17 March 2017: Meta-Analysis
Platelets Cellular and Functional Characteristics in Patients with Atrial Fibrillation: A Comprehensive Meta-Analysis and Systematic Review
Alexander Weymann ABCDEFG 1*, Sadeq Ali-Hasan-Al-Saegh ABCDEFG 2, Anton Sabashnikov ABCDEFG 3,4, Aron-Frederik Popov ABCDEFG 5, Seyed Jalil Mirhosseini ABCDEFG 2, Luis Nombela-Franco ABCDEFG 6, Luca Testa ABCDEFG 7, Mohammadreza Lotfaliani ABCDEFG 2, Mohamed Zeriouh ABCDEFG 3,4, Tong Liu ABCDEFG 8, Hamidreza Dehghan ABCDEFG 9, Senol Yavuz ABCDEFG 10, Michel Pompeu Barros de Oliveira Sá ABCDEFG 11,12,13, William L. Baker ABCDEFG 14, Jae-Sik Jang ABCDEFG 15, Mengqi Gong ABCDEFG 8, Umberto Benedetto ABCDEFG 16, Pascal M. Dohmen DFG 1, Fabrizio D'Ascenzo ABCDEFG 17, Abhishek J. Deshmukh ABCDEFG 18, Giuseppe Biondi-Zoccai ABCDEFG 19,20, Hugh Calkins DE 21, Gregg W. Stone DE 22, Integrated Meta-Analysis of Cardiac Surgery and Cardiology-Group [IMCSC-Group]
DOI: 10.12659/MSMBR.902557
Med Sci Monit Basic Res 2017; 23:58-86
Abstract
BACKGROUND: This systematic review with meta-analysis aimed to determine the strength of evidence for evaluating the association of platelet cellular and functional characteristics including platelet count (PC), MPV, platelet distribution width (PDW), platelet factor 4, beta thromboglobulin (BTG), and p-selectin with the occurrence of atrial fibrillation (AF) and consequent stroke.
MATERIAL AND METHODS: We conducted a meta-analysis of observational studies evaluating platelet characteristics in patients with paroxysmal, persistent and permanent atrial fibrillations. A comprehensive subgroup analysis was performed to explore potential sources of heterogeneity.
RESULTS: Literature search of all major databases retrieved 1,676 studies. After screening, a total of 73 studies were identified. Pooled analysis showed significant differences in PC (weighted mean difference (WMD)=–26.93 and p<0.001), MPV (WMD=0.61 and p<0.001), PDW (WMD=–0.22 and p=0.002), BTG (WMD=24.69 and p<0.001), PF4 (WMD=4.59 and p<0.001), and p-selectin (WMD=4.90 and p<0.001).
CONCLUSIONS: Platelets play a critical and precipitating role in the occurrence of AF. Whereas distribution width of platelets as well as factors of platelet activity was significantly greater in AF patients compared to SR patients, platelet count was significantly lower in AF patients.
Keywords: Atrial Fibrillation, Blood Coagulation, Platelet Count
Background
As the most prevalent cardiac arrhythmia in the general population, atrial fibrillation (AF) is associated with a high risk of developing morbidities, such as thromboembolism, stroke and neurologic injury, major and minor organ injury or failure, and hospital re-admissions resulting in significantly increased health care costs [1–3]. Moreover, this situation might even exacerbate, since the number of AF patients is expected to double by 2050 [3].
The pathophysiological mechanism of increased prothrombotic tendency in patients with AF is highly intricate and multifactorial [4]. The association of increased platelet activity with atherosclerotic disease has been well documented [5]. Activated platelets have numerous vasoactive and prothrombotic factors [5,6]. Mean platelet volume (MPV) is a marker of platelet activation and function reflecting platelet size and changes either in terms of platelet stimulation or the rate of platelet production [6]. Virchow’s triad on prothrombotic state including arterial stasis, vessel wall abnormalities, and coagulant alternations in the hemostatic balance may play a major role in the development of supraventricular arrhythmia [7]. Platelets represent an important part of hemostatic balance and can directly affect prothrombotic state.
Various studies have reported the association of hemostatic markers with the occurrence of AF. However, so far the data from the studies have been largely inconclusive. This systematic review with meta-analysis aimed to determine the strength of evidence for evaluating the association of platelet cellular and functional characteristics including platelet count, MPV, platelet distribution width (PDW), platelet factor 4, beta thromboglobulin (BTG), and p-selectin with the occurrence of AF and consequent stroke.
