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Keywords:

  • HIV infection;
  • hypertension;
  • lipopolysaccharide;
  • nadir CD4 cell count;
  • soluble CD14

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Objectives

The aim of the study was to test the hypothesis that microbial translocation, quantified by levels of lipopolysaccharide (LPS) and subsequent monocyte activation [soluble (sCD14)], is associated with hypertension in HIV-infected individuals.

Methods

In this exploratory substudy, 42 patients were recruited from a larger, longitudinal HIV-infected cohort study on blood pressure. LPS and sCD14 levels were measured retrospectively at the time of nadir CD4 cell count, selecting untreated HIV-infected patients with both advanced immunodeficiency and preserved immunocompetence at the time of nadir. Patients with later sustained hypertension (n = 16) or normotension (n = 26) throughout the study were identified. LPS was analysed using the Limulus Amebocyte Lysate colorimetric assay (Lonza, Walkersville, MD) and sCD14 using an enzyme-linked immunosorbent assay (ELISA). Nonparametric statistical tests were applied.

Results

In the HIV-infected patients [median (interquartile range) age 42 (32–46) years; 79% male and 81% Caucasian], LPS and sCD14 levels were both negatively correlated with nadir CD4 cell count. Plasma levels of LPS (P < 0.001) and sCD14 (P = 0.024) were elevated in patients with later hypertension compared with patients with normotension. There was a stepwise increase in the number of patients with hypertension across tertiles of LPS (P = 0.001) and sCD14 (P = 0.007). Both LPS and sCD14 were independent predictors of elevated blood pressure after adjustment for age and gender. For each 10-unit increase in LPS (range 66–272 pg/ml), the increment in mean blood pressure in the first period of blood pressure recording was 0.86 (95% confidence interval 0.31–1.41) mmHg (P = 0.003).

Conclusions

As LPS and sCD14 were both independently associated with elevated blood pressure, microbial translocation may be linked to the development of hypertension.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Non-AIDS-related morbidities such as hypertension, cardiovascular disease (CVD), malignancy, and renal, liver and bone diseases have emerged as increasing clinical problems in HIV-infected patients [1]. In fact, non-AIDS-related mortality today exceeds AIDS-related mortality in populations with access to antiretroviral therapy (ART) [2]. A premature ageing process has been suggested to occur in HIV-infected individuals for which several contributing factors have been proposed, including viral replication, drug toxicity, lifestyle factors, and persistent immune activation with increased cell proliferation and apoptosis as well as elevated levels of pro-inflammatory markers [1].

Primary HIV infection is characterized by massive T-cell depletion in the gastrointestinal mucosa with subsequent enhanced translocation of bacterial products such as lipopolysaccharide (LPS) and flagellin from the intestinal lumen into the systemic circulation [3, 4]. LPS is a potent inducer of immune response and inflammation through the innate immune system. Soluble CD14 (sCD14) is a marker of monocyte activation and is shed from monocytes upon LPS stimulation [5]. Microbial translocation has been suggested to be a major driver of HIV-associated immune activation through stimulation of Toll-like receptors (TLRs) [4]. Measures of T-cell-related immune activation independently predict disease progression and mortality in patients receiving and not receiving ART [6, 7]. Moreover, gut epithelial barrier dysfunction [8] as well as high levels of sCD14 [9] predicts mortality in HIV infection. Immune activation and microbial translocation are reduced, but often not normalized despite prolonged effective ART with achievement of viral suppression [4, 10-12]. Importantly, increased cardiovascular risk has been linked to lack of CD4 cell count restoration despite effective ART [13, 14], which in turn is associated with low nadir CD4 cell counts [15], persistent microbial translocation [12] and immune activation [10].

Hypertension occurs frequently in HIV-infected populations, and is a major cause of myocardial infarction [16], non-AIDS-related mortality [17], and cardiovascular and all-cause mortality [2]. In the large D:A:D (Data Collection on Adverse Effects of Anti-HIV Drugs) study, predictors of new-onset hypertension such as older age, male gender and higher body mass index (BMI) were similar to those in the general population, but markers related to immune activation were not included in this study [18]. Other studies have shown an association between low nadir CD4 cell counts and the development of hypertension after initiation of ART [19, 20], although the mechanisms have not been addressed. Whether immune activation and microbial translocation play a role in the relationship between immunodeficiency and hypertension is not known. Our group has previously identified low nadir CD4 cell count (i.e. <50 cells/μL) as a predictor of sustained hypertension in the present HIV-infected cohort [21], and in this substudy we wanted to explore microbial translocation as a possible pathogenetic link. Thus, we chose to include patients with previous severe immunodeficiency as well as patients with preserved immunocompetence throughout their ART-naïve course of HIV infection. The aim of the present study was to test the hypothesis that markers of microbial translocation and subsequent monocyte activation before initiation of ART could predict hypertension in HIV-infected individuals.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Study design and population

