Skip Navigation LinksHome > January 28, 2014 - Volume 28 - Issue 3 > HIV therapy, metabolic and cardiovascular health are associa...
AIDS:
doi: 10.1097/QAD.0000000000000094
Clinical Science

HIV therapy, metabolic and cardiovascular health are associated with glomerular hyperfiltration among men with and without HIV infection

Ng, Derek K.a; Jacobson, Lisa P.a; Brown, Todd T.a,b; Palella, Frank J. Jrc; Martinson, Jeremy J.d; Bolan, Roberte; Miller, Edgar R. 3rda,b,f; Schwartz, George J.g; Abraham, Alison G.a; Estrella, Michelle M.b

Free Access
Article Outline
Collapse Box

Author Information

aDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health

bDepartment of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland

cDepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois

dGraduate School of Public Health, Department of Infectious Diseases and Microbiology, University of Pittsburgh, Pittsburgh, Pennsylvania

eLos Angeles Gay and Lesbian Center, Los Angeles, California

fWelch Center for Prevention, Epidemiology and Clinical Research, Baltimore, Maryland

gDepartment of Pediatrics, University of Rochester Medical Center, Rochester, New York, USA.

Correspondence to Derek K. Ng, ScM, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room E7011, Baltimore, MD 21205, USA. E-mail: dng@jhsph.edu

Received 5 June, 2013

Revised 27 August, 2013

Accepted 19 September, 2013

Collapse Box

Abstract

Objective: Diabetes and hypertension, common conditions in antiretroviral-treated HIV-infected individuals, are associated with glomerular hyperfiltration, which precedes the onset of proteinuria and accelerated kidney function decline. In the Multicenter AIDS Cohort Study, we examined the extent to which hyperfiltration is present and associated with metabolic, cardiovascular, HIV and treatment risk factors among HIV-infected men.

Design: Cross-sectional cohort using direct measurement of glomerular filtration rate by iohexol plasma clearance for 367 HIV-infected men and 241 HIV-uninfected men who were free of chronic kidney disease.

Methods: Hyperfiltration was defined as glomerular filtration rate above 140−1 ml/min per 1.73 m2 per year over age 40. Multivariate logistic regression was used to estimate the odds ratios (ORs) of prevalent hyperfiltration for metabolic, cardiovascular, HIV and cumulative antiretroviral exposure factors.

Results: Among individuals without chronic kidney disease, the prevalence of hyperfiltration was higher for HIV-infected participants (25%) compared to uninfected participants (17%; P = 0.01). After adjustment, HIV infection remained associated with hyperfiltration [OR 1.70, 95% confidence interval (CI) 1.11–2.61] and modified the association between diabetes and hyperfiltration, such that the association among HIV-uninfected men (OR 2.56, 95% CI 1.33–5.54) was not observed among HIV-infected men (OR 1.19, 95% CI 0.69–2.05). These associations were independent of known risk factors for hyperfiltration. Indicators of hyperglycemia and hypertension were also associated with hyperfiltration as was cumulative zidovudine exposure.

Conclusion: Hyperfiltration, a potential modifiable predictor of kidney disease progression, is significantly higher among antiretroviral-treated HIV-infected men. Furthermore, HIV-infection nullifies the association of diabetes and hyperfiltration present in HIV-uninfected men.

Back to Top | Article Outline

Introduction

The use of HAART has resulted in marked reductions in AIDS-related mortality and opportunistic disease among HIV-infected persons [1,2], yet these patients are now at increased risk of death as a result of chronic noninfectious age-related comorbid conditions, including chronic kidney disease (CKD) [3,4]. In the context of HAART-treated HIV infection and aging, CKD often results from metabolic abnormalities, such as diabetes mellitus and hypertension [5,6]. In the general population, proteinuria and impaired kidney function related to these conditions can be preceded by glomerular hyperfiltration, in which the glomerular filtration rate (GFR) increases to above normal levels [7–11], due to compensatory hemodynamic alterations within the kidney. Metabolic dysfunction characterized by impaired fasting glucose [12], diabetes mellitus [9,11,13], hypertension, and obesity [9] – conditions for which HIV-infected persons receiving HAART are at higher risk [14,15] – has also been linked to hyperfiltration.

Because hyperfiltration is an early indicator of kidney dysfunction and is potentially reversible by aggressive antihypertensive therapy or dietary changes [7,13], establishing its relevance for HIV clinical care is important. However, whether and to what extent glomerular hyperfiltration is present among HIV-infected persons receiving HAART, and whether metabolic and cardiovascular risk factors and HIV-related factors are associated with hyperfiltration in this population are unclear. To address these questions, we used directly measured GFR data from a representative subsample of the Multicenter AIDS Cohort Study (MACS), a cohort of men with or at risk for HIV infection. We aimed to: determine the prevalence of hyperfiltration; investigate the association of HIV infection and metabolic, cardiovascular and behavioral factors with hyperfiltration; and investigate HIV disease severity and HAART as potential risk factors for hyperfiltration.

