JAIDS Journal of Acquired Immune Deficiency Syndromes:
Epidemiology and Prevention
Prevalence of Proteinuria and Elevated Serum Cystatin C among HIV-Infected Adolescents in the Reaching for Excellence in Adolescent Care and Health (REACH) Study
Aaron, Kristal J. MSPH*,†; Kempf, Mirjam-Colette PhD‡,§,†; Christenson, Robert H. PhD‖,¶; Wilson, Craig M. MD#,†; Muntner, Paul PhD#,*,†; Shrestha, Sadeep PhD, MHS, MS#,†
*Nephrology Research and Training Center, Division of Nephrology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
†University of Alabama at Birmingham, Birmingham, AL
‡Department of Health Behavior, School of Public Health
§Department of Family/Child Health & Caregiving, School of Nursing
‖Departments of Pathology and
¶Medical and Research Technology, University of Maryland School of Medicine, Baltimore, MD
#Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL.
Correspondence to: Sadeep Shrestha, PhD, MHS, MS, Department of Epidemiology, R217, School of Public Health, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35294-0022 (e-mail: firstname.lastname@example.org).
The REACH study (1994–2001) was supported by the National Institute of Child Health and Human Development (U01-HD32830), with supplemental funding from the NIAID, the National Institute on Drug Abuse, and the National Institute of Mental Health. This work was also supported in part by the Adolescent Trials Network for HIV/AIDS Interventions (ATN) which is funded by the National Institutes of Health through NICHD (5 U01 HD040533).
Presented in part at the 19th Conference on Retroviruses and Opportunistic Infections (CROI) meeting, March 5–8, 2012, Seattle, WA.
The authors have no conflict of interest to disclose.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jaids.com).
Received April 27, 2012
Accepted August 2, 2012
Objective: In the United States, kidney dysfunction is prevalent in almost 30% of HIV-infected patients and is an independent predictor of mortality. Proteinuria and elevated serum cystatin C (eCysC) are used as markers of kidney disease in the general population; however, the prevalence of these markers in HIV-infected adolescents is largely unknown.
Methods: This study includes 304 HIV-infected adolescents from the Reaching for Excellence in Adolescent Care and Health (REACH) cohort, an observational study of adolescents recruited from 13 US cities. Clinical and demographic characteristics of participants were evaluated as correlates of proteinuria, a urine protein to creatinine ratio of ≥200 mg/g. Select univariate predictors were assessed to determine the association with urinary protein excretion and serum cystatin C in multivariable linear regression models and proteinuria and eCysC (eCysC ≥ 75th percentile) in multivariable logistic regression models.
Results: Overall, 19.1% of the participants had proteinuria, whereas 23.7% had an eCysC. Low CD4+ T-lymphocyte counts (<200 cells/mm3) were significantly associated with a greater urine protein to creatinine ratio in linear models and with proteinuria in logistic regression models. CD4+ T-lymphocyte counts <500 cells/mm3 were significantly associated with a greater serum cystatin C concentration in linear models and with eCysC in logistic regression models.
Conclusions: Proteinuria among HIV-infected adolescents in the REACH cohort was approximately 2-fold greater than healthy US adolescents. Both proteinuria and eCysC are associated with CD4+ T-lymphocyte counts. Further studies investigating early markers of kidney disease and the association with immune status and inflammation in HIV-infected adolescents are needed.
Combination antiretroviral therapy (cART) has significantly improved the longevity and disease care management of HIV-infected individuals over the last decade.1,2 However, age-related complications, including chronic kidney disease (CKD), tend to occur more frequently and at an earlier age among HIV-infected patients compared with the general public. Kidney function is abnormal in up to 30% of HIV-infected patients,3 while as many as 10% of HIV-infected patients suffer from CKD, which may not be symptomatic until late into the disease progression.4 Two large cohorts in the United States—the HIV Epidemiology Research Study (HERS) and the Women's Interagency HIV Study (WIHS)—demonstrated a 2- to 2.5-fold risk of death among HIV-infected women with proteinuria or a serum creatinine ≥1.4 mg/dL.5,6 HIV care guidelines recommend regular assessment of kidney function.3
Individuals with CD4+ T-lymphocyte cell (CD4+ T-cell) counts less than 200 cells/mm3 are at high risk for HIV-1–related morbidity and mortality, including kidney disease. Before the advent of cART, CKD was largely a result of HIV-associated nephropathy (HIVAN), a disease manifestation associated with black race, high viral load (VL), and a low CD4+ T-cell count.7 In the past decade, widespread use of cART has reduced the incidence of HIVAN8; however, other renal pathologies have emerged.9 Although cART is used to improve the overall health status of HIV-infected individuals, including reduction in kidney disease progression, it has also been associated with pathological kidney manifestations10,11 and nephrotoxicity.9,11,12 Drug metabolism and excretion are a major function of the kidney; thus, cART agents alone or in combination with other medications may result in kidney impairment leading to end-stage renal disease.10,12 Although the nephrotoxic effects of specific combinations of cART are uncertain, examining kidney dysfunction markers in HIV-infected adolescents, with or without cART, could provide valuable insight.
