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doi: 10.1097/QAD.0b013e3281fc9320
Clinical Science

Amprenavir and didanosine are associated with declining kidney function among patients receiving tenofovir

Crane, Heidi M; Kestenbaum, Bryan; Harrington, Robert D; Kitahata, Mari M

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From the Department of Medicine, University of Washington, Seattle, Washington, USA.

Received 2 January, 2007

Revised 5 April, 2007

Accepted 13 April, 2007

Correspondence and reprint requests to Dr H. M. Crane, Center for AIDS and STDs, University of Washington, Harborview Medical Center, Box 359931, 325 9th Avenue, Seattle, WA 98104, USA. E-mail:

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Objective: To examine the effect of antiretroviral agents and clinical factors on the development of tenofovir-associated kidney dysfunction.

Methods: Observational cohort study of HIV-infected patients receiving tenofovir in an HIV clinic population. Patients' kidney function prior to initiating and while receiving tenofovir was evaluated in relation to other antiretroviral medications and demographic and clinical characteristics. Decline in kidney function was assessed by the glomerular filtration rate (GFR) as estimated by the Cockcroft–Gault (CG) equation, which incorporates weight. Logistic regression analysis was used to examine factors associated with GFR of > 90, 60–90, 30–60, and < 30 ml/min per 1.73 m2 while on tenofovir. Secondary analyses used the simplified Modification of Diet in Renal Disease (MDRD) equation.

Results: Among the 445 patients initiating tenofovir, 51 (11%) developed a decline in kidney function. In multivariate analysis, there was a significant association between decline in kidney function and concurrent use of amprenavir [odds ratio (OR) 3.6; P = 0.045] and didanosine (OR, 3.1; P = 0.006), age over 50 years (OR, 4.4; P = 0.03), and lower baseline weight (OR, 0.95/kg; P < 0.001). Patients identified with kidney dysfunction by the MDRD equation did not fully overlap with those identified by the CG equation.

Conclusions: Didanosine and amprenavir use, increased age, and lower baseline weight were significantly associated with risk for kidney dysfunction among patients receiving tenofovir. GFR results using the MDRD equation were inconsistent with those using CG, which highlights the impact of including weight in the estimation of GFR among HIV-infected patients.

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Tenofovir is a nucleotide reverse transcriptase inhibitor (NRTI) used in first-line and salvage HAART regimens [1]. Tenofovir is structurally related to adefovir and cidofovir, which can cause proximal tubular injury and acute renal failure [2]. Early clinical trials [3–6] and in vitro studies [7] indicated initially that tenofovir was without associated nephrotoxicity. Since FDA approval in 2001, however, cases of renal failure with proximal tubular dysfunction associated with tenofovir have been reported [3,8–12]. Use of tenofovir has been associated with a greater decline in kidney function compared with alternative NRTI drugs (nucleotides or nucleosides) [13].

Little is known about the impact of patient characteristics and treatment factors, such as concurrent use of other individual antiretroviral medications, on risk of tenofovir-related kidney abnormalities. Case reports suggest ritonavir, lopinavir/ritonavir, or didanosine [8,9,14–18], and low body weight [9,19], may enhance the risk of developing tenofovir-related kidney dysfunction, but two observational cohort studies did not find an association between use of lopinavir/ritonavir and kidney abnormalities among patients taking tenofovir [13,20]. The study decribed here examined factors associated with development of tenofovir-related kidney dysfunction among HIV-infected patients.

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Study setting

This study was conducted using the University of Washington (UW) HIV Cohort, a longitudinal observational study of HIV-infected patients from the UW Harborview Medical Center HIV Clinic from 1/1/1995 to the present, using standardized data collection methods as previously described [21,22]. Patients in the UW HIV Cohort provide informed consent and are followed from enrollment until death or relocation.

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Study subjects

All HIV-infected patients 18 years of age or older who initiated tenofovir as part of a HAART regimen between 1 November 2001 and 1 January 2006 were eligible for the study. Subjects had to have at least one serum creatinine value within the 6 months before initiating tenofovir, and at least one value after initiating tenofovir and prior to the end of data collection (1 February 2006). The study was approved by the UW Institutional Review Board.

