Diagnostic Markers Of Cardiovascular Disease: Current Practice And New Developments

Circulating Biomarkers

All the diseases involving impaired structure and functioning of the heart or blood vessels are termed as cardiovascular disease. Cardiovascular diseases include coronary artery disease which include angina and myocardial infraction. Along with these other cardiovascular diseasesinclude stroke, heart failure, hypertensive heart disease, rheumatic heart disease, cardiomyopathy, heart arrhythmia, congenital heart disease, valvular heart disease, carditis, aortic aneurysms, peripheral artery disease, thromboembolic disease, and venous thrombosis. High blood pressure, smoking, diabetes, lack of exercise, obesity, high blood cholesterol, poor diet, and excessive alcohol consumption are the causes of cardiovascular disease. It has been estimated that 1 in 3 adults are suffering through cardiovascular diseases. Biomarker can be defined as the measurable and quantifiable biological parameter which specify physiological and pathological status of the individual’s health (Ahearn et al., 2015; Thiara, 2015).

Evaluation of validity biomarkers is important aspect in medical research because it can be helpful in augmenting conventional risk prediction methods for cardiovascular disease. Evaluation of these biomarkers is possible due to emergence of advanced techniques for these biomarkers. For cardiovascular disease there is existence of hundreds of biomarkers (Björnson et al., 2016). These biomarkers are of different types like circulating, genetic, and imaging. Study of biomarkers for cardiovascular disease would be helpful in understanding pathophysiological condition of the patient and initiating relevant treatment (Albert, 2011).

Proteomic research gave further contribution to the discovery of novel biomarkers. It proved helpful in the understanding pathological independence of various biochemical biomarkers. It is evident that combination of different biomarkers corresponding to different pathological conditions can be helpful in improving sensitivity and specificity of their use. As shown in the fig. , marker of myocardial ischemia can be incorporated in the markers of myocardial necrosis.

Novel strategies need to be developed for selecting biomarkers for cardiovascular disease. In cardiovascular disease different events occur like biomechanical stress, cardiac injury, fluid overload and inflammatory reaction. Specific biomarkers for each of these events, novel biomarkers has been discovered. These biomarkers include procalcitonin, copeptin, heart-type fatty acid-binding protein and growth differentiation factor.Detailed information about these biomarkers specific to cardiovascular disease can be helpful in accurate prediction of these diseases (Wang et al, 2017).

Study of biomarkers for cardiovascular diseases is important because it is a leading cause of mortality and it can be successfully prevented by risk prediction in the individuals. Traditionally hypertension, diabetes mellitus, hyperlipidemia, and smoking were used to be risk factors for cardiovascular diseases (Herder et l., 2011). However, these risk factors can’t be used for initiating preventive treatment. Hence, idea of cardiovascular biomarkers was introduced. These biomarkers can be used as indicator of normal physiology, pathology and pharmacological outcome (Nadir et al., 2012).  

Genetic Biomarkers

In case of cardiovascular diseases, it is very important to evaluate correct biomarker at the correct stage of the disease. In case of cardiovascular disease, subclinical stage of the cardiovascular disease precedes actual diseased condition. Atherosclerosis can occur in an individual almost decade prior to the occurrence of myocardial infraction. Varied types of methods can be useful for identifying cardiovascular biomarkers at varied phases of the disease. Imaging biomarkers are more useful in subclinical stage as compared to the earlier stage of the disease. Genetic biomarkers can provide information about the susceptibility of the disease;however, these biomarkers are nor useful in identifying stage of the disease. Circulating biomarkers can provide information about different phases of disease along with different stages of biological pathways (Duffy and Hameed, 2015).

Discrimination, calibration, and reclassification are the important aspects which need to be considered during evaluation of the cardiovascular biomarkers. Discrimination refers to biomarkers which distinguish between disease and normal individuals. Calibration refers to agreement between predicted and observed risks among patients with different stages of disease and its comparison with the baseline values. Different calibrations should be used for varying population with different risks. Otherwise, there would be possibility of false positive or false negative results. Reclassification is required in individuals with narrow range of risks (Kavousi et al., 2012).