Proteinuria has been used as a surrogate marker of CKD13 and is defined as a spot urine protein to urine creatinine ratio of ≥200 mg/g. The prevalence of proteinuria in adults in the general population in the United States ranges from 6% to 10%,14,15 whereas various studies in the United States and abroad have demonstrated a higher prevalence of proteinuria in HIV-infected adults ranging from 17% to 45%16,17 and 21% to 33% in children.18,19 Likewise, due to the significant limitations of serum creatinine–based glomerular filtration rate (GFR) estimation in individuals with HIV, serum cystatin C (CysC) has emerged as a marker for both the evaluation of GFR and the detection of drug-induced kidney injury.20 Independent of GFR, CysC concentration is associated with C-reactive protein, HIV VL, and CD4+ T-cell count.21–23 However, studies evaluating the accuracy of CysC for estimating GFR among HIV-infected individuals are limited, and thus, the use of CysC alone or as a component in GFR estimation in the HIV-infected population remains uncertain.20
Studies have shown that HIVAN is a common cause of CKD in HIV-infected adults and children.24,25 To date, most studies of kidney disease in HIV-infected patients have been conducted in adults, and studies of kidney disease markers in HIV-infected adolescents are lacking. One recent study has found that reduction of the HIV VL by antiretroviral therapy (ART) may prevent progression of proteinuria and improve the clinical outcome of HIV-infected youth.18 Our study explored the association of clinical and demographic factors with proteinuria and CysC among HIV-infected adolescents.
Study Population Characteristics
This study involves HIV-infected adolescents, who participated in the Reaching for Excellence in Adolescent Care and Health (REACH) cohort. In brief, the REACH study is an observational study that included 352 adolescents (13–19 years old) who acquired HIV-1 through risk behaviors, mainly sexual activities (perinatal transmission or blood product contamination were excluded), recruited from 15 clinical sites in 13 US cities from 1995 to 2001.26,27 Urine and serum samples were collected at all visits and stored at −80°C; however, in this cross-sectional analysis, we only included the serum and urine sample from a single visit. Due to pregnancy at baseline or sample depletion, we obtained serum and urine specimens from the earliest visit available that met the inclusion criteria. Participant data for HIV and other health-related clinical evaluations, including documentation of demographics, risk behaviors, medical history, and immunological outcomes were extracted from the REACH database and sorted and matched by participant identifier and visit number. Four men seroconverted during the study follow-up and biological specimens were unavailable from 44 individuals, leading to exclusion from this analysis. Overall, samples from 304 REACH participants, obtained during non-pregnant visits, were included in the current analysis. Body mass index (BMI) was calculated using weight and height measurements, and values were characterized into standard weight status categories.27 At the time of the study visit, cART was defined as a combination of 2 nucleoside reverse transcriptase inhibitors and either a protease inhibitor or a nonnucleoside reverse transcriptase inhibitor. This study was approved by the University of Alabama at Birmingham Institutional Review Board.
Immunomarkers and HIV-Specific Testing
VL was measured in a centralized laboratory using either nucleic acid sequence-based amplification (lower limit = 400 copies/mL) or NucliSens assays (Organon Teknika, Durham, NC; lower limit = 80 copies/mL). Quantitative immunophenotyping of CD4+ and CD8+ T-lymphocyte counts were determined at the individual clinical sites in certified laboratories using AIDS Clinical Trials Group standardized flow cytometry protocols.28,29 Samples for quantitative immunophenotyping of CD8+CD38+ T cells were analyzed centrally every 6 months at the Immunology Core Laboratory at The Children's Hospital of Philadelphia after overnight shipping as previously reported.29 The clinical status of participants at baseline was determined using the absolute CD4+ T-cell count categorized as follows: 0 to 200 cells/mm3 (low), 200 to 499 cells/mm3 (medium), and ≥500 cells/mm3 (high).
