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Epidemiology and Social

Association of tenofovir exposure with kidney disease risk in HIV infection

Scherzer, Rebeccaa; Estrella, Michelleb; Li, Yongmeia; Choi, Andy I.a; Deeks, Steven G.c; Grunfeld, Carla; Shlipak, Michael G.a

Author Information
doi: 10.1097/QAD.0b013e328351f68f

Introduction

Despite the widespread use of HAART, HIV disease remains associated with increased kidney disease risk [1]. Causes and risk factors for kidney disease in the setting of HIV infection include hypertension, diabetes, hepatitis C, and certain antiretroviral drugs [2]. Consequences of kidney disease in HIV-infected persons include increased risk of atherosclerosis and mortality [3,4].

Tenofovir (TDF) is a first-line treatment of HIV infection that is currently used in approximately half of all antiretroviral regimens and as part of postexposure prophylaxis. Whether TDF use is associated with higher risk of kidney disease is controversial [5]. Prior to FDA approval, early TDF studies found no or only limited nephrotoxicity; these studies, however, excluded those with preexisting renal impairment and generally enrolled populations without other risk factors for kidney disease [6,7]. A higher risk of TDF-induced toxicity has been associated with older age [8], lower CD4 cell count [9], and other comorbidities [10]. A retrospective study of 1647 antiretroviral drug-naive patients [11] found a steeper decline in estimated glomerular filtration rate (eGFR) in patients on TDF-containing vs. TDF-sparing regimens. Another study of 324 antiretroviral drug-naive patients found a greater incidence of proximal tubular dysfunction and greater decline in eGFR over 24 months in TDF-treated patients [12]. By contrast, a randomized study of abacavir/lamivudine vs. TDF/emtricitabine in 333 persons found no statistically significant differences in eGFR over 48 weeks [13]. Furthermore, TDF did not appear to be associated with worsening kidney function in the multicenter, observational Study of Fat Redistribution and Metabolic Change in HIV Infection study, despite widespread use at the follow-up visit [14]. A 1-year prospective study of 424 HIV-infected persons also reported no association between TDF use and tubular damage [15]. Possible reasons for these disparate findings include variable patient populations, limited sample sizes, and lack of access to appropriate laboratory data.

Our objective was to evaluate the association of TDF use with kidney disease events in a national sample of 10 841 HIV-infected persons who initiated antiretroviral therapy between 1997 and 2007 within the Veterans Health Administration. We utilized an advanced statistical method, marginal structural models, to account for the possibility that time-dependent covariates may both confound and mediate the effects of antiretroviral treatment, a complexity that conventional methods of analysis cannot address.

Methods

We analyzed kidney disease outcomes in a national sample of HIV-infected US veterans. Data sources used to assemble the analytic cohort have been described in detail [4]. In brief, the Department of Veterans Affairs HIV Clinical Case Registry (CCR) actively monitors all HIV-infected persons receiving care in the Department of Veterans Affairs nationally and automatically extracts demographic, clinical, laboratory, pharmacy, utilization, and death information from the Department of Veterans Affairs electronic medical record to a centralized database [16].

Patients

The target population for this analysis was treatment-naive HIV-infected veterans (i.e., no prior exposure to any antiretroviral drug) at the time they entered clinical care in the Veterans Health Administration (VHA) system, who subsequently received monotherapy or combined antiretroviral therapy with regular care and laboratory monitoring. Among 59 479 HIV-infected persons treated in the VHA between 1985 and 2007, 19 715 patients initiated antiretroviral drugs in the modern era of combination antiretroviral therapy (after 1997). Baseline was defined as the date of starting antiretroviral therapy. We excluded patients with prevalent kidney failure (receipt of chronic dialysis treatment or kidney transplant) and those who did not have at least one HIV-1 viral load, CD4 count, outpatient visit, and assessment of kidney function, leaving 10 841 patients in the analytic cohort. Participants excluded from the analysis were similar in terms of age, race, and sex, but appeared to have a lower prevalence of some comorbid conditions, as well as somewhat higher CD4 cell count and lower HIV RNA levels (Supplemental Digital Content 1, http://links.lww.com/QAD/A206).