In this exploratory substudy, 42 patients were recruited from a larger longitudinal HIV-infected cohort study on blood pressure (BP) (n = 434) [22]. Plasma levels of LPS and sCD14 were measured retrospectively at the time of nadir CD4 cell count (T0) (Fig. 1), selecting both patients with earlier advanced immunodeficiency and patients with preserved immunocompetence, as reflected by low [<50 cells/μL (lowN)] or high [>200 cells/μL (highN)] nadir CD4 cell count. Exclusion criteria for the substudy were diabetes mellitus, ART exposure at T0 or known hypertension [systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg or use of antihypertensive drugs] based on hospital records at T0. Patients with available plasma samples at T0 with sustained hypertension (HT) or sustained normotension (NT) throughout the longitudinal study were recruited to both the highN and lowN groups. Availability of plasma samples turned out to be a major limitation for recruitment, and all eligible patients with available samples in the lowN group (HT and NT) as well as the highN HT group were included. Because matching across the two nadir strata was not possible, we chose to increase the number in the highN NT group to increase statistical strength. Recruitment to the highN NT group was performed in a random fashion. A total of 16 HT patients (lowN, n = 10) and 26 NT patients (lowN, n = 5) were included in the substudy. Written informed consent was obtained before inclusion in the longitudinal study. The study was performed in accordance with the Declaration of Helsinki. A control group of 15 HIV-negative Caucasian subjects were recruited by advertisement in local newspapers. It should be noted that the controls were not specifically matched for risk factors for hypertension, although the age and gender distribution was similar (Table 1). One-third of the control group had hypertensive BP readings at recruitment, but none used antihypertensive drugs.

figure

Figure 1. Graphic outline of the study including blood sampling at the time of nadir (T0) and three blood pressure measurements during time period 1 (T1) and time period 2 (T2). IQR, interquartile range.

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Table 1. Characteristics of the controls and the HIV-infected patients at the time of nadir CD4 cell count (T0) and of the HIV-infected patients according to sustained hypertension status during time periods 1 and 2
Characteristics at T0Controls (n = 15)HIV-infected patients (n = 42)pHIV-infected patientsp
Sustained NT (n = 26)Sustained HT (n = 16)
  1. Data are given as number (%) or median (interquartile range) for skewed distributions.

  2. BMI, body mass index; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; LPS, lipopolysaccharide; sCD14, soluble CD14; NT, normotension; HT, hypertension; NA, not applicable; T0, the time of nadir CD4 cell count.

  3. a

    Recorded during time period 1.

  4. b

    Available data for n = 30.

  5. c

    Within 1 year from the time of nadir.

  6. d

    Available data for n = 37; missing in the normotension (n = 2) and hypertension (n = 3) groups.

Age (years)42 (41–47)42 (32–46)0.23236.0 (30.2–46.2)45.4 (42.7–46.5)0.032
Male gender [n (%)]12 (80)33 (78.6)1.00019 (73.1)14 (87.5)0.442
Caucasian ethnicity [n (%)]0 (0)34 (81.0)0.09520 (76.9)14 (87.5)0.397
Current smoking [n (%)]a22 (52.4)NA14 (53.8)8 (50)0.808
BMI (kg/m2)25.5 (23.8–26.7)22.4 (20.2–24.3)0.00322.8 (20.4–24.3)22.5 (20.0–23.6)0.427
Triglycerides (mmol/L)0.8 (0.6–1.3)1.3 (1.1–1.9)0.0041.2 (0.9−1.6)1.8 (1.4–2.4)0.007
Cholesterol (mmol/L)b4.9 (4.4–5.8)4.6 (4.1–5.3)0.1354.6 (4.1–5.3)4.5 (4.1–5.5)0.824
eGFR (ml/min)106.2 (94.6–113.9)NA106 (98−122)106 (94–112)0.242
Glucose (mmol/L)5.1 (4.7–5.4)NA5.1 (4.6–5.4)5.0 (4.7–5.2)0.866
SBP (mmHg)134 (124–138)NANA
DBP (mmHg)77 (75–92)NANA
LPS (pg/mL)54 (42–65)104 (79–160)<0.00186 (73−110)154 (137–220)<0.001
sCD14 (ng/mL)221 (102–395)1292 (587–1815)<0.001888 (434−1552)1726 (880–2394)0.024
HIV diagnosis (years)1.1 (0.2–4.5)NA1.2 (0.2–3.2)1.0 (0.1–7.3)0.917
Diagnosis of AIDS [n (%)]c12 (28.6)NA6 (23.1)6 (37.5)0.483
HIV RNA (copies/mL)d44000 (3550–165000)NA18000 (3100–115250)130000 (25500–370000)0.148
Nadir CD4 count (cells/μL)240 (36–430)NA339 (222−463)43 (9–23)0.009
Nadir CD4 count <50 cells/μL [n (%)]15 (35.7)NA5 (19.2)10 (62.5)0.004

