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Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
Multi-variable biomarker approach in identifying incident heart failure in chronic kidney disease: results from the
Scott E Janus1, Jamal Hajjari1, Tarek Chami2
1Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Insights
A panel of four blood biomarkers, including B-type natriuretic peptide (BNP), fibroblast growth factor 23 (FGF23), fibrinogen, and high-sensitivity troponin T (hsTnT), can predict heart failure (HF) risk in chronic kidney disease (CKD) patients.
Area of Science:
- Cardiology
- Nephrology
- Biomarker Research
Background:
- Heart failure (HF) is a major cause of death in patients with chronic kidney disease (CKD).
- Early prediction of HF in CKD is crucial for timely intervention.
- Current risk prediction models may not fully capture HF risk in this population.
Purpose of the Study:
- To assess the feasibility of a multi-biomarker approach for predicting incident HF in CKD patients.
- To develop and validate a biomarker score for HF risk stratification.
- To evaluate the added value of a biomarker panel to existing clinical risk models.
Main Methods:
- Analysis of 3182 CKD patients from the Chronic Renal Insufficiency Cohort (CRIC) study.
- Measurement of 11 blood biomarkers including BNP, FGF23, fibrinogen, and hsTnT.
- Development of a biomarker score based on Youden's index cut-offs and Cox regression analysis for prediction of incident HF over a median follow-up of 7 years.
Main Results:
- Four biomarkers (BNP, FGF23, fibrinogen, hsTnT) were significantly associated with incident HF in the derivation cohort.
- HF incidence increased with the biomarker score, from 2.0% to 46.6% in the derivation cohort and 2.4% to 43.5% in the validation cohort.
- Incorporating the biomarker score into clinical models improved risk reclassification by 19% (net reclassification improvement of 0.16).
Conclusions:
- A panel of four biomarkers (BNP, FGF23, fibrinogen, hsTnT) can serve as a standalone score for predicting incident HF in CKD patients.
- This biomarker score enables early identification of high-risk individuals.
- The addition of this biomarker score to clinical risk models modestly improves HF risk prediction and discrimination.
Aims:
Heart failure (HF) is one of the leading causes of cardiovascular morbidity and mortality in the ever-growing population of patients with chronic kidney disease (CKD). There is a need to enhance early prediction to initiate treatment in CKD. We sought to study the feasibility of a multi-variable biomarker approach to predict incident HF risk in CKD.
Methods And Results:
We examined 3182 adults enrolled in the Chronic Renal Insufficiency Cohort (CRIC) without prevalent HF who underwent serum/plasma assays for 11 blood biomarkers at baseline visit (B-type natriuretic peptide [BNP], CXC motif chemokine ligand 12, fibrinogen, fractalkine, high-sensitivity C-reactive protein, myeloperoxidase, high-sensitivity troponin T (hsTnT), fibroblast growth factor 23 [FGF23], neutrophil gelatinase-associated lipocalin, fetuin A, aldosterone). The population was randomly divided into derivation (n = 1629) and validation (n = 1553) cohorts. Biomarkers that were associated with HF after adjustment for established HF risk factors were combined into an overall biomarker score (number of biomarkers above the Youden's index cut-off value). Cox regression was used to explore the predictive role of a biomarker panel to predict incident HF. A total of 411 patients developed incident HF at a median follow-up of 7 years. In the derivation cohort, four biomarkers were associated with HF (BNP, FGF23, fibrinogen, hsTnT). In a model combining all four biomarkers, BNP (hazard ratio [HR] 2.96 [95% confidence interval 2.14-4.09]), FGF23 (HR 1.74 [1.30-2.32]), fibrinogen (HR 2.40 [1.74-3.30]), and hsTnT (HR 2.89 [2.06-4.04]) were associated with incident HF. The incidence of HF increased with the biomarker score, to a similar degree in both derivation and validation cohorts: from 2.0% in score of 0% to 46.6% in score of 4 in the derivation cohort to 2.4% in score of 0% to 43.5% in score of 4 in the validation cohort. A model incorporating biomarkers in addition to clinical factors reclassified risk in 601 (19%) participants (352 [11%] participants to higher risk and 249 [8%] to lower risk) compared with clinical risk model alone (net reclassification improvement of 0.16).
Conclusion:
A basic panel of four blood biomarkers (BNP, FGF23, fibrinogen, and hsTnT) can be used as a standalone score to predict incident HF in patients with CKD allowing early identification of patients at high-risk for HF. Addition of biomarker score to clinical risk model modestly reclassifies HF risk and slightly improves discrimination.
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