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Sources of data

The UW HIV Information System (UWHIS) captures longitudinal data on UW HIV Cohort patients. The UWHIS integrates comprehensive clinical data from all outpatient and inpatient encounters including standardized HIV-related information collected at enrollment (initial clinic visit) regarding prior antiretroviral treatment and diagnosis history. Demographic, clinical, laboratory, medication, and socioeconomic data are obtained from the UW Electronic Medical Record and other institutional data sources. Over 90% of patients in the UW HIV Cohort receive all medications from the on-site pharmacy. Patient data such as height and weight are routinely collected at outpatient visits and integrated in the UWHIS. Weights are collected by nursing staff at the beginning of clinic visits on one of two scales; patients are not asked to remove their clothing before being weighed.

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Estimates of kidney function

Primary analyses used the Cockroft–Gault (CG) creatinine clearance formula [23] to estimate glomerular filtration rate (GFR). The simplified Modification of Diet in Renal Disease (MDRD) equation [24], and the CG equation adjusted for body surface area [25] were also used to estimate GFR as sensitivity analyses. The CG equation incorporates body weight while the MDRD equation does not. The equations used to calculate GFR by both methods is shown in Table 1. GFR was estimated prior to initiating tenofovir (‘baseline’) and at the time of the last creatinine value during tenofovir treatment (‘follow-up’).

Table 1
Table 1
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Definition of outcomes

The GFR values for patients were categorized based on the National Kidney Foundation Kidney Disease Outcome Initiative (K/DOQI) guidelines (estimated GFR groups of > 90, 60–90, 30–60, and < 30 ml/min per 1.73 m2) [26]. A decline in GFR was defined as a fall to a more severe K/DOQI category at follow-up compared with baseline; the decline was severe if patients' drop estimated GFR was ≥ 60 ml/min per 1.73 m2. Sensitivity analyses defined a decline in GFR as a fall to a more severe K/DOQI category and a decline in GFR of ≥ 20 ml/min per 1.73 m2, and also evaluated percentage change in GFR while receiving tenofovir.

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Antiretroviral medications

HAART was defined as antiretroviral regimens containing three or more drugs including a protease inhibitor (PI) or nonnucleoside reverse transcriptase inhibitor (NNRTI), or three or more NRTI. Patients were classified as HAART naive if tenofovir was given as part of their initial HAART regimen, and as HAART experienced if they had received three or more months of HAART prior to initiating tenofovir. Antiretroviral medications were categorized into mutually exclusive and exhaustive groups. Patients receiving regimens containing two PI, boosted PI, or regimens with both an NNRTI and a PI were grouped by their PI other than ritonavir, and patients receiving triple nucleoside regimens were categorized as ‘other’ for the PI/NNRTI variable. Patients receiving two NRTI besides tenofovir were categorized by their NRTI other than lamivudine for the NRTI variable. Ritonavir use was examined separately, including ritonavir at any dosage as well as ritonavir categorized as full dose or boosting dose (> 400 or ≤ 400 mg every 24 h).

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Statistical analysis

Associations were examined between decline in GFR and demographic characteristics (age, risk factor for HIV transmission, race, and sex) and clinical characteristics at baseline [CD4 cell count nadir; plasma HIV-1 RNA prior to initiating HAART; Centers for Disease Control and Prevention clinical classification (A/B/C) [27]; hepatitis C virus (HCV) infection (indicated by either the presence of HCV antibody or HCV RNA); a diagnosis of diabetes mellitus, hypertension, or nephrolithiasis; and body mass index (BMI) and weight at initiation of tenofovir]. The association between decline in GFR and change in weight during treatment with tenofovir was also examined. The impact of antiretroviral therapy was examined based on total HAART duration, which included the regimen containing tenofovir and all prior HAART regimens, and individual antiretroviral medications and medication classes (NRTI, NNRTI, and PI) taken concurrently with tenofovir. The BMI using the traditional Quetelet index was categorized as underweight (< 18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (≥ 30 kg/m2) [28]. Change in weight was calculated as the difference between weight at follow-up and baseline. CD4 cell count nadir was examined as a continuous and categorical variable (≤ 50, 51–200, and > 200 cells/μl).

