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Figure 1.
Meta-analysis of Odds Ratios for CKD Using Creatinine Values Comparing Sickle Cell Trait Carriers With Noncarriers
Meta-analysis of Odds Ratios for CKD Using Creatinine Values Comparing Sickle Cell Trait Carriers With Noncarriers

CKD indicates chronic kidney disease; eGFR, estimated glomerular filtration rate; SCT, sickle cell trait. Chronic kidney disease was defined as an eGFR level lower than 60 mL/min/1.73 m2 at baseline or follow-up. All models adjusted for age, sex, clinic or region, African genetic ancestry, hypertension, and diabetes. The size of data markers indicate the weight of study.

Figure 2.
Meta-analysis of Odds Ratios for Incident CKD Using Creatinine Values Comparing Sickle Cell Trait Carriers With Noncarriers
Meta-analysis of Odds Ratios for Incident CKD Using Creatinine Values Comparing Sickle Cell Trait Carriers With Noncarriers

CKD indicates chronic kidney disease; eGFR, estimated glomerular filtration rate; SCT, sickle cell trait. Incident CKD was defined as development of an eGFR level lower than 60 mL/min/1.73 m2 during follow-up. All models adjusted for age, sex, clinic or region, African genetic ancestry, hypertension, and diabetes. The size of data markers indicate the weight of study.

Figure 3.
Meta-analysis of Odds Ratios for Estimated Glomerular Filtration Rate Decline Comparing Sickle Cell Trait Carriers With Noncarriers
Meta-analysis of Odds Ratios for Estimated Glomerular Filtration Rate Decline Comparing Sickle Cell Trait Carriers With Noncarriers

CKD indicates chronic kidney disease; eGFR, estimated glomerular filtration rate; SCT, sickle cell trait. Estimated glomerular filtration rate decline was defined as a decrease in eGFR level of more than 3 mL/min/1.73 m2 per year. All models adjusted for age, sex, clinic or region, African genetic ancestry, hypertension, and diabetes. The size of data markers indicate the weight of study.

Figure 4.
Meta-analysis of Odds Ratios for Albuminuria Comparing Sickle Cell Trait Carriers With Noncarriers
Meta-analysis of Odds Ratios for Albuminuria Comparing Sickle Cell Trait Carriers With Noncarriers

CKD indicates chronic kidney disease; SCT, sickle cell trait. Albuminuria was defined as spot urine albumin:creatinine ratio higher than 30 mg/g or albumin excretion rate higher than 30 mg/24 hours. All models adjusted for age, sex, clinic or region, African genetic ancestry, hypertension, and diabetes. The size of data markers indicate the weight of study.

