Eun Sook Kim, Eun Yeong Mo, Sung Dae Moon,* and Je Ho Han*

Transcription

1 ORIGINAL ARTICLE Mean Platelet Volume Is Closely Associated With Serum Glucose Level but Not With Arterial Stiffness and Carotid Atherosclerosis in Patients With Type 2 Diabetes Eun Sook Kim, Eun Yeong Mo, Sung Dae Moon,* and Je Ho Han* Department of Internal Medicine (E.S.K., E.Y.M., S.D.M., J.H.H.), The Catholic University of Korea College of Medicine, Seoul , Republic of Korea; and Division of Endocrinology and Metabolism (E.S.K., E.Y.M., S.D.M., J.H.H.), Department of Internal Medicine, Incheon St Mary s Hospital, Incheon , Korea Context: Mean platelet volume (MPV) has been suggested as a predictive biomarker for cardiovascular disease. Objective: This study investigated the association between MPV and subclinical atherosclerosis in Korean patients with type 2 diabetes. Methods: This cross-sectional study involved 1205 patients with type 2 diabetes mellitus. Both brachial-ankle pulse wave velocity measurements and an ultrasound assessment of carotid atherosclerosis were done. Subclinical atherosclerosis was assessed by the presence of high brachialankle pulse wave velocity ( 1743 cm/sec), carotid atherosclerosis (intima-media thickness 0.8 mm or the presence of plaques), and carotid stenosis ( 50% of luminal narrowing). The subjects were stratified into quartiles according to MPV, and the relationship between MPV and subclinical atherosclerosis was analyzed. Results: High MPV quartiles were linearly associated with fasting glucose and glycated hemoglobin but not with diabetic duration or insulin resistance. The prevalence of high pulse wave velocity, carotid atherosclerosis, and carotid stenosis did not differ between the quartiles in men and women. Multivariate logistic regression analyses revealed no association between MPV and high pulse wave velocity, carotid atherosclerosis, and carotid stenosis. Conclusions: MPV was strongly associated with the severity of glycemic control but not significantly associated with the early and late stages of atherosclerotic vascular changes in type 2 diabetes mellitus patients. Our results suggest that MPV is not a reliable marker for subclinical atherosclerosis in a diabetic population. This is possibly confounded by the close association of MPV with poor glycemic control. Further research is needed to broaden and validate the results. (J Clin Endocrinol Metab 100: , 2015) Type 2 diabetes mellitus (T2DM) has reached global epidemic proportions, with more than 382 million people affected according to a 2013 estimate. By 2035, its ISSN Print X ISSN Online Printed in USA Copyright 2015 by the Endocrine Society Received February 27, Accepted June 22, First Published Online June 29, 2015 prevalence is expected to reach 471 million, meaning that 10% of the world s population will have diabetes (1). The substantial rise in the prevalence of diabetes is a major * S.-D.M. and J.-H.H. contributed equally to the study. Abbreviations: bapwv, brachial-ankle PWV; BMI, body mass index; CAD, coronary artery disease; CCA, common carotid artery; CI, confidence interval; CVD, cardiovascular disease; DBP, diastolic blood pressure; egfr, estimated glomerular filtration rate; FPG, fasting plasma glucose; FRS, Framingham risk score; HbA1c, hemoglobin A1c; HOMA-IR, homeostasis model assessment of insulin resistance; IMT, intima-media thickness; LDL-C, lowdensity lipoprotein-cholesterol; MI, myocardial infarction; MPV, mean platelet volume; OR, odds ratio; PWV, pulse wave velocity; SBP, systolic blood pressure; T2DM, type 2 diabetes mellitus; TG, triglyceride press.endocrine.org/journal/jcem J Clin Endocrinol Metab, September 2015, 100(9): doi: /JC

2 doi: /JC press.endocrine.org/journal/jcem 3503 health threat, associated with prevalent long-term complications and a high mortality rate. The coronary risks associated with T2DM are similar to those of nondiabetic patients with previous myocardial infarction (MI) (2). Patients with diabetes often suffer with metabolic abnormalities, poor glycemic control, oxidative stress, insulin resistance, and low-grade inflammation. These conditions trigger vascular dysfunction, which predisposes them to atherothrombosis (3). A comprehensive multifactorial intervention is essential for the proper management of diabetic patients, but the cardiovascular risks remain high, even after optimal glycemic