Circulating levels and prognostic value of soluble ST2 in heart failure are less influenced by age than N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T

Alberto Aimo, James L. Januzzi, Giuseppe Vergaro, A. Mark Richards, Carolyn S.P. Lam, Roberto Latini, Inder S. Anand, Jay N. Cohn, Thor Ueland, Lars Gullestad, Pål Aukrust, Hans Peter Brunner-La Rocca, Antoni Bayes-Genis, Josep Lupón, Rudolf A. de Boer, Yasuchika Takeishi, Michael Egstrup, Ida Gustafsson, Hanna K. Gaggin, Kai M. EggersKurt Huber, Greg D. Gamble, Lieng H. Ling, Kui Tong Gerard Leong, Poh Shuah Daniel Yeo, Hean Yee Ong, Fazlur Jaufeerally, Tze P. Ng, Richard Troughton, Robert N. Doughty, Claudio Passino, Michele Emdin

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


AIMS: N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity troponin T (hs-TnT) and soluble suppression of tumorigenesis-2 (sST2) predict outcome in chronic heart failure (HF). We assessed the influence of age on circulating levels and prognostic significance of these biomarkers.

METHODS AND RESULTS: Individual data from 5301 patients with chronic HF and NT-proBNP, hs-TnT, and sST2 data were evaluated. Patients were stratified according to age: <60 years (n = 1332, 25%), 60-69 years (n = 1628, 31%), 70-79 years (n = 1662, 31%), and ≥ 80 years (n = 679, 13%). Patients (median age 66 years, 75% men, median left ventricular ejection fraction 28%, 64% with ischaemic HF) had median NT-proBNP 1564 ng/L, hs-TnT 21 ng/L, and sST2 29 ng/mL. Age independently predicted NT-proBNP and hs-TnT, but not sST2. The best NT-proBNP and hs-TnT cut-offs for 1-year and 5-year all-cause and cardiovascular mortality and 1- to 12-month HF hospitalization increased with age, while the best sST2 cut-offs did not. When stratifying patients according to age- and outcome-specific cut-offs, this stratification yielded independent prognostic significance over NT-proBNP levels only, or the composite of NT-proBNP and hs-TnT, and improved risk prediction for most endpoints. Finally, absolute NT-proBNP, hs-TnT, and sST2 levels predicted outcomes independent of age, sex, left ventricular ejection fraction category, ethnic group, and other variables.

CONCLUSIONS: Soluble ST2 is less influenced by age than NT-proBNP or hs-TnT; all these biomarkers predict outcome regardless of age. The use of age- and outcome-specific cut-offs of NT-proBNP, hs-TnT and sST2 allows more accurate risk stratification than NT-proBNP alone or the combination of NT-proBNP and hs-TnT.

Original languageEnglish (US)
Pages (from-to)2078-2088
Number of pages11
JournalEuropean Journal of Heart Failure
Issue number11
StatePublished - Nov 2020