Circulating biomarkers are useful in identifying various pathological pathways like inflammation (such as CRP, interleukin-6, lipoprotein-associated phospholipase A2 [LpPLA2]), oxidative stress (oxidized LDL, nitrotyrosine), lipid metabolism [lipoprotein(a)], thrombosis (plasminogen activator inhibitor-1, D-dimer), endothelial dysfunction (homocysteine, urinary microalbuminuria), hemodynamic stress (natriuretic peptides), and cardiomyocyte injury (cardiac troponins) (Reichlin et., 2012; Wensley et al., 2011; Lubrano and Balzan, 2015). Circulating biomarkers are useful in identifying future events of cardiovascular diseases. However, there is scarcity of data related todiscrimination, calibration, or reclassification for circulating biomarkers. Maximum amount of data is available for C-reactive protein (CRP) which is a hepatic origin acute-phase reactant. Several studies established that there is close association between amount of CRP and risk of cardiovascular diseases. Meta-analysis of 32 studies indicated that CRP concentration  ≥ 3.0 mg/dLcan predict cardiovascular disease in approximately 60 % patients. Even tough, clinical evidence is available for association of CRP and cardiovascular disease, mechanistic link between CRP and cardiovascular disease is not completely established. Vascular inflammation is main contributor for atherogensis; however, there is less evidence available for the contribution of CRP in this process. CRP can be used as predictive risk which is useful in clinical management. Lipoprotein-associated phospholipase A2 (Lp-PLA₂) is another circulating biomarker produced by inflammatory cells and bound to low density lipoprotein (LDL). Meta-analysis of 32 studies indicated relationship between Lp-PLA₂ and risk of cardiovascular disease. Interleukin-6 is an inflammatory biomarker and there is robust association between interleukin-6 and risk of cardiovascular diseases. Observational studies established that there is positive relationship between lipoprotein and coronary heart disease. All these circulating biomarkers are helpful in modest augmentation of the risk prediction of cardiovascular disease however these biomarkers are unable to reclassify individuals to low or higher risk of cardiovascular diseases (Hazarika and Annex, 2017; Huang, 2017).  

Imaging Biomarkers

Genetic risk factors can be evaluated in an individual prior to the development of conventional risk factors. Hence, these biomarkers can be implemented to the lager population. It is evident that there is statistically significant association between genetic markers and cardiovascular risks. However, effect size is less because there is approximate 10 – 30 % cardiovascular risk for each copy of risk allele. Efforts are being made to use multiple genetic markers to improve performance of these biomarkers in predicting cardiovascular risks. Even though, there is increase in number of genetic biomarkers for predicting cardiovascular disease, there is not much improvement in gain from the individual biomarker. Single-nucleotide polymorphisms at chromosome 9p21 exhibited modest association with the cardiovascular risk (Lv et al., 2012).

Imaging biomarker can be defined as biologic feature or biomarker which can be detected using an image. This image can be used for patient’s diagnosis. In contrast to the circulating and genetic biomarker, imaging biomarkers would be helpful in evaluating current status of the disease. Imaging biomarkers are with improved productive efficacy. Some of the plaques might be below the size which can notbe detected by imaging techniques. Coronary calcium scanning and carotid ultrasound are the most widely used imaging techniques for subclinical atherosclerosis. Coronary calcium can be detected by electron beam computed tomography or multidetector computed tomography. Most of the observational studies indicated that relative risk of coronary events of ≥2are more in patients with coronary calcium as compared to the no coronary calcium. Carotid ultrasound is useful in measuring carotid intima-media thickness (IMT) and the combined thickness of the intimal and medial layers of the carotid artery.  It can be considered as the earlier biomarker as compared to the coronary calcium for subclinical atherosclerosis (Rana et al., 2012). Meta-analysis of 8 studies indicated that increment in carotid IMT by 0.1 mm can have risk of 10 to 15 % myocardial infraction (Jiang, 2017).

Most of the studies mentioned in the literature included single biomarker for the diagnosis of the cardiovascular disease. A wide spectrum of cardiovascular biomarkers might have prognostic value, however; there was no valid clinical evidence for these biomarkers. These biomarkers can provide improved prediction of cardiovascular abnormalities and mortality in diabetes patients, however novel data need to be generated for these biomarkers. Abundant data is available for CRP and troponin T as the biomarker for the cardiovascular disease, however, less data is available for IL-6 and creatine kinase. Moreover, creatinine kinase is associated with variability in prediction of cardiovascular risks. Hence, these biomarkers should be used in combination to get optimal data for the diagnosis of cardiovascular conditions. Available data for the diagnosis of cardiovascular conditions using multimarker based diagnosis is insufficient in terms of discrimination and calibration. There was tremendous progress in the biomarker study for cardiovascular conditions, these biomarkers were not being utilised in proper way. Factors like sensitivity, cost of the assays for biomarker estimation, duration for estimation of biomarker estimation and clinical implication should be considered while selection of biomarker for diagnosis of cardiovascular conditions. Hence, this multimarker based study was designed considering all these factors (Coffman and Richmond-Bryant, 2015; Junenette et al., 2012).

Discrimination, Calibration, and Reclassification

Measurement of single biomarker for cardiovascular disease is a debatable issue because it is evident that there is statistically significant association between the biomarker and disease outcome which is necessary but not the sufficient measure to predict risk or occurrence of disease. Hence, this study was planned to measure multiple biomarkers to get maximum outcome from the study. In this study biomarkers mainly for myocardial infraction and anginal pain were studied. These two diseases were selected because these are the most prevalent cardiovascular diseases which can occur in all the age group people. Myocardial infraction, in common known a heart attack, occurs due to reduced supply of blood to the heart and damages heart muscles. Its most common symptom is chest pain.