Back to Top | Article Outline

Methods

Study population

The MACS is a prospective observational cohort study of the natural and treated histories of HIV infection among 6972 homosexual and bisexual men enrolled in four United States metropolitan areas from 1984 to 2003. Details of the study design have been previously described [16]. In brief, demographics, medical history, and clinical characteristics were collected semi-annually. Standard protocols included physical examinations, blood pressure measurements, blood and urine collection, and self-administered interviews.

The current study included men who underwent direct iohexol-based GFR measurement (iGFR) [17–19] between August 2008 and January 2011. These participants were a subsample of the full MACS cohort [19], chosen by random selection using a ratio of approximately two HIV-infected participants for each HIV-uninfected participant. Additionally, all participants co-infected with the hepatitis C virus (HCV), as determined by presence of serum anti-HCV antibody or plasma HCV RNA [20], were eligible for iGFR measurement. Participants who received renal replacement therapy, were diagnosed with cancer in the preceding 3 years, or were unable to complete data collection due to contrast allergy were excluded. A central laboratory measured blood glucose, insulin, lipid panels (Heinz, Pittsburgh, Pennsylvania, USA), and HCV antibodies (Tricore, Albuquerque, New Mexico, USA). HIV-1 infection was defined by positive serum ELISA with confirmatory Western blot. Plasma HIV RNA levels were ascertained by the Roche Amplicor assay (Hoffman-LaRoche, Nutley, New Jersey, USA) sensitive to 50 copies/ml, and CD4+ lymphocyte counts/ml were measured using standardized flow cytometry [21]. This study was approved by Institutional Review Boards at all participating sites.

Back to Top | Article Outline
Dependent variables

The primary outcome was hyperfiltration, defined from GFR measured by the plasma disappearance of iohexol using a 2-compartment 4-point model standardized to a body surface area of 1.73 m2 as previously described [19]. To address the expected decline in GFR associated with age, we defined hyperfiltration as iGFR at least 140 ml/min per 1.73 m2 for men 40 years and younger and subtracted 1 ml/min per 1.73 m2 for each year over age 40 [22]. Among men with GFR below this hyperfiltration threshold, we excluded men with GFR below 60 ml/min per 1.73 m2 or with proteinuria [urine protein-to-creatinine ratio (UPCR) >200 mg/g] [7,23,24]. The remaining men without markedly impaired GFR (i.e. ‘normofiltration’) served as the comparison group. UPCR was measured at each site, and was based on the mean of three measurements for 1 year prior to iGFR measurement. Men with missing UPCR data were excluded (n = 8).

Back to Top | Article Outline
Independent variables

Metabolic variables included BMI (kg/m2, obesity defined as ≥30 kg/m2); serum high-density lipoprotein (HDL) and non-HDL cholesterol; dyslipidemia [fasting total cholesterol ≥200 mg/dl, low-density lipoprotein (LDL) ≥130 mg/dl, HDL <40 mg/dl, triglycerides ≥150 mg/dl, or use of lipid-lowering medications with self-reported/clinical diagnosis of dyslipidemia]; fasting glucose level; hemoglobin A1c (HbA1c); insulin resistance [Homeostatic Model Assessment – Insulin Resistance (HOMA-IR]; diabetes (HbA1c ≥6.5%, fasting glucose >126 mg/dl, or diagnosis of diabetes with use of medications); and metabolic syndrome [25]. High fasting glucose (>100 mg/dl) and HbA1c (≥6.5%) were also used as indicators of hyperglycemia [12]. Cardiovascular variables were SBP and DBP, uncontrolled hypertension (SBP ≥140 or DBP ≥90 mmHg), and history of hypertension (uncontrolled hypertension or diagnosis of hypertension with use of antihypertensive medications). Behavioral variables included current smoking status at the time of iGFR measurement, stimulant use (cocaine, amphetamines, or methamphetamines), and other drug use (marijuana and inhalant nitrates). For all continuous variables, the mean of available data from the visits in 1 year prior to and including the iGFR visit was used as a summary level. For binary variables such as diabetes, the presence of these conditions was determined by at least two occurrences in the visits prior to and including the iGFR measurement in a 1-year period (i.e. two out of three measurements, about 6 months apart). Since variables within metabolic and cardiovascular domains were expected to be highly correlated, we did not simultaneously include them in multivariate analyses.

To investigate the association of HIV and HAART on hyperfiltration, we restricted the sample to men receiving HAART at the time of iGFR measurement, as few HIV-infected participants were HAART-naive (n = 4). Exposure to antiretroviral therapy (ART) was characterized by: years from any ART initiation to the time of iGFR measurement (i.e. time since ART initiation, either monotherapy, combination therapy, or potent therapy with three or more agents); years from HAART initiation (i.e. a combination regimen of three or more ART agents) to the time of iGFR measurement; and cumulative exposure [i.e. person-years of use, for each subclass of ARTs: non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors, and nucleoside reverse transcriptase inhibitors (NRTIs)]. Cumulative exposure was also calculated for specific antiretroviral drugs that have been associated with either metabolic or cardiovascular derangements (such as hyperglycemia or dyslipidemia, associated with thymidine-analog reverse transcriptase inhibitor medications used in earlier HAART regimens [15,26]) or for drugs that are considered nephrotoxic [27]. The specific medications evaluated were indinavir (IDV), ritonavir (RTV), atazanavir (ATV) for protease inhibitors; and zidovudine (ZDV), abacavir (ABC), tenofovir (TDF), and stavudine (d4T) for NRTIs. If the participant discontinued ART use between study visits, exposure time was calculated as half the time between study visits. For IDV, RTV, ATV, ABC, and d4T, in which more than 50% of participants were unexposed, these variables were classified by ‘any exposure’. As a sensitivity analysis, we also categorized ART-exposed years as no exposure and by 4-year intervals.

