Impact of cardiac shock wave therapy on the dynamics of biomarkers: results of proteomic analysis

doi:10.21203/rs.3.rs-2322533/v1

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Impact of cardiac shock wave therapy on the dynamics of biomarkers: results of proteomic analysis

https://doi.org/10.21203/rs.3.rs-2322533/v1

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Background: Cardiac shock-wave therapy (CSWT) is a non-invasive treatment based on low-frequency ultrasound waves that stimulate angiogenesis. Current data on the effects of revascularization procedures on angiogenesis biomarkers are limited.

In this study, we aimed to characterize the serum protein profiles of patients with coronary artery disease after CSWT treatment in relation to changes in exercise and imaging parameters.

Methods: The study population included 10 patients from a prospective, randomized, triple-blind, sham-procedure controlled study (NCT02339454), who received CSWT and had demonstrated improvement in myocardial perfusion and/or contractility at 6 months follow-up

The blood samples were collected at baseline, after the last treatment procedure (9^th treatment week), at 6-month follow-up and stored at −80°C until analysis. The cardiovascular-related proteins (n=92) were measured using the Olink Proseek Multiplex Cardiovascular III panel (Olink, Uppsala, Sweden).

Results: The median age was 65.5 years, and 7 patients were males. A total of 20 biomarkers showed significant changes from baseline to 9^th week and 6 months follow-up (p<0.05).

We identified 28 proteins that showed clear association with an increase in exercise tolerance and improvement in myocardial contractility or perfusion after CSWT. Using a volcano plot and results from the paired Mann-Whitney U test at confidence level of 0.95, we identified 3 proteins (PON3, TR-AP, CD163) with elevated values corresponding to increase in exercise duration (p<0.05) and 2 proteins (CPA1, COL1A1) related to improved myocardial contractility (p<0.05).

A pathway analysis including 28 proteins suggested that these biomarkers were related to immune and inflammatory response, cell adhesion, tissue remodeling, proteolysis and catabolic processes.

Conclusions: This study demonstrates the association of an increase in protein levels with an improvement in exercise duration and contractile function. Proteomic analysis suggests that CSWT exerts biological effects including immune and inflammation response, cell adhesion and tissue remodeling, all of which may mediate angiogenesis.

Trial registration: Clinicaltrials.gov (NCT02339454).

cardiac shock wave therapy

regenerative treatment

angiogenesis

stable angina

myocardial ischemia

biomarkers

proteomic analysis

Traditional revascularization methods restore perfusion in patients with coronary artery disease (CAD) on a macrovascular level but remaining microvascular deficit may still induce adverse cardiac remodeling and functional deterioration. The main pathophysiological pathways associated with CAD progression include oxidative stress, inflammation, endothelial dysfunction and extracellular matrix remodeling [1]. Cardiac shock wave therapy (CSWT) as a regenerative treatment option potentially addresses this deficit by promoting angiogenesis, reducing inflammation and ameliorating symptoms. However, understanding the molecular mechanism by which shock waves promote neovascularization and improve cardiac function remains incomplete.

Cardiovascular proteomics is an emerging field of molecular pathophysiology with a significant progress made during the past decade to define candidate biomarkers [2]. The proximity extension assay is a novel technique that has been shown to be useful for biomarker discovery in cardiometabolic disease or heart failure [3, 4, 5], as well as for cardiovascular death prediction in patients with CAD [6]. This method has not been tested yet to evaluate the molecular effects of cardiac shock wave therapy effects related to angiogenesis.

This post hoc study aimed to characterize the changes in serum cardiovascular-related protein profiles of patients with CAD after CSWT treatment and their association with imaging parameters.

Study design and patients’ selection

The study population included a subset of 10 patients from an interventional arm of a prospective, randomized, triple-blind, sham-procedure controlled study designed to assess the antianginal efficacy of CSWT on top of the standard medical therapy in patients with stable angina (NCT02339454). The study was approved by Vilnius Regional Ethics Committee (Approval No. 158200–13–616 − 187) and was conducted following the Good Clinical Practice and Declaration of Helsinki 2013. The study enrolled patients from June 2013 to December 2015 and was completed in June 2016. All participants provided written informed consent. The main trial’s design, methods, and results (NCT02339454) were described previously [7, 8].

Inclusion criteria

This sub-study included patients with angiographically confirmed CAD and exercise-induced angina associated with ST-segment depression ≥ 1 mm on treadmill electrocardiogram (ECG), symptoms not controlled by optimal medical treatment (OMT), who received treatment with cardiac shock wave therapy (CSWT) and demonstrated improvement in myocardial perfusion and/or contractility at 6 months follow up. Improvement in myocardial contractility was defined as the difference in stress wall motion score (WMS) before and after the study treatment at least by 3 points; improvement in perfusion was assessed as the difference in summed difference score (SDS) before and after the treatment at least by 3 points.