Outcomes

Primary study outcomes were time to first occurrence of proteinuria, rapid decline in kidney function, and eGFR less than 60 ml/min per 1.73 m2. Proteinuria was defined as two consecutive urine dipstick measurements 30 mg/dl or more. eGFR was calculated using the abbreviated Modification of Diet in Renal Disease (MDRD) formula based on age, sex, race, and serum creatinine, as this equation is used in Department of Veterans Affairs clinical practice [17]. eGFR levels above 120 ml/min per 1.73 m2 were capped at this level, as higher estimates are unlikely to be accurate or precise [14]. Rapid decline in kidney function was defined as an annual decline of 3 ml/min per 1.73 m2 or more for two consecutive years, a level shown to correspond to elevated mortality risk [18]. Chronic kidney disease (CKD) was defined by two consecutive measures of eGFR less than 60 ml/min per 1.73 m2, wherein consecutive measures were required to be at least 3 months apart, and not obtained during inpatient periods. For analyses of proteinuria and CKD, we excluded those with proteinuria or CKD present at baseline (respectively).

Additional sensitivity analyses were performed for time to first occurrence of doubling of serum creatinine, presence of CKD and proteinuria, annual eGFR decline of more than 3%, annual eGFR decline of more than 5%, presence of eGFR less than 45 ml/min per 1.73 m2, and presence of eGFR less than 30 ml/min per 1.73 m2.

Independent variables

We ascertained drug utilization in CCR medication files based on pharmacy-fill information. Medication exposure was used to define antiretroviral drug predictor variables and identify individuals with chronic diseases based on validated algorithms. Previous work demonstrated that Department of Veterans Affairs pharmacy data are comprehensive and reliable for assessing medication use [19–23]. HAART use was defined as in previous reports [19,24].

Demographic information (age, sex, and race) from CCR was supplemented with Medicare database information. We defined comorbid conditions as described previously [24]. Blood pressure, BMI, CD4 T-cell counts, HIV RNA level, low-density lipoprotein, high-density lipoprotein, total cholesterol, and serum glucose were included in statistical models or used to define clinical characteristics. At any given time, the most recent previous measurement was used to define time-dependent covariates.

Statistical analysis

Our primary objective was to estimate the effects of cumulative and ever use of TDF on kidney disease outcomes. We estimated TDF treatment effects using two Cox proportional hazards regression models adjusted for demographic characteristics only and adjusted for demographics and time-dependent prognostic covariates. Time-dependent multivariable adjusted models included exposure to TDF and all other antiretroviral drugs, age, sex, race, baseline comorbid conditions (diabetes, hypertension, dyslipidemia, prevalent cardiovascular disease, smoking, drug abuse, hepatitis B, and C virus infection), baseline measurements (CKD or proteinuria, BMI category), and current measurements [CD4 cell count (log2), viral load (log10), CKD or proteinuria, lipids, diabetes, and hypertension].

In a final step, we used MSMs to re-estimate effects of cumulative and ever use of TDF, while accounting for the fact that the decision to prescribe a particular antiretroviral drug may be modified over time as a result of changing values for a patient's covariates [25–27]. MSMs are a useful method to minimize selection bias of treatment allocation. To reduce bias due to informative censoring, we also generated stabilized censoring weights, and calculated final weights as the product of the stabilized treatment and censoring weights.

Tests for interaction were performed using cross-product terms between TDF and characteristics of interest defined at baseline. Patients were censored at time of death or last day of follow-up, 31 December 2007, due to lack of availability of additional data. Cox regression model assumptions were checked by comparing plots of log [−log(survival)] vs. log of survival time and the Schoenfeld test.

We tested linearity of the relationship of TDF exposure with kidney disease outcomes by adding quadratic terms to the models and by constructing linear splines. Because TDF did not receive FDA approval until 2001, we examined the interaction between ‘era’ of use (early = before 2003, mid = 2003–2004, late = 2005–2007) and TDF use on kidney disease. Of the 4303 participants with exposure to TDF by the end of the study, 17% had used TDF before 2003, 59% during 2003–2004, and 84% in 2005–2007.