Bibliographical note

Funding Information:
In a cohort of 5301 patients with chronic HF, circulating sST2 levels were influenced by age to a lesser extent than NT‐proBNP and hs‐TnT. Accordingly, the best cut‐offs of NT‐proBNP and hs‐TnT for the prediction of 1‐year and 5‐year all‐cause and cardiovascular mortality and 1‐ to 12‐month HF hospitalization tended to increase with age, while the best sST2 cut‐offs did not. Patient classification according to the age‐specific cut‐offs of the three biomarkers refined risk prediction over NT‐proBNP levels, as well as the combination of NT‐proBNP and hs‐TnT. The three biomarkers yielded independent prognostic significance in models including age, sex, LVEF categories, ethnicity, and other characteristics such as therapies for neurohormonal modulation. while cardiac troponins are released mostly upon cardiomyocyte necrosis. By contrast, extracardiac tissues are a significant source of circulating sST2, whose levels reflect both the activation of inflammatory and profibrotic pathways and haemodynamic overload, which are important determinants of disease progression in HF. This may explain the strong, independent prognostic value of sST2 for all‐cause and cardiovascular mortality and HF hospitalization. The main stimulus to natriuretic peptide release is pressure and/or volume overload, which increases left ventricular wall tension, Across age categories, sST2 remained much more stable than NT‐proBNP or hs‐TnT, despite a progressive increase in disease severity in parallel with age, as demonstrated by the rising proportions of patients in NYHA class III or IV (27% <60 years to 56% in the ≥80 years; ), with a pattern of NYHA class increase similar to the one observed in cohort studies. Accordingly, age predicted NT‐proBNP and hs‐TnT regardless of other variables including NYHA class, while it did not predict sST2. Even though this dataset allows to gain only limited insight on the reasons of this different relationship with age, some mechanisms can be proposed. The prevalence of CKD stages 3–5 increased from 36% in patients aged <60 years to 78% in those aged ≥80 years. Natriuretic peptides are excreted to a significant extent by the kidneys, and their circulating levels increase in patients with CKD. It is more controversial as to whether or not cardiac troponins are cleared by the kidneys, but patients with CKD tend to display higher troponin levels. By contrast, the influence of renal function on sST2 is thought to be less important. Accordingly, in the present study eGFR independently predicted NT‐proBNP and hs‐TnT, but not sST2 ( ). We may also consider that previous studies have reported limited differences in plasma sST2 between men and women, and between patients with HFrEF or HFmrEF or HFpEF, possibly justifying the relative stability of sST2 across age categories despite marked differences in the proportion of women and patients with HFmrEF or HFpEF ( ). Table Table Table This multi‐marker approach sometimes was more predictive than the combination of absolute NT‐proBNP and hs‐TnT, also improving metrics of risk reclassification. The additive prognostic value for the prediction of short‐to‐intermediate term HF hospitalization seems particularly interesting, as HF admissions have a negative impact on the quality of life and natural history of the disease, and can often be prevented if subclinical congestion is detected and addressed through appropriate changes in HF medications, lifestyle advice, and close follow‐up. Interestingly, absolute levels of all three biomarkers were independent predictors of almost all outcome measures, including HF hospitalization at the different time‐points, independent from age, but also from the combination of age, sex, HF category (HFrEF, HFmrEF, HFpEF), and patient ethnicity, and even from other baseline variables including medical therapy for HF. These findings provide a further demonstration of the strong, independent prognostic value of HF biomarkers in chronic HF, and outline that their predictive performance is unaffected by age and other patient characteristics. While dedicated studies are needed to clarify the mechanisms of the different relationships between age and HF biomarkers, the BIOS cohort represents an ideal platform to search for age‐specific cut‐offs for risk prediction, and to evaluate the added value of a multi‐marker strategy over absolute NT‐proBNP levels across age categories. We identified specific cut‐offs for each age category (<60, 60–69, 70–79, and ≥ 80 years), and for each endpoint (1‐year and 5‐year all‐cause and cardiovascular death, and 1‐, 3‐, 6‐, and 12‐month HF hospitalization), and we reported that patient classification according to these cut‐offs yielded independent prognostic significance over absolute NT‐proBNP levels, which are commonly used for risk prediction, according to guideline recommendation. or ≤ 40% in the Controlled Rosuvastatin Multinational Trial in Heart Failure ). Therefore, the results on the prognostic role of biomarkers across age categories were mostly driven by HFrEF patients, as indirectly confirmed by the similar AUC values in the whole population and the HFrEF subgroup (online supplementary Indeed, patient data for this study derived mainly from clinical trials