Abstract
Acute coronary syndrome (ACS) is caused by decreased blood flow to the heart muscle after plaque rupture and thrombus formation, leading to ischemia and infarction. Cofilins are the proteins involved in actin dynamics by severing and dissociating actin filaments. Abnormal cofilins are associated with myopathies, idiopathic dilated cardiomyopathies, etc. As ACS prevalence is increasing and there is a need to find specific biomarkers for ACS diagnosis, the study aimed to assess the diagnostic utility of serum cofilin-1 (CFL-1) and 2 (CFL-2) levels in ACS patients. Forty-five ACS patients as cases and 45 healthy participants as controls were recruited in a case–control pilot study. Collected blood was used to measure serum CFL-1, CFL-2 and heart-type fatty acid-binding protein (H-FABP, a marker of myocardial infarction) levels. Serum CFL-2 levels were significantly lower in cases compared to controls (2.93 [1.95–3.32] vs. 4.35 [3.4–6.61], p < 0.05). No significant difference was observed in serum CFL-1 levels between groups. Serum CFL-2 levels were negatively associated with creatine kinase–total, creatine kinase–MB, creatine kinase–MB relative index, troponin-T, and H-FABP (p < 0.05). Binary logistic regression showed lower CFL-2 levels were associated with a 2.45-fold increased ACS risk. ROC analysis showed no advantage of serum CFL-2 levels over serum H-FABP levels for ACS diagnosis (AUC: 0.120 Vs 0.710, p > 0.05). In conclusion, serum CFL-1 and 2 levels may not have greater significance in ACS diagnosis than traditional cardiac biomarkers. However, further cohort studies with a larger sample size must confirm the study’s findings.
Keywords: Acute coronary syndrome, Cofilin-1, Cofilin-2, Heart-type fatty acid-binding protein
Introduction
Cardiovascular disease (CVD) is among the prime non-communicable conditions and has emerged worldwide and in India over the last few decades. As per the Global Burden of Cardiovascular Disease Study 2019 from 204 countries and territories, CVD prevalence and mortality doubled from 1990 to 2019 [1]. In India, approximately 30 million ACS patients were reported in 2016, and the number of ACS patients is increasing [2]. Acute coronary syndrome (ACS) is characterized by a sudden reduction of blood supply to the heart. It consists of unstable angina (UA), non-ST-elevation myocardial infarction (non-STEMI), and ST-elevation myocardial infarction (STEMI) [3]. Early diagnosis and timely interventions are the keys to decreasing the total incidence rate and death in ACS patients. Early ACS diagnosis is limited due to non-specificity, less reliability, biological variations, variations in analytical assays, etc., in traditional cardiac markers like serum creatine kinase-MB (CK-MB), troponin-T, or troponin-I [4–6]. Therefore, novel cardiac biomarkers are being searched to overcome the problems associated with traditional cardiac markers [7].
The search has been extended to find the markers having origin in heart tissue, release in circulation after insult, remain in circulation for a longer time, detectable with available technology, and helpful in diagnosing and prognosis in patients with ACS. It has been observed that actin dynamics and their regulation are essential for maintaining the typical structure and function of skeletal and cardiac muscles [8, 9]. Among the many molecules regulating actin dynamics in cardiac muscle, actin-depolymerizing proteins (ADP) and cofilins are proteins involved in the actin dynamics by severing and dissociating actin monomers from filaments [10]. ADF/cofilins are vital in maintaining the contractile system, including rings of contractile nature, stress fibres, and muscles, via actin filament mass and extent regulation [11]. The depolymerization role of ADP/Cofilins has been observed during cytokinesis, cell loco-motion, cellular stress responses, and pathological situations [12]. Many heart diseases have been linked with defects in actin length regulation or filament organization resulting from mutation or misregulated expression of cytoskeletal proteins [13].
