Abstract
Aims
Heart failure (HF) is a frequent condition in the elderly, further complicated by associated pulmonary hypertension (PH), with impact on morbidity and mortality. Plasma proteins associated with cardiovascular disease, related to inflammation, neurohormonal changes, and myocyte stress, pathways recognized in the pathophysiology of HF, may provide information on disease severity and prognosis. We aimed to investigate such cardiovascular proteins and their relationship to haemodynamics before and 1 year after heart transplantation (HT), as well as their prognostic value in advanced HF with PH.
Methods and results
In 20 healthy controls and 67 patients with HF and PH, before and 1 year after HT, N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) and 18 cardiovascular proteins were analysed with proximity extension assay. Right heart catheterization was used to measure the haemodynamics of the HF patients pre‐operatively and at 1 year follow‐up after HT. Prognosis was estimated using Kaplan–Meier and Cox regression analyses. Out of 18 plasma proteins, 11 proteins including adrenomedullin peptides and precursor levels (ADM) and protein suppression of tumourigenicity 2 receptor were elevated before HT compared with healthy controls and had decreased 1 year after HT. The decrease in plasma levels 1 year after HT was towards the healthy controls' levels. The decrease in ADM levels before vs. after HT correlated with decreased mean right atrial pressure (r s = 0.61; P = 0.0077), decreased NT‐proBNP (r s = 0.75; P = 0.00025), and decreased stroke volume index (r s = −0.52; P = 0.022). High levels of pre‐operative plasma ADM were associated with worse event‐free survival (HT or death), as well as survival compared with low ADM levels (log‐rank P value = 0.023 and 0.0225, respectively). Univariable Cox regression analysis demonstrated that ADM levels were associated with survival, hazard ratio (HR) 1.007 (95% confidence interval (CI): 1.00–1.015, P = 0.049), and the association remained after adjusting for NT‐proBNP, HR 1.01 (95% CI: 1.00–1.021, P = 0.041).
Conclusions
Elevated plasma levels of ADM may be a marker of pressure/volume overload in HF patients with PH, as well as long‐term prognosis after HT. In line with previous studies, our findings additionally confirm that ADM may be a marker of venous congestion in HF. Further studies are encouraged to establish a deeper understanding of the properties of ADM and its relationship with HF and PH, in order to potentially facilitate clinical management of HF and associated PH.
Keywords: Cardiovascular plasma proteins, Heart failure, Heart transplantation, Haemodynamics, Pulmonary hypertension
Introduction
Heart failure (HF) is a global health issue with an estimated prevalence of 1–2% in the adult population, increasing with age to >10% in those ≥70 years of age. 1 The 5 year mortality rates remain high, ranging from 53% to 67%, 2 , 3 and 10 year survival estimates are ~10%. 4 HF is characterized by a complex interplay of several pathophysiological processes, including myocardial stretch and remodelling, neurohormonal activation, and inflammation. 5 It arises due to myocardial dysfunction: systolic, diastolic, or both. 1
Pulmonary hypertension (PH) is an associated condition with a negative impact on both morbidity and mortality. It can be considered a complication of left‐sided heart disease, including HF. 6 , 7 PH associated with left HF (LHF‐PH) affects up to 75% of patients with HF with reduced ejection fraction and 83% of HF with preserved ejection fraction. 7 The pathophysiology of PH associated with left heart disease (PH‐LHD) is characterized by several mechanisms including backward transmission of increased left‐sided filling pressures into the pulmonary circulation (pulmonary congestion), followed by pulmonary arterial endothelial dysfunction with vasoconstriction, as well as vascular remodelling, affecting both venules and arterioles. 8 In addition, right ventricular dilation, dysfunction and altered right ventricular–pulmonary arterial coupling may ensue. 8
Biochemical markers constitute a tool that could be used to confirm, rule out, and prognostically determine HF. Blood‐borne biomarkers provide increased understanding of mechanisms recognized in the pathophysiology of HF. 9 , 10 For instance, activation of the renin–angiotensin–aldosterone system (RAAS) contributes to hypertension, cardiac hypertrophy, and worsening of HF. 11 , 12 At present, N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) is an important biomarker used in the definition of different subgroups of HF. 13 However, due to the multifaceted pathophysiological mechanisms of HF, it has previously been proposed that the ability of biomarkers to assess the diagnosis, prognosis, and risk stratification can be improved by using multimarker panels. 10 For instance, adrenomedullin peptides and precursor levels (ADM) have previously been shown to add prognostic information in addition to NT‐proBNP. 9 , 14 Due to the clinical importance of the natriuretic peptides, the desire to incorporate other biomarkers into clinical practice has become of interest to facilitate the diagnosis of HF, as highlighted in the American College of Cardiology/American Heart Association/Heart Failure Society of America. 15 We sought to investigate a set of cardiovascular plasma proteins in LHF‐PH in relation to haemodynamic changes following heart transplantation (HT).
Materials and methods
Blood sampling and population selection
The present study was based on venous blood samples from adult participants (≥18 years), including healthy controls, patients with HF, and a subgroup of the HF patients during the 1 year follow‐up after HT. Samples were collected between October 2011 and February 2017 and stored at −80°C in the Lund Cardio Pulmonary Registry, a prospective cohort in Region Skåne's biobank.
