Introduction to CHD occur in low- and middle-income

Introduction

Cardiovascular disease (CVD), a leading
cause of morbidity and mortality worldwide, is accounting for 17.3 million
deaths per year and this number is expected to grow to be more than 23.6
million in 2030 1. In 2008, World Health Organization (WHO) reported that CVD
mortality was about 30% of total global deaths. Of those, approximately 7.4
million deaths were caused by coronary heart disease (CHD) 1, 2. CHD causes a
high morbidity rate ranges from 81 per 100.000 population in China to 718 per
100.000 population in Finland 3. Atherosclerosis of coronary arteries, the
main cause of CHD, is a complex inflammatory disorder, changes the cells of the
artery wall and the blood components 4, 5. Several inflammatory markers such
as troponin, C reactive protein (CRP), and B-type natriuretic peptide (BNP) or
pro-BNP are now used to identify the risk stratification and therefore to
identify patients at higher risk 6. A high-sensitivity CRP (hs-CRP), for example,
is one of the commonest predictor being used in predicting recurrent events, myocardial
infarction, or restenosis after percutaneous coronary intervention 7.
However, its usage in developing country is not routine due to the cost. In fact,
most of deaths due to CHD occur in low- and middle-income countries 1, 2.

 

It has been hypothesized that the injury
of the vascular wall leads to an inflammatory response involving complex
interactions between endothelial and smooth muscle cells, leukocytes, and
platelets. Elevated leukocyte count, a marker of inflammation, has been
identified for a long time as an independent predictor of an increased risk for
long-term mortality and myocardial infarction both in individuals without CVD
at baseline and in patients with established CHD 8. Studies found that
leukocyte count was associated with aortic arch plaque thickness 9, progression
of aortic atheroma in patients with stroke 10, and it increased risk of
stroke andvascular death in patients with symptomatic intracranial
atherosclerotic disease 11. Another study revealed that neutrophil count was
prognostic marker for major adverse cardiac events in acute coronary syndrome
12. In addition, other components of the complete blood count, such as
hematocrit, platelet count, and erythrocyte sedimentation rate are also
associated with CHD. Combination of leukocyte count with other components of
the complete blood count could improve the ability to predict the risk of CHD
13, 14. Assessment of these blood components is inexpensive and widely
available. Therefore, the aim of this study was to investigate the possibility
of complete blood count as predictor of CHD.

 

Methods

Study
designs and patients

A retrospective study was conducted in
Aisyiyah Hospital, Malang, Indonesia. The target population was all CHD
patients treated in Aisyiyah Hospital during January 2011 to December 2015 (610
patients – updated January 7th 2016). The inclusion criteria were (1) suffered
from CHD and (2) aged over 18 years. Patients with one of these clinical
conditions (renal dysfunction (creatinine ? 1.5 mg/dL), hepatic disorder,
concomitant inflammatory disease, neoplastic disease, systemic disorder, acute
or chronic infectious disease, haematological disorder, and on medications
which could affect complete blood count) were excluded. Information related to
gender, age, diagnosis, body mass index, mean arterial blood pressure, the
level of blood glucose, low-density lipoprotein (LDL), ureum and creatinin and
complete blood count was extracted from medical record. A simple random
sampling method was used to select 610 CHD patients (population size). The
sample of this study was 133 CHD patients and 50 controls as theminimum sample
size. Controls were obtained from healthy and or non-CHDindividualswith age-and
gender-matchedrecorded in Aisyiyah Hospital.

 

Study
variables

The response variable in this study was
the incidence of CHD. The explanatory variables in this study were hematocrit
(%), concentration of haemoglobin (g/dl), and the levels of erythrocytes,
leukocytes, thrombocytes, eosinophils, basophils, neutrophils, monocytes, and
lymphocytes (cell/µl). Those variable measurements, measured using XS – 800i
Hematology Analyzer (Sysmex Europe GmbH, Norderstedt, Germany), were retrieved
from medical record.

 

Statistical
analysis

Association between complete blood count
and the risk of CHD were analyzed using double logistic regression. All
significance tests were two tailed and P-value of less than 0.05 was considered
to be statistically significant. All analysis were performed using the
Statistical Package of Social Sciences 17.0 software (SPSS Inc., Chicago, IL).

 

Meta-analysis
design

A meta-analysis was also performed to evaluate
the association between complete blood count and the risk of CHD. The approach
of meta-analysis was adapted from the previous studies 15-18. Briefly,
articles related to the association between complete blood count and the risk
of CHD were collected for calculating combined ORs 95% CI and assessed using
fixed or random effect model.

 

Eligibility
criteria and data extraction for meta-analysis

The inclusion criteria for this study were:
(1) retrospective studies; (2) prospective studies; (3) cross-sectional
studies; (4) randomized-controlled trials (RCTs); (5) controlled
before-and-after studies; (6) cross-over studies; (7) investigating the
association between complete blood count and the risk of CHD; and (8) providing
sufficient data for calculating OR 95% CI. The following information were extracted
from each study: (1) name of the first author; (2) year of publication; (3)
country of origin; (4) sample sizes of cases and controls, and (5) levels of
blood cells count.