The ADP family of proteins primarily includes a triad in mammals, cofilin-1 (CFL1), which is present in a wide variety of cells; ADF, which presents specifically in epithelial/endothelial cells; then cofilin-2 (CFL2), primarily presents in cells of cardiac and skeletal musculature [9]. The significance of ADF/Cofilins is observed in functions and diseases of myocytes and valves of the heart, podocytes of the kidney, neuronal tissues and cancer cells. CFL-2 has been localized between Z-discs in cardiomyocytes closely to the pointed end of the actin filaments [13] and plays a critical role in the formation and maintenance of myofibril structure in cardiac muscle [14]. It has been shown that CFL-2 efficiently binds and disassembles ADP- and ATP/ADP-Pi-actin filaments, providing the basis of myopathies due to cofilin mutation [13]. The abnormality of CFL-1 and 2 has been linked with idiopathic dilated cardiomyopathy [15, 16]. An animal study observed that CFL-2 expression significantly increased at 28 days after permanent ligation of the left anterior descending coronary artery, which was attenuated by Na2S treatment [17]. Animal studies with cofilin knockdown and knockout showed its importance in modulating actin dynamics in podocytes and proposed that cofilin function is significant in developing proteinuria [18]. Cofilin contribution has been demonstrated in synaptic loss and pathology in neurodegenerative diseases [19]. Serum CFL-2 levels were used in the diagnosis and prognosis of patients with Alzheimer’s disease [20]. The role of cofilin has also been found in regulating cancer metastasis and apoptosis in tumour cells [21]. The extensive involvement of cofilin proteins in functions and disorders of various tissues has created interest in the diagnosis and prognosis of respective diseases.
The higher incidence of ACS and limitations of existing cardiac markers make researchers find new biomarkers to diagnose ACS patients as early as possible so that proper treatment is given and morbidity and mortality are reduced. The selection of cofilin proteins for ACS diagnosis was based on their involvement in the typical structure and function of cardiac muscle, increasing their specificity and reliability; animal studies showed a significant association between cofilin and cardiac diseases. As there is limited data in the literature regarding the utility of serum CFL-1 and CFL-2 for the ACS diagnosis and prognosis, the pilot study aimed to determine the diagnostic utility of serum CFL-1 and CFL-2 levels in ACS patients. The study’s objectives were to compare the serum levels of CFL-1 and CFL-2 among ACS patients and healthy participants and to correlate them with markers of myocardial infarction (MI), biochemical and clinical parameters, and the risk scores of ACS.
Methods
Study Design and Participants
This case–control, single-centre, observational and pilot study was conducted from April 2021 to March 2022. Forty-five patients with ACS, including UA, non-STEMI and STEMI, were recruited as cases. ACS is diagnosed based on clinical presentation, electrocardiographic changes and elevation of cardiac enzymes like troponin-T, CK-total or CK-MB [3]. Forty-five age and gender-matched healthy participants (healthy volunteers/patient bystanders other than relatives/patients who were reported to hospitals for minor ailments) were recruited as controls. Patients with connective tissue diseases, myopathies, osteoarthritis, osteoporosis, symptomatic degenerative diseases, cancers, dilated cardiomyopathy, chronic kidney disease (CKD) with serum creatinine levels over 2.5 mg/dL and symptomatic peripheral vascular diseases were excluded from the study.