and metabolic control has been achieved. Thus, identifying novel risk factors can provide better preventative strategies for individuals at high risk of cardiovascular events. Mean platelet volume (MPV) is a measure of the average size of platelets in the circulation. Considerable evidence suggests that MPV can be used as a potential biomarker of cardiovascular disease (CVD). Recent studies reported that increased MPV is associated with the presence of angina pectoris, severity of coronary artery occlusion, and even poor prognosis for acute MI (4 7). Several studies reported that increased MPV is closely associated with cardiovascular risks in patients with diabetes; however, this remains controversial because others failed to observe an association between MPV and CVD (8 10). Arterial stiffness is a manifestation of structural and functional changes in the vascular wall and a strong predictor of cardiovascular events and mortality. It is also a link between diabetes and increased cardiovascular risks (11). Brachial-ankle pulse wave velocity (bapwv) is a reliable and easily accessible measurement of arterial stiffness with good reproducibility (12). Carotid B-mode highresolution ultrasonography is a noninvasive tool to assess intima-media thickness (IMT) and plaque presence in carotid arteries, visualizing changes in vascular morphology (13). In clinical practice, carotid ultrasonography and measurements of bapwv are widely accepted as efficient tools for identifying individuals at high risk for CVD with predictive power for cardiovascular outcomes (11, 14). In this study, we investigated the association between MPV and vascular wall properties in Korean patients with T2DM by measuring bapwv and performing an ultrasound assessment of carotid atherosclerosis. Materials and Methods Subjects We recruited subjects with type 2 diabetes older than 30 years who visited Incheon St Mary s Hospital for the purpose of glucose control between August 2011 and November 2013 in a retrospective manner. We enrolled total of 1490 patients with MPV measurements and subclinical atherosclerosis. Patients with platelet levels of less than per cubic millimeter or greater than per cubic millimeter (n 95), ankle-brachial index ( 0.9 or 1.3) (n 27), or a severe illness such as malignancy (n 64), chronic liver disease (n 11), systemic inflammatory disease, or those taking warfarin or corticosteroids (n 29) were excluded. A total of 1205 patients were included after additionally excluding those with missing values in the final analysis. The Institutional Review Board of the Clinical Research Coordinating Center in Incheon St Mary s Hospital approved the study protocol. Clinical and biochemical assessment Demographic and clinical data were verified by reviewing the electronic medical records. Body mass index (BMI) was calculated by dividing the patients weight in kilograms by height in meters squared. After overnight fasting, venous blood was taken and collected in K2-EDTA tubes, and blood analyses were processed within 2 hours of blood collection. MPVs were analyzed using the Sysmex XE 2100 automated hematology analyzer (Sysmex). The expected values for MPV ranged from 9.1 to 11.9 fl. Serum insulin was measured with a Roche Cobas E601 (Roche), and homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as follows: HOMA-IR fasting insulin (microunits per milliliter) fasting plasma glucose (FPG) (millimoles per liter)/22.5. Low-density lipoprotein-cholesterol (LDL-C) was indirectly measured using the Friedewald formula only in participants with serum triglyceride concentrations below 400 mg/ml. Hyperlipidemia was defined as a triglyceride (TG) concentration of 150 mg/dl or greater or an LDL-C concentration of 100 mg/dl or greater and/or taking cholesterollowering medication. Patients were considered to have hypertension if they had a systolic blood pressure (SBP) of 140 mm Hg or greater and/or a diastolic blood pressure (DBP) of 90 mm Hg or greater or if they were on treatment. The estimated glomerular filtration rate (egfr) was calculated from the Modification of Diet in Renal Disease study equation (15). The Framingham risk score (FRS) was calculated to estimate the 10-year risk for coronary heart disease using validated algorithms (16). Ultrasonic assessment of carotid artery disease Carotid IMT was measured bilaterally using high resolution B-mode