Kidney Markers and Estimation of Kidney Function
Serum and urine creatinine were analyzed enzymatically on previously frozen samples using an in vitro clinical diagnostic integrated system, the Siemens Dimension Vista 1500 (Siemens Healthcare Diagnostics, Inc, Deerfield, IL), at the University of Maryland (Baltimore, MD) in conjunction with National Institute of Standards and Technology's Standard Reference Material 967 traceable to isotope dilution mass spectrometry analysis.30,31 Additional testing included urine protein and CysC, which were measured using automated nephelometric assay methodology on the same system (Siemens Healthcare Diagnostics, Inc). The spot urine protein and creatinine were used to calculate a ratio, in milligrams of protein to grams of creatinine (mg/g), as an estimation of 24-hour urinary protein excretion (urinary protein to creatinine ratio, UP/Cr). A ratio of ≥200 mg/g was defined as abnormal urinary protein excretion or proteinuria, as recommended by the Kidney Disease Outcomes Quality Initiative (KDOQI) of the National Kidney Foundation (NKF).32
For the participants' visit, demographic and clinical parameters were characterized for participants with and without proteinuria. Correlative analysis of each kidney marker—UP/Cr in mg/g, serum creatinine in mg/dL, and CysC in mg/L—was performed. Regression model outcomes included log-transformed UP/Cr ratio and CysC for linear regression models, whereas proteinuria, UP/Cr ≥200 mg/g, and elevated serum cystatin C (eCysC ≥ 0.783 mg/L) corresponding to the 75th percentile cutoff were the outcomes for logistic regression models. The 75th percentile age-adjusted CysC was not significantly different from the unadjusted values. Both linear and logistic regression models included CD4+ T-cell count as the primary predictor (categories low, medium, and high) and adjusted for a priori-selected covariates chosen based on previous literature and those found to be statistically significant at (P < 0.10) in univariate analyses. Additionally, independent variables exhibiting collinearity with CD4+ T-cell count, the main predictor of interest, were excluded. Three models were evaluated for each outcome. The first model was adjusted for age, gender, and race/ethnicity. The second model consisted of variables included in the first model in addition to BMI category. Finally, model 3 included model 2 covariates while adjusting for cART use. Final adjusted and unadjusted models were also performed (data shown in the Tables, Supplemental Digital Content 1, http://links.lww.com/QAI/A352). All statistical analyses were performed using SAS version 9.2 (SAS Institute, Inc, Cary, NC).
Of the 304 participants included in this study, the mean age was 18 ± 1 years and 75% of participants were non-Hispanic black females. Participants were predominantly normal weight according to BMI (45%), and more than half of the cohort reported that they had (ever) smoked cigarettes (67%) and drank alcohol (58%). There were no significant differences for proteinuria status by gender, smoking status, alcohol use, cART status, VL, absolute CD8+ or CD8+CD38+ T-cell counts, or serum creatinine. There were significant differences (P < 0.10) for proteinuria status by age, race/ethnicity, BMI category, CD4+ T-cell count, and CysC (Table 1).
Correlative analysis of UP/Cr in milligrams per gram with serum creatinine in milligrams per deciliter yielded a Pearson correlation (r) [95% confidence interval (CI)] of 0.22 (0.11 to 0.33). However, the correlation was much higher among those with low CD4+ T-cell counts, r = 0.77 (0.51 to 0.89), and not quite significant in the medium and high CD4+ T-cell count groups. The overall correlation between CysC in milligrams per liter and serum creatinine in milligrams per deciliter was 0.46 (0.36 to 0.55). There was a graded statistically significant correlation between CysC and serum creatinine with descending CD4+ T-cell count category, r = 0.79 (0.55 to 0.90), 0.44 (0.15 to 0.65), and 0.31 (0.18 to 0.42), respectively. The overall Pearson correlation between CysC and UP/Cr was 0.43 (0.33 to 0.52); however, the correlation was higher among individuals with low CD4+ T-cell counts, r = 0.81 (0.58 to 0.91).
Of the participants in this study, 19.1% had proteinuria. The geometric mean of UP/Cr was 221, 148, and 134 mg/g among participants with low, medium, and high CD4+ T-cell count categories, respectively (Fig. 1, top panel). The prevalence of proteinuria was 42%, 19%, and 16% among the 3 levels of CD4+ T cells, respectively (Fig. 1, bottom panel). After adjustment for age, gender, and race/ethnicity in linear regression, the beta coefficient ± SE for low and medium CD4+ T-cell count versus high CD4+ T-cell counts were 0.53 ± 0.12 and 0.10 ± 0.09, respectively. With the addition of BMI in model 2 and cART in model 3, the change in the beta coefficients was unremarkable (Table 2).
In logistic regression models, with high CD4+ T-cell count as the referent category, the odds ratio (95% CI) for proteinuria in participants with low CD4+ T-cell counts was 3.7 (1.5 to 8.9) in the unadjusted model (see Table 1, Supplemental Digital Content 1, http://links.lww.com/QAI/A352), 4.4 (1.6 to 11.6) in the first model, 3.2 (1.2 to 8.8) in the second model, and 3.2 (1.2 to 8.7) in the third model (Table 2). Age and race/ethnicity were significant predictors of proteinuria in all models. Participant BMI category significantly predicted proteinuria in models 2–3, with underweight and normal weight individuals being most likely to present with proteinuria.