Bivariate analysis of associations used χ2 and t-tests. One-way analysis of variance (ANOVA) was used to examine the association between PI/NNRTI and NRTI variables and the mean duration of HAART, the CD4 cell count nadir, age, and weight. ANOVA was also used to examine the association between a decline in GFR as estimated by the MDRD or CG equations, both, or neither, and mean weight, weight changes, and creatinine level. Multivariate logistic regression was used to examine predictors of a decline in GFR. Secondary analyses were conducted using multivariate linear regression. Effect modification was assessed using interaction terms. Two-tailed P values of < 0.05 were considered significant for all statistical tests.

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Entry criteria were met by 445 patients during the study period. The mean age of study patients was 41 years; 83% were men; 20% had a prior diagnosis of HCV coinfection, and the mean CD4 cell count nadir was 140 cells/μl (Table 2). The mean BMI for the study cohort was 25.5 kg/m2 prior to initiation of tenofovir and 25.7 kg/m2 while receiving tenofovir. For comorbidities, 22 patients had nephrolithiasis, 20 had diabetes mellitus (type 1 or 2), and six had a diagnosis of hypertension prior to initiating tenofovir.

Table 2
Table 2
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Subjects received the following HAART regimens: 219 patients (49%) received PI-based regimens, 179 patients (40%) received NNRTI-based regimens, and 19 patients (4%) were treated with regimens containing both a PI and an NNRTI. In addition, 28 patients (6%) received triple NRTI regimens without a PI or NNRTI. Nearly half of patients received boosted PIs: lopinavir/ritonavir, 86 (19%); atazanavir with ritonavir, 107 (24%); amprenavir with ritonavir, 22 (5%). Three patients (< 1%) were treated with regimens containing two full-dose PI drugs (ritonavir and saquinavir). Duration of tenofovir therapy averaged 13 months.

The mean creatinine level measured at follow-up was significantly higher than at baseline (0.93 versus 8.6 mg/dL; P < 0.001). GFR estimated by the CG creatinine clearance equation was 7 ml/min per 1.73 m2 lower, on average, at follow-up compared with baseline (P < 0.001). A decline in K/DOQI category while receiving tenofovir developed in 51 patients (11%), of whom seven (2%) experienced a severe decline (> 60 ml/min per 1.73 m2). Severe declines in GFR were ordinarily observed in the third or fourth month of tenofovir treatment (range, 3–9). In contrast, mild to moderate declines in GFR generally occurred after 6 months, though these declines were observed throughout tenofovir treatment (mean, 7 months; range, 1–33). More than 14% of patients over 40 years of age had a decline in K/DOQI category while receiving tenofovir compared with less than 8% of patients under 40 years of age (P = 0.02). A decline in estimated GFR was associated with didanosine (P = 0.002) and amprenavir (P = 0.02) in unadjusted analyses. Over 22% of patients taking didanosine had a decline in K/DOQI category while receiving tenofovir, compared with less than 10% of patients not receiving didanosine (P = 0.001); 25% of patients receiving ampenavir had a decline in category compared with 10% of patients not receiving amprenaivr (P = 0.05). Other antiretroviral medications, including ritonavir, were not associated with declining kidney function.

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Multivariate analyses

Declining kidney function was examined using the CG equation, adjusting models for age, race, sex, CD4 cell count, baseline weight, individual NNRTI and PI, NRTI, and duration of HAART. Patients receiving amprenavir-based regimens were significantly more likely to develop a decline in GFR than patients receiving efavirenz [odds ratio (OR), 3.6; P = 0.045; Table 3]. Patients receiving didanosine concurrently with tenofovir were three times more likely to develop a decline in GFR than patients receiving lamivudine (OR, 3.1; P = 0.006). Patients older than 50 years of age were four times as likely to develop a decline in GFR (OR, 4.4; P = 0.03) compared with patients less than 30 years of age. Patients with lower baseline weight were more likely to develop a decline in GFR compared with patients with a higher weight at initiation of tenofovir (OR, 0.95/kg; P < 0.001).

