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Article

Circulating Cell-Free Nuclear DNA Predicted an Improvement of Systolic Left Ventricular Function in Individuals with Chronic Heart Failure with Reduced Ejection Fraction

by
Tetiana Berezina
1,
Oleksandr O. Berezin
2,
Michael Lichtenauer
3 and
Alexander E. Berezin
3,*
1
Department of Internal Medicine and Nephrology, Vita Center, 69000 Zaporozhye, Ukraine
2
Department of Alter Psychiatrie, Luzerner Psychiatrie AG, 4915 St. Urban, Switzerland
3
Division of Cardiology, Department of Internal Medicine II, Paracelsus Medical University Salzburg, 5020 Salzburg, Austria
*
Author to whom correspondence should be addressed.
Cardiogenetics 2024, 14(4), 183-197; https://doi.org/10.3390/cardiogenetics14040014
Submission received: 23 August 2024 / Revised: 25 September 2024 / Accepted: 27 September 2024 / Published: 1 October 2024
(This article belongs to the Section Biomarkers)
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Abstract

:
Background: Patients with heart failure (HF) with improved ejection fraction (HFimpEF) demonstrate better clinical outcomes when compared with individuals without restoration of cardiac function. The identification of predictors for HFimpEF may play a crucial role in the individual management of HF with reduced ejection fraction (HFrEF). Cell-free nuclear (cf-nDNA) DNA is released from damaged cells and contributes to impaired cardiac structure and function and inflammation. The purpose of the study was to elucidate whether cf-nDNA is associated with HFimpEF. Methods: The study prescreened 1416 patients with HF using a local database. Between October 2021 and August 2022, we included 452 patients with chronic HFrEF after prescription of optimal guideline-based therapy and identified 177 HFimpEF individuals. Circulating biomarkers were measured at baseline and after 6 months. Detection of cf-nDNA was executed with real-time quantitative PCR (qPCR) using NADH dehydrogenase, ND2, and beta-2-microglobulin. Results: We found that HFimpEF was associated with a significant decrease in the levels of cf-nDNA when compared with the patients from persistent HFrEF cohort. The presence of ischemia-induced cardiomyopathy (odds ration [OR] = 0.75; p = 0.044), type 2 diabetes mellitus (OR = 0.77; p = 0.042), and digoxin administration (OR = 0.85; p = 0.042) were negative factors for HFimpEF, whereas NT-proBNP ≤ 1940 pmol/mL (OR = 1.42, p = 0.001), relative decrease in NT-proBNP levels (>35% vs. ≤35%) from baseline (OR = 1.52; p = 0.001), and cf-nDNA ≤ 7.5 μmol/L (OR = 1.56; p = 0.001) were positive predictors for HFimpEF. Conclusions: We established that the levels of cf-nDNA ≤ 7.5 μmol/L independently predicted HFimpEF and improved the discriminative ability of ischemia-induced cardiomyopathy, IV NYHA class, and single-measured NT-proBNP and led to a relative decrease in NT-proBNP levels ≤35% from baseline in individuals with HFrEF.

1. Introduction

Heart failure (HF) is a clinical syndrome resulting from a variety of myocardial diseases that lead to structural and/or functional cardiac abnormalities, including reduced cardiac output and/or increased intracardiac pressure at rest or during exercise and is associated with elevated circulating levels of natriuretic peptides and/or evidence of systemic or pulmonary congestion [1]. Irrespective of its etiology, HF is associated with a poor 5-year mortality rate ranging from 50% to almost 70% depending on staging, age, and comorbidity [2,3,4]. Traditionally, HF has been classified on the basis of left ventricular (LV) ejection fraction (LVEF) and consequently divided into three phenotypes—HF with preserved ejection fraction (HFpEF, LVEF ≥ 50%), HF with mildly reduced ejection fraction (HFmrEF, LVEF 41% to 49%), and HF with reduced ejection fraction (HFrEF, LVEF ≤ 40%) [5]. Although these three phenotypes show strict similarity in 5-year mortality in HF patients, cardiovascular and HF prehospitalization rates were sufficiently higher in those with HFrEF and HFmrEF compared with those with HFpEF [6].
Recent clinical trials have confirmed the improvement of HF clinical outcomes and prognosis in cases of transiting reduced/mildly reduced LVEF to preserved LVEF [7,8,9]. This fact led to the determination of a new HF subgroup with an improved LVEF (HFimpEF) that is defined as LVEF > 40% at ≥3 months or ≥10% increase from baseline LVEF and which became a clinical biomarker of favorable HF-related outcomes [1]. However, causative factors contributing to HFimpEF remain unclear and what remains especially important is a persistence of HFrEF/HFmrEF related to poor clinical outcomes and elevated mortality rates [10,11]. However, current international recommendations do not provide comprehensive information on how to manage patients with HFimpEF and are limited to stating that guideline-directed medical therapy that has led to the improvement of LVEF should be maintained further [1,5]. In this connection, the prediction of LVEF trajectories seems to be a promising approach in the management of HFrEF/HFmrEF. Indeed, there is a large body of evidence that aging, ischemic etiology, and comorbidities, including diabetes mellitus, atrial fibrillation, and acute and chronic kidney disease, as well as LV diameter, diastolic blood pressure, levels of natriuretic peptides, and no digoxin use, could be incorporated into a predictive model for the occurrence of HFimpEF, because this has so far lacked an individualized approach [12,13,14,15].
Cell-free deoxyribonucleic acid (cf-DNA) fragments are released into circulation from various bodily tissues following non-selective cellular membrane permeability, cellular damage, or death [16]. However, profiling cfDNA is associated with various pathological conditions including neutrophil extracellular traps (NETs), oxidative stress, mitochondrial dysfunction, apoptosis, ferroptosis, and necrosis [17,18]. Fragmentation and clearance of DNA may relate to initial triggers of cellular damage (ischemia/hypoxia, infections, inflammation, and immune/autoimmune reactions), physical exercise, and concomitant comorbidities [19]. Two main circulating subpopulations of cfDNA, i.e., nuclear-derived DNA (cf-nDNA) and mitochondrial-derived DNA (cf-mtDNA), which characterize cellular damage and non-selective cellular membrane permeability following oxidative stress, respectively, seem to non-invasively and dynamically assess disease status in patients with acute coronary artery disease, HF, acute kidney injury, chronic kidney disease, allograft rejection, cancer, and sepsis [20,21,22,23,24]. Although in circulating cfDNA mainly cf-nDNA was found to be a reliable circulating marker of myocardial injury with a possible predictive value in acute and chronic HF [25,26], there are no data about the discriminative potency of cf-DNA in HFimpEF individuals with HF. The aim of our study was to elucidate whether cf-nDNA is associated with HFimpEF.

