Abstract
Albuminuria (urinary excretion of more than 30 milligram of albumin per gram of creatinine) serves as an indicator of microvascular injury, which has been associated with atherosclerosis and cardiovascular disease in HIV-seronegative individuals. Albuminuria has been reported to be prevalent among HIV-seropositive individuals, however, the relationship between albuminuria and risk for cardiovascular disease in this population has not been well-studied. We examined the relationships between albuminuria and parameters of atherosclerosis including carotid intima-media thickness and traditional cardiovascular risk assessment among HIV-seropositive individuals receiving stable antiretroviral therapy. We utilized a cross-sectional baseline data from the Hawai‘i Aging with HIV-Cardiovascular Study cohort.
Results
Data was available on 111 HIV-infected patients (median age of 52 (Q1,Q3: 46, 57), male 86%; diabetes 6%; hypertension 33%; dyslipidemia 50%; median CD4 count of 489 cells/mm3 (341, 638); HIV RNA PCR < 48 copies/ml of 85%). Eighteen subjects (16.2%) had microalbuminuria, and two subjects (1.8%) had macroalbuminuria. Albuminuria was significantly associated with increased Framingham Risk Score (P=.002), insulin resistance by HOMA-IR (P=.02), diastolic blood pressure (P=.01), and carotid intima-media thickness (P =.04). The correlation between the amount of albuminuria and carotid intima-media thickness remained significant even after adjusting for age, gender, ethnicity, current smoking status, diabetes mellitus, diastolic blood pressure, fasting insulin level, CD4 count, and HIV-RNA viral load.
Conclusion
Albuminuria is prevalent among HIV-infected patients receiving stable antiretroviral therapy. It is significantly related to previously defined markers of cardiovascular disease and metabolic syndrome among HIV-infected patients receiving stable antiretroviral therapy.
Keywords: HIV, albuminuria, CD4 count, HIV viral load, atherosclerosis, aging, cardiovascular disease
Introduction
Albuminuria is recognized to be associated with renal progression in type 1 diabetes. In type 2 diabetes patients, however, albuminuria is a stronger predictor for cardiovascular disease (CVD) than kidney function. Albuminuria has become a marker of early stage of systemic atherosclerosis.1,2 Meta-analysis studies have revealed that albuminuria is associated with increasing cardiovascular events in high risk populations including patients with diabetes, hypertension, and metabolic syndrome. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure recognized albuminuria as a major CVD risk factor.3 The correlation of CVD appears to be strongly related to the amount of albuminuria, even at levels of albuminuria lower than 30 mg/g.4 The prevalence of microalbuminuria (30-300 mg of urine albumin per gram of creatinine) varies from 10–30% in diabetic, 5–25% in hypertensive, and 5–10% in the non-diabetic, non-hypertensive population.5 However, albuminuria has not been well-studied in HIV-seropositive individuals. A previous study reported an 11% rate of albuminuria in HIV-seropositive individuals compared to 2% among control individuals. Several cardiovascular risk factors including insulin resistance and elevated blood pressure were noted to be associated with higher albumin-to-creatinine ratio in HIV-infected patients.6
Ultrasonographic imaging of carotid intima-media thickness (cIMT) is a non-invasive method of assessing systemic atherosclerosis. It was used as a surrogate endpoint in several clinical intervention studies,7,8 with higher levels of cIMT observed among HIV-seropositive individuals. Hsue, et al, suggested that this may be secondary to an interplay between hemodynamic shear stresses and HIV-associated inflammation.9 We examined the relationships between albuminuria, risks for cardiovascular disease, and carotid intima-media thickness among HIV-seropositive individuals receiving stable antiretroviral therapy using cross-sectional baseline data from the Hawai‘i Aging with HIV-Cardiovascular Study cohort.
Methods
Study Population
The Hawai‘i Aging with HIV-Cardiovascular Study, a natural history longitudinal study of the role of oxidative stress and inflammation in HIV cardiovascular risk, enrolled 158 HIV-infected adults age ≥ 40 years old, living in the state of Hawai‘i. Inclusion criteria included documented HIV-seropositive status and having been on the same regimen of antiretroviral therapy for at least six months. IRB approval was obtained from the University of Hawai‘i and all subjects provided informed consent prior to entry into the study.