Population characteristics and inclusion and exclusion criteria
The characteristics of the study participants are described in Table 1 . The study population consisted of healthy controls (n = 20) without a history of myocardial infarction, HF, diabetes mellitus, or atrial fibrillation, as well as a group of patients with LHF‐PH (n = 70), 19 of which underwent HT and had 1 year follow‐up at the time of blood sampling. Patients with missing haemodynamic data or persistent PH [mean pulmonary arterial pressure (mPAP) ≥25 mmHg] at the 1 year follow‐up after HT were excluded (n = 3). Thirty‐seven (55%) out of the 67 LHF‐PH patients had atrial fibrillation, 14 (21%) diabetes mellitus, and 27 (40%) systemic hypertension (Table 1 ).
Table 1.
Demographic characteristics of the study population
| Variable | Controls (n = 20) | LHF‐PH (n = 67) | Pre‐HT (n = 19) | Post‐HT (n = 19) | ||||
|---|---|---|---|---|---|---|---|---|
| n (%) | Median (IQR) | n (%) | Median (IQR) | n (%) | Median (IQR) | n (%) | Median (IQR) | |
| Female, n (%) | 10 (50) | 27 (40.3) | 4 (21.1) | 4 (21.1) | ||||
| Age (years) | 20 (100) | 41 (27–51) | 67 (100) | 63 (51–75) | 19 (100) | 51 (47–62) | 19 (100) | 52 (49–64) |
| BSA (m2) | 19 (95) | 1.9 (1.8–2.0) | 67 (100) | 1.9 (1.8–2.1) | 19 (100) | 2.0 (1.8–2.0) | 19 (100) | 2.0 (1.8–2.0) |
| Creatinine (μmol/L) | 63 (94) | 108 (86–136)* | 18 (94.7) | 110 (89.5–124.5)** | 19 (100) | 104 (97–121) | ||
| eGFR (mL/min/1.73 m2) | 63 (94) | 53.9 (39.6–66.2) ¤ | 18 (94.7) | 62.8 (54.1–70.5) ¤¤ | 19 (100) | 61.0 (46.2–72.7) | ||
| SaO2 (%) | 20 (100) | 98 (97–98) | 67 (100) | 94.8 (91.8–96.4) | 19 (100) | 95.9 (93–96.4) | 17 (89.5) | 97 (96–98) |
| MAP (mmHg) | 20 (100) | 95 (88.8–99.8) | 67 (100) | 89 (79–99) | 19 (100) | 82 (78–90) | 19 (100) | 101 (90–106) |
| NT‐proBNP (AU) | 20 (100) | 1.1 (1.12–1.17) ○ , ○○ | 67 (100) | 12.97 (7.56–32.12) | 19 (100) | 28.43 (17.07–44.64) ○○○ | 19 (100) | 2.04 (1.53–6.48) |
| HFrEF (EF < 50%) | 36 (53.7) | 18 (94.7) | ||||||
| HFpEF (EF ≥ 50%) | 31 (46.3) | 1 (5.3) | ||||||
| Atrial fibrillation | 37 (55.2) | 7 (36.8) | — | |||||
| Diabetes mellitus | 14 (20.9) | 1 (5.3) | 6 (31.6) | |||||
| Hypertension | 27 (40.3) | 2 (10.5) | 1 (5.3) | |||||
| Medications | n (%) | n (%) | n (%) | |||||
|---|---|---|---|---|---|---|---|---|
| Beta‐blockers | 59 (88.1) | 18 (94.7) | 3 (15.8) | |||||
| Angiotensin‐converting enzyme inhibitor | 30 (44.8) | 9 (47.4) | — | |||||
| Angiotensin II receptor blocker | — | — | — | |||||
| Mineralocorticoid receptor antagonist | 28 (41.8) | 8 (42.1) | 2 (10.5) | |||||
| Furosemide/torasemide | 40 (59.7) | 18 (94.7) | 7 (36.8) | |||||
| Prednisolone | — | — | 18 (94.7) | |||||
| Cyclosporine | 1 (1.5) | — | 2 (10.5) | |||||
| Tacrolimus | — | — | 5 (26.3) | |||||
| Mycophenolate mofetil | — | — | 15 (78.9) | |||||
| Azathioprine | 1 (1.5) | — | 3 (15.8) |
AU, arbitrary units; BSA, body surface area; eGFR, estimated glomerular filtration rate; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; HT, heart transplantation; IQR, inter‐quartile range; LHF‐PH, pulmonary hypertension associated with left heart failure; MAP, mean arterial pressure; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; SaO2, arterial oxygen saturation.
P = 0.88 vs. post‐HT.
P = 0.89 vs. post‐HT.
P = 0.19 vs. post‐HT.
P = 0.58 vs. post‐HT.
P < 0.0001 vs. LHF‐PH.
P < 0.0003 vs. post‐HT.
P < 0.0001 vs. post‐HT.
Plasma protein analysis
NT‐proBNP and 18 plasma proteins were analysed with proximity extension assay (PEA), using three multiplex immunoassay reagent kits (Cardiovascular Disease II, Cardiovascular Disease III, and Oncology II) (Olink Proteomics, Uppsala, Sweden). PEA is based on antibodies linked to unique DNA oligonucleotides. Upon binding of the oligonucleotide‐linked antibodies to the target protein, the DNA oligonucleotides come into proximity, creating a template for quantitative PCR. 16 The plasma proteins' levels are expressed in arbitrary units on a linear normalized protein expression scale.
The plasma proteins in the present study include ADM, angiotensin‐converting enzyme 2 (ACE2), cathepsin D, cathepsin L1, cathepsin V, a disintegrin and metalloproteinase domain‐containing protein 8 (ADAM‐8), a disintegrin and metalloproteinase with thrombospondin motifs 15 (ADAMTS15), elafin, galectin‐1, galectin‐3, galectin‐4, galectin‐9, granzyme B, granzyme H, myeloblastin, myoglobin, protein suppression of tumourigenicity 2 receptor (protein ST2), and renin.