2. Materials and Methods

2.1. Patients’ Characteristics

Between October 2021 and August 2022, we prescreened 1416 patients with HF using local database of the “Vita Center” (Zaporozhye, Ukraine) according to inclusion criteria (Figure 1) and finally enrolled 452 patients with chronic HFrEF. All patients were included in the study at the time of their admission to the hospital with the objective of initiating optimal guideline-directed medical therapy, which was continued after the patients were discharged. Following a six-month period, the patients were divided into two cohorts based on their LVEF trajectory. The first cohort included patients with HFimpEF (n = 177), while the second cohort excluded those with evidence of HFimpHF (n = 275).

2.2. Determination of HFimpEF

HFimpEF was defined as an increase in baseline LVEF by a minimum of 10% over a period of at least three months of guideline-directed medical therapy, with a follow-up measurement of >40% in accordance with the 2021 Universal Definition and Classification of Heart Failure [1].

2.3. Medical Information Collection

Basic patient information included the following: age, gender, height, weight, body mass index (BMI), and body surface area (BSA), as well as the New York Heart Association (NYHA) heart failure functional class. Past medical history included a history of smoking, hypertension, diabetes, atrial fibrillation, coronary artery disease, and dyslipidemia.
The diagnosis of T2DM was made in accordance with the criteria set forth by the American Diabetes Association (2021) [27]. In order to determine the presence of heart failure [5], hypertension [28], dyslipidemia [29], and coronary artery disease [30], the clinical guidelines set out by the European Society of Cardiology (ESC) were consulted. The presence of chronic kidney disease was determined in accordance with the criteria set forth in the Kidney Disease Improving Global Outcomes (KDIGO) Consensus Report [31].
The utilization of pharmacological agents included diuretics, angiotensin-II receptor blockers (ARBs), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor/neprilysin inhibitors (ARNIs), sodium–glucose co-transporter-2 inhibitors (SGLT2is), beta-blockers, antiplatelet agents, anticoagulants, statins, digoxin, calcium channel blockers, and mineralocorticoid receptor antagonists.

2.4. Examination of Hemodynamics

All patients underwent echocardiographic and Doppler examinations conducted by two blinded, highly experienced echocardiographers in accordance with the guidelines set forth by the American Society of Echocardiography [32]. The standard apical two- and four-chamber views were acquired at the outset of the study and at its conclusion using a GE Healthcare Vivid E95 scanner (General Electric Company, Horton, Norway). The conventional hemodynamic parameters included left ventricular ejection fraction (LVEF) using Simpson’s method, left ventricular end-diastolic (LVEDV) and end-systolic (LVESV) volumes, left atrial volume index (LAVI), early diastolic blood filling (E), and mean longitudinal strain ratio (e`). The estimated E/e` ratio was calculated as the ratio of the E-wave velocity to the mean of the medial and lateral e` velocities. Left ventricular hypertrophy was defined as a left ventricular mass index (LVMI) of 95 g/m2 or greater in women and 115 g/m2 or greater in men. The data were stored in DICOM format for subsequent analysis by two independent assessors.

2.5. Glomerular Filtration Rate Calculation

We used CKD-EPI formula to estimate glomerular filtration rate (GFR) [33].

2.6. Blood Sampling

Venous blood samples were taken in the morning before breakfast after an overnight fast. After centrifugation at 3000 rpm for 10 min, the supernatant was collected and stored at −70 °C until analysis.

2.7. Biomarker Evaluation

Conventional biochemical parameters were routinely measured in the local biochemical laboratory using a Roche P800 analyzer (Basel, Switzerland). Serum concentrations of NT-proBNP, tumor necrosis factor-alpha (TNF-alpha), and high-sensitivity C-reactive protein (hs-CRP) were determined using commercially available enzyme-linked immunosorbent assay (ELISA) kits (Elabscience, Houston, TX, USA) in accordance with the manufacturer’s instructions at baseline and at six months following the commencement of optimal guideline-based therapy. All ELISA data were analyzed according to the standard curve, with each sample measured in duplicate and the mean value used for the final analysis. The intra- and inter-assay coefficients of variability for each marker were both found to be less than 10%.

2.8. Cell-Free DNA Extraction

Cell-free DNA was isolated from 4 mL plasma samples using the Biosystems MagMAX Cell-Free DNA Kit (Thermo Fisher Scientific, Vienna, Austria) in accordance with the manufacturer’s instructions. The plasma samples were obtained from the EDTA whole blood samples by subjecting them to two consecutive centrifugations at 4 °C. The initial centrifugation step was performed at 2000× g for a duration of 10 min. Subsequently, the plasma samples were transferred to silicone tubes for a second centrifugation step (20,000× g, 4 °C, within 5 min) to ensure the thorough removal of cell debris. The resulting supernatant was pooled and eluted in 2 mL Tris-EDTA buffer, and the concentration was determined using a Nanodrop (ND-1000 Spectrophotometer v 3.7.1, Waltham, MA, USA) with spectrophotometric analysis at 260/280 nm.