Table 1.  
Baseline Characteristics of Cohorts
Baseline Characteristics of Cohorts
Table 2.  
Characteristics of African American Participants by Sickle Cell Trait Carrier Statusa
Characteristics of African American Participants by Sickle Cell Trait Carrier Statusa
Table 3.  
Association of Sickle Cell Trait With Chronic Kidney Disease and Albuminuria by Cohorta
Association of Sickle Cell Trait With Chronic Kidney Disease and Albuminuria by Cohorta
Table 4.  
Association of Sickle Cell Trait With Incident Chronic Kidney Disease by Hypertension or Diabetes Statusa
Association of Sickle Cell Trait With Incident Chronic Kidney Disease by Hypertension or Diabetes Statusa
Table 5.  
Association of Sickle Cell Trait With Chronic Kidney Disease and Albuminuria by APOL1 Carrier Status in ARIC and JHSa
Association of Sickle Cell Trait With Chronic Kidney Disease and Albuminuria by APOL1 Carrier Status in ARIC and JHSa
1.
Key  NS, Derebail  VK.  Sickle cell trait: novel clinical significance. Hematology Am Soc Hematol Educ Program. 2010;2010:418-422.
PubMedArticle
2.
Tsaras  G, Owusu-Ansah  A, Boateng  FO, Amoateng-Adjepong  Y.  Complications associated with sickle cell trait: a brief narrative review. Am J Med. 2009;122(6):507-512.
PubMedArticle
3.
Becton  LJ, Kalpatthi  RV, Rackoff  E,  et al.  Prevalence and clinical correlates of microalbuminuria in children with sickle cell disease. Pediatr Nephrol. 2010;25(8):1505-1511.
PubMedArticle
4.
Itano  HA, Keitel  HG, Thompson  D.  Hyposthenuria in sickle cell anemia: a reversible renal defect. J Clin Invest. 1956;35(9):998-1007.
PubMedArticle
5.
Heller  P, Best  WR, Nelson  RB, Becktel  J.  Clinical implications of sickle cell trait and glucose-6-phosphate dehydrogenase deficiency in hospitalized black male patients. N Engl J Med. 1979;300(18):1001-1005.
PubMedArticle
6.
Statius van Eps  LW, Pinedo-Veels  C, de Vries  GH, de Koning  J.  Nature of concentrating defect in sickle cell nephropathy: microradioangiographic studies. Lancet. 1970;1(7644):450-452.
PubMedArticle
7.
Goldsmith  JC, Bonham  VL, Joiner  CH, Kato  GJ, Noonan  AS, Steinberg  MH.  Framing the research agenda for sickle cell trait: building on the current understanding of clinical events and their potential implications. Am J Hematol. 2012;87(3):340-346.
PubMedArticle
8.
Grant  AM, Parker  CS, Jordan  LB,  et al.  Public health implications of sickle cell trait: a report of the CDC meeting. Am J Prev Med. 2011;41(6)(suppl 4):S435-S439.
PubMedArticle
9.
Derebail  VK, Nachman  PH, Key  NS, Ansede  H, Falk  RJ, Kshirsagar  AV.  High prevalence of sickle cell trait in African Americans with ESRD. J Am Soc Nephrol. 2010;21(3):413-417.
PubMedArticle
10.
US Renal Data System. USRDS 2013 annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States.http://www.usrds.org/atlas.aspx. Accessed December 12, 2013.
11.
Palmer Alves  T, Lewis  J.  Racial differences in chronic kidney disease (CKD) and end-stage renal disease (ESRD) in the United States: a social and economic dilemma. Clin Nephrol. 2010;74(suppl 1):S72-S77.
PubMed
12.
Muntner  P, Newsome  B, Kramer  H,  et al.  Racial differences in the incidence of chronic kidney disease. Clin J Am Soc Nephrol. 2012;7(1):101-107.
PubMedArticle
13.
 The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives: the ARIC investigators. Am J Epidemiol. 1989;129(4):687-702.
PubMed
14.
Taylor  HA  Jr,.  The Jackson Heart Study: an overview. Ethn Dis. 2005;15(4 suppl 6):S6-1-3.
PubMed
15.
Friedman  GD, Cutter  GR, Donahue  RP,  et al.  CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988;41(11):1105-1116.
PubMedArticle
16.
Bild  DE, Bluemke  DA, Burke  GL,  et al.  Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871-881.
PubMedArticle
17.
The Women’s Health Initiative Study Group.  Design of the Women’s Health Initiative clinical trial and observational study. Control Clin Trials. 1998;19(1):61-109.
PubMedArticle
18.
Pritchard  JK, Stephens  M, Donnelly  P.  Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945-959.
PubMed
19.
Tang  H, Peng  J, Wang  P, Risch  NJ.  Estimation of individual admixture: analytical and study design considerations. Genet Epidemiol. 2005;28(4):289-301.
PubMedArticle
20.
Parsa  A, Kao  WH, Xie  D,  et al; AASK Study Investigators; CRIC Study Investigators.  APOL1 risk variants, race, and progression of chronic kidney disease. N Engl J Med. 2013;369(23):2183-2196.
PubMedArticle
21.
Genovese  G, Friedman  DJ, Ross  MD,  et al.  Association of trypanolytic APOL1 variants with kidney disease in African Americans. Science. 2010;329(5993):841-845.
PubMedArticle
22.
Ito  K, Bick  AG, Flannick  J,  et al.  Increased burden of cardiovascular disease in carriers of APOL1 genetic variants. Circ Res. 2014;114(5):845-850.
PubMedArticle
23.
Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-612.
PubMedArticle
24.
Inker  LA, Schmid  CH, Tighiouart  H,  et al; CKD-EPI Investigators.  Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20-29.
PubMedArticle
25.
Stevens  PE, Levin  A; Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members.  Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825-830.
PubMedArticle
26.
Schneider  RG, Hightower  B, Hosty  TS,  et al.  Abnormal hemoglobins in a quarter million people. Blood. 1976;48(5):629-637.
PubMed
27.
Petrakis  NL, Wiesenfeld  SL, Sams  BJ, Collen  MF, Cutler  JL, Siegelaub  AB.  Prevalence of sickle cell trait and glucose-6-phosphate dehydrogenase deficiency. N Engl J Med. 1970;282(14):767-770.
PubMedArticle
28.
Nath  KA, Katusic  ZS.  Vasculature and kidney complications in sickle cell disease. J Am Soc Nephrol. 2012;23(5):781-784.
PubMedArticle
29.
Hostetter  TH.  Hyperfiltration and glomerulosclerosis. Semin Nephrol. 2003;23(2):194-199.
PubMedArticle
30.
Reese  PP, Hoo  AC, Magee  CC.  Screening for sickle trait among potential live kidney donors: policies and practices in US transplant centers. Transpl Int. 2008;21(4):328-331.
PubMedArticle
31.
Falk  RJ, Scheinman  J, Phillips  G, Orringer  E, Johnson  A, Jennette  JC.  Prevalence and pathologic features of sickle cell nephropathy and response to inhibition of angiotensin-converting enzyme. N Engl J Med. 1992;326(14):910-915.
PubMedArticle
32.
Gupta  AK, Kirchner  KA, Nicholson  R,  et al.  Effects of alpha-thalassemia and sickle polymerization tendency on the urine-concentrating defect of individuals with sickle cell trait. J Clin Invest. 1991;88(6):1963-1968.
PubMedArticle
33.
Hicks  PJ, Langefeld  CD, Lu  L,  et al.  Sickle cell trait is not independently associated with susceptibility to end-stage renal disease in African Americans. Kidney Int. 2011;80(12):1339-1343.
PubMedArticle
Original Investigation
November 26, 2014

Association of Sickle Cell Trait With Chronic Kidney Disease and Albuminuria in African Americans

Author Affiliations
  • 1Division of Hematology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
  • 2Division of Nephrology and Hypertension, Department of Medicine, University of North Carolina Kidney Center, University of North Carolina at Chapel Hill
  • 3Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
  • 4Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill
  • 5Department of Biostatistics, Zilber School of Public Health, University of Wisconsin-Milwaukee
  • 6Center for Human Genetic Research, Boston and Broad Institute, Program in Medical and Population Genetics, Massachusetts General Hospital, Cambridge
  • 7Division of Nephrology, Department of Medicine, Veterans Affairs Puget Sound Health Care System, University of Washington, Seattle
  • 8Montreal Heart Institute and Université de Montréal, Montréal, Québec, Canada
  • 9Division of Nephrology, Department of Medicine, University of California, San Francisco
  • 10Kidney Research Institute, University of Washington, Seattle
  • 11Stroke Center, Department of Neuroscience, Medical University of South Carolina, Charleston
  • 12Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, Georgia
  • 13Human Genetics Center, School of Public Health, University of Texas School Health Science Center at Houston
  • 14Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge
  • 15Center for Public Health Genomics, University of Virginia, Charlottesville
  • 16Department of Genome Sciences, University of Washington, Seattle
  • 17Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
  • 18Department of Medicine and Pediatrics, University of Mississippi Medical Center, Jackson
  • 19Department of Obstetrics and Gynecology, School of Medicine and Public Health, University of Wisconsin, Madison
  • 20Division of Hematology and Oncology, Department of Medicine, University of North Carolina at Chapel Hill
  • 21Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis
  • 22Division of General Internal Medicine, Department of Medicine, University of California, San Francisco
  • 23Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson
  • 24Department of Epidemiology, University of Washington School of Public Health, Seattle
JAMA. 2014;312(20):2115-2125. doi:10.1001/jama.2014.15063
Abstract

Importance  The association between sickle cell trait (SCT) and chronic kidney disease (CKD) is uncertain.

