Original Article

The Relationship of Markers With Carotid Artery Stenosis and Lesion Hardness: Superiority of C-Reactive Protein and Uric Acid

Please cite this article as follows: Yildirim A, Celik M, Fatih Kaleli M, Keles F, Alpaslan M, Sokmen E. The relationship of markers with carotid artery stenosis and lesion hardness: superiority of c-reactive protein and uric acid. Avicenna J Med Biochem. 2023; 11(1):79-87. doi:10.34172/ ajmb.2023.2416

Background

Atherosclerosis is a systemic inflammatory disease characterized by the progressive accumulation of inflammatory cells and oxidized lipoproteins within the arterial walls, and inflammation plays a pivotal role in the onset and every aspect of the atherosclerotic process (1,2).

Carotid artery disease (CAD) is a robust predictive indicator of worldwide cardiovascular morbidity and mortality. CAD is related to an increased risk of stroke and acute coronary syndrome (3). Therefore, aggressive treatment regimens are warranted to prevent affected patients by ischemic/embolic endpoints, especially in severe CAD. Moreover, the degree of carotid stenosis and the composition of plaques were suggested to have possible utilization in determining stroke risk (4,5). In addition, some studies have shown a close relationship between CAD in patients with unstable carotid plaques (6,7). Previous studies have demonstrated that inflammation is associated with parameters such as monocyte/high-density lipoprotein cholesterol (HDL-C) ratio (MHR), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and white blood cell/monocyte (WMR). Other studies have suggested that these parameters are related to the presence and severity of CAD and may be useful in predicting future coronary events and mortality (8-11). It has been found that these parameters are closely related to ischemic stroke and CAD (12). Inflammation is a significant factor in the development of atherosclerosis at every stage, including the destabilization of plaques in the arteries. Soft plaques more are available for destabilization than hard plaques. Hence, inflammatory parameters are higher in patients who have a soft plaque.

Xanthine oxidase is responsible for the production of uric acid (UA) by oxidizing hypoxanthine and xanthine, which are the byproducts of purine metabolism (13). UA is the end product of this process. Elevated levels of UA can potentially contribute to the advancement of arteriolosclerosis and atherosclerosis (14). In this study, we planned to investigate the relationship of routinely checked inflammation parameters and UA with carotid plaque severity and plaque morphology. Plaques that appeared echolucent and echogenic on ultrasonography were defined as soft hard plaques, respectively. An echogenic plaque is a type of atherosclerotic plaque that appears brighter than the surrounding tissue on an ultrasound image due to its high calcium content. Echolucent plaques typically have a higher content of lipids and inflammatory cells. Echolucent carotid artery plaques (soft plaques rich in lipids and/or intraplaque hemorrhage) are linked to a higher risk of stroke regardless of the severity of artery narrowing (13).

Materials and Methods

Study Design and Patient Choice

The study was conducted in the cardiology clinic between May 2021 to November 2021. Our study was designed prospectively and included 209 patients diagnosed with carotid stenosis, including 35% (n = 109) and 32% (n = 100) with mild and severe carotid stenosis, respectively, and 101 patients (33%, n = 101) were included in the control group. The study included patients who were presented to the emergency department with cerebrovascular events and diagnosed with carotid stenosis based on clinical, imaging, and laboratory evaluations. Patients with a history of cancer, acute or chronic coronary syndrome, systemic inflammatory disease, heart failure, significant heart valve disease, chronic obstructive pulmonary disease, hepatic or renal insufficiency, hematological disorders, or acute infection were excluded from the study.

The patients’ demographic information, including age and gender, was documented, and a thorough physical examination was conducted to check for acute or chronic illnesses. The patients were also interviewed to gather information about any significant medical history. Either a composed or an oral-saw informed assent was gotten from every one of the partaking patients given at the emergency service. Our review was performed following the administrators by the Announcement of Helsinki and was endorsed by the local ethics committee.

Blood Sample Collection

Blood samples for all analyses were drawn by aseptic venipuncture from an antecubital vein into routine biochemistry tubes containing K2EDTA. Complete blood and routine biochemistry parameters were measured immediately. The biochemical parameters and hematological were analyzed using the Abbott ARCHITECT c8000 and Cell-Dyn 3700 devices manufactured by Abbott Laboratories in the USA.

