7434

56.1

(49.7, 62.5)

0.7292

exposure

49.5

(43.1, 55.9)

 

49.5

(43.4, 55.6)

 

57.7

(50.9, 64.4)

 

57.7

(51.3, 64.1)

 

WBC (x109/L)

baseline

6.36

(6.21, 6.51)

0.8579

6.36

(6.22, 6.5)

0.8494

6.47

(6.32, 6.63)

0.6118

6.47

(6.33, 6.62)

0.5847

exposure

6.34

(6.19, 6.49)

 

6.34

(6.2, 6.48)

 

6.42

(6.26, 6.57)

 

6.42

(6.27, 6.56)

 
            

RBC (x1012/L)

baseline

4.36

(4.32, 4.4)

0.0015 *

4.36

(4.33, 4.4)

0.0002 *

4.4

(4.36, 4.45)

0.3324

4.4

(4.36, 4.45)

0.2798

 

exposure

4.26

(4.22, 4.31)

 

4.26

(4.23, 4.3)

 

4.37

(4.33, 4.42)

 

4.37

(4.33, 4.41)

  

PLT (x109/L)

 

baseline

221.2

(216.4, 226)

0.8006

221.2

(216.8, 225.6)

0.7845

222.4

(216.8, 227.9)

0.2089

222.4

(217.2, 227.6)

0.1825

 

exposure

222.1

(217.3, 226.9)

 

222.1

(217.7, 226.5)

 

227.4

(221.9, 233)

 

227.4

(222.2, 232.6)

  

Hemoglobin (g/L)

 

baseline

138.0

(136.7, 139.4)

0.0024 *

138

(136.9, 139.1)

0.0002 *

138.5

(137.1, 139.8)

0.0727

138.5

(137.4, 139.6)

0.0315 *

 

exposure

135.1

(133.8, 136.5)

 

135.1

(134, 136.2)

 

136.7

(135.4, 138.1)

 

136.7

(135.6, 137.9)

  

Hematocrit (mmol/mol)

 

baseline

0.407

(0.404, 0.411)

0.0069 *

0.407

(0.404, 0.411)

0.0012 *

0.409

(0.405, 0.413)

0.285

0.409

(0.406, 0.412)

0.2033

 

exposure

0.400

(0.396, 0.404)

 

0.4

(0.397, 0.403)

 

0.406

(0.402, 0.41)

 

0.406

(0.403, 0.409)

  

Abbreviations: TG; triglyceride, TC; total cholesterol, HbA1c; hemoglobin A1c, ALT; alanine aminotransferase, AST; asparate aminotransferase, GGT; γ-glutamyltrasnferase, WBC; white blood cell count, RBC; red blood cell count, PLT; platelet count. *: p<0.05 (baseline vs exposure). † Analyses were adjusted by covariates including age, sex, medical history and previous medication.

Table 4 shows the mean changes in laboratory parameters during the exposure period compared with the baseline period. The change in potassium was significantly greater in ARB users compared with CCB users, and the changes in RBC count, hemoglobin and hematocrit were significantly smaller in ARB users compared with CCB users after adjustment for covariates.
Table 4

Unadjusted and adjusted mean changes in laboratory parameters values during0020exposure period from baseline

Laboratory test

Unadjusted

Adjusted†

 

Mean

(95%CI)

p-value

Mean

(95%CI)

p-value

ΔTG (mmol/L)

CCB

-0.058

(-0.134, 0.017)

0.7509

-0.067

(-0.139, 0.006)

0.5062

ARB

-0.041

(-0.116, 0.035)

 

-0.032

(-0.105, 0.04)

 

ΔTC (mmol/L)

CCB

-0.119

(-0.182, -0.056)

0.4512

-0.123

(-0.185, -0.061)

0.5664

ARB

-0.153

(-0.217, -0.09)

 

-0.149

(-0.211, -0.087)

 

ΔBlood glucose (mmol/L)

CCB

-0.17

(-0.439, 0.099)

0.9651

-0.177

(-0.447, 0.093)

0.9085

ARB

-0.161

(-0.431, 0.108)

 

-0.154

(-0.424, 0.115)

 

ΔHbA1c (%)

CCB

-0.087

(-0.175, 0.002)