Analyses were conducted using SAS version 9.2 (SAS Institute Inc., Cary, North Carolina, USA). This study was approved by the Committee on Human Research at the San Francisco Veterans Affairs Medical Center and the Veterans Affairs Public Health Strategic Healthcare Group.

Results

Baseline demographic and clinical characteristics of the 10 841 HIV-infected persons included in this analysis are shown in Table 1, stratified by end-of-study TDF exposure status. The overall mean age was 46 years, and women comprised 2.3% of the study sample. Individuals exposed to TDF were more often white (46 vs. 39%, P < 0.0001) compared with those without TDF exposure, but were similar in terms of comorbid conditions such as hypertension, diabetes, dyslipidemia, smoking, and hepatitis C virus, and by CD4 cell count, viral load, blood pressure, and proteinuria. Proportion with baseline eGFR less than 60 ml/min per 1.73 m2 was slightly higher among those unexposed to TDF.

Table 1
Table 1:
Baseline clinical characteristics of 10 841 HIV-infected persons, stratified by tenofovir exposure status at end of study.

There were 3400 proteinuria events in 38 132 person-years of follow-up, 3078 rapid decline events (51 589 person-years), and 533 CKD events (56 416 person-years) (Supplemental Digital Content 2, http://links.lww.com/QAD/A206). The median period of observation (before event or censoring) per individual ranged from 3.9 years for proteinuria to 5.5 years for CKD (maximum 11.0 years). At the end of the study, the 4303 participants with TDF exposure had a mean ± SD duration of 1.3 ± 1.1 years of use (median 1 year, interquartile range 0.5–1.9, maximum 6.3 years).

Association of tenofovir exposure with risk of kidney disease outcomes

In Cox proportional hazards models that adjusted for age, sex, and race, each year of cumulative exposure to TDF was associated with a 30% increase in the risk of proteinuria (P < 0.0001, Table 2). After further adjustment for baseline comorbid conditions, other antiretroviral drugs, and current measurements of HIV-related and other factors, there was little change in the association of TDF with proteinuria (34% increase per year, P < 0.0001). TDF use was also associated with an 11% increased risk of rapid decline per year of exposure (P = 0.0033), and a 33% increased risk of CKD per year of exposure (P < 0.0001) in fully adjusted time-dependent Cox models. Results were similar in MSMs designed to correct for drug channeling bias. Ever exposure to TDF was also strongly associated with approximately 50% higher risk of proteinuria, rapid decline, and CKD during follow-up (all P < 0.0001). When we modified the analysis to begin follow-up when participants initiated combination antiretroviral therapy, results were nearly unchanged. We found similar results for the association of TDF with CKD after adjustment for baseline eGFR [hazard ratio (HR) = 1.26 per year, 95% CI 1.10–1.44].

Table 2
Table 2:
Association of tenofovir exposure with riska of kidney disease outcomes.

A greater percentage of TDF users had repeated measures of proteinuria (66 vs. 62%, P < 0.0001) and creatinine (96 vs. 94%, P < 0.0001) compared with nonusers. Controlling for number of assessments did not weaken the association of TDF with proteinuria (HR = 1.40 per year of exposure, 95% CI 1.30–1.50), rapid decline (HR = 1.11, 95% CI 1.04–1.19), or CKD (HR = 1.36, 95% CI 1.20–1.55). There was little evidence of interaction of TDF with concomitant protease inhibitor, non-nucleoside reverse transcriptase inhibitor, and ritonavir use on the three outcomes (all P values for interaction were >0.30).

We also analyzed associations of TDF with more stringent measures of kidney disease (Supplemental Digital Content 3, http://links.lww.com/QAD/A206). Cumulative exposure to TDF was associated with a 10% increased risk of creatinine doubling (432 events) (95% CI 0.92–1.32, P = 0.28) and a 35% (95% CI 12–62) increased risk of combined CKD and proteinuria (237 events) (P = 0.0014). Findings were similar when rapid kidney decline was defined by more than 3% or more than 5% decline in eGFR (Supplemental Digital Content 3, http://links.lww.com/QAD/A206). We also considered lower thresholds of risk for CKD (cutpoints of 45 and 30 ml/min). Cumulative TDF exposure was associated with a marginally increased risk of eGFR less than 45 ml/min per 1.73 m2 (HR = 1.18, 95% CI 0.97–1.45, P = 0.10) but did not appear to be associated with increased risk of eGFR less than 30 ml/min per 1.73 m2 (HR = 0.91, 95% CI 0.62–1.33, P = 0.61). However, the incidence was low for both eGFR less than 45 ml/min per 1.73 m2 (237 events) and eGFR less than 30 ml/min per 1.73 m2 (124 events).