Material and Methods
LITERATURE SEARCH:
A comprehensive literature search was conducted in electronic scientific databases (Medline/PubMed, Web of Science, Embase, and Google Scholar) from their inception through August 10, 2016 to identify relevant studies on the association of platelet cellular and functional characteristics with the occurrence of AF and consequent stroke. Predefined search terms were as follows: “platelet count”, “mean platelet volume”, “platelet distribution width”, “platelet factor 4”, “beta thromboglobulin”, “P-selectin”, and “atrial fibrillation” or “supraventricular arrhythmia”. No restrictions were applied regarding language, time of publication, or sample size of studies. To assess additional studies not indexed in common databases, all retrieved references of the enrolled studies, recent published review articles, and meta-analyses were also checked.
STUDY SELECTION:
Studies were included in the analysis when they met the following criteria: 1) human subjects; 2) cohort or case-control studies; 3) the study investigated the comparison between AF-cases and non-AF-population in terms of platelet biomarkers; 4) the study compared patients with and without stroke focusing on biomarkers. Abstracts without peer-review or from congress presentations only, as well as gray literature were not included.
DATA EXTRACTION AND OUTCOME MEASURES:
Three investigators (S.A-H-S, S-J.M, and A.S) independently extracted the data. Discrepancies were resolved by a consensus standardized abstraction checklist used for recording data in each included study. Disagreements were discussed and resolved by senior authors (A.F-P, A.W, G.B.Z, and H.C). Author’s name, year of publication, country, design of study, sample size, mean age, gender, coexistent cardiovascular diseases and risk factors, such as diabetes mellitus, hypertension and history of myocardial infarction, percentage of used anti-coagulants, type of AF, and details of platelet markers were extracted. For exploration of heterogeneity among trials, subgroup analyses of disparities in the patients’ characteristics were performed for 1) the era of publication (before 2000 versus after 2000); 2) geographical area (Asia, Europe, Africa, North-America, South-America, and Oceania); 3) study design (case-control versus cohort); 4) size of patient cohort (≤300 versus >300); 5) mean age (≤60 years versus >60 years); 6) percentage of male patients (≤70% versus >70%); 7) presence of diabetes (≤30% versus >30%); 8) presence of hypertension (≤70% versus >70%); 9) history of cigarette smoking (≤0% versus >30%); 10) presence of myocardial infarction (≤20% versus >20%); 11) use of cardiovascular drugs, such as diuretics, angiotensin converting enzyme inhibitors, statins and beta-blockers (for each: ≤70% versus >70%); 12) AF-classification (chronic versus non-chronic; duration of AF ≥6 months and ≥1 attempt of electrical cardioversion to restore normal sinus rhythm were considered chronic AF and patients with duration of AF ≤6 months were considered non-chronic AF); 13) type of AF [paroxysmal (spontaneous termination of the arrhythmia within 7 days of its onset), persistent (sustained arrhythmia beyond 7 days), permanent (efforts to restore normal sinus rhythm have either failed or been forgone)]; and 12) anticoagulation (code-1: patients did not receive anticoagulants in both groups, code-2: all participants received anticoagulants in both groups, code-3: range of percentages between both groups >5 0%, code-4: range of percentages between both groups <50%, code-5: no information available about anticoagulation in both groups, and code-6: anticoagulation information not available for one group only).
HOMOGENIZATION OF EXTRACTED DATA:
Continuous data were expressed as mean ± standard deviation (SD). For studies reporting interquartile ranges, the mean was estimated according to [minimum+maximum+2(median)]/4 and SD was calculated based on (maximum–minimum)/4 for groups with sample sizes of n ≤70 and (maximum–minimum)/6 for sample sizes of >70 [8].
QUALITY ASSESSMENT AND STATISTICAL ANALYSIS:
The Newcastle-Ottawa scale was independently used by two investigators (S.A-H-S and M.G) to assess the quality of studies [9]. Total scores ranged from 0 (worst quality) to 9 (best quality) for case-control or cohort studies. Data were analyzed by STATA 11.0 using METAN and METABIAS modules. For non-categorical data, pooled effect size measured was weighted mean difference (WMD) with 95% CI. A p value <0.1 for Q test or I2 >50% showed significant heterogeneity among the studies. Heterogeneity among trials was examined by applying a random effect model when indicated. Publication bias was assessed using the Begg tests. A p value <0.05 was considered statistically significant.