Materials and analyses

Standardized BP was recorded during two prospectively scheduled time periods; time period 1 (T1) and time period 2 (T2). The time interval between the median dates of T1 and T2 was 2.7 [interquartile range (IQR) 2.5–3.1] years. BP was measured at three separate clinical visits days to months apart during both time periods (Fig. 1). The time interval between the first and the last visits during T1 and T2 was 0.5 (IQR 0.3–1.4) and 0.5 (0.3–0.8) years, respectively. BP measurement was performed in duplicate 2 min apart using a semiautomatic oscillometric device (Omron M4; Matsusaka Co. Ltd, Matsusaka, Japan) with appropriate cuff size according to the upper arm circumference. BP was measured in a relaxed sitting position in a quiet room after five minutes of rest by well-trained nurses. The average of six SBP and DBP measurements during T1 and T2, respectively, was used for statistical analyses. Patients with sustained hypertension, defined as hypertension at both T1 and T2 (treated hypertension at T1, n = 2/16; at T2, n = 6/16), and patients with sustained normotension were thereby identified. Mean arterial pressure (MAP) was calculated as DBP + 1/3 (SPB − DBP).

Plasma samples were collected with standard venipuncture as per clinical practice, and fasting state had been recommended but was not recorded. The samples were stored at −20°C until analysis.

LPS was analysed using the Limulus Amebocyte Lysate colorimetric assay (Lonza, Walkersville, MD) according to the manufacturer's instructions, with the following modifications: samples were diluted 10-fold to avoid interference with background colour, and preheated to 68°C for 12 min prior to analyses to dissolve immune complexes, as previously described [11]. sCD14 was analysed using an enzyme-linked immunosorbent assay (ELISA) according to the manufacturer's instructions (R&D, Minneapolis, MN).

Demographic, clinical and laboratory data at T0, T1 and T2 were obtained from the hospital records, the local HIV database and the case report forms (CRFs) at inclusion at T1 and T2. Nadir CD4 cell count was recorded as the lowest CD4 cell count in each individual's history. The estimated glomerular filtration rate (eGFR) was calculated according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula [23].

Statistical analyses

Skewed data (LPS, sCD14 and CD4 cell count) were log-transformed, and performance of parametric bivariate correlation analyses with calculation of Pearson's coefficient (r) was justified in analyses including all the HIV-infected patients. Otherwise nonparametric statistics were used, and Spearman's coefficient (rho) was calculated in bivariate correlation analyses. Between-group differences were evaluated with the Mann–Whitney U-test. A χ2 linear-by-linear association test was used for exploring linear trend in categorical data. Predictors of BP at T1 and T2, respectively, were explored using multivariate linear regression analyses. Statistical assumptions for the use of the linear regression model were satisfied. Because of the small sample size, the models included only age, gender, and LPS or sCD14, respectively, as independent variables. However, models with four independent variables were also created, adding a fourth variable to the original model, one at a time. The independent variables were chosen based on clinical relevance or association with outcome in univariate analyses (P < 0.1). A significance level of 0.05 was used. The statistical analyses were performed with spss software, version 20.0 (SPSS Inc., Chicago, IL).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Characteristics of the controls and the HIV-infected patients at T0, including stratification of the 16 HT and 26 NT patients at T0, are given in Table 1. The HIV-infected study population included 19% non-Caucasians, and they had higher triglyceride levels, lower cholesterol and lower BMI compared with the Caucasian controls. All patients were ART-naïve at T0, whereas 50% and 64% were receiving ART at the time of inclusion at T1 and T2, respectively.