2.9. Measurement of Cell-Free DNA in Plasma Samples

The measurement of cf-nDNA was conducted using real-time quantitative PCR (qPCR) with the specific primers targeting the NADH dehydrogenase, ND2, and beta-2-microglobulin genes. The qPCR reactions were conducted using SYBR Green technology (Thermo Fisher Scientific, Waltham, MA, USA).
The primers used for the analysis of nuclear DNA were as follows: the forward primer was CCCCACACACATGCACTTACC, while the reverse primer was ATCAAACTCAAAGGGCAGGA.
A total of 20 μL of reaction volume was used, comprising 0.1 mL Taq-Man1Universal PCR Master Mix (Applied Biosystems, Branchburg, NJ, USA), 0.5 mL ultra-clear water. Twenty-five microliters of each primer (Sigma-Aldrich, St. Louis, MO, USA), one microliter of a FAM-labeled MT-ATP8 probe, one microliter of an MVIC-labeled GAPDH probe, and two microliters of Tris–EDTA buffer containing cell-free DNA isolated from plasma were combined. The concentrations of the primers and probes in the reaction volume were 0.6 μmol/L and 0.4 μmol/L, respectively. The negative control consisted of 2 μL of Tris–EDTA buffer. The measurements were conducted using a 7500 HT real-time PCR System (Applied Biosystems, Branchburg, NJ, USA) with high-resolution melt software v. 2.0 (Applied Biosystems, Branchburg, NJ, USA), in accordance with conventional methodology [34].

2.10. Statistical Analysis

The statistical analysis was conducted using the statistical software packages SPSS 11.0 for Windows and v. 9 Graph Pad Prism (Graph Pad Software, San Diego, CA, USA). Continuous variables were expressed as means ± SD for parametric data and median and interquartile range (IQR) according to whether the data were normally distributed. The Kolmogorov–Smirnov test was employed to ascertain the normality of the distribution. The distribution of dichotomous values was evaluated using the Chi-square test. A one-way analysis of variance (ANOVA) and Tukey test were employed for the comparison of variables between cohorts. The reliability of the predictive models was determined by receiver operating curve (ROC) analysis, with further calculation of the area under the curve (AUC), its 95% confidence interval (CI), sensitivity (Se), specificity (Sp), and likelihood ratio (LR) for each predictor. The Youden test was employed to ascertain the optimal cut-off points for cf-nDNA. Predictors of HFimpEF were determined by univariate and multivariate logistic regression analyses. We reported odds ratio (OR) and 95% CI for each variable included in regression analysis. Predictive value of cf-nDNA for HFimpEF was reclassified using the integrated discrimination indices (IDIs) and net reclassification improvement (NRI). Differences were considered significant at the level of statistical significance p < 0.05.

3. Results

3.1. General Clinical Characteristics of the Patients

There were 452 patients enrolled in this study, 177 of whom met the criteria of HFimpEF events and 275 did not (Table 1). Patients were on average 59 years old, of which 266 were male, accounting for 58.9%. Mean values of body mass index (BMI) were 25.8 ± 3.5 kg/m2. The comorbidity profile included dyslipidemia (63.2%); hypertension (15.7%); stable coronary artery disease (31.2%); dilated cardiomyopathy (15.0%); paroxysmal, persistent, or permanent forms of atrial fibrillation (30.3%); smoking (37.2%); abdominal obesity (24.8%); LV hypertrophy (69.9%); and chronic kidney disease 1–3 grades (29.2%). Among the entire patient population, 31.9% had I/II NYHA HF classes, 50.8% had III NYHA HF class, and 17.3% had IV NYHA HF class. Patients had an average of LVEF equal to 32% (29–39%); LVMMI was 226 ± 15 g/m2; LAVI was 46 mL/m2 (39–52 mL/m2); and circulating levels of NT-proBNP and hs-CRP were 3228 pmol/mL and 9.68 mg/L, respectively.
There were no significant differences between both cohorts in age, gender, and BMI, as well as in the presence of dyslipidemia, hypertension, smoking, abdominal obesity, atrial fibrillation, T2DM, LV hypertrophy, CKD 1–3 grades, III and IV NYHA HF classes, systolic and diastolic blood pressure, LVMMI, LAVI, E/e`, fasting glucose, total cholesterol and high-density lipoprotein cholesterol (HDL-C), hs-CRP, NT-proBNP, and concomitant medication apart from digoxin. On the contrary, patients from the HFimpEF cohort often had I/II HF NYHA classes, whereas individuals from the persistent HFrEF cohort had frequently ischemia-induced cardiomyopathy and IV HF NYHA class. Furthermore, patients with HFimpEF exhibited significantly elevated LVEDV and LVESV, reduced LVEF and eGFR, and elevated levels of creatinine, low-density lipoprotein cholesterol (LDL-C), TNF-alpha, and cf-nDNA compared to those with HFimpEF. Patients from both cohorts received conventional therapies that were adjusted in accordance with the presence of concomitant diseases.

