GDF-15: A novel biomarker of heart failure predicts 30-day all-cause mortality and 30-day HF rehospitalization in patients with acute heart failure syndrome

doi:10.21203/rs.3.rs-2903414/v1

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Research Article

GDF-15: A novel biomarker of heart failure predicts 30-day all-cause mortality and 30-day HF rehospitalization in patients with acute heart failure syndrome

https://doi.org/10.21203/rs.3.rs-2903414/v1

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published 12 Mar, 2024

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Background: Growth Differentiation Factor-15 (GDF-15) is a distant member of the transforming growth factor-β. The increased expression of GDF-15 has been observed during heart failure and is associated with worse outcomes. However, the relationship between GDF-15 and acute heart failure (AHF) is not well understood with limited evidence among Thai patients.

Purpose: To study the association between cumulative level and biomarker at the time of admission and discharge in patients with AHF and their association with 30-day all-cause mortality and heart failure rehospitalization

Methods: This is a prospective, single-center study of patients admitted for AHF. GDF-15, high-sensitivity troponin (hsTrop) and NT-proBNP were measured at the time of admission and discharge. Outcomes, including 30-day all-cause mortality and rehospitalization, were collected. Logistic regression analysis was used to evaluate outcomes.

Results: A total of 84 patients were enrolled (mean age of 69 years, 52% females). The GDF-15 level significantly decreased during admission (at the time of admission 9,118 pg/mL, at the time of discharge 7,107 pg/mL, mean differences 1528; p = 0.006). The 30-day all-cause mortality was 6%. The 30-day rehospitalization occurred in 22 (26.1%) patients. Univariate analysis showed that the admission GDF-15 level, but not hsTrop or NT-proBNP, was associated with 30-days all-cause mortality (p = 0.029) and multivariate analysis showed a reduction in GDF-15 level from admission to discharge was associated with lower 30-days rehospitalization (p = 0.023).

Conclusion: The GDF-15 levels were elevated during acute HF and improved by the time of discharge. This novel biomarker may offer additional value in predicting outcomes in acute HF.

Acute heart failure syndrome

Orthoedema congestion score

GDF-15

rehospitalization

all-cause mortality

Acute heart failure (AHF) is a potentially life-threatening clinical syndrome, usually requiring hospital admission. AHF can occur de novo, or in patients with chronic heart failure. Globally, there are some variations in the duration of hospitalization with AHF. In many countries, patients are admitted for at least one week, with an inpatient mortality of 5–10%, and up to 25% of the patients are readmitted within a month of discharge¹.

Among readmitted patients within 30 days after discharge from an AHF hospitalization, 50% were caused by heart failure and 23% by non-HF cardiovascular etiology². A quarter of patients hospitalized because of worsening HF have signs of inadequate decongestion at the time of discharge³, and this is associated with an increased risk of readmission and a worse prognosis⁴. Congestion not only has a significant effect on symptoms and quality of life, but is also associated with cardiac, renal, and liver injury, which are in turn associated with worse clinical outcome⁵. The gold standard for evaluating congestion in hospitalized patients is the measurement of pulmonary capillary wedge pressure (PCWP) which provides a good approximation of LV filling pressure, detecting hemodynamic congestion in its preclinical state. However, PCWP measurement involves invasive catheterization, limiting its clinical use, especially in the outpatient setting. While there is not currently a standardized definition of adequate decongestion, several criteria are being used in clinical trials. These include jugular venous distention (JVD) < 8 cm of water, no more than trace peripheral edema, and the absence of orthopnea⁶. These criteria have been simplified into an orthoedema congestion score. Higher orthoedema scores at admission and discharge are associated with poor prognosis (60-day death or HF hospitalization)⁷.

The field of biomarkers has attracted intensive investigation in the last decade in the management and care of HF patients. Circulating cardiac biomarkers, reflecting different aspects of the orchestral molecular interplay involved in HF have been sought after, with the prospect that these markers in combination would reveal the signature of the disease⁸. The natriuretic peptides, which include B-type natriuretic peptide (BNP) and the N-terminal fragment of its prohormone (NT-proBNP), are the approved biomarkers for HF⁹. ST-2, GDF-15, Pentraxin-3, Galectin-3, Osteopontin¹⁰, are some of the novel biomarkers that have been investigated alone or in combination in the context of HF.

Growth-differentiation factor-15 (GDF-15) is a distant member of the transforming growth factor-β (TGF-β) cytokine super family that is constantly shown in the liver. It is also known as Prostate derived factor (PDF), Macrophage inhibitory cytokine-1(MIC-1), NSAID-activated gene (NAG-1) and Placental TGF-Beta (PTGFB. .While the exact function of GDF-15 is still unclear, it has been shown to be weakly expressed in all tissue types under normal physiological states. The increased expression of GDF-15 has been observed during pulmonary, cardiac, or renal diseases.

Multi-biomarkers (GDF-15, NT-proBNP, hs-Troponin T) and orthoedema congestion score are rarely studied in Asian. Therefore, this study aimed to investigate the association between multi-biomarker profile and congestion level at the time of discharge in patients with acute heart failure syndrome and 30-day all-cause mortality and 30-day heart failure rehospitalization.

