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      Is Open Access

      Race, ancestry, and genetic risk for kidney failure

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      Author(s): 1 , 2 , 3 , 1 , 3 , ∗
      Publication date (Electronic):
      Journal: Cell Reports Medicine
      Publisher: Elsevier

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          Abstract

          In a retrospective analysis of over 62,000 Black and non-Black participants from eight United States cohorts, Gutiérrez et al. 1 examined estimated glomerular filtration rate (eGFR) equations to assess racial differences in kidney failure requiring replacement therapy and in mortality across different equations.

          Abstract

          In a retrospective analysis of over 62,000 Black and non-Black participants from eight United States cohorts, Gutiérrez et al. examined estimated glomerular filtration rate (eGFR) equations to assess racial differences in kidney failure requiring replacement therapy and in mortality across different equations.

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          New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race

          Lesley A. Inker, Nwamaka D. Eneanya, Josef Coresh … (2021)
          Current equations for estimated glomerular filtration rate (eGFR) that use serum creatinine or cystatin C incorporate age, sex, and race to estimate measured GFR. However, race in eGFR equations is a social and not a biologic construct.
          Article 0 comments Cited 854 times – based on 0 reviews     Review now
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            A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

            A S Levey, J P Bosch, J B Lewis … (1999)
            Serum creatinine concentration is widely used as an index of renal function, but this concentration is affected by factors other than glomerular filtration rate (GFR). To develop an equation to predict GFR from serum creatinine concentration and other factors. Cross-sectional study of GFR, creatinine clearance, serum creatinine concentration, and demographic and clinical characteristics in patients with chronic renal disease. 1628 patients enrolled in the baseline period of the Modification of Diet in Renal Disease (MDRD) Study, of whom 1070 were randomly selected as the training sample; the remaining 558 patients constituted the validation sample. The prediction equation was developed by stepwise regression applied to the training sample. The equation was then tested and compared with other prediction equations in the validation sample. To simplify prediction of GFR, the equation included only demographic and serum variables. Independent factors associated with a lower GFR included a higher serum creatinine concentration, older age, female sex, nonblack ethnicity, higher serum urea nitrogen levels, and lower serum albumin levels (P < 0.001 for all factors). The multiple regression model explained 90.3% of the variance in the logarithm of GFR in the validation sample. Measured creatinine clearance overestimated GFR by 19%, and creatinine clearance predicted by the Cockcroft-Gault formula overestimated GFR by 16%. After adjustment for this overestimation, the percentage of variance of the logarithm of GFR predicted by measured creatinine clearance or the Cockcroft-Gault formula was 86.6% and 84.2%, respectively. The equation developed from the MDRD Study provided a more accurate estimate of GFR in our study group than measured creatinine clearance or other commonly used equations.
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              Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms

              Debra Malina, Darshali A. Vyas, Leo Eisenstein … (2020)
              Article 0 comments Cited 511 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Keith C. Norris
                Journal
                Journal ID (nlm-ta): Cell Rep Med
                Journal ID (iso-abbrev): Cell Rep Med
                Title: Cell Reports Medicine
                Publisher: Elsevier
                ISSN (Electronic): 2666-3791
                Publication date PMC-release: 16 August 2022
                Publication date Collection: 16 August 2022
                Publication date (Electronic): 16 August 2022
                Volume: 3
                Issue: 8
                Electronic Location Identifier: 100726
                Affiliations
                [1 ]Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
                [2 ]Department of Community Health Sciences, University of California, Los Angeles School of Public Health, Los Angeles, CA, USA
                [3 ]Center for the Study of Racism, Social Justice & Health, University of California, Los Angeles School of Public Health, Los Angeles, CA, USA
                Author notes
                [∗ ]Corresponding author kcnorris@ 123456mednet.ucla.edu
                Article
                Publisher Item ID: S2666-3791(22)00269-5 Publisher ID: 100726
                DOI: 10.1016/j.xcrm.2022.100726
                PMC ID: 9418843
                PubMed ID: 35977464
                SO-VID: 9d3a0dc0-abfa-415e-99f6-ae9796cecb85
                Copyright © © 2022 The Author(s)
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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