3.2. The Dynamics of Circulating Biomarker Levels

In the HFimpEF cohort, there was a decline in NT-proBNP levels from 3015 (1780–5220) pmol/mL to 1940 (1220–2580) pmol/mL (Δ% = −35.6%; p = 0.01), whereas in those who had persistent HFrEF, the levels of NT-proBNP did not exert sufficient changes (Figure 2). However, in the persistent HFrEF cohort, there was a trend toward an increase from 3290 (1820–5470) pmol/mL at baseline up to 3515 (1380–6152) pmol/mL at 6 months (Δ% = 0.6%; p = 0.48). There was a significant difference (p = 0.001) between the 6-month NT-proBNP levels in patients from the persistent HFrEF cohort (3515 [1380–6152 pmol/mL) and the HFimpEF cohort (1940 [1220–2580] pmol/mL).
The levels of hs-CRP revealed a trend toward a decrease in both cohorts from 9.25 (3.45–12.70) mg/L to 7.02 (3.45–10.20) mg/L in HFimpEF (Δ% = −24.1%, p = 0.20) and from 10.70 (5.80–17.50) to 9.50 (4.90–15.10) mg/L (Δ% = −11.2%, p = 0.20) in persistent HFrEF. In fact, the 6-month levels of hs-CRP in the persistent HFrEF patients were significantly higher when compared with individuals with HFimpEF (p = 0.001).
In HFimpEF patients, the TNF-alpha levels decreased from 3.11 (2.62–3.69) pg/mL to 2.88 (2.53–3.27) pg/mL (Δ% = −7.4%, p = 0.04), whereas in the persistent HFrEF patients, the TNF-alpha levels remained unchanged (3.40 [3.00–3.79] pg/mL) over the observation period (Δ% = −0.9%, p = 0.62). Overall, the 6-month levels of TNF-alpha in HFimpEF patients were found to be markedly lower than those with persistent HFrEF (p = 0.026).
In the 6-month observation period, the patients who met criteria of HFimpEF demonstrated a significant decrease in the levels of cf-nDNA (Δ% = −19.4%, p = 0.01) when compared with the patients from the persistent HFrEF cohort (Δ% = −14.0%, p = 0.66). However, patents with HFimpEF had significantly lower 6-month levels of cf-nDNA than those with persistent HFrEF: 8.3 (5.9–10.6) μmol/L versus 13.4 (10.3–16.5) μmol/L, p = 0.01).

3.3. The Reliability of Circulating Levels of cf-nDNA: The Results of the ROC Curve Analysis

The present study demonstrates that circulating levels of cf-nDNA < 7.5 µmol/L (area under curve [AUC] = 0.875; 95% confidence interval [CI] = 0.795–0.950; sensitivity = 87.0%, specificity = 73.5%; likelihood ratio = 3.288; p = 0.001) were associated with HFimpEF (Figure 3).

3.4. The Predictors of HFimpEF: The Univariate and Multivariate Logistic Regressions

We identified several factors with plausible predictive values for HFimpEF using the univariate logistic regression model (Table 2). For this analysis, we used median levels of NT-proBNP (1940 pmol/mL), TNF-alpha (2.88 pg/mL), and hs-CRP (7.02 mg/L) in HFimpEF as cut-offs. Along with it, a relative decrease in NT-proBNP levels of 35% was added to the regression analysis. It was established that the presence of ischemia-induced cardiomyopathy (OR = 0.75; CI = 0.62–0.88; p = 0.044), IV HF NYHA class (OR = 0.71; CI = 0.57–0.92; p = 0.044), T2DM (OR = 0.77; CI = 0.71–0.82; p = 0.042), CKD (OR = 0.89; CI = 0.84–0.96; p = 0.048), and digoxin administration (OR = 0.85; CI = 0.72–0. 97; p = 0.042) were identified as negative factors for HFimpEF. Conversely, NT-proBNP ≤ 1940 pmol/mL (OR = 1.42; 95% CI = 1.19–1.98, p = 0.001) and a relative decrease in NT-proBNP levels (>35% vs. ≤35%) were identified as positive predictors for HFimpEF. A relative decrease in NT-proBNP levels of 35% from baseline (OR = 1.67; CI = 1.51–1.82; p = 0.001) and cf-nDNA ≤ 7.5 μmol/L (OR = 1.56; 95% CI = 1.07–2.94; p = 0.001) were identified as positive predictors for HFimpEF.
The results of the multivariate logistic regression analysis indicate that the presence of ischemic cardiomyopathy (OR = 0.77; CI = 0.60–0.90; p = 0.042), IV HF NYHA class (OR = 0.76; CI = 0.63–0.87; p = 0.001), T2DM (OR = 0.84 (CI = 0.62–0.92; p = 0.046), and NT-proBNP ≤ 1940 pmol/mL (OR = 1.35; 95% CI = 1.12–1.76; p = 0.001) and a relative decrease in NT-proBNP levels (>35% vs. ≤35%) from baseline (OR = 1.70; CI = 1.61–1.83; p = 0.001) and cf-nDNA ≤ 7.5 μmol/L (OR = 1.64; 95% CI = 1.10–2.07; p = 0.001) were identified as independent predictors for HFimpEF.

3.5. Comparison of the Models for HFimpEF

To compare the models, we used area under the curve (AUC) estimation, which failed to show the differences in predictive ability of Model 1 (ischemia-induced cardiomyopathy), Model 2 (IV NYHA class), and Model 3 (NT-proBNP ≤ 1940 pmol/mL). Aligned with it, we found the discriminatory value of the relative decrease in NT-proBNP levels ≤ 35% from baseline and cf-nDNA ≤ 7.5 μmol/L to be superior over ischemia-induced cardiomyopathy (p = 0.001) (Table 3). Nevertheless, cf-nDNA ≤ 7.5 μmol/L improved the risk stratification by increasing the prognostic impact of NT-proBNP levels and NT-proBNP dynamic on HFimpEF independently. However, we found that the predictive value of Model 5 (NT-proBNP levels ≤ 1940 pmol/mL + cf-nDNA) was not better than Model 4 (cf-nDNA ≤ 7.5 μmol/L), whereas it was better than the reference model. Model 6 (relative decrease in NT-proBNP levels ≤ 35% from baseline + cf-nDNA) exerted superiority over the reference model and Model 3 (relative decrease in NT-proBNP levels ≤ 35% from baseline).