CSWT treatment

All patients were maintained on stable doses of medications [9] for 4 weeks before the baseline evaluation and the entire study period. CSWT was performed using the Cardiospec device (Medispec Ltd., Germantown, Maryland, USA) using ECG R-wave gating. The target treatment area stepwise covering the whole left ventricle was determined by a cardiac ultrasound imaging (Vivid I; GE Healthcare, Horten, Norway). Treatment consisted of 9 sessions in total with 3 sessions per week on the 1st, 5th, and 9th study weeks [8]. The study population was evaluated at 3 and 6 months by clinical, exercise and imaging tests.

Sample preparations and protein measurement (proteomic biomarkers)

Blood samples were collected at baseline, after the last treatment procedure (9th treatment week) and at the 6-month follow-up visit (each time 5 ml). The samples were taken according to standard laboratory practice and centrifuged at 2000 rpm for 20 min to collect the serum, then stored at − 80°C for further biomarker analysis.

Serum aliquots were thawed and divided into microwell plates. Internal controls were added to each plate. Plates were frozen at − 80 ◦C and shipped on dry ice to Olink Proteomics AB, Uppsala, Sweden. Baseline, 9th treatment week (after the last treatment procedure) and month 6 serum samples were analyzed by using Olink Proseek Multiplex 96x96 Cardiovascular CVD III panel. This panel was chosen as it is the most recently developed panel with relevant biomarkers candidates. The panel analysis method is based on proximity extension assay technology with a dual-recognition DNA-coupled readout, where 92 oligonucleotide-labelled antibody probe pairs are allowed to bind to their respective targets in the sample [10]. The protein levels are expressed on a log2 scale and delivered in normalized protein expression (NPX) units with relative quantification. A difference of 1 NPX is equivalent to a doubling of protein concentration. Normalization is performed to minimize intra- and inter-assay variation. The limit of detection (LOD) is calculated separately for each Olink assay. The LOD is based on the background, estimated from negative controls on every plate, plus 3 standard deviations. The standard deviation (SD) is assay-specific and estimated during product validation.

The abbreviations, coding genes and full names of the studied proteins are described in Supplementary Table 1.

Statistical analysis

Statistical analysis was performed using the following software: R statistical software package V 4.1.2 (© The R Foundation for Statistical Computing), RStudio 2022.07.1 Build 554 © 2009–2022 Rstudio, PBC, IBM SPSS Statistics V.26.

We calculated relative changes in percents for all 92 biomarkers from baseline to 9th week follow-up, from 9th week to 6 months follow-up and baseline to 6 months follow-up (see Supplementary Table 2).

Interval and ratio variables, not satisfying the conditions of normality, were described by medians, first quartiles (Q1) and third quartiles (Q3). We used Shapiro – Wilk and Kolmogorov – Smirnov tests to check the data for normality. Nominal and ordinal variables were characterized by frequencies and percentages across the corresponding subset of the sample.

An increase in exercise duration by more than 1.5 minutes and improvement in WMS_stress – WMS_rest and SDS at follow-up by at least 3 points were considered significant improvements (SI). According to these criteria, study patients were divided into subgroups: with significant improvement and non-significant (NS) changes.

To evaluate the dependence of the interval or rank indicators of two independent samples, we used the nonparametric Mann-Whitney U test and rank-biserial correlation (r_rb) coefficient. To evaluate the dependence of the interval or rank indicators of three dependent samples, we used the nonparametric Friedman’s test.

To display the average fold differences in protein expression levels between two sample groups we employed a volcano plot, using results from the paired Mann-Whitney U test at confidence level of 0.95 for every protein (by OlinkID) for a given grouping variable.

Relationships between variables were considered statistically significant when p value was less than 0.05 (p < 0.05) and a statistical test power of 1-ß was equal 0.95 (1-ß=0.95). In some cases, we considered relationships between variables statistically significant and when p value was less than 0.07 (p < 0.07).

To show the relationship between protein percentage change and the patient’s gender, exercise and imaging parameters from baseline to 9th week follow-up, from 9th week to 6 months follow-up and baseline to 6 months follow-up respectively, we used heatmap’s. Data in heatmap’s are centered and scaled across assays.

Network analysis

The Search Tool for the Retrieval of Interacting Genes/ Proteins (STRING) database was used to analyze the functional enrichment (Gene Ontology biologic processes, Kyoto Encyclopedia of Genes and Genomes, and Reactome pathways) [11] and connections of 28 proteins that showed associations with an increase in exercise tolerance and improvement in myocardial contractility or perfusion after CSWT. The network analysis identifies proteins limited to the first-degree interactors (intermediate nodes) linking our input proteins (seed nodes), and an interaction network with confidence levels > 0.4 is exhibited. The created network was updated to cluster proteins by k-means based on biological functions.

Characteristics of the population

Patient characteristics, exercise and imaging parameters are presented in Table 1. In the study we included 7 men and 3 women. The mean age of the patients was 65.5 ± 7.9 years. All patients had a multivessel disease, and none were candidates for further revascularization due to the extent and severity of the disease or technical considerations.