Clinical Parameters
General medical and HIV-specific histories, and medication history were obtained. Clinical parameters assessed included height, weight, waist-to-hip ratio, blood pressure, and ankle-brachial index. An EKG was obtained. Blood parameters assessed included fasting lipids, glucose and insulin as well as results from an oral glucose tolerance test. The Framingham Risk Score was calculated using ATP III guideline.10 HIV-specific laboratory measurements included CD4 count and plasma HIV-RNA viral levels.
Urine Albumin
The level of urine albumin was determined by Immunoturbidimetric assay using a Roche/Hitachi MODULAR P analyzer. Albuminuria was defined as urine albumin-to-creatinine ratio (ACR) of more than 30 mg/g, as assessed from random urine collection. Microalbuminuria was defined as urine ACR between 30 and 300 mg/g and macroalbuminuria as urine ACR more than 300 mg/g.11
Common Carotid Artery Intima-Media Thickness (cIMT)
cIMT is an ultrasonographic measurement of the thickness of intima-media of the common carotid artery. High-resolution B-mode ultrasound images of the right common carotid artery (CCA) were obtained from each patient using previously described techniques.12–15 Centralized reading services were provided by the University of Southern California Atherosclerosis Research Unit Core Imaging and Reading Center. A single reader measured the intima-media thickness of the far wall of the distal common carotid artery along a 1-cm length just distal to the carotid artery bulb with automated computerized edge detection.
Statistical Analysis
Differences in clinical and laboratory characteristics between subjects without and with albuminuria were compared using non-parametric Wilcoxon rank test for continuous variables and Chi-squared test for categorical variables. Albuminuria was characterized as both a categorical and a continuous variable. Categorically, albuminuria was defined as ACR of more than or equal to 30 mg/g versus less than 30 mg/g. As a continuous variable, albuminuria including measures below 30 mg/g was normally distributed. The cIMT was logarithmically transformed due to a skewed distribution. Multivariable linear regression analysis was employed to relate cIMT with albuminuria. Multivariable linear regression was adjusted for covariates found to be significant on univariate analysis. A two-sided probability of P < .05 was used to determine statistical significance. Statistical analyses were performed using the JMP statistical program (SAS Institute Inc., Cary, NC).
Results
Demographic and baseline clinical characteristics of the 111 participants are detailed in Table 1. The majority of participants were men (86%) and Caucasian (56%) with a median age of 52 years (Q1,Q3: 46,57). Baseline renal function with glomerular filtration rate was 81.9 ml/min (Q1,Q3: 72.8,93.5) by MDRD (Modification of Diet in Renal Disease) formula and was 85.4 ml/min (Q1,Q3: 73.6,96.5) by CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) formula. Participants had a median CD4 count of 489 cells/mm3 (Q1,Q3: 341,638). The majority of participants had undetectable HIV viral load (85%). All participants were receiving highly-active antiretroviral therapy with 75% receiving a tenofovir-based regimen.
Table 1.
Demographic and baseline clinical characteristics of 111 participants. Continuous variables as listed a median (Q1, Q3).
| Baseline Characteristics | Values, N=111 |
| Age, years | 52 (46, 57) |
| Male gender, n (%) | 95 (86) |
| Ethnicity, n (%) | |
| Caucasian | 62 (56) |
| Asian and Pacific Islander | 23 (21) |
| African American | 5 (5) |
| Others | 21 (19) |
| Past Medical History | |
| History of diabetes, n (%) | 7 (6) |
| History of hypertension, n (%) | 37 (33) |
| History of dyslipidemia, n (%) | 55 (50) |
| Current Smoker, n (%) | 29 (26) |
| Framingham score | 0.07 (0.04, 0.10) |
| BMI, kilogram/m2 | 25.7 (23.7, 28.0) |
| Waist-hip ratio | 0.93 (0.89, 0.98) |
| GFR | |
| MDRD, ml/min | 81.9 (72.8, 93.5) |
| CKD-EPI, ml/min | 85.4 (73.6, 96.5) |
| Fasting Lipids | |
| Total cholesterol, mg/dl | 176 (151, 199) |
| HDL, mg/dl | 40 (32, 50) |
| LDL, mg/dl | 108 (83, 127) |
| Triglyceride, mg/dl | 122 (53, 168) |
| HbA1c, % | 5.5 (5.4, 5.7) |
| Fasting glucose, mg/dl | 104.5 (87.5, 135) |
| Fasting insulin, mg/dl | 15.3(4.2, 33.9) |
| HOMA-IR | 1.37 (0.80, 2.56) |
| HIV Laboratory Parameters | |
| Current CD4 count, cells/mm3 | 489 (341, 638) |
| Nadir CD4 count, cells/mm3 | 135 (29, 253) |
| Undetectable viral load, n (%) | 94 (85) |
| Currently Receiving ART | |
| Tenofovir-based regimen, n (%) | 83 (75) |
The rate of albuminuria in this study group was 18% (20/111), 16.2% (18/111) with microalbuminuria and 1.8% (2/111) with macroalbuminuria. There were significant differences in age, Framingham risk score, diastolic blood pressure, fasting insulin level, HOMA-IR (Homeostatic model assessment index of insulin resistance),16 and common cIMT between those individuals without and with albuminuria (Table 2). There were no differences in ethnicity, history of diabetes, history of hypertension, body mass index (BMI), systolic blood pressure, Glomerular filtration rate (GFR) by MDRD or CKD-EPI formula, lipid profile, CD4 count, and proportion of subjects receiving a tenofovir-based antiretroviral regimen between groups.
