Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment
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 equat...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118200/?tool=EBI |
| _version_ | 1797842250300915712 |
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| author | Ryosuke Fujii Roberto Melotti Martin Gögele Laura Barin Dariush Ghasemi-Semeskandeh Giulia Barbieri Peter P. Pramstaller Cristian Pattaro |
| author_facet | Ryosuke Fujii Roberto Melotti Martin Gögele Laura Barin Dariush Ghasemi-Semeskandeh Giulia Barbieri Peter P. Pramstaller Cristian Pattaro |
| author_sort | Ryosuke Fujii |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T16:45:57Z |
| format | Article |
| id | doaj.art-8d4670cbd35c4b009520af549a6e2e4a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-09T16:45:57Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-8d4670cbd35c4b009520af549a6e2e4a2023-04-23T05:31:35ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01184Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessmentRyosuke FujiiRoberto MelottiMartin GögeleLaura BarinDariush Ghasemi-SemeskandehGiulia BarbieriPeter P. PramstallerCristian PattaroLower 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.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118200/?tool=EBI |
| spellingShingle | Ryosuke Fujii Roberto Melotti Martin Gögele Laura Barin Dariush Ghasemi-Semeskandeh Giulia Barbieri Peter P. Pramstaller Cristian Pattaro Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment PLoS ONE |
| title | Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment |
| title_full | Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment |
| title_fullStr | Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment |
| title_full_unstemmed | Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment |
| title_short | Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment |
| title_sort | structural equation modeling sem of kidney function markers and longitudinal cvd risk assessment |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118200/?tool=EBI |
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