Table 1

Baseline characteristics of the study population
General characteristics
Age, years, median (Q1; Q3)	65.5 (61.8; 71.8)
Male, n (%)	7 (70)
Medical history
Previous myocardial infarction, n (%)	4 (40)
Previous percutaneous intervention, n (%)	5 (50)
Previous CABG, n (%)	6 (60)
Three-vessel disease, n (%)	8 (80)
Cardiovascular risk factors
Hyperlipidemia, n (%)	10 (100)
Hypertension, n (%)	10 (100)
Diabetes, n (%)	3 (30)
Peripheral vascular disease, n (%)	2 (20)
Clinical parameters
Body mass index, kg/m², median (Q1; Q3)	30.5 (29.4; 32.7)
Systolic blood pressure, mmHg, median (Q1; Q3)	128.5 (109.8; 143.8)
Diastolic blood pressure, mmHg, median (Q1; Q3)	72.5 (71.3; 82.3)
LVEF (echocardiographic), %, median (Q1; Q3)	55.5 (54; 62)
Medical treatment
ACE inhibitors / ARB, n (%)	10 (100)
Beta-blocker, n (%)	9 (90)
Long-acting nitrates, n (%)	4 (40)
Calcium channel blocker, n (%)	4 (40)
Other antianginal drugs*, n (%)	4 (40)
Statins, n (%)	10 (100)
Mean dose of atorvastatin, mg, median (Q1; Q3)	40 (40; 40)
Antiplatelets, n (%)	10 (100)
Exercise and imaging test
Exercise duration (Treadmill test), min, median (Q1; Q3)	5.4 (4.5; 7.9)
Wall motion score at stress (DSE), median (Q1; Q3)	25.0 (22.0; 35.8)
Summed difference score (SPECT), median (Q1; Q3)	6.5 (4.5; 8.8)
ACE – angiotensin converting enzyme, ARB angiotensin receptor blocker, CABG – coronary artery bypass grafting, CVD – cardiovascular disease, DSE – dobutamine stress echocardiography, SPECT – single photon emission tomography myocardial perfusion imaging.
* Other antianginal drugs include trimetazidine and ranolazine.

Changes in Olink biomarkers after CSWT

All 92 biomarkers were available for analysis. The baseline levels of all studied proteins are presented in Supplementary Table 2. A total of 20 (21.7%) biomarkers showed significant changes from baseline till 9th week and 6 months follow-up (p < 0.05). The levels of most of the biomarkers increased under CSWT, while 19 (20.7%) first decreased at 9 weeks but increased at 6-month follow up, and concentration of 3 biomarkers (3.3%) reduced at both follow-up periods (Supplementary Table 2). Significant relative changes were found in the levels of collagen alpha-1(I) chain (COL1A1 increase of 14.4% and 10.2%, p = 0.007), contactin-1 (CNTN1 increase of 15.6% and 11.8%, p = 0.025), tartrate-resistant acid phosphatase 5 (TR-AP increase of 8.7% and 12.1% p = 0.016) and epidermal growth factor receptor (EGFR increase of 7.6% and 10.1% p = 0.049) at 9th week and at 6-month follow-up, respectively.

The relative changes of proteins that have associations with improvement in exercise and imaging parameters are presented in Table 2.

