Emerging biomarkers for heart failure: an update

Biomarker profiles in obesity patients and their relation to cardiac dysfunction

Aim: Current knowledge on the role of obesity in causing cardiac dysfunction is insufficient. Several biomarkers reflecting biological processes that may play a role in the occurrence of cardiac dysfunction in obesity patients are available. Purpose: To compare cardiovascular biomarker profiles between obesity patients and nonobese controls, and between obesity patients with and without cardiac dysfunction, in order to better understand the underlying pathophysiology of cardiac dysfunction in obesity patients. Materials & methods: Blood samples were obtained from 100 obesity patients (BMI ≥35 kg/m²) without known cardiovascular disease, and from 50 age- and gender-matched nonobese controls (BMI ≤30 kg/m²). The third cardiovascular panel of the Olink Multiplex platform was used for the measurement of 92 biomarkers. Results: The majority (53%) of biomarkers were elevated in obesity patients compared with nonobese controls. Only 5% of the biomarkers were elevated in obesity patients with cardiac dysfunction compared with those without. Biomarkers discriminating cardiac dysfunction from no cardiac dysfunction in obesity patients differed from those discriminating obese from nonobese patients. An elastic net model for the prediction of cardiac dysfunction in obesity patients had a high area under the receiver operating curve of 0.87 (95% CI: 0.79-0.94; p < 0.001). The sensitivity of this model was 84% and the specificity was 79%. Conclusion: A multiplex immunoassay was used for the first time in obesity patients without known cardiovascular disease. These patients have cardiovascular biomarker profiles that are clearly different from nonobese controls. Comparison of obesity patients with and without cardiac dysfunction suggested an important role for inflammation, atherosclerosis and insulin resistance in the underlying pathophysiology of cardiac dysfunction in obesity patients.

Low YKL-40 in chronic heart failure may predict beneficial effects of statins: analysis from the controlled rosuvastatin multinational trial in heart failure (CORONA)

CONTEXT AND OBJECTIVE

To evaluate if YKL-40 can provide prognostic information in patients with ischemic heart failure (HF) and identify patients who may benefit from statin therapy.

MATERIALS AND METHODS

The association between serum YKL-40 and predefined outcome was evaluated in 1344 HF patients assigned to rosuvastatin or placebo.

RESULTS

YKL-40 was not associated with outcome in adjusted analysis. In YKL-40 tertile 1, an effect on the primary outcome (HR 0.50, p = 0.006) and CV death (HR 0.54, p = 0.040) was seen by rosuvastatin in adjusted analysis.

CONCLUSIONS

A beneficial modification of outcome was observed with statin therapy in patients with low YKL-40 levels.

The multi-biomarker approach for heart failure in patients with hypertension

We assessed the predictive ability of selected biomarkers using N-terminal pro-brain natriuretic peptide (NT-proBNP) as the benchmark and tried to establish a multi-biomarker approach to heart failure (HF) in hypertensive patients. In 120 hypertensive patients with or without overt heart failure, the incremental predictive value of the following biomarkers was investigated: Collagen III N-terminal propeptide (PIIINP), cystatin C (CysC), lipocalin-2/NGAL, syndecan-4, tumor necrosis factor-α (TNF-α), interleukin 1 receptor type I (IL1R1), galectin-3, cardiotrophin-1 (CT-1), transforming growth factor β (TGF-β) and N-terminal pro-brain natriuretic peptide (NT-proBNP). The highest discriminative value for HF was observed for NT-proBNP (area under the receiver operating characteristic curve (AUC) = 0.873) and TGF-β (AUC = 0.878). On the basis of ROC curve analysis we found that CT-1 > 152 pg/mL, TGF-β < 7.7 ng/mL, syndecan > 2.3 ng/mL, NT-proBNP > 332.5 pg/mL, CysC > 1 mg/L and NGAL > 39.9 ng/mL were significant predictors of overt HF. There was only a small improvement in predictive ability of the multi-biomarker panel including the four biomarkers with the best performance in the detection of HF—NT-proBNP, TGF-β, CT-1, CysC—compared to the panel with NT-proBNP, TGF-β and CT-1 only. Biomarkers with different pathophysiological backgrounds (NT-proBNP, TGF-β, CT-1, CysC) give additive prognostic value for incident HF in hypertensive patients compared to NT-proBNP alone.