Among those who discontinued TDF use, the period following cessation was not significantly associated with either higher or lower risks of proteinuria (HR = 1.05 per year, 95% CI 0.93–1.18, P = 0.41) or rapid decline (HR = 1.05 per year, 95% CI 0.94–1.16, P = 0.42), although there was a marginal association of time off TDF with CKD (HR = 1.22 per year, 95% CI 0.99–1.50, P = 0.055). All hazard ratios remained greater than unity, which suggests that the effects of TDF on kidney disease risk were not reversible following discontinuation. When we instead discretized TDF use as never, current, or past, we found that past and current use of TDF had increased risk of outcomes, compared with those never exposed (Supplemental Digital Content 4, http://links.lww.com/QAD/A206). We also considered the 3400 participants who experienced a proteinuria event and looked at subsequent time to resolution of proteinuria. This analysis found that current and past TDF users did not differ statistically from never users in likelihood of resolution of proteinuria (all P > 0.2).

We found evidence of nonlinear associations of cumulative TDF exposure with risk of proteinuria (P = 0.0030), rapid decline (P < 0.0001), and CKD (P = 0.036) (Table 3). Risk of proteinuria appeared strengthened among those with more than 3 years of exposure to TDF, whereas incidence of rapid decline appeared to decrease over time, especially after 3 years of exposure. Each category of exposure to TDF was associated with increased risk of CKD, although associations were not statistically significant for those with less than 0.5 or more than 3 years of exposure.

Table 3
Table 3:
Association of cumulative tenofovir (in different time ranges) with riska of kidney disease outcomes.

The association of cumulative TDF use with proteinuria appeared to be stronger in the earlier era (HR = 2.2, P = 0.0051) than in the mid (HR = 1.5, P < 0.0001) and later eras (HR = 1.2, P < 0.0001; test for difference: P = 0.014) (Supplemental Digital Content 5, http://links.lww.com/QAD/A206). Cumulative TDF exposure was more strongly associated with rapid decline in the earlier era (HR = 1.7, P = 0.071) than in the mid era (HR = 1.3, P < .0001), with little association in the later era (HR = 1.04, P = 0.20; test for difference: P = 0.0018). Likewise, cumulative TDF exposure was more strongly associated with CKD in the earlier era (HR = 3.3, P = 0.018) compared with mid (HR = 1.6, P = 0.0002) and later eras (HR = 1.4, P < 0.0001), although the test for difference did not reach statistical significance (P = 0.12). As a sensitivity analysis, we excluded data collected before 2001, but found little change in the association of TDF exposure with kidney outcomes. We also controlled for whether participants were antiretroviral drug-naive at the time of TDF initiation; this analysis found little change in the association of TDF exposure with kidney outcomes.

Interactions between tenofovir and baseline comorbidity

We examined interactions of TDF and subgroups (age, race, baseline CKD, smoking, etc.) on proteinuria (Fig. 1). TDF use was associated with increased risk of proteinuria in all subgroups, but appeared somewhat stronger in persons with lower compared with higher HIV-1 RNA levels (P value for interaction was 0.011). There was no evidence that preexisting CKD, diabetes, or hypertension increased the risk of proteinuria, relative to those without preexisting comorbidities.

Fig. 1
Fig. 1:
Association between cumulative tenofovir exposure and risk of proteinuria in subgroups defined by baseline characteristics (excluding those with proteinuria at baseline).Cardiovascular risk category based on Framingham risk score (<10% = low, 10–20% = moderate, >20% = high). All estimates based on multivariable-adjusted Cox models described in Table 2. P value for test of interaction between cumulative tenofovir use and characteristic reported. CI, confidence interval; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate.