Ultrasonographic Evaluation

All patients evaluated the carotid intima-media thickness in the longitudinal plane using a 13 MHz linear probe with the Toshiba Aplio 500 (Toshiba Medical Systems Corporation, Tokyo, Japan). Initially, the patient was supine, and the neck was angled approximately 20° to the opposite side. Then, the posterior wall of the middle part of the right and left common carotid arteries were measured, and the carotid intima medial distance was measured from a distance between the echogenicity of the vessel lumen and the echogenicity of the media layer. A space above 1 mm for each carotid artery was considered abnormal (15).

Plaques were classified into echolucent (soft plaques in the article) and echogenic (hard plaques in the article) categories. In the transverse orientation, the most stenotic area of the carotid vessels is carefully examined, and measurements are taken to determine the extent of the narrowing. This is performed by assessing the percentage of area/diameter stenosis and/or residual lumen diameter. The percent diameter stenosis method is used to express the degree of narrowing in the carotid vessels. This measurement is calculated as a percentage of the original vessel diameter using the following formula (16):

Percent diameter stenosis = [(1 - minimal lumen diameter/normal vessel diameter) × 100%]

Plaques were divided into two groups based on the estimated surface area of stenosis, including mild stenosis for surface areas less than 60% and severe stenosis for surface areas greater than or equal to 60% (17).

Statistical Analysis

The data were analyzed using IBM SPSS Statistics for Windows (Version 21.0), and the Kolmogorov-Smirnov and Shapiro-Wilk tests tested the normality assumption. The type of variable and the state of assumptions determined the use of the chi-square test, Fisher’s exact test, Mann-Whitney U test, and independent t test for the univariate analysis of variables in the study. Descriptive statistics for variables are reported as the mean + standard deviation and median (interquartile ranges at the 25th and 75th percentiles). Duncan multiple comparison tests were used to collate the groups with statistical differences between them as a result of variance analysis. After finding significant differences among three or more groups using the Kruskal-Wallis H test, the Mann-Whitney U test was employed to compare each pair of groups. To control for the risk of obtaining false positives from multiple comparisons, the P values were adjusted using the Bonferroni correction. The relationship between the CRP variable and other variables was examined with the Spearman correlation test. Multinomial logistic regression analysis was used to determine whether variables included in the study were an important risk factor for stenosis formation. First, univariate multinomial logistic regression analysis was applied to determine the effects of individual variables. Then, stepwise multivariate multinomial logistics regression analysis was utilized to find the most successful logistic model in predicting stenosis groups. The control group was used as the reference category when applying the multinomial logistic regression analysis. Multivariate logistic regression analysis was applied to examine the importance of variables on calcification formation. For all statistical analyses, results with a P value lower than 0.05 were considered statistically significant and interpreted accordingly.

Results

The study group comprised 209 patients diagnosed with carotid stenosis. A control group was constructed that included 101 healthy control subjects. The baseline clinical, laboratory, and univariate analysis results of the study population are summarized in Table 1. Based on the analysis findings (Table 1), there was a statistically significant difference between the groups in terms of the urea variable (P < 0.01). There was no significant difference in mean Urea values of severe and mild stenosis groups. However, the mean urea value of both groups was statistically different from that of the control group (P < 0.01). The urea value of the severe and mild stenosis groups was higher than the control group. In addition, both mild and severe stenosis groups showed higher creatinine levels compared to the control group (P < 0.01). The difference between mild and severe stenosis groups in the creatinine value was statistically nonsignificant. The UA values of mild and severe stenosis groups were higher than that of the control group (P < 0.01).

Of the compared groups, the severe stenosis group had the highest mean CRP value, while the control group had the lowest CRP value (P < 0.01). The lowest level of alanine transaminase (ALT) was found in the severe stenosis group, whereas the highest level was observed in the control group. The difference between the severe and mild stenosis groups in mean ALT values was statistically nonsignificant. The mean ALT values of both groups were statistically different from the mean ALT value of the control group (P < 0.01). The control group had a higher mean gamma-glutamyl transferase (GGT) value than the other groups (P < 0.05).