In the control group, LPS and sCD14 plasma levels did not correlate with age, BMI, BP or triglycerides (data not shown). Plasma levels of LPS correlated with sCD14 in the HIV-infected population (Pearson's r=0.48; P = 0.001), but not in the control group. In the HIV-infected study population, LPS and sCD14 were both negatively correlated with nadir CD4 cell count (r = −0.57; P < 0.001 and r = −0.66; P < 0.001, respectively). A diagnosis of AIDS within 1 year of nadir occurred in two-thirds of the lowN patients.

Elevated plasma levels of LPS and sCD14 in HIV-infected subjects with hypertension

Plasma levels of LPS and sCD14 were strongly correlated in patients with HT (Spearman's rho=0.62; P = 0.011), but not in patients with NT or controls (Fig. 2a). Plasma levels of LPS (P < 0.001) and sCD14 (P = 0.024) at T0 were elevated in patients with HT compared with those with NT, and in both HT and NT patients compared with controls (P < 0.001 for all) (Fig. 2b and c). Furthermore, there was a stepwise increase in the number of HT subjects through tertiles of LPS (P = 0.001) and sCD14 (P = 0.007) (Fig. 3).

figure

Figure 2. Within-group correlations and between-group comparisons of plasma levels of lipopolysaccharide (LPS) and soluble CD14 (sCD14). (a) Correlations between plasma levels of LPS and sCD14 at the time of nadir in HIV-infected patients with hypertension (HT), HIV-infected patients with normotension (NT) and controls. (b, c) Plasma levels of LPS and sCD14 are elevated in patients with HT compared with NT patients, and in HT and NT patients compared with controls. Horizontal lines in dot plots indicate the median level.

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figure

Figure 3. Increase in the number of hypertensive HIV-infected patients through tertiles of lipopolysaccharide (LPS) and soluble CD14 (sCD14). P-values refer to χ2 linear-by-linear association.

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LPS and sCD14 as independent predictors of elevated blood pressure

In univariate analyses, plasma LPS correlated with MAP at T1 and T2 (r = 0.57; P < 0.001 and r = 0.48; P = 0.001, respectively) (Fig. 4), with SBP at T1 and T2 (r = 0.52; P < 0.001 and r = 0.43; P = 0.004, respectively), and with DBP at T1 and T2 (r = 0.57; P < 0.001 and r = 0.46; P = 0.002, respectively). Furthermore, sCD14 correlated with MAP at T1 and T2 (r = 0.39; P = 0.012 and r = 0.38; P = 0.013, respectively) (Fig. 4) as well as with DBP at T1 and T2 (r = 0.45; P = 0.003 and r = 0.41; P = 0.007, respectively), but not significantly with SBP either at T1 (r = 0.27; P = 0.083) or T2 (r = 0.29; P = 0.060). Univariate linear regression analyses for MAP at T1 are presented in Table 2.

figure

Figure 4. Correlations between plasma levels of log lipopolysaccharide (LPS) and log soluble CD14 (sCD14), respectively, at the time of nadir and mean arterial pressure (MAP) at time period 1 (T1).

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Table 2. Univariate and multivariate linear regression analyses with mean blood pressure at time period 1 as outcome; also presented are adjusted β coefficients for lipopolysaccharide (LPS) and soluble CD14 (sCD14), respectively, when a fourth variable was added to the multivariate regression model
Characteristics at T0Unadjusted unstandardized coefficient β [95% CI]tpModel with LPStpModel with sCD14tp
Adjusted unstandardized coefficient β [95% CI]Adjusted unstandardized coefficient β [95% CI]
  1. ART, antiretroviral therapy; BMI, body mass index; CI, confidence interval; eGFR, estimated glomerular filtration rate.

  2. a

    Recorded during time period 1.