TDF use was associated with increased risk of CKD in nearly all subgroups (SDC Figure 1, http://links.lww.com/QAD/A206). The association of TDF use with CKD was weaker in older vs. younger individuals (P = 0.043), in those with vs. without CVD (P = 0.016), in diabetic patients vs. nondiabetic individuals (P = 0.041), and in those with hypertension vs. those without hypertension (P = 0.018).

Associations of other antiretroviral drugs with kidney disease

We evaluated associations of all antiretroviral drugs in use in this study, summarized by descending prevalence of use (Table 4). TDF was the only antiretroviral drug that showed statistically significant associations with all three outcomes. Ritonavir was associated with increased proteinuria risk in fully adjusted analysis, whereas efavirenz, lopinavir/ritonavir, and saquinavir appeared to be associated with lower proteinuria risk. Atazanavir was associated with increased risk of rapid decline but not with proteinuria or CKD risk. Indinavir was associated with increased risk of CKD, whereas efavirenz and zidovudine were associated with decreased risk.

Table 4
Table 4:
Association of cumulative antiretroviral exposure (per year) with riska of kidney disease outcomes, ordered by prevalence of use.

Discussion

In this large, national sample of predominantly male HIV-infected veterans receiving combination antiretroviral therapy, we found that exposure to TDF was associated with increased risk for proteinuria, rapid decline (a ≥3 unit annual decrease) in kidney function, and development of CKD. Even after accounting for demographics, HIV-related factors, comorbidities, and other antiretroviral drugs, TDF remained independently associated with elevated risk for each kidney disease outcome. These associations were in general similar across subgroups based on baseline comorbidities and characteristics, and few statistically significant interactions were observed. Presence of traditional CKD risk factors at baseline such as preexisting CKD, diabetes, and hypertension did not appear to worsen the effects of TDF. Together, these findings provide strong evidence that TDF may cause clinically significant irreversible toxicity to the kidney.

It is noteworthy that TDF was associated with both proteinuria and CKD in our study. These outcomes are not highly intercorrelated, and each is independently associated with cardiovascular disease and death in the setting of HIV infection [3,4]. The primary mechanism by which TDF causes renal toxicity may involve drug accumulation within proximal renal tubules, leading to mitochondrial injury and depletion [28]. Individuals with certain variants of the ABCC2 gene, the multidrug resistance protein, which facilitates TDF efflux from proximal tubular cells, may be more prone to TDF toxicity [29]. Furthermore, inhibition of TDF entry into proximal tubular cells via the organic anion transporter by probenecid prevents recurrent TDF renal toxicity [30]. Consistent with this proposed mechanism of drug accumulation in the renal proximal tubule, most case reports describe TDF renal toxicity presenting as partial or full Fanconi syndrome characterized by subnephrotic proteinuria with or without hyperphosphaturia and normoglycemic glycosuria [31–33]. However, TDF renal injury may also present as acute tubular necrosis [34], eventually leading to tubulointerstitial scarring, which may account for the lack of reversibility of TDF renal toxicity in some individuals [5].

Few previous large, nationally representative studies in HIV-infected patients have looked at associations of TDF with kidney disease outcomes. TDF was the only antiretroviral drug that showed statistically significant associations with all three kidney disease outcomes in our study. A study of 10 343 patients designed to evaluate the safety of TDF over the first 4 years of use reported that less than 1% of patients experienced a serious renal adverse event [35]; however, all patients were taking TDF, so its effects on renal function could not be compared with other drugs.

A recent longitudinal study of 6843 HIV-infected persons found that TDF, indinavir, and atazanavir were associated with a higher incidence of CKD, even after controlling for traditional CKD risk factors and other antiretroviral drugs [36]. In our study, atazanavir was associated with increased risk of rapid decline, but not with CKD.

By contrast, efavirenz was associated with a lower risk of both proteinuria and CKD. Similarly, a recent prospective study of 62 HIV-infected patients found lower rates of proteinuria and higher levels of eGFR among those who were treated with TDF/lamivudine/efavirenz compared with those treated with TDF/lamivudine/nevirapine [37]. Mechanisms accounting for this potential beneficial effect of efavirenz are unknown.