The hemoglobin values of mild and severe stenosis groups were lower than that of the control group (P < 0.01). The difference between severe and mild stenosis groups in the red blood cell distribution width (RDW) value was statistically nonsignificant. The difference between the RDW value of both groups and the control group was statistically significant (P < 0.05). The procalcitonin (PRCT) value of the control group was higher than the PCT value of the mild and severe stenosis groups (P < 0.01). The highest NLR value was observed in the severe stenosis group, while the lowest value was found in the control group (P < 0.01).

The reference category for the analysis was the control group. The results of the multinomial logistic regression analysis applied to determine which variables are effective predictors of stenosis are provided in Table 2. According to the results of univariate logistic regression analysis (Table 2), the effective predictors of the mild stenosis ( < 60) group were identified as age (β = 0.159, P < 0.001), UA (β = 0.391, P = 0.004), CRP (β = 1.189, P <0.001), ALT (β = -0.048, P = 0.001), NLR (β = 0.461, P = 0.002), HG (β = -0.276, P = 0.001), UREA (β = 0.091, P < 0.001), creatinine (β = 3.914, P < 0.001), LYMP (β = -0.481, P = 0.005), and CT (β = -5.815, P = 0.003). Effective predictors of the severe stenosis ( > 60) group were determined as age (β = 0.166, P < 0.001), UA (β = 0.513, P < 0.001), CRP (β = 1.403, P < 0.001), ALT (β = -0.046, P = 0.002), NLR (β = 0.533, P <0.001), HG (β = -0.273, P = 0.002), RDW (β = 0.274, P = 0.021), UREA (β = 0.094, P < 0.001), creatinine (β = 3.373, P ≤ 0.001), GGT (β = -0.022, P = 0.048), NEUT (β = 0.230, P = 0.018), LYMP (β = -0.618, P = 0.001), and PLR (β = 0.008, P = 0.007).

According to the results of multivariate logistic regression coefficients, effective predictors of the mild stenosis ( < 60) group were identified as age (β = 0.171, P ≤ 0.001) and UA (β = 0.790, P ≤ 0.001). An increase of one unit in the age variable could increase the risk of entering the mild stenosis group by 1.187 times. Moreover, a one-unit increase in UA increased the risk by 2.203 times. The severe stenosis ( > 60) group was identified as age (β = 0.152, P ≤ 0.001), UA (β = 0.871, P ≤ 0.001), and CRP (β = 0.757, P = 0.034). A one-unit increase in the age variable increased the risk of the patient entering the severe stenosis ( > 60) group by 1.165 times. Furthermore, an increase of one unit in UA could increase this risk by 2.389 times. An increase of one unit in the CRP value increased the risk of entering the severe stenosis ( > 60) group by 2.132 times.

The descriptive statistics and univariate analysis results of the means of the groups with and without calcification are presented in Table 3. According to these results, 59.61% (n = 62) of patients in the soft lesion group without calcification had HT, while 81.90% (n = 86) in the hard lesion group with calcification had HT (P < 0.01). The urea value of the hard lesion group was higher than that of the soft lesion group (P < 0.05). The difference in mean creatinine values was statistically significant (P < 0.05). The CRP value was higher in the soft lesion group without calcification than in the hard lesion group with calcification. This difference was statistically significant (P < 0.01). The difference between the groups in terms of the WBC value was statistically significant (P < 0.05). In terms of monocyte, the mean platelet volume (MPV), platelet distribution width (PDW), and MHR values, the mean values of the hard lesion group with calcification were lower than those of the soft lesion group without calcification. These differences between the groups were statistically significant (P < 0.01).