Table 3
Table 3
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No effect modification was found when interactions between individual antiretroviral medications and CD4 cell count or age were included in the models. An interaction between baseline weight and didanosine approached statistical significance (P = 0.06 for interaction term). When the risk associated with didanosine was examined separately among individuals with baseline weight above and below 70 kg, excess risk was found primarily among patients with a baseline weight < 70 kg [those < 70 kg: adjusted OR, 5.0; 95% confidence interval (CI), 1.8–14.0; P = 0.002; those > 70 kg: adjusted OR, 1.3; 95% CI, 0.3–5.8; P = 0.8). The dosage of didanosine was not related to the probability of experiencing a decline in kidney function, though 95% received the recommended lower dosage of didanosine when given with tenofovir or were switched to the lower dosage while on therapy.

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Sensitivity analyses

Sensitivity analyses examined factors associated with both a fall in K/DOQI category and a decrease in estimated GFR of at least 20 ml/min per 1.73 m2 using the CG equation, using baseline BMI rather than weight, using change in weight in addition to baseline weight, using the CG equation adjusted for body surface area, and using linear regression to determine predictors of the percentage change in kidney function. Limited models were also examined which excluded demographic variables that were components of the equations used to estimate kidney function. These limited models included individual PI/NNRTI, NRTI, CD4 cell count nadir, and HAART duration but did not include sex, age, or race. All of these models yielded similar results regarding the associations between individual antiretroviral medications and a decline in GRF as the main model presented in Table 3.

Finally, multivariate analyses were performed using the simplified MDRD equation to estimate GFR adjusting for age, race, sex, CD4 cell count, baseline weight, individual NNRTI and PI, NRTI, and total duration of HAART therapy. Patients older than 30 years of age were more than three times as likely to develop a decline in GFR compared with patients under 30 year (30–40 years: OR, 3.7; 95% CI, 1.0–13.4; P = 0.04; 40–50 years: OR, 5.0; 95% CI, 1.4–18.0; P = 0.01). African-American patients were less likely than white patients to develop a decline in GFR (OR, 0.3; 95% CI, 0.1–0.6; P = 0.001). Individual antiretroviral medications were not associated with developing a decline in GFR in analyses using the MDRD equation.

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Differing methods for estimating glomerular filtration rates

Differences were examined between patients with declines in GFR by the CG equation and those identified by the MDRD equation. Only 31 of 51 patients with a decline in GFR defined as a change in K/DOQI category estimated by the CG equation were also identified with a decline in GFR using the MDRD equation (Fig. 1). Baseline weight and weight change were factors that distinguished individuals with declining GFR detected by the two methods (Table 4). The mean weight prior to tenofovir for the entire cohort was 76.5 kg, while the mean weight for those with a decline in kidney function by the CG equation was 68.1 kg (P < 0.001).

Fig. 1
Fig. 1
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Table 4
Table 4
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In this study of kidney function among HIV-infected patients receiving tenofovir, older age and concurrent use of didanosine or amprenavir were significantly associated with increased risk of declining kidney function. Notably, these findings differed when the MDRD equation was used to estimate kidney function rather than the CG equation.

We had hypothesized that didanosine would be associated with declining GFR based on case reports [12,14–18,29–33]. The decline in kidney function found with didanosine was predominantly among patients with lower baseline weights, suggesting a dose-related effect. Additionally, this decline in GFR associated with didanosine was only detected using the CG equation, not with the MDRD equation; the latter may be less accurate among individuals with lower body weights [34]. Further study is needed to determine the best method for estimating GFR among HIV-infected patients given the significant weight and body morphology changes observed in this group.