Aims :

To implement multimarker based strategy for the diagnosis of cardiovascular conditions.

To evaluate clinical utility of multimarker based strategy for the diagnosis of cardiovascular conditions.

Objectives:

To evaluate whether combination of CRP, IL-6, troponin T and creatinine kinase are associated with the increased risk prediction of cardiovascular conditions like anginal pain and abnormal ECG.

To evaluate effect of the patient age on level of biomarkers like CRP, IL-6, troponin T and creatinine kinase.  

To evaluate effect of sex of the patients on level of biomarkers like IL-6and creatinine kinase.  

Methods

A systematic research review from the meta-analysis were performed to review the biomarker of cardiovascular diseases and the risk and benefit of the multimarker. Randomised controlled trial was performed for the evaluation of diagnostic biomarkers for cardiovascular diseases. Patients were identified from the patient’s case reports and computerised clinic registers from hospitals in Europe (Supino and and  Borer, 2012; Hirsh, 2017).

Literature search:

A literature search was performed in Medline using Pubmed and Embase. Search terms used were Cardiovascular biomarkers, cardiovascular diseases, diagnostic biomarkers for cardiovascular diseases, circulating biomarkers for cardiovascular diseases, imaging biomarkers for cardiovascular diseases and genetic biomarkers for cardiovascular diseases. Titles and abstracts were screened for the mentioned search titles. Duplicate articles were removed from the study.  Eligibility of the articles were decided by reading abstracts and material and methods. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements were used for the literature review. 5024 articles in Medline and 3521 articles EMBASE were found respectively. 8221 articles were excluded based on the titles, abstracts, materials and methods. 324 articles were selected as eligible articles based on titles and abstracts. 244 articles were excluded based on the material and methods.

Importance of Biomarkers in Predicting and Preventing Cardiovascular Diseases

Following studies were included: those mentioned diagnostic accuracy test, mentioned gold standard test for particular biomarker and those mentioned sensitivity and specificity, cut-off values, true and false positive and negative data. Following studies were excluded: which were not prospective, not mentioned other than circulating biomarkers, not mentioned risks related to all cause death, not mentioned cardiovascular risks related to mentioned biomarkers and not mentioned pooled data in the form of tables and figures. Data was abstracted related to the end points, to which group risk applied, whether there was adjustment in the risk assessment, whether biomarkers were evaluated in combination or individual.

Peripheral venous blood was collected for the estimation of the circulating biomarkers. For the estimation of blood markers blood samples should be collected in 5 ml lithium heparin tubes. Blood collection tubes should be directly inserted into instrument for automated measurement. In this study, all the biomarkers were estimated from the single sample. Hence, it proved useful in reducing variation among different biomarkers. Lower limit for detection of different biomarkers were as follows : CRP (0.1 mg/ml), Interleukin- 6 (10 pg/ml), Toponin T (0.001 ng/ml) and creatinine kinase 100U/L. Upper limit for detection of these biomarkers were as follwos : CRP (5 mg/ml), Interleukin- 6 (100 pg/ml), Toponin T (10 ng/ml) and creatinine kinase 1000 U/L. Multimarker strategy was considered positive because all the estimated biomarkers were within upper and lower limit of detection.Human Basic Kit FlowCytomix (BMS8420FF, eBioscience, USA) was used for the estimation of IL-6 and Human FlowCytomix (Simplex BMS8213FF and BMS8288FF, eBioscience, USA) on a BD FACSCalibur instrument (BD Biosciences, USA) according to the manufacturer’s instructions (Dongfang et al., 2013). Data was collected CellQuestsoftware (BDBiosciences) and calculated using the Flow  Cytomix Program (eBioscience, USA). Lower detection limit for IL-6 was 1 pg/ml. C-reactive protein (CRP) measurements were performed with the highly sensitive CRP-Latex (II) immunoturbidimetric assay (Denka Seiken) on a Hitachi 911 immunoanalyzer(Roche Diagnostics) (Vaucher et al. 2014). A discrete fluorimetric immunoassay analyser like Dade-Behring Stratus CS STAT was used for the estimation of troponin T and creatinine kinase. Troponin T and creatinine kinase estimation were performed in the single blood sample (Sadoh et al., 2014). The sensitivities troponin T and creatinine were 0.03 microg/L for cTnI and 0.4 microg/L for CK-MB.

