The Coronary Artery Disease (CAD) remains a major public health problem, with a leading cause of death more than any other disease. In 2015, 17.7 million people died from Cardiovascular Disease (CVD) worldwide that represent the 31% of all global deaths out of which 7.4 million people died due to CAD [1,2]. In India, CAD is responsible for 25% of all death. The burden of CAD in the rural population is 3-5% compared to 7-10% in urban population [3]. The major established risk factors such as hypertension, smoking, dyslipidemia, obesity, and diabetes varies widely between different countries. In a comparative study, Indians have higher C-reactive protein, Plasminogen Activator Inhibitor (PAI-1) and homocysteine level which cannot be explained by conventional risk factor [3]. Diagnosis of CAD is made by invasive Coronary Angiogram (CAG) technique as well as Electrocardiogram (ECG) and Exercise Tolerance Testing (ETT) have also been widely used. Diagnosis of CAD requires careful examination and medical history. The current mainstay of the diagnosis of coronary heart disease is the echocardiogram, Computerised Tomography (CT), angiography and cardiac catheterisation. Many individuals continue to succumb to CAD, despite advances in risk factor management at an epidemiological level [4]. Recently, it has been identified that changes in circulating microRNAs (miRNA) have a potential in identifying cardiac dysfunctions and shown a major interest as a biomarker for CAD diagnosis [5]. MicroRNAs are small (19-25nt), highly specific, endogenous, single-stranded, non-coding Ribonucleic acid (RNA). It regulates the wide variety of biological processes such as endothelial dysfunction, inflammation, apoptosis, angiogenesis, atherosclerosis, and neointimal hyperplasia or restenosis [6-11]. Significantly, dysregulated miRNAs have been reported in patients with acute coronary syndrome, unstable angina, Acute Myocardial Infarction (AMI), heart failure and stroke [12-17]. In stable CAD, expression of miR-135a and miR-31 was found to be up-regulated as compared to healthy subjects [15,16]. Both microRNA-126 and -122 have already been incriminated in the field of coronary vascular disease. However; still there is a lack of use of circulating miRNAs as a biomarker for the diagnosis of CAD. Vascular Endothelial Growth Factor (VEGF) is another angiogenic factor related with endothelial function. Studies demonstrated distinct expression of VEGF and its receptors in atherosclerotic lesions in coronary arteries and VEGF may have some role in progression of coronary atherosclerosis [18]. Endothelial enriched miRNA, miR126 has been reported to play important role in modulating vascular development and angiogenesis [19,20]. The study have hypothesised that the serum miRNA levels and angiogenic markers such as VEGF could predict the presence of CAD in middle aged (46-58 years) Indian population. Hence, the current study was designed to assess the level of circulating miR-126, 122 and VEGF in serum of middle aged angiographically proven CAD cases and healthy controls.
Materials and Methods
Study Population
Study subjects were recruited between December 2018 to December 2019 from Lari Cardiology Centre, Department of Cardiology, King George Medical University, Lucknow, UP, India. CAGs were evaluated by a consultant through visual estimation of luminal narrowing in multiple segments based on the American Heart Association/American College of Cardiology (AHA/ACC) classification [21]. On the basis of this data, significant CAD was defined as atleast one major epicardial vessel with >50% stenosis. Patients with neither detectable coronary stenosis nor atherosclerotic vascular disease were considered as healthy controls. Patients were interviewed to collect medical history and lifestyle habits. Patients aged 40-60 years were interviewed to collect medical history and lifestyle habits. Risk factors were determined by physician. Hyperlipidemia was defined as Total Cholesterol (TC) level of ≥5.72 mmol/L and/or Triglyceride (TG) level of ≥1.70 mmol/L, or if the patient was being treated with lipid-lowering medication. Hypertension was defined as resting systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg or in the presence of active antihypertensive treatment. Diabetes was defined as fasting blood glucose ≥7.0 mmol/L or a diagnosis of diabetes needing diet or anti-diabetic therapy. Individuals who formerly or currently smoked ≥10 cigarettes per day for at least two years were defined as smokers [20]. A written informed consent was taken from the patients. The research related to human use has been complied in accordance the tenets of the Helsinki Declaration, and has been approved by the Institutional Ethical Committee (Number: 81th ECM II-A/P2) of King George Medical University, Lucknow.
