Integrating multiple kidney function markers to predict all-cause and cardiovascular disease mortality: prospective analysis of 366 758 UK Biobank participants
Background Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction. Methods We conducted an explora...
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| Veröffentlicht in: | Clinical Kidney Journal Jg. 17; H. 8; S. sfae207 |
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| Sprache: | Englisch |
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England
Oxford University Press (OUP)
01.08.2024
Oxford University Press |
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| ISSN: | 2048-8505, 2048-8513 |
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| Abstract | Background
Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction.
Methods
We conducted an exploratory factor analysis (EFA) of serum creatinine– and cystatin C–based estimated glomerular filtration rate [eGFRcre and eGFRcys; derived by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and European Kidney Function Consortium (EKFC) equations], blood urea nitrogen (BUN), uric acid and serum albumin among 366 758 participants in the UK Biobank without a history of kidney failure. Fitting Cox proportional hazards models, we compared the ability of the identified latent factors to predict overall mortality and mortality by cardiovascular disease (CVD), also considering CVD-specific causes like coronary heart disease (CHD) and cerebrovascular disease.
Results
During 12.5 years of follow-up, 26 327 participants died from any cause, 5376 died from CVD, 2908 died from CHD and 1116 died from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2, both representing kidney function variations. When using the CKD-EPI equation, EFA1 performed like eGFRcys, with EFA1 showing slightly larger hazard ratios for overall and CVD-related mortality. At 10 years of follow-up, EFA1 and eGFRcys showed moderate discrimination performance for CVD-related mortality, outperforming all other kidney indices. eGFRcre was the least predictive marker across all outcomes. When using the EKFC equation, eGFRcys performed better than EFA1 while all other results remaining similar.
Conclusions
While EFA is an attractive approach to capture the complex effects of kidney function, eGFRcys remains the most practical and effective measurement for all-cause and CVD mortality risk prediction.
Graphical Abstract
Graphical Abstract |
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| AbstractList | ABSTRACT Background Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction. Methods We conducted an exploratory factor analysis (EFA) of serum creatinine– and cystatin C–based estimated glomerular filtration rate [eGFRcre and eGFRcys; derived by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and European Kidney Function Consortium (EKFC) equations], blood urea nitrogen (BUN), uric acid and serum albumin among 366 758 participants in the UK Biobank without a history of kidney failure. Fitting Cox proportional hazards models, we compared the ability of the identified latent factors to predict overall mortality and mortality by cardiovascular disease (CVD), also considering CVD-specific causes like coronary heart disease (CHD) and cerebrovascular disease. Results During 12.5 years of follow-up, 26 327 participants died from any cause, 5376 died from CVD, 2908 died from CHD and 1116 died from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2, both representing kidney function variations. When using the CKD-EPI equation, EFA1 performed like eGFRcys, with EFA1 showing slightly larger hazard ratios for overall and CVD-related mortality. At 10 years of follow-up, EFA1 and eGFRcys showed moderate discrimination performance for CVD-related mortality, outperforming all other kidney indices. eGFRcre was the least predictive marker across all outcomes. When using the EKFC equation, eGFRcys performed better than EFA1 while all other results remaining similar. Conclusions While EFA is an attractive approach to capture the complex effects of kidney function, eGFRcys remains the most practical and effective measurement for all-cause and CVD mortality risk prediction. Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction.BackgroundReduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction.We conducted an exploratory factor analysis (EFA) of serum creatinine- and cystatin C-based estimated glomerular filtration rate [eGFRcre and eGFRcys; derived by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and European Kidney Function Consortium (EKFC) equations], blood urea nitrogen (BUN), uric acid and serum albumin among 366 758 participants in the UK Biobank without a history of kidney failure. Fitting Cox proportional hazards models, we compared the ability of the identified latent factors to predict overall mortality and mortality by cardiovascular disease (CVD), also considering CVD-specific causes like coronary heart disease (CHD) and cerebrovascular disease.MethodsWe conducted an exploratory factor analysis (EFA) of serum creatinine- and cystatin C-based estimated glomerular filtration rate [eGFRcre and eGFRcys; derived by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and European Kidney Function Consortium (EKFC) equations], blood urea nitrogen (BUN), uric acid and serum albumin among 366 758 participants in the UK Biobank without a history of kidney failure. Fitting Cox proportional hazards models, we compared the ability of the identified latent factors to predict overall mortality and mortality by cardiovascular disease (CVD), also considering CVD-specific causes like coronary heart disease (CHD) and cerebrovascular disease.During 12.5 years of follow-up, 26 327 participants died from any cause, 5376 died from CVD, 2908 died from CHD and 1116 died from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2, both representing kidney function variations. When using the CKD-EPI equation, EFA1 performed like eGFRcys, with EFA1 showing slightly larger hazard ratios for overall and CVD-related mortality. At 10 years of follow-up, EFA1 and eGFRcys showed moderate discrimination performance for CVD-related mortality, outperforming all other kidney indices. eGFRcre was the least predictive marker across all outcomes. When using the EKFC equation, eGFRcys performed better than EFA1 while all other results remaining similar.ResultsDuring 12.5 years of follow-up, 26 327 participants died from any cause, 5376 died from CVD, 2908 died from CHD and 1116 died from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2, both representing kidney function variations. When using the CKD-EPI equation, EFA1 performed like eGFRcys, with EFA1 showing slightly larger hazard ratios for overall and CVD-related mortality. At 10 years of follow-up, EFA1 and eGFRcys showed moderate discrimination performance for CVD-related mortality, outperforming all other kidney indices. eGFRcre was the