 

Search
strategy and literature for meta-analysis

We searched published articles up to
June 20th, 2016 in PubMed and Embase with no language restrictions. For searching
stategy, we used the combination of the following key words: (complete blood
count or CBC or hematocrit or haemoglobin or erythrocytes or leukocytes or
thrombocytes or eosinophils or basophils or neutrophils or monocytes or
lymphocytes) and (coronary disease or coronary heart disease or coronary artery
disease or myocardial infarct or ischemic heart disease or CHD or IHD or MI or
cardiovascular disease or heart disease OR angina). The publication languages
were limited to English.

 

Statistical
analysis for meta-analysis

The correlation between complete blood
count and the risk of CHD was determined by calculating pooled ORs and 95% CIs.
The significance of pooled ORs was estimated by Z-tests (P value of less than 0.05
was considered statistically significant). A Q-test was conducted to evaluate
whether heterogeneity existed. A random effects model was used to calculate the
OR 95% CI if heterogeneity existed (P<0.10), otherwise a fixed effects model was used. Publication bias was evaluated using Egger's test (P<0.05 was considered statistically significant). All analyses was performed using a Comprehensive Meta-analysis (CMA) 2.0 software.   Results Characteristics of patients A total of 133 and 50 controls were analysed. The average age of the CHD patients were 59.5 (±11.2) years old with the average body mass index (BMI) was 26.07 (±3.65 kg/m2) (Table 1). Other laboratory parameters of the patients such as mean arterial blood pressure and the level of blood glucose, LDL, ureum and creatinin are presented in Table 1.   Association between complete blood cells count and CHD The average number of complete blood cells count in 133 CHD patients are presented in Table 2. We found that the number of hemoglobin, leukocyte, hematocrit, eosinophil, and monocyte were associated with the risk of CHD (Table 3). While, other components of blood cells count such as erythrocyte, thrombocyte, basophil, neutrophil, and lymphocyte had no significant association with the risk of CHD.   Meta-analysis A total of 56 962 potentially relevant papers were identified based on the search strategy. Of these, 56 943 papers were excluded because of obvious irrelevance by reading their titles and abstracts. After the full texts were read, three papers were excluded because of reviews; ten papers were excluded because they did not provide sufficient data for calculation of OR with 95% CI; and another paper was excluded because of study bias. A flow chart demonstrating the inclusion or exclusion of studies is displayed as Fig. 1. A total of five studies were included in the meta analysis.   We found several studies have been conducted previously to assess the association between the complete blood account and CHD. Detail as follow: seven studies for hematocrit 19-25, five for haemoglobin 26-30, 16 for leukocytes 24, 31-45, eight for eosinophils 32, 40-42, 45-48, and four for monocytes 32, 40, 41, 49. Only data regarding three blood components (leukocyte, eosinophil and monocytes) were compatible for meta-analysis.   We found 16 papers evaluated the association between leukocyte and the risk of CHD in which seven retrospective studies 24, 32-34, 37, 41, 42, four cross sectional studies 31, 35, 43, 45, four cohort studies 36, 38, 40, 44, and one meta-analysis study 39. Of 16 studies, six studies 32, 40-43, 45 were compatible for meta-analysis with a total of 889 CHD patients and 4306 controls (Table 4). The result indicated that leukocyte count was not associated with the risk of CHD (OR 95%CI = 1.37 0.32 – 5.94, P=0.670) (Table 4, Fig.2A). After re-evaluating the results, a study 42 had the opposite result and therefore, we excluded this study and re-analyzed the data. We found that elevated leukocyte had a significant association with the incidence of CHD (OR 95%CI = 3.57 1.84 – 6.93, P<0.001) (Table 4, Fig. 2B).   For eosinophil, eight studies were identified, four retrospective model studies 32, 41, 42, 46, three cross sectional studies 45, 47, 48 and one cohort study 40. Of these, five studies 32, 40-42, 45 and our results with a total 513 CHD patients and 3998 controls were included in meta-analysis. The result found that eosinophil count had the significant association with the risk of CHD (OR 95%CI = 5.34 1.17 – 24.77, P=0.031) (Table 4, Fig. 2C). Four previous studies evaluating the association between monocyte count and the risk of CHD were identified 32, 40, 41, 49. Of these studies, three studies 32, 40, 41 together with our study with a total 393 CHD patients and 3871 controls were included in meta-analysis. The result revealed that monocyte was associated with the risk of CHD (OR 95%CI = 2.77 2.11 – 3.64, P<0.001) (Table 4, Fig. 2D).   Evidence for heterogeneity was found in eosinophil and leukocyte (P<0.001) and therefore, data were analyzed using random effects model. No publication bias could be detected (P>0.05). Forest
plots the association between leukocyte, eosinophil, and monocyte and the
risk of CHD are presented in Fig 2A-D.

