Population-based estimated Glomerular Filtration Rate distributions and associated health outcomes provide opportunities for early identification of and primary prevention of chronic kidney disease

Kidney International, vol. 109, pp. 559–568

Abstract

INTRODUCTION: There are currently no established strategies for early identification and primary prevention of chronic kidney disease (CKD). Automatic reporting of estimated glomerular filtration rate (eGFR) allows opportunistic CKD screening. Here, we hypothesized that comparison with population-based eGFR distributions may further help identify individuals at elevated risk. METHODS: A population-based observational cohort study including adults aged 40 to 100 years with routine serum/plasma creatinine tests (Stockholm CREAtinine Measurements project) between 2006 and 2021 was conducted. The cohort captured 1,179,501 unique individuals (80% of the population in the region) with 6,914,993 repeated annual eGFR measurements. After computing eGFR distributions by age and sex, cause-specific Cox regressions evaluated the associations between eGFR percentiles and risks of kidney failure with replacement therapy (KFRT) and death. RESULTS: or more who were below the 25th percentile, only 24% underwent albuminuria/proteinuria testing in the adjacent year and could have benefited from additional diagnostic work-up. CONCLUSIONS: Our study shows that eGFR values below the 25th percentile of the population distribution are associated with increased risks of kidney failure and death. Population-based eGFR charts may complement current automatic reporting systems and provide opportunities for early identification and primary prevention of CKD.

Authors 10

1. Yuanhang Yang

   Karolinska Institutet

   Affiliation as printed

   Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

2. Antoine Créon

   Karolinska Institutet

   Affiliation as printed

   Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

3. Andrew S. Levey

   Tufts Medical Center

   Affiliation as printed

   Division of Nephrology, Department of Internal Medicine, Tufts Medical Center, Boston, Massachusetts, USA

4. Anne‐Laure Faucon

   Karolinska Institutet

   Affiliation as printed

   Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

5. Aurora Caldinelli

   Karolinska Institutet

   Affiliation as printed

   Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

6. Marie Evans

   Karolinska Institutet

   Affiliation as printed

   Division of Nephrology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden

7. Arvid Sjölander

   Karolinska Institutet

   Affiliation as printed

   Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

8. A. Ortiz

   Instituto de Investigación Sanitaria Fundación Jiménez Díaz

   Affiliation as printed

   Department of Nephrology and Hypertension, IIS-Fundacion Jimenez Diaz UAM, Madrid, Spain

9. Edouard L Fu Aachen

   Leiden University · Leiden University Medical Center · Karolinska Institutet

   Affiliation as printed

   Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands

10. Juan Jesús Carrero

    Danderyds sjukhus · Karolinska Institutet

    Affiliation as printed

    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Division of Nephrology, Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden. Electronic address: juan.jesus.carrero@ki.se