Table 2

Biomarkers with significant changes during the study periods and effect of cardiac shock wave therapy
	Baseline level	Relative change				Effect size
		Baseline – 9th week	9th week – 6-month	Baseline – 6-month	P – value^a	W_Kendall	CI 95%	P – value^b
Cell adhesion
CD166 antigen (ALCAM)	6.4 (6.3; 6.6)	2.7%	1.2%	4%	0.031	0.21	(0.07; 1.0)	0.12
Epidermal growth factor receptor (EGFR)	2.1 (1.9; 2.2)	7.6%	2.4%	10.1%	0.049	0.21	(0.03; 1.0)	0.12
Contactin-1 (CNTN1)	2.4 (2.1; 2.8)	15.6%	-3.3%	11.8%	0.025	0.43	(0.19; 1.0)	0.01
Ephrin type-B receptor 4 (EPHB4)	3.6 (3.5; 3.9)	4%	1.7%	5.8%	0.234	0.5	(0.01; 1.0)	0.5
Spondin-1 (SPON1)	0.8 (0.7; 0.9)	1.4%	-4.2%	-2.9%	0.903	0.04	(0.0; 1.0)	0.67
Transferrin receptor protein 1 (TR)	3.8 (3.4; 4.2)	-1.3%	3.5%	2.1%	0.149	0.19	(0.01; 1.0)	0.15
Integrin beta-2 (ITGB2)	4.9 (4.7; 5.4)	1.5%	2.9%	4.4%	0.637	0.09	(0.01; 1.0)	0.4
Aminopeptidase N (AP-N)	4.9 (4.7; 5.2)	2.8%	3.6%	6.5%	0.053	0.36	(0.12; 1.0)	0.027
Proprotein convertase subtilisin/kexin type 9 (PCSK9)	1.9 (1.7; 2.2)	4.9%	-9.6%	-5.2%	0.733	0.21	(0.04; 1.0)	0.12
Immune and inflammatory response
Tartrate-resistant acid phosphatase 5 (TR-AP)	3.9 (3.7; 4.1)	8.7%	3.2%	12.1%	0.016	0.36	(0.12; 1.0)	0.027
Scavenger receptor cysteine-rich type 1 protein M130 (CD163)	7.6 (7.1; 7.7)	3.8%	-0.2%	3.6%	0.098	0.07	(0.01; 1.0)	0.49
Intercellular adhesion molecule 2 (ICAM-2)	4.4 (4.2; 4.6)	4.2%	-0.5%	3.7%	0.161	0.05	(0.01; 1.0)	0.67
Osteopontin (OPN)	6.7 (6.2; 6.9)	5.7%	0.3%	6.0%	0.053	0.28	(0.09; 1.0)	0.06
Myeloblastin (PRTN3)	3.6 (2.7; 3.8)	-5.3%	6.9%	1.2%	0.367	0.16	(0.04; 1.0)	0.20
Myeloperoxidase (MPO)	2.8 (2.4; 3.3)	2.2%	2.8%	5.0%	0.258	0.16	(0.01; 1.0)	0.20
Interleukin-18-binding protein (IL-18BP)	5.1 (5.1; 5.6)	3.6%	3%	6.7%	0.155	0.39	(0.13; 1.0)	0.02
Tissue remodeling
Collagen alpha-1(I) chain (COL1A1)	1.6 (1.4; 1.8)	14.4%	-3.6%	10.2%	0.007	0.49	(0.12; 1.0)	0.008
Trefoil factor 3 (TFF3)	4.2 (3.8; 4.5)	1.3%	2.3%	3.6%	0.095	0.21	(0.03; 1.0)	0.12
Insulin-like growth factor-binding protein 2 (IGFBP-2)	7.0 (6.5; 7.9)	7.7%	-3.2%	4.3%	0.062	0.28	(0.09; 1.0)	0.061
Growth/differentiation factor 15 (GDF-15)	4.0 (3.9; 4.6)	5.4%	3.2%	8.8%	0.237	0.07	(0.01; 1.0)	0.49
Caspase-3 (CASP-3)	3.8 (3.6; 4.7)	6.7%	4.7%	11.8%	0.561	0.12	(0.03; 1.0)	0.30
Proteolysis and catabolic process
Carboxypeptidase B (CPB1)	5.6 (4.9; 5.7)	7.4%	-1.6%	5.7%	0.036	0.49	(0.28; 1.0)	0.007
Carboxypeptidase A1 (CPA1)	5.1 (4.4; 5.2)	9.1%	-0.7%	8.3%	0.041	0.37	(0.25;1.0)	0.025
Kallikrein-6 (KLK6)	3.1 (2.8; 3.5)	8%	-1.5%	6.3%	0.162	0.21	(0.01; 1.0)	0.122
Cystatin-B (CSTB)	3 (2.6; 3.4)	-1.4%	7.9%	6.4%	0.490	0.16	(0.04; 1.0)	0.20
Paraoxonase (PON3)	4.6 (3.9; 5.0)	9.0%	2.7%	11.9%	0.058	0.19	(0.04; 1.0)	0.15
Fatty acid-binding protein (FABP4)	4.3 (3.9; 4.9)	7.2%	-1.0%	6.1%	0.762	0.21	(0.07; 1.0)	0.12
Bleomycin hydrolase (BLMH)	2.3 (2.2; 2.7)	13.1%	7.7%	21.8%	0.217	0.16	(0.04; 1.0)	0.20
Baseline protein level is presented as median and IQR, and expressed in Normalized Protein eXpression (NPX), arbitrary units.
CI – confidence interval.
a – comparison of biomarker levels between periods performed using Friedman’s test.
b - to determine the effect size and its statistical significance Kendall's test was used.

CSWT was associated with at least moderate effect for variation in levels of CNTN1, EPHB4, COL1A1, CPB1 and IL-1BP (Table 2).

The majority of the studied proteins had strong correlations with each other during study periods (Spearman rho > 0.7 for all comparisons). This is shown in the heatmaps based on Spearman correlations of the levels between the 92 proteins during study time points (Supplementary Fig. 1).

Association of Changes in Biomarker Levels with exercise and imaging tests parameters

The median increase in exercise duration of 10 study patients made 0.9 (0.5; 2.4) minutes at the 6-month follow-up. The reduction in stress-induced ischemia at 6-month follow-up expressed by median change of WMS_stress – WMS_rest was 3.0 (0.3; 4.0) points and by perfusion SDS − 4.0 (2.0; 4.8) points after CSWT treatment.

According to volcano plots, dynamic levels of 28 proteins showed an association with positive changes in exercise capacity, myocardial contractility and perfusion.