Study design and Studied population

This is a prospective, single center study of patients admitted at King Chulalongkorn Memorial Hospital (KCMH) for AHF syndrome regardless of ejection fraction (acute de novo heart failure, acute decompensated heart failure) between December 2018 to June 2019. All patients were symptomatic (New York Heart Association [NHYA] functional class II-IV). Key inclusion criteria include; 1. Adult, aged more than 18 years old; 2. Being an inpatient at the time of screening; 3. Admission diagnosis of AHF per primary; accepted terms include heart failure (HF), acute decompensated heart failure (ADHF), congestive heart failure; 4. Patient presence of at least 2 major criteria or 1 major criteria in conjunction with 2 minor criteria of Framingham criteria for congestive heart failure. (Table 1). The patient will be excluded from study if confused, in delirium state, diagnosed with liver cirrhosis or nephrotic syndrome, has an unacceptable vision or hearing to respond to the questionnaire, unwilling to be reached by phone, being an outpatient or did not give consent.

Our study protocol complied with The ethical guidelines of the 1975 Declaration of Helsinki and was conducted under the approval of the Institutional Review Board of the Research Ethics Review Committee for Research Involving Human Research Participants, Health Sciences Group, Chulalongkorn University (198/61). We obtained informed consent from all the enrolled patients. We registered in Thai Clinical Trials Registry (TCTR20190521002).

Data collection and data analysis

After screening inclusion/exclusion criteria, the enrolled patients undergoing optimization of medical therapy decided by a treating primary physician or heart failure cardiologist. We collected demographic data, previous left ventricular ejection fraction (LVEF), co-morbid diseases e.g., diabetic mellitus (DM), coronary arterial disease (CAD), previous PCI (stents, balloon), CABG, atrial fibrillation (AF), hypertension (HTN). Data about general physical and cardiovascular examination (included vital signs, jugular venous distention (JVD), rale or crepitation, and peripheral edema), treatment (length of stays, pharmacological treatment, investigation, intervention) were collected by case record form (CRF).

Orthoedema congestion score based on the presence of orthopnea (≥ 2 pillow = 2, < 2 pillows = 0), and peripheral edema (trace: pitting edema grade 0 or 1 + = 0, moderate: pitting edema grade 2 + = 1, severe: pitting edema grade 3 + or 4 + = 2) with the components added to classify congestion as no congestion (score 0), low grade (score 1–2), high grade (score 3–4).

After patients gave informed consent to participate in this study, the researcher collected 3 ml of blood specimen (in lithium heparin tube) within 24 hours after admission and 24 hours prior to discharge. (Total 6 ml of blood specimen) for further biomarker investigations (GDF-15, NT-proBNP, and hs-Troponin T). The specimens were analyzed for GDF-15, hs-Troponin T, NT-proBNP by Cobas^® Machine (Roche Diagnostics, Basel, Switzerland).

After discharge, patients were contacted by phone at 30 days after discharge date for the outcome, including mortality, re-hospitalization, numbers and causes of re-hospitalization, and recent NYHA status.

Outcomes

The primary objective was association between biomarkers (GDF-15, NT-proBNP and hs-Troponin T) in patients with AHF and their association with 30-day heart failure rehospitalization or 30-day all-cause mortality.

The secondary end points were the association between congestion level at the time of discharge in patient with AHF with 30-day heart failure rehospitalization and 30-day all-cause mortality, prognostic factors of heart failure rehospitalization and all causes mortality.

Statistical analysis

Categorical and continuous variables were analyzed by Chi-squared and student T tests, respectively. Most numerical values with normal distribution were expressed as the mean and standard deviation. In multivariate analysis, a backward stepwise binary logistic regression with adjustment for covariates was used to evaluate the relationship between biomarkers (GDF-15, NT-proBNP, and hs-Troponin T) and outcomes.

All statistical analyses were performed, using the SPSS statistical analysis package (version 22.0; SPSS Inc., Chicago, Illinois, USA), and a P value of < 0.05 was considered statistically significant.

A total of 103 patients were screened, 12 patients were excluded, and 92 patients were enrolled into study. Seven patients had intra-hospital death. One patient was lost to follow-up due to lack of telephone contact. Therefore, a total of 84 patients were included for analysis (Figure 1). Median follow-up time was 213 days.

Baseline Characteristics

The baseline characteristics of the study population, which were divided into 30-day readmission group and non-readmission group, are described in Table 2 (mean age of 69 ± 14 years, 52.4% females). Ischemic heart diseases were reported in 38.1%, 75% had hypertension, 59.5% had diabetes mellitus, 56% had dyslipidemia, 39.3% had chronic kidney disease at least stage III, and 23.1% of patients had history of HF with reduced ejection fraction (EF). Mean JVD of 9.0 ± 1.4 cm, a mean body weight of 69.9 ± 22.0 kg. Patients in the non-rehospitalization group had hypertension and dyslipidemia more than the rehospitalization group, respectively (80.3% vs 46.2%; p = 0.015), and 60.6% vs 30.8%, p = 0.047). No significant differences were observed for age, sex, LVEF, number of prior cardiovascular diseases, HF hospitalization or prehospital drugs used (diuretic, betablocker, ACEI/ARB/ARNI, digoxin, ivabradine, hydralazine, nitrate, anticoagulant, and P2Y12 inhibitor).

Patients in the rehospitalization group had less statin usage prior to admission (23.1% vs 73.2%; p = 0.001), and less statin usage at discharge (30.8% vs 66.2%; p = 0.016)

Orthoedema congestion score

The orthoedema congestion score of the study population are described in Table 3 and 4.

At the time of admission: the orthoedema congestion score at the time of admission was 2.29 ± 0.1. Grading orthoedema congestion score consists of no congestion (score 0) 7.1%, low grade (score 1-2) 56%, and high grade (score 3-4) 36.9%.

