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Abstract: TH-PO624

Structural Equation Modeling of Kidney Function Biomarkers Improves Incident Cardiovascular Risk Estimation

Session Information

Category: Hypertension and CVD

  • 1501 Hypertension and CVD: Epidemiology‚ Risk Factors‚ and Prevention

Authors

  • Fujii, Ryosuke, Eurac Research, Bolzano, Italy
  • Melotti, Roberto, Eurac Research, Bolzano, Italy
  • Gögele, Martin, Eurac Research, Bolzano, Italy
  • Barin, Laura, Eurac Research, Bolzano, Italy
  • Ghasemi-Semeskandeh, Dariush, Eurac Research, Bolzano, Italy
  • Barbieri, Giulia, Eurac Research, Bolzano, Italy
  • Pramstaller, Peter Paul, Eurac Research, Bolzano, Italy
  • Pattaro, Cristian, Eurac Research, Bolzano, Italy
Background

While there is no biochemical trait that alone can represent the true kidney function of an individual, formulas have been developed to obtain the estimated glomerular filtration rate (eGFR) based on serum creatinine (eGFRcrea) or cystatin C (eGFRcys) or their combination (eGFRcreacys). However, a more general method that takes advantage of kidney function-related changes observed in other biomarkers is lacking.

Methods

In the Microisolates in South Tyrol (MICROS) study, we applied structural equation modeling (SEM) to derive a latent kidney function biomarkers based on serum creatinine, cystatin C, eGFRcrea, and eGFRcys estimated with the CKD-Epi equations, uric acid (UA), and blood urea nitrogen (BUN), and accounting for sex and age (n=647). In an independent longitudinal dataset (n=670), we assessed the ability of the identified latent trait to predict increased risk of cardiovascular disease (CVD) over 10 years.

Results

Based on standard goodness-of-fit statistics, the best model was selected that included eGFRcrea, eGFRcys, UA, and BUN. The corresponding latent trait showed a C-statistics [95% CI] of 0.70 [0.65–0.74] for a 10-year prediction of a Framingham risk score of ≥5%. The corresponding C-statistics for CKD-EPI eGFRcrea, eGFRcys, and eGFRcreacys were of 0.63 [0.58–0.68], 0.69 [0.65–0.74], and 0.66 [0.62–0.71], respectively.

Conclusion

The SEM-derived latent kidney trait showed better performance in 10-year CVD risk prediction over conventional eGFR estimation methods.

Conceptual figure of our SEM analysis and factor loadings from the best fitting model

Funding

  • Private Foundation Support