Haemodynamic assessment
Right heart catheterization (RHC) was utilized to assess all patients before HT and at 1 year follow‐up after HT. RHC was performed in supine position, using a Swan–Ganz catheter (Baxter Healthcare Corp., Santa Ana, CA, USA), inserted predominantly via the right internal jugular vein. Experienced cardiologists diagnosed PH‐LHD according to guidelines present at the time of analysis, defined as a resting mPAP ≥ 25 mmHg and a pulmonary arterial wedge pressure (PAWP) >15 mmHg. Isolated post‐capillary PH was defined according to the prevailing guidelines at that time by diastolic pulmonary pressure gradient (DPG) <7 mmHg and/or pulmonary vascular resistance (PVR) ≤3 Wood units (WU). Combined post‐capillary and pre‐capillary PH was defined by DPG ≥ 7 mmHg and/or PVR > 3 WU. 17 In case of multiple assessments with RHC before HT, the haemodynamic data closest to HT or the data before implantation of left ventricular (LV) assist device were used. The HT procedures were done in accordance with the International Society for Heart and Lung Transplantation 18 , 19 and were performed at Skåne University Hospital, Lund, Sweden.
The mean arterial pressure (MAP), mPAP, mean right atrial pressure (MRAP), PAWP, systolic pulmonary arterial pressure (sPAP), diastolic pulmonary arterial pressure (dPAP), arterial oxygen saturation, and mixed venous oxygen saturation were measured during RHC. Cardiac output (CO) was obtained using thermodilution, and electrocardiography was used for heart rate (HR) determination. The following formulas were used to calculate the remaining haemodynamic parameters: cardiac index = CO/body surface area; stroke volume (SV) = CO/HR; SV index (SVI) = cardiac index/HR; DPG = dPAP − PAWP; PVR = mPAP − PAWP/CO; pulmonary arterial compliance = SV/(sPAP − dPAP); LV stroke work index = (MAP − PAWP) × SVI; and right ventricular stroke work index = (mPAP − MRAP) × SVI. The revised Lund–Malmö formula was used to calculate the estimated glomerular filtration rate (eGFR). 20 Echocardiography and magnetic resonance imaging were used to diagnose diastolic and systolic dysfunction, and diagnosis was made in accordance with European Society of Cardiology (ESC) guidelines. 13 , 21 , 22
Statistical analysis and study set‐up
The data were tested for normal distribution using histograms. Data were presented as medians with [inter‐quartile range (IQR): 25th–75th percentiles] unless otherwise stated, due to the presence of non‐normally distributed data. Wilcoxon signed‐rank test and Mann–Whitney U test were used as appropriate. Correlations were expressed with Spearman's rank coefficient (r s ). The false discovery rate (FDR) was calculated using Benjamini, Krieger, and Yekutieli's two‐stage step‐up method, to accommodate for mass significance. P values below the thresholds established using FDR were considered statistically significant. In cases where FDR was not applied due to the low number of statistical tests (n < 20), P values <0.05 were considered statistically significant. Analyses were performed using GraphPad Prism Version 9.1.2.
To identify plasma proteins associated with haemodynamics and potentially prognosis as well as to limit the number of statistical tests performed, plasma proteins underwent initially three analyses: post‐HT vs. pre‐HT, pre‐HT vs. controls, and the presence of a normalization pattern post‐HT vs. controls. Next, baseline values of the qualified plasma protein were correlated with haemodynamics as well as NT‐proBNP in the LHF‐PH cohort. The proteins displaying significant associations with haemodynamics were thereafter analysed using the delta (post‐HT vs. pre‐HT values) of both haemodynamics and plasma proteins to identify proteins for prognostic analyses. Finally, proteins displaying significant associations were prognostically analysed using the Kaplan–Meier method and Cox regressions. The optimal threshold to differentiate patients with high vs. low plasma protein levels in relation to the outcome measures [(i) event‐free survival: all‐cause mortality or HT as events and (ii) survival:all‐cause mortality as events] for the Kaplan–Meier analysis was calculated using the area under the receiver operating characteristic (ROC) curve in conjunction with Youden's index. Comparisons between survival curves in the Kaplan–Meier analyses were made using the log‐rank test.
Ethical considerations
The study was conducted in accordance with the Declaration of Helsinki and Istanbul. Ethical approval was obtained by the local ethics board in Lund, Sweden (Dnr: 2010/114, 2010/442, 2011/368, 2011/777, 2014/92, and 2015/270). All participants provided informed, written consent.
Results
Population characteristics
The patients' characteristics are displayed in Table 1 and have previously been described. 23 , 24 , 25 , 26 , 27 eGFR (P = 0.58) and creatinine (P = 0.89) did not change in the 19 patients post‐HT vs. pre‐HT (Table 1 ). The median follow‐up time of the 67 LHF‐PH patients was 4.6 (IQR: 2.9–6.8) years, and data were censored on 21 August 2020. Patients' haemodynamics are presented in Table 2 .
Table 2.