Case reports suggest that lopinavir/ritonavir, atazanavir, and ritonavir may be associated with tenofovir-associated kidney abnormalities [3,8,9,11,12,18,29,30]. It has been suggested that concurrent atazanavir or lopinavir/ritonavir may increase serum concentrations of tenofovir [35–37], and ritonavir may increase proximal tubule tenofovir concentrations [18]. Others have suggested that patients treated with lopinavir/ritonavir often have more advanced HIV disease and thus a higher risk of adverse events [38]. Our results agree with two prior studies that found no increase in tenofovir-associated kidney dysfunction among patients receiving lopinavir/ritonavir, atazanavir, or ritonavir [13,20]. However, we found a significant association between the coadministration of amprenavir and tenofovir and a decline in GFR, which has not been previously reported. We suspect that prior cohort studies have not detected this association because of the more limited use of amprenavir compared with, for example, lopinavir/ritonavir in many clinical settings. The basis for this association is unclear and additional studies are needed to confirm our findings as well as to examine possible mechanisms. Further characterization of interactions between amprenavir and drug efflux transporters may provide insight into mechanisms for this finding.

Low baseline weight was a significant risk factor for development of tenofovir-associated kidney dysfunction, suggesting that monitoring kidney function may be particularly important for lighter patients. Baseline body weight has not been included in most prior studies [39,40]. Our findings are consistent with several case reports [9,19,41] and the hypothesis that low body weight may lead to high tenofovir concentrations and increased risk of kidney impairment [10].

We did not find an association between tenofovir-associated kidney dysfunction and diabetes, hypertension, kidney stones, or HCV coinfection. However, the small numbers of patients with these conditions in our study sample limited our ability to examine these effects. As expected, older age was associated with increased risk of kidney abnormalities. We also found an increased risk among white patients when we used the MDRD equation to estimate GFR, but not when using the CG equation. This difference could reflect race-specific polymorphisms in drug membrane transporters; however, we suspect it is caused by the way race is included in the MDRD equation.

We observed two patterns of tenofovir-related kidney dysfunction. A few patients (seven) developed a severe decline in kidney function early in the course of treatment (3–4 months), while more patients (44) developed a mild to moderate decline occurring throughout tenofovir therapy. These findings are consistent with results from pooled clinical trial data (studies 902 and 907), which showed that 7% of patients had mild (grade 1) creatinine elevations, while only 1% discontinued tenofovir because of kidney-related effects, and only a single patient developed Fanconi syndrome [42]. Another study that examined change in creatinine over 24 months also found that a large number of patients receiving tenofovir (42%) had a mild decline in kidney function, while a smaller number (4%) developed severe kidney dysfunction [43]. The reason(s) for these patterns of toxicity associated with tenofovir is unknown, but it highlights the importance of designing future studies to detect both mild and severe changes in kidney function.

Reference methods for measuring GFR such as inulin are not suitable for routine clinical care [44]. Therefore, clinicians rely on GFR estimates from equations incorporating clinical and demographic characteristics and serum creatinine. The validity of these equations has not been well established among HIV-infected patients [45]. Single-sample serum creatinine level is the most widely used indirect estimate of GFR [46]. However, creatinine levels are insensitive to even substantial declines in GFR [47], especially among individuals with HIV [48]. The K/DOQI guidelines state that creatinine levels alone should not be used to assess kidney function [26]. Despite their limitations, estimates of GFR using the CG and MDRD equations provide substantial improvement over just using serum creatinine [49].

We chose change in K/DOQI category as our primary measure of decline in GFR because both CG and MDRD equations have decreased accuracy at higher levels of GFR [49]. A limitation of this approach is that small decreases in GFR in patients whose baseline value is close to a category cut-off will result in a category drop. We therefore conducted additional analyses requiring a change in category plus a decline in GFR of ≥ 20 ml/min per 1.73 m2. We also examined percentage change in GFR to decrease the impact of increased measurement variability of the CG and MDRD equations at higher levels of GFR [50]. We used estimated GFR based on last creatinine value rather than an average value to maximize sensitivity of detecting decreasing levels over time. Another limitation of the CG equation is decreased accuracy among people with unusual body morphologies, such as those with obesity [51] or substantial muscle wasting [49]. To explore this, we performed sensitivity analyses using the CG equation adjusted for body surface area and found no differences. Nevertheless, the impact of lipodystrophy (lipoatrophy and/or lipohypertrophy) on estimates of GFR will require additional studies.