Personal, family and medical histories from all the participants were recorded. The same observer measured height, weight, waist circumference and seated blood pressure. The nearest half-kilogram and half-centimetre were recorded for body weight and height, respectively. The waist circumference was determined by measuring the shortest point below the lowermost rib cage margin and the iliac crest and was recorded to the nearest half-centimetre. Body mass index (BMI) was calculated as weight (kg) divided by height (metre) square. To determine the risk of major adverse cardiac events (MACEs) in ACS patients, Thrombolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) Risk scores were assessed in ACS patients [22]. TIMI score (between 0 to 7) consists of seven elements (age > 65 yrs., more than three CAD risk factors, significant coronary artery stenosis, severe angina symptoms, ST-deviation, elevated cardiac enzymes and use of aspirin in the last 7 days), each with a score of 0 or 1. GRACE score consists of eight elements (age, heart rate, systolic blood pressure, creatinine, Killip class, cardiac arrest on admission, ST-deviation and elevation of cardiac enzymes) [23]. Under strict aseptic conditions, five millilitres of peripheral venous blood were collected from cases at admission and controls at the time of enrolment in the study. The blood samples were centrifuged at 4000 rpm for 10 min, and the serum obtained was used for biochemistry investigations and measurement of serum CFL-1, CFL-2 and H-FABP levels. Biochemistry investigations, i.e., serum total cholesterol, triglycerides, high-density lipoprotein (HDL), low-density lipoprotein (LDL), CK-total, CK-MB, troponin-T, blood urea nitrogen (BUN) and creatinine levels were assessed on the fully automated analyzer (Beckmann AU 5800 and Beckmann AU 680) by an appropriate method. CK-MB Relative index (RI) was calculated by CK-MB (ng/ml)/ CK-total (IU/L) × 100.
Serum CFL-1 and 2 levels were measured by a commercially available human double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) kit (Wuhan Fine Biotech Co. Ltd., China). All ELISA kits were validated by the quality control department of the manufacturers with three quality controls (low, medium, and high). The CFL-1 ELISA kit had measuring range between 0.156 to 10 ng/ml, sensitivity of 0.094 ng/ml, intraday and interday precision of less than 10%, recovery between 93 to 105%, and stability at 37 °C for 1 month and 2–8 °C for 6 months. The CFL-2 ELISA kit had measuring range between 15.625 to 1000 pg/ml, sensitivity of 9.375 pg/ml, intraday and interday precision of less than 10%, recovery between 85 to 105%, and stability at 37 °C for 1 month and 2–8 °C for 6 months. Serum H-FABP levels were measured by a commercially available human double-antibody sandwich ELISA kit (Optics Valley International Biomedicine Park, Wuhan, China). The H-FABP ELISA kit had measuring range between 102 to 25,000 pg/ml, sensitivity of 102 pg/ml, intraday and interday precision of less than 10%, recovery between 95 to 105%, and stability at 37 °C for 1 month and 2–8 °C for 6 months.
Statistical Analysis
Statistical analysis was performed using the SPSS program for Windows (version 26.0, IBM, New York, NY). The Shapiro-Walk test assessed the normality of data distribution. The continuous data were expressed as mean ± standard deviation (SD) or median (interquartile range). The distribution of categorical data was expressed as percentages. Normally distributed continuous variables like age, time between onset of chest pain and admission, weight, height, waist circumference, BMI, systolic and diastolic blood pressure, pulse rate, random blood glucose, total cholesterol, triglycerides, LDL, HDL, BUN, creatinine, CK-total, CK-MB, and troponin-T were compared using the independent sample Student’s t-test, whereas the Mann–Whitney test was used for those variables that were not normally distributed like cofilin-1, cofilin-2 and H-FABP. Categorical variables were analyzed using the Chi-square test of homogeneity (alcoholics, smokers, and history of hypertension) or Fisher’s exact test (diabetes, family history of diabetes, hypertension, and ischemic heart disease). Pearson correlation coefficient was used to find the correlation of study parameters (CFL-1, CFL-2 and H-FABP) with age, the time between the onset of chest pain and admission, weight, height, waist circumference, BMI, systolic and diastolic blood pressure, pulse rate, random blood glucose, CK-MB, CK-total, CK-MB RI, troponin-T, total cholesterol, triglycerides, HDL, LDL, BUN, creatinine, TIMI and GRACE scores adjusted for age, gender, smokers, and hypertensives. Spearman’s correlation coefficient was used to find the correlation of study parameters (CFL-1, CFL-2 and H-FABP) with the history of diabetes and hypertension adjusted for age, gender, smokers, and hypertensives. To differentiate between cases and controls, the receiver operating characteristic (ROC) curve analysis was done to determine cut-off values, sensitivity, and specificity for serum CFL-1 and 2 levels. ROC analysis draws a plot of sensitivity (true positive rate) by 1-specificity (false positive rate) at every test value by dichotomizing patients into having a disease. The cut-off point was determined by the test value with the highest sensitivity and specificity, indicating that the value can classify whether the patient has the disease best [24]. Binary logistics regression was done to determine the predictive value (odd ratio and 95% confidence interval) of study parameters for ACS. Also, the C-statistics was calculated using predicted probability variables generated from binary logistics regression in ROC curve. For all statistical tests, a p < 0.05 was considered statistically significant.