0.2669

-0.089

(-0.175, -0.003)

0.2887

ARB

-0.157

(-0.246, -0.069)

 

-0.155

(-0.241, -0.069)

 

ΔCreatinine (μmol/L)

CCB

1.575

(-0.59, 3.74)

0.5503

1.667

(-0.466, 3.801)

0.6275

ARB

2.508

(0.343, 4.673)

 

2.416

(0.282, 4.549)

 

ΔSodium (mmol/L)

CCB

-0.24

(-0.462, -0.017)

0.8194

-0.239

(-0.458, -0.019)

0.8275

ARB

-0.203

(-0.425, 0.019)

 

-0.204

(-0.424, 0.016)

 

ΔPotassium (mmol/L)

CCB

-0.005

(-0.037, 0.027)

0.0173 *

-0.005

(-0.037, 0.027)

0.0182 *

ARB

0.05

(0.018, 0.082)

 

0.05

(0.018, 0.081)

 

ΔALT (U/L)

CCB

-0.639

(-2.463, 1.185)

0.2966

-0.633

(-2.433, 1.167)

0.2871

ARB

-2.012

(-3.835, -0.188)

 

-2.018

(-3.818, -0.218)

 

ΔAST (U/L)

CCB

0.667

(-0.859, 2.193)

0.1633

0.664

(-0.847, 2.175)

0.1618

ARB

-0.867

(-2.393, 0.659)

 

-0.864

(-2.375, 0.647)

 

ΔGGT (U/L)

CCB

1.599

(-4.675, 7.873)

0.238

1.648

(-4.644, 7.939)

0.2319

ARB

-3.74

(-10.015, 2.534)

 

-3.789

(-10.081, 2.502)

 

ΔWBC (x109/L)

CCB

-0.057

(-0.185, 0.072)

0.6864

-0.048

(-0.176, 0.08)

0.8298

ARB

-0.019

(-0.148, 0.109)

 

-0.028

(-0.156, 0.099)

 

ΔRBC (x1012/L)

CCB

-0.032

(-0.058, -0.006)

0.0005 *

-0.032

(-0.058, -0.006)

0.0004 *

ARB

-0.097

(-0.123, -0.072)

 

-0.098

(-0.124, -0.072)

 

ΔPLT (x109/L)

CCB

5.03

(1.71, 8.35)

0.0825

5.057

(1.793, 8.321)

0.0743

ARB

0.872

(-2.448, 4.192)

 

0.845

(-2.419, 4.109)

 

ΔHemoglobin (g/L)

CCB

-1.722

(-2.542, -0.903)

0.0476 *

-1.721

(-2.538, -0.904)

0.047 *

ARB

-2.894

(-3.713, -2.074)

 

-2.895

(-3.712, -2.078)

 

ΔHematocrit (mmol/mol)

CCB

-0.003

(-0.005, -0.001)

0.0103 *

-0.003

(-0.005, -0.001)

0.0092 *

ARB

-0.007

(-0.01, -0.005)

 

-0.007

(-0.01, -0.005)

 

Δ indicates mean change in laboratory test value between baseline and exposure period. Abbreviations: TG; triglyceride, TC; total cholesterol, HbA1c; hemoglobin A1c, ALT; alanine aminotransferase, AST; asparate aminotransferase, GGT; γ-glutamyltransferase, WBC; white blood cell count, RBC; red blood cell count, PLT; platelet count. *: p<0.05 (ARB vs CCB). † Analyses were adjusted by covariates including age, sex, medical history and previous drugs.

We further analyzed the data divided by sex, because the standard values of hemoglobin, hematocrit and RBC count differ by sex. Table 5 shows the mean changes in laboratory parameters during the exposure period compared with the baseline period after adjustment for covariates, in subclass analysis. In women, the change in potassium was significantly greater in ARB users than in CCB users, and the changes in hemoglobin, hematocrit and RBC count were significantly smaller in ARB users than in CCB users. In men, the mean change in RBC count was significant smaller in ARB users than in CCB users.
Table 5

Adjusted mean changes in laboratory parameters during exposure period from baseline by sex

Laboratory test

Adjusted Women

Adjusted Men

 

Mean

(95%CI)

p-value

Mean

(95%CI)

p-value

ΔTG (mmol/L)