At the time of discharge: the orthoedema congestion score at the time of admission was 0.38 ± 0.8. Grading orthoedema congestion score consists of no congestion (score 0) 81%, low grade (score 1-2) 15%, and high grade (score 3-4) 0%.

No significant differences between orthoedema congestion score in patients with AHF with 30-day heart failure rehospitalization and 30-day all-cause mortality were observed.

30-day rehospitalization and 30-day all-cause mortality

The 30-day all-cause mortality was 5.95%. The 30-day rehospitalization occurred in 22 (26.1%) patients (13 HF rehospitalization and 9 non-HF rehospitalization). 15 patients (25.86%) of the surviving 30-day readmission were readmitted between 30 to 180 days, and total 35 patients (44.3%) were readmitted at least once within 180 days. The highest period of readmission was within first 30 days after discharge, with median time to readmit at day 12.

Biomarkers

The biomarkers of the study population are described in Table 3-5.

The GDF-15 level significantly decreased during admission (at the time of admission 9,118 ± 6,010 pg/mL, at time of discharge 7,090.68 ± 11,041.07 pg/mL, mean differences 1,528 ± 4,469; p = 0.006).

The NT-proBNP level was not significantly different during admission (at the time of admission 9,900.93 ± 10,132.29 pg/mL, at time of discharge 7,107 ± 4,787 pg/mL, mean differences 2,170.04 ± 9,390.23; p = 0.056).

The hs-Troponin T level had no significant difference during admission (at the time of admission 217.16 ± 851.52 pg/mL, at time of discharge 92.2 ± 164.5 pg/mL, mean differences 138.85 ± 816.53; p = 0.156).

Univariate analysis showed that the admission GDF-15 level, but not troponin or NT-proBNP, was associated with 30-days all-cause mortality (p = 0.029) and multivariate analysis showed reduction in GDF-15 level from admission to discharge was associated with lower 30-days rehospitalization (p = 0.023), in Table 5.

The ROC curve showed 95% Confidence Interval (CI) of GDF-15 at time of admission was 0.54 (0.39-0.69) (p = 0.712) and time of discharge was 0.75 (0.62-0.88) (p = 0.013). Sensitivity and specificity of GDF-15 were described in Figure2.

This prospective single-center study of patients admitted for acute heart failure syndrome regardless of ejection fraction at KCMH demonstrated that discharge GDF-15, but not troponin or NT-proBNP, and percentage change in GDF-15 levels from admission to discharge were associated with 30-day all-cause mortality and 30-day HF rehospitalization, respectively. No significant differences were observed between the orthoedema congestion score in AHF patient with 30-day heart failure rehospitalization and 30-day all-cause mortality.

Previous clinical trials have an orthoedema congestion score based on the presence of orthopnea and peripheral edema. A post hoc analysis of the DOSE-HF and CARRESS-HF trials of patients with AHF with congestion (and cardiorenal syndrome in the case of CARRESS-HF) found that baseline orthoedema was moderate in 22% of patients and severe in 62%⁷. After aggressive inpatient therapy aimed at decongestion, more than a third of the patients (35%) had persistent moderate to severe congestion at discharge. Higher orthoedema scores at admission and discharge were associated with an increased risk of death at 60 days or hospitalization for heart failure.

GDF-15 was a distant member of the superfamily transforming growth factor -β- superfamily¹¹ maintaining tissue homeostasis and adaptation. When ischemia and reperfusion injury occurred, its expression was significantly increased through phosphoino- sitide 3-OH kinase (PI3K) and Akt-dependent signaling pathways, GDF-15 protected cardiomyocytes from apoptotic¹², thus, as our study revealed, the value of GDF-15 predicted the degree of inflammatory response and the risk of cardiovascular events.

Experimental studies suggest that various forms of cardiac stress, including pressure overload, increase the concentration of GDF-15. Animal studies indicate that GDF-15 is protective against cardiac injury by virtue of its anti-hypertrophic¹³, anti-inflammatory and anti-apoptotic properties¹⁴. In addition to cardiomyocytes, this biomarker is also produced by vascular smooth muscle cells, pulmonary epithelial cells, macrophages, and adipocytes in response to oxidative stress and proinflammatory signaling molecules¹⁵. However, clinical studies in humans indicate that a higher concentration of GDF-15 is associated with increased mortality. For example, studies by Lok et al. and Kempf et al. observe that GDF-15 is a marker of increased mortality in CHF^16,17. Lok et al. observe that GDF-15 is an even stronger predictor than NTproBNP¹⁶. Furthermore, patients with HF with reduced ejection fraction had higher levels of GDF-15 compared to patients with HF with a mid-range ejection fraction¹⁸. Therefore, GDF-15 seems to display a series of different functions, rendering protection in some instances, while simultaneously being associated with poor outcomes.

Previous study¹⁸ shown receiver operating characteristic (ROC) curves estimated associations between GDF-15 and clinical indicators in cardiac remodeling of GDF-15 with NT-proBNP (AUC = 0.905, 95%CI: 0.868–0.942, P < 0.001) was superior to NT-proBNP alone (AUC = 0.869, 95%CI: 0.825–0.913, P < 0.001) in HF. These findings promised that GDF-15 combined with NT-proBNP significantly improves the accuracy of the diagnosis of HF. Plasma levels of GDF-15 can indirectly reflect the degree of cardiac remodeling and fibrosis. Therefore, this study supports our results to promote GDF-15 as an additional prognostic factor in addition to NT-proBNP.