Inclusion criteria: All the patients who fulfilled the inclusion criteria were recruited for the study.
Exclusion criteria: Subjects were excluded from the study if they were affected by a hepatic failure, renal failure, abnormal liver function, hepatitis, cardiomyopathy, congenital heart disease, bleeding disorders, previous thoracic irradiation therapy, and malignant diseases in view of principal investigator.
Sample Collection and Serum Isolation
A 3 mL of peripheral blood was collected from cases and controls in plain, Ethylenediamine Tetraacetic Acid (EDTA) and Fluoride vials (NOVAC, Polymed, Poly Medicure LTD., India). Serum was separated by centrifugation at 1900 g for 10 minutes, followed by a 10 minutes high-speed centrifugation at 16,000 g and stored at -80°C until further processing. All samples were processed within one hour of the collection to avoid potential contamination of leukocytes.
Biochemical Examination
All biochemical parameters were measured by fully automated biochemical analyser (ARCHITECT i2000SR, Abbott Diagnostic & Selectra ProXL, ELITech Group).
RNA Isolation from Serum
Total RNA was extracted using a Trizol-based miRNA isolation protocol (Invitrogen, Carlsbad, CA, USA) (1). RNA concentrations were measured with a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Inc. Wilmington, USA), and the RNA samples were stored at -80°C for future use.
cDNA synthesis and quantitative real-time PCR: Total RNA extracted from serum were initially reverse transcribed using (Multiscribe) MuLV reverse transcriptase kit (Cat. no. K1622, Thermo Fisher Scientific, USA). Complementary DNA (cDNA) was amplified with specific primer sets: miR-122 (hsa-miR-122-5p, Cat. no. 4427975), miR126 (hsa-miR-126-5p, Cat no. 4427975) and RNU6 (Cat no. 4427975). Data were normalised for RNU6 (housekeeping gene), the relative expression levels of miRNAs were calculated using the 2∆∆Ct method and fold-changes were calculated for each miRNA [22,23].
ELISA for VEGF: Serum VEGF level was determined by Enzyme-linked Immunosorbent Assay (ELISA) using RayBio Human VEGF ELISA kit and the reading were recorded by iMarkTM microplate absorbance reader (BIOS) at 450 nm. The level of VEGF concentration in cases was determined by comparing the Optical Density (OD) of the samples with the standard curve.
Statistical Analysis
Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) V.20.0 (SPSS, Chicago, Illinois, USA). Data for age, gender, HbA1c, TC, TG, High-density lipoprotein cholesterol (HDL-C), Low-density lipoprotein cholesterol (LDL-C), Very Low-density lipoprotein cholesterol (VLDL-C), Folate II, Vitamin B12, Small dense LDL (sdLDL), Vit.D 25-OH, Thyroid-stimulating Hormone (TSH) and total Homocysteine (HCY) were presented as Mean±Standard Deviation (SD), median and 25th-75th quartile. The significance of the comparison was determined by the student t-test and Mann-Whitney U test. The association of levels of miR-122 and 126 in more than two groups was compared by non-parametric Kruskal-Wallis one-way ANOVA. Data for circulating miRNA levels were presented as Mean±SD and significance of the comparison was determined by Mann-Whitney test. The diagnostic value of miRNA expression was determined using the area under the Receiver Operating Characteristic (ROC) curve. A p-value of <0.05 was considered to be statistically significant.
Results
Basic characteristics of the study population
In the present study, 100 CAD patients and 100 healthy controls between December 2018 to December 2019 were included. The baseline characteristics of all the subjects enrolled in this study are listed in [Table/Fig-1]. Laboratory data including biochemical parameters are listed in [Table/Fig-2]. There was a significant difference in age, HbA1c, HDL, LDL and total cholesterol level between CAD cases and controls (p=0.001, 0.0004, 0.0015, 0.0004 and 0.0002, respectively). The sdLDL was significantly higher in CAD cases compared to controls (p=0.001). However, there was no significant difference in weight, BMI, VLDL and TG between CAD cases and controls. In CAD cases, the level of Folate II, Vit.D and Vit.B12 was significantly different as compared to normal controls (p=0.001, 0.0083 and 0.0016, respectively) and described in [Table/Fig-2].