Haemodynamic characteristics of the study population including before and 1 year after heart transplantation
| Haemodynamic parameters | LHF‐PH (n = 67) | Pre‐HT (n = 19) | Post‐HT (n = 19) |
|---|---|---|---|
| Median (IQR) | Median (IQR) | Median (IQR) | |
| sPAP (mmHg) | 49 (43–65) | 47 (40–57) | 23 (18–27) |
| dPAP (mmHg) | 24 (20–29) | 24 (23–29) | 8 (6–11) |
| mPAP (mmHg) | 34 (29–43) | 31 (29–39) | 13 (12–17) |
| PAWP (mmHg) | 22 (18–26.3) a | 23 (19–27) a | 6 (4–8) |
| MRAP (mmHg) | 13 (8–16) | 14 (9–17) | 2.5 (0–4) a |
| CO (L/min) | 3.7 (3–4.6) | 3.2 (2.6–4) | 5.44 (4.9–6.5) |
| a‐vO2diff (mL O2/L) | 61.1 (51.4–78.8) | 74.3 (68.9–81.9) | 41.9 (39.6–50.9) b |
| SvO2 (%) | 56 (48.3–63.3) | 48.8 (46.4–56.7) | 69.6 (67.1–72.1) |
| SV (mL/beat) | 50 (42.4–63.1) | 45.4 (34.3–58.8) | 71.8 (66.2–77.6) |
| SVI (mL/beat/m2) | 27.3 (21.7–33.7) | 22.6 (17.7–29) | 36.2 (33.8–39.2) |
| Cardiac index (L/min/m2) | 1.93 (1.55–2.41) | 1.6 (1.4–2.1) | 2.9 (2.6–3.2) |
| PVR (WU) | 3.36 (2.42–4.08) a | 3.2 (2.3–3.6) a | 1.4 (0.9–1.9) |
| RVSWI (mmHg × mL/m2) | 643 (385–888) | 389 (303–725) | 432 (320–524) a |
| LVSWI (mmHg × mL/m2) | 1850 (1133–2510) a | 1461 (1028–1805) a | 3278 (3186–3876) |
| PAC (mL/mmHg) | 1.81 (1.45–2.8) | 1.8 (1.7–3.1) | 5.1 (4–6.3) |
a‐vO2diff, arteriovenous oxygen difference; CO, cardiac output; dPAP, diastolic pulmonary arterial pressure; HT, heart transplantation; IQR, inter‐quartile range; LHF‐PH, pulmonary hypertension associated with left heart failure; LVSWI, left ventricular stroke work index; mPAP, mean pulmonary arterial pressure; MRAP, mean right atrial pressure; PAC, pulmonary arterial compliance; PAWP, pulmonary arterial wedge pressure; PVR, pulmonary vascular resistance; RVSWI, right ventricular stroke work index; sPAP, systolic pulmonary arterial pressure; SV, stroke volume; SVI, stroke volume index; SvO2, mixed venous oxygen saturation; WU, Wood units.
Indirect Fick was used before HT to calculate one CO value. Pre‐HT is a subgroup of LHF‐PH.
n − 1
n − 2.
Plasma protein qualification
Fourteen out of 18 plasma proteins displayed a significant change post‐HT vs. pre‐HT (P < 0.035; FDR = 0.01) and controls vs. LHF‐PH (P < 0.035; FDR = 0.01). Three out of the 14 proteins were excluded as they did not display a normalization pattern towards healthy controls' levels at 1 year follow‐up after HT. Thus, 11 plasma proteins qualified for correlation analyses including ACE2, ADAM‐8, ADAMTS15, ADM, cathepsin D, cathepsin L1, elafin, galectin‐1, myoglobin, protein ST2, and renin (Table 3 ).
Table 3.
Levels of cardiovascular plasma proteins of the study population
| Plasma protein (AU) | Controls (n = 20) | LHF‐PH (n = 67) | Pre‐HT (n = 19) | Post‐HT (n = 19) | P values | |
|---|---|---|---|---|---|---|
| Controls vs. LHF‐PH | Post‐HT vs. Pre‐HT | |||||
| ACE2 | 7.1 (6.2–7.5) | 19.2 (14.3–28.7) | 23.2 (16.7–28.7) | 12 (10.3–15.2) | <0.0001 | <0.0001 |
| ADAM‐8 | 14.9 (13.5–16.6) a | 19.3 (16.3–21.9) a | 20.6 (16.8–23.2) a | 14.9 (13.4–18.8) | <0.0001 | 0.00033 |
| ADAMTS15 | 11.8 (10.4–12.9) a | 17.9 (13.8–24.1) a | 16.4 (12.6–23.8) a | 12.5 (9.4–17.9) | <0.0001 | 0.0027 |
| ADM | 57.9 (45.9–71.6) | 197.8 (152.1–234.9) | 228.5 (161.3–238.5) | 144.1 (114.9–171.6) | <0.0001 | 0.00027 |
| Cathepsin D | 9 (7.2–10.1) | 12.1 (10.2–16.2) | 12.7 (10.2–17) | 9.3 (8–10.2) | <0.0001 | <0.0001 |
| Cathepsin L1 | 25.1 (21–28.9) | 54 (38.9–76.7) | 53.4 (38.9–115) | 39.4 (31.6–50.5) | <0.0001 | 0.00013 |
| Cathepsin L2 | 11.6 (9.3–12.1) a | 6.8 (5.2–9.9) | 10 (7.4–11.3) a | 5.8 (4.9–6.8) | <0.0001 | <0.0001 |
| Elafin | 6 (4.9–8) | 10 (7.4–14.7) | 8.3 (6–11.9) | 5.7 (4.6–10) | <0.0001 | 0.0023 |
| Galectin‐1 | 37.4 (36.2–43.7) a | 54 (48.5–62.1) a | 51.2 (46–56.5) a | 44.5 (41.3–55.7) | <0.0001 | 0.016 |