An increase in levels of paraoxonase (PON3, + 14.9%), tartrate-resistant acid phosphatase type 5 (TR-AP, + 13.9%) and scavenger receptor cysteine-rich type 1 protein M130 (CD163, + 3.1%) at 6 months follow-up were significantly correlated with increased exercise capacity at the same time point (p < 0.05 for all), Fig. 1A. Similarly, an increase in levels of carboxypeptidase A1 (CPA1) by 7.8% and collagen alpha-1 chain (COL1A1) by 7% at 6 months follow-up showed a significant correlation with improvement in myocardial contractile function (expressed as changes in WMS during DSE) at that time point (p < 0.05 for both), Fig. 1B. The link of remaining biomarkers, which appeared in volcano plots, to clinical endpoints narrowly missed the statistical significance (Fig. 1A, B, C).

Furthermore, this analysis indicates large impact of the change in biomarkers levels (PON3, TR-AP, CD163, CPA1, COL1A1) on improvement in exercise duration and myocardial contractility (Table 3, effect sizes from 0.58 to 0.92).

Table 3

The effect sizes and biological roles of candidate biomarkers on improvement in exercise and imaging tests parameters
	Effect size			Biologic pathway
	R_{rank biserial}	CI 95%	P – value^a	Biologic pathway
Exercise duration (Treadmill test)
PON3	0.92	[0.7; 1.0]	0.03	Proteolysis and catabolic process / Immune and inflammatory response
TR-AP	0.92	[0.7; 1.0]	0.03	Immune and inflammatory response / Tissue remodeling
CD163	0.83	[0.4; 1.0]	0.04	Immune and inflammatory response
TFF3	0.67	[0.0; 0.9]	0.11	Tissue remodeling / Cell adhesion
IGFBP-2	-0.67	[-0.9, -0.0]	0.11	Tissue remodeling / Cell adhesion
CNTN1	0.58	[-0.1;0.9]	0.17	Cell adhesion / Immune and inflammatory response
ICAM-2	0.58	[-0.1;0.9]	0.17	Immune and inflammatory response
ALCAM	0.58	[-0.1;0.9]	0.17	Cell adhesion
EGFR	0.58	[-0.1;0.9]	0.17	Cell adhesion
EPHB4	0.58	[-0.1;0.9]	0.17	Cell adhesion
Wall motion score at stress (DSE)
CPA1	-0.83	[-1.0; -0.4]	0.04	Proteolysis and catabolic process
COL1A1	-0.83	[-1.0; -0.4]	0.04	Tissue remodeling
CPB1	-0.75	[-0.9; -0.2]	0.07	Proteolysis and catabolic process
SPON1	-0.75	[-0.9; -0.2]	0.07	Cell adhesion / Proteolysis and catabolic process
FABP4	-0.67	[-0.9; -0.0]	0.11	Proteolysis and catabolic process
CSTB	-0.67	[-0.9; -0.0]	0.11	Proteolysis and catabolic process
BLM hydrolase	-0.58	[-0.9; 0.1]	0.17	Proteolysis and catabolic process
TR	-0.58	[-0.9; 0.1]	0.17	Cell adhesion
GDF-15	-0.50	[-0.9; 0.2]	0.24	Tissue remodeling
KLK-6	-0.50	[-0.9; 0.2]	0.24	Proteolysis and catabolic process
Summed difference score (SPECT)
ITGB2	0.75	[0.2; 0.9]	0.07	Cell adhesion / Immune and inflammatory response
AP-N (ANPEP)	0.75	[0.2; 0.9]	0.07	Cell adhesion / Proteolysis and catabolic process
OPN (SPP1)	0.75	[0.2; 0.9]	0.07	Immune and inflammatory response / Tissue remodeling
PRTN3	0.67	[0.0; 0.9]	0.11	Immune and inflammatory response
SPON1	-0.67	[-0.9; -0.0]	0.11	Cell adhesion / Proteolysis and catabolic process
FABP4	0.67	[0.0; 0.9]	0.11	Proteolysis and catabolic process
MPO	0.58	[-0.1; 0.9]	0.17	Immune and inflammatory response
IL-18BP	-0.58	[-0.9; 0.1]	0.17	Immune and inflammatory response
CASP-3	0.58	[-0.1; 0.9]	0.17	Tissue remodeling / Cell adhesion
PCSK9	0.58	[-0.1; 0.9]	0.17	Other
a – comparison performed using Mann-Whitney test.
DSE – dobutamine stress echocardiography, SPECT – single photon emission computed tomography.
PON3 – Paraoxonase, TR-AP – Tartrate-resistant acid phosphatase type 5, CD163 – Scavenger receptor cysteine-rich type 1 protein M130, TFF3 – Trefoil factor 3, IGFBP-2 – Insulin-like growth factor-binding protein 2, CNTN1 – Contactin-1, ICAM-2 – Intercellular adhesion molecule 2, ALCAM – CD166 antigen, EGFR – Epidermal growth factor receptor, EPHB4 – Ephrin type-B receptor 4, CPA1 – Carboxypeptidase A1, COL1A1 – Collagen alpha-1(I) chain, SPON1 – Spondin-1, CPB1 – Carboxypeptidase B, FABP4 – Fatty acid-binding protein, adipocyte, CSTB – Cystatin-B, TR – Transferrin receptor protein 1, BLM hydrolase – Bleomycin hydrolase, GDF-15 – Growth/differentiation factor 15, KLK6 – Kallikrein-6, ITGB2 – Integrin beta-2, AP-N – Aminopeptidase N, OPN – Osteopontin, PRTN3 – Myeloblastin, MPO – Myeloperoxidase, IL-18BP – Interleukin-18-binding protein, CASP-3 – Caspase-3, PCSK9 – Proprotein convertase subtilisin/kexin type 9.