Literature has been published for the evaluation of cardiovasucalr biomarkers like Myoglobin (MYO), Cardiac troponin I (cTnI), Creatine phosphokinase MB (CK-MB), Myeloperoxidase (MPO), brain natriuretic peptide (NT-proBNP), Exosomal miRNA, C-Reactive Protein (CRP), Matrix metalloproteinase-8 (MMP-8), MMP-9 and tissue inhibitor of MMP-8 (TIMP-1). Most commonly used biomarker protocol was combination of CK-MB and cTn.  Usage of this protocol was decreased by approximately 20 % from 2009 to 2014. In few of the hospitals, combination of CK-MB, myoglobin, and cTn was being used. However, application of this protocol also was being reduced by approximately 50 % in the recent years.  On the other hand, application of cTn-only protocol was increased by 20 % in the recent years. Combination of cTn and myoglobin protocol was rarely used for the diagnosis of cardiovascular disease. It was evident that approximately 60 %  hospitals used same biomarker and remaining hospitals added new biomarkers for the improvement in the protocol (Feldman et al., 2011; Wang, 2011).It was evident that approximately 50 % protocols analysed the biomarkers with interval of > 10 hours and 30 % protocols analysed the biomarkers with interval of 4 to 6 hours. cTn was used in all the protocols either in individually or in combination. 90 % of the protocols used cTnI and remaining 10 % used cTnT as a biomarker. Most common time points used for the evaluation of CK-MB were 0, 6 and 12 hours. Immunoassays were used for the evaluation of Tn, while enzymatic assays were used for the evaluation of creatinine kinase (Montgomery and Brown, 2013; Berezin, 2015; Halim et al., 2012).

Descriptive statistics were used in the analysis of the obtained data. One way ANOVA followed by Dunnetttest was used to compare data obtained from the normal participants with the diabetic patients. Data were represented as Mean±S.D. Data of the normal participants were compared with the diabetes patients of different age groups like above  50yrs age, between 30 to 50 yrs and betwee 20 to 30yrs. The statistical significance was set at P? 0.05. Graph pad prism was used for the statistical analysis.

Table 1: Demographic data for the participants?

Parameters	G1 – Control	G2 – Above 50 yrs	G3- Between 30 – 50 yrs	G4- Between 20 – 30 yrs
N	100	100	100	100
Sex (M/F)	50/50	50/50	50/50	50/50
Smokers	25/20	34/29	32/25	36/27
Diabetes duration (years)	Nil	7.5±2.6	3.4±1.8	1.2±0.8
Weight (kg)	68±2.6	70±4.6	71±3.2	69±2.9
Height (m)	1.70±0.04	1.69±0.03	1.70±0.02	1.68±0.04
BMI (kg/m2)	27.6±1.1	26.6±1.0	27.1±1.1	26.9±0.9

The baseline demographic information about the participants is presented in the table 1. Mean age of different participant group were (mean±standard deviation) Group 1- 40±15yrs, Group 2- 50±9 yrs, Group 3- 30±5 yrs and Group 4- 20±3 yrs. Average BMI for control patients were 27.6±1.1 kg/m² and average BMI for diabetic patients were G2-26.6±1.0, G3- 27.1±1.1 and G4-26.9±0.9 kg/m².

Table 2: CRP levels at baseline and after 5 years.

Group	CRP level at baseline (mg/ml)	CRP level after 5 yrs (mg/ml)
G1 – Control	0.2±0.0	0.2±0.1
G2 – Above 50 yrs	0.3±0.01	4.0±0.1***
G3- Between 30 – 50 yrs	0.2±0.01	3.0±0.1***
G4- Between 20 – 30 yrs	0.2±0.01	1.0±0.1*

Data are expressed as Mean±S.D. G – Group; n- 100;  ^***p?0.001, ^*p?0.01 as compared to G1.

Graph 1: CRP levels after 5 years.

Data are expressed as Mean±S.D. G – Group; n- 100;  ^***p?0.001, ^*p?0.01 as compared to G1.

CRP levels for different groups were found to be : Group I- 0.2±0.1 mg/ml, Group II-  4±0.1 mg/ml, Group III- 3±0.1 mg/ml and Group IV- 1±0.1 mg/ml (Table 2; Graph 1).

IL –  6 biomarker :

Table 3: IL-6 levels at baseline and after 5 years.

Group	IL-6 level at baseline (pg/ml)	IL-6 level after 5 yrs (pg/ml)
G1 – Control	0.1±0.0	0.1±0.1
G2 – Above 50 yrs	0.2±0.01	5.0±0.2***
G3- Between 30 – 50 yrs	0.2±0.0	3.0±0.1***
G4- Between 20 – 30 yrs	0.1±0.0	1.0±0.1**

Data are expressed as Mean±S.D. G – Group; n- 100;  ^***p?0.001, ^*p?0.01 as compared to G1.

Graph 2: IL-6 levels at baseline and after 5 years.

Data are expressed as Mean±S.D. G – Group; n- 100;  ^***p?0.001, ^*p?0.01 as compared to G1.

Table 4: IL-6 levels in male population at baseline and after 5 years.