Results
Baseline Characteristics
All the details of patients with ACS as cases and healthy participants as controls are compared by the student’s t-test, the Mann–Whitney, the Chi-square of homogeneity or Fisher’s exact tests, and is shown in Table 1. Cases had a significantly higher percentage of diabetes than controls. However, no difference was found in the percentage of hypertension patients among cases and controls. Also, cases had a significantly higher percentage of patients with a family history of diabetes, hypertension and ischemic heart disease than controls. Out of 45 cases, 12 had a previous history of MI. The average time between the onset of chest pain and admission in cases was 11.02 ± 2.17 h. Of 45 cases, 16 had STEMI, 21 had non-STEMI, and 8 had UA. Also, out of 45 cases, 19 had single-vessel disease, 23 had double-vessel disease, and 3 had triple-vessel disease. Cases had significantly lower weight and BMI than controls. However, no difference was found in height, waist circumference, pulse rate, systolic and diastolic blood pressure, and the percentage of smokers and alcoholics among cases and controls. Cases had significantly higher random blood glucose, serum total cholesterol and triglycerides, and lower serum HDL levels than controls. No significant difference was observed in serum LDL, creatinine and BUN levels among cases and controls. All patients were on hypolipidemic treatment, and some of them were on anti-diabetic treatment as well.
Table 1.
Clinical and biochemical properties of the study population
| Parameters | Patients with ACS, Cases (n = 45) | Healthy participant, Controls (n = 45) | p |
|---|---|---|---|
| Age (y) | 57 ± 11 | 56 ± 7 | 0.527 |
| Male/Female (n/n) | 35/10 | 34/11 | 0.806 |
| Smoker, n (%) | 15 (33) | 22 (49) | 0.531 |
| Alcoholics, n (%) | 19 (42) | 26 (58) | 0.550 |
| Presence of diabetes, n (%) | 16 (36) | 0 (00) | < 0.001 |
| Presence of hypertension, n (%) | 18 (40) | 12 (27) | 0.184 |
| Family History of diabetes, n (%) | 17 (38) | 0 (00) | < 0.001 |
| Family History of hypertension, n (%) | 17 (38) | 0 (00) | < 0.001 |
| Family History of ischemic heart disease, n (%) | 17 (38) | 0 (00) | < 0.001 |
| Time between onset of chest pain and admission (h) | 11.02 ± 2.17 | − | − |
| Type of ACS | |||
| STEMI, n (%) | 16 (36) | − | − |
| Non-STEMI, n (%) | 21 (47) | − | − |
| UA, n (%) | 08 (17) | − | − |
| Type of vessel diseases | |||
| Single-vessel disease, n (%) | 19 (41) | − | − |
| Double-vessel disease, n (%) | 23 (52) | − | − |
| Triple-vessel disease, n (%) | 03 (07) | − | − |
| Weight (kg) | 56 ± 10 | 62 ± 13 | 0.025 |
| Height (cm) | 159 ± 9 | 161 ± 11 | 0.359 |
| Waist circumference (inch) | 33.64 ± 5.28 | 34.31 ± 4.30 | 0.514 |
| BMI (kg/m2) | 21.20 ± 2.17 | 22.97 ± 3.72 | 0.007 |
| Systolic blood pressure (mm Hg) | 118 ± 16 | 118 ± 13 | 0.814 |
| Diastolic blood pressure (mm Hg) | 79 ± 16 | 77 ± 10 | 0.550 |
| Pulse rate (beats/min) | 91 ± 18 | 89 ± 15 | 0.660 |
| Random blood glucose (mg/dL) | 168.27 ± 46.17 | 135.49 ± 10.92 | < 0.001 |
| Total cholesterol (mg/dL) | 157.24 ± 35.40 | 130.10 ± 20.60 | < 0.001 |
| Triglycerides (mg/dL) | 171.18 ± 50.52 | 124.40 ± 13.85 | < 0.001 |
| LDL (mg/dL) | 100.64 ± 34.95 | 103.64 ± 25.16 | 0.642 |
| HDL (mg/dL) | 30.61 ± 4.81 | 44.44 ± 2.35 | < 0.001 |