Objective  To describe the relationship between SCT and CKD and albuminuria in self-identified African Americans.

Design, Setting, and Participants  Using 5 large, prospective, US population-based studies (the Atherosclerosis Risk in Communities Study [ARIC, 1987-2013; n = 3402], Jackson Heart Study [JHS, 2000-2012; n = 2105], Coronary Artery Risk Development in Young Adults [CARDIA, 1985-2006; n = 848], Multi-Ethnic Study of Atherosclerosis [MESA, 2000-2012; n = 1620], and Women’s Health Initiative [WHI, 1993-2012; n = 8000]), we evaluated 15 975 self-identified African Americans (1248 participants with SCT [SCT carriers] and 14 727 participants without SCT [noncarriers]).

Main Outcomes and Measures  Primary outcomes were CKD (defined as an estimated glomerular filtration rate [eGFR] of <60 mL/min/1.73 m2 at baseline or follow-up), incident CKD, albuminuria (defined as a spot urine albumin:creatinine ratio of >30 mg/g or albumin excretion rate >30 mg/24 hours), and decline in eGFR (defined as a decrease of >3 mL/min/1.73 m2 per year). Effect sizes were calculated separately for each cohort and were subsequently meta-analyzed using a random-effects model.

Results  A total of 2233 individuals (239 of 1247 SCT carriers [19.2%] vs 1994 of 14 722 noncarriers [13.5%]) had CKD, 1298 (140 of 675 SCT carriers [20.7%] vs 1158 of 8481 noncarriers [13.7%]) experienced incident CKD, 1719 (150 of 665 SCT carriers [22.6%] vs 1569 of 8249 noncarriers [19.0%]) experienced decline in eGFR, and 1322 (154 of 485 SCT carriers [31.8%] vs 1168 of 5947 noncarriers [19.6%]) had albuminuria during the study period. Individuals with SCT had an increased risk of CKD (odds ratio [OR], 1.57 [95% CI, 1.34-1.84]; absolute risk difference [ARD], 7.6% [95% CI, 4.7%-10.8%]), incident CKD (OR, 1.79 [95% CI, 1.45-2.20]; ARD, 8.5% [95% CI, 5.1%-12.3%]), and decline in eGFR (OR, 1.32 [95% CI, 1.07-1.61]; ARD, 6.1% [95% CI, 1.4%-13.0%]) compared with noncarriers. Sickle cell trait was also associated with albuminuria (OR, 1.86 [95% CI, 1.49-2.31]; ARD, 12.6% [95% CI, 7.7%-17.7%]).

Conclusions and Relevance  Among African Americans in these cohorts, the presence of SCT was associated with an increased risk of CKD, decline in eGFR, and albuminuria, compared with noncarriers. These findings suggest that SCT may be associated with the higher risk of kidney disease in African Americans.

Introduction

Sickle cell trait (SCT) is defined as inheritance of a single copy of the sickle mutation that results from a single base pair substitution in the gene encoding the β-globin chain of hemoglobin. It is estimated that SCT affects 1 in 12 African Americans and nearly 300 million people worldwide.1,2

Quiz Ref IDRenal disease in individuals with 2 copies of the sickle hemoglobin mutation (sickle cell disease) has been well characterized and includes impaired urinary concentrating ability, hematuria, chronic kidney disease (CKD), albuminuria, and end-stage renal disease (ESRD).3,4 Although SCT largely has been considered a benign condition, renal manifestations are the most commonly reported complications and include impaired urinary concentration, asymptomatic hematuria, and papillary necrosis.1,2,5,6 Nonetheless, the relationship of SCT to long-term functional impairment of the kidney has not been firmly established. Prior studies demonstrated a higher than expected prevalence of SCT among participants with ESRD, leading a National Institutes of Health (NIH) consensus panel to identify kidney disease as an area of priority in SCT research.1,79

Quiz Ref IDAfrican Americans have a disproportionately higher risk of CKD and progression to ESRD compared with white or Asian American populations.1012 Sickle cell trait may be an important and unrecognized risk factor for renal disease in this population. We hypothesized that SCT is associated with CKD, decline in estimated glomerular filtration rate (eGFR), and albuminuria.

Methods
Study Population

All participants who were included in the analyses provided written informed consent for genetic studies, and institutional review board approval was obtained separately at each participating institution. The study population was derived from 5 population-based, prospective cohort studies from the United States: the Atherosclerosis Risk in Communities Study (ARIC), Jackson Heart Study (JHS), Coronary Artery Risk Development in Young Adults (CARDIA), Multi-Ethnic Study of Atherosclerosis (MESA), and Women’s Health Initiative (WHI). The design and methods of each study have been previously described1317 and are summarized in Table 1 and described in the eAppendix in the Supplement. Clinical information was collected by self-report and in-person examination. Data from years 1987-2013 were used for analysis in ARIC, 2000-2012 for JHS, 1985-2006 for CARDIA, 2000-2012 for MESA, and 1993-2012 for WHI.

Exposure Assessment

Genotype data for rs334 encoding the sickle cell mutation (HBB p.Glu7Val) were obtained by custom genotyping or exome sequencing, or by imputation into the remaining sample of African Americans with genome-wide genotyping (eAppendix in the Supplement). We examined kidney function outcomes by the number of risk alleles (0 or 1).