Serum Cystatin C
Of the participants in this study, 23.7% had an eCysC value ≥0.783 mg/L (≥75th percentile). Mean values for CysC were 0.78, 0.75, and 0.71 mg/L among participants with low, medium, and high CD4+ T-cell counts, respectively (Fig. 2, top panel). The prevalence of eCysC was 33%, 40%, and 20%, respectively, in the 3 groups (Fig. 2, bottom panel). Linear regression models for CysC demonstrated an increase in value as the category of CD4+ T-cell count decreased from high to low. After adjustment for age, gender, and race/ethnicity, the beta coefficient ± SE for low and medium CD4+ T-cell count versus high CD4+ T-cell count were 0.06 ± 0.03 and 0.05 ± 0.02, respectively (Table 3). After controlling for BMI in model 2 and cART in model 3, the change in beta coefficient values was minimal (Table 3). Gender was statistically significant in all the linear regression models (eg, 0.78 mg/L in males versus 0.72 mg/L in females, P < 0.001 in the overall adjusted model) (see Table 2, Supplemental Digital Content 1, http://links.lww.com/QAI/A352). In logistic regression models, a CD4+ T-cell count <500 cells/mm3 was associated with an eCysC. The odds ratio (95% CI) for low and medium CD4+ T-cell count was 1.9 (0. 7 to 4.9) and 3.5 (1.7 to 7.3) in model 1; 2.4 (0.84 to 6.6) and 3.5 (1.7 to 7.5) in model 2; and 2.7 (0.93 to 7.6) and 3.8 (1.8 to 8.1), respectively, in the final model (Table 3).
In the current study, the prevalence of proteinuria among HIV-infected adolescents who participated in the REACH cohort was 19.1%, almost 2 times the prevalence in healthy US adolescents.33 Logistic regression analyses revealed age, race/ethnicity, BMI category, and low CD4+ T-cell count as significant predictors of proteinuria; in the linear regression with the natural log-transformed UP/Cr ratio as the dependent variable, there was an inverse relationship between both BMI category and CD4+ T-cell count category with UP/Cr. The association with risk factors such as severity of HIV-infection and nutrition status inferred by these relationships is similar to what other studies have demonstrated in different HIV-infected adult populations.34,35 Diabetes and hypertension are known cardiovascular and kidney disease risk factors associated with proteinuria in the general population and among those who are HIV-infected.36,37 In the US, the prevalence of diabetes and hypertension in adolescents is <2%38; consequently, although these data were not available, hypertension and diabetes are expected to be minimal among the adolescents in this cohort. Other factors, such as the metabolic syndrome, exposure to multiple medications over time, or myopathic disorders related to HIV, may also play a role in the development of proteinuria and subsequent kidney dysfunction.23,39
Our finding that low CD4+ T-cell count was associated with proteinuria is consistent with other US studies among various HIV-infected populations.17,18,40–42 REACH participants with abnormal urinary protein excretion were more likely to be young, underweight or normal weight, and nonblack individuals. However, the association of race/ethnicity in these models is most likely a result of a small number of nonblack individuals and should be interpreted cautiously. In contrast to other studies conducted in the general population,43,44 the association of low BMI with proteinuria and kidney dysfunction is more common among those with HIV infection.34,35,45,46 In addition, body fat distributions in HIV-infected children and adolescents have demonstrated patterns associated with cardiovascular disease risk and it is possibly related to specific antiretroviral drugs.47,48 Association between low BMI and proteinuria has also been reported in at least one other study and was explained as being due to advanced renal disease.35 However, in our study, renal impairment was not advanced enough for the association to be interpreted in this way. Consequently, in our study, BMI category could potentially be a proxy for the relation of nutritional status and body fat distribution, sicker individuals, and disease progression among those with HIV infection.
Classification of the REACH cohort by proteinuria status demonstrated a significant difference between mean CysC (P = 0.01) of 0.77 mg/L in those with proteinuria versus 0.71 mg/L in those with normal urinary protein excretion, respectively. The values obtained for serum creatinine and cystatin C were both slightly lower than those reported from NHANES III, including non-Hispanic blacks with values reported as 0.76 mg/dL for serum creatinine and 0.80 mg/L for CysC, respectively.49 This difference may be due to the differences in study populations as the mean age of our cohort was 18 years compared with 15 years in NHANES III. Additionally, there have been changes in calibration and sensitivity of the methods during the past 5 years50; however, this should not result in differential misclassification.