We further explored the association of biomarkers levels with clinical outcomes dividing study patients into those with significant improvement in exercise duration, WMS_stress – WMS_rest and SDS and those with non-significant changes in these parameters. Comparison of these subgroups revealed that increase in PON3, TR-AP, and CD163 levels were significantly higher in patients with prominent increase of exercise duration in contrast to the remaining patients (Fig. 2A, B, C), as well as significantly higher levels of CPA1 and COL1A1 were found in patients with remarkable decrease of WMS_stress – WMS_rest (Fig. 2D, E).

The strength of association in an expression of relative changes in biomarkers levels with changes in exercise and imaging parameters during study periods for 10 study patients are detailed in Fig. 3. There was high variability in the protein expression between the patients and different study periods with no overall pattern.

Network analysis

We selected 28 proteins that showed association with an increase in exercise tolerance and improvement in myocardial contractility or perfusion after CSWT as input to create a focused network. Overall, 4 clusters of protein were identified and fitted to biological functions: inflammation and immune response, cell adhesion, tissue remodeling, proteolysis and catabolic processes (Fig. 4). The strongest associations were found between PRTN3 and MPO, ICAM and ITGB2, TFF3 and EGFR (with the highest interaction score > 0.9) reflecting immune and inflammatory responses and cell adhesion mechanism and associated with improvement in exercise duration and myocardial perfusion. Eleven of 28 biomarkers interact in several biological processes such as cell adhesion and tissue remodeling, for example, and thus are presented in bicolour bubbles (Fig. 4).

This sub-study investigates Olink Cardiovascular III panel protein biomarkers in patients with stable angina who were apparent responders to cardiac shock wave therapy. The purpose was to explore the potential link of protein candidates with the expected activation of angiogenesis mechanism induced by shear endothelial stress. We identified 28 serum proteins that showed clear associations with clinically significant improvement in exercise tolerance, myocardial contractility and perfusion after CSWT. The discriminated proteins according to network analysis are involved in biological pathways promoting angiogenesis: cell adhesion, immune and inflammation response, tissue remodeling, proteolysis and catabolic process.

A recent review summarized potential plasma and serum proteomic biomarkers in cardiovascular disease which are associated with inflammation, wound healing and coagulation, proteolysis and extracellular matrix organization, handling of cholesterol and low-density lipoproteins [12]. Currently, there is a lack of clinical studies investigating angiogenesis process in humans after revascularization procedures. Some studies showed raised levels of circulating VEGF after percutaneous intervention [13, 14] and coronary artery bypass grafting [15]. Most protein candidates were tested in oncology, and most pathophysiological mechanisms are associated with tumor angiogenesis and progression. Only a few of these biomarkers were tested in patients with myocardial ischemia.

A cardiac shockwave therapy is a regenerative treatment method that utilizes non-invasive application of low-intensity shock waves to stimulate angiogenesis. Several studies demonstrated that the application of low-intensity shockwaves (SW) might induce the release of endothelial nitric oxide synthase (eNOS), vascular endothelial growth factor (VEGF), and proliferating cell antinuclear antigen (PCNA) [16, 17, 18, 19]. There are some data that CSWT induces recruitment of progenitor cells to the ischemic zones [20, 21], ameliorates left ventricular remodeling after myocardial infarction [20, 22]. The ultrasound-mediated stimulation of endothelial cells culture increased proliferation rates, and enhanced migration and sprouting in the 3-D spheroid cultures [23].

The regulation of angiogenesis involves several growth factors, cytokines, signaling cascades and cellular processes that are triggered in response to either an inflammatory or ischemic stimulus. Inflammatory and ischemia-driven angiogenesis are regulated via two different yet slightly overlapping pathways [24], which are promoted by circulating endothelial progenitor cells (EPCs) [25], VEGF [26, 27] and involve cell migration, proliferation and tubulogenesis [28] to form new blood vessels. EPCs have also been shown to incorporate into tubules and participate in angiogenesis directly [29].