Basic characteristic of cases and controls.
Characteristics | No. of patients cases (%) | No. of patients control (%) |
---|
Age (Years) | ≤45 | 26 (26.0) | 87 (87.0) |
>45 | 74 (74.0) | 13 (13.0) |
Sex | Male | 73 (73.0) | 66 (66.0) |
Female | 27 (27.0) | 34 (34.0) |
Occupation | Business | 15 (15.0) | 23 (23.0) |
Farmer | 21 (21.0) | 03 (3.00) |
Government employee | 16 (16.0) | 06 (6.00) |
House wife | 19 (19.0) | 13 (13.0) |
Labour | 12 (12.0) | 00 (00.0) |
Other | 17 (17.0) | 55 (55.0) |
Educational status | Basic | 42 (42.0) | 40 (40.0) |
High school | 20 (20.0) | 10 (10.0) |
Graduate | 19 (19.0) | 36 (36.0) |
Illiterate | 19 (19.0) | 14 (14.0) |
Nature of work | Hard | 27 (27.0) | 03 (3.33) |
Moderate | 51 (51.0) | 70 (70.0) |
Sedentary | 22 (22.0) | 27 (27.0) |
Exercise | Regular | 11 (11.0) | 37 (37.0) |
Occasionally | 08 (8.00) | 17 (17.0) |
Sedentary | 12 (12.0) | 03 (3.00) |
None | 69 (69.0) | 43 (43.0) |
Smoking | Current | 16 (16.0) | 10 (10.0) |
Ex-smoker | 24 (24.0) | 07 (7.0) |
Never | 60 (60.0) | 83 (83.0) |
Alcohol | Current drinker | 09 (9.00) | 00 (0.0) |
Occasionally | 08 (8.00) | 90 (90.0) |
Never | 83 (83.0) | 10 (10.0) |
Hypertension | Yes | 62 (62.0) | 24 (24.0) |
No | 38 (38.0) | 76 (76.0) |
Diet | Vegetarian | 43 (43.0) | 27 (27.0) |
Non-vegetarian | 50 (50.0) | 40 (40.0) |
Veg+Non-vegetarian | 07 (7.00) | 33 (33.0) |
Chest pain | High | 09 (9.0) | 00 (0.0) |
Intermediate | 20 (20.0) | 00 (0.0) |
Low | 56 (56.0) | 06 (6.00) |
No pain | 15 (15.0) | 94 (94.0) |
Laboratory data from the study subjects.
Variable | CAD cases (Mean±SD) Median (Q1-Q3) | Normal controls (Mean±SD) Median (Q1-Q3) | p-value* |
---|
Age (Years) | 52.07±9.94, 52(46-58.7) | 36.13±8.12, 35(32-40) | <0.0001 |
Height (Inch) | 5.3±0.39, 5.4(5.0-5.6) | 5.4±0.46, 5.4(5.2-5.7) | 0.259 |
Weight (Kg) | 67.2±13.4, 64.5(55.2-77.2) | 62.2±11.8, 60.5(55-70) | 0.06 |
BMI (kg/m2) | 25.9±4.8, 25.8(22.7-28.0) | 25.2±4.7, 24.3(21.4-29.1) | 0.496 |
HbA1c (%) | 6.8±2.0, 6.1(5.6-6.9) | 5.4±1.0, 5.3(4.8-5.7) | 0.0004 |
HDL (mg/dL) | 44.9±15.9, 40.7(34.3-50.9) | 55.4±12.3, 49.3(42.0-54.7) | 0.0015 |
LDL (mg/dL) | 56.3±37.4, 51(30-74) | 87.6±44.4, 76.5(55.2-110.0) | 0.0004 |
VLDL (mg/dL) | 39.5±31.4, 28.5(20.2-42.5) | 38.3±33.9, 27.5(19.2-38.7) | 0.862 |
TG (mg/dL) | 145.7±72.6, 137.4(99.8-165.7) | 157.2±91.4, 139.5(94.7-172.2) | 0.243 |
TC (mg/dL) | 132.7±46.2, 95.3(98.6-156.8) | 172.2±48.8, 168(130.5-207.4) | 0.0002 |
sdLDL (mmol/l) | 24.7±15.7, 22.1(12.3-34.1) | 7.2±4.2, 6.7(5.3-9.0) | 0.0001 |
Folate (nmol/L) | 14.6±10.6, 10.2(6.9-19.5) | 7.8±5.0, 6.5(4.4-8.5) | 0.001 |
Vit.D (nmol/L) | 19.2±9.8, 16.5(10.3-28.8) | 31.8±38.5, 17.8(12.4-32.7) | 0.0083 |