Our final model in logistic regression analyses revealed a greater likelihood of an eCysC among participants who were obese, nonblack, male gender, and had an elevated VL. In contrast to the logistic regression results for proteinuria, in which a low CD4+ T-cell count (<200 cells/mm3) was found to be a strong predictor, participants with medium CD4+ T-lymphocyte counts (200–499 cells/mm3) were most likely to have an eCysC. In linear regression models with CysC as the dependent variable, least square mean comparisons demonstrated similar findings. Participants with both low and medium CD4+ T-cell counts demonstrated significantly higher CysC concentrations than those with high CD4+ T-cell counts. CysC-derived GFR estimating equations have not been validated in healthy children and adolescent populations; however, they have shown greater sensitivity and accuracy in those with kidney dysfunction, particularly in those with a GFR <90 mL/min per 1.73 m2.51 In this study, estimated glomerular filtration rate (eGFRs) were calculated using different equations.52–55 At higher GFRs, serum creatinine estimates are less precise and cystatin C provides more valid estimation.56 GFR estimates as calculated from serum creatinine and/or CysC are known to be unreliable for values >60 mL/min per 1.73 m2; thus, those that are calculated to be >60 mL/min per 1.73 m2 are generally reported as such in clinical laboratory reports. The majority of participants in the REACH cohort had an eGFR over 60 mL/min per 1.73 m2. Therefore, we selected to report CysC values rather than an eGFR calculated from CysC. The eCysC is associated with many of the abnormalities present in moderate to advanced CKD.56 Recent data presented by the Chronic Kidney Disease Epidemiology Collaboration suggest that cystatin C should not replace creatinine for GFR estimation in general practice; however, it may be useful in specific cases, such as confirmation of the diagnosis of CKD in patients with a decreased GFR as estimated from serum creatinine and more accurate estimation of GFR in patients with muscle wasting or chronic illness.57 Studies have shown that CysC is higher in those who are HIV infected when compared with uninfected individuals and that there is a correlation with VL,58 as found in our analyses, and inflammatory markers.23 It is unclear if the biological associations of CysC are with HIV infection or kidney disease. Although the finding should be cautiously interpreted, it is possible that these are potential kidney disease progression indicators in HIV-infected adolescents.
Gupta et al59 reported the association of the percent of activated CD8+CD38+HLA-DR+ with proteinuria in HIV-infected adults but not with absolute counts or other T-cell markers of immune activation. In our study, we did not observe any association between immune activation by way of CD8+CD38+ T cells, VL, and proteinuria. The extent of viral replication has been shown to be a strong determinant of kidney disease,60 along with peripheral CD4+ T-cell count.34,61 The correlations of CD4+ T-cell count and VL with proteinuria could be high among adults. There seems to be less variation and overall lower VL among nonwhite females in our adolescent population compared with other studies.26 Furthermore, it is often challenging to distinguish antiretroviral-related kidney toxicity from direct effects of HIV-1 infection on the kidney or from a multitude of non–HIV-related kidney diseases.62 If suppression of viral replication is the mechanism by which antiretroviral medications have a beneficial effect on kidney disease, the inclusion of CD4+ T-cell count would be expected to minimize the estimated relationship between these medications and progression of nephropathy by controlling for mechanism of effect. Alternatively, although we adjusted for cART, the efficacy of antiretroviral medications may not be uniform across antiretroviral classes. Variations in effect due to the use of different antiretrovirals and other issues such as compliance, however, would bias the investigation of this association toward the null.
The current study has potential limitations. We were unable to assess causality because of the study's observational cross-sectional design. In addition, the assessment of proteinuria was determined from spot urine samples as opposed to a timed or 24-hour urine collection; however, spot urine measurement has been shown to perform well at detecting abnormal urinary protein excretion in those with HIV, and the one time collection avoids error introduced by inadequate collections over time.57 Another limitation is that proteinuria was measured at only one time point. This may lead to misclassification of some individuals with regard to proteinuria status as the KDOQI of the NKF guidelines recommend a second measurement to confirm the persistence of proteinuria.63 Additionally, the study population was primarily composed of women and our results may not be generalizable to a broader population. In support of the study's internal validity, evaluation of participant descriptors for those not included in the study demonstrated similar characteristics as the majority of the missing individuals were normal weight (44.8%), non-Hispanic black females (72.4%) with a mean age of 17 years. In addition, REACH participants not included in the study presented with an average CD4+ T-cell count of 532 ± 273 cells/mm3 and 41.4% were on cART. Despite study limitations, the REACH Cohort has several notable strengths, including being a representative sample of urban HIV-infected adolescents in the US. In addition, serum and urine measurements in this study were conducted at a central laboratory following standardized procedures.
In conclusion, in the current study, kidney disease as indicated by proteinuria was present in 19.1% of the HIV-infected adolescents participating in REACH. HIV-infected adolescents in the REACH cohort with a low CD4+ T-cell count and low BMI were more likely to be diagnosed with proteinuria. In REACH, there were significant correlations with increasing CysC concentration, including low and medium CD4+ T-cell counts and a high BMI. With the level of current evidence, the added value of CysC in assessment of kidney dysfunction in HIV-infected adolescents will have to be both clinically useful and economically acceptable for its widespread adoption.20 Early detection of kidney disease in HIV-infected adolescents would allow for appropriate evaluation and treatment, as well as modification of medication regimens to avoid systemic toxicity and worsening kidney function. Further studies investigating earlier markers of kidney damage and systemic therapies targeting kidney disease risk in this vulnerable population are warranted.