Back to Top | Article Outline
Statistical analyses

The one-way Wilcoxon rank-sum test and Fisher's exact test were used to compare differences by filtration status within each HIV infection group with the hypothesis that comorbidities were more common among participants with hyperfiltration. Multivariate logistic regression was used to estimate odds ratios (ORs) of prevalent hyperfiltration. Confounders were identified a priori based on biological plausibility and published research, and included age, race, BMI, angiotensin-converting enzyme inhibitor (ACEi)/angiotensin receptor blocker (ARB) use, stimulant drug use, current smoking status [28,29], and HIV disease severity (as measured by CD4+ cell counts) [30]. Although age after 40 years was part of the hyperfiltration definition, age was also included in multivariate models to control for potential residual confounding since it is strongly related to renal function. In investigating the effect of ART exposure on the odds of prevalent hyperfiltration, we additionally adjusted for time since any ART initiation and ART use in the pre-HAART era (i.e. prior to 1 July 1996 [31]). As weight gain may be in the causal pathway between ART exposure and hyperfiltration, we also performed a sensitivity analysis excluding BMI as a confounder, and instead used height as an indicator of body size.

Interactions between HIV status and categorical putative hyperfiltration risk factors (diabetes, hypertension, and metabolic syndrome) were assessed and included if significant. Multiple imputation (10 repetitions) was used to complete missing data for unbiased estimates of effects, and the corresponding 95% confidence intervals (95% CIs) are presented. A dataset comprising complete cases was analyzed as a sensitivity analysis and yielded similar inferences as the multiple imputation approach. Statistical significance was assessed at the 0.05 level. All statistical analyses were completed using SAS 9.2 (SAS Institute, Cary, North Carolina, USA).

Back to Top | Article Outline

Results

Cohort characteristics

Of the 741 men who underwent an iGFR study, 720 (97%) had a valid iGFR and were included in our analysis (260 HIV-uninfected and 460 HIV-infected men). Of the HIV-infected men, 456 (99%) had initiated ART prior to the iGFR study. The HIV-uninfected group was older, with a median age of 53 years, vs. 50 years in the infected group. The median iGFR was 105 and 109 ml/min per 1.73 m2 for HIV-uninfected and infected men, respectively. About 4% (n = 10) of HIV-uninfected and 3% (n = 16) of HIV-infected men had iGFR below 60 ml/min per 1.73 m2; these men were excluded from further analysis. We also excluded 6 HIV-uninfected and 69 HIV-infected men with other evidence of CKD: near-normal iGFR (i.e. 60 ml/min per 1.73 m2 < iGFR < hyperfiltration threshold) and proteinuria [24], resulting in an analytic sample of 608 men free of CKD. Among HIV-uninfected men with hyperfiltration, 2.6% had proteinuria. In contrast, among HIV-infected men with hyperfiltration, 10% had proteinuria.

Back to Top | Article Outline
Metabolic, cardiovascular and behavioral characteristics by HIV status and filtration status

Table 1 compares participant demographic and clinical characteristics by HIV serostatus and filtration status, comprising 241 HIV-uninfected participants (17% with hyperfiltration) and 367 HIV-infected participants (25% with hyperfiltration), without CKD (17 vs. 25%; P = 0.01). Among HIV-uninfected participants, men with hyperfiltration had a higher proportion of diabetes (38 vs. 21%) compared to men with normofiltration; however, both groups had similar proportions of individuals with obesity, hypertension, and metabolic syndrome.

Table 1
Table 1
Image Tools

Among HIV-infected men, those with hyperfiltration were older (median 51 vs. 49 years) than men with normofiltration. HIV-infected participants with hyperfiltration also had higher fasting blood glucose (105 vs. 100 mg/dl) and HOMA-IR levels (3.9 vs. 3.2), and were more likely to be obese (26 vs. 15%). Men with hyperfiltration were more likely to have a history of hypertension (50 vs. 37%) and uncontrolled hypertension (26 vs. 15%), but the same proportion reported ACEi/ARB use as those with normofiltration (14%). Stimulant use was more common among men with hyperfiltration (22 vs. 14%), but current smoking and other drug use did not differ between groups.

Table 2 presents ORs of prevalent hyperfiltration associated with HIV infection adjusted for age, race, stimulant use, current smoking status, ACEi/ARB use, and BMI. HIV-infected participants were 1.70 times more likely to exhibit hyperfiltration than HIV-uninfected participants (95% CI 1.11–2.61). The effect of stimulant use was similar and borderline significant (OR 1.71, 95% CI 0.99–2.95). In contrast, race, age, and current ACEi/ARB use were not associated with odds of hyperfiltration.