Results
LITERATURE SEARCH STRATEGY AND INCLUDED STUDIES:
A total of 1,676 studies were retrieved from the literature search and screened databases, of which 1,005 studies (59.9%) were excluded after meticulous evaluation during the first review due to either unnecessary information (n=710), inadequate report of endpoints of interest (n=265) or report of non-matched data based on mean ±SD or median [minimum-maximum] (n=30). In total, 671 potentially relevant full-text articles were reviewed, and finally 73 studies were analyzed in the meta-analysis (Supplementary Table 1).
PLATELET COUNT: A total of 6,255 cases were selected from 45 studies, of which 2,964 were allocated to the AF group and 3,291 to the SR group. Patient populations in the selected studies ranged from 27 to 621 patients. Mean platelet count was 237.3×109/L in AF group and 240.04×109/L in SR (Tables 1, 2). Using a random effect model, pooled assessment effect analysis indicated that the mean platelet count was significantly lower in patients with AF than in patients with SR with WMD of −26.93 (95% CI: −28.35 to −25.51; p<0.001, Figure 1). Significant heterogeneity was observed among the studies (I2=93.5%; heterogeneity p<0.001).
MPV: A total of 3,609 cases were included from 19 studies, of which 1,646 were allocated to the AF group and 1,963 to the SR. Patient populations of the included studies ranged from 57 to 621 patients. Mean level of MPV was 9.22 FL in the AF group and 8.40 FL in the SR group (Tables 1, 2). Pooled analysis revealed that MPV level was significantly higher in patients with AF compared to those with SR with WMD of 0.61 (95% CI: 0.56 to 0.65; p<0.001, Figure 2) using a random effect model. There was a significant heterogeneity among the studies (I2=94.3%; heterogeneity p<0.001).
PDW: A total of 1,117 cases were included from three studies, of which 290 were allocated to the AF group and 827 to the SR group. Using a random effect model, pooled analysis revealed that PDW was statistically lower in the AF group than in the SR group with WMD of −0.22 (95% CI: −0.37 to −0.08; p=0.002, Supplementary Figure 1). There was significant heterogeneity among the studies (I2=87.4%; heterogeneity p<0.001)
BTG: A total of 1,781 patients were included from 22 studies, of whom 1,043 were allocated to the AF group and 738 to the SR. Mean level of BTG was 83.62 ng/mL in patients with AF and 58.72 ng/mL in those with SR (Tables 1, 2). Pooled analysis revealed that the mean level of BTG was significantly higher in AF patients compared to those with SR with WMD of 24.69 (95% CI: 24.07 to 25.32; p<0.001, Figure 3) with considerable heterogeneity among the studies (I2=97.6%; heterogeneity p<0.001).
PF4: A total of 1,220 cases were selected from 16 studies, of which 651 were allocated to the AF group and 569 to the SR group. Mean levels of PF4 were 41.43 ng/mL in the AF group and 24.78 ng/mL in the SR group (Tables 1, 2). Pooled analysis showed that the level of PF4 was remarkably higher in patients suffering AF compared to controls with WMD of 4.59 ng/mL (95% CI: 4.33 to 4.86; p<0.001, Figure 4) using a random effect model. There was significant heterogeneity among the studies (I2=99.6%; heterogeneity p<0.001).
P-SELECTIN: A total of 2,725 cases were included from 24 studies, of which 1,469 were allocated to the AF group and 1,256 to the SR. Mean level of P-selectin was 69.52 ng/mL in the AF group and 51.51 ng/mL in the SR group (Tables 1, 2). Using a random effect model, pooled analysis showed that the level of P-selectin was significantly higher in the AF group compared to the SR group with WMD of 4.90 ng/mL (95% CI: 4.36 to 5.45; p<0.001, Figure 5). Significant heterogeneity was observed among the studies (I2=98.6%; heterogeneity p<0.001).