Vit. B12 (pg/mL) | 472.2±417.0, 274.0(146.9-759.5) | 215.6±189.6, 145.0 (99.1-265.0) | 0.0016 |
TSH (uIU/mL) | 3.2±2.5, 2.7(1.3-4.0) | 2.4±1.2, 2.1(1.7-2.1) | 0.096 |
Total HCY (umol/L) | 21.1±10.6, 21.0(12.8-28.5) | 25.0±22.2, 17.7 (13.1-30.1) | 0.163 |
*p-value of <0.05 was considered as significant; BMI: Body mass index; HDL: High density lipoproteins; LDL: Low density lipoproteins; VLDL: Vey low density lipoproteins; TG: Triglycerides; TC: Total cholesterol; sdLDL: Small dense low density lipoproteins; TSH: Thyroid stimulating hormone; HCY: Homocysteine; SD: Standard deviation
The expression levels of circulating miR-122 and miR-126 and VEGF in CAD and healthy controls
The fold change in miR expression level is summarised in [Table/Fig-3]. The level of serum miR-122 in CAD cases was significantly higher as compared to controls (1.60±1.06 vs. 0.93±0.43, p=0.001). However, the circulating miR-126 was significantly higher in controls compared to CAD cases (1.01±0.47 vs. 0.82±0.51, p=0.02). The circulating serum VEGF level was significantly higher in CAD cases compared to control (182.97±156.49 vs. 105.49±103.88, p=0.02).
Fold change in Circulating MicroRNA levels and VEGF in coronary Artery disease (CAD) cases and controls.
MicroRNAs | CAD patients (Mean±SD) | Healthy controls (Mean±SD) | p-value* |
---|
miR-122 | 1.60±1.06 | 0.93±0.43 | 0.001 |
miR-126 | 0.82±0.51 | 1.01±0.47 | 0.02 |
VEGF | 182.97±156.49 | 105.49±103.88 | 0.02 |
*p-value of <0.05 was considered as significant; SD: Standard deviation; VEGF: Vascular endothelial growth factor: miR; MicroRNA
Association of circulating miR-122, miR-126 and VEGF with demographic and biochemical parameters
The association of circulating miR-122, 126 and VEGF levels with demographic and clinicopathological characteristic of CAD patients is summarised in [Table/Fig-4]. The study did not found any association of miR-122 and miR-126 with demographic and clinicopathological characteristics. The miR-122 and 126 levels were higher in cases that were presented with high and intermediate chest pain as compared to low and no chest pain (p=0.433, 0.343), current and ex-smoker as compared to non-smoker (p=0.082, 0.983). The level of miR-122 was higher in cases with the total triglyceride level of <30, HDL with range of 40-60 and LDL with ≤130, however the difference was not statistically significant (p=0.255, 0.679 and 0.07, respectively). The study did not found any association of VEGF level with demographic and clinical characteristics of the cases.
Association of circulating miR-122, miR-126 and VEGF with demographic, clinical and laboratory parameters.