The Olink panel contains 92 protein candidates potentially important for the pathogenesis of cardiovascular diseases and inflammation. Based on widely used public-access bioinformatic databases, including Uniport, Human Protein Atlas, Gene Ontology and DisGeNET, the vendor classified biomarkers according to biological process, disease area and tissue expression into 17 biological pathways, including angiogenesis, blood vessel morphogenesis, catabolic process, cell adhesion, chemotaxis, coagulation, heart development, immune, inflammatory response, mitogen-activated protein kinase (MAPK) cascade, platelet activation, proteolysis, regulation of blood pressure, response to hypoxia, to peptide hormone, wound healing and other gene ontology terms [30].

Cell adhesion mechanism

We determined a significant increase in 9 biomarkers levels that mediates cell adhesion (Table 3 and Fig. 4).

CD166 antigen (ALCAM) enables the isolation and differentiation of progenitor cells into cardiomyocytes, endothelial and smooth muscle cells. A rat model of myocardial infarction demonstrated that ALCAM improves angiogenesis by activating AKT-MAPK signaling, thus being included in tissue recovery and cardiac function restoration [31].

Ephrin type-B receptor 4 (EPHB4) maintains vascular integrity and heart homeostasis [32].

Overexpression of integrin beta-2 precursor (ITGB2) enhances the homing and engraftment of transplanted adipose-derived stem cells to infarcted myocardium through ICAM-1/ITGB2 interactions, augments angiogenesis, and improves myocardial perfusion [33].

Aminopeptidase N (APN) is expressed in vascular endothelial cells and is involved in capillary tube formation [34].

The data on remaining three proteins - contactin-1 (CNTN-1), spondin 1 (SPON1) and epidermal growth factor receptor (EGFR) - activation in ischemic myocardium are limited. They are known to have a role in tumor angiogenesis (EGFR [35]) and in the development of the central nervous system through its adhesion functions and promoting axonal growth (CNTN-1 and SPON1[36, 37]).

Immune and inflammatory response

Seven biomarkers, that appeared to react to CSWT, reflect immune and inflammatory response. Tartrate-resistant acid phosphatase 5 (TR-AP), a marker of systemic inflammatory burden, is secreted by activated macrophages [38]. TR-AP promotes inflammation-induced angiogenesis [39], while osteopontin (OPN) acts through regulating the VEGF pathway [40]. This could explain the relationship of increase of TR-AP level with prolonged exercise duration in our patients.

Hemoglobin scavenger receptor (CD163) is exclusively expressed on macrophages, where it acts as a receptor for haemoglobin [41] and stimulates muscle regeneration after ischaemic injury [42].

The intercellular adhesion molecule 2 (ICAM-2) is expressed at the endothelial junctions, regulates angiogenesis via cell migration and Rac activation during tube formation [43].

Myoloblastin (PRTN3) is a protease secreted by myeloid cells and allocated on the cell surface of neutrophils and endothelial cells. PRTN3 is involved in inflammatory processes through MMP activation and potentially in tumor invasion [44].

A recent in vivo study showed that neutrophil-derived myeloperoxidase (MPO) activates proteins that drive post-ischemic angiogenesis [45].

Animal studies have shown that treatment with IL-18 binding protein (IL-18BP) improves postischemic myocardial dysfunction by reducing IL-18 levels [46].

Tissue remodeling

We identified an increase of 5 biomarkers that participate in tissue remodeling. Growth differentiation factor-15 (GDF15) participates in tissue repair by exerting antiapoptotic and anti-inflammatory effects whilst maintaining vascular endothelial functions [47].

Collagen type 1 alpha 1 (COL1A1) is related to diabetic kidney disease [48] and might play role in the development of diabetic retinopathy [49] and cancer progression [50]. Of note, we found an association of elevated COL1A1 levels with improved myocardial contractility.

Trefoil factor 3 (TFF3) is the driving factor of cancer, which is involved in the proliferation, invasion, resistance to apoptosis, and angiogenesis of cancer cells [51].

Insulin-like growth factor-binding protein-2 (IGFBP-2) promotes angiogenesis by activating VEGF gene expression [52]. IGFBP-2 has also been identified as a pro-angiogenic factor in cancer studies [53, 54]. IGFBP-2 can augment tube formation in HUVECs [53], also it can be excreted by cancer cells, mediates activation of IGF-1R (insulin-like growth factor receptor 1) on endothelial cells and promoting their recruitment for metastatic angiogenesis [54].

Caspase 3 (CASP-3) is a marker of apoptosis. Activation of CASP-3 is considered a deleterious event in different pathophysiologic processes, such as degenerative diseases, ischemia, inflammation and cancer. Nevertheless, all nonapoptotic roles of CASP-3 rely on its proteolytic activity [55]. Recently, CASP-3 was described as a mediator of neoangiogenesis in dying cells after X-ray therapy [56].

Proteolysis and catabolic process

Cathepsin B (CTSB) is essential in regulating cell death, inflammatory response and angiogenesis [57].

Kallikrein kinase 6 (KLK 6), carboxypeptidase A1 (CPA1) and carboxypeptidase B1 (CPB1) have not previously been tested in patients with cardiovascular diseases. Proteases are involved in the pathogenesis of cancer, inflammatory diseases, disorders of the musculoskeletal system, and immunogenesis [58].