The study was scientifically reviewed by the ATN's Therapeutic Leadership Group. Network, scientific, and logistical support was provided by the ATN Coordinating Center (C. Wilson, C. Partlow) at the University of Alabama at Birmingham. We thank the REACH investigators, staff, and participants for their valuable contributions (listed in J Adolescent Health 2001; 29: S5–S6). The parent study and this substudy conformed to the procedures for informed consent (parental permission was obtained wherever required) approved by institutional review boards at all sponsoring organizations and to human experimentation guidelines set forth by the United States Department of Health and Human Services.
1. Seal PS, Jackson DA, Chamot E, et al.. Temporal trends in presentation for outpatient HIV medical care 2000-2010: implications for short-term mortality. J Gen Intern Med. 2011;26:745–750.
2. Centers for Disease Control and Prevention (CDC). HIV surveillance—United States, 1981-2008. MMWR Morb Mortal Wkly Rep. 2011;60:689–693.
3. Gupta SK, Eustace JA, Winston JA, et al.. Guidelines for the management of chronic kidney disease in HIV-infected patients: recommendations of the HIV Medicine Association of the Infectious Diseases Society of America. Clin Infect Dis. 2005;40:1559–1585.
4. Fernando SK, Finkelstein FO, Moore BA, et al.. Prevalence of chronic kidney disease in an urban HIV infected population. Am J Med Sci. 2008;335:89–94.
5. Szczech LA, Hoover DR, Feldman JG, et al.. Association between renal disease and outcomes among HIV-infected women receiving or not receiving antiretroviral therapy. Clin Infect Dis. 2004;39:1199–1206.
6. Wyatt CM, Hoover DR, Shi Q, et al.. Microalbuminuria is associated with all-cause and AIDS mortality in women with HIV infection. J Acquir Immune Defic Syndr. 2010;55:73.
7. Winston JA. HIV and CKD epidemiology. Adv Chronic Kidney Dis. 2010;17:19–25.
8. Lucas GM, Eustace JA, Sozio S, et al.. Highly active antiretroviral therapy and the incidence of HIV-1-associated nephropathy: a 12-year cohort study. AIDS. 2004;18:541–546.
9. Lescure FX, Flateau C, Pacanowski J, et al.. HIV-associated kidney glomerular diseases: changes with time and HAART. Nephrol Dial Transplant. 2012;27:2349–2355.
10. Kalyesubula R, Perazella MA. Nephrotoxicity of HAART. AIDS Res Treat. 2011;2011:562790.
11. Daugas E, Rougier JP, Hill G. HAART-related nephropathies in HIV-infected patients. Kidney Int. 2005;67:393–403.
12. Maggi P, Bartolozzi D, Bonfanti P, et al.. Renal complications in HIV disease: between present and future. AIDS Rev. 2012;14:37.
13. Stoycheff N, Pandya K, Okparavero A, et al.. Early change in proteinuria as a surrogate outcome in kidney disease progression: a systematic review of previous analyses and creation of a patient-level pooled dataset. Nephrol Dial Transplant. 2011;26:848–857.
14. Garg AX, Kiberd BA, Clark WF, et al.. Albuminuria and renal insufficiency prevalence guides population screening: results from the NHANES III. Kidney Int. 2002;61:2165–2175.
15. Coresh J, Selvin E, Stevens LA, et al.. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298:2038–2047.
16. Estrella MM, Parekh RS, Astor BC, et al.. Chronic kidney disease and estimates of kidney function in HIV infection: a cross-sectional study in the multicenter AIDS cohort study. J Acquir Immune Defic Syndr. 2011;57:380–386.
17. Yanik EL, Lucas GM, Vlahov D, et al.. HIV and proteinuria in an injection drug user population. Clin J Am Soc Nephrol. 2010;5:1836–1843.
18. Chaparro AI, Mitchell CD, Abitbol CL, et al.. Proteinuria in children infected with the human immunodeficiency virus. J Pediatr. 2008;152:844–849.
19. Esezobor CI, Iroha E, Onifade E, et al.. Prevalence of proteinuria among HIV-infected children attending a tertiary hospital in Lagos, Nigeria. J Trop Pediatr. 2010;56:187–190.
20. Gagneux-Brunon A, Mariat C, Delanaye P. Cystatin C in HIV-infected patients: promising but not yet ready for prime time. Nephrol Dial Transplant. 2012;27:1305–1313.
21. Choi A, Scherzer R, Bacchetti P, et al.. Cystatin C, albuminuria, and 5-year all-cause mortality in HIV-infected persons. Am J Kidney Dis. 2010;56:872–882.