| Galectin‐3 | 21 (19.2–23.7) | 26.4 (22.2–35.1) | 24 (22.2–35) | 26 (20.7–28) | 0.00036 | 0.24 |
| Galectin‐4 | 4.9 (4.4–5.9) | 9.4 (6.5–12.2) | 7.8 (5.6–9.6) | 9.3 (6.9–11.9) | <0.0001 | 0.055 |
| Galectin‐9 | 65.4 (58.6–68.6) | 110.5 (90.9–134) | 106.3 (80.2–121.9) | 104.3 (87–119.5) | <0.0001 | 0.92 |
| Granzyme B | 6.6 (5.3–8.7) a | 7.6 (5.6–9.6) a | 9.1 (6.4–11.7) a | 6.3 (3.9–9.3) | 0.23 | 0.096 |
| Granzyme H | 12.6 (7.8–16.7) a | 15.6 (11.4–25.6) a | 11.9 (8.5–21.6) a | 23.8 (14.8–52.4) | 0.026 | 0.0056 |
| Myeloblastin | 15.9 (13.9–19) | 19.8 (15.4–24.8) | 21.1 (15.4–25.9) | 28.4 (18.5–44.9) | 0.0031 | 0.032 |
| Myoglobin | 50 (37.4–59.8) | 80.1 (56.6–143.7) | 67.3 (51.3–127.5) | 53.5 (43.4–67.5) | <0.0001 | 0.0082 |
| NT‐proBNP | 1.1 (1.1–1.2) | 13 (7.6–32.1) | 28.4 (17.1–44.6) | 2 (1.5–6.5) | <0.0001 | <0.0001 |
| Protein ST2 | 11.2 (7–15.9) | 16.8 (12.3–30) | 23.8 (13.5–36.2) | 10.2 (6.7–12.2) | 0.00031 | <0.0001 |
| Renin | 90.2 (79–113.8) | 400.8 (247.4–614.6) | 570 (342.8–694.9) | 277.2 (199–424.4) | <0.0001 | 0.0046 |
ACE2, angiotensin‐converting enzyme 2; ADAM‐8, a disintegrin and metalloproteinase domain‐containing protein 8; ADAMTS15, a disintegrin and metalloproteinase with thrombospondin motifs 15; ADM, adrenomedullin peptides and precursor levels; AU, arbitrary units; LHF‐PH, pulmonary hypertension associated with left heart failure; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; protein ST2, protein suppression of tumourigenicity 2 receptor.
Statistical significance was defined as P < 0.033; false discovery rate = 0.01.
n − 1.
Baseline correlations between plasma proteins as well as haemodynamics and N‐terminal pro‐brain natriuretic peptide
Next, corrections between baseline values of the 11 qualified plasma proteins as well as baseline invasive haemodynamics and NT‐proBNP using the LHF‐PH cohort were made. Six out of 11 plasma proteins correlated significantly (P < 0.0026; FDR = 0.1) with haemodynamics or NT‐proBNP (Table 4 ), including ACE2, ADM, cathepsin D, cathepsin L1, protein ST2, and renin.
Table 4.
Correlations between cardiovascular plasma proteins and haemodynamics as well as NT‐proBNP
| Variable | MAP (mmHg) | mPAP (mmHg) | PAWP (mmHg) | MRAP (mmHg) | CO (L/min) | SVI (mL/min/m2) | NT‐proBNP (AU) |
|---|---|---|---|---|---|---|---|
| Baseline correlations [r s (P value)]—LHF‐PH cohort (n = 67) | |||||||
| ACE2 (AU) | −0.35 (0.0035) | 0.48 (0.000046) | 0.43 (0.00029) | 0.66 (<0.0001) a | −0.20 (0.11) | −0.32 (0.0074) | 0.61 (<0.0001) |
| ADAM‐8 (AU) | −0.049 (0.70) | 0.21 (0.092) | 0.018 (0.89) | 0.21 (0.092) a | −0.059 (0.64) | −0.11 (0.40) | 0.32 (0.0090) |
| ADAMTS15 (AU) | −0.021 (0.87) | 0.19 (0.12) | −0.040 (0.75) | 0.30 (0.015) a | 0.16 (0.20) | −0.028 (0.83) | 0.19 (0.14) |
| ADM (AU) | −0.22 (0.071) | 0.28 (0.020) | 0.26 (0.034) | 0.67 (<0.0001) a | −0.0426 (0.73) | −0.23 (0.057) | 0.55 (<0.0001) |
| Cathepsin D (AU) | −0.055 (0.66) | 0.43 (0.00032) | 0.27 (0.027) | 0.51 (0.000012) a | 0.035 (0.78) | −0.14 (0.25) | 0.35 (0.0039) |
| Cathepsin L1 (AU) | −0.042 (0.74) | 0.41 (0.00065) | 0.15 (0.22) | 0.43 (0.00026) a | −0.0092 (0.94) | −0.020 (0.87) | 0.29 (0.019) |
| Elafin (AU) | 0.080 (0.52) | 0.0032 (0.98) | −0.18 (0.15) | 0.094 (0.45) a | 0.33 (0.0061) | 0.13 (0.29) | 0.15 (0.23) |
| Galectin‐1 (AU) | 0.16 (0.20) | 0.078 (0.53) | −0.21 (0.092) | −0.052 (0.68) a | 0.27 (0.031) | 0.072 (0.56) | −0.045 (0.72) |
| Myoglobin (AU) | −0.0083 (0.95) | 0.12 (0.32) | −0.062 (0.62) | 0.21 (0.094) a | 0.29 (0.019) | −0.0034 (0.98) | 0.088 (0.48) |
| Protein ST2 (AU) | −0.27 (0.026) | 0.37 (0.0024) | 0.28 (0.022) | 0.61 (<0.0001) a | −0.15 (0.24) | −0.31 (0.012) | 0.55 (<0.0001) |
| Renin (AU) | −0.51 (<0.0001) | 0.12 (0.34) | 0.22 (0.073) | 0.31 (0.011) a | −0.14 (0.26) | −0.44 (0.00020) | 0.49 (<0.0001) |