Paraoxonase 3 (PON3) protects cells and tissues from oxidative stress by reducing reactive oxygen species [59] and abrogates the development of atherosclerosis [60]. Interestingly, among others this protein showed an association with improvement in exercise capacity of study patients.

The physiological role of bleomycin hydrolase (BLMH) is unknown. However, it has been suggested that BLM hydrolase may play a part in inflammation by regulating the secretion of chemokines [61].

Fatty acid binding protein 4 (FABP4) regulates glucose and lipid homeostasis as well as inflammation through its actions in adipocytes and macrophages. FABP4 has been shown to be expressed specifically within capillaries and small veins of various tissues. In vitro study has demonstrated that FABP4 is required for vascular endothelial growth factor signaling and cell proliferation, associated with angiogenesis [62].

Network analysis of biomarkers

The extended network analysis revealed eleven biomarkers that likely are involved in several biological processes such as cell adhesion and proteolysis and catabolic process (ANPEP and SPON1), cell adhesion and tissue remodeling (CASP3, TFF3 and IGFBP-2), proteolysis and catabolic process and immune and inflammatory response (KLK6 and PON3), immune and inflammatory response and tissue remodeling (SPP1 and TR-AP), immune and inflammatory response and cell adhesion (CNTN1 and ITGB2), Table 2, Fig. 4.

This is an exploratory substudy with a small sample size. A greater number of participants with a control group might produce more reliable and significant results.

The blood samples were collected at baseline, after the last treatment session and at 6-month follow-up, but not after the first CSWT session limiting the assessment of the acute effect of SW. The lack of additional baseline data such as C-reactive protein, troponin and BNP levels and the degree of a collateral network also should be mentioned.

The results of the biomarker analysis are shown in arbitrary units, making it difficult to compare with other studies; for convenience, we have presented relative changes of protein levels.

Finally, the statistical analysis was challenged by the large amount of data generated by the proximity extension assay technique. We chose to focus on protein levels that demonstrated significant changes between the baseline and follow-up periods. However, the remaining proteins may hold valuable important clinical information as well.

The present study provides novel data on 92 biomarkers of Olink panel measured in patients with stable angina after cardiac shock wave therapy. This study demonstrates the association of an increase in selected protein levels with an improvement in exercise duration and contractile function. These biomarkers are involved in several biological processes activated by shock waves application, including immune and inflammation response, cell adhesion and tissue remodeling, presumably associated with angiogenesis.

CAD - coronary artery disease

CSWT - cardiac shock-wave therapy

DSE - dobutamine stress echocardiography

ECG - electrocardiogram

eNOS - endothelial nitric oxide synthase

EPCs - endothelial progenitor cells

LOD - limit of detection

MAPK - mitogen-activated protein kinase

OMT - optimal medical treatment

PCNA - proliferating cell antinuclear antigen

SD - standard deviation

SPECT – single photon emission tomography myocardial perfusion imaging.

Q1 - first quartiles

Q3 - third quartiles

SDS - summed difference score

SW - shockwaves

STRING - Search Tool for the Retrieval of Interacting Genes/ Proteins

VEGF - vascular endothelial growth factor

WMS - wall motion score

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by Vilnius Regional Ethics Committee, Lithuania (Approval No.158200-13-616-187). The patients provided their written informed consent to participate in this study.

Consent for publication

Not applicable.

Availability of data and materials

All data analysed during this study are included in this published article and its supplementary information files. The datasets analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This research was funded by the European Social Fund project No 09.3.3-LMT-K-712-19-0175 under grant agreement with the Research Council of Lithuania (LMT).

Authors Contributions

GB, PR, and JC designed the writing framework. EJ performed the statistical analysis and drew pictures. GB wrote the first draft of the manuscript. ES, EJ, GZ, NM, BP, EK, AL, FS, PR and JC revised and refined the manuscript. All authors have contributed, read, and approved the final manuscript.

Acknowledgements

The authors would like to thank Asta Brazdžionytė for blood sample collection, Louise Courtaillac, Martin Le Corf and Adam Rouet for serum aliquots preparation.

Many thanks to Hüseyin Firat, Eglė Kvederaitė, Justina Staigytė and Roma Puronaitė for their support and suggestions.

Many thanks to Medispec for Research support (CSWT applicators for study NCT02339454).

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Impact of cardiac shock wave therapy on the dynamics of biomarkers: results of proteomic analysis

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Abstract

Figures

Background

Methods

Study design and patients’ selection

Inclusion criteria

CSWT treatment

Sample preparations and protein measurement (proteomic biomarkers)

Statistical analysis

Network analysis

Results

Characteristics of the population

Changes in Olink biomarkers after CSWT

Association of Changes in Biomarker Levels with exercise and imaging tests parameters

Network analysis

Discussion

Cell adhesion mechanism

Immune and inflammatory response

Tissue remodeling

Proteolysis and catabolic process

Network analysis of biomarkers

Limitations

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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