Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment

PLoS ONE, vol. 18, pp. e0280600

Abstract

Lower kidney function is known to enhance cardiovascular disease (CVD) risk. It is unclear which estimated glomerular filtration rate (eGFR) equation best predict an increased CVD risk and if prediction can be improved by integration of multiple kidney function markers. We performed structural equation modeling (SEM) of kidney markers and compared the performance of the resulting pooled indexes with established eGFR equations to predict CVD risk in a 10-year longitudinal population-based design. We split the study sample into a set of participants with only baseline data (n = 647; model-building set) and a set with longitudinal data (n = 670; longitudinal set). In the model-building set, we fitted five SEM models based on serum creatinine or creatinine-based eGFR (eGFRcre), cystatin C or cystatin-based eGFR (eGFRcys), uric acid (UA), and blood urea nitrogen (BUN). In the longitudinal set, 10-year incident CVD risk was defined as a Framingham risk score (FRS)>5% and a pooled cohort equation (PCE)>5%. Predictive performances of the different kidney function indexes were compared using the C-statistic and the DeLong test. In the longitudinal set, a SEM-based estimate of latent kidney function based on eGFRcre, eGFRcys, UA, and BUN showed better prediction performance for both FRS>5% (C-statistic: 0.70; 95% CI: 0.65-0.74) and PCE>5% (C-statistic: 0.75; 95%CI: 0.71-0.79) than other SEM models and different eGFR formulas (DeLong test p-values<3.21×10-6 for FRS>5% and <1.49×10-9 for PCE>5%, respectively). However, the new derived marker could not outperform eGFRcys (DeLong test p-values = 0.88 for FRS>5% and 0.20 for PCE>5%, respectively). SEM is a promising approach to identify latent kidney function signatures. However, for incident CVD risk prediction, eGFRcys could still be preferrable given its simpler derivation.

Authors 8

1. Ryosuke Fujii corresponding

   Eurac Research · Fujita Health University · Institute for Biomedicine

   Affiliation as printed

   Department of Preventive Medical Science, Fujita Health University School of Medical Sciences, Toyoake, Japan

   Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano, Bozen, Italy

2. Roberto Melotti

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano, Bozen, Italy

3. Martin Gögele

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano, Bozen, Italy

4. Laura Barin

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano, Bozen, Italy

5. Dariush Ghasemi-Semeskandeh Aachen Department of Human Genetics

   Eurac Research · Leiden University Medical Center · Institute for Biomedicine

   Affiliation as printed

   Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands

   Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano, Bozen, Italy

6. Giulia Barbieri

   University of Verona · Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy

   Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano, Bozen, Italy

7. Peter Paul Pramstaller

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano, Bozen, Italy

8. Cristian Pattaro

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, Bolzano, Bozen, Italy