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      Risk of End-Stage Renal Disease in HIV-Positive Potential Live Kidney Donors

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          Abstract

          New federal regulations allow HIV-positive individuals to be live kidney donors; however, potential candidacy for donation is poorly understood given the increased risk of end-stage renal disease (ESRD) associated with HIV infection. To better understand this risk, we compared the incidence of ESRD among 41 968 HIV-positive participants of North America AIDS Cohort Collaboration on Research and Design followed for a median of 5 years with the incidence of ESRD among comparable HIV-negative participants of National Health and Nutrition Examination III followed for a median of 14 years. We used risk associations from multivariable Cox proportional hazards regression to derive cumulative incidence estimates for selected HIV-positive scenarios (no history of diabetes, hypertension, AIDS, or hepatitis C virus coinfection) and compared these estimates with those from similarly selected HIV-negative scenarios. For 40-year-old HIV-positive individuals with health characteristics that were similar to those of age-matched kidney donors, viral load <400 copies/mL, and CD4(+) count ≥500 cells/μL, the 9-year cumulative incidence of ESRD was higher than that of their HIV-negative peers, yet still low: 2.5 versus 1.1 per 10 000 among white women, 3.0 versus 1.3 per 10 000 among white men, 13.2 versus 3.6 per 10 000 among black women, and 15.8 versus 4.4 per 10 000 among black men. HIV-positive individuals with no comorbidities and well-controlled disease may be considered low-risk kidney donor candidates.

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          Most cited references39

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Likelihood of a model and information criteria

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              Body mass index and the risk of development of end-stage renal disease in a screened cohort.

              Obesity is associated with proteinuria and could be a risk factor for end-stage renal disease (ESRD). However, few studies have examined the significance of body mass index (BMI) as a risk factor for the development of ESRD in the general population. We examined the relationship between BMI and the development of ESRD using data from a 1983 community-based screening in Okinawa, Japan. Screenees who developed ESRD by the end of 2000 were identified through the Okinawa Dialysis Study registry. BMI data were available for 100,753 screenees (47,504 men and 53,249 women) aged >/=20 years. The cumulative incidence of ESRD was analyzed according to the quartile of BMI: /=25.5 kg/m(2). The mean (SD) BMI of the screenees was 23.4 (3.3) kg/m(2) (range 7.9 to 59.1 kg/m(2)); the mean was 23.4 kg/m(2) for both men and women. During the follow-up period, 404 screenees (232 men and 172 women) developed ESRD. The cumulative incidences of ESRD per 1000 screenees were, from the lowest to highest BMI quartile, 2.48, 3.79, 3.86, and 5.81. The odds ratio (95% CI) of BMI for developing ESRD, after adjustment for age, sex, systolic blood pressure, and proteinuria, was 1.273 (1.121-1.446, P= 0.0002) for men and 0.950 (0.825-1.094, not significant) for women. We found that BMI was associated with an increased risk of the development of ESRD in men in the general population in Okinawa. The maintenance of optimal body weight may reduce the risk of ESRD.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                American Journal of Transplantation
                Am J Transplant
                Wiley
                16006135
                July 2017
                July 2017
                May 12 2017
                : 17
                : 7
                : 1823-1832
                Affiliations
                [1 ]Department of Surgery; Johns Hopkins University School of Medicine; Baltimore MD
                [2 ]Department of Epidemiology; Johns Hopkins School of Public Health; Baltimore MD
                [3 ]Department of Medicine; Johns Hopkins University School of Medicine; Baltimore MD
                [4 ]University of Washington Center for AIDS Research; Seattle WA
                [5 ]Mid-Atlantic Permanente Institute; Rockville MD
                [6 ]Veterans Affairs Connecticut Healthcare System; West Haven CT
                [7 ]Jesse Brown VA Medical Center and Hines VA Hospital; Chicago IL
                [8 ]Kaiser Permanente Division of Research; Oakland CA
                [9 ]Hamad Healthcare Quality Institute; Hamad Medical Corporation; Doha Qatar
                [10 ]Weill Cornell Medical College; Doha Qatar
                [11 ]Weill Cornell Medical College; New York NY
                [12 ]Fenway Health HIV Cohort; Boston MA
                [13 ]Infectious Diseases Division; Sunnybrook Health Sciences Centre; Toronto Ontario Canada
                [14 ]Universidad Central del Caribe; Bayamón PR
                [15 ]Southern Alberta HIV Clinic; Sheldon M. Chumir Health Centre; Calgary Alberta Canada
                [16 ]University of North Carolina; HIV Clinic Cohort; Chapel Hill NC
                [17 ]The Polyclinic Madison Center; Seattle WA
                [18 ]Department of Epidemiology and Biostatistics; University of California; San Francisco CA
                [19 ]Department of Biostatistics; Harvard T.H. Chan School of Public Health; Boston MA
                [20 ]Department of Surgery; University of Alabama; Birmingham AL
                Article
                10.1111/ajt.14235
                5489376
                28497525
                77e3c8ab-a1d0-4c30-9856-caf055117354
                © 2017

                http://doi.wiley.com/10.1002/tdm_license_1

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