scanner (ALOKA; prosound- 10) with a 10-MHz transducer. Carotid IMT measurement and plaque assessment were done as recommended by the Manheim Carotid Intima-Media Thickness and the American Society of Echocardiography Consensus. IMT was measured from three contiguous sites at the region mm proximal to the carotid bulb, and the mean IMT was calculated as the average of right and left mean IMT. Plaque was defined as a localized or broad lesion and the broad lesion was defined as more than 50% of the surrounding IMT or a thickness of 1.5 mm. As for the plaque quantification scoring method, the method suggested by Chien et al (17) was used. In brief, common carotid artery (CCA) segments, including the proximal CCA, distal CCA, bulb, internal carotid artery, and external carotid artery were examined bilaterally. A grade was assigned for each segment: grade 0 for normal or no observable plaque; grade 1 for 1 small plaque with diameter stenosis less than 30%; grade 2 for one medium plaque with 30% 49% diameter stenosis or multiple small plaques; grade 3 for one large plaque with 50% 99% diameter stenosis or multiple plaques

3 3504 Kim et al Cardiovascular Significance of MPV in T2DM J Clin Endocrinol Metab, September 2015, 100(9): with at least one medium plaque; and grade 4 for 100% occlusion. The highest value at any segment was used for the analysis. We defined carotid atherosclerosis as carotid IMT greater than 0.8 mm or the presence of plaques and carotid stenosis as 50% or greater of luminal narrowing (plaque score 2). Measurement of bapwv The bapwv was measured using an automated pulse wave velocity (PWV)/ankle-brachial index analyzer (VP-2000; Colin Co Ltd) after the subjects had rested in the supine position for at least 5 minutes. The electrocardiogram electrodes were placed on both wrists and both ankles, and blood pressure cuffs were wrapped around both upper arms and both ankles. To measure the bapwv, pulse waves obtained from the brachial and tibial arteries were recorded simultaneously, and the transmission time was calculated as the time interval between the initial increase in the brachial and ankle waveforms. The path length from the suprasternal notch to the brachium and from the suprasternal notch to the ankle was automatically obtained based on the subject s height. The bapwv was calculated using the equation, bapwv (length from the suprasternal notch to the ankle the length from the suprasternal notch to the brachium)/transmission time (centimeters per second), and the mean bapwvs for the left and right sides were used for the analysis. A high bapwv was defined as the highest quartile of values among the subjects ( 1743 cm/sec). Statistical analysis Statistical analyses were performed using the SAS software (version 9.1; SAS Institute). Continuous data were expressed as means SD or as median with interquartile range (25th to 75th percentile) in case of skewed distribution. Categorical data were expressed as numbers (percentage). The comparison of continuous variables was done by a one-way ANOVA or the Kruskal- Wallis test, as appropriate and categorized variables by 2 test to compare characteristics of the study population. A Spearman rank correlation analyses were performed to examine the association between MPV and various clinical parameters. Multivariate logistic regression analyses were performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for high bapwv, carotid atherosclerosis, and carotid stenosis according to the MPV quartiles and a 1-fL increase in the MPV. A value of P.05 was considered statistically significant. Results Clinical characteristics of the subjects The characteristics of the study patients according to the MPV quartile groups are shown in Table 1. Subjects in the highest quartile group were younger, more likely to be men, had lower platelet counts, and were more likely to be insulin users. Metabolic profiles including BMI, lipid, and blood pressure profiles were similar among the quartile groups. The glycemic status, measured by FPG and hemoglobin A1c (HbA1c), linearly increased across the MPV quartiles, but no association was observed between MPV and HOMA-IR. Separate analyses by gender showed the same trend (Supplemental Figure 1). Regarding subclinical atherosclerosis, no significant differences were observed in bapwv, carotid IMT, and plaque grade across the quartiles. The