ASSOCIATION OF PLATELET CHARACTERISTICS WITH THE INCIDENCE OF STROKE IN PATIENTS WITH AF:
Five studies examined the association of platelet markers with stroke (Table 3). Platelet count and MPV were investigated in at least two studies which were included in the meta-analysis (Table 3). According to pooled assessment analysis, the level of MPV (number of studies=2, WMD of 0.97, 95% CI: 0.70 to 1.24; p<0.001 and I2=95%%; heterogeneity p<0.001, Supplementary Figure 2) was significantly higher in patients with stoke compared to those without major cerebrovascular events. Pooled analysis showed that platelet count (number of studies=4, WMD of 7.23, 95% CI: −4.96 to 19.42; p=0.245 and I2=35.2%%; heterogeneity p=0.21, Supplementary Figure 3) was not significantly different in patients with or without stroke.
PUBLICATION BIAS AND SUBGROUP ANALYSIS:
Begg tests suggested that there might be publication bias for studies examining the levels of MPV and BTG (Supplementary Figures 4–8). Details of subgroup analysis are reported in Supplementary Tables 2 and 3.
Discussion
The incidence of cardiovascular diseases has been dramatically increasing in developed and developing countries in recent decades [1]. AF represents one of the most critical and prevalent cardiac arrhythmias precipitating morbidity and mortality in short- and long-term periods of time and adversely affecting patient’s quality of life [1,2]. Despite the wide range of investigations on diagnosis and treatment of AF conducted and published in recent years, the pathophysiology of this multifactorial disease is not completely understood [2]. Due to a number of complex mechanisms that are involved in the development of AF the current controversies regarding diagnosis and treatment of AF seem to be justifiable [2,3]. Among other things the mechanism of oxidation and release of free radical oxygen has been defined as one of the main precipitating mechanisms in development of AF [2]. Also, the Virchow’s triad, which plays a critical role in predicting AF and includes arterial stasis, vessel wall abnormalities, and coagulant alternations in the hemostatic balance, indicates that prothrombotic state is another important pathophysiological mechanism of AF. However, the exact mechanism involving prothrombotic state in AF is ambiguous [6,7]. Nevertheless, it is known that platelets are involved in both thrombosis and inflammation becoming a key factor in pathogenesis of cardiovascular diseases [6]. In the present study, we attempted conducting a meticulous and multilateral investigation on platelets cellular and functional characteristics in patients with AF compared to patients with sinus rhythm. Our findings revealed that from statistical and clinical points of view, AF was significantly associated with reduced platelet count. However, an undeniable fact is that a considerable heterogeneity among the studies was present in this analysis. A subgroup analysis revealed that the type of AF (chronic or non-chronic) could probably be a factor of heterogeneity: there was an inverse relationship between the occurrence of AF and platelet count in non-chronic AF, while such an association was not observed in patients with chronic AF. On the other hand, reduced platelet count was not observed in paroxysmal and permanent AF, while this relationship only existed in persistent AF. In general, it can be concluded that the type of AF is one of the heterogeneity factors in platelet count analysis. Barura et al. reported that exposure to cigarette smoking could change the hemostatic process through multiple mechanisms including alteration of the function of endothelial cells, platelets, and coagulation factors [10]. However, our subgroup analysis demonstrated that platelet count was not significantly reduced in cigarette smokers with AF compared to smokers with SR, while lower platelet count was observed in non-smokers with AF compared to smokers with SR. This can be explained by the fact that cigarette smoking can disturb the actual platelet count via increasing aggregation and adhesion of the platelets [10]. In fact, we believe that the occurrence of AF is strongly associated with reduced platelet count while the type of AF, cigarette smoking, and the geographical area of the studies represent factors of heterogeneity.
MPV is also an important biomarker of platelet activity. Large platelets secrete many critical mediators of coagulation, inflammation, thrombosis, and atherosclerosis. A close relationship has been found between MPV and cardiovascular risk factors, such as diabetes mellitus, hypertension, and hypercholesterolemia [11,12]. The results of this study revealed that the average MPV was significantly higher in AF patients than in SR patients, thus implying the direct relationship between MPV and the risk of AF. According to our subgroup analysis, study sample size and diabetes mellitus could probably result in heterogeneity. Our findings also showed that levels of the platelet markers were notably higher in both chronic and non-chronic AF patients compared to the SR group. Interestingly, Sansanayudh et al. recently found an association between elevated MPV and CAD. Patients with CAD and slow coronary blood flow showed larger MPV compared to controls [13]. The mean difference in MPV in patients with an acute coronary event was higher than those with stable coronary disease [13]. They suggested that MPV might be used for risk stratification or to add diagnostic accuracy to the traditional risk stratification markers in patients with CAD [13].