least predictive marker across all outcomes. When using the EKFC equation, eGFRcys performed better than EFA1 while all other results remaining similar.While EFA is an attractive approach to capture the complex effects of kidney function, eGFRcys remains the most practical and effective measurement for all-cause and CVD mortality risk prediction.ConclusionsWhile EFA is an attractive approach to capture the complex effects of kidney function, eGFRcys remains the most practical and effective measurement for all-cause and CVD mortality risk prediction. Background Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction. Methods We conducted an exploratory factor analysis (EFA) of serum creatinine– and cystatin C–based estimated glomerular filtration rate [eGFRcre and eGFRcys; derived by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and European Kidney Function Consortium (EKFC) equations], blood urea nitrogen (BUN), uric acid and serum albumin among 366 758 participants in the UK Biobank without a history of kidney failure. Fitting Cox proportional hazards models, we compared the ability of the identified latent factors to predict overall mortality and mortality by cardiovascular disease (CVD), also considering CVD-specific causes like coronary heart disease (CHD) and cerebrovascular disease. Results During 12.5 years of follow-up, 26 327 participants died from any cause, 5376 died from CVD, 2908 died from CHD and 1116 died from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2, both representing kidney function variations. When using the CKD-EPI equation, EFA1 performed like eGFRcys, with EFA1 showing slightly larger hazard ratios for overall and CVD-related mortality. At 10 years of follow-up, EFA1 and eGFRcys showed moderate discrimination performance for CVD-related mortality, outperforming all other kidney indices. eGFRcre was the least predictive marker across all outcomes. When using the EKFC equation, eGFRcys performed better than EFA1 while all other results remaining similar. Conclusions While EFA is an attractive approach to capture the complex effects of kidney function, eGFRcys remains the most practical and effective measurement for all-cause and CVD mortality risk prediction. Graphical Abstract Graphical Abstract Graphical Abstract Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction. We conducted an exploratory factor analysis (EFA) of serum creatinine- and cystatin C-based estimated glomerular filtration rate [eGFRcre and eGFRcys; derived by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and European Kidney Function Consortium (EKFC) equations], blood urea nitrogen (BUN), uric acid and serum albumin among 366 758 participants in the UK Biobank without a history of kidney failure. Fitting Cox proportional hazards models, we compared the ability of the identified latent factors to predict overall mortality and mortality by cardiovascular disease (CVD), also considering CVD-specific causes like coronary heart disease (CHD) and cerebrovascular disease. During 12.5 years of follow-up, 26 327 participants died from any cause, 5376 died from CVD, 2908 died from CHD and 1116 died from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2, both representing kidney function variations. When using the CKD-EPI equation, EFA1 performed like eGFRcys, with EFA1 showing slightly larger hazard ratios for overall and CVD-related mortality. At 10 years of follow-up, EFA1 and eGFRcys showed moderate discrimination performance for CVD-related mortality, outperforming all other kidney indices. eGFRcre was the least predictive marker across all outcomes. When using the EKFC equation, eGFRcys performed better than EFA1 while all other results remaining similar. While EFA is an attractive approach to capture the complex effects of kidney function, eGFRcys remains the most practical and effective measurement for all-cause and CVD mortality risk prediction. Background. Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction. Methods. We conducted an exploratory factor analysis (EFA) of serum creatinine- and cystatin C-based estimated glomerular filtration rate [eGFRcre and eGFRcys; derived by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and European Kidney Function Consortium (EKFC) equations], blood urea nitrogen (BUN), uric acid and serum albumin among 366 758 participants in the UK Biobank without a history of kidney failure. Fitting Cox proportional hazards models, we compared the ability of the identified latent factors to predict overall mortality and mortality by cardiovascular disease (CVD), also considering CVD-specific causes like coronary heart disease (CHD) and cerebrovascular disease. Results. During 12.5 years of follow-up, 26 327 participants died from any cause, 5376 died from CVD, 2908 died from CHD and 1116 died from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2, both representing kidney function variations. When using the CKD-EPI equation, EFA1 performed like eGFRcys, with EFA1 showing slightly larger hazard ratios for overall and CVD-related mortality. At 10 years of follow-up, EFA1 and eGFRcys showed moderate discrimination performance for CVD-related mortality, outperforming all other kidney indices. eGFRcre was the least predictive marker across all outcomes. When using the EKFC equation, eGFRcys performed better than EFA1 while all other results remaining similar. Conclusions. While EFA is an attractive approach to capture the complex effects of kidney function, eGFRcys remains the most practical and effective measurement for all-cause and CVD mortality risk prediction. GRAPHICAL ABSTRACT Keywords: cardiovascular disease, cystatin C, eGFR, factor analysis, kidney function |
| Audience | Academic |
| Author | Roberto Melotti Ryosuke Fujii Anna Köttgen Alexander Teumer Cristian Pattaro Daniele Giardiello |
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| Keywords | eGFR cardiovascular disease cystatin C factor analysis kidney function |
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Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function... Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but... Background. Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function... GRAPHICAL ABSTRACT ABSTRACT Background Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney... Graphical Abstract |
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| SubjectTerms | Biobanks Cardiovascular disease cardiovascular disease; cystatin C; eGFR; factor analysis; kidney function Cardiovascular diseases Cerebrovascular disease Chronic kidney failure Complications and side effects Health aspects Kidney diseases Kidney function tests Mortality Original Original Article Patient outcomes Risk factors |
| Title | Integrating multiple kidney function markers to predict all-cause and cardiovascular disease mortality: prospective analysis of 366 758 UK Biobank participants |
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