prevalence of high PWV, carotid atherosclerosis, and carotid stenosis did not differ between the quartiles both in men and in women (Figure 1). Correlation between MPV and clinical parameters Spearman correlation analyses of MPV revealed a positive correlation with male gender, FPG, and HbA1c and a negative correlation with age, bapwv, and carotid IMT (Table 2). However, no significant correlation with plaque presence or grade was observed. After adjusting for age and gender, bapwv and IMT showed no correlation with MPV. Association of MPV with high PWV, carotid atherosclerosis, and carotid stenosis Table 3 shows the OR and 95% CI for high PWV, carotid atherosclerosis, and carotid stenosis across the MPV quartiles. The risks of high PWV, carotid atherosclerosis, or carotid stenosis were not correlated with MPV values both as continuous variables and quartiles in all models. Discussion The present study showed that MPV is significantly associated with glycemic parameters such as fasting glucose and HbA1c levels in a graded manner. However, no association was observed between MPV and any measures of subclinical atherosclerosis including bapwv, carotid atherosclerosis, and carotid stenosis. CVD is the leading cause of death and disability in T2DM, affecting about 65% 80% of diabetic patients (18). It is caused by atherosclerosis, a chronic disease that begins during adolescence and slowly progresses over decades. Platelets are anucleate cell fragments with a life span of 7 10 days. They are implicated in the pathogenesis of atherosclerosis as well as playing a major role in regulating hemostasis (19). Platelet hyperactivity is characteristically seen in T2DM, causing a proatherothrombic state along with altered hemostasis and impaired endothelial function (20). In addition, a substantial portion of diabetic patients show resistance to antiplatelet drugs, which poses a major health care challenge to preventing heart attacks and stroke (21). Clinical research showed that platelet activity is associated with vascular thrombotic disease and can predict coronary heart disease outcomes (22). Although turbidimetric platelet aggregometry is regarded as the gold standard test for platelet function, it cannot be used in routine

4 doi: /JC press.endocrine.org/journal/jcem 3505 Table 1. Characteristics of the Study Subjects Q1 <9.50 MPV, fl Q Q Q4 >10.5 P Value n Age, y Male gender, % 114 (40.3) 144 (44.3) 123 (44.7) 171 (53.1).013 Current smokers 50 (17.7) 61 (18.8) 51 (18.6) 80 (24.8).097 Duration, y 5 (1 11) 4 (1 10) 5 (1 13) 4 (0 10).414 BMI, kg/m Platelet, 10 9 /L FPG, mg/dl HbA1c, % HOMA-IR a 2.8 ( ) 2.5 ( ) 2.8 ( ) 2.7 ( ).120 TC, mg/dl TG, mg/dl 148 ( ) 149 ( ) 147 ( ) 149 ( ).936 HDL-C, mg/dl LDL-C, mg/dl SBP, mm Hg DBP, mm Hg egfr, ml/min per 1.73 m Usage of medication, % Insulin 66 (23.3) 76 (23.4) 71 (25.8) 106 (32.9).019 Aspirin 53 (18.7) 65 (20.0) 54 (19.6) 56 (17.4).841 Clopidogrel 4 (1.4) 3 (0.9) 2 (0.7) 5 (1.6).753 CCB 62 (21.9) 52 (16.0) 55 (20.0) 61 (18.9).306 ACEI/ARB 99 (35.0) 106 (32.6) 98 (35.6) 102 (31.7).699 -Blocker 17 (6.0) 28 (8.6) 21 (7.6) 27 (8.4).629 Statin 87 (30.7) 104 (32.0) 101 (36.7) 116 (36.0).332 History of CVD, % 32 (11.3) 23 (7.1) 22 (8.0) 32 (9.9) year FRS, % 4 (2 10) 4 (1 10) 4 (1 10) 5 (1 12).089 bapwv, cm/sec IMT, mm Carotid plaque, %.770 Grade (39.9) 148 (45.5) 109 (39.6) 136 (42.2) Grade 1 31 (11.0) 36 (11.1) 29 (10.6) 41 (12.7) Grade 2 74 (26.2) 68 (20.9) 76 (27.6) 76 (23.6) Grade 3 61 (21.6) 72 (22.2) 58 (21.1) 65 (20.2) Grade 4 4 (1.4) 1 (0.3) 3 (1.1) 4 (1.2) Abbreviations: ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker; CCB, calcium channel blocker; HDL-C, highdensity lipoprotein-cholesterol; Q, quartile; TC, total cholesterol. Data were expressed as means SD, median with interquartile range, or numbers (percentage). a n 897. practice due to its time-consuming nature and operatordependent variability (23). MPV is a simple and inexpensive parameter, which is automatically analyzed as part of a complete blood count. It reflects platelet reactivity because large platelets are likely to be younger and physiologically more active, have more -granules, express increased adhesion molecules, and produce more thromboxane A2, subsequently showing greater thrombogenic potential, compared with small platelets (24). Some studies also suggested that large platelets might be derived from an elevated