Covariate Assessment

Baseline characteristics for each study participant were determined at the time of the first creatinine measurement. Hypertension was defined as a baseline systolic blood pressure measurement of 140 mm Hg or higher, a diastolic blood pressure measurement of 90 mm Hg or higher, or self-reported use of antihypertensive medication. Diabetes was defined as baseline fasting glucose measurement of 126 mg/dL or higher (to convert to mmol/L, multiply by 0.0555), self-reported physician diagnosis of diabetes, or self-reported use of oral hypoglycemic medication or insulin. To correct for any cryptic population structure, the global percentage of African ancestry for each participant was estimated based on high-density genome-wide genotyping data using population genetics software programs (Structure or Frappe), as previously described.18,19 All participants included in this analysis had complete baseline data for age, sex, estimated African ancestry proportion, hypertension, and diabetes. The APOL1 risk variants (G1 and G2), which are associated with CKD in African Americans,20,21 were directly genotyped in all individuals in ARIC and a subset of participants in JHS.22

Kidney Function Outcome Assessment

Serum creatinine values were calibrated to the Cleveland Clinic or isotope dilution mass spectrometry reference standard. The eGFR was estimated from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration equation.23 The eGFR was also estimated using cystatin C measurements in available cohorts as described previously.24 Chronic kidney disease was defined as an eGFR lower than 60 (stage G3 or higher in the Kidney Disease: Improving Global Outcomes [KDIGO] CKD definition)25 at any time during the study, including baseline or any follow-up visit. Incident CKD was defined as development of an eGFR lower than 60 mL/min/1.73 m2 during follow-up with a baseline eGFR of 60 mL/min/1.73 m2 or higher. Albuminuria was defined as spot urinary albumin:creatinine ratio (UACR) higher than 30 mg/g or an albumin excretion rate of more than 30 mg/24 hours (stage A2 or higher per KDIGO).25 Each cohort has been followed longitudinally; the number and frequency of repeated measures of serum creatinine, cystatin C, and UACR vary by cohort (Table 1). Participants with incident ESRD were identified in ARIC and WHI through linkage with the US Renal Data System, a national registry of all participants with ESRD.10

Statistical Methods

The association of SCT carrier status with CKD outcomes was assessed using linear regression for quantitative eGFR and log-transformed urinary albumin, logistic regression for CKD, incident CKD, albuminuria, and presence of decline in kidney function (defined as a decrease in eGFR of >3 mL/min/1.73 m2 per year), and Cox regression for incident ESRD. To evaluate the association between SCT and eGFR decline, we used linear mixed models with random intercepts and slopes to estimate and compare linear trends in mean eGFR. Linear mixed models account for the correlation of observations within individual participants. Annual decline was estimated using eGFR data from examinations 1, 2, 4, and 5 in ARIC; examinations 1, 4, 5, 6, and 7 in CARDIA; examinations 1, 3, 4, and 5 in MESA; examinations 1 and 3 in JHS; and examination 1 and a subsequent visit about 15 years later in a subsample of WHI (n = 2139).

All regression models were minimally adjusted for covariates of age, sex, clinic or region, and African genetic ancestry to account for population stratification (model 1). We additionally adjusted for 2 major clinical risk factors: baseline diabetes and hypertension (model 2). All tests were 2-sided and a P value of less than .05 was considered statistically significant. The following sensitivity analyses were performed: (1) confining to ARIC and JHS, the 2 cohorts with the highest percentage of direct genotyping for rs334 (100% in ARIC; 98% in JHS; n = 5507) (eTable 2 in the Supplement), (2) excluding hemoglobin C carriers in genotyped samples, and (3) adjusting for the presence of 2 copies of the APOL1 risk variants (G1 and G2) in individuals with available genotypic data (n = 4490).

Effect modification of SCT on the development of incident CKD by baseline hypertension or diabetes status or by the presence of 2 APOL1 risk variants was evaluated using 2 distinct methods: performing stratified analyses and including an interaction term in the original model. We defined the significance level for interaction as a P value of less than .05.

Because of the variability of risk across cohorts of various age ranges, we used ARIC prevalence and incidence rates as a fixed standard and calculated absolute risk differences from the meta-analyzed effect measures. Using ARIC as a standard allowed for the applicability of risk estimates to a middle-aged population.

Given the demographic heterogeneity of the cohorts included in this study, all data were analyzed separately within each cohort. Although the I2 for heterogeneity for all analyzed outcomes was less than 50%, with the I2 statistic being 0% for the primary outcomes, the cohort-specific results were meta-analyzed using a random-effects model to provide a conservative pooled estimate given the inherent heterogeneity in design and population between cohorts. Forest plots were generated for the primary outcomes. All statistical analyses were performed using Stata (StataCorp), version 12.

Results
Baseline Characteristics

After excluding participants with missing data for SCT (n = 9657), kidney phenotypes (n = 241) or covariates (n = 891), and those with homozygous hemoglobin S (n = 8), the present analysis included 15 975 African Americans from 5 population-based, prospective cohort studies (ARIC, JHS, CARDIA, MESA, and WHI). Genotype data for rs334 encoding the SCT mutation (HBB p.Glu7Val) were obtained by custom genotyping (n = 3402), exome sequencing (n = 2791), or by imputation into the remaining sample of African Americans with genomewide genotyping (n = 9782). The APOL1 risk variants (G1 and G2), which are associated with CKD in African Americans were directly genotyped in 3221 individuals in ARIC and a subset of 1269 participants in JHS. Baseline characteristics of African American participants by cohort and SCT carrier status are shown in Table 2. CARDIA participants were younger than participants of all other cohorts. A total of 1248 individuals in our study had SCT. The prevalence of SCT among the cohorts ranged from 6.4% to 9.3%, consistent with previous population prevalence estimates in African Americans.5,26,27 There was little difference in age, sex, body mass index (BMI, calculated as weight in kilograms divided by height in meters squared), hypertension, and diabetes between African American participants with SCT (SCT carriers) and without SCT (noncarriers) in each cohort.

