Dynamic personalized risk prediction in chronic heart failure patients: a longitudinal, clinical investigation of 92 biomarkers (Bio-SHiFT study)

Dominika Klimczak-Tomaniak, Marie de Bakker, Elke Bouwens, K Martijn Akkerhuis, Sara Baart, Dimitris Rizopoulos, Henk Mouthaan, Jan van Ramshorst, Tjeerd Germans, Alina Constantinescu, Olivier Manintveld, Victor Umans, Eric Boersma, Isabella Kardys, Dominika Klimczak-Tomaniak, Marie de Bakker, Elke Bouwens, K Martijn Akkerhuis, Sara Baart, Dimitris Rizopoulos, Henk Mouthaan, Jan van Ramshorst, Tjeerd Germans, Alina Constantinescu, Olivier Manintveld, Victor Umans, Eric Boersma, Isabella Kardys

Abstract

The aim of our observational study was to derive a small set out of 92 repeatedly measured biomarkers with optimal predictive capacity for adverse clinical events in heart failure, which could be used for dynamic, individual risk assessment in clinical practice. In 250 chronic HFrEF (CHF) patients, we collected trimonthly blood samples during a median of 2.2 years. We selected 537 samples for repeated measurement of 92 biomarkers with the Cardiovascular Panel III (Olink Proteomics AB). We applied Least Absolute Shrinkage and Selection Operator (LASSO) penalization to select the optimal set of predictors of the primary endpoint (PE). The association between repeatedly measured levels of selected biomarkers and the PE was evaluated by multivariable joint models (mvJM) with stratified fivefold cross validation of the area under the curve (cvAUC). The PE occurred in 66(27%) patients. The optimal set of biomarkers selected by LASSO included 9 proteins: NT-proBNP, ST2, vWF, FABP4, IGFBP-1, PAI-1, PON-3, transferrin receptor protein-1, and chitotriosidase-1, that yielded a cvAUC of 0.88, outperforming the discriminative ability of models consisting of standard biomarkers (NT-proBNP, hs-TnT, eGFR clinically adjusted) - 0.82 and performing equally well as an extended literature-based set of acknowledged biomarkers (NT-proBNP, hs-TnT, hs-CRP, GDF-15, ST2, PAI-1, Galectin 3) - 0.88. Nine out of 92 serially measured circulating proteins provided a multivariable model for adverse clinical events in CHF patients with high discriminative ability. These proteins reflect wall stress, remodelling, endothelial dysfunction, iron deficiency, haemostasis/fibrinolysis and innate immunity activation. A panel containing these proteins could contribute to dynamic, personalized risk assessment.Clinical Trial Registration: 10/05/2013 https://ichgcp.net/clinical-trials-registry/NCT01851538?term=nCT01851538&draw=2&rank=1 .

Conflict of interest statement

Henk Mouthaan is employed by Olink Proteomics AB. Other authors have no competing interests.

© 2022. The Author(s).

Figures

Figure 1
Figure 1
Boxplot figures presenting baseline and last available measurements of biomarkers retained in the model by the LASSO analysis in 250 patients with the endpoint and those who remained endpoint free. CHIT1 Chitotriosidase-1, FABP4 Fatty acid-binding protein 4, IGFBP-1 Insulin-like growth factor-binding protein 1, NT-proBNP N-terminal prohormone brain natriuretic peptide, PAI-1 Plasminogen activator inhibitor 1, PON3 Paraoxonase 3, ST2 protein Suppressor of tumorigenicity 2, TfR Transferrin receptor protein 1, vWF von Willebrand factor.