CCB

-0.076

(-0.178, 0.025)

0.3449

-0.061

(-0.162, 0.039)

0.8484

ARB

-0.007

(-0.108, 0.095)

 

-0.047

(-0.148, 0.053)

 

ΔTC (mmol/L)

CCB

-0.127

(-0.239, -0.014)

0.2907

-0.117

(-0.19, -0.044)

0.9665

ARB

-0.214

(-0.326, -0.101)

 

-0.115

(-0.188, -0.042)

 

ΔBlood glucose (mmol/L)

CCB

-0.17

(-0.593, 0.253)

0.6908

-0.152

(-0.505, 0.202)

0.8447

ARB

-0.293

(-0.717, 0.131)

 

-0.101

(-0.454, 0.252)

 

ΔHbA1c (%)

CCB

-0.075

(-0.216, 0.066)

0.0777

-0.092

(-0.201, 0.016)

0.9247

ARB

-0.257

(-0.398, -0.116)

 

-0.1

(-0.208, 0.009)

 

ΔCreatinine (μmol/L)

CCB

0.475

(-0.776, 1.726)

0.0592

2.346

(-0.967, 5.658)

0.923

ARB

2.202

(0.948, 3.456)

 

2.578

(-0.73, 5.886)

 

ΔSodium (mmol/L)

CCB

-0.09

(-0.434, 0.254)

0.1124

-0.279

(-0.567, 0.009)

0.3479

ARB

-0.49

(-0.835, -0.145)

 

-0.082

(-0.37, 0.206)

 

ΔPotassium (mmol/L)

CCB

-0.015

(-0.067, 0.038)

0.0188 *

0.0002

(-0.041, 0.041)

0.2423

ARB

0.075

(0.023, 0.128)

 

0.035

(-0.006, 0.076)

 

ΔALT (U/L)

CCB

-0.921

(-3.478, 1.636)

0.1991

-0.31

(-2.767, 2.147)

0.5436

ARB

-3.32

(-5.883, -0.758)

 

-1.393

(-3.847, 1.06)

 

ΔAST (U/L)

CCB

1.125

(-1.37, 3.621)

0.0796

0.59

(-1.334, 2.514)

0.6034

ARB

-2.43

(-4.931, 0.072)

 

-0.135

(-2.057, 1.786)

 

ΔGGT (U/L)

CCB

-1.498

(-6.821, 3.824)

0.3965

3.176

(-6.48, 12.832)

0.3908

ARB

-4.794

(-10.129, 0.541)

 

-2.839

(-12.482, 6.804)

 

ΔWBC (x109/L)

CCB

-0.092

(-0.278, 0.094)

0.3973

-0.02

(-0.191, 0.152)

0.7449

ARB

0.023

(-0.164, 0.209)

 

-0.06

(-0.232, 0.111)

 

ΔRBC (x1012/L)

CCB

-0.025

(-0.061, 0.012)

0.0004 *

-0.032

(-0.067, 0.003)

0.0286 *

ARB

-0.12

(-0.157, -0.083)

 

-0.088

(-0.123, -0.053)

 

ΔPLT(x109/L)

CCB

4.675

(-0.389, 9.738)

0.2057

4.86

(0.551, 9.17)

0.3242

ARB

-0.008

(-5.083, 5.067)

 

1.776

(-2.528, 6.08)

 

ΔHemoglobin (g/L)

CCB

-1.286

(-2.417, -0.156)

0.0135 *

-1.811

(-2.931, -0.692)

0.2222

ARB

-3.333

(-4.466, -2.2)

 

-2.804

(-3.922, -1.686)

 

ΔHematocrit (mmol/mol)

CCB

-0.002

(-0.006, 0.001)

0.0076 *

-0.003

(-0.006, 0.0004)

0.0796

ARB

-0.009

(-0.012, -0.005)

 

-0.007

(-0.01, -0.004)

 

Δindicates mean change in laboratory test value between baseline and exposure period. Abbreviations: TG; triglyceride, TC; total cholesterol, HbA1c; hemoglobin A1c, ALT; alanine aminotransferase, AST; asparate aminotransferase, GGT; γ-glutamyltransferase, WBC; white blood cell count, RBC; red blood cell count, PLT; platelet count. *: p<0.05 (ARB vs CCB). † Analyses were adjusted by covariates including age, sex, medical history and previous drugs.