Association of SCT With CKD and Incident CKD

Chronic kidney disease using creatinine values (eGFR <60 mL/min/1.73 m2) was present in 2233 individuals (239 of 1247 SCT carriers [19.2%] vs 1994 of 14 722 noncarriers [13.5%]) in all 5 cohorts and in 1164 participants (113 of 516 SCT carriers [21.9%] vs 1051 of 6468 noncarriers [16.2%]) from 4 cohorts based on cystatin C measurements. In the meta-analysis, SCT carriers had an increased risk of CKD compared with noncarriers (odds ratio [OR], 1.57 [95% CI, 1.34-1.84]; absolute risk difference [ARD], 7.6% [95% CI, 4.7%-10.8%]) (Figure 1). The meta-analyzed risk for CKD in the sensitivity analysis using ARIC and JHS alone was similar (82 of 381 SCT carriers [21.5%] vs 754 of 5126 noncarriers [14.7%]; OR, 1.69 [95% CI, 1.28-2.22]; ARD, 9.1% [95% CI, 3.9%-14.8%]) (eTable 2 in the Supplement). Similar results were also obtained using cystatin C–based eGFR (OR, 1.76 [95% CI, 1.37-2.27]; ARD, 12.8% [95% CI, 6.8%-19.0%]) in the meta-analysis of available values from ARIC, JHS, CARDIA, and MESA (Table 3, model 2).

A total of 1298 (140 of 675 SCT carriers [20.7%] vs 1158 of 8481 noncarriers [13.7%]) experienced incident CKD. Incident CKD was more common in SCT carriers compared with noncarriers in all cohorts. (OR, 1.79 [95% CI, 1.45-2.20]; ARD, 8.5% [95% CI, 5.1%-12.3%]) (Figure 2). Results were similar using data from ARIC and JHS alone (60 of 325 SCT carriers [18.5%] vs 525 of 4506 noncarriers [11.7%]; OR, 1.81 [95% CI, 1.33-2.45]; ARD, 8.7% [95% CI, 3.8%-14.4%]) (eTable 2 in the Supplement).

Association of SCT With Decline in eGFR and ESRD

Sickle cell trait was significantly associated with a faster decline in eGFR, with a pooled adjusted estimate of an increased rate of decline in eGFR of 0.218 mL/min/1.73 m2 per year (95% CI, 0.062 to 0.374) compared with noncarriers (Table 3, model 2). The rate of decline was 0.254 mL/min/1.73 m2 per year (95% CI, 0.089 to 0.418) faster for SCT carriers compared with noncarriers among the subgroup of 8923 individuals without baseline CKD. Sickle cell trait was not associated with faster decline in the subset of 361 participants with baseline eGFR lower than 60 mL/min/1.73 m2 (β = 0.024 [95% CI, −0.484 to 0.533])

In our study, a total of 1719 individuals (150 of 665 SCT carriers [22.6%] vs 1569 of 8249 noncarriers [19.0%]) from the 5 cohorts experienced eGFR decline. Evaluation of decline in eGFR (>3 mL/min/1.73 m2 decline per year) demonstrated that SCT carriers had an increased risk of decline compared with noncarriers in the meta-analysis (OR, 1.32 [95% CI, 1.07 to 1.61]; ARD, 6.1% [95% CI, 1.4% to 13.0%]) (Figure 3). Using ARIC and JHS only, the results were similar (81 of 305 SCT carriers [26.6%] vs 1040 of 4272 noncarriers [24.3%]; OR, 1.21 [95% CI, 0.92 to 1.59]; ARD, 4.1% [95% CI, −1.7% to 10.5%]) (eTable 2 in the Supplement). As with CKD and incident CKD, the direction of the association was consistent among all cohorts analyzed, though the relationship was not statistically significant in any individual cohort.

In the 2 cohorts with available US Renal Data System follow-up data on ESRD (ARIC and WHI), a total of 314 individuals (26 of 852 SCT carriers [3.1%] vs 288 of 10 411 noncarriers [2.8%]) developed incident ESRD. Incidence of ESRD did not differ significantly between SCT carriers and noncarriers either in the individual cohorts or in meta-analysis (hazard ratio [HR], 1.02 [95% CI, 0.59 to 1.76]; ARD, 0.1% [95% CI, −1.8% to 3.3%]) (Table 3, model 2).

Association of SCT and Albuminuria

Using both UACR and albumin excretion rate data, the mean baseline urinary albumin was 0.49 natural log units (95% CI, 0.351-0.628) of milligrams per gram or milligrams per 24 hours higher among SCT carriers compared with noncarriers (Table 3, model 2). A total of 1322 individuals (154 of 485 SCT carriers [31.8%] vs 1168 of 5947 noncarriers [19.6%]) from the 4 cohorts with available data had albuminuria. Similar to CKD, albuminuria (>30 mg/g or >30 mg/24 hours) was more common in SCT carriers compared with noncarriers (OR, 1.86 [95% CI, 1.49-2.31; ARD, 12.6% [95% CI, 7.7%-17.7%]) in the pooled analysis (Figure 4). Similar results for albuminuria were observed using ARIC and JHS cohorts only (88 of 283 SCT carriers [31.1%] vs 801 of 3880 noncarriers [20.6%]; OR, 1.75 [95% CI, 1.17-2.62]; ARD, 11.2% [95% CI, 2.9%-20.7%]) (eTable 2 in the Supplement).

Association of SCT and Incident CKD by Hypertension and Diabetes Status

There was no evidence that the association of SCT with incident CKD was modified by either baseline hypertension (P = .09) or diabetes status (P = .60) (Table 4).

Association of SCT and APOL1 Risk Variants on CKD and Albuminuria

We assessed the association of SCT and APOL1 on CKD and albuminuria in a subset of 3221 participants in ARIC and 1269 participants in JHS with genotypes available for the G1 and G2 alleles (eAppendix in the Supplement). Carrier status for SCT and APOL1 risk genotypes segregated independently, with 22 individuals (1%) carrying both genetic risk factors in ARIC and 11 individuals (1%) in JHS. There was no significant genetic interaction by APOL1 status on the relationship of SCT and CKD (P = .17) or albuminuria (P = .18) (Table 5).

Discussion

Although SCT is known to be associated with hematuria and renal papillary necrosis,1,2,5 the relationship between SCT and CKD has not been clearly established. Quiz Ref IDIn this pooled analysis of more than 15 000 individuals from 5 population-based cohorts of African Americans, SCT was associated with an increased risk of CKD and incident CKD, decline in eGFR, and albuminuria. Our results were reproducible within each individual cohort. Our findings show an association of SCT with the development of CKD in African Americans.