Table 2
Table 2
Image Tools

Figure 1 presents results when extending the above model to include the main effects for metabolic and cardiovascular factors. The effect of HIV remained consistently strong across all models, with ORs ranging from 1.63 to 1.80. Indicators of hyperglycemia were associated with higher odds of prevalent hyperfiltration (HbA1c ≥6.5%, OR 3.03, 95% CI 1.25–7.38; fasting glucose >100 mg/dl, OR 1.58, 95% CI 1.03–2.42). The prevalence of hyperfiltration was similar by metabolic syndrome status. The association of diabetes with hyperfiltration differed by HIV status (P = 0.05 for interaction). Specifically, diabetes was associated with hyperfiltration among HIV-uninfected individuals (OR 2.56, 95% CI 1.33–5.54), but not among HIV-infected individuals (OR 1.19, 95% CI 0.69–2.05; not shown). However, compared to HIV-uninfected individuals without diabetes (reference in this model represented by the vertical line at 1), HIV-infected individuals (regardless of diabetes status) were more than twice as likely to have hyperfiltration; this comparison was statistically significant. Uncontrolled hypertension was associated with higher odds of hyperfiltration (OR 1.61, 95% CI 0.99–2.60) as was history of hypertension (OR 2.10, 95% CI 1.33–3.33). The latter association was similar by HIV status (P = 0.91 for interaction). The inclusion of hypertension in the model assessing the interaction of diabetes and HIV on hyperfiltration yielded similar results (model 7; Fig. 1).

Fig. 1
Fig. 1
Image Tools
Back to Top | Article Outline
HIV and antiretroviral therapy characteristics associated with hyperfiltration

Table 3 displays HIV and treatment-related factors among HIV-infected participants stratified by filtration status. Indices of HIV disease severity were similar by hyperfiltration status, whereas some ART factors differed between the two groups. The hyperfiltration and normofiltration groups had similar median times since any ART initiation, but the median cumulative HAART use was longer in the hyperfiltration group than the normofiltration group (median time was 7.3 vs. 6.3 years). Cumulative protease inhibitor and NNRTI years were similar between the two groups, whereas men with hyperfiltration had higher median cumulative NRTI years (19.7 vs. 17.4). This difference in cumulative ART use was primarily accounted for by ZDV use (median 4.1 vs. 2.0 years).

Table 3
Table 3
Image Tools

The association of HIV disease severity indicators and therapy with hyperfiltration, adjusted for age, race, current and nadir CD4+ cell count, time since first ART exposure and ART use in the pre-HAART era, BMI, ACEi/ARB use, current smoking status, and stimulant drug use, are presented by the adjusted odds of prevalent hyperfiltration ratios in the right-most column of Table 3. None of the indicators of HIV disease stage were associated with hyperfiltration, including nadir/current CD4+ cell count below 350/μl, and detectable plasma HIV RNA level. Indicators of ART exposure were not associated with hyperfiltration, with the exception of ZDV. For a 5-year increase in ZDV exposure, the odds of hyperfiltration significantly increased by 53% (95% CI 1.12–2.09). A sensitivity analysis of cumulative ART exposure by 4-year categories yielded similar results. For ZDV specifically and with participants with no exposure to ZDV as the reference group, there was no association for less than 4 years of exposure (OR 0.85, 95% CI 0.42–1.70). However, the OR of prevalent hyperfiltration was 1.88 for 4–8 years of exposure (95% CI 0.93–3.81) and was 2.11 for more than 8 years of ZDV exposure (95% CI 0.99–4.49).

Back to Top | Article Outline

Discussion

In this MACS GFR substudy, the overall prevalence of measured glomerular hyperfiltration for men without CKD was significantly higher among those infected with HIV compared to uninfected men: 25 and 17%, respectively. From this group, men with HIV had 1.7 times higher odds of hyperfiltration than uninfected men, independent of demographic, metabolic, and cardiovascular factors. We further confirmed the findings of previous studies which identified hyperglycemia [12], diabetes mellitus [32], and hypertension [9], as independently associated with hyperfiltration in this unique population, although the effect of diabetes was mainly observed among HIV-uninfected men. Our findings suggest that hyperfiltration is common in this cohort and may be an important clinical consideration as an early indicator of kidney dysfunction among HIV-infected persons.

HIV infects renal epithelial cells [33] and disturbs podocyte structure and function [34]. The latter appears to involve up-regulation of the renin–angiotensin system [35], which is central to the pathophysiology of hyperfiltration. Alternatively, HIV itself, ART, and associated metabolic derangements may contribute to hyperfiltration via perturbations in insulin-like growth factor [36], leading to visceral adiposity and abnormal glucose metabolism [14,37]. In our cohort, since nearly all HIV-infected men were also being treated with ART, we were unable to discern whether the elevated prevalence of hyperfiltration was due to HIV infection alone, ART use, or both.

