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.

Clinical Kidney Journal
|October 6, 2017
PubMed

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.
Abstract

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