Quiz Ref IDPotential pathophysiological mechanisms for kidney injury in individuals with sickle cell disease have been described. Chronic reversible sickling induced by hypoxia in the renal medulla results in ischemia and microinfarction of the renal tubules.6 Local ischemia and hemolysis cause release of vasoactive factors,28 which promotes glomerular hyperfiltration, ultimately resulting in glomerulosclerosis and proteinuria.29 In SCT, injection radiographs demonstrate renal medullary vascular disruption, though to a lesser extent than seen in sickle cell disease, suggesting that sickle hemoglobin may have a dose-dependent relationship with kidney injury.6 Our finding that SCT was related to both CKD and albuminuria is consistent with these proposed mechanisms.

In our study, the association of SCT with CKD appeared to be independent of APOL1 risk variants, which have recently drawn attention as an explanation for the higher risk of CKD in African Americans.20,21 Although APOL1 risk variants are associated with CKD progression in African Americans, they do not fully explain the observed increased risk of CKD among African Americans compared with other populations.20 Our findings suggest that SCT may additionally relate to the racial disparity in CKD, with a population attributable risk for incident CKD of approximately 6%. Although we observed no gene-gene interaction with APOL1 or gene-environment interaction with diabetes or hypertension, further studies with even larger sample sizes are needed to sufficiently address potential interactions between SCT and these other CKD risk factors.

Quiz Ref IDIn contrast to APOL1, genetic testing for SCT is performed widely in the United States. Universal newborn screening for sickle hemoglobin is mandated in all 50 states, and screening for SCT is also variably implemented in college athletics, pregnancy, and renal transplant donor evaluations.1,7,30 As a result, SCT carriers are identified at a young age, and high-risk individuals may benefit from early intervention. Studies investigating angiotensin-converting enzyme inhibition to delay the progression of albuminuria in participants with sickle cell disease and sickle nephropathy have had promising results,31 although the long-term benefits remain unknown. Angiotensin-converting enzyme inhibition in SCT-associated CKD may provide a similar potential benefit but has not been evaluated.

Our study has limitations. The clinical and pathological cause of CKD in each individual was not explicitly recorded. In addition, direct genotyping for SCT was not available on all individuals; however, sensitivity analysis using the cohorts with the highest percentage of direct genotyping yielded substantially similar results to the overall meta-analysis. We were also unable to evaluate the modifying effects of coexisting hemoglobin mutations, such as α-thalassemia.32 Our ability to assess the potential for interaction of APOL1 was limited because genotypic data were only available in 2 cohorts. The strengths of our study include the large prospective, population-based sample of African Americans with detailed genotypic and phenotypic information and the reproducibility of results across cohorts with differing geographic, age, and sex compositions. In addition, we were able to evaluate outcomes that have not been previously evaluated in SCT, including CKD, decline in eGFR, and albuminuria.

Sickle cell trait was not associated with ESRD in our analysis. Our study is, to our knowledge, the first prospective study of SCT and ESRD; however, the relatively small number of incident ESRD cases, which was available by US Renal Data System linkage in only 2 cohorts, may have resulted in insufficient power to detect an association with SCT. Two prior studies examining the relationship between SCT and ESRD have demonstrated discordant findings, which can be potentially explained by limitations and variation in design.9,33 A single-center study demonstrated a higher prevalence of SCT compared with the population prevalence obtained from birth records.9 Another study, a cross-sectional analysis of a preexisting genetic cohort, did not show a higher prevalence of SCT among participants with ESRD compared with nonnephropathy controls.33 Although we were unable to demonstrate a clear association with ESRD, the observed relationship with eGFR decline suggests that SCT may be associated with severe renal phenotypes. Additional prospective studies are needed to resolve this question.

In this large multicohort study, SCT was associated with CKD and incident CKD, decline in eGFR, and albuminuria in African Americans. These associations were independent of APOL1 risk variants and may offer an additional genetic explanation for the increased risk of CKD observed among African Americans compared with other racial groups. Our study also highlights the need for further research into the renal complications of SCT. Because screening for SCT is already being widely performed, accurate characterization of disease associations with SCT is critical to inform policy and treatment recommendations.

Conclusions

Among African Americans in these cohorts, the presence of SCT was associated with an increased risk of CKD, decline in eGFR, and albuminuria, compared with noncarriers. These findings suggest that SCT may be related to the higher risk of kidney disease in African Americans.

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Article Information

Corresponding Author: Rakhi P. Naik, MD, MHS, Division of Hematology, Department of Medicine, Johns Hopkins University, 1830 E Monument St, Ste 7300, Baltimore, MD 21287 (rakhi@jhmi.edu).

Published Online: November 13, 2014. doi:10.1001/jama.2014.15063.

Author Contributions: Drs Naik and Reiner 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. Drs Naik and Derebail are co-first authors and Drs Kshirsagar, Wilson, and Reiner are co-senior authors.

Study concept and design: Naik, Derebail, Auer, Rich, Lanzkron, Key, Kathiresan, Bibbins-Domingo, Kshirsagar, Wilson, Reiner.

Acquisition, analysis, or interpretation of data: Naik, Grams, Franceschini, Auer, Peloso, Young, Lettre, Peralta, Katz, Hyacinth, Quarells, Grove, Bick, Fontanillas, Rich, Smith, Boerwinkle, Rosamond, Ito, Coresh, Correa, Sarto, Jacobs, Bibbins-Domingo, Kshirsagar, Wilson, Reiner.

Drafting of the manuscript: Naik, Derebail, Auer, Hyacinth, Quarells, Grove, Key, Kshirsagar, Reiner.

Critical revision of the manuscript for important intellectual content: Naik, Derebail, Grams, Franceschini, Auer, Peloso, Young, Lettre, Peralta, Katz, Hyacinth, Bick, Fontanillas, Rich, Smith, Boerwinkle, Rosamond, Ito, Lanzkron, Coresh, Correa, Sarto, Key, Jacobs, Kathiresan, Bibbins-Domingo, Kshirsagar, Wilson, Reiner.