In the general population, previous studies have found that elevated blood glucose and HbA1c are associated with higher odds of hyperfiltration, with ORs ranging from 1.3 [9] and 1.6–2.2 [12,38], as well as hypertension (OR 1.8) [9,39]. Studies also suggest hyperfiltration precedes the onset of albuminuria and kidney function decline, and is therefore an important marker of future CKD [23]. Indeed, among diabetic individuals, hyperfiltration has been associated with increased risk of diabetic nephropathy and CKD progression [32]. Among individuals with hypertension, the proportion of those with persistent hyperfiltration and those with hyperfiltration progressing to normofiltration who developed microalbuminuria was 16 and 36%, respectively, compared to only 5% of those who never experienced hyperfiltration, over a median follow-up of 8.5 years [40].

Our results show that HIV is also associated with higher odds of hyperfiltration, and that HIV infection modifies the diabetes–hyperfiltration relationship: among HIV-uninfected individuals, diabetes was strongly associated with hyperfiltration; however, among HIV-infected individuals, there was no further association between diabetes and hyperfiltration, but these individuals were at higher risk for hyperfiltration than their HIV-uninfected peers. This association was observed univariately (Table 1) and in adjusted models (Fig. 1). The effect of diabetes on hyperfiltration may be secondary to the dominant effect of HIV and its treatment.

Two recent publications documented the association of diabetes and HIV on decreased renal function [41] and end-stage renal disease (ESRD) [42]. In these studies, the presence of either HIV or diabetes was associated with a higher risk of diminished GFR [41] and ESRD among African Americans [42], a relationship which was similar to the increased odds of hyperfiltration in our study. In contrast to our findings, however, those who had both diabetes and HIV were at even greater risk of incidence of low eGFR below 45 ml/min compared to those with HIV or diabetes only [41]. Similar to the results for ESRD [42], we did not find an additive effect of HIV and diabetes on hyperfiltration. These results suggest that hyperfiltration may mediate some portion of the effects of diabetes and HIV on ESRD, but future research is needed to investigate this relationship.

Although not all associations were statistically significant, we consistently found increased odds of hyperfiltration in association with most ART factors, suggesting that prolonged ART exposure may increase the risk of hyperfiltration. Whereas this relationship was strongest for ZDV use (OR 1.53 per 5-year increase in ZDV exposure), increased ZDV exposure may be a marker for longer HIV infection duration, and longer HIV infection is the risk factor for hyperfiltration. Alternatively, ZDV use may be a proxy for historically poorer HIV suppression, since it was commonly used in the pre-HAART era; this was a possibility we could not explore. Despite the diminished use of ZDV, ZDV exposure may be clinically relevant for identifying patients with hyperfiltration since there are HIV-infected patients in care with prior or current exposure to ZDV. Indeed, there may also be a threshold effect of ZDV on hyperfiltration that is more common with chronic use, or the effect may manifest later among persons with a longer duration of HIV infection. The protective, but nonsignificant trend, associated with TDF exposure was also notable since TDF is nephrotoxic. Individuals at risk for kidney disease may have been less likely to be prescribed TDF than others (i.e. channeling bias). Alternatively, increased TDF exposure may have caused pathologic nephron loss, potentially counteracting any hyperfiltration effects due to other disease or therapy processes. Third, TDF renal toxicity is thought to be directed towards the proximal tubule rather than the glomerulus; therefore, TDF-treated individuals may not necessarily be at higher risk of glomerular hyperfiltration. Lastly, better virologic control and HIV management with TDF may reduce the risk of hyperfiltration.

Of note, in multivariate analysis, stimulant drug use was associated with increased odds of hyperfiltration (OR 1.71). The relationship between stimulant drug use and hyperfiltration has not been well characterized in the literature, but may be related to the effects of stimulant drug use on the sympathetic nervous system and on the regulation of beta cell function and dopamine (potentially affecting glomerular perfusion). Studies have suggested that cocaine and other stimulants may perturb insulin secretion and lipid/glucose homeostasis [43,44]. The observed association between stimulants and hyperfiltration may be related to a shared relationship with hormonal dysregulation, hyperglycemia, and renal function. Alternatively, stimulant use may be an indicator for socioeconomic or lifestyle factors that adversely affect disease management, adherence to medications, or overall general health, and thereby increase the risk of hyperfiltration.

Whereas ACEi/ARB use was not significantly associated with decreased odds of hyperfiltration, the directionality of effect was consistent with reduced GFR due to preferential dilation of the efferent arteriole (OR 0.77). In contrast, we were unable to explain the lower prevalence of hyperfiltration among black participants (OR 0.66), although this effect was also nonsignificant. This was surprising given the higher risk of diabetes, diabetic nephropathy, and ESRD in the black population, and the documented higher risk of hyperfiltration among blacks with hypertension [45].

One limitation of this study is that we are unable to causally link identified metabolic, cardiovascular, and HIV infection factors with hyperfiltration. However, the physiologic framework of the HIV renal reservoir, the influence of ART on metabolic and cardiovascular health, as well as previous literature, suggest that metabolic and cardiovascular changes likely initiate hyperfiltration [10,32]. Furthermore, since previous direct measurements of GFR had not been obtained, we were unable to determine the duration of hyperfiltration, although a second iGFR measurement will be obtained in these men for future analyses. Whereas hyperfiltration is a known precursor to kidney damage, the duration of hyperfiltration necessary to precipitate kidney function decline is unclear. An early study among Pima Indians demonstrated GFR decline over a 4-year period after hyperfiltration was identified [46]. Future longitudinal data of iGFR in our cohort are needed to further understand risk factors for and consequences of hyperfiltration. An additional limitation is that the study sample was exclusively men, and whether these inferences extend to women is unknown.

In summary, we found that among men without CKD, HIV infection was associated with a higher prevalence of hyperfiltration compared to being uninfected. A major strength of this study was the use of directly measured GFR by iohexol to detect hyperfiltration, a better and more reliable method than estimating equations, particularly for high GFR levels [10,47]. Noninfectious metabolic conditions found commonly in aging HIV-infected individuals were associated with hyperfiltration. Additionally, among HIV-infected men, increased exposure to ART, particularly ZDV, was associated with higher odds of hyperfiltration. Since poor metabolic and cardiovascular health are known risk factors for glomerular hyperfiltration, which is an early clinical marker of kidney damage, evaluation and aggressive treatment of these risk factors [9,10] should be an important priority for health management in HIV-infected as well as non-HIV-infected populations.

Back to Top | Article Outline

Acknowledgements

The Multicenter AIDS Cohort Study (MACS) includes the following: Baltimore: The Johns Hopkins University Bloomberg School of Public Health: Joseph B. Margolick (PI), Barbara Crain, Adrian Dobs, Homayoon Farzadegan, Joel Gallant, Lisette Johnson-Hill, Michael W. Plankey, Ned Sacktor, Ola Selnes, James Shepard, Chloe Thio. Chicago: Feinberg School of Medicine, Northwestern University, and Cook County Bureau of Health Services: Steven M. Wolinsky (PI), John P. Phair, Sheila Badri, Maurice O’Gorman, David Ostrow, Frank Palella, Ann Ragin. Los Angeles: University of California, UCLA Schools of Public Health and Medicine: Roger Detels (PI), Otoniel Martínez-Maza (Co-PI), Aaron Aronow, Robert Bolan, Elizabeth Breen, Anthony Butch, Beth Jamieson, Eric N. Miller, John Oishi, Harry Vinters, Dorothy Wiley, Mallory Witt, Otto Yang, Stephen Young, Zuo Feng Zhang. Pittsburgh: University of Pittsburgh, Graduate School of Public Health: Charles R. Rinaldo (PI), Lawrence A. Kingsley (Co-PI), James T. Becker, Ross D. Cranston, Jeremy J. Martinson, John W. Mellors, Anthony J. Silvestre, Ronald D. Stall. Data Coordinating Center: The Johns Hopkins University Bloomberg School of Public Health: Lisa P. Jacobson (PI), Alvaro Muñoz (Co-PI), Alison Abraham, Keri Althoff, Christopher Cox, Gypsyamber D'Souza, Priya Duggal, Elizabeth Golub, Janet Schollenberger, Eric C. Seaberg, Sol Su, Pamela Surkan. NIH: National Institute of Allergy and Infectious Diseases: Robin E. Huebner; National Cancer Institute: Geraldina Dominguez. Website located at http://www.statepi.jhsph.edu/macs/macs.html. We are grateful to GE Healthcare, Amersham Division, for providing the MACS study with ioxehol (Omnipaque) for GFR measurements.

Author contributions: D.K.N., L.P.J. and M.M.E. had full access to all of the data used in the analysis. D.K.N., L.P.J., and M.M.E. take responsibility for the integrity of the data and the accuracy of the data analysis presented here.

MACS GFR Study concept and design: L.P.J., G.J.S., F.J.P.

Acquisition of MACS data: L.P.J., F.J.P., G.J.S.

Analysis and interpretation of data: D.K.N., L.P.J., M.M.E., T.T.B., A.G.A.

Drafting of the manuscript: D.K.N., L.P.J., M.M.E.

Critical revision of the manuscript for important intellectual content: D.K.N., L.J.P., T.T.B., F.J.P., J.J.M., R.B., E.R.M., G.J.S., A.G.A., M.M.E.

Statistical analysis: D.K.N., L.P.J.

MACS Study supervision: L.P.J., F.J.P.

Sources of support: National Institute of Allergy and Infectious Diseases and National Cancer Institute (UO1-AI-35042, UM1-AI-35043, UO1-AI-35039, UO1-AI-35040, UO1-AI-35041); the National Center for Advancing Translational Sciences (UL1TR000424); and the National Institutes of Health (1K23DK081317 to M.M.E.).

Back to Top | Article Outline
Conflicts of interest

The authors have no conflict of interests to declare.

Back to Top | Article Outline

References

1. Palella FJ Jr, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med 1998; 338:853–860.

2. Wada N, Jacobson LP, Cohen M, French A, Phair J, Muñoz A. Cause-specific life expectancies after 35 years of age for human immunodeficiency syndrome-infected and human immunodeficiency syndrome-negative individuals followed simultaneously in long-term cohort studies, 1984–2008. Am J Epidemiol 2013; 177:116–125.

3. Adih WK, Selik RM, Hu X. Trends in diseases reported on US death certificates that mentioned HIV infection, 1996–2006. J Int Assoc Physicians AIDS Care (Chic) 2011; 10:5–11.

4. Wyatt CM, Morgello S, Katz-Malamed R, Wei C, Klotman ME, Klotman PE, et al. The spectrum of kidney disease in patients with AIDS in the era of antiretroviral therapy. Kidney Int 2009; 75:428–434.

5. Brown TT, Cole SR, Li X, Kingsley LA, Palella FJ, Riddler SA, et al. Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study. Arch Intern Med 2005; 165:1179–1184.

6. George E, Lucas GM, Nadkarni GN, Fine DM, Moore R, Atta MG. Kidney function and the risk of cardiovascular events in HIV-1-infected patients. AIDS 2010; 24:387–394.

7. Brenner BM, Lawler EV, Mackenzie HS. The hyperfiltration theory: a paradigm shift in nephrology. Kidney Int 1996; 49:1774–1777.

8. Chaiken RL, Eckert-Norton M, Bard M, Banerji MA, Palmisano J, Sachimechi I, et al. Hyperfiltration in African-American patients with type 2 diabetes. Cross-sectional and longitudinal data. Diabetes Care 1998; 21:2129–2134.

9. Tomaszewski M, Charchar FJ, Maric C, McClure J, Crawford L, Grzeszczak W, et al. Glomerular hyperfiltration: a new marker of metabolic risk. Kidney Int 2007; 71:816–821.

10. Helal I, Fick-Brosnahan GM, Reed-Gitomer B, Schrier RW. Glomerular hyperfiltration: definitions, mechanisms and clinical implications. Nat Rev Nephrol 2012; 8:293–300.

11. Ruggenenti P, Porrini EL, Gaspari F, Motterlini N, Cannata A, Carrara F, et al. Glomerular hyperfiltration and renal disease progression in type 2 diabetes. Diabetes Care 2012; 35:2061–2068.

12. Melsom T, Mathisen UD, Ingebretsen OC, Jenssen TG, Njølstad I, Solbu MD, et al. Impaired fasting glucose is associated with renal hyperfiltration in the general population. Diabetes Care 2011; 34:1546–1551.

13. Okada R, Yasuda Y, Tsushita K, Wakai K, Hamajima N, Matsuo S. Glomerular hyperfiltration in prediabetes and prehypertension. Nephrol Dial Transplant 2012; 27:1821–1825.

14. Tebas P. Insulin resistance and diabetes mellitus associated with antiretroviral use in HIV-infected patients: pathogenesis, prevention, and treatment options. J Acquir Immune Defic Syndr 2008; 49:S86–S92.

15. Vu CN, Ruiz-Esponda R, Yang E, Chang E, Gillard B, Pownall HJ, et al. Altered relationship of plasma triglycerides to HDL cholesterol in patients with HIV/HAART-associated dyslipidemia: further evidence for a unique form of metabolic syndrome in HIV patients. Metab Clin Exp 2013; 62:1014–1020.

16. Kaslow RA, Ostrow DG, Detels R, Phair JP, Polk BF, Rinaldo CR Jr. The Multicenter AIDS Cohort Study: rationale, organization, and selected characteristics of the participants. Am J Epidemiol 1987; 126:310–318.

17. Schwartz GJ, Furth S, Cole SR, Warady B, Muñoz A. Glomerular filtration rate via plasma iohexol disappearance: pilot study for chronic kidney disease in children. Kidney Int 2006; 69:2070–2077.

18. Schwartz GJ, Abraham AG, Furth SL, Warady BA, Muñoz A. Optimizing iohexol plasma disappearance curves to measure the glomerular filtration rate in children with chronic kidney disease. Kidney Int 2010; 77:65–71.

19. Ng DKS, Schwartz GJ, Jacobson LP, Palella FJ, Margolick JB, Warady BA, et al. Universal GFR determination based on two time points during plasma iohexol disappearance. Kidney Int 2011; 80:423–430.

20. Caliendo AM, Valsamakis A, Zhou Y, Yen-Lieberman B, Andersen J, Young S, et al. Multilaboratory comparison of hepatitis C virus viral load assays. J Clin Microbiol 2006; 44:1726–1732.

21. Hultin LE, Menendez FA, Hultin PM, Jamieson BD, O’Gorman MRG, Borowski L, et al. Assessing immunophenotyping performance: proficiency-validation for adopting improved flow cytometry methods. Cytometry B Clin Cytom 2007; 72:249–255.

22. Premaratne E, Macisaac RJ, Tsalamandris C, Panagiotopoulos S, Smith T, Jerums G. Renal hyperfiltration in type 2 diabetes: effect of age-related decline in glomerular filtration rate. Diabetologia 2005; 48:2486–2493.

23. Palatini P. Glomerular hyperfiltration: a marker of early renal damage in prediabetes and prehypertension. Nephrol Dial Transplant 2012; 27:1708–1714.

24. National Kidney FoundationK/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39:S1–S266.

25. Mondy K, Overton ET, Grubb J, Tong S, Seyfried W, Powderly W, et al. Metabolic syndrome in HIV-infected patients from an urban, midwestern US outpatient population. Clin Infect Dis 2007; 44:726–734.

26. Curran A, Ribera E. From old to new nucleoside reverse transcriptase inhibitors: changes in body fat composition, metabolic parameters and mitochondrial toxicity after the switch from thymidine analogs to tenofovir or abacavir. Expert Opin Drug Saf 2011; 10:389–406.