A regression model, whose coefficients are presented in Table 4, was derived from the multivariate logistic regression analysis to identify the risk factors that affect calcification. The logistic regression model developed using the backward elimination method achieved a percentage of correct predictions of 82.4%. The accurate estimation value of the model obtained according to the Hosmer-Lemeshow test score was quite good (χ² = 15.216, P > 0.05). Multivariate logistic regression results are also listed in Table 4. Based on the results (Table 4), CRP, MPV, WBC, and LYMP were found to be important risk factors in determining whether the patients belonged to the soft or hard lesion group. A 1-unit increase in the CRP value increased the risk of belonging to the soft lesion group by 2.624 times (β = 0.965, P = 0.004). A one-unit increase in MPV could increase the patient’s risk of the soft lesion by 2.168 times (β = 0.774, P = 0.003). A one-unit increase in the WBC variable increased the patient’s risk of soft lesion by 2.785 times (β = 1.024, P = 0.011). Finally, a one-unit increase in the LYMPH variable could increase the risk of the soft lesion by 2.451 times (β = 0.896, P = 0.022).

In the study, the effect of CRP on both stenosis formation and calcification was found to be statistically significant. Therefore, a Spearman-Rho correlation analysis was conducted to investigate the linear relationship between CRP and other variables. The Spearman Rho coefficients showing the direction and size of the relationship between CRP and other variables are provided in Table 5. According to these coefficients, a significant negative correlation was found between CRP and HDL (Rho = -0.179, P < 0.05). There was a significant positive correlation between GGT and CRP (Rho = 0.214, P < 0.05). Statistically, a significant positive relationship was observed between the neutrophile variable and CRP (Rho = 0.162, P < 0.05). Additionally, a significant positive correlation was identified between the PCT variable and CRP (Rho = 0.183, P < 0.05). Similarly, there was a significant positive correlation between NLR and CRP (Rho = 0.162, P < 0.05). Moreover, a strong positive correlation was detected between CRP and MHR (Rho = 0.224, P < 0.01). Eventually, the positive relationship between CRP and PLR variable was statistically significant (Rho = 0.167, P < 0.05).

Discussion

Carotid artery stenosis has mortal complications such as stroke, transient ischemic attack, aneurysm, and carotid artery dissection. The determinants of carotid artery stenosis risk include life-saving conditions. Inflammatory parameters facilitate detecting and monitoring risks and conditions associated with carotid stenosis. In our study, age, UA, and CRP values were determined as independent predictors for the development of carotid stenosis. Regarding plaque classification, CRP, MPV, WBC, and lymphocyte values were determined as negative predictors. CRP alone was found to be useful in predicting stenosis severity and soft plaque formation. Furthermore, serum UA levels were highly associated with the risk of stenosis than the other variables because a one-unit increase in UA could extremely increase the risk of stenosis. Both CRP and UA are effective in predicting the severity of stenosis and the formation of soft plaques. Soft plaques, which are non-calcified, are considered more unstable than hard plaques and pose a greater risk of thromboembolism, stroke, and rupture. Soft plaques are associated with inflammation in the arterial wall, and this is associated with the higher CRP value in the soft lesion group. Van Lammeren et al demonstrated that individuals with asymptomatic carotid plaques exhibit more stable plaque characteristics such as greater smooth muscle cell content and calcification rate, and a lower incidence of intraplaque bleeding than those with a history of cerebrovascular accident (17). Inflammation has an effect on plaque morphology and instability, thus it has been known that inflammatory markers may be predictive of thromboembolic events. Inflammation plays a critical role in both the initiation and progression of atherosclerosis, including its complications. PLR and NLR are new markers that have been shown to increase in various inflammatory diseases, and studies indicate that they are associated with atherosclerotic diseases. İdil Soylu et al concluded that PLR is useful in predicting the degree of stenosis and the risk of stroke in patients with carotid stenosis (18). In our study, the PLR value increased significantly in the group with advanced stenosis compared to the control group. On the other hand, it was observed that there is a significant increase in the NLR value in both mild stenosis and advanced stenosis groups compared to the control group.