From systematic review¹⁷, there is reasonable evidence to suggest that GDF-15 is an independent predictor of all-cause mortality in HF. GDF-15 may offer additional value in predicting the risk of HF and death in patients with MI. A multi-biomarker strategy with GDF-15 as one of the components may be superior to conventional risk scores, especially for systemic conditions such as HF. On a biological scale, the exact role of GDF-15 in the pathophysiology of HF remains to be elucidated. It is also essential to carry out studies to see how the available information on GDF-15 can be used to make therapeutic decisions about HF management.

In a recent meta-analysis¹⁹ involving 6,244 patients with chronic heart failure revealed that elevated circulating GDF-15 concentration is associated with an increased risk of 6% in all-cause mortality with an increase per 1LnU in baseline GDF-15 concentration with pooled risk ratios 1.06 (95% CI:1.03–1.10, P < 0.001) in chronic heart failure patients, especially among those with ischemic etiology (e.g., coronary atherosclerosis). Furthermore, the association becomes stronger after removing patients with a nonreduced ejection fraction, suggesting that GDF-15 could have a higher prognostic value in individuals with a reduced ejection fraction. Subgroup analyzes show that age, NYHA class and duration of follow-up may not affect the relationship of GDF-15 with the risk of all-cause death in chronic heart failure.

This study still has limitations, although we designed a prospective analytical study. First, this is a small, single-center study, which additional research may be required to strengthen the results. Second, we enroll patients admitted only with AHF, not including patients who visit the emergency room or outpatient visits. Finally, the total number of death cases was too small to identify the independent factors associated with this condition. However, these patients were included in the relatively short period of time, representing a population of acute HF in the real world. As a result, future research should study on a large scale, including all patients who visit the hospital to give a precise result.

Our study identified a connection between GDF-15 levels and numerous outcomes in patients with heart failure, including all-cause mortality at 30 days and rehospitalization. These encouraging findings may pave the way for additional research on this biomarker. Furthermore, the findings of this study may encourage the use of GDF-15 as an additional predictive marker in patients with heart failure in current clinical practices.

In this single center study, the rate of mortality and rehospitalization is very high in patients who were discharged after acute HF hospitalization. The GDF-15 levels were elevated during acute HF and improved by the time of discharge. Univariate analysis showed that the admission GDF-15 level, but not troponin or NT-proBNP, was associated with 30-days all-cause mortality and multivariate analysis showed reduction in GDF-15 level from admission to discharge was associated with lower 30-days rehospitalization. This novel biomarker may offer additional value in predicting the risk of outcomes in patient with acute heart failure syndrome.

Acknowledgement

We thank Chonlada Phathong for her assistance in designed study and statistical analysis.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflict of interest statement

All authors have no conflicts of interest to declare.

Funding

This study was supported in part by the Ratchadapiseksompotch Fund, Chulalongkorn University (Grant No. RA 62/072).

Conflicts of interest: The authors declare no competing interests.

Ethics approval and consent to participate: The Institutional Review Board of the Research Ethics Review Committee for Research Involving Human Research Participants, Health Sciences Group, Chulalongkorn University (198/61) approved this study. Our study protocol complied with the ethical guidelines of the 1975 Declaration of Helsinki. All the enrolled patients provided informed consent. The ethics committee of Chulalongkorn University approved this form of consent due to the registry-based nature of the study.

Consent for publication: Not applicable.