27. Kalyesubula R, Perazella MA. Nephrotoxicity of HAART. AIDS Res Treat 2011; 2011:562790.

28. Crowe AV, Howse M, Bell GM, Henry JA. Substance abuse and the kidney. QJM 2000; 93:147–152.

29. Maeda I, Hayashi T, Sato KK, Koh H, Harita N, Nakamura Y, et al. Cigarette smoking and the association with glomerular hyperfiltration and proteinuria in healthy middle-aged men. Clin J Am Soc Nephrol 2011; 6:2462–2469.

30. Shoptaw S, Stall R, Bordon J, Kao U, Cox C, Li X, et al. Cumulative exposure to stimulants and immune function outcomes among HIV-positive and HIV-negative men in the Multicenter AIDS Cohort Study. Int J STD AIDS 2012; 23:576–580.

31. Mateen FJ, Shinohara RT, Carone M, Miller EN, McArthur JC, Jacobson LP, et al. Neurologic disorders incidence in HIV+ vs. HIV- men: Multicenter AIDS Cohort Study, 1996–2011. Neurology 2012; 79:1873–1880.

32. Jerums G, Premaratne E, Panagiotopoulos S, MacIsaac RJ. The clinical significance of hyperfiltration in diabetes. Diabetologia 2010; 53:2093–2104.

33. Bruggeman LA, Ross MD, Tanji N, Cara A, Dikman S, Gordon RE, et al. Renal epithelium is a previously unrecognized site of HIV-1 infection. J Am Soc Nephrol 2000; 11:2079–2087.

34. Lu T-C, He JC, Klotman PE. Podocytes in HIV-associated nephropathy. Nephron Clin Pract 2007; 106:c67–c71.

35. Chandel N, Sharma B, Husain M, Salhan D, Singh T, Rai P, et al. HIV compromises integrity of podocyte actin cytoskeleton through down regulation of vitamin D receptor. Am J Physiol Renal Physiol 2013; 304:F1347–F1357.

36. Congote LF. Monitoring insulin-like growth factors in HIV infection and AIDS. Clin Chim Acta 2005; 361:30–53.

37. Rao MN, Mulligan K, Tai V, Wen MJ, Dyachenko A, Weinberg M, et al. Effects of insulin-like growth factor (IGF)-I/IGF-binding protein-3 treatment on glucose metabolism and fat distribution in human immunodeficiency virus-infected patients with abdominal obesity and insulin resistance. J Clin Endocrinol Metab 2010; 95:4361–4366.

38. Okada R, Wakai K, Naito M, Morita E, Kawai S, Yin G, et al. Renal hyperfiltration in prediabetes confirmed by fasting plasma glucose and hemoglobin A1c. Ren Fail 2012; 34:1084–1090.

39. Schmieder RE, Veelken R, Gatzka CD, Rüddel H, Schächinger H. Predictors for hypertensive nephropathy: results of a 6-year follow-up study in essential hypertension. J Hypertens 1995; 13:357–365.

40. Palatini P, Mos L, Ballerini P, Mazzer A, Saladini F, Bortolazzi A, et al. Relationship between GFR and albuminuria in stage 1 hypertension. Clin J Am Soc Nephrol 2013; 8:59–66.

41. Medapalli RK, Parikh CR, Gordon K, Brown ST, Butt AA, Gibert CL, et al. Comorbid diabetes and the risk of progressive chronic kidney disease in HIV-infected adults: data from the Veterans Aging Cohort Study. J Acquir Immune Defic Syndr 2012; 60:393–399.

42. Choi AI, Rodriguez RA, Bacchetti P, Bertenthal D, Volberding PA, O’Hare AM. Racial differences in end-stage renal disease rates in HIV infection versus diabetes. J Am Soc Nephrol 2007; 18:2968–2974.

43. Wierup N, Björkqvist M, Kuhar MJ, Mulder H, Sundler F. CART regulates islet hormone secretion and is expressed in the beta-cells of type 2 diabetic rats. Diabetes 2006; 55:305–311.

44. Banke E, Riva M, Shcherbina L, Wierup N, Degerman E. Cocaine- and amphetamine-regulated transcript is expressed in adipocytes and regulate lipid- and glucose homeostasis. Regul Pept 2013; 182:35–40.

45. Kotchen TA, Piering AW, Cowley AW, Grim CE, Gaudet D, Hamet P, et al. Glomerular hyperfiltration in hypertensive African Americans. Hypertension 2000; 35:822–826.

46. Nelson RG, Bennett PH, Beck GJ, Tan M, Knowler WC, Mitch WE, et al. Development and progression of renal disease in Pima Indians with noninsulin-dependent diabetes mellitus. Diabetic Renal Disease Study Group. N Engl J Med 1996; 335:1636–1642.

47. Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function: measured and estimated glomerular filtration rate. N Engl J Med 2006; 354:2473–2483.

Keywords:

antiretroviral therapy; glomerular filtration rate; glomerular hyperfiltration; HIV; iohexol

© 2014 Lippincott Williams & Wilkins, Inc.

Login

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.