LPS (+10 pg/mL)1.05 [0.52–1.57]4.03<0.0010.86 [0.31–1.41]3.170.003   
sCD14 (+100 ng/ml)0.40 [0.00–0.80]2.170.036   0.40 [0.10–0.80]2.580.014
Age (+1 year)0.45 [0.11–0.78]2.650.0120.29 [−0.03–0.61]1.840.0730.36 [0.037–0.69]2.260.030
Male gender8.43 [0.33–16.54]2.100.0422.56 [−5.05–10.17]0.680.5007.03 [−0.54–14.59]1.880.068
    Basic model with additional adjustment for each of the following variables
    Adjusted unstandardized coefficient β [95% CI] for LPS (+10 pg/mL)tpAdjusted unstandardized coefficient β [95% CI] for sCD14 (+100 ng/ml)tp
Caucasian ethnicity8.08 [−0.45–16.64]1.910.0630.96 [0.40–1.53]3.460.0010.40 [0.10–0.80]2.560.015
Current smokinga2.46 [−4.51–9.44]0.710.4790.88 [0.32–1.44]3.200.0030.40 [0.10–0.80]2.530.016
BMI (+1 kg/m2)1.01 [−0.19–2.21]1.700.0971.08 [0.43–1.73]3.360.0020.50 [0.10–0.80]2.800.008
Triglycerides (+1 mmol/L)3.39 [−1.14–9.91]1.510.1380.86 [0.24–1.48]2.820.0080.40 [0.10–0.70]2.410.021
eGFR (+1 ml/min)−0.19 [−0.39–0.01]−1.950.0580.78 [0.21–1.34]2.780.0090.50 [0.10–0.80]2.840.007
Glucose (+1 mmol/L)2.93 [−2.91–8.77]1.010.3170.85 [0.28–1.41]3.040.0040.40 [0.10–0.70]2.540.015
Current ARTa2.63 [−4.33–9.59]0.760.4490.91 [0.24–1.58]2.760.0090.40 [0.00–0.80]2.100.042
Log HIV RNA (+1 copies/mL)−0.76 [−3.53–2.01]0.560.5831.06 [0.47–1.65]3.650.0010.50 [0.10–0.90]2.790.009
Nadir CD4 count (+10 cells/μL)−0.05 [−0.16–0.07]−0.790.4330.92 [0.30–1.55]2.980.0050.50 [0.10–0.90]2.410.021
Nadir CD4 count <50 cells/μL−4.0 [−11.19–3.23]−1.120.2711.05 [0.32–1.79]2.910.0060.40 [0.10–0.80]2.580.014

Both LPS and sCD14 remained independent predictors of MAP at T1 after adjustment for age and gender (Table 2). Moreover, for each 10 pg/ml increase in LPS (range 66–272 pg/ml), the increment in BP at T1 was 1.03 [95% confidence interval (CI) 0.23–1.83] mmHg for SBP (P = 0.013) and 0.78 (95% CI 0.29–1.26) mmHg for DBP (P = 0.002) after adjustment for age and gender. For each 100 ng/ml increase in sCD14 (range 135–4218 ng/ml), the increment was 0.40 (95% CI 0.10–0.70) mmHg for DBP (P = 0.004) after adjustment, but with no independent association with SBP (P = 0.101). Similarly, for each 10 pg/ml increase in LPS, the increment in BP at T2 was 0.75 (95% CI 0.20–1.29) mmHg for MAP (P = 0.009), 0.97 (95% CI 0.15–1.80) mmHg for SBP (P = 0.022) and 0.63 (95% CI 0.15–1.11) mmHg for DBP (P = 0.011) after adjustment for age and gender. For each 100 ng/ml increase in sCD14, the increment in BP at T2 was 0.40 (95% CI 0.10–0.70) mmHg for both MAP and DBP (P = 0.008 and P = 0.007, respectively) and 0.50 (95% CI 0.00–1.00) mmHg for SBP (P = 0.036) after adjustment for age and gender. The multivariate regression models including LPS and sCD14 yielded R2 of 0.17–0.29 and 0.19–0.28, respectively.

When a fourth variable was added to the multivariate regression model, i.e. ethnicity, viral load, nadir CD4 cell count, triglyceride concentration, BMI, eGFR, glucose concentration, smoking or current ART, plasma levels of LPS and sCD14 remained independent predictors of MAP at both T1 (Table 2) and T2. In addition, LPS independently predicted SBP and DBP, whereas sCD14 independently predicted DBP but not SBP at T1 and T2 (data not shown).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

In the present study, LPS and sCD14 levels were measured at the time of nadir CD4 cell count, selecting both patients with advanced immunodeficiency and preserved immunocompetence at the time of nadir. LPS and sCD14 levels were both independent predictors of later BP levels and were associated with subsequent sustained hypertension. These findings could not be explained by cardiometabolic risk factors, viral load, nadir CD4 cell count or later use of ART. Our data suggest that microbial translocation and immune activation might contribute to increased BP and the development of hypertension.

The significant association between LPS and hypertension found in this study can be explained by several mechanisms. First, LPS is well known to induce endothelial dysfunction [24], among other mechanisms by activating the endothelium through a receptor complex consisting of TLR4, CD14 and myeloid differentiation (MD)-2 [25], and by inducing apoptosis of endothelial cells [26]. Endothelial dysfunction and impaired arterial elasticity are present throughout the atherosclerotic process [27], and an association of LPS with carotid atherosclerosis has been described in the general population [28] as well as in HIV-infected individuals [29]. In non-HIV-infected cohorts, we and others have shown that pro-inflammatory cytokines [30] and sCD14 are associated with arterial stiffness [31] which is mechanistically linked to hypertension.