Among those who discontinued TDF use in our study, time following cessation was not significantly associated with either higher or lower risks of proteinuria, or rapid decline, and appeared to be weakly associated with increased CKD risk. Past users of TDF remained at increased risk of outcomes, compared with those never exposed to TDF. Proteinuria appeared to be similarly persistent among users, current and former, as among nonusers, suggesting that TDF-induced proteinuria is not uniquely transient. A small study of HIV-infected men found little recovery on average in eGFR following TDF cessation [10]. Similarly, both current and past TDF uses were associated with increased risk of proximal renal tubular dysfunction in a cross-sectional study of 399 HIV-infected persons [38]. These findings suggest that kidney damage and loss of function do not quickly reverse after cessation of TDF use.

A major strength of our study is the large number of participants, which gave us power to detect relatively small hazard ratios for the risk of renal outcomes per year of TDF exposure. Previous studies may have been less powered to detect statistically significant associations between TDF use and kidney disease. Assuming 5 years of follow-up and a type I error rate of 5% with equal allocation to treatment arms (TDF vs. no TDF), a study would need to enroll 3544 participants to achieve 80% power to detect a hazard ratio of 1.3 or greater (the TDF effect observed in our study for CKD and proteinuria).

Study limitations include our inability to measure GFR directly, similar to all large studies of kidney disease. There may have been incomplete or inadequate control for factors that may confound or explain the association between TDF and kidney disease. However, we utilized MSMs to account for the possibility that the decision to prescribe a particular antiretroviral drug may change over time due to changes in a patient's covariates. Mean exposure to TDF in our study was 1.3 years, and among those who discontinued TDF, mean follow-up time was 1.2 years; this limits our ability to extrapolate risk of longer exposure. Antiretroviral drugs other than TDF showed inconsistent associations with kidney disease risk; the few results reaching statistical significance may be due to chance despite meeting the conventional cutoff for statistical significance. Prospective studies should be undertaken to validate our findings. Additionally, our results may not generalize to nonveterans, women, or patients not receiving regular clinical care. However, our population includes those who are often excluded from clinical trials and do not qualify or volunteer for cohort studies. Finally, our analyses excluded patients with inadequate data collection, and these persons on average were healthier than patients included in our study; we cannot discern whether this would have a bias on our findings.

In conclusion, this large, national sample of 10 841 HIV-infected persons indicates that TDF is associated with increased risk of proteinuria, rapid decline, and CKD. Clinicians treating HIV-infected patients should recognize that although traditional risk factors such as hypertension, older age, and diabetes may increase the risk for kidney disease, TDF is associated with elevated risk even in patients without preexisting kidney risk factors. Despite TDF's association with progressive kidney disease, it is an important component of effective antiretroviral therapy that may be required in many patients to control viral load. The balance between its efficacy and probable adverse effects requires further study.

Acknowledgements

The authors would like to thank Eric Vittinghoff, PhD, for statistical assistance and Cristin Weekley, BA, for assistance with figures and administrative help. R.S., M.G.S., and Y.L. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflicts of interest

This study was supported by the National Institutes of Health [K23DK080645-1A1 (A.I.C.), 1R03AG034871-01 (A.I.C./M.G.S.), K24AI069994, R01 DK066488-01 (M.G.S./A.I.C.), K23DK081317 (M.E.)], the National Center for Research Resources (KL2 RR024130), the American Heart Association Established Investigator Award (MGS), and the Veterans Affairs Public Health Strategic Healthcare Group, which were administered by the Northern California Institute for Research and Education, and with resources of the Veterans Affairs Medical Center, San Francisco, California. These funding sources had no involvement in the design or execution of this study. S.G.D. receives research support from Merck, Bristol-Myers Squibb, Gilead, and Roche Molecular Sciences. C.G. has received prior research funding and/or honorarium from Merck, Bristol-Myers Squibb, Abbott, Gilead Sciences, EMD Serono and Theratechnologies.

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Keywords:

antiretroviral therapy; HIV; kidney disease; tenofovir

© 2012 Lippincott Williams & Wilkins, Inc.