Cowie MR, Anker SD, Cleland JGF, Felker GM, Filippatos G, Jaarsma T, Jourdain P, Knight E, Massie B, Ponikowski P, López-Sendón J. Improving care for patients with acute heart failure: before, during and after hospitalization. ESC Heart Fail. 2014 Dec;1(2):110-145. doi: 10.1002/ehf2.12021. Epub 2015 Jan 21. PMID: 28834628.
Vader JM, LaRue SJ, Stevens SR, Mentz RJ, DeVore AD, Lala A, Groarke JD, AbouEzzeddine OF, Dunlay SM, Grodin JL, Dávila-Román VG, de Las Fuentes L. Timing and Causes of Readmission After Acute Heart Failure Hospitalization-Insights From the Heart Failure Network Trials. J Card Fail. 2016 Nov;22(11):875-883. doi: 10.1016/j.cardfail.2016.04.014. Epub 2016 Apr 28. PMID: 27133201; PMCID: PMC5085925.
Maggioni AP, Dahlström U, Filippatos G, Chioncel O, Leiro MC, Drozdz J, Fruhwald F, Gullestad L, Logeart D, Metra M, Parissis J, Persson H, Ponikowski P, Rauchhaus M, Voors A, Nielsen OW, Zannad F, Tavazzi L; Heart Failure Association of ESC (HFA). EURObservational Research Programme: the Heart Failure Pilot Survey (ESC-HF Pilot). Eur J Heart Fail. 2010 Oct;12(10):1076-84. doi: 10.1093/eurjhf/hfq154. Epub 2010 Aug 29. PMID: 20805094.
Maggioni AP, Dahlström U, Filippatos G, Chioncel O, Crespo Leiro M, Drozdz J, Fruhwald F, Gullestad L, Logeart D, Fabbri G, Urso R, Metra M, Parissis J, Persson H, Ponikowski P, Rauchhaus M, Voors AA, Nielsen OW, Zannad F, Tavazzi L; Heart Failure Association of the European Society of Cardiology (HFA). EURObservational Research Programme: regional differences and 1-year follow-up results of the Heart Failure Pilot Survey (ESC-HF Pilot). Eur J Heart Fail. 2013 Jul;15(7):808-17. doi: 10.1093/eurjhf/hft050. Epub 2013 Mar 28. PMID: 23537547.
Metra M, Cotter G, Davison BA, Felker GM, Filippatos G, Greenberg BH, Ponikowski P, Unemori E, Voors AA, Adams KF Jr, Dorobantu MI, Grinfeld L, Jondeau G, Marmor A, Masip J, Pang PS, Werdan K, Prescott MF, Edwards C, Teichman SL, Trapani A, Bush CA, Saini R, Schumacher C, Severin T, Teerlink JR; RELAX-AHF Investigators. Effect of serelaxin on cardiac, renal, and hepatic biomarkers in the Relaxin in Acute Heart Failure (RELAX-AHF) development program: correlation with outcomes. J Am Coll Cardiol. 2013 Jan 15;61(2):196-206. doi: 10.1016/j.jacc.2012.11.005. PMID: 23273292.
Bart BA, Goldsmith SR, Lee KL, Givertz MM, O'Connor CM, Bull DA, Redfield MM, Deswal A, Rouleau JL, LeWinter MM, Ofili EO, Stevenson LW, Semigran MJ, Felker GM, Chen HH, Hernandez AF, Anstrom KJ, McNulty SE, Velazquez EJ, Ibarra JC, Mascette AM, Braunwald E; Heart Failure Clinical Research Network. Ultrafiltration in decompensated heart failure with cardiorenal syndrome. N Engl J Med. 2012 Dec 13;367(24):2296-304. doi: 10.1056/NEJMoa1210357. Epub 2012 Nov 6. PMID: 23131078; PMCID: PMC3690472.
Lala A, Dunlay S, Vader J, Zakeri R, Ravichandran A, AbouEzzadine O, Khazanie P, McNulty S. A Two-Symptom Congestion Score In Relation to Outcomes after Discharge with Acute Decompensated Heart Failure. J Card Fail. 2013; 19(8) Supplement S39.
O'Connell JB. The economic burden of heart failure. Clin Cardiol. 2000 Mar;23(3 Suppl):III6-10. doi: 10.1002/clc.4960231503. PMID: 10754775; PMCID: PMC6654942.
Fonarow GC; ADHERE Scientific Advisory Committee. The Acute Decompensated Heart Failure National Registry (ADHERE): opportunities to improve care of patients hospitalized with acute decompensated heart failure. Rev Cardiovasc Med. 2003;4 Suppl 7:S21-30. PMID: 14668697.
Publication Committee for the VMAC Investigators (Vasodilatation in the Management of Acute CHF). Intravenous nesiritide vs nitroglycerin for treatment of decompensated congestive heart failure: a randomized controlled trial. JAMA. 2002 Mar 27;287(12):1531-40. doi: 10.1001/jama.287.12.1531. Erratum in: JAMA 2002 Aug 7;288(5):577. PMID: 11911755.
Ago T, Sadoshima J. GDF15, a cardioprotective TGF-beta superfamily protein. Circ Res. 2006 Feb 17;98(3):294-7. doi: 10.1161/01.RES.0000207919.83894.9d. PMID: 16484622.
Kempf T, Eden M, Strelau J, Naguib M, Willenbockel C, Tongers J, Heineke J, Kotlarz D, Xu J, Molkentin JD, Niessen HW, Drexler H, Wollert KC. The transforming growth factor-beta superfamily member growth-differentiation factor-15 protects the heart from ischemia/reperfusion injury. Circ Res. 2006 Feb 17;98(3):351-60. doi: 10.1161/01.RES.0000202805.73038.48. Epub 2006 Jan 5. PMID: 16397141.
Xu X, Li Z, Gao W. Growth differentiation factor 15 in cardiovascular diseases: from bench to bedside. Biomarkers. 2011 Sep;16(6):466-75. doi: 10.3109/1354750X.2011.580006. Epub 2011 Jul 1. PMID: 21718220.
Kempf T, von Haehling S, Peter T, Allhoff T, Cicoira M, Doehner W, Ponikowski P, Filippatos GS, Rozentryt P, Drexler H, Anker SD, Wollert KC. Prognostic utility of growth differentiation factor-15 in patients with chronic heart failure. J Am Coll Cardiol. 2007 Sep 11;50(11):1054-60. doi: 10.1016/j.jacc.2007.04.091. Epub 2007 Aug 24. PMID: 17825714.
Milks MW, Nambi V (2019). Cardiac injury, maladaptation, and heart failure incidence. In: Biomarkers in Cardiovascular Disease. Elsevier. pp 81–96.
Lok DJ, Klip IT, Lok SI, Bruggink-André de la Porte PW, Badings E, van Wijngaarden J, Voors AA, de Boer RA, van Veldhuisen DJ, van der Meer P. Incremental prognostic power of novel biomarkers (growth-differentiation factor-15, high-sensitivity C-reactive protein, galectin-3, and high-sensitivity troponin-T) in patients with advanced chronic heart failure. Am J Cardiol. 2013 Sep 15;112(6):831-7. doi: 10.1016/j.amjcard.2013.05.013. Epub 2013 Jun 29. PMID: 23820571.
George M, Jena A, Srivatsan V, Muthukumar R, Dhandapani VE. GDF 15--A Novel Biomarker in the Offing for Heart Failure. Curr Cardiol Rev. 2016;12(1):37-46. doi: 10.2174/1573403x12666160111125304. PMID: 26750722; PMCID: PMC4807717.
Li J, Cui Y, Huang A, Li Q, Jia W, Liu K, Qi X. Additional Diagnostic Value of Growth Differentiation Factor-15 (GDF-15) to N-Terminal B-Type Natriuretic Peptide (NT-proBNP) in Patients with Different Stages of Heart Failure. Med Sci Monit. 2018 Jul 18;24:4992-4999. doi: 10.12659/MSM.910671. PMID: 30019695; PMCID: PMC6067023.
Luo JW, Duan WH, Song L, Yu YQ, Shi DZ. A Meta-Analysis of Growth Differentiation Factor-15 and Prognosis in Chronic Heart Failure. Front Cardiovasc Med. 2021 Nov 5;8:630818. doi: 10.3389/fcvm.2021.630818. PMID: 34805295; PMCID: PMC8602355.