4. Discussion

The results of our investigation show that the level of cf-nDNA ≤ 7.5 μmol/L is associated with a presence of HFimpEF and seems to improve risk stratification by increasing the prognostic value of NT-proBNP ≤ 1940 pmol/mL and by a relative decrease in NT-proBNP levels ≤ 35% from baseline HFimpEF. Along with it, the combined model composed of a relative decrease in NT-proBNP levels ≤ 35% from baseline + cf-nDNA demonstrated the best discriminative value on the dependent variable when compared with a presence of ischemia-induced cardiomyopathy, IV NYHA class, NT-proBNP ≤ 1940 pmol/mL, and relative decrease in NT-proBNP levels ≤ 35% from baseline alone.
These findings can demonstrate how to predict LVEF improvement and improve the prognosis of persistent HFrEF. Indeed, a recent meta-analysis revealed that HFimpEF compared with HFpEF was associated with a moderately lower risk of mortality and hospitalization [35]. Moreover, based on different evidence, 23% to 61% of patients with HFrEF would be effectively treated to reach the criteria of HFimpEF and thereby improve their prognosis [13,35,36,37]. However, ischemic cardiomyopathy, T2DM, E/e`, LAVI, left bundle branch block, higher platelet count, and implantable cardioverter-defibrillator therapy were found to be negative predictive factors for HFimpEF [13,38,39]. Although single-measured values of NT-proBNP > 5000 pg/mL and >1000 pg/mL predicted a worse outcome in hospitalized patients and at discharge for patients with HFrEF, there are serious concerns about the predictive ability of NT-proBNP being associated with HFimpEF [40,41]. Indeed, elevated baseline NT-proBNP was detected as the powerful prognostic factor associated with an increased risk of CV events in HFrEF patients regardless of improved EF and independent of age, sex, duration of HFrEF, and other clinical risk factors [41]. On the other hand, a trajectory of NT-proBNP in patients with HFrEF corresponded to the dynamic in LVEF, and a reduction in NT-proBNP concentration was related to a reverse of LV remodeling during HF management [42]. Finally, NT-proBNP and the trend toward a decrease in the levels of NT-proBNP seem to be obvious biomarkers of LVEF restoration.
In our study, we found that HFimpEF was associated with better LVEDV, LVESV, and LVEF at baseline along with a significant decrease in NT-proBNP (>35% from baseline), whereas circulating levels of NT-proBNP remained unchanged in patients with persistent HFimpEF. However, hemodynamic characteristics were not found to be independent prognostic factors for HFimpEF in a multivariate regression analysis. This is consistent with previous studies in which LVEF and LV cavity volumes were not associated with the likelihood of restoring LV systolic function [9,10,11,12,13,14,15,38,39]. In contrast, cf-DNA levels ≤ 7.5 μmol/L were associated with the occurrence of recovery of LVEF, whereas levels of pro-inflammatory cytokines were not. Although the baseline levels of cf-DNA in patients with HFrEF differed significantly from the cut-off point obtained by ROC analysis, we acknowledge that the trend toward a decrease in its concentration more accurately characterizes the likelihood of LVEF recovery than the baseline concentration of NT-proBNP. However, the models constructed from cf-nDNA showed strict similarity in their predictive ability for HFimpEF.
These findings partially agree with some previous studies in which optimal guideline-based therapy improved patients’ clinical status, cardiac hemodynamic parameters, and prognosis irrespective of the baseline NT-proBNP level, although a reduced NT-proBNP concentration was frequently associated with restoring LVEF [43,44]. In fact, the proportion of HFrEF patients with a meaningful improvement of their HF-related health status or NT-proBNP level did not exceed 62% in the actively treated patient cohort [44,45]. This means that NT-proBNP dynamics is probably a sufficient argument in favor of a high probability of LVEF recovery, but not for all cases. To improve the predictive ability of a decrease in NT-proBNP on HFimpEF, we used the measurement of cf-nDNA levels, which reflect either cellular damage or impaired permeability of cell membranes [46,47].
Non-specific circulating molecular pattern of cf-DNA fragments are considered the biomarker of inflammation and immune reaction including neutrophil extracellular traps [48,49]. On the other hand, cf-DNA can modulate intra- and intercellular signaling cascades to upregulate the transcriptional expression of pro-inflammatory genes and inflammasome synthesis and induce oxidative stress within cells leading to an increased susceptibility to CVD including HF [50,51]. Although in this study we did not evaluate circulating cardiomyocyte-specific cf-DNA, elevated levels of cf-nDNA derived from other cells, such as adult hematopoietic cells, is common for the patients with HF. They may be associated with transforming growth factor-beta-1/Smad-dependent activation of cardiac fibroblasts, macrophages, and other immune cells, which intervene in cardiac remodeling through the activation of apoptosis, necrosis, and extracellular matrix accumulation, and autophagy alteration resulted in myocardial stiffness and reduced LVEF [52]. Predictably, circulating cf-nDNA fragments may play a pivotal role in the progression to HFrEF during pathological insults to the heart via interstitial fibrosis and cardiomyocyte cell death.
The results of this study reveal that a circulating pool of cf-nDNA fragments was negatively correlated with LVEF and positively associated with the ischemic etiology of HFrEF and several concomitant comorbidities, including chronic kidney disease, T2DM, severity of HFrEF, and hemodynamic performance reflection fluid overload (LAVI, LVEDV), as well as with conventional biomarkers of biomechanical stress (NT-proBNP) and inflammation (TNF-alpha and hs-CRP). Interestingly, individuals with wide QRS on ECG (with left or right bundle branch block) exhibited a mildly positive association with the levels of cf-nDNA. These findings indicate that cf-nDNA may correspond to the previously established non-specific predictor for HFimpEF that has been detected in previous studies on this issue [11,35,53,54,55].
In contrast to these studies, we were able to establish the fact that a decrease in NT-proBNP concentration is inferior in its predictive value to the cf-nDNA concentration in the blood of patients with HFrEF receiving optimal therapy with the aim of reaching HFimpEF. Thus, it can be hypothesized that taking serial measurements not only of NT-proBNP, but also of cf-nDNA is likely to lead to the application of a more individualized approach in respect to the risk stratification and management among patients with HFrEF and thereby to decrease the proportion of those who have persistent HFrEF. At least avoiding the use of digoxin and extending the implementation of beta-blockers, ARNIs, and SGLT2 inhibitors is probably the simplest solution to increase the likelihood of achieving LVEF reversal. Nevertheless, the identification of a target cohort of patients with the highest probability of LV function improvement may be encouraging.
Another aspect of the potential application of the results obtained is the prospect of monitoring the risk of a decline in the previously improved LVEF and timely individual correction of treatment. Although this idea may have serious practical value, further research is needed to clarify the situation and to develop and validate an effective predictive model.

5. Study Limitations

The study has several limitations, the first of which concerns the origin of the DNA molecules. Although some molecular features, such as the methylation pattern, the proportion of circular versus single-stranded form, and the distribution of cell-free DNA fragments, provide important information about their tissue source and reflect the severity of target organ damage, we studied a circulating pool of cf-nDNA without categorizing its correspondence to cardiac myocytes. A second limitation may be due to the study design, in which patients were enrolled according to the presence of HFrEF but were not randomized. Nevertheless, a small sample size can be considered as one of the limitations of the study. However, in studies with a small sample size, this is a reasonable price to pay for identifying the predictor(s), the significance of which will need to be further established in larger studies in the future. We believe that these limitations do not have a serious impact on the interpretation of the results of our study, but they highlight the possibilities to investigate our hypothesis in the future.

6. Conclusions

In this study, we established that the levels of cf-nDNA ≤ 7.5 μmol/L independently predicted HFimpEF and improved the discriminative ability of ischemia-induced cardiomyopathy, IV NYHA class, and single-measured NT-proBNP and led to a relative decrease in NT-proBNP levels ≤ 35% from baseline in individuals with HFrEF.

Author Contributions

Conceptualization, A.E.B.; methodology, M.L. and A.E.B.; software, A.E.B. and O.O.B.; validation, T.B., A.E.B. and M.L.; formal analysis, O.O.B., T.B. and A.E.B.; investigation, O.O.B. and T.B.; resources, T.B. and O.O.B.; data curation, A.E.B. and T.B.; writing—original draft preparation, T.B., O.O.B., M.L. and A.E.B.; writing—review and editing, T.B., O.O.B., M.L. and A.E.B.; visualization, O.O.B. and T.B.; supervision, A.E.B.; project administration, T.B. and O.O.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee of Zaporozhye Medical Academy of Post-graduate Education (protocol number: 8; date of approval: 10 October 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy restrictions.