Group (Male)	IL-6 level at baseline (pg/ml)	IL-6 level after 5 yrs (pg/ml)
G1 – Control	0.1±0.0	0.1±0.1
G2 – Above 50 yrs	0.2±0.01	2.0±0.2**
G3- Between 30 – 50 yrs	0.2±0.0	1.8±0.1**
G4- Between 20 – 30 yrs	0.1±0.0	1.0±0.0*

Data are expressed as Mean±S.D. G – Group; n – 100 for G1; n – 50 for G2 to G4;  ^**p?0.01, ^*p?0.05 as compared to G1.

Graph 3: IL-6 levels in male population after 5 years.

Data are expressed as Mean±S.D. G – Group; n – 100 for G1; n – 50 for G2 to G4;  ^**p?0.01, ^*p?0.05 as compared to G1.

Table 5: IL-6 levels in female population at baseline and after 5 years.

Group (Female)	IL-6 level at baseline (pg/ml)	IL-6 level after 5 yrs (pg/ml)
G1 – Control	0.1±0.0	0.1±0.0
G2 – Above 50 yrs	0.2±0.01	5.5±0.3***
G3- Between 30 – 50 yrs	0.2±0.0	4.5±0.2***
G4- Between 20 – 30 yrs	0.1±0.0	3.5±0.1**

Data are expressed as Mean±S.D. G – Group; n – 100 for G1; n – 50 for G2 to G4;  ^***p?0.001, ^*p?0.01 as compared to G1.

Graph 4: IL-6 levels in female population after 5 years.

Data are expressed as Mean±S.D. G – Group; n – 100 for G1; n – 50 for G2 to G4;  ^***p?0.001, ^*p?0.01 as compared to G1.

IL-6 levels for different groups were found to be : Group I- 0.1±0.1 pg/ml, Group II-  5±0.2 pg /ml, Group III- 3±0.1 pg /ml and Group IV- 1±0.1 pg/ml. IL-6 levels in males for different groups were found to be : Group I- 0.1±0.1 pg/ml, Group II-  2±0.2 pg/ml, Group III- 1.8 ±0.1 pg/ml and Group IV- 1.0±0.0 pg/ml. IL-6 levels in females for different groups were found to be : Group I- 0.1±0.0 pg/ml, Group II-  5.5±0.3 pg/ml, Group III- 4.5±0.2 pg/ml and Group IV- 3.5±0.1 pg/ml (Table 3, 4 and 5; Grpah 2, 3, 4).

Troponin biomarker :

Table 6: Troponin T levels at baseline and after 5 years.

Group	Troponin T at baseline (ng/ml)	Troponin T after 5 yrs (ng/ml)
G1 – Control	0.005±0.0	0.005±0.0
G2 – Above 50 yrs	0.006±0.0	0.05±0.0***
G3- Between 30 – 50 yrs	0.005±0.0	0.02±0.0**
G4- Between 20 – 30 yrs	0.005±0.0	0.01±0.0*

Data are expressed as Mean±S.D. G – Group; n – 100;  ^***p?0.001, ^***p?0.01, ^*p?0.05 as compared to G1.

Graph 5: Troponin T levels after 5 years.

Data are expressed as Mean±S.D. G – Group; n – 100;  ^***p?0.001, ^***p?0.01, ^*p?0.05 as compared to G1.

Troponin levels for different groups were found to be : Group I- 0.005±0.0 µg/ml, Group II-  0.05±0.0 µg/ml, Group III- 0.02±0.0 µg/ml and Group IV- 0.01±0.0 µg/ml (Table 6; Graph 5).

Table 7: Creatinine kinase levels at baseline and after 5 years.

Group	Creatinine kinase at baseline (U/L)	Creatinine kinase after 5 yrs (U/L)
G1 – Control	145±20	150±25
G2 – Above 50 yrs	152±27	200±40*
G3- Between 30 – 50 yrs	147±22	175±35
G4- Between 20 – 30 yrs	149±23	170±30

Data are expressed as Mean±S.D. G – Group; n – 100;  ^*p?0.05  as compared to G1.

Graph 6: Creatinine kinase levels after 5 years.

Data are expressed as Mean±S.D. G – Group; n – 100;  ^*p?0.05  as compared to G1.

Table 8: Creatinine kinase levels in male population at baseline and after 5 years.

Group (Male)	Creatinine kinase at baseline (U/L)	Creatinine kinase after 5 yrs (U/L)
G1 – Control	146±21	175±20
G2 – Above 50 yrs	149±26	210±35
G3- Between 30 – 50 yrs	146±19	185±35
G4- Between 20 – 30 yrs	148±24	180±35

Data are expressed as Mean±S.D. G – Group; n – 100 for G1; n – 50 for G2 to G4

Graph 7: Creatinine kinase levels in male population after 5 years.