Table 1 Framingham criteria for congestive heart failure

Criteria for enrollment: 2 Major or 1 major + 2 minor

Table 2 Baseline characteristics of patients diagnosed with acute heart failure syndrome with 30-day HF rehospitalization

Factors	Total (N=84)	Non rehospitalization (N=71)	Rehospitalization (N=13)	P value
Age (mean±S.D.)	69±14 (19-97)	70±13 (34-97)	66±20 (19-92)	0.535
Male gender	40 (47.6%)	36 (50.7%)	4 (30.8%)	0.186
Ethnicity (Thai)	83 (98.8%)	70 (98.6%)	13 (100%)	1.000
Past History
Hx of HF	60 (73.2%)	48 (69.6%)	12 (92.3%)	0.169
LVEF				0.310
No previous echo	11 (16.9%)	11 (20.4%)	0
pEF (>= 50%)	34 (52.3%)	28 (51.9%)	6 (54.5%)
mrEF (40-49%)	5 (7.7%)	4 (7.4%)	1 (9.1%)
rEF (< 40%)	15 (23.1%)	11 (20.4%)	4 (36.4%)
Hx of HF hospitalization	44 (67.7%)	36 (64.3%)	8 (88.9%)	0.251
Diabetes mellitus	50 (59.5%)	44 (62%)	6 (46.2%)	0.285
Ischemic heart disease	32 (38.1%)	26 (36.6%)	6 (46.2%)	0.546
Atrial fibrillation	27 (32.1%)	23 (32.4%)	4 (30.8%)	1.000
Hypertension	63 (75%)	57 (80.3%)	6 (46.2%)	0.015
Dyslipidemia	47 (56%)	43 (60.6%)	4 (30.8%)	0.047
CKD at least stage III	33 (39.3%)	30 (42.2%)	3 (23.1%)	0.362
Treatment (Prior to admission) (mean±S.D.)/median (IQR)
Furosemide	51 (60.7%)	40 (56.3%)	11 (84.6%)	0.055
Furosemide (mg/day)	171.57±272.78 40 (20-240)	145±237.17 40 (20-120)	268.18±373.60 60 (40-250)	0.327
Betablocker (BB)	49 (58.3%)	41 (57.7%)	8 (61.5%)	0.799
ACEI	11 (13.1%)	10 (14.1%)	1 (7.7%)	1.000
ARB	15 (17.9%)	13 (18.3%)	2 (15.4%)	1.000
Spironolactone	7 (8.3%)	5 (7%)	2 (15.4%)	0.295
Digoxin	5 (6%)	3 (4.2%)	2 (15.4%)	0.169
Treatment at discharge (mean±S.D.)/median (IQR)
Furosemide	66 (78.6%)	54 (76.1%)	12 (92.3%)	0.281
Furosemide (mg/day)	190.92±276.18 80 (40-160)	174.34±269.93 80 (20-160)	264.17±303.6 120 (40-500)	0.210
HCTZ	2 (2.4%)	1 (1.4%)	1 (7.7%)	0.287
Tolvaptan	1 (1.2%)	0	1 (7.7%)	0.155
Betablocker	50 (59.5%)	42 (59.2%)	8 (61.5%)	0.872
ACEI	9 (10.7%)	8 (11.3%)	1 (7.7%)	1.000
ARB	8 (9.5%)	8 (11.3%)	0	0.347
Spironolactone	4 (4.8%)	2 (2.8%)	2 (15.4%)	0.111
Digoxin	5 (6%)	4 (5.6%)	1 (7.7%)	0.578