| BUN (mg/dL) | 27.69 ± 11.33 | 24.87 ± 7.60 | 0.169 |
| Creatinine (mg/dL) | 0.90 ± 0.31 | 0.83 ± 0.29 | 0.294 |
| CK-total (IU/L) | 76.10 ± 22.58 | 37.93 ± 13.30 | < 0.001 |
| CK-MB (IU/L) | 40.38 ± 8.67 | 14.16 ± 4.19 | < 0.001 |
| CK-MB RI | 0.53 ± 0.11 | 0.28 ± 0.12 | < 0.001 |
| Troponin-T (pg/mL) | 82.44 ± 14.81 | 8.62 ± 2.84 | < 0.001 |
| Cofilin-1 (ng/mL) | 0.57 (0.46 − 0.73) | 0.58 (0.41 − 0.88) | 0.939 |
| H-FABP (pg/mL) | 488.51 (395.60 − 737.80) | 406.61 (308.70 − 504.60) | < 0.001 |
| Cofilin-2 (pg/mL) | 2.93 (1.95 − 3.32) | 4.35 (3.4 − 6.61) | < 0.001 |
Data are expressed as median (inter-quartile range), mean ± SD or number (percentage). Differences between the two groups were tested by the student’s t-test for normally distributed continuous variables, the Mann–Whitney U test for non-normally distributed variables, and the Chi-square test of homogeneity or Fisher’s exact test for categorical variables. ACS: Acute coronary syndrome; STEMI: ST-segment elevation myocardial infarction; Non-STEMI: Non ST-segment elevation myocardial infarction; UA: Unstable angina; BMI: Body mass index; HDL: High-density lipoprotein cholesterol; LDL: Low-density lipoprotein cholesterol; BUN: Blood urea nitrogen; CK-total: Creatine kinase-total; CK-MB: Creatine kinase-MB; CK-MB RI: Creatine kinase-MB relative index; H-FABP: Heart-fatty acid binding protein
Cases had lower serum CFL-1 levels than controls (0.57 [0.46–0.73] vs. 0.58 [0.41–0.88], p > 0.05), which was not statistically significant. Cases had significantly lower serum CFL-2 levels than controls (2.93 [1.95–3.32] vs. 4.35 [3.40–6.61], p < 0.05). ROC analysis showed that serum CFL-2 levels didn’t lead to a more significant advantage for diagnosing ACS than serum H-FABP levels (area under the curve: 0.120 vs. 0.710, p > 0.05, Fig. 1).
Fig. 1.
Receiver operating curve of serum cofilin-2 and heart-type fatty acid binding proteins (H-FABP). The area under the curve represents the diagnostic performance of cofilin-2, using H-FABP as a standard
Correlation analyses were done in all the study populations (Table 2). Serum CFL-2 levels were significantly positively associated with the percentage of diabetes, BMI, and serum HDL levels and negatively associated with random blood glucose, serum total cholesterol, triglycerides, CK-total, CK-MB, CK-MB RI, troponin-T and BUN levels (p < 0.05) adjusted for age, gender, smokers, and hypertensives. Serum H-FABP levels were positively associated with serum CK-MB, CK-MB RI, troponin-T and triglyceride levels and negatively with serum HDL levels (p < 0.05) adjusted for age, gender, smokers, and hypertensives. Serum CFL-1 levels were not significantly associated with physical characteristics and biochemical parameters. TIMI and GRACE risk scores were significantly positively associated with troponin-T and serum H-FABP levels among cases (p < 0.05). Also, binary logistics regression (Table 3) showed that lower serum CFL-2 levels were associated with increased ACS risk (OR 2.45, 95% CI 1.56–3.87, p < 0.001); however, serum CFL-1 levels were not associated with ACS risk (OR 0.70, 95% CI 0.15–3.30, p = 0.653). The serum CFL-2 levels had C-statistics of 0.152, which is less than 0.500, indicating that it had less predicted accuracy for ACS diagnosis.