Discussion

In this study, we evaluated and compared the effects of ARB and CCB monotherapy on biochemical parameters including serum TG, TC, non-fasting blood glucose, HbA1c, sodium, potassium, creatinine, ALT, AST and GGT and hematological parameters including hemoglobin, hematocrit, and WBC, RBC and PLT counts in patients with mild to moderate hypertension and type 2 diabetes mellitus. We found a significant reduction of serum TC, HbA1c, hemoglobin, hematocrit and RBC count in ARB users, and a reduction of serum TC and hemoglobin level in CCB users, from the baseline period to during the exposure period. The reductions of RBC count, hemoglobin and hematocrit in ARB users were significantly greater than those in CCB users. The increase of serum potassium level in ARB users was significantly greater than that in CCB users. These results suggest that hematological adverse effects and electrolyte imbalance are greater with ARB monotherapy than with CCB monotherapy.

It is known that renin-angiotensin system inhibitors, ACEIs and ARBs, occasionally cause anemia, while having protective effects on various organs. Valsartan decreases hematocrit in recipients of kidney transplantation [21]. Losartan decreases hematocrit, hemoglobin and erythrocyte count in recipients of kidney transplantation [6, 22]. In animals, candesartan decreases hematocrit, hemoglobin, erythrocyte count, and erythropoietin level in the rat [23]. Confirming these previous reports, our 'real-world' study showed adverse effects of ARB monotherapy on hemoglobin, hematocrit and RBC count.

There are some reports that the use of renin-angiotensin system inhibitors, including ARBs, is associated with hyperkalemia. The serum level of potassium is significantly higher in ARB users than in CCB users after renal transplantation [24]. The relative risk of hyperkalemia was 2-fold higher with dual therapy (ARB plus ACEI) than with monotherapy (ARB or ACEI) [25]. Use of ARBs and ACEIs is associated with a high prevalence of hyperkalemia, and the prevalence of hyperkalemia is significantly higher in ARB users than in ACEI users [5]. Supporting these previous reports of hyperkalemia, our study showed that ARB monotherapy caused electrolyte imbalance with respect to the serum level of potassium. Our study, in combination with previous reports, suggested that regular checks of serum potassium level may be advisable in ARB users.

There are few reports of ARBs affecting hepatic function. In patients with hypertension and abdominal obesity, there was no significant difference in the levels of ALT, AST and GGT between the candesartan group and placebo [26]. There was no significant difference in the levels of ALT and AST from baseline to six months of use of losartan in hypertensive diabetic patients [27]. Supporting these reports, there was no statistically significant difference in the serum levels of ALT and AST between baseline and the exposure period in both ARB users and CCB users in our study. In addition, those changes from baseline to during the exposure period were not significantly different between ARB and CCB users. Therefore, the influence of ARB and CCB monotherapy on hepatic function may be minimal and not of clinical concern.

TC and HbA1c levels in ARB users decreased during the exposure period compared to the baseline period in this study. Some ARBs modulate peroxisome proliferator-activated receptor-γ (PPAR-γ), which regulates lipid metabolism and is associated with insulin resistance [28, 29]. There are some reports that telmisartan, which is a strong modulator of PPAR-γ, has a favorable effect on glucose metabolism. Telmisartan significantly improved HOMA-IR in hypertensive patients and also significantly decreased HbA1c in type 2 diabetic patients, especially in those with poor glycemic control [30]. Treatment with telmisartan significantly improved the hyper-insulin response to glucose loading in patients with hypertension and obesity showing insulin resistance [31]. The favorable effect of ARBs on lipid and glucose metabolism that we observed may be caused in part by activation of PPAR-γ. Another reason for the decrease in HbA1c level in ARB users in our study may be the effect of the reduction of hemoglobin level. Sinha et al. suggested that both serum hemoglobin and HbA1c levels are significantly increased in patients with treatment of iron-deficiency anemia [32]. Ford et al. suggested that hemoglobin concentration is positively correlated with the concentration of HbA1c [33]. The effect of ARBs on the HbA1c level that we observed may have been partly influenced by the reduction of hemoglobin level.