Table 3. Orthoedemascore and biomarker in patient with 30-day rehospitalization

Factors	Total (N=84)	Non rehospitalization (N=71)	Rehospitalization (N=13)	P value
30-day heart failure rehospitalization
Admission
Total orthoedema score mean±S.D. median (IQR)	2.29±0.98 2 (2-3)	2.31±1.01 2 (2-3)	2.15±0.80 2 (2-3)	0.638
Grading orthoedema score				0.898
No congestion	6 (7.1%)	5 (7%)	1 (7.7%)
Low grade	47 (56%)	39 (54.9%)	8 (61.5%)
High grade	31 (36.9%)	27 (38%)	4 (30.8%)
NT-proBNP (pg/mL) mean±S.D. median (IQR)	9900.93±10132.29 5529 (2234-14280)	10505.10±10477.22 5628 (2222-16544)	6601.23±7469.63 3513 (1719-9923.5)	0.296
hs-Troponin T (pg/mL) mean±S.D. median (IQR)	217.16±851.52 40.28 (26.65-96.03)	244.68±923.10 44.34 (27.28-102)	66.87±124.63 31.57 (21.78-42.77)	0.112
GDF-15 (pg/mL) mean±S.D. median (IQR)	9118.76±6010.68 6879 (4387-12658)	9317.58±6365.77 6855 (4118-13700)	7942.42±3111.48 7330 (5290.75-11159)	0.907
Discharge
Total orthoedema score mean±S.D. median (IQR)	0.38±0.79 0 (0-0)	0.30±0.72 0 (0-0)	0.77±1.01 0 (0-2)	0.052
Grading orthoedema score				0.114
No congestion	64 (81%)	56 (84.8%)	8 (61.5%)
Low grade	15 (19%)	10 (15.2%)	5 (38.5%)
High grade	0	0	0
NT-proBNP (pg/mL) mean±S.D. median (IQR)	7090.68±11041.07 3306 (678.8-8159)	7297.78±11870.05 2862 (656.75-7527)	5961.05±4514.15 4739 (2974-9758)	0.280
hs-Troponin T (pg/mL) mean±S.D. median (IQR)	92.2±164.50 36.6 (21.22-64.9)	96.87±173.98 38.28 (20.03-78.27)	66.71±109.70 27.8 (25.48-56.98)	0.634
GDF-15 (pg/mL) mean±S.D. median (IQR)	7107.06±4787.63 5659 (3654-8930)	6736.60±4892.72 5375.5 (3295.25-8256)	9127.73±3727.82 8539 (6653-10950)	0.027
Diff NT-proBNP (pg/mL) mean±S.D. median (IQR)	2170.04±9390.23 1328.3 (166.1-4841.2)	2437±9788.74 1548.95 (340.4-5199.55)	712.32±7009.60 -1227 (-3323 - 261)	0.008
Diff hs-Troponin T (pg/mL) mean±S.D. median (IQR)	138.84±816.53 4.21 (-1.30 - 10.15)	162.96±884.38 4.46 (-0.09 - 11.28)	7.31±173.04 -1.3 (-4.95 - 808)	0.259
Diff GDF-15 (pg/mL) mean±S.D. median (IQR)	1528.41±4469.26 525 (-212.5 - 2239)	2100.33±4319.62 707.5 (-49.5 – 3000.5)	-1903.1±3943.48 -391.5 (-4054.5 - 808)	0.028

Table 4. Orthoedema score and biomarker in patient with 30-day mortality

30-day all-cause mortality
Factors	Total (N=84)	Alive (N=79)	Death (N=5)		P value
Admission
Total orthoedema score mean±S.D. median (IQR)	2.29±0.98 2 (2-3)	2.23±0.96 2 (2-3)		3.2±0.84 3 (2.5-4)	0.026
Grading orthoedema score					0.174
No congestion	6 (7.1%)	6 (7.6%)		0
Low grade	47 (56%)	46 (58.2%)		1 (20%)
High grade	31 (36.9%)	27 (34.2%)		4 (80%)
NT-proBNP (pg/mL) mean±S.D. median (IQR)	9900.93±10132.29 5529 (2234-14280)	9393.74±9680.17 5520 (2222-12234)		17914.6±14752.92 19629 (2976-31996)	0.212
hs-Troponin T (pg/mL) mean±S.D. median (IQR)	217.16±851.52 40.28 (26.65-96.03)	216.82±876.37 38.95 (26-84.08)		222.56±262.20 150.8 (44.55-436.45)	0.044
GDF-15 (pg/mL) mean±S.D. median (IQR)	9118.76±6010.68 6879 (4387-12658)	8702.08±5886.34 6345.5 (4139-11800.5)		15619±4175.39 14297 (11899-20000)	0.012
Discharge
Total orthoedema score mean±S.D. median (IQR)	0.38±0.79 0 (0-0)	0.38±0.79 0 (0-0)		N=1 0	0.628
Grading orthoedema score					1.000
No congestion	64 (81%)	63 (80.8%)		1 (100%)
Low grade	15 (19%)	15 (19.2%)		0
High grade	0	0		0
NT-proBNP (pg/mL) mean±S.D. median (IQR)	7090.68±11041.07 3306 (678.8-8159)	7185.14±11091.86 3447 (695.08-8201.75)		N=1 479	0.223
hs-Troponin T (pg/mL) mean±S.D. median (IQR)	92.2±164.50 36.6 (21.22-64.9)	92.66±165.64 36.32 (20.93-65.69)		N=1 59.83	0.435
GDF-15 (pg/mL) mean±S.D. median (IQR)	7107.06±4787.63 5659 (3654-8930)	6999.04±4734.26 5631.5 (3643.5-8636.75)		N=1 14668	0.157
Factors	Total (N=84)	Alive (N=79)		Death (N=5)	P value
Diff NT-proBNP (pg/mL) mean±S.D. median (IQR)	2170.04±9390.23 1328.3 (166.1-4841.2)	2191.54±9456.27 1390.65 (124.57-4960.65)		N=1 665	0.626
Diff hs-Troponin T (pg/mL) mean±S.D. median (IQR)	138.84±816.53 4.21 (-1.30 - 10.15)	140.94±822.23 4.29 (-0.79 – 10.53)		N=1 -7.81	0.172
Diff GDF-15 (pg/mL) mean±S.D. median (IQR)	1528.41±4469.26 525 (-212.5 - 2239)	1604.91±4455.60 546 (-133.5 – 2271)		N=1 -3750	0.119

P value by Mann-Whitney U test

Table 5 Logistic regression analysis of outcomes.