In our study, markers such as MPV and PDW, which were found to be useful in predicting the development of atherosclerotic disease in previous studies, were evaluated as well (19,20). This study reported no association between the development of carotid stenosis and MPV and RDW values. However, both markers were markedly elevated in individuals with soft plaques compared to those with hard plaques. It was concluded that MPV might be useful in predicting soft plaque formation. This finding is consistent with those of previous studies, representing high MPV levels in instances of arterial thrombotic events, including acute myocardial infarction and ischemic stroke (21,22). Chen et al determined that MHR could be a predictive marker for developing and progressing subclinical carotid atherosclerosis in diabetic patients (23). In our study, no significant difference was observed between the development of stenosis and MHR levels; it can be concluded that it can be a valuable marker in predicting soft plaque formation. The relationship between CRP and carotid artery stenosis has been detected in some studies (24,25). In our study, it was found to be the only marker with a predictive value in evaluating the degree of stenosis and plaque structure. In the study of Xiong et al on 115 patients undergoing carotid endarterectomy, the correlation between plaque fragility and CRP levels was remarkable (24). In a study conducted by Huang et al on 5349 asymptomatic people, it was observed that both basal and chronic CRP elevations were associated with carotid artery stenosis (25).

Carotid plaques from symptomatic patients (cerebrovascular events) had a higher quantity of UA than those from asymptomatic patients. The levels of UA (measured in micrograms per gram) present in atherosclerotic plaques demonstrated a positive correlation with the levels of SUA (serum UA). In addition, UA is useful for the degree of stenosis, and UA was detected to be associated with cerebrovascular events (26).

In a study performed by Ma et al on 11382 people, including 7597 participants in the high UA group and 3785 in the control group, the high UA group was significantly higher than that in the control group, and the difference was significant (27).

Limitations of the Study

There were several limitations to our study. First, the sample size needed to be more significant to draw definitive conclusions. Additionally, the study was conducted at a single centre, which might have limited its generalizability to the broader population.

Conclusion

Overall, our findings revealed that the CRP level is associated with both the stenosis level and plaque morphology. According to the results, this relationship was more valuable than the other inflammatory parameters. Therefore, CRP is most superior in terms of its association with the degree of carotid stenosis. In addition to these results, one unit increase of the UA level was more related to the risk of carotid stenosis than the other inflammatory markers, demonstrating that CRP and serum UA levels are precious for the detection of carotid artery stenosis and risk of carotid artery stenosis.

Authors’ Contribution

Conceptualization: Yildirim Alp, Mustafa Celik.

Data curation:Yildirim Alp, Fikret Keles.

Formal analysis: Fikret Keles, Erdogan Sokmen.

Funding acquisition: Fikret Keles, Muhammed Alpaslan.

Investigation: Mustafa Celik, Muhammed Fatih Kaleli.

Methodology: Muhammed Alpaslan, Mustafa Celik.

Project administration: Mustafa Celik, Yildirim Alp.

Resources: Muhammed Fatih Kaleli, Yildirim Alp.

Supervision: Yildirim Alp, Mustafa Celik.

Validation: Muhammed Fatih Kaleli, Mustafa Celik.

Visualization: Muhammed Alpaslan.

Writing–original draft: Yildirim Alp, Erdogan Sokmen.

Writing–review & editing: Muhammed Fatih Kaleli, Mustafa Celik.

Competing Interests

All authors declare that they have no conflict of interests.

Ethical Approval

The Ethics Committee Resolution number 2019-10/118, dated 28.05.2019, was obtained from the Clinical Research Ethics Committee of the Faculty of Medicine, Ahi Evran University.

