It is time for consistency in the use of biomarkers in heart failure clinical trials.

Abstract

This article refers to ‘Cardiovascular and non-cardiovascular death distinction: the utility of troponin beyond N-terminal pro-B-type natriuretic peptide. Findings from the BIOSTAT-CHF study’ by J.P. Ferreira et al., published in this issue on pages 81–89. In nearly every issue of nearly every heart failure (HF) journal, one may find scientific reports regarding biomarker testing. Indeed, over the past 20 years, the role of ‘biomarker science’ has taken on a life of its own in the HF literature: analyses have focused on a broad range of topics including the role of HF markers to support clinical judgement, provide information regarding mechanism of disease, and to predict complications of the diagnosis. Increasingly, HF biomarker studies have focused on the role of circulating blood substances to reliably predict adverse outcomes such as death or HF progression; these applications may have unique uses, one of which is a growing role as a tool in HF clinical trials. As we have recently discussed,1 measurement of B-type natriuretic peptide (BNP) or its amino-terminal pro-peptide equivalent (NT-proBNP) is now ubiquitous in development programmes for HF therapeutics. Given associations with presence or severity of HF, concentrations of both peptides are routinely used (i) as an inclusion criterion to ensure enrolment of appropriate patients; (ii) as a measure of drug toxicity (especially in oncology trials); (iii) as an outcome or endpoint measure; (iv) to explain efficacy of therapeutics; and (v) as a target for therapy.1 Natriuretic peptides are by far the most frequently used for these applications.1 That said, other biomarkers such as high-sensitivity cardiac troponin are also increasingly used. Of these, the first – namely to include appropriate patients in studies – is of great interest. In the present issue of the Journal, Ferreira and colleagues report results from a substudy out of the BIOSTAT-CHF (A systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) trial.2 In this analysis, the investigators sought to develop risk models to predict cardiovascular and non-cardiovascular events. Both models included clinical and laboratory variables; and interestingly, both models incorporated NT-proBNP, however, high-sensitivity cardiac troponin T (hs-cTnT) helped predict cardiovascular death only. These findings are important and potentially informative to clinical trialists regarding trial design when it comes to enrolment criteria in particular, for the achievement of desired outcomes. When used to include patients in trials, a biomarker that is considered prognostic is ‘used to identify likelihood of a clinical event, disease recurrence or progression in patients who have the disease or medical condition of interest’.3 The ability of biomarkers such as hs-cTnT to predict risk allows for enrolment of study patients with higher event rates, minimizing Type II error in underpowered studies,1 as elevated concentrations are associated with worse prognosis.4, 5 And so, as Ferreira and colleagues demonstrated,2 a clinical trialist interested in cardiovascular death as a clinical trial endpoint may opt to use hs-cTnT concentrations as an inclusion criterion in combination with BNP and NT-proBNP. The potential for use of biomarker inclusion criteria concentration to attain a certain event rate is vast, and intuition would tell us that higher concentration cut-offs are associated with higher event rates, but this has yet to be examined across the universe of HF clinical trials. Importantly, biomarkers are a continuous variable, with higher concentrations less commonly found, but perhaps more tightly associated with risk. When considering biomarker cut-points for risk enrichment it is imperative to choose concentrations that (i) identify high-risk patients, but (ii) that risk is not ‘too high’ that the patients are not representative of the general population. Hitting the sweet spot to ensure enrolment of patients likely to benefit is important: in the Prospective Angiotensin–Neprilysin Inhibition versus Enalapril in Heart Failure (PARADIGM-HF) trial, HF with reduced ejection fraction (EF) patients were enrolled based on a modestly elevated BNP or NT-proBNP; with high event rates, this allowed for more timely completion of this event-driven study.1, 6 In the Spironolactone for Heart Failure with Preserved Ejection Fraction (TOPCAT) trial, in HF with preserved EF patients enrolled based on elevated BNP (≥100 pg/mL) or NT-proBNP (≥360 pg/mL), significant benefit was seen (hazard ratio 0.65; 95% confidence interval 0.49–0.87, P = 0.003).7 Interestingly, varying responses to spironolactone as a function of natriuretic peptide values was seen in TOPCAT, such that patients with intermediate baseline concentrations of BNP or NT-proBNP benefitted most, while those with considerably higher concentrations were not treatment responsive. Thus, biomarkers may also identify patients ‘too sick to benefit’ or those with a treatment non-responsive disease: the lack of response to spironolactone in those with very elevated natriuretic peptide concentrations might reflect a sinister unrecognized diagnosis such as cardiac amyloidosis. Though biomarkers may provide utility for clinical trials, caveats abound, and a note of caution is needed: it is not so simple as to merely include a biomarker in the design of a study and expect similar performance across patient types. For example, several populations need special consideration whose natriuretic peptide or troponin concentrations are higher or lower than would be expected based on the severity of HF. The former includes patients with atrial fibrillation,8 chronic kidney disease,9 and elderly patients.10 Conversely, Black patients reportedly have lower (in some cases undetectable) concentrations of BNP and NT-proBNP than White patients; reasons for which remain unclear.11 As well, concentrations of natriuretic peptides are lower in obese patients.10 Lastly, effects of therapies such as sacubitril/valsartan may result in unpredictable changes in certain biomarkers.12 Thus, while we strongly advocate for use of biomarkers in clinical trial programmes, we also emphasize the need for caution and attention to detail when doing so. The data by Ferreira and colleagues suggest different utilities of NT-proBNP and hs-cTnT to predict different outcomes. These results suggest clinical trialists might leverage each biomarker for different purposes, which is an appealing option. More widespread use of accurate biomarkers to enhance trial enrolment would be expected to improve inclusion of correct patients, allow for greater representation of the general population, include patients normally underrepresented in clinical trials, and strengthen the science being delivered. That is interesting and exciting. There is enthusiasm, to be sure, about the use of biomarkers to enrich trials and speed their conclusion through enrolment of patients at higher risk for events but little has been done to standardize the approaches used in trials that use biomarkers. Furthermore, despite a literal universe of biomarkers that might predict cause-specific outcomes (such as cardiac vs. non-cardiac death), this application has not been leveraged with most studies only using BNP or NT-proBNP. Standardization of when and how biomarkers are included as a tool for clinical trial design would be a welcome – and much needed – step. Dr. Januzzi is supported in part by the Hutter Family Professorship (Boston, MA, USA). Dr. Ibrahim is supported in part by the Dennis and Marilyn Barry Fund in Cardiology Research (Boston, MA, USA). Conflict of interest: J.L.J. is a Trustee of the American College of Cardiology, a Board member of Imbria Pharmaceuticals, has received grant support from Novartis Pharmaceuticals, Roche Diagnostics, Abbott, Singulex and Prevencio, consulting income from Abbott, Janssen, Novartis, Pfizer, Merck, and Roche Diagnostics, and participates in clinical endpoint committees/data safety monitoring boards for Abbott, AbbVie, Amgen, Boehringer-Ingelheim, Janssen, and Takeda. N.E.I. has received consulting income from Novartis.