on HF, where women are traditionally underrepresented, with an average representation of 20%. Overall, sex differences in the prognostic value of HF biomarkers could be more accurately searched in real‐world HF registries with available biomarker values. Third, the relatively small number of women and patients with non‐Caucasian ethnicity across age categories did not allow a reliable assessment of the relative prognostic performance of the three biomarkers in men vs. women, and Caucasian vs. non‐Caucasian ethnicity in the different age categories. Indeed, the AUC values calculated for these patient subsets were probably affected by the highly different patient numbers (online supplementary and age‐specific cut‐offs defined through the Youden method are more influenced by the size and composition of patient groups than continuous biomarker values. Seventh, the best age‐specific cut‐offs were calculated only in the whole population, instead than in smaller patient subgroups identified by sex, LVEF categories, different ethnicities, etc. Eighth, as stated above, the study design did not allow to define the mechanisms of the different relationships observed between age and HF biomarkers. Finally, limited information was available on patient co‐morbidities (particularly chronic inflammatory conditions) potentially affecting sST2 values. Several limitations to this analysis should be acknowledged. First, patients with HFrEF accounted for 83% of the whole population as a consequence of the inclusion of several trials (for example, LVEF <40% in the Valsartan Heart Failure Trial, Table S2 ). Further studies including a larger proportion of patients with HFmrEF and HFpEF are then warranted to gain a deeper insight on the impact of age on the prognostic value of HF biomarkers across LVEF categories. Second, the percentage of female patients was 27%, while study registries suggest a higher proportion of female patients, up to 50%. Table S7 ). Fourth, all studies containing these patient data were published before 2016, thus not considering treatment with sacubitril/valsartan, now prescribed in a significant number of patients with HFrEF. Fifth, the boundaries between age categories were rather arbitrarily set at 60, 70, and 80 years, in order to have an adequate number of patients within each category. Sixth, cut‐offs might be much more easily used in current clinical practice than continuous values, but dichotomizing continuous predictors in multiple regression might entail a loss of prognostic information compared to absolute biomarker values, In conclusion, sST2 is less influenced by age than NT‐proBNP or hs‐TnT; all these biomarkers predict outcome regardless of age. The use of age‐ and outcome‐specific cut‐offs of NT‐proBNP, hs‐TnT and sST2 allows a more accurate risk stratification than NT‐proBNP alone, or the combination of NT‐proBNP and hs‐TnT. Conflict of interest: J.L.J. is supported in part by the Hutter Family Professorship; is a Trustee of the American College of Cardiology; 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. A.M.R. has sat on advisory boards and/or received speakers honoraria, travel support and/or grants from Novartis, Roche Diagnostics, Abbott Laboratories, Thermo Fisher and Critical Diagnostics. C.S.P.L. is supported by a Clinician Scientist Award from the National Medical Research Council of Singapore; has received research support from Boston Scientific, Bayer, Roche Diagnostics, AstraZeneca, Medtronic, and Vifor Pharma; has served as consultant or on the Advisory Board/ Steering Committee/ Executive Committee for Boston Scientific, Bayer, Roche Diagnostics, AstraZeneca, Medtronic, Vifor Pharma, Novartis, Amgen, Merck, Janssen Research & Development LLC, Menarini, Boehringer Ingelheim, Novo Nordisk, Abbott Diagnostics, Corvia, Stealth BioTherapeutics, JanaCare, Biofourmis, Darma, Applied Therapeutics, MyoKardia, WebMD Global LLC, Radcliffe Group Ltd and Corpus. R.L. has received grant support and travel reimbursements from Roche Diagnostics. H.P.B.L.R. reports unrestricted research grants and consulting fees from Roche Diagnostics, as well as unrestricted research grants from Novartis and GlaxoSmithKline outside this work. A.B.G. has received grant support from Roche Diagnosis, lecture honoraria from Roche Diagnostics and Critical Diagnostics, and consulting income from Roche Diagnostics, Critical Diagnostics, and Novartis. J.L. has received lecture honoraria from Roche Diagnostics and reports relationship with Critical Diagnostics. The UMCG, which employs R.A.d.B., has received research grants and/or fees from AstraZeneca, Abbott, Bristol‐Myers Squibb, Novartis, Novo Nordisk, and Roche. R.A.d.B. is a minority shareholder of scPharmaceuticals, Inc.; received personal fees from Abbott, AstraZeneca, MandalMed Inc, and Novartis, outside the submitted work. H.K.G. has received grant support from Roche and Portola; consulting income from Roche Diagnostics, Amgen and Ortho Clinical; research payments for clinical endpoint committees for EchoSense and Radiometer. All other authors have nothing to disclose.

Publisher Copyright:
© 2019 European Society of Cardiology


  • Age
  • Biomarkers
  • Heart failure
  • Prognosis
  • sST2


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