References

1. Badimón L, Vilahur G, Padró T. Lipoproteins, platelets and atherothrombosis. Rev Esp Cardiol 2009; 62(10):1161-78. doi: 10.1016/s1885-5857(09)73331-6 [Crossref] [ Google Scholar]
2. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med 2005; 352(16):1685-95. doi: 10.1056/NEJMra043430 [Crossref] [ Google Scholar]
3. Störk S, van den Beld AW, von Schacky C, Angermann CE, Lamberts SW, Grobbee DE. Carotid artery plaque burden, stiffness, and mortality risk in elderly men: a prospective, population-based cohort study. Circulation 2004; 110(3):344-8. doi: 10.1161/01.cir.0000134966.10793.c9 [Crossref] [ Google Scholar]
4. Liapis CD, Bell PR, Mikhailidis D, Sivenius J, Nicolaides A, Fernandes e Fernandes J. ESVS guidelines Invasive treatment for carotid stenosis: indications, techniques. Eur J Vasc Endovasc Surg 2009; 37(4 Suppl):1-19. doi: 10.1016/j.ejvs.2008.11.006 [Crossref] [ Google Scholar]
5. van Lammeren GW, den Ruijter HM, Vrijenhoek JE, van der Laan SW, Velema E, de Vries JP. Time-dependent changes in atherosclerotic plaque composition in patients undergoing carotid surgery. Circulation 2014; 129(22):2269-76. doi: 10.1161/circulationaha.113.007603 [Crossref] [ Google Scholar]
6. Hamada S, Kashiwazaki D, Yamamoto S, Akioka N, Kuwayama N, Kuroda S. Impact of plaque composition on risk of coronary artery diseases in patients with carotid artery stenosis. J Stroke Cerebrovasc Dis 2018; 27(12):3599-604. doi: 10.1016/j.jstrokecerebrovasdis.2018.08.031 [Crossref] [ Google Scholar]
7. Cicorella N, Zanolla L, Franceschini L, Cacici G, De Cristan B, Arieti M. Usefulness of ultrasonographic markers of carotid atherosclerosis (intima-media thickness, unstable carotid plaques and severe carotid stenosis) for predicting presence and extent of coronary artery disease. J Cardiovasc Med (Hagerstown) 2009; 10(12):906-12. doi: 10.2459/JCM.0b013e32832e62fd [Crossref] [ Google Scholar]
8. Çiçek G, Açıkgoz SK, Bozbay M, Altay S, Uğur M, Uluganyan M. Neutrophil-lymphocyte ratio and platelet-lymphocyte ratio combination can predict prognosis in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Angiology 2015; 66(5):441-7. doi: 10.1177/0003319714535970 [Crossref] [ Google Scholar]
9. Ji H, Li Y, Fan Z, Zuo B, Jian X, Li L. Monocyte/lymphocyte ratio predicts the severity of coronary artery disease: a syntax score assessment. BMC Cardiovasc Disord 2017; 17(1):90. doi: 10.1186/s12872-017-0507-4 [Crossref] [ Google Scholar]
10. Kose N, Akin F, Yildirim T, Ergun G, Altun I. The association between the lymphocyte-to-monocyte ratio and coronary artery disease severity in patients with stable coronary artery disease. Eur Rev Med Pharmacol Sci 2019; 23(6):2570-5. doi: 10.26355/eurrev_201903_17406 [Crossref] [ Google Scholar]
11. Kundi H, Kiziltunc E, Cetin M, Cicekcioglu H, Cetin ZG, Cicek G. Association of monocyte/HDL-C ratio with SYNTAX scores in patients with stable coronary artery disease. Herz 2016; 41(6):523-9. doi: 10.1007/s00059-015-4393-1 [Crossref] [ Google Scholar]
12. Wang HY, Shi WR, Yi X, Zhou YP, Wang ZQ, Sun YX. Assessing the performance of monocyte to high-density lipoprotein ratio for predicting ischemic stroke: insights from a population-based Chinese cohort. Lipids Health Dis 2019; 18(1):127. doi: 10.1186/s12944-019-1076-6 [Crossref] [ Google Scholar]
13. Grønholdt ML, Nordestgaard BG, Schroeder TV, Vorstrup S, Sillesen H. Ultrasonic echolucent carotid plaques predict future strokes. Circulation 2001; 104(1):68-73. doi: 10.1161/hc2601.091704 [Crossref] [ Google Scholar]