References

    1. Cresci S, et al. Heart failure in the era of precision medicine: A scientific statement from the American Heart Association. Circ. Genom. Precis. Med. 2019;12(10):458–485.
    1. Anker SD, et al. Hormonal changes and catabolic/anabolic imbalance in chronic heart failure and their importance for cardiac cachexia. Circulation. 1997;96(2):526–534.
    1. Enjuanes C, et al. Iron status in chronic heart failure: Impact on symptoms, functional class and submaximal exercise capacity. Rev. Esp. Cardiol. (Engl. Ed.) 2016;69(3):247–255.
    1. Jug B, et al. Prognostic impact of haemostatic derangements in chronic heart failure. Thromb. Haemost. 2009;102(2):314–320.
    1. Yancy CW, et al. 2013 ACCF/AHA guideline for the management of heart failure: A report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation. 2013;128(16):e240–327.
    1. Ponikowski P, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur. Heart J. 2016;37(27):2129–2200.
    1. Lanfear DE, et al. Targeted metabolomic profiling of plasma and survival in heart failure patients. JACC Heart Fail. 2017;5(11):823–832.
    1. Cao, T. H. et al. Plasma proteomic approach in patients with heart failure: Insights into pathogenesis of disease progression and potential novel treatment targets. Eur. J. Heart Fail.22(1), 70–80 (2019).
    1. Dubin RF, et al. Proteomic analysis of heart failure hospitalization among patients with chronic kidney disease: The Heart and Soul Study. PLoS ONE. 2018;13(12):e0208042.
    1. Stenemo M, et al. Circulating proteins as predictors of incident heart failure in the elderly. Eur. J. Heart Fail. 2018;20(1):55–62.
    1. Brankovic M, et al. Cardiometabolic biomarkers and their temporal patterns predict poor outcome in chronic heart failure (Bio-SHiFT study) J. Clin. Endocrinol. Metab. 2018;103(11):3954–3964.
    1. van Boven N, et al. Toward personalized risk assessment in patients with chronic heart failure: Detailed temporal patterns of NT-proBNP, troponin T, and CRP in the Bio-SHiFT study. Am. Heart J. 2018;196:36–48.
    1. Klimczak-Tomaniak D, et al. Temporal patterns of macrophage- and neutrophil-related markers are associated with clinical outcome in heart failure patients. ESC Heart Fail. 2020;7(3):1190–1200.
    1. Brankovic M, et al. Patient-specific evolution of renal function in chronic heart failure patients dynamically predicts clinical outcome in the Bio-SHiFT study. Kidney Int. 2018;93(4):952–960.
    1. Assarsson E, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE. 2014;9(4):e95192.
    1. Pavlou M, Ambler G, Seaman S, De Iorio M, Omar RZ. Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events. Stat. Med. 2016;35(7):1159–1177.
    1. Rizopoulos D. JM: An R package for the joint modelling of longitudinal and time-to-event data. J. Stat. Softw. 2010;35(9):33.
    1. Rizopoulos D. Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics. 2011;67(3):819–829.
    1. Pencina MJ, D'Agostino RB, Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat. Med. 2011;30(1):11–21.
    1. Klimczak-Tomaniak, D. et al. Longitudinal patterns of N-terminal pro B-type natriuretic peptide, troponin T, and C-reactive protein in relation to the dynamics of echocardiographic parameters in heart failure patients. Eur. Heart J. Cardiovasc. Imaging.21(9), 1005–1012 (2019).
    1. Emdin M, et al. sST2 predicts outcome in chronic heart failure beyond NT-proBNP and high-sensitivity Troponin T. J. Am. Coll. Cardiol. 2018;72(19):2309–2320.
    1. Giblin JP, Hewlett LJ, Hannah MJ. Basal secretion of von Willebrand factor from human endothelial cells. Blood. 2008;112(4):957–964.
    1. Popovic B, et al. Endothelial-driven increase in plasma thrombin generation characterising a new hypercoagulable phenotype in acute heart failure. Int. J. Cardiol. 2019;274:195–201.
    1. Kleber ME, et al. Von Willebrand factor improves risk prediction in addition to N-terminal pro-B-type natriuretic peptide in patients referred to coronary angiography and signs and symptoms of heart failure and preserved ejection fraction. Circ. Heart Fail. 2015;8(1):25–32.