Statistical analysis: Naik, Derebail, Grams, Franceschini, Auer, Peloso, Lettre, Hyacinth, Bick, Fontanillas, Ito, Coresh, Jacobs, Kathiresan, Bibbins-Domingo, Reiner.

Obtained funding: Derebail, Franceschini, Young, Quarells, Grove, Rich, Rosamond, Key, Jacobs, Kathiresan, Kshirsagar, Wilson.

Administrative, technical, or material support: Auer, Hyacinth, Bick, Boerwinkle, Correa, Sarto, Bibbins-Domingo, Wilson.

Study supervision: Lanzkron, Kathiresan, Kshirsagar, Wilson, Reiner.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Peralta reports grant funding from LabCorp. Dr Lanzkron reports funding from Emmaus, Glycomimetrics, and Novartis. Dr Kathiresan reports grant funding from AstraZeneca and Merck and serving on the advisory board for Amarin, Catabasis, and Regeneron. Dr Bibbins-Domingo reports receiving personal fees from Roche. No other disclosures were reported.

Funding/Support: The Atherosclerosis Risk in Communities Study (ARIC) is carried out as a collaborative study supported by the National Heart, Lung, and Blood Institute (NHLBI; contracts HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C and grants R01HL087641, R01HL59367, and R01HL086694); National Human Genome Research Institute (NHGRI; contract U01HG004402); and the National Institutes of Health (NIH; contract HHSN268200625226C). Funding for “Building on Genome Wide Association Studies for NHLBI-diseases: the US CHARGE Consortium” was provided by grant 5RC2HL102419 from the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA). Sequencing was funded by grant U54 HG003273 from the Baylor Genome Center. This ancillary study was supported by grant 5K12 HL087097-0 from the Duke University Medical Center and University of North Carolina Clinical Hematology Research Career Development Program (Dr. Derebail). The Jackson Heart Study (JHS) is supported by the NHLBI and the National Institute on Minority Health and Health Disparities (contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C). The Coronary Artery Risk Development in Young Adults Study (CARDIA) is conducted and supported by NHLBI in collaboration with the University of Alabama at Birmingham (contracts HHSN268201300025C and HSN268201300026C), Northwestern University (contract HHSN268201300027C), University of Minnesota (contract HHSN268201300028C), Kaiser Foundation Research Institute (contract HHSN268201300029C), and Johns Hopkins University School of Medicine (contract HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging (NIA) and intra-agency agreement AG0005 between NIA and NHLBI. The Multi-Ethnic Study of Atherosclerosis (MESA) was supported by the NHLBI (contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169) and by grants UL1-TR-000040 and UL1-RR-025005 from National Center for Research Resources. The Women's Health Initiative (WHI) program is funded by NHLBI, NIH, and the US Department of Health and Human Services (contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C). Funding for the Grand Opportunity Exome Sequencing Project (GO ESP) was provided by NHLBI grants RC2 HL-103010 (HeartGO), RC2 HL-102923 (LungGO), and RC2 HL-102924 (WHISP). The exome sequencing was performed through NHLBI grants RC2 HL-102925 (BroadGO) and RC2 HL-102926 (SeattleGO). Exome sequencing was also performed through the Type 2 Diabetes Genetic Exploration by Next Generation Sequencing (T2D-GENES) consortium, with support from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; contracts DK085501, DK085524, DK085526, DK085545, and DK085584). Additional exome sequencing was performed through the Minority Health Genomics and Translational Research Bio-repository Database (MH-GRID) with support from grant RC4MD005964 from the National Institute on Minority Health and Health Disparities. The MH-GRID investigators (Rakale C. Quarells, Donna K. Arnett, Gary H. Gibbons, Robert L. Davis, Suzanne M. Leal, Deborah A. Nickerson, James Perkins, Charles N. Rotimi, Joel H. Saltz, and Herman A. Taylor, James G. Wilson) collected and provided data for this study. Chip genotyping and exome sequencing in JHS was also supported by grant R01HL107816 from NHLBI (Dr Kathiresan) and NHGRI (contract 5u54HG003067) (Drs Gabriel and Lander). Support for targeted sequencing was provided by grants U54 HG003067 from the NHGRI (Medical Sequencing Program) and HL080494-05 from NHLBI. Funding for cystatin C and APOL1 in ARIC was provided by grants R01 DK089174 for cystatin C (Dr Selvin) and R01 DK076770–01 for APOL1 from the NIH and NIDDK. Funding for this work was also provided by grant 1R01DK102134-01 from the NIH and NIDDK. This material is the result of work supported by resources from the Veterans Affairs Puget Sound Health Care System (Dr Young), Seattle, Washington. Funding was also provided in part by grant 2K12HL087169-06 (Dr Naik), by grant T32HL007208 (Dr Peloso), by grant 1UO1HL117659 (Dr Key), and by grant R01HL071862 (Dr Reiner) from NHLBI. Some of the data reported here have been supplied by the United States Renal Data System.

Role of the Funders/Sponsors: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Veterans Affairs Puget Sound. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US government.

Additional Contributions: We thank the investigators, staff, and participants of the ARIC, JHS, CARDIA, MESA, and WHI for their valuable contributions.