In HIV-infected individuals, viral replication has been associated with endothelial dysfunction [32]. Furthermore, increased LPS levels are associated with activated T cells [4], consistent with a recent study reporting that a predominance of such cells was associated with arterial stiffness in HIV-infected women [33]. Taken together, these findings suggest that microbial translocation and subsequent activation of macrophages and T cells may contribute to HIV-associated endothelial dysfunction and arterial stiffness, which over time may cause hypertension.

Other mechanisms may also be involved. LPS has been shown to activate the sympathetic nervous system in experimental settings [34], and high autonomous nervous activity and elevated levels of noradrenaline have been linked to impaired ART response and enhanced replication of HIV [35]. Furthermore, the renin-angiotensin system is involved in LPS-induced endothelial dysfunction, and can be reversed by the angiotensin-converting enzyme-inhibitor enalapril [36].

Nadir CD4 cell count was associated with LPS and sCD14 in our study. In fact, microbial translocation could explain the reported independent association between low nadir CD4 cell count and the development of hypertension [19-21]. However, other mechanisms could also be at play. Hypertension has previously been related to inflammation [37, 38]. Immune activation is linked to HIV progression, and could be driven by factors other than microbial translocation, for example viral coinfections, other opportunistic infections and amplified HIV replication during immunodeficiency [39]. Dyslipidaemia during advanced HIV infection [40] could have an adverse effect on endothelial cells, as could viral toxicity [32]. Furthermore, the combination of severe immunodeficiency and subsequent ART-mediated immune reconstitution could possibly have an adverse influence on BP [19, 20].

There are limitations in this study that need to be acknowledged. First, the small sample size increases the risk of statistical type II errors. However, the risk of type I errors is lower, and the significant association between microbial translocation and elevated BP levels is likely to be reliable. Secondly, the study was not originally designed for the purpose of exploring a link between microbial translocation and hypertension. Consequently, the study lacks systematic BP measurements at T0. Because of limited availability of plasma samples in all groups except the NT highN group, the HT and NT patients could not be matched across the two nadir strata, either in number or in other characteristics. However, adjusting for relevant covariates in linear regression analyses did not alter the main result. Thirdly, measurement of LPS has many pitfalls, including technical difficulties and possible interference from plasma turbidity [9]. A fasting state was advised but not formally recorded, and thus post-prandial hypertriglyceridaemia could have influenced the LPS levels, in particular in samples with elevated triglycerides. Furthermore, storage of plasma samples over a long period of time could possibly have an adverse influence. Fourthly, the controls were recruited with the purpose of adding context to the data. Because of the lack of matching between the HIV-infected cohort and controls, the comparisons should be interpreted with caution. Finally, although all samples were run in duplicate, inter- and intra-assay variability cannot be excluded.

Our study also has several strengths. The close association between LPS and BP was supported by the finding of a similar predictive power for sCD14, which is pathogenetically linked to LPS [5]. The longitudinal design in the assessment of hypertension status, the thorough validation of BP measurements, and the use of standardized BP readings and sustained hypertension as predefined endpoints are additional strengths.

The majority of patients with a low nadir CD4 cell count reached nadir at the time-point at which ART was introduced (1996). Thus, the study population does not reflect the situation today, and this study should therefore be considered a proof of concept study. Prospective studies should be performed in populations on stable ART in which non-AIDS-related complications are an increasing clinical problem [1].