| Variable | MAP (mmHg) | mPAP (mmHg) | PAWP (mmHg) | MRAP (mmHg) | CO (L/min) | SVI (mL/min/m2) | NT‐proBNP (AU) |
|---|---|---|---|---|---|---|---|
| Delta correlations [r s (P value)]—heart transplantation cohort (n = 19) | |||||||
| ACE2 (AU) | −0.035 (0.89) | 0.47 (0.041) | 0.066 (0.79) | 0.31 (0.22) a | −0.021 (0.93) | −0.36 (0.13) | 0.25 (0.31) |
| ADM (AU) | −0.16 (0.52) | 0.2 (0.41) | 0.36 (0.13) | 0.61 (0.0077) a , * | −0.33 (0.17) | −0.52 (0.022)* | 0.75 (0.00025)* |
| Cathepsin D (AU) | −0.15 (0.54) | 0.11 (0.64) | −0.017 (0.94) | 0.46 (0.053) a | −0.29 (0.22) | −0.35 (0.14) | 0.46 (0.047) |
| Cathepsin L1 (AU) | −0.27 (0.27) | 0.066 (0.79) | −0.28 (0.25) | 0.28 (0.27) a | 0.21 (0.4) | 0.016 (0.95) | 0.33 (0.17) |
| Protein ST2 (AU) | −0.43 (0.064) | 0.35 (0.15) | 0.14 (0.58) | 0.54 (0.02) a , * | −0.28 (0.24) | −0.49 (0.032)* | 0.61 (0.0053)* |
| Renin (AU) | −0.35 (0.14) | 0.21 (0.39) | 0.082 (0.74) | 0.31 (0.21) a | −0.32 (0.18) | −0.16 (0.52) | 0.4 (0.091) |
ACE2, angiotensin‐converting enzyme 2; ADAM‐8, a disintegrin and metalloproteinase domain‐containing protein 8; ADAMTS15, a disintegrin and metalloproteinase with thrombospondin motifs 15; ADM, adrenomedullin peptides and precursor levels; AU, arbitrary units; CO, cardiac output; LHF‐PH, pulmonary hypertension associated with left heart failure; MAP, mean arterial pressure; mPAP, mean pulmonary arterial pressure; MRAP, mean right atrial pressure; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; PAWP, pulmonary arterial wedge pressure; r s , Spearman's rank correlation coefficient; ST2, protein suppression of tumourigenicity 2 receptor; SVI, stroke volume index.
Statistical significance for baseline correlations was defined as P < 0.0026; false discovery rate = 0.01.
n − 1.
Statistical significance for delta correlations was defined as P < 0.033; false discovery rate = 0.25.
Plasma adrenomedullin peptides and precursor levels and protein suppression of tumourigenicity 2 receptor correlated with improved haemodynamics and decreased N‐terminal pro‐brain natriuretic peptide levels
Delta (Δ; post‐HT − pre‐HT values) correlations were performed between the Δ of the six plasma proteins' levels as well as the Δ of NT‐proBNP and six haemodynamic parameters, which improved after HT (Table 4 ). Δ ADM and Δ protein ST2 each correlated with improved MRAP (r s = 0.61; P = 0.0077 and r s = 0.54; P = 0.02, respectively), SVI (r s = −0.52; P = 0.022 and r s = −0.49; P = 0.032, respectively), and NT‐proBNP (r s = 0.75; P = 0.00025 and r s = 0.61; P = 0.0053, respectively) (FDR = 0.25) (Table 4 ).
Prognostic analyses of plasma adrenomedullin peptides and precursor levels and protein suppression of tumourigenicity 2 receptor
Pre‐operative levels of plasma ADM and protein ST2 were analysed with respect to event‐free survival (HT or all‐cause mortality), as well as survival using the Kaplan–Meier method. To find the optimal plasma protein thresholds for each of the outcome measures, ROC curve analyses were performed (Table 5 ). High plasma ADM levels were associated with worse survival and event‐free survival compared with low ADM levels (log‐rank P value = 0.0225 and 0.0230, respectively) (Figure 1 ). High plasma protein ST2 was associated with worse event‐free survival compared with low protein ST2 levels (log‐rank P value = 0.0275). High baseline levels of protein ST2 were, however, not related to worse survival (log‐rank P value = 0.091) (Table 5 ). Univariable Cox regression analysis demonstrated that baseline plasma ADM was associated with survival, hazard ratio (HR) 1.007 [95% confidence interval (CI): 1.00–1.015, P = 0.049], and the association remained after adjusting for NT‐proBNP, HR 1.01 (95% CI: 1.00–1.021, P = 0.041) (Table 5 ).
Table 5.