References
1.
Key  NS, Derebail  VK.  Sickle cell trait: novel clinical significance. Hematology Am Soc Hematol Educ Program. 2010;2010:418-422.
PubMedArticle
2.
Tsaras  G, Owusu-Ansah  A, Boateng  FO, Amoateng-Adjepong  Y.  Complications associated with sickle cell trait: a brief narrative review. Am J Med. 2009;122(6):507-512.
PubMedArticle
3.
Becton  LJ, Kalpatthi  RV, Rackoff  E,  et al.  Prevalence and clinical correlates of microalbuminuria in children with sickle cell disease. Pediatr Nephrol. 2010;25(8):1505-1511.
PubMedArticle
4.
Itano  HA, Keitel  HG, Thompson  D.  Hyposthenuria in sickle cell anemia: a reversible renal defect. J Clin Invest. 1956;35(9):998-1007.
PubMedArticle
5.
Heller  P, Best  WR, Nelson  RB, Becktel  J.  Clinical implications of sickle cell trait and glucose-6-phosphate dehydrogenase deficiency in hospitalized black male patients. N Engl J Med. 1979;300(18):1001-1005.
PubMedArticle
6.
Statius van Eps  LW, Pinedo-Veels  C, de Vries  GH, de Koning  J.  Nature of concentrating defect in sickle cell nephropathy: microradioangiographic studies. Lancet. 1970;1(7644):450-452.
PubMedArticle
7.
Goldsmith  JC, Bonham  VL, Joiner  CH, Kato  GJ, Noonan  AS, Steinberg  MH.  Framing the research agenda for sickle cell trait: building on the current understanding of clinical events and their potential implications. Am J Hematol. 2012;87(3):340-346.
PubMedArticle
8.
Grant  AM, Parker  CS, Jordan  LB,  et al.  Public health implications of sickle cell trait: a report of the CDC meeting. Am J Prev Med. 2011;41(6)(suppl 4):S435-S439.
PubMedArticle
9.
Derebail  VK, Nachman  PH, Key  NS, Ansede  H, Falk  RJ, Kshirsagar  AV.  High prevalence of sickle cell trait in African Americans with ESRD. J Am Soc Nephrol. 2010;21(3):413-417.
PubMedArticle
10.
US Renal Data System. USRDS 2013 annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States.http://www.usrds.org/atlas.aspx. Accessed December 12, 2013.
11.
Palmer Alves  T, Lewis  J.  Racial differences in chronic kidney disease (CKD) and end-stage renal disease (ESRD) in the United States: a social and economic dilemma. Clin Nephrol. 2010;74(suppl 1):S72-S77.
PubMed
12.
Muntner  P, Newsome  B, Kramer  H,  et al.  Racial differences in the incidence of chronic kidney disease. Clin J Am Soc Nephrol. 2012;7(1):101-107.
PubMedArticle
13.
 The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives: the ARIC investigators. Am J Epidemiol. 1989;129(4):687-702.
PubMed
14.
Taylor  HA  Jr,.  The Jackson Heart Study: an overview. Ethn Dis. 2005;15(4 suppl 6):S6-1-3.
PubMed
15.
Friedman  GD, Cutter  GR, Donahue  RP,  et al.  CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988;41(11):1105-1116.
PubMedArticle
16.
Bild  DE, Bluemke  DA, Burke  GL,  et al.  Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871-881.
PubMedArticle
17.
The Women’s Health Initiative Study Group.  Design of the Women’s Health Initiative clinical trial and observational study. Control Clin Trials. 1998;19(1):61-109.
PubMedArticle
18.
Pritchard  JK, Stephens  M, Donnelly  P.  Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945-959.
PubMed
19.
Tang  H, Peng  J, Wang  P, Risch  NJ.  Estimation of individual admixture: analytical and study design considerations. Genet Epidemiol. 2005;28(4):289-301.
PubMedArticle
20.
Parsa  A, Kao  WH, Xie  D,  et al; AASK Study Investigators; CRIC Study Investigators.  APOL1 risk variants, race, and progression of chronic kidney disease. N Engl J Med. 2013;369(23):2183-2196.
PubMedArticle
21.
Genovese  G, Friedman  DJ, Ross  MD,  et al.  Association of trypanolytic APOL1 variants with kidney disease in African Americans. Science. 2010;329(5993):841-845.
PubMedArticle
22.
Ito  K, Bick  AG, Flannick  J,  et al.  Increased burden of cardiovascular disease in carriers of APOL1 genetic variants. Circ Res. 2014;114(5):845-850.
PubMedArticle
23.
Levey  AS, Stevens  LA, Schmid  CH,  et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration).  A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-612.
PubMedArticle
24.
Inker  LA, Schmid  CH, Tighiouart  H,  et al; CKD-EPI Investigators.  Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20-29.
PubMedArticle
25.
Stevens  PE, Levin  A; Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members.  Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825-830.
PubMedArticle
26.
Schneider  RG, Hightower  B, Hosty  TS,  et al.  Abnormal hemoglobins in a quarter million people. Blood. 1976;48(5):629-637.
PubMed
27.
Petrakis  NL, Wiesenfeld  SL, Sams  BJ, Collen  MF, Cutler  JL, Siegelaub  AB.  Prevalence of sickle cell trait and glucose-6-phosphate dehydrogenase deficiency. N Engl J Med. 1970;282(14):767-770.
PubMedArticle
28.
Nath  KA, Katusic  ZS.  Vasculature and kidney complications in sickle cell disease. J Am Soc Nephrol. 2012;23(5):781-784.
PubMedArticle
29.
Hostetter  TH.  Hyperfiltration and glomerulosclerosis. Semin Nephrol. 2003;23(2):194-199.
PubMedArticle
30.
Reese  PP, Hoo  AC, Magee  CC.  Screening for sickle trait among potential live kidney donors: policies and practices in US transplant centers. Transpl Int. 2008;21(4):328-331.
PubMedArticle
31.
Falk  RJ, Scheinman  J, Phillips  G, Orringer  E, Johnson  A, Jennette  JC.  Prevalence and pathologic features of sickle cell nephropathy and response to inhibition of angiotensin-converting enzyme. N Engl J Med. 1992;326(14):910-915.
PubMedArticle
32.
Gupta  AK, Kirchner  KA, Nicholson  R,  et al.  Effects of alpha-thalassemia and sickle polymerization tendency on the urine-concentrating defect of individuals with sickle cell trait. J Clin Invest. 1991;88(6):1963-1968.
PubMedArticle
33.
Hicks  PJ, Langefeld  CD, Lu  L,  et al.  Sickle cell trait is not independently associated with susceptibility to end-stage renal disease in African Americans. Kidney Int. 2011;80(12):1339-1343.
PubMedArticle
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