Hypertension is a non-AIDS-related complication which deserves particular clinical attention. As our data show that hypertension was closely associated with markers of microbial translocation in HIV-infected individuals, a key aspect of HIV pathogenesis may be linked to the development of hypertension. We propose that the gastrointestinal mucosal barrier may present as a potential therapeutic target for the prevention of future hypertension and long-term cardiovascular complications in HIV infection, and this needs to be explored in future studies.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We thank the patients for their participation. We acknowledge the skilled assistance of the staff at the Outpatient Clinic of Infectious Diseases at Oslo University Hospital, Ullevål. Financial support was received from Signe and Albert Bergsmarken's fund for investigation of kidney diseases, the HIV fund of the Department of Infectious Diseases at Oslo University Hospital, Ullevål and the South-Eastern Norway Regional Health Authority and Novo Nordisk Foundation.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 1
    Deeks SG. HIV infection, inflammation, immunosenescence, and aging. Annu Rev Med 2011; 62: 141155.
  • 2
    Smith C, Sabin CA, Lundgren JD et al. Factors associated with specific causes of death amongst HIV-positive individuals in the D:A:D Study. AIDS 2010; 24: 15371548.
  • 3
    Brenchley JM, Douek DC. HIV infection and the gastrointestinal immune system. Mucosal Immunol 2008; 1: 2330.
  • 4
    Brenchley JM, Price DA, Schacker TW et al. Microbial translocation is a cause of systemic immune activation in chronic HIV infection. Nat Med 2006; 12: 13651371.
  • 5
    Landmann R, Knopf HP, Link S, Sansano S, Schumann R, Zimmerli W. Human monocyte CD14 is upregulated by lipopolysaccharide. Infect Immun 1996; 64: 17621769.
  • 6
    Giorgi JV, Hultin LE, McKeating JA et al. Shorter survival in advanced human immunodeficiency virus type 1 infection is more closely associated with T lymphocyte activation than with plasma virus burden or virus chemokine coreceptor usage. J Infect Dis 1999; 179: 859870.
  • 7
    Hunt PW, Cao HL, Muzoora C et al. Impact of CD8+ T-cell activation on CD4+ T-cell recovery and mortality in HIV-infected Ugandans initiating antiretroviral therapy. AIDS 2011; 25: 21232131.
  • 8
    Hunt PW, Rodriguez B, Shive C et al. Gut epithelial barrier dysfunction, inflammation, and coagulation predict higher mortality during treated HIV/AIDS. 19th Conference on Retroviruses and Opportunistic Infections. Seattle, WA, March 2012.
  • 9
    Sandler NG, Wand H, Roque A et al. Plasma levels of soluble CD14 independently predict mortality in HIV infection. J Infect Dis 2011; 203: 780790.
  • 10
    Hunt PW, Martin JN, Sinclair E et al. T cell activation is associated with lower CD4+ T cell gains in human immunodeficiency virus-infected patients with sustained viral suppression during antiretroviral therapy. J Infect Dis 2003; 187: 15341543.
  • 11
    Troseid M, Nowak P, Nystrom J, Lindkvist A, Abdurahman S, Sonnerborg A. Elevated plasma levels of lipopolysaccharide and high mobility group box-1 protein are associated with high viral load in HIV-1 infection: reduction by 2-year antiretroviral therapy. AIDS 2010; 24: 17331737.
  • 12
    Jiang W, Lederman MM, Hunt P et al. Plasma levels of bacterial DNA correlate with immune activation and the magnitude of immune restoration in persons with antiretroviral-treated HIV infection. J Infect Dis 2009; 199: 11771185.
  • 13
    Lichtenstein KA, Armon C, Buchacz K et al. Low CD4+ T cell count is a risk factor for cardiovascular disease events in the HIV outpatient study. Clin Infect Dis 2010; 51: 435447.
  • 14
    Obel N, Thomsen HF, Kronborg G et al. Ischemic heart disease in HIV-infected and HIV-uninfected individuals: a population-based cohort study. Clin Infect Dis 2007; 44: 16251631.
  • 15
    Kelley CF, Kitchen CM, Hunt PW et al. Incomplete peripheral CD4+ cell count restoration in HIV-infected patients receiving long-term antiretroviral treatment. Clin Infect Dis 2009; 48: 787794.
  • 16
    Triant VA, Regan S, Lee H, Sax PE, Meigs JB, Grinspoon SK. Association of immunologic and virologic factors with myocardial infarction rates in a US healthcare system. J Acquir Immune Defic Syndr 2010; 55: 615619.
  • 17