Prognostic analyses of plasma ADM and protein ST2
| AUC (95% CI) | Cut‐off a (AU) | Sensitivity (%) | Specificity (%) | |
|---|---|---|---|---|
| ROC curve analyses to determine optimal thresholds for Kaplan–Meier analysis | ||||
| ADM (AU): event (transplantation or death)‐free survival | 0.68 (0.52–0.84) | <147 | 88.68 | 42.86 |
| ADM (AU): survival | 0.57 (0.42–0.72) | <273 | 24.0 | 95.24 |
| Protein ST2 (AU): event (transplantation or death)‐free survival | 0.66 (0.49–0.82) | <16.3 | 64.15 | 71.43 |
| Protein ST2 (AU): survival | 0.57 (0.42–0.71) | <14.3 | 80 | 45.24 |
| HR (95% CI) | P value | |||
|---|---|---|---|---|
| Univariable Cox regression analyses: event‐free survival | ||||
| ADM (AU) | 1.004 (0.9996–1.009) | 0.065 | ||
| Protein ST2 (AU) | 1.01 (0.9975–1.020) | 0.086 | ||
| NT‐proBNP (AU) | 1.019 (1.009–1.029) | 0.0002 | ||
| Bivariable Cox regression models: event‐free survival | ||||
| Model 1 (ADM + NT‐proBNP) | ||||
| ADM (AU) | 0.9978 (0.9913–1.004) | 0.51 | ||
| NT‐proBNP (AU) | 1.023 (1.008–1.038) | 0.028 | ||
| Model 2 (protein ST2 + NT‐proBNP) | ||||
| Protein ST2 (AU) | 0.9968 (0.9817–1.010) | 0.66 | ||
| NT‐proBNP (AU) | 1.021 (1.008–1.033) | 0.001 | ||
| Univariable Cox regression analyses: survival | ||||
| ADM (AU) | 1.007 (1.00–1.015) | 0.049 | ||
| Protein ST2 (AU) | 1.011 (0.9931–1.025) | 0.17 | ||
| NT‐proBNP (AU) | 1.006 (0.9848–1.023) | 0.57 | ||
| Bivariable Cox regression models: survival | ||||
| Model 1 (ADM + NT‐proBNP) | ||||
| ADM (AU) | 1.01 (1.000–1.021) | 0.041 | ||
| NT‐proBNP (AU) | 0.9895 (0.9653–1.013) | 0.39 | ||
| Model 2 (protein ST2 + NT‐proBNP) | ||||
| Protein ST2 (AU) | 1.011 (0.9910–1.029) | 0.24 | ||
| NT‐proBNP (AU) | 0.9995 (0.9752–1.020) | 0.9646 |
ADM, adrenomedullin peptides and precursor levels; AU, arbitrary units; AUC, area under the receiver operating characteristic curve; CI, confidence interval; HR, hazard ratio; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; ROC, receiver operating characteristic; ST2, protein suppression of tumourigenicity 2 receptor.
Cut‐offs were defined using Youden's index.
Figure 1.
Kaplan–Meier survival estimates stratified according to high or low levels of baseline plasma adrenomedullin peptides and precursor levels (ADM) in the pulmonary hypertension associated with left heart failure cohort (n = 67). Optimal thresholds were assessed using receiver operating characteristic curve analyses followed by Youden's index. In (A), events were defined as heart transplantation or death, whereas in (B), events were defined as death.
Discussion
The present study investigated cardiovascular‐related proteins in patients with LHF‐PH, before and 1 year after HT in relation to haemodynamics and prognosis. Eleven out of 18 plasma proteins, including ADM and protein ST2, were elevated in advanced HF compared with controls, and these elevated levels decreased towards healthy controls' levels after HT. Plasma ADM and protein ST2 correlated with RHC haemodynamics, and prognostic analyses of ADM demonstrated that higher baseline ADM levels were associated with worse event‐free survival and crude survival. The prognostic data on survival obtained from ADM were independent of NT‐proBNP, as demonstrated by bivariable Cox regression analysis (Table 5 ).
Adrenomedullin peptides and precursor levels are secreted from different parts of the body, including the kidneys, lungs, and endocrine tissues. 9 It is also released by the cardiovascular system, including vascular smooth muscle cells, fibroblasts, and endothelial cells, as a result of an increased volume overload due to increased shear stress. 28 , 29 , 30 ADM preserve endothelial barrier function by improving vascular integrity, specifically reducing vascular permeability, and causing dilation. 30 , 31 In HF, the circulating ADM levels are elevated, and this elevation is positively associated with the severity of the HF. 9 Increased ADM levels lead to reduced pre‐load and afterload, through stimulating natriuresis, diuresis, and vasodilation of resistance and capacitance vessels, resulting in lower systemic blood pressure. Furthermore, some studies suggest that ADM could reduce hypertrophy, remodelling, and fibrosis of the myocardium. 29 , 30 A recent study found that bioactive plasma ADM were associated with MRAP and PAWP measured by RHC, thus proposing it as a biomarker of systemic venous congestion in patients with HF. 32 This is in line with our results demonstrating that ADM levels in patients with advanced HF correlated with MRAP and NT‐proBNP. Elevated ADM levels have been demonstrated to successfully predict disease severity and prognosis in chronic HF. 14 , 33 A previous multi‐centre study found that levels of MR‐proADM, a precursor of ADM, increased with New York Heart Association (NYHA) class and were a predictor of 12 month mortality, independent of LV ejection fraction, NYHA class, creatinine, and age, suggesting MR‐proADM as a potential biomarker for additional prognostic information in addition to NT‐proBNP. 14 Another study found that, in acute HF, MR‐proADM was superior than BNP and NT‐proBNP in predicting 90 day all‐cause mortality. 33
A notable characteristic of ADM is its function as an RAAS inhibitor and consequently its relation to cardiac pathophysiology. 30 In an experimental pig model of HF, treatment with furosemide has, apart from its positive diuretic effect, shown to increase plasma levels of aldosterone. This may, in turn, potentially affect LV function and contribute to adverse effects through perivascular and interstitial myocardial fibrosis, thus accelerating systolic dysfunction. 34 , 35 According to the 2021 ESC HF guidelines, improved morbidity and mortality have been observed in individuals with systolic dysfunction who received RAAS inhibitors. 1 RAAS inhibitors exert positive effects on the cardiovascular system and renal function (eGFR). 36 ADM, as an addition to furosemide, may be a putative alternative treatment combination that can be used for HF due to their ability to inhibit the RAAS and thus prevent the potential adverse hypertrophy and remodelling resulting from the furosemide‐induced increase in plasma aldosterone. However, the effect of furosemide‐induced increase in plasma aldosterone along with ADM needs to be further investigated in future HF studies to see if administration of ADM in HF patients is beneficial beyond what is released in the body during disease development. Another notable characteristic of ADM is that its degradation has been shown to be catalyzed by neprilysin. 37 This implies that the effect of angiotensin receptor/neprilysin inhibitor medications, which are outlined among the base HF treatments in the 2021 ESC HF guidelines, 1 could partially already be by inhibiting the degradation of ADM.