Data are expressed as Mean±S.D. G – Group; n – 100 for G1; n – 50 for G2 to G4

Table 9: Creatinine kinase levels in female population at baseline and after 5 years.

Group (Female)	Creatinine kinase at baseline (U/L)	Creatinine kinase after 5 yrs (U/L)
G1 – Control	144±20	125±20
G2 – Above 50 yrs	150±28	160±30
G3- Between 30 – 50 yrs	144±21	150±25
G4- Between 20 – 30 yrs	145±23	145±25

Data are expressed as Mean±S.D. G – Group; n – 100 for G1; n – 50 for G2 to G4

Graph 8: Creatinine kinase levels in female population after 5 years.

Data are expressed as Mean±S.D. G – Group; n – 100 for G1; n – 50 for G2 to G4

Creatine kinase levels for different groups were found to be : Group I- 150±25 U/L, Group II-  200±40 U/L, Group III- 175±35 U/L and Group IV- 170±30 U/L. Creatine kinase levels in males for different groups were found to be : Group I- 175±20 U/L, Group II-  210±35 U/L, Group III- 185±35 U/L and Group IV- 180±35 U/L. Creatine kinase levels in females for different groups were found to be : Group I- 125±20 U/L, Group II-  160±30 U/L, Group III- 150±25 U/L and Group IV- 145±25 U/L (Table 7,8 and 9; Graph 6, 7 and 8).

In this study, evaluation of circulating biomarkers for cardiovascular diseases were selected because these biomarkers can give cost effective and rapid results as compared to the imaging and genetic biomarkers. Hence, these methods can be applicable to all the population in the Europe.

CRP biomarker :

Data collected for all the biomarkers is primary data and all the data were collected by instrumental methods. Traditional assays for CRP estimation were associated with drawback like inadequate sensitivity for the detection of required amount of CRP for cardiovascular disease prediction. Hence, in this study highly sensitive assay was used for estimation of CRP. CRP was selected as biomarker because it is highly stable and it can be accurately measured in both fresh and frozen plasma (Linden et al., 2014).

CRP levels for different groups were found to be : Group I- 0.2±0.1 mg/ml, Group II-  4±0.1 mg/ml, Group III- 3±0.1 mg/ml and Group IV- 1±0.1 mg/ml (Table 2; Graph 1). From the obtained data, it is evident that CRP levels are good indicators of cardiovascular diseases in age dependent manner. Obtained CRP levels were well corelated to the cardiovascular abnormalities like anginal pain and abnormal ECG which are good indicators of cardiovascular risk. In epidemiological studies, CRP has been used for prediction of multiple cardiovascular conditions like myocardial infarction, stroke, peripheral arterial disease, and sudden cardiac death. In this study also, raised CRP levels exhibited good relation with anginal pain and abnormal ECG, which are the main predisposing factors for myocardial infarction, stroke, peripheral arterial disease, and sudden cardiac death. It is evident that CRP is more predictive than LDL and cholesterol for cardiovascular events (Ndrepepa et al., 2014). CRP levels of <1, 1 to 3, and >3 mg/L can be considered as the low-, moderate-, and high-risk groups respectively for cardiovascular risks (Wang et al., 2017; Hermida et al., 2012). In this study, it has been observed that patients above age group 50  yrs were at high risk, patients between age 30 to 50 yrs with moderate risk and patients between 20 to 30 yrs age group with low cardiovascular risk. 60 % patients above age group 50, 35 % patients of age group 30 to 50 yrs and 12 % patients of age group 20 to 30 yrs exhibited anginal pain and abnormal ECG. CRP levels have longer duration predictive value because abnormal cardiovascular events can occur after 20 yrs. Distribution of CRP among male and female were similar, hence CRP were measured in male and female collectively (Sánchez-Muniz et al., 2017).

IL-6  biomarker :