Table 2.
Correlation of serum cofilin-1, cofilin-2 and H-FABP levels with other variables
| Parameters n = 90 | Cofilin-1 | Cofilin-2 | H-FABP | |||
|---|---|---|---|---|---|---|
| r | p | r | p | r | p | |
| Age | − 0.094 | 0.380 | − 0.075 | 0.481 | − 0.052 | 0.627 |
| Presence of diabetes | − 0.030 | 0.957 | 0.310 | 0.005 | − 0.101 | 0.342 |
| Presence of hypertension | 0.104 | 0.330 | 0.056 | 0.601 | 0.075 | 0.483 |
| Onset of chest pain | − 0.183 | 0.228 | − 0.22 | 0.140 | − 0.243 | 0.107 |
| Weight | − 0.058 | 0.587 | 0.182 | 0.086 | 0.033 | 0.759 |
| Height | 0.044 | 0.679 | 0.065 | 0.540 | 0.107 | 0.314 |
| Waist circumference | 0.195 | 0.065 | 0.054 | 0.614 | 0.008 | 0.938 |
| BMI | − 0.123 | 0.248 | 0.271 | 0.020 | − 0.030 | 0.778 |
| Systolic blood pressure | 0.066 | 0.958 | − 0.015 | 0.887 | − 0.092 | 0.388 |
| Diastolic blood pressure | − 0.017 | 0.872 | − 0.034 | 0.754 | 0.087 | 0.415 |
| Pulse rate | 0.171 | 0.108 | − 0.086 | 0.422 | − 0.026 | 0.807 |
| Random blood glucose | − 0.043 | 0.687 | − 0.370 | < 0.001 | 0.010 | 0.925 |
| CK-MB | − 0.010 | 0.929 | − 0.631 | < 0.001 | 0.314 | 0.003 |
| CK- total | 0.049 | 0.647 | − 0.499 | < 0.001 | 0.158 | 0.136 |
| CK- MB RI | 0.057 | 0.591 | − 0.668 | < 0.001 | 0.442 | 0.001 |
| Troponin-T | 0.136 | 0.372 | − 0.328 | 0.028 | 0.645 | 0.017 |
| Total cholesterol | − 0.058 | 0.587 | − 0.254 | 0.016 | 0.074 | 0.491 |
| Triglycerides | 0.135 | 0.206 | − 0.471 | < 0.001 | 0.359 | 0.001 |
| HDL | − 0.042 | 0.697 | 0.515 | < 0.001 | − 0.271 | 0.009 |
| LDL | − 0.015 | 0.890 | 0.173 | 0.103 | 0.003 | 0.981 |
| BUN | 0.008 | 0.941 | − 0.276 | 0.008 | − 0.134 | 0.207 |
| Creatinine | 0.112 | 0.295 | − 0.165 | 0.120 | − 0.179 | 0.091 |
| TIMI score | 0.124 | 0.418 | 0.183 | 0.229 | 0.785 | < 0.001 |
| GRACE score | 0.203 | 0.181 | 0.236 | 0.118 | 0.901 | < 0.001 |
Pearson and Spearman’s correlation coefficient were tested. BMI: Body mass index; CK-MB: Creatine kinase-MB; CK-total: Creatine kinase-total; CK-MB RI: Creatine kinase-MB relative index; HDL: High-density lipoprotein cholesterol; LDL: Low-density lipoprotein cholesterol; BUN: Blood urea nitrogen; H-FABP: Heart-fatty acid binding protein; TIMI: Thrombolysis in myocardial infarction; GRACE: Global registry of acute coronary events
Table 3.