22. Overton ET, Patel P, Mondy K, et al.. Cystatin C and Baseline Renal Function Among HIV-Infected Persons in the SUN Study. AIDS Research and Human Retroviruses. 2012;28:148–155.
23. Neuhaus J, Jacobs DR Jr, Baker JV, et al.. Markers of inflammation, coagulation, and renal function are elevated in adults with HIV infection. J Infect Dis. 2010;201:1788–1795.
24. Wyatt CM, Klotman PE. HIV-1 and HIV-associated nephropathy 25 years later. Clin J Am Soc Nephrol. 2007;2(suppl 1):S20–S24.
25. Purswani MU, Chernoff MC, Mitchell CD, et al.. Chronic kidney disease associated with perinatal HIV infection in children and adolescents. Pediatric Nephrology. 2012;27:981–989.
26. Wilson CM, Houser J, Partlow C, et al.; Adolescent Medicine HIVARN. The REACH (Reaching for Excellence in Adolescent Care and Health) project: study design, methods, and population profile. J Adolesc Health. 2001;29(3 suppl):8–18.
27. Rogers AS, Futterman DK, Moscicki AB, et al.. The REACH Project of the Adolescent Medicine HIV/AIDS Research Network: design, methods, and selected characteristics of participants. J Adolesc Health. 1998;22:300–311.
28. Wilson CM, Ellenberg JH, Douglas SD, et al.; Reach Project Of The Adolescent Medicine HIVARN. CD8+CD38+ T cells but not HIV type 1 RNA viral load predict CD4+ T cell loss in a predominantly minority female HIV+ adolescent population. AIDS Res Hum Retroviruses. 2004;20:263–269.
29. Douglas SD, Rudy B, Muenz L, et al.. T-lymphocyte subsets in HIV-infected and high-risk HIV-uninfected adolescents: retention of naive T lymphocytes in HIV-infected adolescents. The Adolescent Medicine HIV/AIDS Research Network. Arch Pediatr Adolesc Med. 2000;154:375–380.
30. Dodder NG, Tai SS, Sniegoski LT, et al.. Certification of creatinine in a human serum reference material by GC-MS and LC-MS. Clin Chem. 2007;53:1694–1699.
31. Myers GL, Miller WG, Coresh J, et al.; National Kidney Disease Education Program Laboratory Working G. Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin Chem. 2006;52:5–18.
32. Levey AS, Coresh J, Balk E, et al.; National Kidney F. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Int Med. 2003;139:137–147.
33. Hogg RJ, Furth S, Lemley KV, et al.; National Kidney Foundation's Kidney Disease Outcomes Quality I. National Kidney Foundation's Kidney Disease Outcomes Quality Initiative clinical practice guidelines for chronic kidney disease in children and adolescents: evaluation, classification, and stratification. Pediatrics. 2003;111(6 pt 1):1416–1421.
34. Msango L, Downs JA, Kalluvya SE, et al.. Renal dysfunction among HIV-infected patients starting antiretroviral therapy. AIDS. 2011;25:1421–1425.
35. Emem CP, Arogundade F, Sanusi A, et al.. Renal disease in HIV-seropositive patients in Nigeria: an assessment of prevalence, clinical features and risk factors. Nephrol Dial Transplant. 2008;23:741–746.
36. de Zeeuw D, Parving HH, Henning RH. Microalbuminuria as an early marker for cardiovascular disease. J Am Soc Nephrol. 2006;17:2100–2105.
37. Jotwani V, Li Y, Grunfeld C, Choi AI, et al.. Risk Factors for ESRD in HIV-Infected Individuals: Traditional and HIV-Related Factors. Am J Kidney Dis. 2011;59:628–635.
38. Chavers BM, Rheault MN, Foley RN. Kidney function reference values in US adolescents: National Health And Nutrition Examination Survey 1999-2008. Clin J Am Soc Nephrol. 2011;6:1956–1962.
39. Tien PC, Choi AI, Zolopa AR, et al.. Inflammation and mortality in HIV-infected adults: analysis of the FRAM study cohort. J Acquir Immune Defic Syndr. 2010;55:316–322.
40. Fulop T, Olivier J, Meador RS, et al.. Screening for chronic kidney disease in the ambulatory HIV population. Clin Nephrol. 2010;73:190–196.
41. Gupta SK, Smurzynski M, Franceschini N, et al.. The effects of HIV type-1 viral suppression and non-viral factors on quantitative proteinuria in the highly active antiretroviral therapy era. Antivir Ther. 2009;14:543–549.
42. Szczech LA, Gange SJ, van der Horst C, et al.. Predictors of proteinuria and renal failure among women with HIV infection. Kidney Int. 2002;61:195–202.