PWD is a platelet biomarker and predictive factor in cardiovascular diseases. Varastehravan et al. indicated that PDW in patients with ST-segment elevation myocardial infarction could be used for prediction of ST-segment resolution and clinical outcomes [14]. According to the results of the present study, PDW was greater in patients with AF than those with SR and thus had a direct relationship to the risk of AF. However, due to the limited number of studies on PDW no subgroup analysis could be performed to examine heterogeneity factors. Nevertheless, our evidence shows that AF might be associated with both larger volume of platelets as well as distribution width.
Platelet activation is demonstrated by the release of platelet granules and their components into the circulation. BTG and platelet factor 4 (PF4) represent specific platelet proteins of alpha-granules, which can be secreted into surrounding medium during cell activation [15,16]. Based on the results of this study, increased levels of BTG might be also directly related to the risk of AF. Our subgroup analysis revealed the type of AF (chronic or non-chronic), history of CS, and gender as factors of heterogeneity. The present study also indicated that the level of PF4 was remarkably higher in AF patients compared to those with SR, while the level of BTG and PF4 were significantly increased compared to SR patients in both chronic and non-chronic AF as well as paroxysmal and permanent AF. Therefore, it can be suggested that platelet activity and release of specific proteins from their granules may also play a vital role in pathophysiology of AF.
P-selectin, an integral membrane glycoprotein of platelets and endothelial cells, is involved in the onset of atherosclerosis and cardiovascular diseases [17]. P-selectin functions as a cell adhesion molecule (CAM) on the surfaces of activated endothelial cells, which line the inner surface of blood vessels, and activated platelets. In unactivated endothelial cells, it is stored in α-granules [17]. The present study revealed that P-selectin marker was notably higher in AF patients compared to SR group. The subgroup analysis proposed the type of studies and the type of AF as factors of heterogeneity. In brief, cohort studies did not show any relationship between the level of P-selectin and occurrence of AF, whereas case-control studies strongly confirmed this relationship. It is necessary to mention that the number of cohort studies was remarkably less than case-control studies. Increased level of P-selectin was observed in both chronic and non-chronic AF in our meta-analysis. On the other hand, this association was found in persistent and permanent AF but not in paroxysmal AF. Overall, taking into account the evidence from the present study, platelet count and other biomarkers may considerably influence the development of AF underlying the role of platelets in pathophysiology of AF as well as the predictive function of platelet factors.
The results of our study showed that the level of MPV was obviously higher in AF patients with stroke as compared to AF patients without cerebrovascular events. However, we found no association between platelet count and the occurrence of stroke.
There is a hypothesis that cardiac risk factors might also affect the occurrence of AF. Feng et al. proposed a hypothesis that the relationship between hemostatic markers and AF became insignificant after stratifying based on cardiovascular disease status [18]. Our results showed that cardiac risk factors including diabetes, hypertension, and history of MI were not recognized as heterogeneity factors. However, it should be mentioned that an important cardiac risk factor affecting our results was cigarette smoking.
Lip et al. argued that using anticoagulants could reduce the level of hemostatic factors in AF patients, and consequently, differences in receiving anticoagulants in various studies could be considered as a factor of heterogeneity [19]. According to the results of our subgroup analyses of platelet count and level of MPV and PF4, differences in using anticoagulants could possibly play a considerable role in the occurrence of heterogeneity. It should also be noted that in our meta-analysis on non-experimental studies more heterogeneity was found which may be explained by the following reasons: 1) biases are less controlled, 2) more confounding factors, and 3) differences in defining outcomes. As a result, performing analysis on non-experimental studies, finding associations, effect size, and estimating heterogeneity as well as appropriate method for finding the factors of heterogeneity should be the aim of such meta-analyses.
Conclusions
In summary, considering the results of this study, we strongly state that platelets play a critical and precipitating role in the occurrence of AF as the volume and distribution width of platelets as well as the factors of platelet activity appeared to be significantly higher in AF patients compared to SR patients. On the other hand, AF was associated with lower platelet count. Therefore, emphasizing the potential predictive role of platelet factors in the occurrence of AF, we strongly recommend adding cellular and functional characteristics of platelets to the diagnostic criteria of AF in the future.
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