Characteristics | miR-122Mean±SD | miR-126Mean±SD | VEGFMean±SD | p-value* 122 | p-value* 126 | p-value*VEGF |
---|
Age (Year) | ≤45 | 1.73±1.14 | 0.75±0.31 | 178.57±146.95 | 0.360 | 0.879 | 0.740 |
>45 | 1.56±1.04 | 0.84±0.57 | 169.54±160.55 |
Sex | Male | 1.50±1.07 | 0.80±0.47 | 183.34±156.97 | 0.097 | 0.990 | 0.05 |
Female | 1.90±1.00 | 0.88±0.64 | 117.80±147.34 |
Occupation | Business | 1.55±1.45 | 0.66±0.33 | 212.38±164.04 | 0.842 | 0.259 | 0.05 |
Farmer | 1.44±0.98 | 1.07±0.68 | 202.23±141.43 |
Govt. Employee | 1.99±1.02 | 0.72±0.30 | 174.40±206.49 |
House wife | 1.68±0.98 | 0.76±0.31 | 123.89±147.82 |
Labour | 1.58±1.02 | 0.65±0.25 | 202.23±140.01 |
Other | 1.30±1.06 | 0.94±0.80 | 68.13±46.94 |
Educational status | Basic | 1.58±0.87 | 0.83±0.32 | 156.92±150.53 | 0.612 | 0.380 | 0.254 |
High school | 1.39±1.31 | 0.90±0.74 | 195.28±116.14 |
Graduate | 1.71±1.09 | 0.71±0.28 | 163.38±205.16 |
Illiterate | 1.79±1.19 | 0.83±0.77 | 175.13±153.81 |
Nature of work | Hard | 1.64±1.27 | 0.77±0.35 | 182.40±115.32 | 0.958 | 0.636 | 0.426 |
Moderate | 1.60±1.11 | 0.84±0.49 | 182.10±176.50 |
Sedentary | 1.56±0.98 | 0.83±0.72 | 155.30±163.91 |
Exercise | Regular | 1.92±0.85 | 0.72±0.42 | 72.25±91.95 | 0.584 | 0.683 | 0.482 |
Occasionally | 1.55±0.94 | 0.66±0.26 | 116.29±85.84 |
Sedentary | 1.69±0.63 | 0.93±0.89 | 147.64±125.32 |
None | 1.55±1.17 | 0.83±0.46 | 187.41±167.18 |
Smoking | Current | 1.62±1.32 | 0.74±0.26 | 166.21±161.22 | 0.082 | 0.983 | 0.509 |
Ex-smoker | 1.98±0.90 | 0.90±0.70 | 184.25±131.97 |
Never | 1.45±1.03 | 0.81±0.48 | 166.29±170.85 |
Alcohol | Current | 1.43±0.80 | 0.83±0.38 | 126.34±120.26 | 0.898 | 0.702 | 0.325 |
Ex-drinker | 1.68±1.13 | 0.72±0.44 | 185.89±117.25 |
Never | 1.61±1.09 | 0.83±0.54 | 173.12±171.22 |
Hypertension | Yes | 1.59±1.02 | 0.77±0.48 | 187.28±145.90 | 0.193 | 0.991 | 0.347 |
No | 1.63±1.44 | 0.89±0.56 | 165.31±161.44 |
Diet | Vegetarian | 1.33±0.99 | 0.94±0.68 | 190.60±172.00 | 0.401 | 0.04 | 0.429 |
Non-vegetarian | 1.71±1.00 | 0.72±0.33 | 139.71±126.37 |
Veg.+Non-vegetarian | 2.64±1.37 | 0.73±0.27 | 129.77±112.36 |
Chest pain | High | 1.47±0.68 | 0.78±0.36 | 231.41±186.73 | 0.433 | 0.343 | 0.145 |
Intermediate | 1.99±1.40 | 0.95±0.70 | 145.68±141.38 |
Low | 1.39±1.34 | 0.74±0.28 | 177.05±147.47 |
No pain | 1.19±0.63 | 0.61±0.29 | 146.49±164.57 |
BMI (kg/m2) | ≤25 | 1.65±1.04 | 0.79±0.26 | 171.78±162.34 | 0.628 | 0.416 | 0.721 |
>25 | 1.58±1.08 | 0.83±0.61 | 171.77±146.19 |
HbA1c (%) | <8 | 1.62±1.03 | 0.57±0.23 | 127.51±115.11 | 0.937 | 0.051 | 0.270 |
6-8 | 1.58±1.18 | 0.87±0.59 | 172.40±178.46 |
