Adding traditional and emerging biomarkers for risk assessment in secondary prevention: a prospective cohort study of 20 656 patients with cardiovascular disease
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View abstract on PubMed
Summary
This summary is machine-generated.Adding biomarkers like Cystatin C and Hemoglobin A1c (HbA1c) modestly improved risk prediction for recurrent atherosclerotic cardiovascular disease (ASCVD) events. These markers enhanced discrimination in secondary prevention models, offering better identification of high-risk individuals.
Area Of Science
- Cardiovascular Medicine
- Biomarker Discovery
- Risk Stratification
Background
- Secondary prevention of atherosclerotic cardiovascular disease (ASCVD) relies on accurate risk assessment.
- Existing models may not fully capture individual risk for recurrent events.
- Novel biomarkers could enhance current risk prediction tools.
Purpose Of The Study
- To evaluate if conventional and emerging biomarkers improve risk discrimination and calibration in secondary ASCVD prevention.
- To assess the added value of biomarkers to the SMART2 (Secondary Manifestations of ARTerial Disease) model.
- To compare creatinine-based versus Cystatin C-based estimated glomerular filtration rate (eGFR) in risk models.
Main Methods
- Analysis of 20,658 UK Biobank participants with a history of ASCVD.
- Addition of biomarkers including Lipoprotein A (LP-a), apolipoprotein B, Cystatin C, Hemoglobin A1c (HbA1c), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase, and alkaline phosphatase (ALP) to the SMART2 model.
- Evaluation of C indices and Net Reclassification Index (NRI) for predicting recurrent major cardiovascular events.
- Comparison of creatinine-based eGFR (eGFRCr) with Cystatin C-based eGFR (eGFRCysC) and combined eGFRCr-CysC.
Main Results
- Modest improvements in C indices were observed with the addition of HbA1c (ΔC = 0.0064), Cystatin C (ΔC = 0.0037), GGT (ΔC = 0.0023), AST (ΔC = 0.0007), and ALP (ΔC = 0.0010).
- Replacing eGFRCr with eGFRCysC (ΔC = 0.0036) or eGFRCr-CysC (ΔC = 0.00336) also modestly improved discrimination.
- HbA1c and Cystatin C showed the strongest improvements in NRI.
- No significant modification in model calibration was observed with the addition of these biomarkers.
Conclusions
- Adding biomarkers, particularly Cystatin C and HbA1c, to the SMART2 model modestly enhances risk discrimination for recurrent ASCVD.
- Lipoprotein A (LP-a) did not significantly improve discrimination in this model.
- Cystatin C-based eGFR offers a potential improvement over creatinine-based eGFR for risk stratification in ASCVD secondary prevention.
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