Cardiovascular risk prediction in people with chronic kidney disease

Area of Science:

• Nephrology
• Cardiology
• Epidemiology

Background:

• Clinical guidelines lack consistency on using chronic kidney disease (CKD) measures for cardiovascular disease (CVD) risk prediction.
• Existing studies often use varied definitions and statistical methods, hindering direct comparisons.

Purpose of the Study:

• To review recent literature on the utility of CKD measures in predicting CVD risk.
• To inform potential updates to clinical guidelines regarding CVD risk assessment in CKD patients.

Main Methods:

• Review of recent literature on CKD and CVD risk prediction.
• Analysis of a large individual-level meta-analysis from the CKD Prognosis Consortium (over 630,000 participants).
• Inclusion of studies examining creatinine-based eGFR, albuminuria, cystatin C, β2-microglobulin, coronary artery calcium, and cardiac troponins.

Main Results:

• Estimated glomerular filtration rate (eGFR) and albuminuria improve CVD risk prediction beyond traditional factors, especially for CVD mortality and heart failure.
• Albuminuria provides a more significant improvement in CVD risk prediction compared to eGFR.
• Additional biomarkers like cystatin C, β2-microglobulin, coronary artery calcium, and cardiac troponins may further enhance CVD prediction in CKD.

Conclusions:

• CKD measures, particularly albuminuria, are valuable additions to CVD risk prediction models.
• Further research and guideline updates are necessary to integrate these biomarkers effectively.
• The utility of specific biomarkers depends on the CVD outcome, population, and data availability.