<6 | 1.61±1.00 | 0.87±0.51 | 185.21±141.78 |
Total cholesterol (mg/dL) | <130 | 1.61±1.17 | 0.73±0.31 | 167.21±141.70 | 0.446 | 0.462 | 0.413 |
130-200 | 1.66±0.94 | 0.95±0.69 | 167.65±166.39 |
>200 | 1.06±0.93 | 0.66±0.26 | 242.49±156.33 |
Total triglyceride (mg/dL) | <30 | 2.60±0.59 | 0.61±0.32 | 164.43±123.57 | 0.255 | 0.516 | 0.643 |
30-200 | 1.55±1.06 | 0.76±0.32 | 173.80±145.31 |
>200 | 1.72±1.09 | 1.17±1.04 | 187.01±93.13 |
HDL (mg/dL) | <40 | 1.62±1.20 | 0.74±0.31 | 167.35±134.54 | 0.697 | 0.179 | 0.228 |
40-60 | 1.66±1.01 | 0.88±0.69 | 167.90±117.18 |
>60 | 1.37±0.75 | 0.97±0.34 | 226.78±116.60 |
LDL (mg/dL) | ≤130 | 1.63±1.06 | 0.82±0.52 | 165.11±155.43 | 0.07 | 0.357 | 0.07 |
>130 | 0.54±0.68 | 0.69±0.43 | 278.42±148.32 |
sdLDL (mmol/l) | ≤30 | 1.58±0.99 | 0.84±0.59 | 167.42±137.64 | 0.168 | 0.235 | 0.352 |
>30 | 1.84±1.16 | 0.75±0.32 | 191.96±124.87 |
Folate II (nmol/l) | 0-44 | 1.62±1.07 | 0.85±0.53 | 177.55±157.78 | 0.603 | 0.092 | 0.09 |
>44 | 1.43±1.01 | 0.57±0.28 | 54.85±51.60 |
Vit.D (nmol/l) | ≤25 | 1.65±0.97 | 0.83±0.58 | 164.67±147.64 | 0.160 | 0.866 | 0.357 |
26-50 | 1.49±1.26 | 0.79±0.35 | 182.96±138.87 |
Vit. B12 (pg/mL) | 0-300 | 1.61±1.11 | 0.83±0.48 | 177.00±165.17 | 0.685 | 0.522 | 0.733 |
>300 | 1.63±1.00 | 0.82±0.56 | 164.30±145.21 |
TSH (uIU/mL) | 0-5 | 1.58±0.90 | 0.84±0.54 | 177.95±154.27 | 0.741 | 0.615 | 0.087 |
>5 | 1.77±1.82 | 0.69±0.26 | 119.63±90.43 |
Total HCY (umol/L) | 0-20 | 1.64±1.06 | 0.80±0.50 | 187.93±157.38 | 0.670 | 0.602 | 0.348 |
>20 | 1.56±1.12 | 0.77±0.33 | 154.43±125.57 |
SD: Standard deviation; VEGF: Vascular endothelial growth factor: miR; MicroRNA; BMI: Body mass index; HDL: High density lipoproteins; LDL: Low density lipoproteins; sdLDL: Small dense low density lipoproteins; TSH: Thyroid stimulating hormone; HCY: Homocysteine
Diagnostic role of serum miR-122 and miR-126 and VEGF in CAD cases
ROC curves were drawn for distinguishing CAD patients from controls. AUC for miR-122 was 0.700. At a cut-off point of ≥1.17, miR-122 discriminated CAD cases from controls with sensitivity, specificity and diagnostic accuracy of 66.67%, 70% and 67.59%, respectively. The AUC for miR-126 was 0.644. At a cut-off point of ≤0.894, miR-126 discriminated CAD cases from controls with sensitivity, specificity and diagnostic accuracy of 56.41%, 60.00% and 57.41% each respectively. The AUC for VEGF was 0.649. At a cut-off point of >106.1, VEGF discriminated CAD cases from controls with sensitivity, specificity and diagnostic accuracy of 61.18%, 64.00% and 61.82%, respectively [Table/Fig-5].
