CHICAGO — Circulating biomarkers that predicted future heart failure (HF) varied by whether incident HF was characterized by reduced LVEF (HFrEF) or preserved LVEF (HFpEF) in a pooled analysis of four large, well-known observational community-based cohort studies.[1]
The variation seemed to reflect differences in the pathophysiology of the two types of heart failures.
Combining biomarker predictors separately for each form of HF improved on conventional risk prediction for both, but more strongly for HFrEF, in the study published January 10 in JAMA Cardiology.
The findings suggest that more traditionally measured circulating cardiovascular risk biomarkers are more predictive of incident HFrEF than HFpEF, observe the authors, led by Dr Rudolf A de Boer (University of Groningen, the Netherlands).
Preserved ejection fraction HF “is a very complex entity that’s probably very heterogeneous,” senior author, Dr Jennifer E Ho (Massachusetts General Hospital, Boston, MA), told theheart.org | Medscape Cardiology.
That the biomarkers’ predictive value seems to favor HFrEF “highlights our existing knowledge gaps,” she said. The two forms of HF are about equally prevalent among patients with HF. For this analysis, HFrEF was characterized by an LVEF of less than 50% and HFpEF by an LVEF of 50% or greater.
Among the dozen biomarkers documented at baseline for the pooled cohorts, the six that predicted incident HFrEF tended to reflect neurohormonal activation, inflammation, and myocyte damage.
They included natriuretic peptides (NPs), troponins by high-sensitivity assay (hs-Tn), C-reactive protein (CRP), and urinary albumin-to-creatinine ratio (UACR).
Only two biomarkers, NPs and UACR, solidly predicted incident HFpEF, according to the authors. But there were less significant “suggestive associations” with incident HFpEF for three others: hs-Tn, fibrinogen, and plasminogen activator inhibitor 1 (PAI-1).
NPs Predict Both HF Types
Even though NP predicted both HFrEF and HFpEF, the effect was much stronger for HFrEF, a finding that seems to mirror what is seen clinically in HF, Ho observed.
“I think it’s consistent with what’s seen in patients with actual disease, except now we’re dialing back the clock and looking at healthy patients without existing heart failure.”
It was surprising in the study that markers of inflammation did not predict HFpEF, she said, because “we postulate that systemic inflammation in the context of other comorbidities like obesity or diabetes is really one of the central drivers of cardiac remodeling in HFpEF.”
Ho speculated on a few potential explanations that reflect limitations of the analysis. “The way we assessed inflammation was with these biomarkers that really represent systemic inflammation. They don’t necessarily always reflect localized inflammation.”
Another possibility is that “perhaps part of it is that we’re assessing all the biomarkers in relatively healthy adults, then looking at the development of future heart failure down the line.” It may be, she said, that the represented inflammatory pathways aren’t activated until the patient’s clinical disease is more imminent.
Pooled Patient-Level Data
The analysis took in patient-level data from 22,756 persons in four community-based longitudinal cohorts in whom at least one cardiovascular biomarker was measured at baseline. These cohorts were the Cardiovascular Health Study, the Framingham Heart Study, the Multi-Ethnic Study of Atherosclerosis, and the Prevention of Renal and Vascular End-stage Disease study.
Across the cohorts, there were 841 cases of documented incident HFrEF, 633 such cases of HFpEF, and an all-cause mortality of 22.8% over follow-up averaging 12 years.
Table. Hazard Ratios for Biomarker Prediction of Incident HFrEF and HFpEF
| Biomarkers | Hazard Ratio (95% Confidence Interval) |
|---|---|
| Significant for incident HfrEF | |
| NPs | 1.54 (1.41–1.68) |
| hs-Tn | 1.37 (1.29–1.46) |
| D-dimer | 1.22 (1.11–1.35) |
| UACR | 1.21 (1.11–1.32) |
| Cystatin-C | 1.19 (1.11–1.27) |
| CRP | 1.19 (1.11–1.28) |
| Significant for incident HFrEF | |
| UACR | 1.33 (1.20–1.48) |
| NPs | 1.27 (1.16–1.40) |
| For all hazard ratios, P<0.001 adjusted for age, sex, race or ethnicity, myocardial infarction history, systolic blood pressure, hypertension treatment, body mass index, diabetes, smoking status, left ventricular hypertrophy, and left bundle-branch block. | |
Measured biomarkers that didn’t shake out as significant predictors of either form of HF were interleukin-6, PAI-1, fibrinogen, galectin-3, aldosterone-to-renin ratio, and soluble suppressor of tumorigenicity 2 (sST2). As is true for NP, sST2 is considered a marker of myocardial stretch.
For argument’s sake, the authors developed a risk score for both forms of HF comprising biomarkers that were available in at least three of the four cohorts, which were used on top of standard clinical risk prediction.
The score for HFrEF combined NP, UACR, hs-Tn, cystatin-C, and CRP; the hazard ratio was 10.95 (95% confidence interval, 5.67–21.17) for quartile 4 vs quartile 1.
The HFpEF score consisted of NA, UACR, and hs-Tn; the corresponding hazard ratio was 4.96 (95% confidence interval, 2.54–9.70).
The exercise, Ho said, showed that “if you add different biomarkers representing different pathophysiologic pathways, you can potentially better risk-stratify patients into high- and low-risk categories.”
But that’s probably a long way off, she added. The usefulness of biomarkers for predicting HF risk “is still in its infancy.”
Choice of biomarkers in the analysis was limited by those measured in the four cohort studies, she observed, so it’s certainly possible that any successful biomarker panel for future HF risk might include other measures.
De Boer reports receiving grants from AstraZeneca, Bristol-Meyers Squibb, and Trevena and serving as a consultant for and receiving research and/or personal honoraria from Roche and Novartis. Ho has disclosed no relevant financial relationships. Disclosures for the other authors and funding sources are in the report.
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