    1. Winter MP, et al. Prognostic significance of tPA/PAI-1 complex in patients with heart failure and preserved ejection fraction. Thromb. Haemost. 2017;117(3):471–478.
    1. Jankowska EA, et al. Iron deficiency defined as depleted iron stores accompanied by unmet cellular iron requirements identifies patients at the highest risk of death after an episode of acute heart failure. Eur. Heart J. 2014;35(36):2468–2476.
    1. Sierpinski, R. et al. High soluble transferrin receptor in patients with heart failure: A measure of iron deficiency and a strong predictor of mortality. Eur. J. Heart Fail. (2020).
    1. Umbarawan Y, et al. Glucose is preferentially utilized for biomass synthesis in pressure-overloaded hearts: Evidence from fatty acid-binding protein-4 and -5 knockout mice. Cardiovasc. Res. 2018;114(8):1132–1144.
    1. Zhang J, et al. Cardiomyocyte overexpression of FABP4 aggravates pressure overload-induced heart hypertrophy. PLoS ONE. 2016;11(6):e0157372.
    1. Lymperopoulos A, Rengo G, Koch WJ. Adrenergic nervous system in heart failure: Pathophysiology and therapy. Circ. Res. 2013;113(6):739–753.
    1. Jabs M, et al. Inhibition of endothelial notch signaling impairs fatty acid transport and leads to metabolic and vascular remodeling of the adult heart. Circulation. 2018;137(24):2592–2608.
    1. Lamounier-Zepter V, et al. Interaction of epoxyeicosatrienoic acids and adipocyte fatty acid-binding protein in the modulation of cardiomyocyte contractility. Int. J. Obes. (Lond.) 2015;39(5):755–761.
    1. Cabre A, et al. Parallel evolution of circulating FABP4 and NT-proBNP in heart failure patients. Cardiovasc. Diabetol. 2013;12:72.
    1. Hoeflich A, David R, Hjortebjerg R. Current IGFBP-related biomarker research in cardiovascular disease—we need more structural and functional information in clinical studies. Front. Endocrinol. (Lausanne) 2018;9:388.
    1. Ho JE, et al. Protein biomarkers of cardiovascular disease and mortality in the community. J. Am. Heart Assoc. 2018;7(14):e008108.
    1. Faxén UL, et al. HFpEF and HFrEF display different phenotypes as assessed by IGF-1 and IGFBP-1. J. Card. Fail. 2017;23(4):293–303.
    1. Duan C, Xu Q. Roles of insulin-like growth factor (IGF) binding proteins in regulating IGF actions. Gen. Comp. Endocrinol. 2005;142(1–2):44–52.
    1. Cittadini, A. et al. Multiple hormonal and metabolic deficiency syndrome predicts outcome in heart failure: the . Registry. Eur. J. Prev. Cardiol. (2021).
    1. Kumar A, Zhang KYJ. Human chitinases: Structure, function, and inhibitor discovery. Adv. Exp. Med. Biol. 2019;1142:221–251.
    1. Korolenko TA, Pisareva EE, Filyushina EE, Johnston TP, Machova E. Serum cystatin C and chitotriosidase in acute P-407 induced dyslipidemia: Can they serve as potential early biomarkers for atherosclerosis? Exp. Toxicol. Pathol. 2015;67(9):459–466.
    1. Mann DL. Innate immunity and the failing heart: The cytokine hypothesis revisited. Circ. Res. 2015;116(7):1254–1268.
    1. Priyanka K, Singh S, Gill K. Paraoxonase 3: Structure and its role in pathophysiology of coronary artery disease. Biomolecules. 2019;9(12):817.
    1. Pei JF, et al. Human paraoxonase gene cluster overexpression alleviates angiotensin II-induced cardiac hypertrophy in mice. Sci. China Life Sci. 2016;59(11):1115–1122.
    1. Ibrahim NE, Januzzi JL., Jr Established and emerging roles of biomarkers in heart failure. Circ. Res. 2018;123(5):614–629.
    1. Salzano A, et al. Biomarkers in heart failure: Clinical insights. Heart Fail. Clin. 2021;17(2):223–243.
    1. Packer M, et al. Cardiovascular and renal outcomes with empagliflozin in heart failure. N. Engl. J. Med. 2020;383(15):1413–1424.
    1. Ge Z, Li A, McNamara J, Dos Remedios C, Lal S. Pathogenesis and pathophysiology of heart failure with reduced ejection fraction: Translation to human studies. Heart Fail. Rev. 2019;24(5):743–758.
    1. Zelniker, T. A. et al. Relationship between baseline cardiac biomarkers and cardiovascular death or hospitalization for heart failure with and without SGLT2 inhibitor therapy in DECLARE-TIMI 58. Eur. J. Heart Fail.23(6), 1026–1036 (2020).

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe