As far as we know, this is the first study conducted in a Caucasian population to evaluate for HFpEF in diabetic patients the diagnostic performance of three specific biomarkers that are involved in different types of pathological mechanisms of HF (FGF21 regulates glucose and lipid homeostasis, copeptin is involved in neurohormonal activation and galectin-3 promotes inflammation and fibrosis). FGF21 is a stress inducible hormone that plays important roles in regulating energy balance and glucose and lipid homeostasis [56] and there are studies demonstrating that myocytes secrete FGF21 as an autocrine factor, in order to protect the heart from adverse cardiac remodeling [7,15]. Studies conducted on rodents or on primates demonstrated that the administration of FGF21 brings considerable pharmacological benefits on a cluster of obesity-related metabolic complications, including

a reduction in fat mass and alleviation of hyperglycemia, insulin resistance, dyslipidemia, cardiovascular disorders and non- alcoholic steatohepatitis (NASH).

Because native FGF21 is not suitable for clinical use due to its poor pharmacokinetic and biophysical properties, there has developed a large number of long-acting FGF21 analogues–ß-and agonistic monoclonal antibodies for theFGFR1–ß-klotho receptor complexes [56]. The clinical trials including patients with obesity, DM type 2 and nonalcoholic steatohepatitis, demonstrated substantial improvements in dyslipidemia, hepatic fat fractions and serum markers of liver fibrosis, whereas the primary end points of glycemic control have not been met [56]. In this context, several current drugs used for treatment inDM type 2 are highly effective for glucose lowering, but lack the considerable therapeutic effects of FGF21 on dyslipidemia. An association of FGF21- based pharmacotherapy with these glucose- lowering agents to treat multiple obesity- related metabolic complications might constitute a promising strategy that worth further exploration [56]. In our study we showed that FGF21 can represent a novel candidate biomarker for diagnosis in HFpEF in diabetic patients, facilitating its early detection, but further studies are needed to confirm our finding, with a larger number of patients. Furthermore, the utility of this biomarker is enhanced by the fact that FGF21 could be used in the future as a target therapy in metabolic and cardiovascular disease.

The main limitation of the present study was the relatively small sample size, which also restricted the testing of a multivariable model containing other known covariates such as coronary artery disease or NYHA class. The biomarker levels were measured only at the time of admission, and we do not have data about the change in their concentration at the time of readmission. Further assessment of these biomarkers (FGF21, galectin-3, copeptin) for HF diagnosis in patients with HFpEF should be conducted with a larger study cohort.

#### **5. Conclusions**

Neither galectin-3 nor copeptin showed promise as a biomarker in diagnosing HFpEF. In contrast with the negative findings related to galectin-3 and copeptin, fibroblast growth factor 21 may be a novel candidate biomarker for the diagnosis of HFpEF in diabetic patients. Further studies are needed with a larger sample of patients.

**Author Contributions:** Conceptualization, R.D.I.; Data curation, R.D.I. and R.R.; Formal analysis, M.I.; Investigation, R.D.I. and C.P.; Methodology, A.C. and L.M.P.; Project administration, L.M.P.; Resources, R.D.I., C.P., R.R. and A.C.; Software, M.I.; Supervision, C.P. and L.M.P.; Validation, M.I. and L.M.P.; Visualization, C.P. and L.M.P.; Writing—original draft, R.D.I. and M.I.; Writing—review & editing, C.P. and L.M.P. All authors have read and agreed to the published version of the manuscript. All authors had equal contributions to this article.

**Funding:** This research was funded by Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, grant number 1529/18 January 2019.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania (protocol code 48/11 March 2019) and Emergency County Hospital Baia Mare, Romania (no. 6361/28 February 2020).

**Informed Consent Statement:** An informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The raw data involved in this study can be obtained upon reasonable request addressed to Lucia M. Procopciuc (luciamariaprocopciuc@yahoo.com).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Review* **Past, Present, and Future of Blood Biomarkers for the Diagnosis of Acute Myocardial Infarction—Promises and Challenges**

**Ioan Tilea 1,2 , Andreea Varga 2,3, \* and Razvan Constantin Serban 4**

**Abstract:** Despite important advancements in acute myocardial infarction (AMI) management, it continues to represent a leading cause of mortality worldwide. Fast and reliable AMI diagnosis can significantly reduce mortality in this high-risk population. Diagnosis of AMI has relied on biomarker evaluation for more than 50 years. The upturn of high-sensitivity cardiac troponin testing provided extremely sensitive means to detect cardiac myocyte necrosis, but this increased sensitivity came at the cost of a decrease in diagnostic specificity. In addition, although cardiac troponins increase relatively early after the onset of AMI, they still leave a time gap between the onset of myocardial ischemia and our ability to detect it, thus precluding very early management of AMI. Newer biomarkers detected in processes such as inflammation, neurohormonal activation, or myocardial stress occur much earlier than myocyte necrosis and the diagnostic rise of cardiac troponins, allowing us to expand biomarker research in these areas. Increased understanding of the complex AMI pathophysiology has spurred the search of new biomarkers that could overcome these shortcomings, whereas multiomic and multi-biomarker approaches promise to be game changers in AMI biomarker assessment. In this review, we discuss the evolution, current application, and emerging blood biomarkers for the diagnosis of AMI; we address their advantages and promises to improve patient care, as well as their challenges, limitations, and technical and diagnostic pitfalls. Questions that remain to be answered and hotspots for future research are also emphasized.

**Keywords:** acute myocardial infarction; blood biomarkers; diagnosis

#### **1. Introduction**

Cardiovascular diseases are responsible for almost half of all fatalities worldwide, causing more than four million deaths each year in Europe alone [1]. Among these, coronary artery disease is the leading cause of death, with acute myocardial infarction (AMI) accounting for most of the mortality related to coronary artery disease [2]. Changes in human lifestyle and behavior, particularly in developing countries, have led to a continuous, rapid increase in AMI incidence over recent decades, with annual growth rates of more than 3.5% [3]. About 10% of patients who present to emergency departments with chest pain every year are diagnosed with AMI [4].

Recognition that AMI most commonly occurs as a result of intracoronary thrombosis and that early opening of the occluded coronary artery significantly ameliorates outcomes in this high-risk population has reshaped AMI diagnosis over the years. Major interest emerged in developing strategies that would allow both early recognition and exclusion of AMI. The former would enable rapid, often life-saving interventions; the latter would allow rapid and safe patient discharge, considerably reducing healthcare costs. Diagnosis

**Citation:** Tilea, I.; Varga, A.; Serban, R.C. Past, Present, and Future of Blood Biomarkers for the Diagnosis of Acute Myocardial Infarction— Promises and Challenges. *Diagnostics* **2021**, *11*, 881. https://doi.org/ 10.3390/diagnostics11050881

Academic Editors: Inna P. Gladysheva and Ryan D. Sullivan

Received: 23 April 2021 Accepted: 14 May 2021 Published: 15 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

of AMI continues to be mainly driven by the occurrence of acute chest pain in the presence of typical ECG changes. However, approximately 90% of patients presenting with chest pain do not have AMI, and the sensitivity and specificity of ECG changes in AMI are rather low [4,5]. Moreover, there is a small, although non-negligible, proportion of patients that do not exhibit obvious symptoms and/or ECG changes. This context has emphasized the need for additional diagnostic criteria, and cardiac biomarkers have emerged as the most obvious approach.

Initially, serial, daily measurement of cardiac biomarkers served only as a strategy to retrospectively confirm AMI diagnosis. Since then, their role has become increasingly larger, and cardiac biomarker changes are now included as major diagnostic criteria in AMI (Figure 1) [6].

— — — **Figure 1.** Schematic diagram of the universal definition of acute myocardial infarction, adapted from [7]: CMR—cardiac magnetic resonance; CTCA—computed tomographic angiography; MI—myocardial infarction; cTn—cardiac troponins.

> — This article reviews the evolution, current application, and emerging biomarkers for the diagnosis of AMI, addressing their advantages and promises to improve patient care, as well as their challenges, limitations, and technical and diagnostic pitfalls. Questions that remain to be answered and hotspots for future research are also emphasized.

#### **2. Out-of-Date Biomarkers for Acute Myocardial Infarction Diagnosis**

Severe myocardial ischemia and the consequent myocardial necrosis lead to the release of a plethora of cardiac enzymes into the circulation. Thus, markers such as myoglobin, lactate dehydrogenase (LDH), aspartate aminotransferase (AST), and creatine kinase (CK), including the concept of delta change, were introduced as initial indicators of AMI. Historically, AST and LDH were the first cardiac enzymes used for AMI diagnosis [8].

#### *2.1. Aspartate Aminotransferase*

–

Aspartate aminotransferase is a ubiquitous, soluble, intracellular enzyme critical in amino acid metabolism. The largest amounts of AST are expressed in the liver, the myocardium, the kidney, and the skeletal muscle. In 1954, Ladue et al. demonstrated a significant rise in AST 3–4 h after an AMI, beginning the age of enzyme-based AMI di-

agnosis [9]. Blood levels of AST increase within the first 12–24 h after AMI, reach a peak 1–2 days after the acute event, and return to baseline within 10–14 days after AMI (Table 1).

**Table 1.** Features of historical and current biomarkers used for acute myocardial infarction diagnosis.

TFPT—time to first positive test; h—hours; TPL—time to peak levels; TRB—time to return to baseline PPV—positive predictive value; NPV—negative predictive value; AST—aspartate aminotransferase; CK—creatine kinase; CK-MB—creatine kinase-myocardial band; cTn—cardiac troponins (i.e., T and I); LDH—lactate dehydrogenase. \* Data reflect values obtained for serial measurement.

> The ubiquitous expression of AST in a wide variety of tissues significantly affects its specificity for myocardial injury, limiting its use as a cardiac biomarker. Currently, AST is no longer used for AMI diagnosis.

#### *2.2. Lactate Dehydrogenase*

Only one year after the advent of AST as an AMI biomarker, LDH, an enzyme that reversibly converts lactate to pyruvate, emerged as a new promising indicator of AMI [10]. Blood levels of LDH typically increase within 6–12 h after the onset of AMI, peak within the following 1 to 3 days, and return to baseline within 8–14 days (Table 1). Similarly to AST, LDH is also expressed in a wide variety of tissues, including the liver, the kidney, the heart, the red blood cells, the lung, and particularly, the skeletal muscle, which makes LDH a marker with poor specificity for cardiac injury (Table 2).

**Table 2.** Most common non-acute myocardial infarction-related causes of cardiac biomarkers elevation.

**Table 2.** *Cont.*

AST—aspartate aminotransferase; CK-MB—creatine kinase-myocardial band; cTn—cardiac troponins (i.e., T and I); LDH—lactate dehydrogenase.

Of the five LDH izoenzymes, the heart expresses LDH-1 with four heart subunits (H4); the isomeric form LDH-2 exists in a tetrameric combination of three heart and one muscle subunit (H3M1) [11]. Hence, an LDH-1/LDH-2 ratio >1 has been proposed as a specific AMI marker. The LDH-1 isoenzyme is not highly specific to the heart, however, and the LDH-1/LDH-2 ratio did not impose as a relevant AMI biomarker [12].

Nowadays, the only use of LDH is in distinguishing between acute and subacute AMI in the late stage of the ischemic event when other cardiac markers have already returned to their normal levels.

#### *2.3. Myoglobin*

Myoglobin is a low molecular weight iron- and oxygen-binding protein abundantly expressed in the myocardium and skeletal muscle. Myoglobin is rapidly released by the injured myocardium. Its blood levels start to increase within the first 30 min to 2 h after the onset of ischemia, which makes myoglobin an important marker for the early detection/exclusion of cardiac injury. Its levels increase during the first 6–10 h after AMI, reach a peak ≈12 h after the acute event, and return to baseline by 24 h after AMI (Table 1). Myoglobin is not found in any other tissue than the muscle, making it a sensitive marker for AMI, with high negative predictive value, and therefore is a useful test to rapidly rule out AMI in the emergency room. However, because myoglobin expression is not restricted to the myocardium (Table 2), its specificity and positive predictive value are rather low [13].

#### *2.4. Creatine Kinase and Creatine Kinase Myocardial Band*

Creatine kinase is an enzyme abundantly expressed in the myocardial cells, where it catalyzes reversible transfer of high-energy phosphate from ATP to creatine, producing creatine phosphate. Since the early 1970s, and particularly since the 1980s, with the advent of the ELISA technique, CK has become a crucial laboratory parameter for the identification of myocardial damage and AMI. The enzyme is present, however, in a large variety of other tissues (Table 2), strongly affecting its specificity as a biomarker of myocardial damage. This issue has been partly overcome by use of the CK-myocardial band (CK-MB) isoform, found in the heart, where it represents ≈20% of total CK, but also in the skeletal muscle, diaphragm, uterus, and several other issues [14]. With ≈91% sensitivity and specificity for the diagnosis of AMI during the first 6 h after symptoms onset, CK-MB has become widely used in emergency department settings [15]. Its levels start to increase 4–9 h after the onset of AMI, reach a peak within the first 24 h, and return to baseline during the next 48–72 h (Table 1). In patients with AMI, there is a strong correlation between CK and

CK-MB levels and infarct size, making these markers suitable for estimating the severity of AMI. Evaluation of CK-MB has, however, several major drawbacks. Firstly, due to its high molecular weight, CK-MB has limited ability to detect minor myocardial damage. Secondly, CK and CK-MB expression is not restricted to the heart. Hence, a number of other conditions can also lead to significant CK and CK-MB increases (Table 2), reducing their diagnostic specificity for AMI. Thirdly, cross reactivity can occur with other different compounds, including heterophilic antibodies such as rheumatoid factor. Due to these flaws, CK-MB evaluation is no longer recommended for AMI diagnosis, although it still has a role in estimating infarct size [7,16].

#### **3. Cardiac Troponins—The Current Gold Standard in the Laboratory Diagnosis of Acute Myocardial Infarction**

Along with the introduction of new myocardial injury biomarkers, the sensitivity for the diagnosis of AMI also recorded an important rise. However, because all previously identified markers were also expressed in a variety of other tissues, specificity continued to be problematic (see Table 2). Hence, numerous patients presenting with non-AMI and even non-cardiac chest pain syndromes continued to undergo extensive, costly, and often invasive evaluations. At the same time, traditional enzyme evaluation was largely incapable of detecting AMI patients with small amounts of cellular death. Introduction of cardiac troponins (cTn) as biomarkers of myocardial injury has radically changed the landscape of AMI diagnosis. At present, cTn are the most widely used, evidence-based, and guideline-endorsed biomarkers of AMI, manifesting a major and immediate impact on AMI patients' management [16].

Nevertheless, values above the upper reference limit of cardiac troponins can be detected in other circumstances unrelated to a thrombotic acute coronary event [17].

Patients with moderate chronic kidney disease or dialyzed patients present persistent elevated cTn values, associated with increased cardiac death and all-cause mortality [18]. In clinical and ECG features suggestive for an AMI, in an end-stage chronic kidney disease patient, dynamic changes higher or equal to 20% in cTn over 6 to 9 h should be interpreted as positive for an AMI [17].

However, the prognostic role of cardiac troponins in the assessment of other potential causes of elevation showed an important independent value, such as in acute heart failure. Serial measurements of troponin can be used for risk assessment in heart failure outpatients, because regular or persistent elevations of cTn are corelated with high risk of death or hospital readmission [19]. The cut off values of troponin T in predicting mortality in acute heart failure is variable in studies with different endpoints, varying from 0.01 ng/mL to cTn above URL or the 99th percentile. [20]

Elevated plasma troponin levels in acute phase of pulmonary embolism are defined as concentrations above upper reference limits; these limits are assay dependent [21].

The rule-in or rule-out of AMI diagnosis based entirely on positive values of troponins addresses challenges, and these values should be interpreted considering clinical presentation, serial ECGs, and other patient variables.

In the myocardium, troponin exists as a hetero-trimer composed of troponin I (TnI), T (TnT), and C (TnC) as subunits. The troponin complex interacts with tropomyosin as part of the thin filaments of the cardiac sarcomere, regulating the calcium-dependent interaction of actin and myosin in response to cytosolic calcium changes. TnC also exists in the striated muscle, rendering it unsuitable for AMI diagnosis, whereas TnT and TnI are specific to the heart and are therefore termed "cardiac troponins". Although TnI assays have generally been affected by more technical problems, this isoform has been shown to have greater early diagnostic accuracy and to be more specific for myocardial injury than TnT, which has also been shown to increase in settings such as skeletal muscle injury, kidney disease, malignancy, or sepsis [22,23]. In average, the amount of cTn per gram of myocardium is ≈13–15-fold higher than that of CK-MB, explaining the higher sensitivity of cTn in detecting early and/or minor myocardial damage [14]. Myocardial cells possess very small pools of cytosolic troponin, whereas the troponin bulk is located within the

contractile apparatus of these cells. In contrast, circulating troponin levels are extremely low in the healthy individual. Hence, troponin plasma levels allow easy identification of even small myocyte injury. In patients with AMI, troponin release occurs initially from the cytosolic pool and later from the contractile apparatus of the damaged cells. This specific dynamic of release explains why cTn rises rapidly after the onset of myocardial ischemia, reaches a peak within the first 12–24 h, and remains high for 1–3 weeks after the acute event (Table 1).

With the advent of high-sensitivity cTn (hs-cTn) assays, sensitivity has become even higher, enabling the detection of troponin levels 10-fold lower than the initial values [24]. By definition, these assays must have an imprecision of less than 10% at the 99th percentile of a reference population and be able to measure cTn levels in at least 50% of a healthy reference population, although the most recent assays have been shown to detect troponin in >95% of healthy reference cohorts and to have an imprecision level in the range of 2–5% [16,25,26]. Overall, the added value of hs-cTn assays is not to identify more AMI cases, but rather to identify AMI more rapidly and to more quickly exclude those patients that do not have AMI [27]. Current rule-in and rule-out diagnostic algorithms rely on very low cTn cut-offs at the first presentation, and rapid, sequential measurements on admission and shortly (1–2 h) after, together with the calculation of delta troponin (i.e., rate of troponin change) [28]. Tables 3 and 4 present the specific cut-off levels of different hs-cTn T and hs-cTn I used in the current diagnosis of myocardial infarction, by each manufacturer.

**Table 3.** Troponin T assay specific cut-off levels in the rule-in or rule-out 0 h/1 h and 0 h/2 h algorithms in NSTEMI diagnosis; values are expressed in ng/L; adapted from [7].

**Table 4.** Troponin I assay specific cut-off levels in the rule-in or rule-out 0 h/1 h and 0 h/2 h algorithms in NSTEMI diagnosis; values are expressed in ng/l; adapted from [7].

Mortality attributable to AMI has significantly declined over the years, mainly due to early recognition and rapid, effective, myocardial revascularization. Repeated testing by principal laboratories is a significant logistic challenge, however, that cannot be overcome by many of the diagnostic laboratories. Implementation of algorithms requiring fast decision-making thus had to be paralleled by earlier access to biomarkers, particularly troponin. Point-of-care tests (POCTs) for cTnI with diagnostic sensitivity comparable to that of central laboratory testing have become available, allowing markedly improved turnaround times and immediacy of results, along with improved therapeutic decisionmaking, patient flow and experience, and reduced costs [26]. Currently available hs-cTn POCT systems only provide diagnostic sensitivity comparable to that of central laboratory testing ≈6 h after admission, thus limiting their use. Randomized trials on the impact of

POCTs in patients presenting with chest pain have also provided rather inconsistent results. However, in general, POCTs appeared to improve patient flow and decrease the length of stay in the emergency departments, while increasing hospital and coronary care unit admissions [26].

#### **4. Newer Biomarkers for Laboratory Diagnosis of Acute Myocardial Infarction**

The introduction of cTn testing has dramatically changed the diagnosis of AMI, allowing earlier therapeutic interventions in AMI patients and the more rapid discharge of patients without AMI. Although clearly superior to earlier AMI biomarkers in detecting myocardial ischemia, cTn are far from ideal. The level of oxidation/reduction of the troponin molecule, phosphorylation, fibrin strands, and heterophilic antibodies have the potential to interfere with TnI assays [29]. Circulating troponin levels increase as early as 2–4 h after the onset of AMI, but still leave a "troponin gap" that prevents earlier detection of AMI, which could further improve prognosis in these high-risk patients. Finally, whereas the use of hs-cTn testing has massively increased diagnostic sensitivity, this has come at the cost of a decrease in diagnostic specificity. Indeed, several cardiac and non-cardiac factors (Table 2) have been shown to affect troponin levels [30]. Even mild increases in troponin levels, regardless of the underlying cause, have been shown to predict adverse prognoses. This makes troponin assessment in the emergency department an excellent test to reliably rule out low-risk individuals. However, based solely on troponin levels, many individuals will continue to undergo further evaluations, potentially delaying therapy for other, noncardiac causes of troponin elevation [31]. Moreover, the high sensitivity of new generation hs-cTn assays could increase emergency department workload by blurring the line between health and disease (i.e., designating "healthy" individuals as being "ill") and, by including such patients in the AMI category, could also lead to an apparent improvement in AMI outcomes [32].

The continuous progress in the treatment of cardiac diseases and the introduction of new therapeutic approaches has imposed the rapid and continuous development of new laboratory assays [33]. The wide use of percutaneous coronary interventions, the introduction of newer generation antiplatelet agents, and recognition that in AMI, such strategies need to be applied as early as possible, have spurred the search of novel AMI biomarkers. These possible key tools can improve AMI diagnostic accuracy or enable the instant ruling out of AMI without the need for serial measurement (i.e., increase specificity) and enable faster detection of AMI (i.e., increase sensitivity). To accomplish these goals, novel biomarkers will have to meet two crucial criteria: accuracy and speed [34]. Any new AMI biomarker will therefore have to be specifically expressed in the myocardial tissue, at relatively high levels, and be released into the bloodstream rapidly after symptom onset. Several novel biomarkers that promise to fulfil at least part of these needs have been recently identified (Figure 2) [7,35–38].

–

— — — — — — — — — — — — — — — — — — — — — **Figure 2.** Pathophysiological pathways in acute myocardial infarction and examples of associated candidate circulating biomarkers; ANGPTL2—angiopoietin-like 2; BNP—B-type natriuretic peptide; CD40—cluster of differentiation 40; cMyC cardiac myosin-binding protein C; CRP—C-reactive protein; cys-C—cystatin C; Gal-3—galectin-3; GDF-15—growth differentiation factor 15; hFABP—heart-type fatty acid binding protein; IL-6—interleukin 6; IMA—ischemia-modified albumin; LncRNAs—long non-coding ribonucleic acids; MPO—myeloperoxydase; MR-proADM—midregional proadrenomedullin; NT-proBNP—N-terminal fragment of the B-type natriuretic peptide precursor; PAPP-A—pregnancy-associated plasma protein-A; PCT—procalcitonin; RNA—ribonucleic acids; sCD40L—soluble ligand of cluster of differentiation 40; SIRT sirtuins; sST2—soluble suppression of tumorigenicity factor 2; TNF-α—tumor necrosis factor α.

#### *4.1. Biomarkers of Myocardial Necrosis*

Recent studies have supplemented the set of classic biomarkers of myocardial necrosis with a number of additional biomarkers, including the heart-type fatty acid binding protein (hFABP), the ischemia-modified albumin (IMA), and the sarcomeric cardiac myosin-binding protein C (cMyC).

—

within ≈3 h) released into the circul – Similarly to myoglobin, hFABP, a low-molecular-weight, non-enzymatic protein involved in the intracellular buffering and transport of long-chain fatty acids, is rapidly (i.e., within ≈3 h) released into the circulation after the onset of myocardial injury, its levels returning to baseline 12–24 h after the acute ischemic event [39]. Although hFABP appears to add incremental value to cTn, increasing diagnostic accuracy and accelerating clinical diagnostic decisions, hFABP is also expressed in the kidney and skeletal muscle, and its elimination is highly dependent on kidney function [40]. Thus, its value as a diagnostic marker of AMI remains controversial.

> Acute myocardial ischemia induces major protein changes, including alterations of the N-terminus of albumin, leading to the formation of IMA. In the same manner to myoglobin and hFABP, IMA levels also increase rapidly (i.e., within 3 h) after AMI onset. A combined approach, using IMA and TnT at presentation for chest pain, appeared to increase AMI diagnostic accuracy compared with TnT alone [41]. However, when used alone, IMA displays sensitivity too low (i.e., 70%) to allow useful clinical decision-making, and its specificity is significantly affected by the fact that IMA levels also increase in a large variety of other medical conditions, as well as infections, liver, and advanced kidney diseases, cancer, or brain ischemia [42].

> Sarcomeric cMyC is one of the most promising novel myocardial necrosis biomarkers. This myosin-binding protein isoform is exclusively expressed at the level of the heart, making it a specific marker of myocardial injury; it is more rapidly released into the bloodstream as a result of myocardial necrosis than troponin, allowing earlier detection of myocardial injury and disease [43]. The cardiac myosin-binding protein C myocardial concentration is almost twice that of cTn, which makes cMyC a more sensitive AMI

biomarker than troponin, and the addition of cMyC to high-sensitive cardiac troponin T provides supplementary AMI-related diagnostic information [44].

Over the years, cardiovascular medicine has seen numerous, often major, paradigm changes. In atherosclerosis, concepts have moved from a purely lipid-related, to a more complex, lipid-inflammatory pathophysiology [45]. In atrial fibrillation, concepts have moved from a purely electrical disease to a more tangled combination of electrical, structural, autonomic, and molecular underlying changes [46–48]. In AMI, for years, biomarkers have been focused on the concept of myocardial necrosis. However, recent years have brought a major paradigm change in AMI biomarker research, and studies focusing on other ischemia-related mechanisms including neurohormonal activation, inflammation, plaque instability, and myocyte membrane rupture have started to emerge. With the extend of newer, more complex molecular techniques, multi-omic approaches have also started to be used for improvement of AMI diagnosis.

#### *4.2. Biomarkers of Neurohormonal Activation*

The observation that neurohormonal activation is a major change occurring in AMI patients has drawn attention to potentially novel AMI biomarkers, including the B-type natriuretic peptide (BNP) and the N-terminal fragment of its precursor (NT-proBNP), copeptin, and the midregional proadrenomedullin (MR-proADM).

Secreted by the ventricular myocytes in response to stretch, the proBNP precursor is cleaved into BNP and the inactive fragment NT-proBNP. Once released, BNP exerts vasodilating, natriuretic, hypocoagulative, inotropic, antiarrhythmic, and anti-renin–angiotensin– aldosterone and sympathoadrenal system effects, while promoting cell differentiation and tissue repair and supporting immunity, metabolic responses, and inflammation [49]. In patients with AMI, BNP and NT-proBNP levels have been associated with infarct size [50]. However, both BNP and NT-proBNP increase in a large variety of non-AMI-related settings, including some that may be accompanied by clinical symptoms comparable with AMI, such as acute heart failure and pulmonary embolism, as well as in kidney dysfunction, hypertension, chronic heart failure, myocarditis, cardiac arrhythmias, electrical cardioversion, or sepsis, precluding the use of these peptides as diagnostic AMI biomarkers, although they remain crucial for prognostic assessment in these patients [51]. According to 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation, the BNP and NT-proBNP plasma concentrations should be considered to contribute to patient's prognosis (death, acute heart failure) [7].

Copeptin, a stable glycopeptide derived from the C-terminal fragment of the vasopressin prohormone, has been shown to be co-released with arginine vasopressin within the first 4 h after AMI. Circulating copeptin levels have been shown to exhibit linear correlation with those of arginine vasopressin, a key regulator of water homeostasis and plasma osmolality [52]. This makes copeptin an excellent surrogate marker of arginine vasopressin secretion, which has a very short half-life. When combined with cTn, copeptin has been shown to improve sensitivity and easily rules out AMI early [53]. However, copeptin is not a specific cardiac marker, neither is it recommended to routinely measure it for constant risk or prognosis judgement [7]. Other conditions, including kidney disease, heart failure, or sepsis, can also influence copeptin levels, and its levels have been shown to be affected by gender, body mass, hydration status, blood pressure, and glomerular filtration rate [51], thus precluding its use as a stand-alone diagnostic AMI biomarker.

Adrenomedullin is a stress hormone expressed in a large variety of tissues, including the brain, kidney, vasculature, and adrenal medulla, involved in diuresis and natriuresis, vasodilation, and inotropism regulation. Adrenomedullin is generated from its more stable precursor MR-proADM. In clinical studies, MR-proADM has been shown to significantly increase in AMI patients, particularly in those developing post-AMI heart failure, and high MR-proADM levels have been associated with significant increases in short- and longterm mortality and hospitalization for heart failure following AMI [54,55]. Increased MR-proADM levels have even been proposed as a rule-in criteria for AMI, but its exact

value as a diagnostic biomarker in addition to the already established criteria remains to be evaluated.

Immediately after the onset of AMI, the renin–angiotensin–aldosterone system (RAAS) is activated, and this phenomenon is linked with an unfavorable prognosis [56]. High levels of angiotensin II aggravate myocardial ischemia-mediated vasoconstriction. In hypertensive women who experience an AMI, the renin–angiotensin–aldosterone axis is suggested to be upregulated [57].

Heightened RAAS activity demonstrates a determining implication not only within the pathogenesis of AMI, but also as an estimated model with 5 years post-AMI mortality, as revealed by the RAAS polymorphisms in the presence of AGT CC genotype and ACE allele, individual or in association [58].

Increased plasma renin activity (PRA) values were detected in both hypertensive and normotensive patients, in whom the diagnosis of AMI was ruled in; these were independently associated with a poor prognosis in AMI patients [59,60]. In the myocardium, increased aldosterone synthesis is induced by an acute coronary event, triggering ventricular fibrosis post-AMI [61].

#### *4.3. Biomarkers of Inflammation*

Myocardial inflammation is a critical process occurring in the setting of AMI that has been related to a variety of deleterious consequences, including electrical instability and increased risk of cardiac arrhythmias, autonomic dysfunction, and fibrosis development [48,62–64]. A wide variety of pro-inflammatory cytokines have been shown to increase in the setting of AMI and to predict prognosis in this population. This is particularly the case for C-reactive protein (CRP), interleukin- (IL-) 6, and procalcitonin (PCT).

Increased CRP levels have been linked with the extent of myocardial injury and with poorer prognosis in AMI patients. In animal studies, CRP removal was able to significantly reduce infarct size and improve outcomes [65]. Ries et al., in a pilot study in humans with AMI (CAMI-1 study), reported that CRP apheresis managed to reduce circulating CRP levels and a loss of correlation between CRP levels and infarct size, although CRP reduction was not associated with a significant reduction in infarct size [66]. However, because CRP increases in a wide variety of other settings, its levels are not sufficiently specific to qualify it as a reliable marker of AMI.

In the setting of AMI, increased IL-6 levels favor activation and recruitment of inflammatory cells, promote CRP release, and exert cardiac negative inotropic effects, predicting poor prognosis in this population [67]. Meanwhile, the IL-6 receptor blockade has been shown to reduce the inflammatory response and cTn release in patients with non-STE segment elevation AMI, suggesting that, similarly with CRP, IL-6 could emerge as not only a diagnostic AMI biomarker, but also as a potentially new therapeutic target in AMI [68]. However, the low specificity of IL-6 and the lack of large confirmatory studies limit IL-6 use as a diagnostic AMI biomarker.

The peptide precursor of the hormone calcitonin, PCT, has also been shown to increase in inflammatory settings, including AMI, and to have prognostic value in ischemic heart disease [69].

The potential role in the development of atherosclerosis and acute coronary syndromes of a group of zinc-dependent endopeptidases, namely, matrix metalloproteinases (MMPs), was investigated in previous studies [70–72].

The increased circulating levels of MMP 2, 3, 9 and 28 in AMI do not distinguish between atherothrombotic and non-atherothrombotic events. [70,73].

Due to matrix metalloproteinase-9 linking with plaque rupture and myocardial necrosis, MMP-9 has demonstrated its position as an early-stage biomarker of AMI [70]. It was also suggested that the value of MMP-28 correlates with GRACE score and could be a predictor of short-term prognoses in AMI cases [72].

Other inflammatory biomarkers, including IL-1β, IL-37, and angiopoietin-like protein 2, have also been shown to increase in AMI patients, but further studies are needed before drawing a definitive conclusion in their regard [35].

#### *4.4. Biomarkers of Plaque Destabilization and Myocyte Rupture*

Myeloperoxidase (MPO), a component of neutrophil granules, is abundantly released from unstable atherosclerotic plaques and plays a critical role in myocardial inflammation and oxidative stress. When used alone, MPO does not seem to be appropriate for AMI diagnosis [74]. However, when combined with TnI and CK-MB, MPO appears to improve the early diagnostic accuracy of AMI [75].

Cluster of differentiation 40 protein, CD40, and its soluble ligand, sCD40L, are expressed by a wide variety of cell types and have been shown to be released during cardiac myocyte rupture. Both molecules are involved in inflammation and thrombosis and have been proposed as diagnostic AMI biomarkers [76]. However, their exact value remains to be established [35].

Biomarkers, including lipid markers (lipoprotein A, apolipoproteins A and B), endothelial cells- (endocan) and platelet- (mean platelet volume, mean platelet volume-toplatelet count ratio, beta-thromboglobulin, platelet miR-126) related markers have been proposed as AMI biomarkers [14,51]. Further enhanced studies are required to establish the additional value in AMI diagnosis for other biomarkers such as the degree of mobilization of mononuclear and endothelial progenitor cells, triggering receptors expressed on myeloid cells 1 and 4, cystatin C, pregnancy-associated plasma protein-A, class II phosphatidylinositol 3-phosphate kinase, sirtuins, arginine methyltransferase 5, and chitinase-3-like protein 1 [30].

The main characteristics of newer biomarkers potentially used in the diagnosis of AMI are depicted in Table 5.

**Table 5.** Characteristics of newer biomarkers used in the diagnosis of acute myocardial infarction.

TFPT—time to first positive test; h—hours; TPL—time to peak levels; TRB—time to return to baseline PPV—positive predictive value; NPV—negative predictive value.

> Studies about other newer biomarkers, such as CRP, IL-6, procalcitonin, CD40, myeloperoxydase, and sCD40L, could be valuable in the diagnosis and prognosis of AMI, although further studies are needed [14,35]. Guidelines recommend the use of BNP/NTproBNP in the setting of different situations, especially in patients with suspected heart failure, as a prognostic indicator against AMI diagnosis biomarker and supporting the risk stratification of adverse outcomes [81].

#### **5. A Glimpse into the Future—Emerging Molecular Candidate Biomarkers for Acute Myocardial Infarction**

The development of new, high-technology laboratory methods continues to improve therapy monitoring, as well as diagnostic and prognostic accuracy in cardiovascular medicine [36,82]. With the advent of novel molecular biology techniques, evaluation of the entire molecular chain, from DNA to RNA, proteins, and metabolites has become possible. Multi-omic approaches based on genomic, transcriptomic, proteomic, and metabolomic studies that allow the evaluation of RNAs, peptides and proteins, and metabolites using

whole blood, plasma, or serum, or even selected circulating cell types or extracellular vesicles, promise to be a game changer in AMI assessment.

#### *5.1. Novel Peptide, Protein, and Enzyme Candidates for Acute Myocardial Infarction Diagnosis*

Growth differentiation factor 15 (GDF-15), galectin-3 (Gal-3), and soluble suppression of tumorigenicity factor 2 (sST2) appear to be among the most promising novel AMI biomarkers and have been extensively studied over the past years.

Circulating levels of GDF-15, a member of the transforming growth factor beta cytokine superfamily expressed in a wide variety of cardiovascular and non-cardiovascular cells, have been shown to increase in inflammatory and high oxidative stress states and tissue injury, including AMI, and to predict prognosis in this population [83]. Galectin-3 has also been shown to participate in inflammation, cardiac fibrosis and repair, and maladaptive ventricular remodeling, as well as in atherosclerotic plaques formation, destabilization, and rupture. Increased Gal-3 levels have been reported in AMI patients, in whom Gal-3 has also been proposed as a valuable predictive marker [84]. Moreover, experimental data suggest that whereas Gal-3 could initially contribute to myocardial repair and the preservation of cardiac geometry and function, over the long term, Gal-3 activation could promote myocardial fibrosis and adverse ventricular remodeling [85]. Likewise, sST2, a member of the IL-1 receptor family, has been shown to increase in AMI patients and to predict prognosis in this setting [86]. However, due to insufficient specificity, sST2 is unlikely to enter clinical practice as a diagnostic AMI biomarker. Other potential peptide and protein biomarkers, including glycogen phosphorylase isoenzyme BB, involved in myocardial carbohydrate metabolism regulation and released rapidly after myocardial injury; S100A, a class of small-molecule calcium-binding proteins involved in cell division and metabolism regulation and highly sensitive in depicting myocardial injury; irisin, a recently identified hormone that facilitates glucose uptake and improves cardiac muscle activity; and adropin, a secretory protein involved in regulation of energy metabolism and insulin resistance, are currently under evaluation [14]. Several of them have been proposed as independent predictors of post-AMI outcomes, but none has demonstrated clear advantage over cTn in AMI diagnosis to date.

#### *5.2. Metabolomics—Emerging Targets for Acute Myocardial Infarction Diagnosis*

By allowing rapid assessment of various products of cell metabolism, including during acute events, metabolomics has emerged as a promising new source of highly specific diagnostic AMI biomarkers. Studies in patients with septal ablation-induced AMI identified several myocardial cells metabolites released very early (i.e., within 10 min) after AMI onset [87]. These include molecules involved in the pentose phosphate, pyrimidine, and tricarboxylic acid pathways. However, the exact value of these new biomarkers in AMI diagnosis in addition to or instead of cTn remains to be established.

#### *5.3. Circulating Ribonucleic Acids as Potential Diagnostic Biomarkers of Acute Myocardial Infarction*

Non-coding RNAs, including microRNAs (miRNAs), circular RNAs, and long noncoding RNAs (lncRNAs), act as strong, tissue- and cell-specific epigenetic regulators of cardiac gene expression, homeostasis, and function, and have recently emerged as promising biomarkers in a wide variety of cardiovascular diseases. These molecules circulate in the peripheral blood, either bound to transport proteins or packaged in microparticles, thus being stably transported in the blood, protected from degradation, and detectable in blood samples. A number of these circulating non-coding RNAs have been or are currently under investigation as potential diagnostic AMI biomarkers.

MicroRNAs are small, single-stranded RNA molecules that work as post-transcriptional protein synthesis suppressors via gene silencing. Four miRNAs—miR-1, miR-133a/b, miR-208b, and miR-499, the latter two being expressed solely in the cardiac myocytes—have been consistently reported to increase in AMI patients, although controversies still remain regarding the value of individual miRNAs as AMI biomarkers [68]. With 78% sensitivity

and 82% specificity, total miRNA levels appear to be suitable diagnostic biomarkers of AMI, with miR-499 appearing to be among the most significantly associated with AMI, increasing very early (i.e., within few hours after AMI onset), to very high (i.e., up to 3 × 10<sup>5</sup> ) levels, and returning to baseline within hours or days after the acute event, while also reaching high (i.e., 88% and 87%) sensitivity and specificity [88]. Other miRNAs, including miR-21, miR-92a, miR-122, miR-181a, miR-320a, miR-328, and miR-375, have also been shown to increase in the peripheral blood in the setting of AMI [89]. Certain miRNAs appear to be highly specific for AMI and to display faster release kinetics than cTn. Moreover, studies have suggested that the miRNA signature could even distinguish between ST and non-ST segment elevation myocardial infarction [90]. In addition, in rats with experimental AMI, antagonizing certain miRNAs, such as miR-31, managed to reduce infarct size and post-AMI ventricular remodeling, preserving cardiac structure and function [91]. Several lncRNAs, namely, myocardial infarction-associated transcript (MIAT), H19, and metastasisassociated lung adenocarcinoma transcript 1 (MALAT1), have also been reported to be altered in AMI patients, but studies in this regard are still in their infancy [92–95].

#### **6. Gaps in Knowledge and Future Research**

The widespread use of cTn has revolutionized AMI diagnosis. Particularly, with the advent of hs-cTn, we can confidently say that sensitivity is no longer an issue in AMI diagnosis. However, two problems remain to be solved. Firstly, the increased sensitivity provided by hs-cTn assays came with a cost—an inevitable decrease in diagnostic specificity. Secondly, although cTn increase relatively early after the onset of AMI, they still leave a time gap between the onset of myocardial ischemia and our ability to detect it, thus precluding very early management of AMI. Historical markers, such as myoglobin, increase much faster than cTn, but they do not provide sufficient diagnostic specificity.

To date, all AMI biomarkers have focused on molecules released by the cardiac myocytes as a result of myocardial necrosis. More recently, understanding of the complex AMI pathophysiology has opened the way for new biomarker identification. Acknowledgment that processes such as inflammation, neurohormonal activation, or myocardial stress occur much earlier than myocyte necrosis has allowed us to expand biomarker research in these areas (Figure 1). Although most of the emerging biomarkers are not specific enough to impose as stand-alone AMI biomarkers, and are therefore unlikely to replace cTn in the foreseeable future, they could bring additional value for AMI diagnosis when included in multi-biomarker approaches. Including tissue-specific markers, such as certain miRNAs, could also be of invaluable help.

Multi-omic approaches hold great promise for more accurate and faster AMI diagnosis. MicroRNAs appear to be particularly promising in this regard, given that myocyte stress induced by anoxia, acidosis, and/or edema precedes myocardial necrosis in AMI patients and is rapidly reflected in circulating miRNAs levels. The rapid progress in molecular biomarker research must be matched, however, by similar progresses in laboratory techniques. Indeed, bioluminescence, solution-phase bioluminescence, and high-throughput sequencing methods have been developed for miRNA detection, but improvement is still necessary to provide instruments that can accurately, conveniently, inexpensively, and rapidly quantify circulating miRNAs in a large number of hospital laboratories. Integrated interpretation of multi-omics data also remains rather difficult at present, and may require machine learning to transform this approach into a clinically useful diagnostic tool.

Multi-biomarker strategies that reflect several AMI-related pathophysiological processes and include several enzymatic, non-enzymatic, and/or molecular markers may provide the solution for obtaining highly sensitive, highly specific, and very rapid AMI diagnoses. Such an approach was already shown to be superior to individual marker assessment for predicting post-AMI outcomes [96]. In a pilot study, a multi-marker panel termed "plaque disruption index" has been shown to present higher diagnostic accuracy for type 1 AMI than coronary angiography [97]. However, large clinical trials are still required before drawing definitive conclusions in this regard.

Regardless of future AMI diagnoses relying on single- or multi-marker approaches, any new approach will have to provide more readily available, more affordable, and more clinically relevant information than the strategies currently used in clinical practice, while also complying with high standards of precision and accuracy. Markers that do not fulfill these requirements will most likely not find their place in clinical practice. The ideal AMI biomarker, which is exclusively expressed in the myocardial tissue and whose plasma levels are not affected by any other pathology, remains to be identified. MicroRNAs could prove of invaluable help in this regard. It is unlikely that a single miRNA will prove superior to the traditional markers for AMI diagnosis. However, a combination of miRNAs, reflecting cardiomyocyte stress, inflammation, endothelial cells, and fibroblast damage, and/or other AMI-related pathophysiological processes, could change the future of AMI diagnosis. Moreover, miRNA-based therapies have the potential to modulate an entire pathway by regulating multiple genes at the same time. The advent of such therapies, and particularly of anti-miRNA strategies, is therefore expected to spur further research in this area.

The massive number of emerging biomarkers and the impressive progress achieved over the past decades in AMI diagnosis were spurred by a revolutionary paradigm shift one that has drawn AMI diagnosis out of the 'myocardial necrosis box'. Further understanding of AMI pathophysiology is expected to open new directions of research and enable the identification of even better diagnostic AMI biomarkers. Laboratory techniques will have to parallel this effort and ideally provide POCT systems able to detect single analytes as well as panels of circulating markers, and thus to enable accurate and rapid AMI diagnosis, including during patient transport to hospital. This would allow further cutting down time in AMI management, which in turn is expected to result in considerable improvement in AMI outcomes, both over the short and the long term.

#### **7. Conclusions**

Circulating biomarkers are key to AMI diagnosis. With their increased specificity, cTn have revolutionized the diagnosis, as well as the management of AMI. The advent of hs-cTn covered an unmet need and boosted diagnostic sensitivity, allowing more rapid therapeutic interventions. Unfortunately, this increased sensitivity was accompanied by a drop in specificity, not for the analyte, but for the diagnosis of AMI. Up to twothirds of patients presenting with cTn levels that overtly exceed the threshold for AMI diagnosis do not have AMI and undergo costly, time-consuming, often invasive, although completely futile investigations. Further advancement in AMI diagnosis is therefore required. Traditional markers have all been focused on detecting myocardial necrosis. Acknowledgment that other processes, such as inflammation, neurohormonal activation, or myocardial stress, occur much earlier than myocyte necrosis, has opened the way for new approaches in AMI biomarker research. The ideal biomarker, that would allow rapid and reliable AMI rule in and rule out, is still to be discovered. However, biomarker research in AMI has massively advanced over the past years, and a solution to this problem is expected to be found in the near future, most likely in the form of multi-marker assessment, based on a combination of biomarkers that reflect several axes involved in the natural evolution of AMI. Future clinical trials, with large sample sizes, will have to confirm the utility of biomarkers that are currently under investigation, to identify novel potential biomarkers, and to validate the clinical impact of multi-biomarker-based AMI diagnosis.

**Author Contributions:** Conceptualization, I.T., A.V. and R.C.S.; methodology, I.T., A.V. and R.C.S.; software, I.T., A.V. and R.C.S.; validation, I.T., A.V. and R.C.S.; formal analysis, I.T., A.V. and R.C.S.; resources, I.T., A.V. and R.C.S.; data curation, I.T., A.V. and R.C.S.; writing—original draft preparation, I.T., A.V. and R.C.S.; writing—review and editing, I.T., A.V. and R.C.S.; visualization, I.T., A.V. and R.C.S.; supervision, I.T., A.V. and R.C.S.; project administration, I.T., A.V. and R.C.S.; funding acquisition, I.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS-UEFISCDI, project number PN-III-P1-1.1-PD-2019-0181, within PNCDI III.

**Acknowledgments:** The authors would like to thank Alina Scridon from "G.E. Palade" University of Medicine, Pharmacy, Science and Technology of Targu Mures, Romania for critically revising the manuscript and for her helpful suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

*Article*

## **Serum and Echocardiographic Markers May Synergistically Predict Adverse Cardiac Remodeling after ST-Segment Elevation Myocardial Infarction in Patients with Preserved Ejection Fraction**

## **Tamara Pecherina, Anton Kutikhin \* , Vasily Kashtalap, Victoria Karetnikova, Olga Gruzdeva, Oksana Hryachkova and Olga Barbarash**

Research Institute for Complex Issues of Cardiovascular Diseases, 6 Sosnovy Boulevard, 650002 Kemerovo, Russia<EMAIL_ADDRESS>(T.P<EMAIL_ADDRESS>(V.K<EMAIL_ADDRESS>(V.K<EMAIL_ADDRESS>(O.G<EMAIL_ADDRESS>(O.H<EMAIL_ADDRESS>(O.B.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +7-384-960-907-7067

Received: 25 April 2020; Accepted: 13 May 2020; Published: 14 May 2020

**Abstract:** Improvement of risk scoring is particularly important for patients with preserved left ventricular ejection fraction (LVEF) who generally lack efficient monitoring of progressing heart failure. Here, we evaluated whether the combination of serum biomarkers and echocardiographic parameters may be useful to predict the remodeling-related outcomes in patients with ST-segment elevation myocardial infarction (STEMI) and preserved LVEF (HFpEF) as compared to those with reduced LVEF (HFrEF). Echocardiographic assessment and measurement of the serum levels of NT-proBNP, sST2, galectin-3, matrix metalloproteinases, and their inhibitors (MMP-1, MMP-2, MMP-3, TIMP-1) was performed at the time of admission (1st day) and on the 10th–12th day upon STEMI onset. We found a reduction in NT-proBNP, sST2, galectin-3, and TIMP-1 in both patient categories from hospital admission to the discharge, as well as numerous correlations between the indicated biomarkers and echocardiographic parameters, testifying to the ongoing ventricular remodeling. In patients with HFpEF, NT-proBNP, sST2, galectin-3, and MMP-3 correlated with the parameters reflecting the diastolic dysfunction, while in patients with HFrEF, these markers were mainly associated with LVEF and left ventricular end-systolic volume/diameter. Therefore, the combination of the mentioned serum biomarkers and echocardiographic parameters might be useful for the prediction of adverse cardiac remodeling in patients with HFpEF.

**Keywords:** biomarkers; ST-segment elevation myocardial infarction; preserved left ventricular ejection fraction; reduced left ventricular ejection fraction; heart failure; NT-proBNP; soluble ST2; galectin-3; matrix metalloproteinases; tissue inhibitors of metalloproteinases

#### **1. Introduction**

Despite cardiovascular mortality trending to decline worldwide in the recent decade [1], it is still unacceptably high in Russian Federation [2,3]. Advances in the diagnosis and treatment of coronary artery disease (CAD), particularly ST-segment elevation myocardial infarction (STEMI), considerably reduced in-hospital but not long-term mortality rates, even in patients with preserved (≥50%) left ventricular ejection fraction (LVEF) [4]. The main cause of adverse outcomes upon MI is ventricular remodeling—a deposition of a disorganized extracellular matrix governed by activated resident fibroblasts—which ultimately results in chronic heart failure (CHF) [5,6]. Timely diagnostics of ventricular remodeling requires sensitive and specific biomarkers, which should be detectable in the serum, permitting noninvasive sample collection [7]. Unfortunately, albeit serum biomarkers are

routinely measurable and can be combined with other diagnostic and prognostic modalities, they generally provide little information on the actual state of ventricular remodeling, which restricts their implementation into its management [8].

Promising serum markers of ventricular remodeling include N-terminal pro-B-type natriuretic peptide (NT-proBNP, a protein released from cardiomyocytes at myocardial stretch), soluble suppression of tumorigenicity 2 (sST2, a cytokine-related protein and an established marker of inflammation, hemodynamic stress, and cardiomyocytes strain), galectin-3 (an inflammatory β-galactoside-binding lectin acting as a matricellular protein), matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) regulating the profibrotic remodeling [7,8], and exosomes enriched by pro-fibrotic miRNA (miR-1, -21, -34a, -133, -192, -194, -208a, -425, -744) [9], although none of them have been recommended for the specific assessment of ventricular remodeling progression so far [8]. Hence, the search for novel systemic markers of ventricular remodeling is ongoing, and various patient cohorts are involved.

Currently, >70% of patients with CHF aged >65 years have a preserved LVEF (HFpEF), and this proportion tends to increase together with the rising prevalence of associated underlying health conditions (obesity, metabolic syndrome, and diabetes mellitus) [10]. In contrast to CHF with a reduced (<50%) LVEF (HFrEF), which progression can be directly assessed in a noninvasive manner by echocardiography, efficient monitoring of the cardiac status in the patients with HFpEF is challenging [11], although a number of studies in this direction have been reported [12,13]. Taken together, these facts underscore the importance of finding the informative and routinely measurable biomarkers of ventricular remodeling in this patient category [14]. Ideally, the candidate molecules should be involved in the development of ventricular remodeling rather than simply reflecting the stiffness of the heart extracellular matrix. In addition, a panel of serum biomarkers can be combined with echocardiographic parameters, possibly correlating with each other and thereby reinforcing the association with ventricular remodeling.

Here, we evaluated whether the combination of serum biomarkers (NT-proBNP, sST2, galectin-3, MMP-1, MMP-2, MMP-3, and TIMP-1) and echocardiographic parameters may be useful to predict the ventricular remodeling-related outcomes in patients with HFpEF as compared to those with HFrEF.

#### **2. Materials and Methods**

In this prospective study, we enrolled 100 consecutive patients with HFpEF and 154 patients with HFrEF who have been admitted to Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russian Federation) in 2015. The investigation was carried out in accordance with the Good Clinical Practice and the Declaration of Helsinki. The study protocol was approved (date of approval: 04 February 2015) by the Local Ethical Committee of Research Institute for Complex Issues of Cardiovascular Diseases (Protocol No. 20150204). All patients provided a written informed consent after receiving a full explanation of the study. Criteria of inclusion were: (1) STEMI diagnosed by means of the respective European Society of Cardiology guidelines [14]; (2) age between 18 and 75; (3) successful percutaneous coronary intervention (PCI). Patients with no clinical signs of heart failure (Killip class I) and LVEF ≥ 40% were assigned to HFpEF group while those with heart failure clinical signs (Killip class to II-IV) and LVEF <40% were considered as having HFrEF. Criteria of exclusion were: (1) Diagnosed acute/chronic liver or kidney failure, cancer, chronic obstructive pulmonary disease, autoimmune, endocrine, or mental disorders; (2) STEMI occurred as a result of unsuccessful PCI or coronary artery bypass graft surgery; (3) in-hospital death of the patient. The study design is represented in Figure 1.

**Figure 1.** Study design. STEMI—ST-segment elevation myocardial infarction, HFpEF—STEMI with preserved ejection fraction, HFrEF—STEMI with reduced ejection fraction, ECG—electrocardiography, ECHO—echocardiography, PCI—percutaneous coronary intervention, MMP—matrix metalloproteinase, TIMP—tissue inhibitor of metalloproteinases, NT-proBNP—N-terminal pro-B-type natriuretic peptide, sST2—soluble suppression of tumorigenicity 2.

Thrombolytic therapy was applied in 11 (11%) patients. Biochemical profile and complete blood count were determined in all patients in addition to the collection of clinicopathological and demographic data from the case histories (Table 1).

**Table 1.** Clinicopathological features of the patients diagnosed with ST-segment elevation myocardial infarction with preserved ejection fraction (HFpEF, *n* = 100) and ST-segment elevation myocardial infarction with reduced ejection fraction (HFrEF, *n* = 154).

Prior to MI, cardiovascular drugs were rarely used in patients with HFpEF (Table 2).

**Table 2.** The list of medications used in patients with ST-segment elevation myocardial infarction with preserved ejection fraction (HFpEF).

ACE—angiotensin-converting enzyme.

During the hospital stay, treatment was performed in accordance with the respective European Society of Cardiology guidelines [15]. Angina pectoris, chronic heart failure, atrial fibrillation, peripheral artery disease, arterial hypertension, diabetes mellitus/glucose intolerance, and hypercholesterolemia were diagnosed according to the respective ESC guidelines [16–22], while chronic kidney disease and overweight/obesity were defined as recommended in KDIGO [23] and NICE [24] guidelines. Past medical history of MI, stroke/transient ischemic attack, PCI or CABG surgery, smoking status, and family history of CAD were defined using the medical records. Coronary angiography was performed within the first hours after hospital admission using GE Healthcare Innova 3100 Cardiac Angiography System (General Electric Healthcare, Chicago, IL, USA). Although 26 out of 100 (26%) patients with HFpEF suffered from multivessel CAD and 17 out of 100 (17%) patients had complications during in-hospital stay, none of them died (Table 3).

**Table 3.** Coronary angiography features and in-hospital complications in patients with ST-segment elevation myocardial infarction with preserved ejection fraction (HFpEF).

Echocardiography (Sonos 2500 Diagnostic Ultrasound System, Hewlett Packard, Palo Alto, CA, USA) and enzyme-linked immunosorbent assay (ELISA) measurement of serum NT-proBNP (SK-1204, Biomedica, Vienna, Austria), sST2 (BC-1065X, Critical Diagnostics, San Diego, CA, USA), galectin-3 (BMS279/4, Thermo Fisher Scientific, Waltham, Massachusetts, MA, USA), matrix metalloproteinase (MMP)-1 (PDMP100, R&D Systems, Minneapolis, MN, USA), MMP-2 (PDMP200, R&D Systems, Minneapolis, MN, USA), MMP-3 (KAC1541, Thermo Fisher Scientific, Waltham, Massachusetts, MA, USA), and tissue inhibitor of metalloproteinases (TIMP-1, PDTM100, R&D Systems, Minneapolis, MN, USA) were performed on the 1st and 10th–12th day of hospitalization.

Statistical analysis was carried out utilizing Statistica 8.0 (Dell, Round Rock, TX, USA). Binary data (frequencies) were compared by Yates′ s chi-squared test. A sampling distribution was assessed by the Shapiro-Wilk test. Descriptive data were represented by median and interquartile range (25th and 75th percentiles). Unpaired and serial (before-after) measurements were compared by Mann-Whitney U-test and Wilcoxon matched-pairs signed rank test, respectively. To assess the correlation, Spearman's rank correlation coefficient was employed. *p* values ≤ 0.05 were regarded as statistically significant.

#### **3. Results**

As compared to patients with HFrEF, patients with HFpEF had significantly lower prevalence of past medical history of myocardial infarction, angina pectoris, atrial fibrillation, chronic heart failure, arterial hypertension, and chronic kidney disease, as well as major cardiovascular risk factors such as smoking, hypercholesterolemia, and family history of coronary artery disease (Table 1).

In patients with HFpEF, serial echocardiographic examination revealed a significant increase in LVEF, stroke volume, early mitral filling velocity to early diastolic mitral annular velocity ratio, and early mitral inflow velocity, along with a concurrent decrease in left ventricular end-diastolic volume, left ventricular end-systolic volume, left ventricular end-systolic diameter, deceleration time, left ventricular ejection time, early diastolic myocardial velocity, early to late diastolic myocardial velocity ratio, and early diastolic myocardial velocity to early mitral inflow velocity ratio from the 1st to the 10th–12th day after STEMI onset (Table 4), suggesting moderate improvement of the cardiac function.

**Table 4.** Echocardiographic parameters in patients diagnosed with ST-segment elevation myocardial infarction with preserved ejection fraction (HFpEF, *n* = 100) at the time of admission and on 10th–12th day of hospital stay.

**Table 4.** *Cont.*

In patients with HFpEF, serum levels of MMP-1, MMP-2, and MMP-3 did not exceed reference ranges at any of measured time points albeit augmented to the 10th–12th day after STEMI onset in comparison with the time of admission (Table 5). In contrast, TIMP-1 and galectin-3 were significantly higher at both of the time points, while NT-proBNP and sST2 demonstrated an increase exclusively on the first day of hospital stay (Table 5). Notably, serum concentrations of all elevated biomarkers (NT-proBNP, sST2, galectin-3, TIMP-1) were significantly reduced on the 10th–12th day after STEMI onset as compared with the time of admission (Table 5). The most pronounced decrease was documented for NT-proBNP and sST2 (3.8- and 1.8-fold, respectively).

Patients with HFrEF had normal levels of MMP-1, MMP-2, MMP-3, and TIMP-1 but elevated NT-proBNP, sST2, and galectin-3 at both of the time points, although the latter markers significantly reduced to the 10th–12th day after STEMI onset (Table 5). Notably, MMP-1, MMP-2, and MMP-3 increased to the 10th–12th day after STEMI onset, similar to the patients with HFpEF (Table 5).

In comparison with patients with HFrEF, those with HFpEF had significantly higher levels of NT-proBNP, sST2, galectin-3, MMP-2, and MMP-3 in conjunction with lower concentration of MMP-1 and TIMP-1 (Table 5).

**Table 5.** Serum concentrations of biomarkers in patients diagnosed with ST-segment elevation myocardial infarction with preserved ejection fraction (HFpEF, *n* = 100) and ST-segment elevation myocardial infarction with reduced ejection fraction (HFrEF, *n* = 154) at the time of admission and on 10th–12th day of hospital stay.

**Table 5.** *Cont.*

HFpEF—ST-segment elevation myocardial infarction with preserved ejection fraction, HFrEF—ST-segment elevation myocardial infarction with reduced ejection fraction, NT-proBNP—N-terminal pro-B-type natriuretic peptide, sST2—soluble suppression of tumorigenicity 2, MMP—matrix metalloproteinase, TIMP—tissue inhibitor of metalloproteinases.

Correlation analysis performed in regards to the patients with HFpEF (Figure 2) demonstrated that, at the time of admission, the serum concentration of sST2 directly correlated with a number of echocardiographic parameters (left ventricular end-diastolic (r = 0.42, *p* = 0.026) and end-systolic volume (r = 0.41, *p* = 0.030), as well as pulmonary artery pressure (r = 0.44, *p* = 0.001)). Likewise, serum galectin-3 on the first day after STEMI onset positively correlated with a myocardial performance (Tei) index (r = 0.41, *p* = 0.001). Serum NT-proBNP negatively correlated with early to late diastolic myocardial velocity ratio (r = −0.49, *p* = 0.001) on the 1st day and with LVEF on the 10th–12th day of hospital stay (r = −0.47, *p* = 0.007) and positively correlated with left ventricular end-systolic volume (r = 0.47, *p* = 0.002) left ventricular end-systolic diameter (r = 0.41, *p* = 0.020), and left ventricular end-diastolic pressure (r = 0.41, *p* = 0.030) at the latter time point.

At the time of admission, serum MMP-1 correlated with left ventricular mass to body surface area index (r = 0.46, *p* = 0.006), yet on the 10th–12th day after STEMI onset, TIMP-1, but not MMPs, showed a significant correlation (r = 0.56, *p* = 0.020). MMP-3, determined at the time of admission, negatively correlated with LVEF on the 10th–12th day of hospital stay (r = −0.68, *p* = 0.010), while MMP-1 positively correlated with left ventricular end-diastolic volume/body surface area (r = 0.57, *p* = 0.040) and left ventricular end-systolic volume/body surface area (r = 0.58, *p* = 0.030) if both were measured at the latter time point.

**Figure 2.** Correlation analysis of echocardiographic parameters and serum biomarkers in patients with ST-segment elevation myocardial infarction and preserved left ventricular ejection fraction (HFpEF, only statistically significant correlations with Spearman's rho > 0.4–0.7 are marked red). Abbreviations used: A—late diastolic ventricular filling velocity, Am—late diastolic myocardial velocity, E—early diastolic ventricular filling, E/A—early to late diastolic ventricular filling velocity ratio, Em—early diastolic myocardial velocity, Em/Am—early to late diastolic myocardial velocity ratio, EDP—left ventricular end-diastolic pressure, EDV—left ventricular end-diastolic volume, EDVI—end-diastolic volume index (left ventricular end-diastolic volume/body surface area), EMIV—early mitral inflow velocity, ESV—left ventricular end-systolic volume (ESV), ESVI—end-systolic volume index (left ventricular end-systolic volume/body surface area), IVRT—isovolumic relaxation time, LVDD—left ventricular end-diastolic diameter, LVEF—left ventricular ejection fraction, LVESD—left ventricular end-systolic diameter, LVMI—left ventricular mass index (left ventricular mass/body surface area), mPAP—pulmonary artery pressure, MMP—matrix metalloproteinase, NT-proBNP—N-terminal pro-B-type natriuretic peptide, sST2—soluble suppression of tumorigenicity 2, SV—stroke volume, TIMP—tissue inhibitor of metalloproteinases.

Patients with HFrEF generally had similar correlation trends (Figure 3). Serum NT-proBNP, sST2, galectin-3, and intriguingly, MMP-3, negatively correlated with LVEF at the time of admission (r = −0.57, *p* = 0.0019; r = −0.44, *p* = 0.0032; r = −0.45, *p* = 0.0021; r = −0.43, *p* = 0.0010, respectively), and similar trend was found regarding the hospital discharge. However, TIMP-1 directly correlated with LVEF (r = 0.49; *p* = 0.0003) and isovolumic relaxation time (r = 0.41; *p* = 0.0012) at the same time point.

Further, NT-proBNP and MMP-3 measured at the time of admission directly correlated with left ventricular end-systolic volume and diameter (r = 0.47, *p* = 0.0017 and r = 0.42, *p* = 0.0234, respectively, for NT-proBNP; r = 0.41, *p* = 0.0138 and r = 0.43, *p* = 0.0085, respectively, for MMP-3) at the 10th–12th day after STEMI onset. At the latter time point, MMP-3 also correlated with left ventricular end-systolic diameter (r = 0.42, *p* = 0.0026).

**Figure 3.** Correlation analysis of echocardiographic parameters and serum biomarkers in patients with ST-segment elevation myocardial infarction and reduced left ventricular ejection fraction (HFrEF, only statistically significant correlations with Spearman's rho > 0.4–0.7 are marked red). Abbreviations used: A—late diastolic ventricular filling velocity, Am—late diastolic myocardial velocity, E—early diastolic ventricular filling, E/A—early to late diastolic ventricular filling velocity ratio, Em—early diastolic myocardial velocity, Em/Am—early to late diastolic myocardial velocity ratio, EDP—left ventricular end-diastolic pressure, EDV—left ventricular end-diastolic volume, EDVI—end-diastolic volume index (left ventricular end-diastolic volume/body surface area), EMIV—early mitral inflow velocity, ESV—left ventricular end-systolic volume (ESV), ESVI—end-systolic volume index (left ventricular end-systolic volume/body surface area), IVRT—isovolumic relaxation time, LVDD—left ventricular end-diastolic diameter, LVEF—left ventricular ejection fraction, LVESD—left ventricular end-systolic diameter, LVMI—left ventricular mass index (left ventricular mass/body surface area), mPAP—pulmonary artery pressure, MMP—matrix metalloproteinase, NT-proBNP—N-terminal pro-B-type natriuretic peptide, sST2—soluble suppression of tumorigenicity 2, SV—stroke volume, TIMP—tissue inhibitor of metalloproteinases.

Therefore, serum markers well correlate with echocardiographic parameters at both of the studied time points in patients with both HFpEF and HFrEF are suggestive of a similar negative long-term prognosis in both of the groups despite better clinical symptoms in those with HFpEF in early HF stages. Hence, patients with HFpEF also have a high risk of adverse outcome.

#### **4. Discussion**

Unfortunately, preserved LVEF also does not guarantee the favorable outcome after STEMI and is frequently followed by CHF, arrhythmia, or heart block [25]. Multiple studies reported that patients with preserved LVEF after STEMI have similar prevalence of cardiovascular events as those with reduced LVEF [26], yet the data on available serum biomarkers informative of ventricular remodeling are still scarce [8]. Identification of patients with pathological cardiac remodeling and its sequelae is of crucial importance for the risk stratification.

Among the plethora of biomarkers, serum NT-proBNP is widely employed for the diagnostics of CHF, as well as control of treatment efficacy and prognostication in such patients [27]. Natriuretic peptides inhibit renin-angiotensin system and enhance sodium excretion, promoting peripheral vasodilation [28]. In our study, serum NT-proBNP was elevated at the time of admission and returned to the normal values to the 10th–12th day upon STEMI onset. In addition, we found a direct correlation of serum NT-proBNP with left ventricular end-systolic volume, left ventricular end-systolic diameter, and left ventricular end-diastolic pressure on 10th–12th day upon STEMI onset, along with the inverse correlation with early to late diastolic myocardial velocity ratio at the time of admission. Importantly, serum NT-proBNP also negatively correlated with LVEF on 10th–12th day upon STEMI onset. Previous studies well confirmed the role of serum NT-proBNP in predicting adverse cardiovascular outcome in patients with HFpEF [29,30].

The interleukin-1 receptor family member sST2 is considerably increased in response to the disrupted extracellular matrix homeostasis and subsequent cardiac remodeling [31,32]. In keeping with these data, serum sST2 declined from the 1st to the 10th–12th day after STEMI onset and directly correlated with left ventricular end-diastolic and end-systolic volume along with pulmonary artery pressure at the time of admission. In other studies, serum sST2 was elevated in patients with congestive heart failure, correlating with left ventricular end-diastolic pressure and serum NT-proBNP [33]. Hypertensive patients with HFpEF had elevated serum sST2 which demonstrated better predictive value as compared with NT-proBNP (AUC 0.8 and 0.7, respectively), with 13.5 ng/mL as a cutoff in multivariate analysis [34]. Predicting efficacy of serum sST2 in relation to the outcomes was higher for HFpEF as compared to the patients with reduced LVEF, indicating a positive association with all-cause death and repeated HF-related hospitalization [35].

Galectin-3 is a beta-galactoside-binding pleiotropic lectin released into the extracellular matrix by activated cardiac macrophages and regulating apoptosis, proliferation, inflammation, and fibrosis [36,37], being notable for its association with left ventricular remodeling [38], incident heart failure [39], and its severity [37]. In patients with HFpEF, serum galectin-3 was significantly elevated as compared to the control subjects and strongly correlated with NT-proBNP [40,41]. Further, both serum galectin-3 and NT-proBNP well distinguished patients with HFpEF from healthy individuals in ROC analysis [41]. In contrast to NT-proBNP, serum galectin-3 retained its association with ventricular remodeling in HFpEF patients in multivariate analysis, although NT-proBNP was significantly associated with HF symptoms [42]. Regarding to the long-term outcomes, elevated galectin-3 at 6 or 12 months was associated with all-cause death and hospitalization independent of treatment arm and NT-proBNP [43]. We found an increase in serum galectin-3 at both time points, yet no significant correlations with echocardiographic parameters were detected excepting a positive correlation with myocardial performance (Tei) index on the first day after STEMI onset.

MMPs represent a large family of zinc-dependent proteases degrading extracellular matrix and are informative of ventricular remodeling following the first hours after STEMI onset [44]. However, it is unknown whether the initial elevation of serum MMPs and TIMP-1 affects further cardiac remodeling and development of CHF. Concentrations of MMP-9 and TIMP-1 are frequently augmented in patients with CHF, being associated with a number of echocardiographic parameters [45,46]. Notably, NT-proBNP and MMP-9 levels have been found increased at all time points from the acute phase until >4 years after STEMI, and their levels showed a significant correlation [47]. In addition, higher serum MMP-9 correlated with lower LVEF and functional myocardial mass in long-term survivors of complicated STEMI [47] and with increased left ventricular end-diastolic diameter and left ventricular wall thickness in Framingham study participants free of previous MI and CHF [48]. In relation to the HF progression, the predictive value of serum MMP-2 in patients with HFpEF was found to be higher as compared with NT-proBNP [49]. Here, we did not reveal a significant increase in MMPs in the early period upon STEMI, yet MMP-1 and TIMP-1 showed a positive correlation with left ventricular mass to body surface area index at sequential time points. Further, higher MMP-1 on the 10th–12th day after STEMI correlated with left ventricular end-diastolic volume and left ventricular end-systolic volume normalized to the body surface area while MMP-3 at the time of admission inversely correlated with LVEF before hospital discharge. Intriguingly, serum levels of MMP-2 and MMP-3, although being

within the reference range, significantly augmented from the admission to the discharge regardless of the ejection fraction, suggesting these molecules are gradually released during ventricular remodeling. Further studies in this direction might address this point.

An important limitation of the study includes differences in risk factors and treatments between the patients resulting in a relatively heterogeneous variable. However, it was expected when considering the patient groups with distinct LVEF (HFpEF and HFrEF). A replication of our study on a larger sample would be beneficial for the field to confirm our results. Another limitation is relatively short observation interval (two weeks), which should possibly be extended in replicating studies.

Notably, patients with HFrEF had multiple correlations of serum markers (NT-proBNP, sST2, galectin-3, MMP-3) with LVEF, as well as left ventricular end-systolic volume and diameter, whereas in those with HFpEF, only NT-proBNP showed significant negative correlations with LVEF. Probably, patients with HFpEF share a number of pathophysiological pathways with those suffering from HFrEF, yet cardiac remodeling in the former patient category is related to the diastolic dysfunction rather than LVEF.

#### **5. Conclusions**

In the present study, we found a reduction in NT-proBNP, sST2, galectin-3, and TIMP-1 in patients with HFpEF and HFrEF from hospital admission to the discharge, as well as numerous correlations between the indicated biomarkers and echocardiographic parameters, testifying to the ongoing ventricular remodeling. In patients with HFpEF, studied biomarkers of ventricular remodeling (NT-proBNP, sST2, galectin-3, and MMP-3) correlated with the parameters, reflecting the diastolic dysfunction (myocardial stretch, transmitral flow profile, and myocardial performance (Tei) index). Contrariwise, in patients with HFrEF, these markers are mainly associated with LVEF and left ventricular end-systolic volume/diameter. Therefore, the combination of serum biomarkers and echocardiographic parameters might be useful for the prediction of adverse cardiac remodeling in patients with HFpEF, particularly after STEMI.

**Author Contributions:** Conceptualization, T.P., A.K., V.K. (Vasiliy Kashtalap) and O.B.; Data curation, T.P. and O.B.; Formal analysis, T.P., A.K., V.K. (Vasiliy Kashtalap) and O.B.; Funding acquisition, O.B.; Investigation, T.P., V.K. (Victoria Karetnikova), O.G. and O.H.; Methodology, T.P., V.K. (Vasiliy Kashtalap), V.K. (Victoria Karetnikova), O.G. and O.H.; Project administration, V.K. (Vasiliy Kashtalap) and O.B.; Resources, V.K. (Vasiliy Kashtalap) and O.G.; Supervision, O.B.; Validation, T.P. and V.K. (Vasiliy Kashtalap); Visualization, T.P.; Writing—original draft, T.P. and A.K.; Writing—review & editing, T.P., A.K. and O.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Complex Program of Basic Research under the Siberian Branch of the Russian Academy of Sciences within the Basic Research Topic of Research Institute for Complex Issues of Cardiovascular Diseases NO. 0546-2019-0003 "Atherosclerosis and its comorbidities. Features of diagnostics and risk management in a large industrial region of Siberia".

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Prediction of Adverse Post-Infarction Left Ventricular Remodeling Using a Multivariate Regression Model**

**Valentin Oleynikov, Lyudmila Salyamova, Olga Kvasova and Nadezhda Burko \***

Department of Therapy, Medical Institute, Penza State University, 440026 Penza, Russia<EMAIL_ADDRESS>(V.O<EMAIL_ADDRESS>(L.S<EMAIL_ADDRESS>(O.K.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +7-986-936-6220

**Abstract:** Background. In order to provide personalized medicine and improve cardiovascular outcomes, a method for predicting adverse left ventricular remodeling (ALVR) after ST-segment elevation myocardial infarction (STEMI) is needed. Methods. A total of 125 STEMI patients, mean age 51.2 (95% CI 49.6; 52.7) years were prospectively enrolled. The clinical, laboratory, and instrumental examinations were performed between the 7th and 9th day, and after 24 and 48 weeks, including plasma analysis of brain natriuretic peptide (BNP), transthoracic echocardiography, analysis of left ventricular-arterial coupling, applanation tonometry, ultrasound examination of the common carotid arteries with RF signal amplification. Results. Patients were divided into 2 groups according to echocardiography: "ALVR" (*n* = 63)—end-diastolic volume index (EDVI) >20% and/or end-systolic volume index (ESVI) >15% after 24 weeks compared with initial values; "non-ALVR" (n = 62)— EDVI <20% and ESVI <15%. In the ALVR group, hard endpoints (recurrent myocardial infarction, unstable angina, hospitalization for decompensated heart failure, ventricular arrhythmias, cardiac surgery, cardiovascular death) were detected in 19 people (30%). In the non-ALVR group, hard endpoints were noted in 3 patients (5%). The odds ratio of developing an adverse outcome in ALVR vs. non-ALVR group was 8.5 (95% CI 2.4–30.5) (*p* = 0.0004). According to the multivariate analysis, the contribution of each of the indicators to the relative risk (RR) of adverse cardiac remodeling: waist circumference, RR = 1.02 (95% CI 1.001–1.05) (*p* = 0.042), plasma BNP—RR = 1.81 (95% CI 1.05–3.13) (*p* = 0.033), arterial elastance to left ventricular end-systolic elastance (Ea/Ees)—RR = 1.96 (95% CI 1.11–3.46) (*p* = 0.020). Conclusion. Determining ALVR status in early stages of the disease can accurately predict and stratify the risk of adverse outcomes in STEMI patients.

**Keywords:** myocardial infarction; adverse left ventricular remodeling; echocardiography; left ventricular-arterial coupling

#### **1. Introduction**

Chronic heart failure (CHF) is one of the main causes of hospitalization and mortality in the world, including the Russian Federation [1,2]. The progressive nature of the course, early disability, the need for long-term pharmacotherapy and cardiac surgery are associated with significant economic costs for the healthcare system.

The results of a meta-analysis conducted by N.R. Jones et al. showed a significant improvement in the survival rates of patients with CHF from the 1970s to the 1990s. However, mortality has declined slightly over the past two decades. In 2010–2019, oneyear and five- year survival amounted to 89.3% (84.3–93.4%) and 59.7% (54.7–64.6%), respectively [3]. Another study showed five times increase in the risk of a fatal outcome in the development of CHF [4].

According to the American Heart Association report, CHF prevalence is predicted to increase by 46% in the USA by 2030 [5]. CHF often complicates the postinfarction period. According to the results of the Russian EPOCHA-CHF study, along with arterial hypertension and chronic ischemic heart disease, myocardial infarction (MI) has become a

**Citation:** Oleynikov, V.; Salyamova, L.; Kvasova, O.; Burko, N. Prediction of Adverse Post-Infarction Left Ventricular Remodeling Using a Multivariate Regression Model. *Diagnostics* **2022**, *12*, 770. https://doi.org/10.3390/ diagnostics12030770

Academic Editors: Inna P. Gladysheva and Ryan D. Sullivan

Received: 1 February 2022 Accepted: 17 March 2022 Published: 21 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

competing cause of CHF, accounting for 15.8% in 2017 vs. 5.8% in 1998 [6]. The results of the European Society of Cardiology Heart Failure Long-Term Registry (ESC-HF-LT-R) also demonstrate the prevailing role of coronary heart disease in the development of CHF [7].

The widespread introduction of high-tech methods for the treatment of acute MI has led to a decrease in hospital mortality; this has been accompanied, however, by an increase in the number of patients with CHF [1,6]. Due to the very typical prolongation of myocardial revas cularization terms, adverse postinfarction remodeling often develops being a key pathogenetic element of CHF and a significant predictor of mortality.

Adverse postinfarction left ventricular remodeling (ALVR) is characterized by an increase in the end-diastolic volume (EDV) >20% or the end-systolic volume (ESV) >15% compared with baseline values [8]. Ventricular remodeling already occurs within the first hours after cardiomyocyte necrosis and proceeds for several months. This process is characterized by a change in the left ventricular (LV) shape and size, and its dysfunction [8,9]. Early remodeling develops within three months after acute MI; mid-term and late remodeling develop within six and twelve months, respectively [10].

Identification of patients with a high probability of ALVR development in the early stages of the disease is important for the stratification of cardiovascular risk, the choice of personalized anti-remodeling therapy and rehabilitation.

The aim of this study was to search for early predictors and develop a model for predicting adverse remodeling in patients who had ST-segment elevation myocardial infarction (STEMI).

#### **2. Materials and Methods**

An open prospective single-center study involving 141 STEMI patients was conducted at the Department of Therapy of Penza State University (Penza, Russia). The study protocol and informed consent were approved by the Local Ethics Committee at Penza State University (approval code 317, on 15 May 2020).

The study included patients which met the following criteria: aged 35–65 years; acute STEMI, confirmed by an electrocardiogram, a diagnostically significant increase in specific cardiac enzymes (troponin I, CPK-MB); the presence of hemodynamically significant stenosis of the infarct-related artery according to coronary angiography with occlusion of other coronary arteries less than 50%, including the trunk of the left coronary artery less than 30%. The main exclusion criteria included: repeated or recurrent MI; type 1 or type 2 diabetes (requiring insulin therapy); NYHA class II-IV of CHF, severe concomitant diseases.

The treatment of STEMI patients has been carried out in full accordance with the guidelines [11] over the entire follow-up period.

A comprehensive clinical, laboratory and instrumental examination has been conducted with preserved pharmacotherapy initially in the period from the 7th to the 9th day of the STEMI, and after 24- and 48-week follow up (Figure 1).

Plasma brain natriuretic peptide (BNP) (with EDTA-ethylenediaminetetraacetic acid) has been measured using the OLYMPUS AU400 chemistry analyzer (Olympus Corporation, Tokyo, Japan).

Transthoracic echocardiography (EchoCG) has been performed using the MyLab90 ultrasound scanner (Esaote, Genoa, Italy) for determination of standard parameters and subsequent calculation of indexed values for the end-diastolic volume (EDVI) and the endsystolic volume (ESVI). An increase in EDVI >20% and/or ESVI >15% has been taken as ALVR after 24 weeks compared with the initial values (7–9th day). The interaction of the left ventricle and the arterial bed has been analyzed according to the following parameters: LV end-systolic elastance, which is the ratio of the end-systolic pressure to the end-systolic volume and reduced to body surface area (Ees/BSA); the arterial elastance calculated as the ratio of the end-systolic pressure over stroke volume divided by body surface area (Ea/BSA); LV-arterial coupling (LVAC) index, defined as the ratio of arterial elastance to left ventricular end-systolic elastance (Ea/Ees) [12].

**Figure 1.** Flowchart of the study.

The structural and functional state of the common carotid arteries (CCA) has been assessed with the MyLab ultrasound machine (Esaote, Genoa, Italy) using radiofrequency (RF)- based technology. The following indicators were recorded: quality intima-media thickness (QIMT); stiffness index β; local systolic arterial pressure in CCA (loc Psys); local diastolic arterial pressure in CCA (loc Pdia). The above indicators of local pressure and stiffness are calculated using special software based on the level of blood pressure in the brachial artery, changes in the diameter and volume of the CCA in systole and diastole [13].

Applanation tonometry has been used to determine systolic aortic pressure (SBPao) and diastolic (DBPao) aortic pressure, and carotid-femoral pulse wave velocity (cfPWV) using the SphygmoCor device (AtCor Medical, Sydney, Australia) [14].

#### *Statistical Analysis*

Statistical data processing was performed using the licensed version of STATISTICA 13.0 program (StatSoft, Inc., Tulsa, OK, USA). All indicator values were given with the 95% confidence interval (CI). The dynamics of indicators was analyzed by the method of oneway analysis of variance (ANOVA) using the Newman-Keuls test. The Cox multiple linear regression was used when constructing a multivariate model. The frequency of the endpoint development was determined by the Kaplan-Meier method. The level of statistical significance was *p* < 0.05.

#### **3. Results**

We have summed up a 48-week follow-up for 125 patients (88.7%). There were some reasons for early termination of study participation: 1 patient died due to myocardial rupture on the 16th day; 1 patient died from pulmonary edema (according to the autopsy report) at the 10th month; 3 patients moved to another city; 11 patients discon tinued the follow-up due to low adherence.

The age of the patients included in the study was 51.2 (49.6; 52.7) years, men prevailed— 109 patients (87.2%). The body mass index was 27.4 (26.7; 28.0) kg/m<sup>2</sup> . Symptoms of abdominal obesity were diagnosed in 74 patients (59.2%) [14]. Coronary heart disease was noted in 21 patients in the history (16.8%). Heredity cardiovascular diseases burdened 51 patients (40.8%) and 80 patients were smokers (64%). Arterial hypertension was observed in 61.6% (n = 77) with a disease duration of 6.5 (5.2; 7.8) years. The mean level of systolic blood pressure (SBP) was 118.8 (116.3; 121.3) mmHg and that of diastolic blood pressure (DBP)—76.2 (74.6; 77.9) mmHg. Prior to the STEMI, antihypertensive therapy was

received regularly by 16 patients (20.8%), and irregularly by 24 patients (31.2%); 37 patients (48%) were not treated.

Primary percutaneous coronary intervention has been performed in 56 patients (44.8%); pharmacoinvasive strategy has been performed in 68 cases (54.4%). One patient only received thrombolytic therapy at the pre-hospital stage.

The analysis of EDVI and ESVI dynamics within 24 weeks after acute myocardial infarction has made it possible to divide the patients into 2 groups. The first group included 63 patients with ALVR revealed after 24 weeks according to the echocardiography. The second group included 62 patients without ALVR (non-ALVR group). A comparative analysis of patients by age, some anthropometric and anamnestic indicators and therapy is presented in Table 1.

**Table 1.** A comparative analysis of ALVR and non-ALVR groups (n = 125).

Note: the data are presented as M ± SD with a normal distribution, and as Me (Q25%; Q75%) with an incorrect distribution; n is the number of patients; BMI is body mass index; CHD is coronary heart disease; AH is arterial hypertension; SBP is systolic blood pressure; DBP is diastolic blood pressure; HR is heart rate.

The study of echocardiographic parameters in patients with ALVR revealed a progressive increase in EDVI and ESVI during the entire follow-up period. Thus, within 24–48 weeks, EDVI increased by 22.3–21.1%, and ESVI by 26.9–25.7%, respectively (Figure 2, Supplementary Materials). Besides, negative dynamics of ejection fraction (EF) has been noted during the repeated studies. At the same time, some negative dynamics of ESVI

were accompanied by an increase in EF by 3.4% in patients without ALVR by the end of the follow-up. Differences in the presented echocardiographic parameters of the compared groups were noted for 48 weeks (Figure 1).

**Figure 2.** Dynamics of EF values in the comparison groups. Note: \* *p* < 0.05, \*\* *p* < 0.01 are significant differences between the initial values and subsequent visits; ## *p* < 0.01 are significant intergroup differences. EF—ejection fraction.

Patients with adverse LV remodeling and without it initially varied by BNP level: 231.9 (95% CI 122.9; 340.9) vs. 72.1 (95% CI 51.0; 93.2) (*p* = 0.003). Despite the improvement in laboratory values for each group, the differences persisted at subsequent visits (Figure 3).

In the analyzed groups, the inverse dynamics of LVAC indicators were revealed. Initially, the patients of two groups had comparable values of arterial elastance. Ea/BSA indicator in ALVR group decreased after 24 weeks and returned to the baseline after 48 weeks; in non-ALVR group, it decreased by the end of the follow-up (Table 2).

**Table 2.** Comparative characteristics of LVAC indicators, and structural and functional features of arteries in the comparison groups.

Note: the data are presented with 95% confidence interval; \* *p* < 0.05, \*\* *p* < 0.01 is significance for intergroup differences; # *p* < 0.05, ## *p* < 0.01 is significance for intragroup differences on the baseline with subsequent visits; Ea/BSA is arterial elastance normalized to body surface area; Ees/BSA is end-systolic left ventricular elastance normalized to body surface area; Ea/Ees is left ventricular-arterial coupling index; SBPao is aortic systolic pressure; DBPao is aortic diastolic pressure; cfPWV is carotid-femoral pulse wave velocity; QIMT is quality intima-media thickness; loc Psys is local systolic blood pressure; loc Pdia is local diastolic blood pressure.

Initially, in patients with ALVR, the level of LV elastance was significantly reduced, and LVAC index predominated compared to non-ALVR group. After 24 and 48 weeks, a decrease in Ees/BSA by 18.6–14%, and an increase in Ea/Ees were denoted by the end of the follow-up in ALVR group. At the same time in the comparison group the LV elastance has not changed, and LVAC index decreased by 10.6% after 24 weeks and by 16% after 48 weeks. Intergroup differences in Ees/BSA and Ea/Ees values persisted over the entire follow-up period.

A detailed analysis of LVAC index has elicited abnormal values in ALVR group: initially in 29 patients (46%), then in 36 patients (57%; unreliable) after 24 weeks, and finally in 33 patients (52.4%; unreliable) after 48 weeks. As for non-ALVR group, there were initially 7 patients (11.3%), and 2 patients (3%; *p* = 0.08) at the interval visit with abnormal LVAC values. By the end of the follow-up, all of the patients of the comparison group demonstrated a normal level of LVAC index (0%; *p* = 0.008).

An analysis of applanation tonometry indicators has shown low SBPao values in ALVR group. Within 48 weeks, a comparable increase in central pressure parameters in the comparison groups has been found. In patients with ALVR and without it cfPWV has not shown differences and was stable over the entire follow-up period.

According to the ultrasound of CCA using the RF-technology, the groups had a comparable QIMT level and β index in the period from the 7th to the 9th day of the STEMI. In ALVR group, indicators have decreased after 24 and 48 weeks; in non-ALVR group, QIMT values have also improved during therapy.

A comparative analysis of the dynamics of local pressure in CCA is essential. The level of loc Psys and loc Pdia has been initially reduced in patients with ALVR compared with non-ALVR group. Subsequently, the pressure values in CCA in ALVR group were restored after 24–48 weeks.

One of the important results of this study is the relationship between ALVR and cardiovascular events. In the comparison groups, the frequency of hard endpoints, such as recurrent MI, unstable angina, hospitalization for decompensated heart failure, ventricu lar arrhythmias, cardiac surgery, and death from cardiac causes has been analyzed (Figure 4). In ALVR group, the above endpoints were detected in 19 patients (30%): 7 patients were

hospitalized for unstable angina (11.1%); 1 patient (1.6%) was diagnosed with recur- rent acute MI and 2 patients (3.2%) due to decompensated CHF. Cardiac surgery was performed in 7 patients (11.1%). Moreover, 2 patients were diagnosed with life-threatening arrhythmias (3.2%). In non-ALVR group, hard endpoints were noted in 3 patients (5%); 1 patient underwent cardiac surgery (1.6%), 2 patients (3.2%) were hospitalized for unstable angina. The odds ratio of developing an adverse outcome in group 1 compared with group 2 was 8.5 [95% CI 2.4–30.5] (*p* = 0.0004).

The predictors of adverse postinfarction remodeling were identified according to the univariate and multivariate logistic regression analysis of clinical, laboratory and instrumental parameters recorded in the period of the 7th-9th days from the index event. Since the determination of postinfarction remodeling is based on echocardiographic parameters, the latter were excluded from the analysis [8]. According to the univariate analysis, the independent variables of ALVR were waist circumference (WC), determination of BNP in a bimodal distribution ("0"—with BNP <100 pg/mL; "1"—BNP ≥100 pg/mL), EF, Ees/BSA, Ea/Ees, Ea/Ees in bimodal distribution ("0"—at Ea/Ees 0.6–1.2; "1"—at Ea/Ees <0.6 or >1.2), Loc Psys (Table 3).

**Table 3.** Predictors of adverse left ventricular remodeling in patients after STEMI according to univariate analysis.

Note: β is regression coefficient; *p* is significance; RR is relative risk; CI is confidence interval; WC is waist circumference; BNP is brain natriuretic peptide; EF is ejection fraction; Ees/BSA is end-systolic left ventricular elastance normalized to body surface area; Ea/Ees is Ea/Ees is left ventricular-arterial coupling index; loc Psys is local carotid systolic blood pressure.

A multivariate model for the development of various types of postinfarction remodeling including WC, abnormal values of BNP and Ea/Ees was created based on the results of the univariate analysis and considering the correlations between the indicators (Table 4).

**Table 4.** A multivariate model for the development of ALVR in STEMI patients.

Note: β—regression coefficient, *p*—significance, RR—relative risk, CI—confidence interval. WC, waist circumference; BNP, brain natriuretic peptide; Ea/Ees, left ventricular-arterial coupling index.

The multivariate regression model presented in Table 4 uses the Formula (1):

$$\mathbf{h} = \mathbf{h}0 \cdot \exp\overline{\mathbf{x}}\overline{\mathbf{e}}\overline{\mathbf{e}}(0.024 \cdot \mathbf{X}1 + 0.59 \cdot \mathbf{X2} + 0.68 \cdot \mathbf{X3})\tag{1}$$

where: X1 is WC, cm; X2 is equal to 1.0 with BNP ≥100 pg/mL, and it is equal to 0 with BNP <100 pg/mL; X3 is equal to 1.0 with Ea/Ees ranging between 0.6 and 1.2, and it is equal to 0 with a normal value of Ea/Ees; h0(t) is baseline risk of 0.024329 at the 24th week after STEMI. If the value of h is higher than 1.0, the development of ALVR is predicted; if the value of h is less than 1.0, a conclusion is made about the absence of ALVR.

The created model for determining the risk of developing the postinfarction remodeling has shown good informative results: the Wilks' lambda = 0.65256; F (3.105) = 18.63467 (*p* < 0.00001).

#### **4. Discussion**

The development of CHF post-MI is driven by the complex pathophysiological mechanisms underlying cardiac remodeling: an inflammatory reaction in the area of myocardial necrosis, isolation of intracellular signaling proteins, activation of neurohumoral systems, followed by the development of hypertrophy and cardiac dilatation, and the formation of a connective tissue scar.

The structural and functional remodeling of LV was followed by a decrease in its contractile function leading to impairment of hemodynamics in organs and tissues [15,16]. The adverse cardiac remodeling post-MI leads to CHF development, associated with increased re-hospitalization rate, disability, and mortality of patients [10,17]. The present study demonstrates that adverse postinfarction remodeling is associated with a high risk of cardiovascular events, being 8.5 times higher than that compared with non-ALVR group during the 48-week follow-up. Thus, the authors have developed a model for predicting various forms of cardiac remodeling to timely prescribe medication and conduct dynamic monitoring for patients with a high risk of an adverse outcome.

Traditional factors have little effect on prognosis after STEMI [18]. In our study, the patients who subsequently developed different types of postinfarction remodeling did not experience differences in most risk factors, except for WC. In this connection, a search for new predictors of unfavorable structural and functional cardiac remodeling seems to be highly relevant.

The high frequency of thrombolytic therapy as part of the pharma-coinvasive treatment strategy should be noted; this was present in 54.4% of cases, which is due to the late presentation of the patients and the territorial remoteness of the place of residence of the patients from the hospital.

In accordance with the clinical practice guidelines for CHF, the determination of natriuretic peptides is used in diagnosing this complication and in assessing the prognosis [2,19]. A high level of N-terminal pro-brain natriuretic peptide (NT-proBNP) indicates an increasing risk of sudden death, recurrent MI, and CHF in patients with MI and unstable

angina [20]. The level of BNP was significantly higher in patients with ALVR as compared to non-ALVR group over the entire follow-up period. As a result, the indicator was included in both univariate and multivariate models for predicting adverse postinfarction cardiac remodeling.

The development and progression of cardiovascular diseases are associated with deterioration in the structural and functional properties of the vascular wall, being an important predictor of an adverse outcome, regardless of traditional factors [21]. According to Lechner I. et al., an enhanced level of pulse wave velocity (PWV) in STEMI patients predicted the development of cardiac and cerebrovascular adverse events 13 months after the index event. In another study, cfPWV appeared to be an important predictor of recovery of LV contractile function three and six months after STEMI [22].

An increased aortic stiffness causes impairment of the coronary blood flow and the development of ischemia of the subendocardial layer even without the coronary artery stenosis. Early return of the reflected pulse wave is accompanied by an increase in SBP and decrease in DBP. The LV load and myocardial oxygen demand increase, but perfusion pressure deteriorates, and myocardial ischemia develops due to lower DBP [23]. Besides, a decrease in the damping function of the aorta, combined with an increase in total peripheral vascular resistance, significantly reduces the efficiency of LV contraction [24].

In this study, the compared groups initially differed in the level of SBPao, and SBP and DBP in CCA. Loc Psys has only shown predictive value in the development of ALVR in univariate logistic regression analysis. However, the indicators of the structural and functional state of the aorta and CCA have not been included in the multivariate model.

The functioning of the cardiovascular system as a whole is determined by the adequacy of the interaction between the heart and the arterial system during the ejection of blood from the LV and is called LVAC [25]. The predictive significance of this parameter has been demonstrated in a number of studies. In particular, in patients with ischemic cardiomyopathy, the ratio Ea/Ees < 1.47 was characterized by better survival rate compared to those whose indicator exceeded the specified threshold value [12]. LVAC can be used both to clarify cardiovascular risk and to study the efficiency of treatment.

LVAC indicator is calculated as the ratio of arterial elastance (Ea) to LV end-systolic elastance (Ees). The Ea parameter indicates the arterial load exerted on LV during the blood ejection, regardless of its functional ability. Arterial afterload includes aortic valve resistance, systemic vascular resistance (SVR), arterial capacitance and stiffness, and duration of systole and diastole. The Ees parameter indicates LV contractility and systolic stiffness. In our study, a low level of Ees/BSA and higher LVAC values were initially diagnosed in the ALVR group. These differences have remained over the entire follow-up period. Besides, these indicators of LV-arterial interaction have evidenced predictive significance according to univariate regression analysis. The determination of the normal/abnormal LVAC level was adequate for inclusion in the mul tivariate model based on the studied parameters.

With the results obtained, a complex model for predicting ALVR in the period from the 7th to the 9th day of the STEMI was developed based on the analysis of WC, BNP, and LVAC.

#### **5. Conclusions**

In the present study, the development of adverse postinfarction LV remodeling is associated with an 8.5-fold increase in the risk of cardiovascular events. The following independent factors of adverse cardiac remodeling were determined 24 weeks after STEMI: waist circumference; abnormal values of brain natriuretic peptide; end-systolic left ventricular elastance normalized to body sur- face area; abnormal level of Ea/Ees index; local systolic pres sure in the common carotid arteries.

Based on the results of multivariate regression analysis, a model for predicting various types of postinfarction remodeling based on waist circumference, abnormal values of brain natriuretic peptide, and the LV arterial coupling index has been developed.

#### **6. Study limitations**

The study was conducted in patients with single-vessel lesion of the coronary bed according to coronary angiography: the presence of hemodynamically significant stenosis of only the infarct-related artery with occlusion of other coronary arteries less than 50%, including the trunk of the left coronary artery—less than 30%. Moreover, the proposed multivariate model was developed for patients with primary STEMI aged 35 to 65 years and is not applicable to patients with recurrent MI, as well as MI without ST segment elevation, younger or older than this age.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/diagnostics12030770/s1, Figure S1: Dynamics of indexed values of EDV and ESV in the comparison groups.

**Author Contributions:** Conceptualization, V.O. and L.S.; methodology, software O.K.; validation, V.O., L.S., O.K.; formal analysis, investigation, L.S., O.K.; resources, V.O.; data curation, L.S.; writing original draft preparation, O.K.; writing—review and editing, L.S., N.B.; project administration, V.O.; funding acquisition, V.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was carried out with financial support: a grant from the President of the Russian Federation for the government support of the young Russian scientists—Candidates and Doctors of sciences.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Local Ethics Committee of the Penza State University (protocol code 317 and date of approval—15 May 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. The data are not publicly available due to ethical reasons.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

#### *Article*

## **Association of Non-Alcoholic Fatty Liver Disease and Hepatic Fibrosis with Epicardial Adipose Tissue Volume and Atrial Deformation Mechanics in a Large Asian Population Free from Clinical Heart Failure**

**Yau-Huei Lai 1,2,3 , Cheng-Huang Su 1,3,4 , Ta-Chuan Hung 1,3,4 , Chun-Ho Yun 1,3,5, \* ,† , Cheng-Ting Tsai 1,3,4, \* ,† , Hung-I Yeh 1,3,4 and Chung-Lieh Hung 1,3,4**

**Abstract:** Non-alcoholic fatty liver disease (NAFLD) and cardiovascular disease share several cardiometabolic risk factors.  This study assessed the associations of NAFLD and liver fibrosis with cardiac structural and functional disturbances. We assessed 2161 participants using ultrasound, and categorized them as per the NAFLD Fibrosis Score into three groups: (1) non-fatty liver; (2) fatty liver with low fibrosis score; and (3) fatty liver with high fibrosis score.  All participants underwent echocardiographic study, including tissue Doppler-based E/e' ratio and speckle tracking-based left ventricular global longitudinal strain, peak atrial longitudinal strain (PALS), and atrial longitudinal strain rates during systolic, early and late-diastolic phases (ALSRsyst , ALSRearly . ALSRlate). Larger EFV, decreased e' velocity, PALS, ALSRsyst , and ALSRearly , along with elevated E/e' ratio, were seen in all groups, especially in those with high fibrosis scores. After multivariate adjustment for traditional risk factors and EFV, fibrosis scores remained significantly associated with elevated E/e' ratio, LA stiffness, and decreased PALS (β: 0.06, 1.4, −0.01, all *p* < 0.05). Thus, NAFLD is associated with LV diastolic dysfunction and subclinical changes in LA contractile mechanics.

**Keywords:** fatty liver; cardiovascular disease; fibrosis; epicardial fat; left atrial strain

#### **1. Introduction**

Obesity and metabolic disorders have long been recognized as global health issues. Non-alcoholic fatty liver disease (NAFLD), also known as metabolic-associated fatty liver disease (MAFLD) [1,2], is one of the most widespread forms of chronic liver disease. It represents a spectrum of conditions ranging from simple steatosis to non-alcoholic steatohepatitis, which can progress to various grades of fibrosis, cirrhosis, and hepatocellular carcinoma [3,4]. NAFLD has also been identified as a precipitating factor for early subclinical cardiac disorders since it shares several cardiometabolic risk factors with cardiovascular

**Citation:** Lai, Y.-H.; Su, C.-H.; Hung, T.-C.; Yun, C.-H.; Tsai, C.-T.; Yeh, H.-I.; Hung, C.-L. Association of Non-Alcoholic Fatty Liver Disease and Hepatic Fibrosis with Epicardial Adipose Tissue Volume and Atrial Deformation Mechanics in a Large Asian Population Free from Clinical Heart Failure. *Diagnostics* **2022**, *12*, 916. https://doi.org/10.3390/ diagnostics12040916

Academic Editor: Gian Paolo Caviglia

Received: 11 March 2022 Accepted: 5 April 2022 Published: 6 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

disease (CVD) [5]. Numerous studies have evaluated the pathological mechanisms connecting these two entities [6,7]. For example, NAFLD has been shown to induce hepatic insulin resistance and atherogenic dyslipidemia [8], which makes the affected individuals susceptible to premature atherosclerosis.

Excessive visceral fat can accumulate at various body sites besides the liver, most notably presenting as epicardial fat. As a biologically active source of pro-atherogenic cytokines that mediate systemic vascular inflammation and metabolic derangements [9], epicardial fat has been shown to influence left ventricular (LV) structure and function through mechanical or paracrine effects [10]. The term "cardiac steatosis" has been used to describe the exaggerated lipid deposition and elevated oxidative stress that up-regulates myocardial fibrosis and cellular apoptosis, leading to cardiomyopathy [11,12]. The release of a variety of pro-inflammatory and pro-fibrogenic mediators in this condition may play important roles in the pathophysiology of cardiac and arrhythmic complications. Furthermore, the surrounding ectopic fat depots may mechanistically impede diastolic filling due to the physical constraints on the epicardium [13].

Previous studies utilizing strain imaging have shown both cross-sectional and longitudinal association of NAFLD with subclinical myocardial remodeling and diastolic dysfunction [14,15]. It has been hypothesized that these changes may be mediated by the interaction between epicardial fat and cardiac structures. A few small histological studies have demonstrated a graded relationship between LV systolic dysfunction, epicardial fat thickness, and liver fibrosis severity in NAFLD, suggesting that systemic inflammation may also contribute to the development and progression of myocardial dysfunction [16,17]. Due to the cost of biopsy and procedural risks, alternative noninvasive tools have been developed to predict the fibrosis stage, including the NAFLD Fibrosis Score [18], which we employed in this study.

Whether or not NAFLD is an independent risk factor for cardiovascular mortality and other cardiovascular events has been studied, but remains controversial [19,20]. Relatively little is known about the underlying pathophysiological mechanisms behind the cardiac remodeling process in Asians with NAFLD. Therefore, we aim to investigate the potential impact of fatty liver and epicardial fat on various aspects of diastolic function and myocardial deformation mechanics in a large Asian population with normal LV ejection fraction, and who free from clinical heart failure (HF).

#### **2. Materials and Methods**

#### *2.1. Study Population*

We retrospectively examined individuals who participated in an ongoing cardiovascular health screening program between June 2009 and January 2013 at a tertiary medical center in Taipei, Taiwan. The original study setting and design have been published previously [21]. The baseline clinical information, medical history, symptoms/signs, and lifestyle patterns were obtained. Informed consent was waived for each participant owing to the retrospective study design. This study conformed to the principles outlined in the Declaration of Helsinki, and was approved by the local ethical board committee (18MMHIS180e, 8 January 2019). Subjects with a history of chronic viral hepatitis, liver cirrhosis, heavy alcohol consumption, any prevalent clinical HF, CVD (defined as a history of previous myocardial infarction, symptom-driven angioplasty, peripheral arterial disease, or cerebrovascular disease), and significant valvular diseases or cardiac arrhythmias were excluded from the analysis. Those with a high fibrosis risk score but diagnosed as non-fatty liver by abdominal ultrasound were also excluded, since it is unlikely for them to have liver fibrosis. (Figure 1).

**Figure 1.** Study design and exclusion flowchart. Abbreviations: HF = heart failure, MI = myocardial infarction, CAD = coronary artery disease, PAD = peripheral artery disease, VHD = valvular heart disease.

#### *2.2. Laboratory Data and Body Fat Assessment*

All biochemical and laboratory parameters, including homeostatic model assessment for insulin resistance (HOMA-IR) and high-sensitivity C-reactive protein (Hs-CRP), were measured at a standardized central laboratory using a Hitachi 7170 Automatic Analyzer (Hitachi Corp., Hitachinaka, Ibaraki, Japan).  Assessment of Fatty Liver and Fibrosis Score*

Abdominal sonography was performed with a Toshiba Nemio SSA-550A instrument (Toshiba, Tochigi-ken, Japan) by hepatology specialists who were completely blinded to other laboratory results. The degree of fatty liver was graded semi-quantitatively according to the level of echoes arising from the hepatic parenchyma [22]. Since interpretation of fatty liver by abdominal ultrasound can be subjective, we defined subjects with at least moderate-degree fatty liver disease as significant NAFLD. By applying the low cut-off value (−1.455) of the NAFLD Fibrosis Score [18] (calculated as [−1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m<sup>2</sup> ) + 1.13 × hyperglycemia/diabetes mellitus (yes = 1, no = 0) + 0.95 × AST (U/L) to ALT (U/L) ratio − 0.013 × platelet count (10−9/L) − 0.66 × albumin (g/dL)]), the study population was categorized into three groups: (1) non-fatty liver; (2) NAFLD with low fibrosis score; and (3) NAFLD with high fibrosis score. APRI and FIB-4 scores were also calculated for comparison [23].

#### *2.4. Assessment of Epicardial Fat*

Multidetector computed tomography study was performed using a 16-slice scanner (Sensation 16, Siemens Medical Solutions, Forchheim, Germany) with 16 mm × 0.75 mm collimation, rotation time of 420 ms, and tube voltage of 120 kV. In one breath-hold, images were acquired from a level above the tracheal bifurcation to below the base of the heart, using prospective ECG-triggering at 70% of the R-R interval. From the raw data, the images were reconstructed with a standard kernel in 3-mm thick axial, non-overlapping slices, and a 25-cm field of view. Epicardial fat volume (EFV) was measured offline on a single workstation (Aquarius iNtuition Cloud, TeraRecon, SanMateo, CA, USA), using methods validated in previous studies [24].

#### *2.5. Conventional Echocardiography and Diastolic Function Indices*

Each participant underwent an extensive two-dimensional (2D) and tissue Doppler echocardiography with strain analysis. All assessments were performed by a single experienced sonographer blinded to the participants' clinical information, using a commercially available ultrasound system equipped with a 2–4 MHz multifrequency transducer (Vivid 7; GE Medical System, Vingmed, Norway), in adherence with the American Society of Echocardiography guidelines [25]. Using the modified biplane Simpson's method, the maximum values of left atrial volume (LAV) were presented in this study. All measurements were the average value derived from three consecutive cardiac cycles. Diastolic functional indices were assessed using transmitral pulsed-wave Doppler and tissue Doppler-derived mitral annular velocities. Systolic and early diastolic velocities (LV s' and LV e') were averaged from the basal septal and lateral LV segments at the mitral annulus level.

#### *2.6. Two-Dimensional Speckle-Tracking Analysis Protocol*

Speckle-tracking analysis was performed offline using 2D cardiac performance software (EchoPAC version 10.8; GE Vingmed Ultrasound, Norway). Semi-automated tracing of endocardial borders was performed at the end-diastolic frame, with minor manual adjustments to ensure optimal delineation. The LV global longitudinal strain (GLS) was calculated as the average peak global values derived from three LV apical planes of the 4-chamber, the 2-chamber, and long-axis views, as described in our previous published work [25]. Peak atrial longitudinal strain (PALS) and triphasic LA strain rates [systolic, early, and late diastolic atrial longitudinal strain rate (ALSRsyst, ALSRearly, and ALSRlate, respectively)] were determined as the average values obtained from both apical 2- and 4-chamber views. The endocardial border of the LA was traced manually so that the LA appendage and pulmonary veins were excluded. LA stiffness (LAstiff) was derived from dividing E/e' by PALS. To avoid confusion regarding the directionality of strain changes, the absolute values of GLS, ALSRearly, and ALSRlate were reported. The inter-and intraobserver analysis of the LA and LV strain/strain rate components in our lab was reported in our previous work [21].

#### *2.7. Statistical Analysis*

Data were presented as mean (standard deviation) for continuous variables, and as proportions for categorical variables. One-way ANOVA was used to assess differences of anthropometric, metabolic, and echocardiography parameters between groups with post hoc paired comparisons using the Bonferroni correction. Fisher's exact test was used to test differences between categorical data. Multiple linear regression models were used to assess the independent association of EFV and NAFLD Fibrosis Score with diastolic function and deformation parameters. For EFV, clinical covariates (age, sex, BMI, blood pressure, and clinical risk factors) were sequentially entered into the models. Since age and BMI are components of the NAFLD Fibrosis Score, they were omitted from the multivariate models for its analysis, while ALT was added into them. Statistical analyses were performed using the STATA statistical software package (Version 14. Stata Corp. College Station, Texas). A two-sided *p*-value of less than 0.05 was considered statistically significant.

#### **3. Results**

#### *3.1. Baseline Demographics, Adiposity Measures, and Metabolic Profiles*

Among the 2161 eligible study participants (mean age: 48.3 ± 9.9 years, 36.5% female), progressive epicardial fat burden, central obesity, stepwise increases in HbA1c, HOMA-IR, GOT, and decreases in platelet count were observed across all three groups (all *p* < 0.05). Both fatty liver groups shared similarly unfavorable lipid profiles and elevated Hs-CRP compared to the normal group. Patients with high fibrosis scores were also older and more likely to have hypertension, diabetes, and hyperlipidemia (Table 1). After excluding the youngest quartile from the normal group, differences in adiposity measures and biomarkers remained mostly unchanged (Supplementary Table S1).

**Table 1.** Baseline demographics, adiposity measures and biomarkers.

**Table 1.** *Cont.*