Receiver operator characteristic curve (ROC) analysis of miR-122, miR-126 and VEGF for predicting coronary artery disease.
Diagnostic feature | Cut-off value | AUC | p-value* | Sensitivity (95% CI) | Specificity (95% CI) | PPV | NPV | Diagnostic accuracy |
---|
miR-122 vs. Control | ≥1.17 | 0.700 | 0.001 | 66.67(50.08-76.94) | 70.00(50.60-85.27) | 85.25(76.59-91.07) | 44.68(35.32-54.44) | 67.59 |
miR-126 vs. Control | ≤0.894 | 0.644 | 0.02 | 56.41(44.70-67.61) | 60.00(40.60-77.34) | 78.57(69.41-85.56) | 43.48(26.46-43.79) | 57.41 |
VEGF | >106.1 | 0.649 | 0.02 | 61.18(49.99-71.56) | 64.00(42.52-82.03) | 82.25(76.93-90.92) | 32.65(24.58-41.90) | 61.82 |
miR-122+ miR-126 vs. Controls | 88.46(79.22-94.59) | 90.00(73.47-97.89) | 95.83(88.69-98.54 | 75.00(61.60-84.87) | 88.89 |
miR-122+ miR-126+VEGF vs. Controls | 98.72(93.06-99.97) | 96.00(79.65-99.90) | 98.72(91.86-99.81) | 96.00(77.36-99.41) | 98.06 |
*p-value of <0.05 was considered as significant; AUC: Area under curve; PPV: Positive predictive value; NPV: Negative predictive value; VEGF: Vascular endothelial growth factor: miR: MicroRNA; CI: Confidence interval
Combined diagnostics of miR-122 and miR-126 and VEGF in CAD cases
Compared with individual diagnostics of miR-122 and miR-126, combined diagnostics using one or more test positivity as a positive test, and all two tests negative as negative interpretation, the combination of miR-122 and miR-126 shall provide efficacy in distinguishing CAD cases from controls. Combination of miR-122 and miR-126 with VEGF provides higher sensitivity and specificity of 98.72 and 96.00%, respectively with diagnostic accuracy of 98.06 [Table/Fig-5].
Discussion
In developed and developing countries, CAD is a major cause of heart attack. It is estimated that an approx. of 7.8 billion deaths will result from a heart attack [24]. Circulating miRNA played a crucial role in various pathophysiological processes. As consequences of pathological changes in a different form of CVD, altered level of miRNA has been reported [25,26].
In different pathophysiological conditions, circulating miRNA has been identified as an important biomarker [27]. Compared to proteomic biomarker identification, circulating miRNA has been reported as a more efficient biomarker. It has been demonstrated that there is an emerging role of miRNA in cardiovascular disease and cardiac arrhythmia, however, most of these biomarkers showed reduced sensitivity, specificity, or unsuitability for early diagnosis [28-30].
Fichtlscherer S et al., 2010, reported that miR-133 and miR-208a were up-regulated in CAD cases compared to controls [31]. Study of D’Alessandra Y et al., reported the positive correlation of miR-1, miR-122, miR-126, miR-133a, miR-133b, and miR-199a in patients with stable and unstable angina, whereas miR-337-5p and miR-145 found up-regulated in stable or unstable angina patients compared to controls [32]. In CAD patients; miR-133a, miR-208a, miR-1, miR-122, miR-133b, miR-337-5p, miR-433 and miR-485-3p were found to be significantly up-regulated, whereas miR-126, miR-17, miR-92a, miR-145, miR-155, and miR-199a levels were markedly down-regulated in CAD [33].
In this study group, the miR-122 and 126 levels were higher in cases with an LDL level of ≤130, however, the difference was not statistically different (p=0.07, 0.357). The level of miR-126 was also lowered in CAD cases with a total cholesterol level of >200 mg/dL. These findings entail a link between the deregulation of serum miR-126 and elevated levels of LDL cholesterol in CAD patients. Sun X et al., found a significantly decreased level of miR-126 in patients with CAD and high Low-Density Lipoprotein (LDL) cholesterol. In contrast, the miR-126 level increased significantly when LDL cholesterol was high in patients who had risk factors for CAD but did not have angiographically significant CAD [34].