Table 2.
Comparison of HIV-seropositive participants with and without albuminuria. Continuous variables as listed a median (Q1, Q3).
| Characteristics | Patients with Albuminuria (n=20) | Patients without Albuminuria (n=91) | P-value |
| Age, years | 57 (49, 62) | 51 (46, 56) | .01 |
| Male gender, n (%) | 18 (90) | 77 (85) | .52 |
| Ethnicity, n (%) | |||
| Caucasian | 13 (65) | 49 (54) | .19 |
| Asian and Pacific Islander | 3 (15) | 21 (23) | |
| African American | 1 (5) | 4 (4) | |
| Others | 3 (15) | 17 (19) | |
| Past Medical History | |||
| History of diabetes, n (%) | 3 (15) | 4 (4) | .11 |
| History of hypertension, n (%) | 10 (50) | 27 (30) | .11 |
| History of dyslipidemia, n (%) | 12 (60) | 43 (47) | .33 |
| Current Smoker, n (%) | 6 (30) | 23 (26) | .78 |
| Framingham Score | 0.10 (0.08, 0.16) | 0.07 (0.04, 0.10) | .002 |
| BMI, Kilograms/m2 | 25.9 (22.4, 28.4) | 25.8 (23.9, 27.9) | .86 |
| Waist-hip Ratio | 0.96 (0.91, 0.99) | 0.92 (0.89, 0.97) | .07 |
| Systolic Blood Pressure, mmHg | 126 (120, 140) | 129 (112, 129) | .05 |
| Diastolic Blood Pressure, mmHg | 83.8 (75.3, 83.8) | 74 (68, 80) | .01 |
| GFR | |||
| MDRD, ml/min | 71.41 (65.92, 95.96) | 83.40 (74.42, 93.40) | .10 |
| CKD-EPI, ml/min | 72.82 (65.91, 99.57) | 86.76 (76.24, 96.49) | .08 |
| Lipids | |||
| Total cholesterol, mg/dl | 184 (144.3, 260) | 175 (153, 195) | .22 |
| HDL, mg/dl | 36 (32, 53) | 41 (32, 50) | .66 |
| LDL, mg/dl | 119 (73.8, 169) | 107 (84, 125) | .44 |
| Triglyceride, mg/dl | 139 (94, 198.8) | 116 (82, 166) | .21 |
| HbA1c, % | 5.7 (5.4, 5.9) | 5.5 (5.3, 5.7) | .43 |
| Fasting glucose, mg/dl | 91.5 (82.5, 100.3) | 88 (81, 94) | .25 |
| Fasting insulin, mg/dl | 10.1 (5.1, 14.8) | 5.9 (3.7, 10.1) | .02 |
| HOMA-IR | 2.54 (0.97, 3.47) | 1.19 (0.79, 2.19) | .02 |
| HIV Laboratory Parameters | |||
| Current CD4 count, cells/mm* | 470 (394.3, 545.8) | 502 (333, 660) | .57 |
| Nadir CD4 count, cells/mm* | 92 (35.3, 193.8) | 160 (27, 266) | .40 |
| Undetectable viral load, n (%) | 17 (85) | 77 (84.6) | .96 |
| HIV viral load, copies/mm* | 69 (53, 77) | 59 (50, 282) | .94 |
| Currently on ART | |||
| Tenofovir-based regimen, n (%) | 17 (85) | 66 (73) | .39 |
| cIMT, mm | 0.83 (0.70, 0.91) | 0.70 (0.65, 0.81) | .04 |
HIV viral load among participants with a detectable HIV viral load
When albuminuria was analyzed as a continuous variable, age, fasting insulin, diastolic blood pressure, HIV-RNA viral load, and albuminuria all significantly correlated with log (cIMT) by univariate analysis. The correlation between the amount of albuminuria and log (cIMT) remained significant even after adjustment for age, gender, ethnicity, current smoking status, diabetes mellitus, CD4 count, HIV-RNA viral load, fasting insulin, and diastolic blood pressure (Table 3).