There was no statistically significant difference in the level of blood glucose between the baseline and exposure periods in ARB users; however, we have previously reported that ARB monotherapy decreases the level of non-fasting blood glucose during a 6-month exposure period in non-diabetic patients with hypertension [13]. This discrepancy could be explained in part by differences in the duration of treatment or history of diabetes mellitus. It is possible that the glucose-lowering effect of ARB monotherapy could be weaker in patients with diabetes mellitus than in non-diabetic patients. We will evaluate these issues in our next study.

A decrease of TC was also observed in CCB users in our study. Nakamura et al. reported that CCBs decrease TC in patients with CKD [34]. Supporting the previous report, our results revealed a beneficial effect on lipid metabolism in CCB users in patients with hypertension and type 2 diabetes mellitus.

Subclass analysis showed that the reduction of RBC count was significantly greater in ARB users than in CCB users, in both men and women. On the other hand, the mean changes of potassium, hemoglobin and hematocrit in women were significantly different between ARB users and CCB users, but were not significantly different in men (Table 5). The reason for this discrepancy may be as follows. First, the effects of ARBs on hematological parameters are stronger in patients with low hemoglobin and hematocrit than in those with high levels. It is well known that there is a sex difference in hematological parameters; RBC count, hemoglobin and hematocrit are generally lower in women than in men. Second, the effect of ARBs on hemoglobin and hematocrit may reflect their effects on hormones. Testosterone is known to increase hemoglobin and hematocrit [35]. However, the reason for this discrepancy between women and men is still unclear.

Our study has several limitations. First, the retrospective and non-randomized nature of the design involved inherent issues of selection bias and confounding. We used rigorous statistical methods to balance potential confounding variables between ARB and CCB users, including propensity score matching. However, their ability to control for differences was limited to variables that were available or measurable. Second, we compared the effects of ARBs and CCBs in this study. However, the effects of ARBs on lipid and glucose metabolism slightly differ among these drugs [3638], and further studies are needed to compare the effects of individual drugs. Third, we did not fix the daily dosage in both ARB and CCB users, because the achievement of blood pressure goal requires various doses of an agent across different individuals or even in the same individual in clinical practice. This study was not designed to assess the effects of ARBs and CCBs at each dosage, because it is difficult to determine whether or not pharmacodynamics are dose-dependent in clinical settings. However, the findings of our study, using a sophisticated statistical method in a real-world setting, are reliable and informative for clinicians.

Conclusions

In this study, we observed greater reductions of hemoglobin, hematocrit and RBC count, and a greater increase of serum potassium level in patients who had received ARB monotherapy compared with CCB monotherapy. We observed significant differences between ARB and CCB users, although the mean values of these parameters remained within normal limits during the baseline and exposure periods. On the other hand, there was no significant difference in parameters of lipid metabolism, glucose metabolism and hepatic function and WBC and PLT counts between ARB and CCB users. Our findings support the clinical evidence that ARB therapy is associated with hematological adverse effects and electrolyte imbalance.

Abbreviations

ARB: 

Angiotensin II type I receptor blocker

CCB: 

Calcium channel blocker

NUSM: 

Nihon University School of Medicine

CDW: 

Clinical Data Warehouse

ACEI: 

Angiotensin-converting enzyme inhibitor

TG: 

Triglyceride

TC: 

Total cholesterol

HbA1c: 

Hemoglobin A1c

ALT: 

Alanine aminotransferase

AST: 

Aspartate aminotransferase

GGT: 

Gamma-glutamyltransferase

WBC: 

White blood cell

RBC: 

Red blood cell

PLT: 

Platelet

NSAID: 

Non-steroidal anti-inflammatory drug

PPAR-γ: 

Peroxisome proliferator-activated receptor-γ

PS: 

Propensity score.

Declarations

Acknowledgement

This work was supported in part by Tempstaff Co., Ltd. (Tokyo, Japan).

Authors’ Affiliations

(1)
Division of Genomic Epidemiology and Clinical Trials, Advanced Medical Research Center, Nihon University School of Medicine
(2)
Division of Clinical Trial Management, Advanced Medical Research Center, Nihon University School of Medicine
(3)
Division of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine
(4)
Division of Laboratory Medicine, Department of Pathology and Microbiology, Nihon University School of Medicine

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