It is noteworthy that, in our results, NT‐proBNP was not a significant predictor of survival in the univariable Cox regression model. There are several possible reasons for this. Firstly, the present cohort is relatively small and could thus be insufficient for finding a statistically significant result for NT‐proBNP in this regard. Another explanation is that the capability of BNP to reflect the haemodynamics is altered in transplanted patients, where persistently elevated BNP levels have been recorded. This could imply that BNP levels do not correlate with right atrial pressure or systolic and diastolic function, thus affecting its ability as a biomarker of volume overload and prognosis in this patient group. 38 , 39 This further highlights the need for additional biomarkers in HF that reflect the various underlying pathophysiological mechanisms of HF and, as in this case, the HT cohort.
In the present study, we consider the use of RHC for acquiring haemodynamic data and PEA for protein‐level analysis as strengths. PEA has displayed high sensitivity and specificity compared with other multiplex methods. 16 Despite these strengths, there are some limitations that are important to address and discuss. The study was small and single centred, and the controls were slightly younger than the patients. The purpose of the controls was to serve as a normal reference point for the different plasma proteins to get an overview of the protein change and more easily exclude proteins that do not show interesting changes. The patients' eGFR did not change in response to HT, which may indicate a minimal effect of renal function on the proteins' expression.
Moreover, patients' medication, age, and comorbidities may have affected the plasma proteins' levels. For instance, prednisolone was prescribed to a greater proportion of patients post‐HT. According to a previous study, prednisolone can contribute to B‐cell dysfunction and impaired insulin release, which may lead to diabetes mellitus. 40 Diabetes in turn is associated with greater release of ADM. 41 This study did not report the effects of immunosuppressive agents on the plasma proteins' levels. A larger sample size is needed to enable a reliable statistical adjustment of various possible factors such as medications and cormorbidities. This study does not address the causal relationship between the proteins and haemodynamics and should therefore be interpreted with caution. However, the present study is hypothesis generating, and our results may be of interest in HF and associated PH, supporting prior findings showing that ADM levels are related to improved haemodynamics in patients with HF. Future studies are needed to address causality and validate our findings.
Conclusions
The present study identified elevated plasma levels of ADM in patients with HF and PH prior to HT, which decreased following HT towards healthy controls' levels. Decreased plasma ADM correlated with improved MRAP and NT‐proBNP. Higher levels of ADM pre‐operatively were associated with worse survival. Pre‐operative plasma ADM were prognostic in a univariable Cox regression model and when adjusted for NT‐proBNP levels in a bivariable model. Our study further confirms the prior associations between plasma ADM, HF, and venous congestion. It also adds to prior findings of ADM being a prognostic marker of HF survival independent of NT‐proBNP, thus demonstrating it as a potential marker to include alongside NT‐proBNP to further refine prognostic assessment. Further research on ADM to gain a deeper understanding of its properties and relationship with HF and associated PH is warranted to implement it in healthcare and facilitate the management of HF diagnosis and treatment.
Conflict of interest
A.A. and S.A. report personal lecture fees from Janssen outside the submitted work. K.K. and H.A.R. report no conflicts of interest. G.R. reports unrestricted research grants from Avtal om Läkarutbildning och Forskning (ALF) and Actelion Pharmaceuticals Sweden AB during the conduct of the study; reports personal lecture fees from Actelion Pharmaceuticals Sweden AB, GlaxoSmithKline, Bayer HealthCare, and Nordic Infucare outside the submitted work; and is and has been a primary investigator or co‐investigator in clinical PAH trials for GlaxoSmithKline, Actelion Pharmaceuticals Sweden AB, Pfizer, Bayer, and United Therapeutics and in clinical heart transplantation immunosuppression trials for Novartis. The companies had no role in the data collection, analysis, and interpretation and had no right in disapproving of the manuscript.
Funding
The work was supported by unrestricted research grants from ALF and Janssen‐Cilag AB. The funding organizations played no role in the collection, analysis, or interpretation of the data and had no right to restrict the publishing of the manuscript.
Acknowledgements
We acknowledge the support and assistance provided by the staff of The Haemodynamic Lab, The Section for Heart Failure and Valvular Disease, Skåne University Hospital, Lund, Sweden, and The Section for Cardiology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden. We thank Anneli Ahlqvist for the assistance, involving management of plasma samples and registration into Lund Cardio Pulmonary Registry. Additionally, we acknowledge the biobank services and retrieval of blood samples from the Lund Cardio Pulmonary Registry performed at Labmedicine Skåne, University and Regional Laboratories, Region Skåne, Sweden.
Ahmed, A. , Kania, K. , Abdul Rahim, H. , Ahmed, S. , and Rådegran, G. (2023) Adrenomedullin peptides and precursor levels in relation to haemodynamics and prognosis after heart transplantation. ESC Heart Failure, 10: 2427–2437. 10.1002/ehf2.14399.
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