Table 3.
Multivariable regression model of common carotid intima-media thickness*
| Variable | β | SE | P-value |
| Age, years | 0.000990 | 0.001061 | .3540 |
| Gender, female | − 0.013163 | 0.011208 | .2445 |
| Ethnicity | 0.003819 | 0.002743 | .1684 |
| Smoking status, current | 0.006758 | 0.009304 | .4702 |
| Diabetes | 0.011243 | 0.024816 | .6520 |
| CD4 count, cells/mm3 | 0.000602 | 0.000731 | .4138 |
| HIV-RNA viral load, copies/ml | 0.000034 | 0.000011 | .0027 |
| Fasting insulin, mg/dl | − 0.000408 | 0.000469 | .3874 |
| Diastolic blood pressure, mmHg | 0.000671 | 0.000934 | .4753 |
| Albuminuria, mg/g | 0.000092 | 0.000045 | .0451 |
Dependent variable was log (cIMT)
Discussion
In diabetic patients, microalbuminuria is recognized as one of the earliest indicators for diabetic nephropathy. The association between microalbuminuria and cardiovascular disease is also well recognized in the general population. Microalbuminuria is a key indicator of a need for intensified treatment with angiotensin converting enzyme inhibitor or angiotensin II receptor antagonist. In HIV-seropositive individuals, the data on albuminuria and risk for cardiovascular disease is limited and not well understood. Previous studies suggested higher rate of cardiovascular disease and higher rate of albuminuria in this population. Among a cohort of women with HIV infection, 39% of African-American and 25% of Caucasian women had clinically significant albuminuria.17 These levels of albuminuria have subsequently been demonstrated to be associated with poorer outcomes including an increased risk of hospitalization and mortality.18,19 From our study, we found the rate of albuminuria among HIV-seropositive individuals receiving stable antiretroviral therapy was 18%.
Several hypotheses have been proposed to explain mechanisms that can cause an increase in albuminuria in the HIV-seropositive population. Albuminuria has been hypothesized to be due to HIV directly damaging the glomeruli causing HIV nephropathy (HIVAN), to deposition of immune complexes generated as an immune response to the HIV, to opportunistic infections that lead to glomerular damage,20 and/or to the side effects of highly-active antiretroviral therapy that may directly affect the kidneys such as tenofovir or cause higher rates of hyperlipidemia and an increased tendency towards atherosclerotic change.
Our study found associations between albuminuria and markers of subclinical atherosclerosis and CVD such as Framingham risk score, HOMA-IR, and cIMT in our HIV-seropositive participants. This data also supports the finding from previous studies that found associations between albuminuria in HIV-seropositive individuals and insulin resistance, and elevated systolic blood pressure.4 Our study is limited by its cross-sectional nature and lack of a HIV-seronegative control. Our results demonstrate an association between albuminuria and clinical and laboratory markers of subclinical atherosclerosis, while correlation indicates that future prospective studies relating albuminuria and CVD clinical outcomes are an important area for future investigation.
Conclusion
Albuminuria is prevalent among HIV-infected patients receiving stable antiretroviral therapy. It is significantly related to cardiovascular and metabolic parameters among HIV-infected patients receiving stable antiretroviral therapy. As albuminuria increases morbidity and mortality, routine urine albumin assessments as part of HIV care, particularly among patients with CVD risk factors, are warranted.
Acknowledgement
We thank our study participants and community physicians for their roles in this study.
Conflict of Interest
The authors report no conflict of interest.
Disclosure Statement
Dr. Cecilia M. Shikuma has received research support from NIH (U54MD007584, U54NS43049, P20RR011091, and R01HL095135), Pfizer, Merck, and Gilead Pharmaceuticals, and has served on an advisory board for Glaxo Smith Kline.
Dr. Beau K. Nakamoto has received research support from NIH (U54MD007584).
Dr. Kalpana J. Kallianpur has received research support from NIH (U54MD007584, U19MH081835, U54MD007584).
Dr. Dominic C. Chow has received research support from NIH (K23HL088981).
No other authors reported any financial disclosures.
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