 

Discussions

Atherosclerosis, the main cause of CHD,
is a multifactoral disease that involve chronic inflammation at every stage
50. The inflammation process in atherosclerosis is a complex, and consist of
several steps 51. Complete blood count has been proven to be associated with
inflammation 52 and here we reported the correlation between complete blood
count and CHD. To the best of our knowledge, this report is the first study
conducted to assess the association between complete blood count and CHD in
Indonesia. The first was conducted in Japan 19. Basically, the concept of
this study is similar with previous studies and therefore the results of this
study are expected to be able to be used as the comparison.

 

Some studies have shown that
leukocytosis to be an independent risk factors of cardiovascular events 8,
42. There are several theories that explain the role of leukocytes in CHD.
Leukocytes cause vascular injury through several ways, including damage to the
endothelial cells caused by proteolytic and oxidative 53, 54, 55, plug the
microvasculature through binding to the ischemicendothelium via the leukocyte
integrin CD 11b/CD18 54, 56, induce hypercoagulability by increased
expression of tissue factor on leukocyte 54, 57, promote infarct expansion by
releasing pro-inflammatory cytokines 54, 57, 58, and destabilize of coronary
artery plaques 54.       Our study
revealed that leukocytes count increased the risk of CHD by 1.01. Although this
result indicates very small impact, our meta-analysis reveled that elevated
leukocyte increased the odds of having CHD by 3.5 times (OR 95%CI = 3.57 1.84
– 6.93, P<0.001). A previous meta-analysis also found that there was a significant association between leukocyte and the risk of CHD (OR 95%CI: 1.33 1.17 – 1.50 P=0.001 39.   Studies have identified that elevated levels of almost all subtypes of leukocyte, including eosinophils 47, monocytes 41, neutrophils 59, and lymphocytes (an inverse relationship) 60    have been associated with increased risk of CHD. We identified eight studies that have been conducted to assess the association between eosinophils count and the risk of CHD. Two retrospective studies 42, 46 and three cross sectional studies 45, 47, 48 showed that there was a significant association between eosinophil count and the risk of CHD, but other studies found no association 32, 40, 41. Our retrospective study and meta-analysis found that eosinophil count had the significant association with the risk of CHD.   The mechanism how eosinophils involved in CHD pathogenesis is still unclear. However, the interaction between eosinophils, platelets, and endothelium has the important role in thrombosis. Several studies have suggested the possible mechanism of eosinophils in thrombosis. First, eosinophils granules activated platelets and as the result, larger and hyper-reactive platelets accelerate the formation of thrombin 61. Second, eosinophils synthesis and release somebioactive mediators, such as leukotriene C4, histamine, and prostaglandin D2 from mast cells and basophils. These bioactive mediators are though to have an important role in cardiovascular system 62. Third, degranulating eosinophils release toxic cationic protein that is a cytotoxic property in CHD 62. This toxin stimulates the formation of mural thrombin throught bind to anion-binding exosite of thrombomodulin 63. As a result, thrombomodulin is unable to form thrombomodulin-thrombin complex, a potent physiological inhibitor of coagulation, and therefore it loses its anti – thrombotic role 64.   Several studies found that monocytes have pivotal roles in the pathogenesis of CHD 32, 40, 41, 49. A cohort study 40 and three retrospective studies conducted 32, 41, 49 found that elevated monocyte count had the significant association with the risk of CHD. These results were consistent with our study. Our meta-analysis also indicated that elevated monocyte is a riskfactor for CHD. Monocytes have central role for plaque development in CHD patient 65. Monocytes are short – lived cells, and do not proliferate in the blood. The functions of monocytes in homeostatic conditions are involved in scavenging dead cells and toxic molecules, and or have a potential role in the renewal of resident tissue macrophages and dendritic cells 66.       During atherosclerosis, monocytes are recruited into intima and subintima 66 through the luminal endothelium 67 and via specific integrin receptors macrophage adhesion ligand-1 (Mac-1) that interacts with the endothelial adhesion molecule 68. This process is facilitated by platelets that secrete factors influencing the monocyte – macrophage phenotype 67. In intima, monocytes phagocytose toxic molecules such as oxidized LDL through their scavenger receptors. Furthermore, monocytes produce inflammatory cytokines and differentiate into dendritic cells, macrophages or foam cells 66.   There were several limitations in the study. First, in this study did not include data regarding the factors associated with CHD such as smoking, physical activity, and non modifiable factors of CHD. Second, false negative results could be occurred in this study due to the small sample size. Therefore, further studies with a larger sample size are needed to determine the actual association. Third, the study regarding the association between complete blood count and the risk of CHD is very limited. Therefore, we could not conduct-meta analysis on all complete blood count components.   Conclusion In conclusion, our study indicates that elevated leukocytes, hemoglobin, hematocrit, eosinophils, and monocyte are correlated with the incidence of CHD in Indonesian population. Our meta-analysis reveals the evidence that leukocyte, eosinophil, and monocyte are the risk factor for CHD.