14. Ruggiero C, Cherubini A, Ble A, Bos AJ, Maggio M, Dixit VD. Uric acid and inflammatory markers. Eur Heart J 2006; 27(10):1174-81. doi: 10.1093/eurheartj/ehi879 [Crossref] [ Google Scholar]
15. Dos Santos SN, de Alcantara ML, Freire CMV, Cantisano AL, Teodoro JAR, Porto CLL. Vascular ultrasound statement from the department of cardiovascular imaging of the Brazilian Society of Cardiology - 2019. Arq Bras Cardiol 2019; 112(6):809-49. doi: 10.5935/abc.20190106 [Crossref] [ Google Scholar]
16. Fox AJ. How to measure carotid stenosis. Radiology 1993; 186(2):316-8. doi: 10.1148/radiology.186.2.8421726 [Crossref] [ Google Scholar]
17. Kuyumcu A. The relationship between nesfatin-1 and carotid artery stenosis. Scand Cardiovasc J 2018; 52(6):328-34. doi: 10.1080/14017431.2018.1547840 [Crossref] [ Google Scholar]
18. İdil Soylu A, Arıkan Cortcu S, Uzunkaya F, Atalay YO, Bekçi T, Güngör L. The correlation of the platelet-to-lymphocyte ratio with the severity of stenosis and stroke in patients with carotid arterial disease. Vascular 2017; 25(3):299-306. doi: 10.1177/1708538116673770 [Crossref] [ Google Scholar]
19. Adam G, Kocak E, Özkan A, Reşorlu M, Çınar C, Bozkaya H. Evaluation of platelet distribution width and mean platelet volume in patients with carotid artery stenosis. Angiology 2015; 66(4):375-8. doi: 10.1177/0003319714548682 [Crossref] [ Google Scholar]
20. Tsiara S, Elisaf M, Jagroop IA, Mikhailidis DP. Platelets as predictors of vascular risk: is there a practical index of platelet activity?. Clin Appl Thromb Hemost 2003; 9(3):177-90. doi: 10.1177/107602960300900301 [Crossref] [ Google Scholar]
21. Arévalo-Lorido JC, Carretero-Gómez J, Villar-Vaca P. Mean platelet volume predicting carotid atherosclerosis in atherothrombotic ischemic stroke. Ir J Med Sci 2012; 181(2):179-83. doi: 10.1007/s11845-011-0755-8 [Crossref] [ Google Scholar]
22. Yılmaz F, Köklü E, Kizilirmak Yilmaz F, Sarıönder Gencer E, Alparslan A, Yıldırımtürk Ö. Evaluation of mean platelet volume and platelet distribution width in patients with asymptomatic intermediate carotid artery plaque. Kardiol Pol 2017; 75(1):35-41. doi: 10.5603/KP.a2016.0129 [Crossref] [ Google Scholar]
23. Chen JW, Li C, Liu ZH, Shen Y, Ding FH, Shu XY. The role of monocyte to high-density lipoprotein cholesterol ratio in prediction of carotid intima-media thickness in patients with type 2 diabetes. Front Endocrinol (Lausanne) 2019; 10:191. doi: 10.3389/fendo.2019.00191 [Crossref] [ Google Scholar]
24. Xiong L, Sun WJ, Cai HY, Yang Y, Zhu J, Zhao BW. Correlation of enhancement degree on contrast-enhanced ultrasound with histopathology of carotid plaques and serum high sensitive C-reactive protein levels in patients undergoing carotid endarterectomy. J Huazhong Univ Sci Technolog Med Sci 2017; 37(3):425-8. doi: 10.1007/s11596-017-1751-7 [Crossref] [ Google Scholar]
25. Huang X, Wang A, Liu X, Chen S, Zhu Y, Liu Y. Association between high sensitivity C-Reactive protein and prevalence of asymptomatic carotid artery stenosis. Atherosclerosis 2016; 246:44-9. doi: 10.1016/j.atherosclerosis.2015.12.024 [Crossref] [ Google Scholar]
26. Nardi V, Franchi F, Prasad M, Fatica EM, Alexander MP, Bois MC. Uric acid expression in carotid atherosclerotic plaque and serum uric acid are associated with cerebrovascular events. Hypertension 2022; 79(8):1814-23. doi: 10.1161/hypertensionaha.122.19247 [Crossref] [ Google Scholar]
27. Ma M, Wang L, Huang W, Zhong X, Li L, Wang H. Meta-analysis of the correlation between serum uric acid level and carotid intima-media thickness. PLoS One 2021; 16(2):e0246416. doi: 10.1371/journal.pone.0246416 [Crossref] [ Google Scholar]