Binary logistics regression to determine the association between study parameters with ACS risk
| Parameters | Adjusted Odd Ratio (95% Confidence Interval) | p |
|---|---|---|
| Cofilin-1 | 0.70 (0.15 − 3.30) | 0.653 |
| Cofilin-2 | 2.45 (1.56 − 3.87) | < 0.001 |
ACS: Acute coronary syndrome
Discussion
Due to the higher prevalence of ACS patients and the significant need to find novel diagnostic markers, the study aimed to determine the diagnostic utility of serum CFL-1 and 2 levels in ACS patients. We concluded that serum CFL-1 and 2 levels may not have a significant advantage in the ACS diagnosis than traditional biochemical markers.
Among ACS patients, we found a higher percentage of non-STEMI (47%) and double-vessel disease (51%) compared to other forms of ACS and vessel disorders, indicating the high scope of biochemical markers in ACS diagnosis. It has been shown that the prevalence of non-STEMI is increasing, and non-STEMI has a high rate of multi-vessel disease due to co-morbid conditions [25, 26]. However, the predominance of STEMI as initial ACS presentations were reported in a single tertiary referral center from North India [27]. Also, a higher STEMI rate than non-STEMI among ACS patients was reported from South India, and it was shown that single-vessel disease was more common in STEMI groups, and multi-vessel disease was common in non-STEMI groups [3]. In the present study, cases had a higher percentage of diabetes than controls, a well-known risk factor for ACS. Also, cases had a higher percentage of patients with a family history of diabetes, hypertension, and ischemic heart disease than the controls, indicating the significance of inheritance among them. Other significant risk factors like age, gender, height, waist circumference, systolic and diastolic blood pressure, and percentage of smokers and alcoholics were similar between cases and controls. We found significantly lower weight and BMI among cases compared to controls, which may be due to extensive treatment, diet management and exercise. Also, BMI and waist circumference are within the acceptable range, excluding obesity as a cause for altered serum cofilin levels among study participants. Though we found no significant difference in the percentage of alcoholics and smokers among study participants, it was observed that persistent exposure to alcohol activates and dephosphorylates cofilin by inhibiting the tyrosine kinase pathway [28], and nicotine inhibits cofilin-induced disassembly of F-actin [29]. Cases had higher serum lipid levels except for serum LDL and HDL levels than controls, which may be attributed to lifestyle, eating habits, and workout routines. Hyperglycemia [30] and hyperlipidemia [31] are important risk factors for ACS. No significant difference in serum creatinine levels and BUN among cases and controls ruled out kidney dysfunction as a cause for altered serum cofilin levels.
Besides cardiac markers, we found a positive association between serum CFL-2 levels and a history of diabetes and a negative association with RBS. It is justified because higher blood glucose levels induced phosphorylation and inactivation of cofilin [32]. Also, serum CFL-2 levels were positively associated with BMI. The changes in adipose size were associated with actin cytoskeleton remodelling through altered expression of actin-regulating proteins and increased Rho kinase activity, favouring actin polymerization [33]. As cofilin proteins are associated with cardiac structure and function, we assumed their levels may be used for ACS diagnosis. We found that serum CFL-2 levels were significantly lower in ACS patients. Also, serum CFL-2 levels showed a negative association with cardiac markers. As ROC curve analysis showed that serum CFL-2 levels had lower AUC than the AUC of H-FABP, we have not determined sensitivity, specificity, likelihood ratio, etc. Binary logistic regression analysis showed that serum CFL-2 level was a predictor of ACS. Thus, serum CFL-2 levels may not be considered a novel and ideal diagnostic marker for ACS.