From the literature, it is evident that levels of IL-6 are good predictors of ischemic conditions, unstable angina and ST-elevated myocardial infarction (STEMI) and coronary artery disease (Fan et al., 2011). However, less studies were conducted to establish relationship between IL-6 and cardiovascular events. Hence, IL-6 was included in this multimarker based biomarker evaluation. In previous studies, it was indicated that IL-6 predicted mortality in female due to cardiovascular events, however in  male it didn’t predicted mortality due to cardiovascular events (Bacchiega et al., 2017). Hence, in this study IL-6 was evaluated separately in male and female as a cardiovascular risk factor. In the literature, conflicting results are available for the relation between IL-6 levels and cardiovascular mortality.Few studies indicated that IL-6 is good predictor of cardiovascular mortality as compared to the CRP levels(Dongfang et al., 2013). However, it was also established that elevated levels of IL-6 are not good predictor of all-cause mortality in patient population of average age 100 yrs. These conflicting results for IL-6 might be due to small sample size, old age of the patients and heterogenous population (Sarwar et al., 2012). However, in this study association between raised levels of IL-6 and cardiovascular events was clearly established. IL-6 levels above 5 pg/ml, 3 pg/ml and 1 pg/ml were considered as high, moderate and low risk for cardiovascular events(Bacchiega et al., 2017).In this study, it has been observed that patients above age group 50  yrs were at high risk, patients between age 30 to 50 yrs with moderate risk and patients between 20 to 30 yrs age group with low cardiovascular risk. 45 % patients above age group 50, 25 % patients of age group 30 to 50 yrs and 7 % patients of age group 20 to 30 yrs exhibited anginal pain and abnormal ECG. Standard commercial kits were used for the measurement of IL-6. Individually, it would be difficult to obtain robust data for IL-6 as cardiovascular risk factor due to its short half-life. Hence, it should be included in the multimarker based evaluation of the cardiovascular diagnostic biomarkers. In the epidemiological studies, imprecise measurements were mentioned for IL-6. It can be overcome by  repeatedly estimating IL-6 in the same patient (Swerdlow et al., 2012; McInnes et al., 2013).   

Troponin T biomarker :

Assay employed for the estimation of Troponin T can detect 10 times lower concentration as compared to the conventional assays. EDTA treated plasma samples were used for the estimation of troponin T and these samples were stored at or below −70°C until analysis. Cardiac troponin T are the components of the contractile apparatus of the cardiomyocytes, hence these can be employed as the biomarkers for the diagnosis of myocardial necrosis. It has been established that little rise in the troponin T levels were associated with increased risk of acute coronary syndromes (Pareek et al., 2015; Starnberg et al., 2014). Troponin T levels greater than 0.01 µg per ml are associated with the cardiac mortality.

Troponin levels for different groups were found to be : Group I- 0.005±0.0 µg/ml, Group II-  0.05±0.0 µg/ml, Group III- 0.02±0.0 µg/ml and Group IV- 0.01±0.0 µg/ml (Table 6; Graph 5). Obtained data indicated that, troponin T values are good indicators of cardiovascular risk in age dependent manner. Troponin T levels were also corelated with cardiovascular conditions like anginal pain and abnormal ECG. Cardiac troponin T was found to be beneficial biomarker for short term risk of cardiovascular risks like acute myocardial infraction and acute coronary syndrome. Troponin T was found to be sensitive marker in combination with myoglobin for myocardial infraction (McEvoy et al., 2015). Assay employed for estimation of Troponin T was with high sensitivity, low limit of detection, low imprecision and low reference limits. Assay employed for the estimation of troponin T was specific to the cardiac muscles. In this assay, two specific antibodies directed against epitopes in part of the cTnT molecule differentiate cardiac and muscle isoforms (Saunders et al., 2011). It was evident that, alteration in the troponin T levels were associated with the new Q waves and ST segment elevation. In this study also, same observations were found for elevated levels of troponin T. Sensitivity and specificity of troponin T were found to be 99 % and 82 % respectively. Troponin T can predict myocardial necrosis below the detection limit of creatinine kinase. Hence, troponin T should be used in combination with the creatinine kinase for the diagnosis of the cardiovascular events to avoid false positive results. Establishing relationship between anginal pain, abnormal ECG and troponin T would be difficult. There was no ST elevation in patients with more than 12 hours anginal pain and elevated levels of troponin T. In this study, it was evident that there were changes in ST segment of ECG in patients with age group above 50 yrs because in these patients anginal pain lasts for approximately 20 hours. In patients between age group 20 to 50, anginal pain lasts for approximately 10 hours in 70 % of patients. In these patients, there was no alteration in the ST segment, despite increase in the troponin T levels. Variability in the estimation of troponin T can be avoided by maintaining same sampling timing for all the patients and employing same analytical method (HitsumotoShirai, 2015).Troponin T can be useful in predicting cardiovascular risk in the absence of traditional risk factors and other biomarkers like CRP and BNP. However, in this study troponin T was incorporated to assess effectiveness of troponin T as cardiovascular biomarker in the presence of other biomarkers like IL-6 and creatinine kinase. From the results it was evident that very low levels of troponin can be stronger predictors of cardiovascular risk as compared to the absolute values.