Acknowledgments

We would like to thank all patients who gave their consent to participate in the study and all administrative staff and doctors of Private Hospital “Vita-Centre LTD” for study assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Cardiogenetics 14 00014 g001
Figure 1. Study design and patient selection for study procedures. Abbreviations: HF, heart failure; HFrEF, heart failure with reduced ejection fraction; HFimpEF, heart failure with improved LVEF; eGFR, estimated glomerular filtration rate; cf-nDNA, cell-free nuclear-derived DNA; TIA, transient ischemic attack; NT-proBNP, N-terminal brain natriuretic pro-peptide.
Figure 1. Study design and patient selection for study procedures. Abbreviations: HF, heart failure; HFrEF, heart failure with reduced ejection fraction; HFimpEF, heart failure with improved LVEF; eGFR, estimated glomerular filtration rate; cf-nDNA, cell-free nuclear-derived DNA; TIA, transient ischemic attack; NT-proBNP, N-terminal brain natriuretic pro-peptide.
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Figure 2. Dynamic changes in circulating biomarkers during period of observation. Abbreviations: HF, heart failure; cf-nDNA, cell-free nuclear DNA; hs-CRP, high-sensitivity C-reactive protein; TNF-alpha, tumor necrosis factor alpha; NT-proBNP, N-terminal brain natriuretic pro-peptide; *, a significant difference between variables at baseline and at 6 months.
Figure 2. Dynamic changes in circulating biomarkers during period of observation. Abbreviations: HF, heart failure; cf-nDNA, cell-free nuclear DNA; hs-CRP, high-sensitivity C-reactive protein; TNF-alpha, tumor necrosis factor alpha; NT-proBNP, N-terminal brain natriuretic pro-peptide; *, a significant difference between variables at baseline and at 6 months.
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Figure 3. Receiver operation curve analysis for HFimpEF: the Youden cut-off point of cf-nDNA. Abbreviations: AUC, area under curve; CI, confidence interval; LR, likelihood ratio.
Figure 3. Receiver operation curve analysis for HFimpEF: the Youden cut-off point of cf-nDNA. Abbreviations: AUC, area under curve; CI, confidence interval; LR, likelihood ratio.
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Table 1. Baseline general characteristics of eligible patients with heart failure.
Table 1. Baseline general characteristics of eligible patients with heart failure.
VariablesEntire Patient Cohort
(n = 452)		Patients with HFimpEF
(n = 177)		Patients with Persistent HFrEF
(n = 275)		p-Value
Demographic and anthropomorphic parameters
Age, year 59 (50–68)59 (52–65)60 (49–72)0.48
Male/female n (%)266 (58.9)/186 (41.2)102 (57.6)/75 (42.3)164 (59.6)/111 (40.4)0.36
BMI, kg/m225.8 ± 3.525.1 ± 2.926.1 ± 2.70.44
Comorbidities and CV risk factors
Dyslipidemia, n (%)286 (63.2)115 (64.5)171 (62.2)0.77
Hypertension, n (%)71 (15.7)28 (15.8)43 (15.6)0.88
Ischemia-induced cardiomyopathy, n (%)141 (31.2)44 (24.9)97 (35.3)0.04
Dilated cardiomyopathy, n (%)68 (15.0)21 (11.9)47 (17.1)0.52
AF, n (%)137 (30.3)47 (26.6)90 (32.7)0.28
Smoking, n (%)168 (37.2)65 (36.7)103 (37.5)0.88
Abdominal obesity, n (%)112 (24.8)46 (26.0)66 (24.0)0.87
T2DM, n (%)146 (32.3)54 (30.5)92 (33.5)0.26
LVH, n (%)316 (69.9)120 (67.8)196 (71.3)0.44
CKD 1–3 grades, n (%)132 (29.2)45 (25.4)87 (31.6)0.42
Complete LBBB/RBBB on ECG, n (%)98 (21.7)35 (19.8)63 (22.9)0.18
CRT, n (%)13 (2.9%)5 (2.8%)8 (2.9%)0.94
NYHA functional classification
I/II HF NYHA classes, n (%)144 (31.9)71 (40.1)73 (26.6)0.001
III HF NYHA class, n (%)230 (50.8) p * = 0.02285 (48.0) p * = 0.48145 (52.7) p * = 0.0180.06
IV HF NYHA class, n (%)78 (17.3) p * = 0.01; p ** = 0.02421 (11.9) p * = 0.001; p ** = 0.00157 (20.7) p * = 0.012; p ** = 0.460.036
Hemodynamic performances
SBP, mm Hg128 ± 11129 ± 9125 ± 100.22
DBP, mm Hg78 ± 1077 ± 874 ± 90.64
LVEDV, mL171 (149–192)168 (136–188)181 (150–202)0.04
LVESV, mL115 (89–127)109 (87–124)126 (90–131)0.01
LVEF, %32 (29–39)35 (31–39)30 (27–34)0.02
LVMMI, g/m2226 ± 15218 ± 15234 ± 130.46
LAVI, mL/m246 (39–52)44 (35–51)47 (39–54)0.12
E/e`, unit17.3 ± 5.416.6 ± 4.119.1 ± 3.30.56
Biochemistry parameters
eGFR, mL/min/1.73 m272 ± 1180 ± 965 ± 70.04
Fasting glucose, mmol/L5.11 ± 0.775.06 ± 0.605.19 ± 1.10.66
Creatinine, µmol/L99.6 ± 12.878.9 ± 9.1115.2 ± 8.20.04
TC, mmol/L5.88 ± 0.905.61 ± 0.525.92 ± 0.700.62
HDL-C, mmol/L0.97 ± 0.140.97 ± 0.150.98 ± 0.180.68
LDL-C, mmol/L3.93 ± 0.183.80 ± 0.174.00 ± 0.120.02
TGs, mmol/L1.98 ± 0.171.90 ± 0.122.03 ± 0.150.64
hs-CRP, mg/L9.68 (4.31–13.70)9.25 (3.45–12.70)10.70 (5.80–17.50)0.22
TNF-alpha, pg/mL3.24 (2.70–3.98)3.11 (2.62–3.69)3.43 (2.95–4.12)0.04
NT-proBNP, pmol/mL3228 (1910–5215)3015 (1780–5220)3290 (1820–5470)0.44
cf-nDNA, μmol/L11.6 (7.68–15.7)9.8 (7.2–12.2)14.1 (11.8–16.5)0.02
Concomitant medications
ACEI, n (%)198 (43.8)79 (44.6)119 (43.3)0.88
ARNI, n (%)134 (29.6)53 (29.9)81 (29.5)0.90
ARB, n (%)86 (19.0)35 (19.7)51 (18.5)0.82
Ivabradine, n (%)78 (17.3)28 (15.8)50 (18.2)0.56
Beta-blockers, n (%)426 (94.2)165 (93.2)261 (94.9)0.90
Calcium channel blocker, n (%)67 (14.8)23 (13.0)44 (16.0)0.44
MRA, n (%)405 (89.6)161 (91.0)244 (88.7)0.86
Digoxin, n (%)51 (11.3)14 (7.9)37 (13.5)0.010
Loop diuretic, n (%)412 (91.2)159 (89.8)253 (92.0)0.46
Antiplatelet, n (%)141 (31.2)54 (30.5)87 (31.6)0.84
Anticoagulants, n (%)139 (30.8)55 (31.1)84 (30.5)0.82
Metformin, n (%)138 (30.5)54 (30.5)84 (31.0)0.86
SGLT2 inhibitors, n (%)434 (96.0)175 (98.9)259 (94.2)0.86
Statins, n (%)350 (77.4)139 (78.5)211 (76.7)0.88