Factor	Univariate analysis		Multivariate analysis
Factor	OR (95%CI)	p-value	OR (95%CI)	p-value
30-day heart failure rehospitalization
Admission
Total orthoedema score	0.85 (0.47-1.55)	0.595
Grading orthoedema score
No congestion	1.00 (reference)
Low grade	1.03 (0.10-10.01)	0.983
High grade	0.74 (0.07-8.08)	0.806
No congestion+ Low grade	1.00 (reference)
High grade	0.72 (0.20-2.58)	0.619
No congestion	1.00 (reference)
Low grade+ High grade	0.91 (0.10-8.48)	0.933
NT-proBNP (pg/mL)	1.00 (1.00-1.00)	0.213
< 5,000	1.00 (reference)
5,000-10,000	0.57 (0.11-3.04)	0.512
10,001-15,000	1.33 (0.22-7.89)	0.751
> 15,000	0.21 (0.02-1.82)	0.156
hs-Troponin T (pg/mL)	0.99 (0.99-1.00)	0.456
< 50	1.00 (reference)
50-100	0.56 (0.11-2.86)	0.484
> 100	0.22 (0.03-1.82)	0.159
GDF-15 (pg/mL)	1.00 (1.00-1.00)	0.464
< 5,000	1.00 (reference)
5,000-10,000	3.95 (0.71-21.78)	0.115
> 10,000	1.85 (0.31-11.01)	0.498
Discharge
Total orthoedema score	1.87 (0.97-3.59)	0.060
Grading orthoedema score
No congestion	1.00 (reference)
Congestion	3.50 (0.95-12.90)	0.060
NT-proBNP (pg/mL)	1.00 (1.00-1.00)	0.712
<5,000	1.00 (reference)
5,000-10,000	1.73 (0.37-8.06)	0.487
>10,000	1.15 (0.20-6.53)	0.873
hs-Troponin T (pg/mL)	0.99 (0.99-1.00)	0.583
< 50	1.00 (reference)
50-100	1.08 (0.20-5.99)	0.927
> 100	0.41 (0.05-3.58)	0.417
GDF-15 (pg/mL)	1.00 (1.00-1.00)	0.136
< 5,000	1.00 (reference)
5,000-10,000	2.80 (0.47-16.80)	0.260
> 10,000	5.83 (0.99-34.38)	0.051
diff_NTProBNP (pg/mL)	1.00 (1.00-1.00)	0.580
< 0	1.00 (reference)
0-5,000	0.07 (0.01-0.40)	0.003
5,000-10,000	0.16 (0.02-1.61)	0.120
> 10,000	0.18 (0.02-1.86)	0.152
diff_hsTroponin T (pg/mL)	0.99 (0.99-1.00)	0.530
< 0	1.00 (reference)
0-10	0.21 (0.04-1.16)	0.074
> 10	0.53 (0.11-2.49)	0.422
diff_GDF15 (pg/mL)	1.00 (1.00-1.00)	0.012	1.00 (1.00-1.00)	0.023
< 0	1.00 (reference)
0-1,000	0.50 (0.10-2.44)	0.392
1,000-2,000	0.33 (0.03-3.33)	0.349
> 2,000	0.17 (0.02-1.59)	0.119
30-day all-cause mortality
Admission
Total orthoedema score	3.54 (0.10-11.40)	0.034
Grading orthoedema score
No congestion+ Low grade	1.00 (reference)
High grade	7.70 (0.82-72.37)	0.074
NT-proBNP (pg/mL)	1.00 (1.00-1.00)	0.087
< 5,000	1.00 (reference)
5,000-10,000	0.00	0.999
10,001-15,000	0.00	0.999
> 15,000	3.35	0.207
hs-Troponin T (pg/mL)	1.00 (0.99-1.00)	0.988
< 50	1.00 (reference)
50-100	3.20 (0.19-54.32)	0.421
> 100	9.00 (0.87-92.76)	0.065
GDF-15 (pg/mL)	1.00 (1.00-1.00)	0.029	1.00 (1.00-1.00)	0.315
< 5,000	1.00 (reference)
5,000-10,000	0.00	1.000
> 10,000	0.00	0.998
Discharge
Total orthoedema score	0.00	0.999
Grading orthoedema score
No congestion	1.00 (reference)
Congestion	0.00	0.999
NT-proBNP (pg/mL)	0.99 (0.99-1.00)	0.531
< 5,000	1.00 (reference)
5,000-10,000	0.00	0.999
> 10,000	0.00	0.999
hs-Troponin T (pg/mL)	0.99 (0.98-1.02)	0.848
< 50	1.00 (reference)
50-100	0.00	0.999
> 100	0.00	1.000
GDF-15 (pg/mL)	1.00 (1.00-1.00)	0.193
< 5,000	1.00 (reference)
5,000-10,000	0.00	0.999
> 10,000	0.00	0.999
diff_NT-proBNP (pg/mL)	1.00 (1.00-1.00)	0.871
< 0	1.00 (reference)
0-5,000	0.00	0.999
5,000-10,000	0.00	1.000
> 10,000	0.00	1.000
diff_hsTroponinT (pg/mL)	0.99 (0.98-1.02)	0.740
< 0	1.00 (reference)
0-10	0.00	0.998
> 10	0.00	0.998
diff_GDF-15 (pg/mL)	1.00 (0.99-1.00)	0.170
< 0	1.00 (reference)
0-1,000	0.00	0.998
1,000-2,000	0.00	0.999
> 2,000	0.00	0.998

No competing interests reported.

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GDF-15: A novel biomarker of heart failure predicts 30-day all-cause mortality and 30-day HF rehospitalization in patients with acute heart failure syndrome

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