    Mocroft A, Reiss P, Gasiorowski J et al. Serious fatal and nonfatal non-AIDS-defining illnesses in Europe. J Acquir Immune Defic Syndr 2010; 55: 262270.
  • 18
    Thiebaut R, El-Sadr WM, Friis-Moller N et al. Predictors of hypertension and changes of blood pressure in HIV-infected patients. Antivir Ther 2005; 10: 811823.
  • 19
    Crane HM, Van Rompaey SE, Kitahata MM. Antiretroviral medications associated with elevated blood pressure among patients receiving highly active antiretroviral therapy. AIDS 2006; 20: 10191026.
  • 20
    Palacios R, Santos J, Garcia A et al. Impact of highly active antiretroviral therapy on blood pressure in HIV-infected patients. A prospective study in a cohort of naive patients. HIV Med 2006; 7: 1015.
  • 21
    Manner IW, Troseid M, Oektedalen O, Baekken M, Os I. Low nadir CD4 cell count predicts sustained hypertension in HIV-infected individuals. J Clin Hypertens 2012; doi:10.1111/jch.12029 [Epub ahead of print].
  • 22
    Manner IW, Baekken M, Oektedalen O, Os I. Hypertension and antihypertensive treatment in HIV-infected individuals. A longitudinal cohort study. Blood Press 2012; 21: 311319.
  • 23
    Levey AS, Stevens LA, Schmid CH et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150: 604612.
  • 24
    Draisma A, Bemelmans R, van der Hoeven JG, Spronk P, Pickkers P. Microcirculation and vascular reactivity during endotoxemia and endotoxin tolerance in humans. Shock 2009; 31: 581585.
  • 25
    Dauphinee SM, Karsan A. Lipopolysaccharide signaling in endothelial cells. Lab Invest 2006; 86: 922.
  • 26
    Bannerman DD, Goldblum SE. Mechanisms of bacterial lipopolysaccharide-induced endothelial apoptosis. Am J Physiol Lung Cell Mol Physiol 2003; 284: L899L914.
  • 27
    Cohn JN, Duprez DA, Grandits GA. Arterial elasticity as part of a comprehensive assessment of cardiovascular risk and drug treatment. Hypertension 2005; 46: 217220.
  • 28
    Wiedermann CJ, Kiechl S, Dunzendorfer S et al. Association of endotoxemia with carotid atherosclerosis and cardiovascular disease: prospective results from the Bruneck Study. J Am Coll Cardiol 1999; 34: 19751981.
  • 29
    Kelesidis T, Kendall MA, Yang OO, Hodis HN, Currier JS. Biomarkers of microbial translocation and macrophage activation: association with progression of subclinical atherosclerosis in HIV-1 infection. J Infect Dis 2012; 206: 15581567.
  • 30
    Troseid M, Seljeflot I, Weiss TW, Klemsdal TO, Hjerkinn EM, Arnesen H. Arterial stiffness is independently associated with interleukin-18 and components of the metabolic syndrome. Atherosclerosis 2010; 209: 337339.
  • 31
    Amar J, Ruidavets JB, Bal Dit SC et al. Soluble CD14 and aortic stiffness in a population-based study. J Hypertens 2003; 21: 18691877.
  • 32
    Torriani FJ, Komarow L, Parker RA et al. Endothelial function in human immunodeficiency virus-infected antiretroviral-naive subjects before and after starting potent antiretroviral therapy: the ACTG (AIDS Clinical Trials Group) Study 5152s. J Am Coll Cardiol 2008; 52: 569576.
  • 33
    Kaplan RC, Sinclair E, Landay AL et al. T cell activation predicts carotid artery stiffness among HIV-infected women. Atherosclerosis 2011; 217: 207213.
  • 34
    Zhang ZH, Yu Y, Wei SG, Felder RB. Centrally administered lipopolysaccharide elicits sympathetic excitation via NAD(P)H oxidase-dependent mitogen-activated protein kinase signaling. J Hypertens 2010; 28: 806816.
  • 35
    Cole SW, Naliboff BD, Kemeny ME, Griswold MP, Fahey JL, Zack JA. Impaired response to HAART in HIV-infected individuals with high autonomic nervous system activity. Proc Natl Acad Sci U S A 2001; 98: 1269512700.
  • 36
    Lund DD, Brooks RM, Faraci FM, Heistad DD. Role of angiotensin II in endothelial dysfunction induced by lipopolysaccharide in mice. Am J Physiol Heart Circ Physiol 2007; 293: H3726H3731.
  • 37
    Harrison DG, Guzik TJ, Lob HE et al. Inflammation, immunity, and hypertension. Hypertension 2011; 57: 132140.
  • 38
    Sesso HD, Buring JE, Rifai N, Blake GJ, Gaziano JM, Ridker PM. C-reactive protein and the risk of developing hypertension. JAMA 2003; 290: 29452951.
  • 39
    Sodora DL, Silvestri G. Immune activation and AIDS pathogenesis. AIDS 2008; 22: 439446.
  • 40
    Grunfeld C, Kotler DP, Arnett DK et al. Contribution of metabolic and anthropometric abnormalities to cardiovascular disease risk factors. Circulation 2008; 118: e20e28.