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Prediction of cardiovascular events and all-cause mortality using race and race-free estimated glomerular filtration rate in African Americans: the Jackson Heart Study.

Journal articles  - Journal Article
Wang, H; Cai, J; Fan, H; Diamantidis, CJ; Young, BA; Bidulescu, A
Published in: Front Med (Lausanne)
2024

BACKGROUND: New Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations without a race adjustment were developed to estimate the glomerular filtration rate (eGFR). We aimed to compare the performance of five CKD-EPI eGFR equations, with or without race, in predicting cardiovascular disease (CVD) events and all-cause mortality in Black Americans from the Jackson Heart Study. METHODS: JHS is an ongoing population-based prospective cohort study of African Americans in the Jackson, Mississippi, metropolitan area. Five CKD-EPI equations were used to estimate GFR at baseline using serum creatinine (Cr) or cystatin C (cys), including 2009 eGFRcr(ASR [age, sex, race]), 2021 eGFRcr(AS [age and sex]), 2012 eGFRcr-cys(ASR), 2021 eGFRcr-cys(AS), 2012 eGFRcys(AS). Endpoints were incident CVD events and all-cause mortality. Cox proportional hazards regression was used to assess the associations between different eGFRs and outcomes adjusting for atherosclerotic risk factors. Harrell's C-statistics and Net Reclassification Index (NRI) were used to assess the predictive utility. RESULTS: Among 5,129 participants (average age 54.8 ± 12.8 yrs), 1898 were male (37.0%). eGFRcr(AS) provided lower estimates and resulting in a greater proportion of participants categorized as CKD than eGFRcr(ASR), eGFRcr-cys(ASR), eGFRcr-cys(AS) and eGFRcys(AS). A median follow-up of 13.7 and 14.3 years revealed 411 (9.3%) CVD incidents and 1,207 (23.5%) deaths. Lower eGFRs were associated with CVD incidents and all-cause mortality. eGFRcr-cys(ASR), eGFRcr-cys(AS) and eGFRcys(AS) were strongly associated with incident CVD events and all-cause mortality than eGFRcr(ASR) and eGFRcr(AS). A significant discrimination improvement was found in C-statistics for predicting incident CVD events and all-cause mortality after adding each eGFR measure to the basic model including atherosclerotic risk factors. Across a 7.5% 10-year risk threshold, eGFRcys(AS) improved net classification of all-cause mortality (NRI: 2.19, 95%CI: 0.08, 4.65%). CONCLUSION: eGFR based on creatinine omit race has the lowest mean and detects more CKD patients in Black population. The eGFRs incorporating cystatin C strengthens the association between the eGFR and the risks of incident CVD and all-cause mortality. Cystatin C-based eGFR equations might be more appropriate for predicting CVD and mortality among Black population.

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Published In

Front Med (Lausanne)

DOI

ISSN

2296-858X

Publication Date

2024

Volume

11

Start / End Page

1432965

Location

Switzerland

Related Subject Headings

  • 42 Health sciences
  • 32 Biomedical and clinical sciences
 

Citation

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Wang, H., Cai, J., Fan, H., Diamantidis, C. J., Young, B. A., & Bidulescu, A. (2024). Prediction of cardiovascular events and all-cause mortality using race and race-free estimated glomerular filtration rate in African Americans: the Jackson Heart Study. Front Med (Lausanne), 11, 1432965. https://doi.org/10.3389/fmed.2024.1432965
Wang, Haiping, Jiahui Cai, Hao Fan, Clarissa J. Diamantidis, Bessie A. Young, and Aurelian Bidulescu. “Prediction of cardiovascular events and all-cause mortality using race and race-free estimated glomerular filtration rate in African Americans: the Jackson Heart Study.Front Med (Lausanne) 11 (2024): 1432965. https://doi.org/10.3389/fmed.2024.1432965.
Wang, Haiping, et al. “Prediction of cardiovascular events and all-cause mortality using race and race-free estimated glomerular filtration rate in African Americans: the Jackson Heart Study.Front Med (Lausanne), vol. 11, 2024, p. 1432965. Pubmed, doi:10.3389/fmed.2024.1432965.

Published In

Front Med (Lausanne)

DOI

ISSN

2296-858X

Publication Date

2024

Volume

11

Start / End Page

1432965

Location

Switzerland

Related Subject Headings

  • 42 Health sciences
  • 32 Biomedical and clinical sciences