Subjects with similar clinical features were recorded to ensure that the study populations were comparable. Statistical analysis showed that these factors do not affect circulating miR-122 and miR-126 levels in present study population. The present study findings suggest that serum circulating miRNAs might be feasible biomarkers for the diagnosis of CAD patients. The serum miR-122 level was significantly up-regulated in CAD cases compared to healthy controls (p=<0.0001) while the miR-126 level was significantly down-regulated in cases compared to controls (p=0.02). It is reported that low Vitamin D level negatively affects cardiac function. In present study samples, vitamin D levels were significantly lower in CAD cases compared to controls (p=0.0083), levels of miR-122 and 126 were higher in cases with lower Vitamin D level (≤25 vs. 26-50 ng/mL). Observational studies of dietary Vitamin D showed that both measured 25(OH)D and estimated 25(OH)D were inversely associated with risk of incident hypertension in both men and women [15]. Interestingly; the miR-122 and 126 levels were higher in cases with a lower level of folate. Vitamin B12 was significantly higher in CAD cases compared to controls (p=0.0016). The miR-122 and 126 levels were higher in cases with >300 pg/mL. The present study finding of Vitamin B12 is consistent with a finding of Hung J et al., reporting that the lower Vit.B12 concentration does not increase the risk of fatal cardiovascular disease in a general population [35]. However, in present study group, the total homocysteine level was lowered in CAD cases compared to controls (p=0.163) and miR-122 and 126 levels were higher in cases with homocysteine level with 0-20 umol/L. However; the effect of folate and vitamin B12 on homocysteine concentrations and risk of cardiovascular disease in the general population is still not clear so far [36].
The AUC values for miR-122 and 126 were 0.700, 0.644, respectively when compared to non-CAD patients. The combined sensitivity and specificity of miR-122 with miR-126 when either test positive was taken as positive for CAD cases showed a maximum sensitivity and specificity of 88.46% and 90.00 with a diagnostic accuracy of 88.89 to discriminate CAD cases from controls. The combination of miR-126 and miR122 with VEGF gives much higher sensitivity and specificity with a diagnostic accuracy of 98.06 compared to the combination of miR-122 and 126.
In present study, the VEGF level in CAD cases was significantly higher (182.97±156.49) compared to normal control (105.49 ±103.88) (p=0.02). The finding of present study is supported by the study of Blann AD et al., [37]. Lin TH et al., reported higher VEGF level in CAD cases compared to control [18]. However, any association of VEGF level with demographic and clinical characteristics of cases was not found [Table/Fig-4]. In a study of Amr KS et al., and Sasahira T et al., reported the down-regulation of miR-126 with increased activity of VEGF [38,39].
Limitation(s)
The major limitation of the present study was the small sample size.
Conclusion(s)
To conclude, serum miR-122 and 126 and level of circulating VEGF might be a valuable novel non-invasive biomarker for early diagnosis of CAD patients, further leading to therapeutic attenuation of CAD patients by targeting these miRNAs i.e., 122 and 126 in addition to the anti-VEGF therapy may be a promising candidate for the treatment of CAD subjects.
*p-value of <0.05 was considered as significant; BMI: Body mass index; HDL: High density lipoproteins; LDL: Low density lipoproteins; VLDL: Vey low density lipoproteins; TG: Triglycerides; TC: Total cholesterol; sdLDL: Small dense low density lipoproteins; TSH: Thyroid stimulating hormone; HCY: Homocysteine; SD: Standard deviation*p-value of <0.05 was considered as significant; SD: Standard deviation; VEGF: Vascular endothelial growth factor: miR; MicroRNASD: Standard deviation; VEGF: Vascular endothelial growth factor: miR; MicroRNA; BMI: Body mass index; HDL: High density lipoproteins; LDL: Low density lipoproteins; sdLDL: Small dense low density lipoproteins; TSH: Thyroid stimulating hormone; HCY: Homocysteine*p-value of <0.05 was considered as significant; AUC: Area under curve; PPV: Positive predictive value; NPV: Negative predictive value; VEGF: Vascular endothelial growth factor: miR: MicroRNA; CI: Confidence interval