We assumed that serum cofilin-2 levels may be higher and correlate positively with traditional diagnostic markers and TIMI and GRACE risk scores of MI. However, the contradictory results may be due to the very low half-life of CFL-2, like myoglobin, small sample size, the mutation in genes associated with cofilin, effects of smoking and drugs, etc. Although ADF and cofilin have a half-life of 24 h in neuronal cells [19], their half-life in serum is unavailable. The molecule’s half-life is critical in determining excretion rate and steady-state concentration. As we found lower serum CFL-2 levels in ACS patients, CFL-2 may be cleared from the circulation rapidly, making it inefficient to detect at higher levels as assumed. Also, this is a pilot study with a total sample size of 90, which affected the study’s power, increased the margin of error, undermined the validity and increased the variability. The activity of CFL-1 and 2 is suppressed due to the phosphorylation of their threonine residues by various kinases like ERK1/2, LIM-kinase1, Rho kinase, etc. [34]. It was found that mutation of the lamin A/c gene adversely affects the ERK1/2 pathway, which phosphorylates the cofilin molecule, reduces its levels and suppresses its function [35]. Thus, the lower serum CFL-2 levels may be due to its higher phosphorylation in ACS patients due to unknown mutations in various kinases. Inhibition of actin dynamics is one of the causes of lower circulating levels of CFL-1 or 2. Cytochalasin D inhibits actin dynamics in mammalian cells by barbed-end capping mechanism or inhibition of G-and F-actin binding to cofilin [36]. CapZ is a Z-disc protein and has a function in ischemia–reperfusion injury [37]. Therefore, lower serum CFL-2 levels in ACS patients may be due to altered CapZ function, affecting actin dynamics. Altered serum CFL-2 levels in ACS patients may be associated with metformin therapy, as sixteen ACS patients had diabetes and are on anti-diabetic treatment. Metformin inhibits Rho kinase, reducing the phosphorylation of cofilin by LIM kinase [38]. Thus, other anti-diabetic or hypolipidemic drugs may influence the serum cofilin levels in ACS patients. The lower serum CFL-2 levels in ACS patients may be the effect of various reasons mentioned above, or it may not be associated with ischemic events.
This study has various limitations. First, a case–control study on smaller samples is the main limitation. Future research over larger samples with follow-up can show the significance of cofilin in ACS patients. ELISA may not be sensitive to determine serum cofilin levels. Advanced techniques like LC–MS or GC–MS may be used to estimate serum cofilins.
Conclusion
Patients with ACS as cases and healthy participants as controls are similar to some risk factors for ACS, such as age, gender, hypertension, and percentage of alcoholics and smokers. In contrast, some risk factors like hyperlipidemia, hyperglycemia, family history of diabetes, hypertension and ischemic heart disease showed clear-cut distinctions. Serum cofilin-2 levels were drastically lower in cases compared to controls and were significantly negatively correlated with markers of MI such as serum CK-MB, CK-total, CK-MB RI, troponin-T and H-FABP levels. Although theoretically, the role of cofilin is vital for cardiac musculature functioning, based on the analysis and the limitations mentioned above, the role of serum CFL-1 and 2 levels in diagnosing ACS may not be significant. However, the present study’s findings may be helpful to form a cohort study on a larger population with advanced analytical methods for measuring different forms of cofilin (phosphorylated and non-phosphorylated), which may pinpoint their significance in ACS patients.
Author Contributions
A.K.G. obtained patient data and blood samples, analyzed blood samples and data; P.S.A. designed the study, analyzed the data and contributed to the writing of the manuscript; K.V.V designed the study and contributed for the selection of study participants. All authors approved the final version of the manuscript.
Funding
This work was supported by an Intramural research grant from JIPMER, Puducherry, India (No: JIP/Res/Intramural/Phs 1/2021-22 dated May 20, 2021).
Declarations
Conflict of interest
None of the authors report any conflict of interest.
Ethics Approval
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (Institutional and national) and with the Helsinki Declaration of 1964, as revised in 2013. Approval of the Institute Human Ethics Committee was taken (JIP/IEC/2021/014 Dated 03/03/2021). Written informed consent was taken from the study participants before recruitment and after informing them that the study was not part of their treatment.
Footnotes
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