Creatinine kinase biomarker :

Creatinine kinase can be helpful in the diagnosis of myocardial infarction (MI) and cerebrovascular accidents. In acute myocardial infraction, injury to myocardium can occur. Following injury, creatinine kinase can be released from the damaged myocardial cells (Wang et al., 2014). Elevated levels of creatinine kinase can be observed within 4 to 8 hours after infraction and it reaches peak between 12 to 24 hours and return back to normal range between 3 to 4 days. It has been observed that considerable difference in creatinine reference values of male and female. In male reference values range between 52-336 U/L and in female its range is between 81-176 U/L (Strunz et al., 2011). Hence, creatine kinase levels were estimated both in male and female. Even though, creatinine kinase can be used as good predictor of myocardial infraction, it was replaced by more sensitive biomarker like troponin T (D’Souza et al., 2015). However, in this multimarker study creatinine kinase was included because cardiovascular risk for male and female can be studied separately using this biomarker. Results indicated that creatinine kinase is not a sensitive biomarker for the risk prediction of cardiovascular conditions because there was no statistically significant difference between the normal and diabetic patients (Herder et al., 2011). However, in these patients, anginal pain and abnormal ECG were evident. In case of separate male and female patients, there was no statistically significant difference of creatinine kinase level. Creatinine kinase cannot be used as risk indicator for the stroke because raised levels of creatinine kinase can occur due to leakage from the myocardium. From the results, it was evident that creatinine kinase can predict myocardial infraction prior to occurrence of abnormal ECG (Chin et al., 2012).

It is evident that single biomarker would provide modest risk prediction for the cardiovascular diseases. Hence, data related to multiple biomarkers should be collected for diagnosis of cardiovascular disease. Several studies are available for the multiple circulating biomarkers and very few studies are available for multiple genetic and imaging studies (Kim, 2012). Hence, this study was planned for circulating biomarkers because outcome of this study would be helpful for comparison with the previous studies. In a study comprising of 3000 individuals, it is evident that combination of BNP and the ratio of urinary albumin to creatinine are predictive of cardiovascular risks as compared to the individual biomarker. Another study was carried out comprising of 5000 participants and six biomarkers like CRP, BNP, midregional pro–atrial natriuretic peptide, midregionalproadrenomedullin, LpPLA2, and cystatin C. From this study, it was evident that BNP and midregionalproadrenomedullin were predictive of coronary risks and BNP and CRP for cardiovascular risks (Wang et al., 2017). It is evident from the studies that combination of circulatory biomarkers with the imaging and genetic biomarkers would give confounding results. In a study comprising of 5000 patients, it was evident that raised CRP levels predicted cardiovascular diseases in patients with carotid plaque, however Carotid IMT and plaque by ultrasound did not predicted cardiovascular risks irrespective of the raised levels of CRP (Sabatine et al., 2012).

Outcomes:

Outcomes of these studies were measured in terms of circulating levels of CRP, Interleukin- 6, Troponin T and creatinine kinase and anginal pain and abnormal ECG. From the results, correlation was evident among all the estimated parameters.

Future prospects:

Previous studies including this study used upto10 biomarkers with modest improvement in the cardiovascular risk prediction. Most of these results can be used as proof to the traditional risk factors for the cardiovascular diseases. However, from these multibiomarker studies, there was no substantial improvement in prediction of cardiovascular disease. In most of the studies, basis for the selection of combination of biomarkers was not completely elucidated. In the theoretical models, it was established that biomarkers with weak or nil correlation with each could be more beneficial. In future studies, less number of biomarkers with less correlation among each other should be used in combination for the prediction of cardiovascular disease. In combination evaluation of biomarkers, these should be from varied biological pathways and these should be clinically valid. Novel biomarkers like procalcitonin, copeptin, heart-type fatty acid-binding protein and growth differentiation factor need to be studied in combination with the existing biomarkers to improve the prediction of cardiovascular diseases. If one biomarker from particular pathway is being used, another biomarker from the same pathway would not add considerable information to the existing knowledge.  

Limitations:

In most the studies using single biomarkers, it was evident that serial measurements can be stronger predictors of the cardiovascular risks. However, using multiple biomarkers serial measurements were not evaluated. In this study also, serial measurements were not performed. Serial measurements would have given better prediction of cardiovascular risk using multiple biomarkers. Another limitation of the study include, enrolled participants in the study may not represent general population. Heart failure was not incorporated as the primary outcome in the study design. In this study, there was no provision to detect asymptomatic cardiovascular disease. More than 95 % participants enrolled in this study were white, hence these results cannot be extrapolated to other population.

Conclusion:

In European population, it has been observed that raised levels of concentration of biomarkers like CRP, troponin T, IL-6 and creatinine kinase are associated with increased risk of cardiovascular conditions like anginal pain and abnormal ECG. These biomarkers provided reliable evidence for progression and prognosis of cardiovascular conditions. Findings of this study are consistent with the previous findings. There should be accurate diagnosis of the cardiovascular conditions. Diagnostic methods should be sensitive, reliable and cost effective which can be applied to general population. Genetic and imaging techniques require long duration and are costlier. Hence, circulatory biomarkers can be used effectively in general population because large number of samples can be analysed in a single assay and these assays are less costly. However, it has been observed that single marker can give false positive or false negative results. Hence, multiple biomarkers were used in this study for the diagnosis of cardiovascular conditions. This multimarker strategy proved beneficial in diagnosis of cardiovascular conditions in European population.Q

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