43. de Jong PE, Verhave JC, Pinto-Sietsma SJ, et al.; group Ps. Obesity and target organ damage: the kidney. Int J Obes Relat Metab Disord. 2002;26(suppl 4):S21–S24.
44. Chen J, Muntner P, Hamm LL, et al.. The metabolic syndrome and chronic kidney disease in U.S. adults. Ann Int Med. 2004;140:167–174.
45. Deti EK, Thiebaut R, Bonnet F, et al.; Groupe d'Epidemiologie Clinique du SeA. Prevalence and factors associated with renal impairment in HIV-infected patients, ANRS C03 Aquitaine Cohort, France. HIV Med. 2010;11:308–317.
46. Menezes AM, Torelly J Jr, Real L, et al.. Prevalence and risk factors associated to chronic kidney disease in HIV-infected patients on HAART and undetectable viral load in Brazil. PloS One. 2011;6:e26042.
47. Lindsey JC, Jacobson DL, Li H, et al.. Using cluster heat maps to investigate relationships between body composition and laboratory measurements in HIV-infected and HIV-uninfected children and young adults. J Acquir Immune Defic Syndr. 2012;59:325–328.
48. Jacobson DL, Patel K, Siberry GK, et al.. Body fat distribution in perinatally HIV-infected and HIV-exposed but uninfected children in the era of highly active antiretroviral therapy: outcomes from the Pediatric HIV/AIDS Cohort Study. Am J Clin Nutr. 2011;94:1485–1495.
49. Groesbeck D, Kottgen A, Parekh R, et al.. Age, gender, and race effects on cystatin C levels in US adolescents. Clin J Am Soc Nephrol. 2008;3:1777–1785.
50. Larsson A, Hansson LO, Flodin M, et al.. Calibration of the Siemens cystatin C immunoassay has changed over time. Clin Chem. 2011;57:777.
51. Zappitelli M, Parvex P, Joseph L, et al.. Derivation and validation of cystatin C-based prediction equations for GFR in children. Am J Kidney Dis. 2006;48:221–230.
52. Levey AS, Coresh J, Greene T, et al.; Chronic Kidney Disease Epidemiology C. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247–254.
53. Schwartz GJ, Haycock GB, Edelmann CM Jr, et al.. A simple estimate of glomerular filtration rate in children derived from body length and plasma creatinine. Pediatrics. 1976;58:259–263.
54. Schwartz GJ, Work DF. Measurement and estimation of GFR in children and adolescents. Clin J Am Soc Nephrol. 2009;4:1832–1843.
55. Stevens LA, Coresh J, Schmid CH, et al.. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis. 2008;51:395–406.
56. Muntner P, Vupputuri S, Coresh J, et al.. Metabolic abnormalities are present in adults with elevated serum cystatin C. Kidney Int. 2009;76:81–88.
57. Inker LA, Schmid CH, Tighiouart H, et al.. Estimating Glomerular Filtration Rate from Serum Creatinine and Cystatin C. N Engl J Med. 2012;367:20–29.
58. Jaroszewicz J, Wiercinska-Drapalo A, Lapinski TW, et al.. Short communication Does HAART improve renal function? An association between serum cystatin C concentration, HIV viral load and HAART duration. Antivir Ther. 2006;11:641–645.
59. Gupta SK, Komarow L, Gulick RM, et al.. Proteinuria, creatinine clearance, and immune activation in antiretroviral-naive HIV-infected subjects. J Infect Dis. 2009;200:614–618.
60. Choi AI, Shlipak MG, Hunt PW, et al.. HIV-infected persons continue to lose kidney function despite successful antiretroviral therapy. AIDS. 2009;23:2143–2149.
61. Baker JV, Peng G, Rapkin J, et al.; Terry Beirn Community Programs for Clinical Research on AIDS. CD4+ count and risk of non-AIDS diseases following initial treatment for HIV infection. AIDS. 2008;22:841–848.
62. Atta MG, Deray G, Lucas GM. Antiretroviral nephrotoxicities. Semin Nephrol. 2008;28:563–575.
63. Coresh J, Astor BC, McQuillan G, et al.. Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis. 2002;39:920–929.
HIV; adolescent; kidney; CD4+ T cells; proteinuria; serum cystatin C
Supplemental Digital Content
© 2012 Lippincott Williams & Wilkins, Inc.
What does "Remember me" mean?
By checking this box, you'll stay logged in until you logout. You'll get easier access to your articles, collections,
media, and all your other content, even if you close your browser or shut down your
To protect your most sensitive data and activities (like changing your password),
we'll ask you to re-enter your password when you access these services.
What if I'm on a computer that I share with others?
If you're using a public computer or you share this computer with others, we recommend
that you uncheck the "Remember me" box.
Highlight selected keywords in the article text.
Data is temporarily unavailable. Please try again soon.