Notes: Data of variables are given mean ± SD and median (25–75% interquartile range); p-value, a difference between patient cohorts; p *, a difference between patients with I/II HF NYHA classes and III and IV HF NYHA classes; p **, a difference between patients with III and IV HF NYHA classes. Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-II receptor blockers; ARNI, angiotensin receptor neprilysin inhibitor; CKD, chronic kidney disease; CRT, cardiac resynchronization therapy; BMI, body mass index; DBP, diastolic blood pressure; E/e`, early diastolic blood filling to longitudinal strain ratio; GFR, glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVMMI, left ventricle myocardial mass index, left atrial volume index, LAVI; left atrial volume index; LDL-C, low-density lipoprotein cholesterol; LBBB, left bundle branch block; LVH, left ventricular hypertrophy; RBBB, right bundle branch block; MRA, mineralocorticoid receptor antagonist; NT-proBNP, N-terminal brain natriuretic pro-peptide; SBP, systolic blood pressure; SGLT2, sodium–glucose linked transporter 2; TGs, triglycerides; TC, total cholesterol; T2DM, type 2 diabetes mellitus.
Table 2. Predictors for HFimpEF in study population. The results of the univariate and multivariate logistic regression analyses.
Table 2. Predictors for HFimpEF in study population. The results of the univariate and multivariate logistic regression analyses.
Dependent Variable: HFimpEF
VariablesUnivariate Logistic RegressionMultivariate Logistic Regression
OR (95% CI)p-ValueOR (95% CI)p-Value
Ischemia-induced cardiomyopathy (presence vs. absent)0.75 (0.62–0.88)0.0440.77 (0.60–0.90)0.042
IV HF NYHA class0.71 (0.57–0.92)0.0010.76 (0.63–0.87)0.001
T2DM (presence vs. absent)0.77 (0.71–0.82)0.0400.84 (0.62–0.92)0.042
CKD (presence vs. absent)0.89 (0.84–0.96)0.0480.88 (0.80–0.10)0.050
AF (presence vs. absent)0.94 (0.80–1.09)0.064-
LVEDV0.93 (0.90–1.01)0.052-
LAVI0.95 (0.92–0.98)0.0420.96 (0.90–1.00)0.050
E/e`0.92 (0.89–0.97)0.080-
NT-proBNP (≤1940 pmol/mL vs. >1940 pmol/mL)1.42 (1.19–1.98)0.0011.35 (1.12–1.76)0.001
Relative decrease in NT-proBNP levels (>35% vs. ≤35%) from baseline1.67 (1.51–1.82)0.0011.70 (1.61–1.83)0.001
TNF-alpha (≤2.88 pg/mL vs. >2.88 pg/mL)1.06 (1.00–1.12)0.48-
hs-CRP (≤7.02 mg/L vs. >7.02 mg/L)1.08 (1.00–1.17)0.60-
cf-nDNA (≤7.5 μmol/L vs. >7.5 μmol/L)1.56 (1.07–2.94)0.0011.64 (1.10–2.07)0.001
Digoxin (presence vs. absent)0.85 (0.72–0.97)0.0420.93 (0.86–1.00)0.052
Abbreviations: AF, atrial fibrillation; OR, odds ratio; CI, confidence interval; cf-nDNA, cell-free nuclear DNA; E/e`, early diastolic blood filling to longitudinal strain ratio; LVEF, left ventricular ejection fraction; hs-CRP, high-sensitivity C-reactive protein; LAVI; left atrial volume index; LVEDV, left ventricular end-diastolic volume; NT-proBNP, N-terminal brain natriuretic pro-peptide; HFimpEF, heart failure with improved ejection fraction; NYHA, New York Hear Association; TNF-alpha, tumor necrosis factor-alpha.
Table 3. The comparisons of ischemia-induced cardiomyopathy, NT-proBNP, its respective decrease, and cf-nDNA discriminative potencies for HFimpEF.
Table 3. The comparisons of ischemia-induced cardiomyopathy, NT-proBNP, its respective decrease, and cf-nDNA discriminative potencies for HFimpEF.
Predictive ModelsAUCNRIIDI
M (95% CI)p-ValueM (95% CI)p-ValueM (95% CI)p-Value
Model 1 (ischemia-induced CMP)0.766 (0.712–0.836)-Reference-Reference-
Model 2 (IV NYHA class)0.771 (0.720–0.811)0.2600.12 (0.10–0.15)0.3600.11 (0.09–0.13)0.520
Model 3 (NT-proBNP ≤ 1940 pmol/mL)0.783 (0.700–0.840)0.1440.18 (0.12–0.23)0.1960.17 (0.12–0.23)0.280
Model 3 (relative decrease in NT-proBNP levels ≤ 35% from baseline)0.795 (0.745–0.861)0.060.23 (0.17–0.30)0.1700.21 (0.18–0.25)0.240
Model 4 (cf-nDNA ≤ 7.5 μmol/L)0.875 (0.795–0.950)0.0010.54 (0.43–0.67)0.0010.51 (0.45–0.58)0.001
Model 5 (NT-proBNP levels ≤ 1940 pmol/mL + cf-nDNA)0.872 (0.820–0.941)0.0010.48 (0.42–0.55)0.0010.49 (0.41–0.56)0.001
Model 6 (relative decrease in NT-proBNP levels ≤ 35% from baseline + cf-nDNA)0.893 (0.844–0.962)0.0010.58 (0.45–0.72)0.0010.55 (0.49–0.62)0.001
Note: p-value estimated in comparison with reference model (ischemia-induced cardiomyopathy). Abbreviations: AUC, area under curve; CMP, cardiomyopathy; NT-proBNP, N-terminal brain natriuretic pro-peptide; cf-nDNA, cell-free nuclear DNA; CI, confidence interval; M, median; IDI, integrated discrimination index; NRI, net reclassification improvement.
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Berezina, T.; Berezin, O.O.; Lichtenauer, M.; Berezin, A.E. Circulating Cell-Free Nuclear DNA Predicted an Improvement of Systolic Left Ventricular Function in Individuals with Chronic Heart Failure with Reduced Ejection Fraction. Cardiogenetics 2024, 14, 183-197. https://doi.org/10.3390/cardiogenetics14040014

AMA Style

Berezina T, Berezin OO, Lichtenauer M, Berezin AE. Circulating Cell-Free Nuclear DNA Predicted an Improvement of Systolic Left Ventricular Function in Individuals with Chronic Heart Failure with Reduced Ejection Fraction. Cardiogenetics. 2024; 14(4):183-197. https://doi.org/10.3390/cardiogenetics14040014

Chicago/Turabian Style

Berezina, Tetiana, Oleksandr O. Berezin, Michael Lichtenauer, and Alexander E. Berezin. 2024. "Circulating Cell-Free Nuclear DNA Predicted an Improvement of Systolic Left Ventricular Function in Individuals with Chronic Heart Failure with Reduced Ejection Fraction" Cardiogenetics 14, no. 4: 183-197. https://doi.org/10.3390/cardiogenetics14040014

APA Style

Berezina, T., Berezin, O. O., Lichtenauer, M., & Berezin, A. E. (2024). Circulating Cell-Free Nuclear DNA Predicted an Improvement of Systolic Left Ventricular Function in Individuals with Chronic Heart Failure with Reduced Ejection Fraction. Cardiogenetics, 14(4), 183-197. https://doi.org/10.3390/cardiogenetics14040014

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