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Updated: Feb 21, 2026

Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
A clinical-genetic approach to assessing cardiovascular risk in patients with CKD
Emilio Rodrigo1,2,3, Sara Pich4, Isaac Subirana5,6
1Nephrology Service, University Hospital Marques de Valdecilla, Santander, Spain.
Insights
Adding a genetic risk score (GRS) to existing formulas significantly improves coronary heart disease (CHD) risk prediction in chronic kidney disease (CKD) patients. This enhanced assessment aids in more effective prevention strategies for high-risk individuals.
Area of Science:
- Cardiovascular Medicine
- Nephrology
- Genetics
- Epidemiology
Background:
- Coronary heart disease (CHD) is the leading cause of mortality in patients with chronic kidney disease (CKD).
- Current coronary risk assessment tools demonstrate limited accuracy in the CKD population.
- Previous research indicated that incorporating a genetic risk score (GRS) enhances coronary risk prediction in the general population.
Purpose of the Study:
- To investigate the association between a specific GRS and the incidence of coronary events in individuals with CKD.
- To determine if adding the GRS to established coronary risk prediction models improves risk estimation, particularly in early stages of kidney disease.
Main Methods:
- A cohort of 632 CKD patients (Stages 4-5, dialysis, or post-transplant) aged 35-74 years were followed for a mean of 9.3 years.
- Coronary events and transitions between CKD disease states were recorded.
- The incremental predictive value of the GRS was quantified using the C-statistic and net reclassification index.
Main Results:
- The GRS was independently associated with an increased risk of CHD (HR 1.34, P=0.022), notably in Stages 4-5 CKD and kidney transplant recipients.
- A revised risk prediction model incorporating CKD status, age, sex, and GRS demonstrated significantly improved predictive capacity (AUC 70.1, P=0.01).
- The enhanced model showed substantial reclassification improvement (net reclassification improvement 28.6).
Conclusions:
- A novel coronary risk prediction function combining genetic and clinical data offers more accurate identification of high-risk CKD patients.
- This improved risk stratification facilitates more effective preventive strategies for coronary events in the CKD population.
Background:
Coronary heart disease (CHD) is the primary cause of death in individuals with chronic kidney disease (CKD), but current equations for assessing coronary risk have low accuracy in this group. We have reported that the addition of a genetic risk score (GRS) to the Framingham risk function improved its predictive capacity in the general population. The aims of this study were to evaluate the association between this GRS and coronary events in the CKD population and to determine whether the addition of the GRS to coronary risk prediction functions improves the estimation of coronary risk at the earliest possible stages of kidney disease.
Methods:
A total of 632 CKD patients, aged 35-74 years, who had Stage 4-5 CKD, were on dialysis, had a functioning renal transplant or had returned to dialysis after transplant failure were included and followed up for a mean of 9.3 years. The transitions between disease states and the development of coronary events were registered. The increase in predictive ability that was obtained by including the GRS was measured as the improvement in the C-statistic and as the net reclassification index.
Results:
The GRS was independently associated with the risk of CHD (hazards ratio 1.34; 95% confidence interval 1.04-1.71; P = 0.022), especially in Stages 4 and 5 CKD, and kidney transplant patients. A coronary risk prediction function that incorporated chronic kidney disease (CKD) disease state, age, sex and the GRS had significantly greater predictive capacity (AUC 70.1, P = 0.01) and showed good reclassification (net reclassification improvement 28.6).
Conclusion:
This new function, combining genetic and clinical data, identifies CKD patients with a high risk of coronary events more accurately, allowing us to prevent such events more effectively.
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