The primary distinction between the CG and MDRD estimates of GFR is the inclusion of weight in the CG equation. The MDRD has been shown to overestimate GFR in underweight individuals [34], underestimate GFR at high GFR levels, and overestimate GFR at low levels [52]. The CG equation may overestimate GFR at low GFR levels [52] and among patients with higher BMI [50]. Therefore, using either equation for HIV-infected patients, who may have wide fluctuations in weight during the course of their disease, is problematic. Guidelines for HIV-infected individuals do not recommend the use of one method over the other [45].

Notably, the CG and MDRD equations identified groups of patients with kidney dysfunction that only partly overlapped (Fig. 1). Only four of the seven patients with severe kidney dysfunction identified by the CG equation were detected using the MDRD equation. Studies comparing the accuracy of the MDRD and CG formulae have been conducted in other patient populations, with mixed results that tended to favor MDRD [34,50,53–55]. The CG equation may provide a better estimate of GFR in HIV-infected patients, however, since it incorporates changes in weight commonly seen in this patient population. It is possible that the CG equation is imperfectly impacted by weight and that a decline in kidney function estimated by this method might represent changes in weight during follow-up. The findings were generally unchanged, however, when we accounted for change in weight in the multivariate model.

Strengths of our study included the ability to examine the comprehensive clinical data and the accurate antiretroviral treatment data captured in the UWHIS. Patients seen in routine care are more heterogeneous than those who enroll in clinical trials and have a broader range of characteristics and comorbid conditions. It is important to examine the effects of these characteristics on the outcome of tenofovir-associated kidney dysfunction and to determine the impact of these factors in real-world settings. As the cohort continues to be followed, additional information will become available with which to examine the effects of newer antiretroviral agents.

This study had a number of limitations. As with any observational study, unknown or unmeasured confounding is a concern. This was minimized by evaluating changes over time within individuals rather than comparing tenofovir-exposed patients with controls. Bias may be introduced by differences in the frequency of laboratory measurements in the clinical setting, but in our study, creatinine levels were routinely collected at 3–6 month intervals in the outpatient setting. Urine protein and phosphate levels were not routinely collected in the clinic and so were not available for analysis. The weights used in the CG equation were collected in a clinical setting by nursing staff prior to appointments, which may have resulted in less precision than weights collected using a research-based protocol. While the presence of HCV was noted, defined by the presence of HCV antibody or HCV RNA, we were unable to categorize HCV disease severity. Estimates of GFR relied on the use of the CG and MDRD equations but neither has been well validated among HIV-infected patients. Finally, we lacked any information regarding other potential risk factors that might impact tenofovir-related kidney dysfunction, such as genetic factors.

Tenofovir is a frequently prescribed antiretroviral medication because of its convenient dosing and favorable tolerability profile. Our study suggests that a significant proportion of patients who take tenofovir develop kidney dysfunction and that concomitant treatment with didanosine and amprenavir, as well as older age, increase this risk. The results of this study provide another reason to avoid didanosine with tenofovir [56,57]. Further study is needed to determine the appropriateness of using the CG and MDRD equations among HIV-infected patients, particularly for those who experience large shifts in weight or who have lipodystrophy. Additional large cohort studies are needed to define further the clinical and treatment factors affecting kidney function of HIV-infected patients taking regimens containing tenofovir.

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The authors thank Dr Ashley Jefferson for his suggestions and the staff and patients of the University of Washington Harborview HIV clinic.

Sponsorship: This work was supported by grants from the Mentored Patient-Oriented Research Career Development Award NIAID Grant (AI-060464) and the University of Washington Center for AIDS Research NIAID Grant (AI-27757).

No authors have any affiliation with or financial involvement in any organization, matter, or materials discussed in this manuscript.

This work was presented in part at the 13thConference on Retroviruses and Opportunistic Infections, Denver, February, 2006.

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amprenavir; didanosine; HAART; kidney disease; renal disease; tenofovir

© 2007 Lippincott Williams & Wilkins, Inc.


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