turnover platelet pool and reflect the number of reticulocyte platelets (25). Thus, recent studies paid particular attention to MPV as a potent biomarker for risk stratification and progression of CVD. A meta-analysis on case-control and cross-sectional studies showed that a large MPV is positively associated with coronary artery disease (CAD) in a graded manner and also with slow coronary blood flow (4). MPV is also correlated with the angiographic severity of CAD in patients with CAD (5). Moreover, several studies showed that MPV is a predictor of high mortality after an MI, in-stent restenosis after coronary angioplasty, and mortality reduction from glycoprotein IIb/IIIa inhibitor use (6, 7). The discrepancy between the results of the present study and those of previous studies may have been due to the differences in the characteristics of the study populations; unlike other studies, the current study was performed only on patients with diabetes. Until now, there has been very few data available on the association between MPV and CV risk in T2DM patients. Some previous

5 3506 Kim et al Cardiovascular Significance of MPV in T2DM J Clin Endocrinol Metab, September 2015, 100(9): Table 2. Spearman Correlation Coefficients Between MPV With Clinical Parameters Unadjusted Age and Sex Adjusted r P r P Age Male gender BMI, kg/m Duration, y Platelets, 10 9 /L FPG, mg/dl HOMA-IR HbA1c, % TC, mg/dl TG, mg/dl HDL-C, mg/dl LDL-C, mg/dl SBP, mm Hg DBP, mm Hg egfr, ml/min per m 2 10-year FRS bapwv, m/sec IMT, mm Plaque presence Plaque grade Abbreviations: HDL-C, high-density lipoprotein-cholesterol; TC, total cholesterol. Figure 1. The prevalence of high bapwv, carotid atherosclerosis, and carotid stenosis according to MPV quartiles in males and in females. reports suggested that MPV is an indicator of increased CV risk but without adequately controlling for the confounding effect of glycemic status. Lekston et al (8) showed that MPV had a good prognostic value for inhospital and late mortality in 539 diabetic patients undergoing primary percutaneous coronary intervention but did not present any information on HbA1c levels. Han et al (9) showed that MPV was a significant predictor of stroke or CAD in 200 diabetic patients during a mean follow-up period of 28.4 months, but the study number was small and did not adjusted HbA1c levels, which, unlike in most previous reports, were not different among MPV quartiles at baseline. De Luca et al (10) demonstrated that MPV was not related to platelet reactivity and the prevalence and extent of CAD in 1016 diabetic patients undergoing coronary angiography, which is consistent with our data. We could not explain the reason for the lack of association between MPV and subclinical atherosclerosis, but it may be related to the close association between MPV and glycemic controls. Many studies reported that MPV is larger in diabetic patients than in nondiabetic subjects, and a continuous association between MPV and glucose levels was demonstrated in diabetic patients (26). Consistent with our data, and unlike to its close association with glucose levels, a few studies have shown that MPV is not associated with diabetic duration (27 30). Furthermore, it was reported that increased MPV can be reversed after glycemic control (27). Therefore, one could speculate that MPV simply mirrors osmotic swelling of platelets as a temporary response to hyperglycemia rather than a reflection of increased platelet activity from long-term hyperglycemia in T2DM patients (31). Several studies suggested that MPV contributes to the development and progression of atherosclerosis, at least in part, via a close association with metabolic profiles and insulin resistance (32). Thus, the lack of an association between MPV and unfavorable cardiovascular risk profiles including hypertension, hy-

6 doi: /JC press.endocrine.org/journal/jcem 3507 Table 3. MPV ORs (95% CIs) for High bapwv, Carotid Atherosclerosis, and Carotid Stenosis According to Quartiles of High bapwv a Carotid Atherosclerosis b Carotid Stenosis c OR (95% CI) P Value OR (95% CI) P Value OR (95% CI) Model 1 Per MPV, fl 1.19 ( ) ( ) ( ).713 Q Q ( ) ( ) ( ).739 Q ( ) ( ) ( ).596 Q ( ) ( ) ( ).714 Model 2 Per MPV, fl 1.07 ( ) ( ) ( ).293 Q Q ( ) ( ) ( ).956 Q ( ) ( ) ( ).976 Q ( ) ( ) ( ).808 Model 3 Per MPV, fl 1.02 ( ) ( ) ( ).195 Q Q ( ) ( ) ( ).747 Q ( ) ( ) ( ).839 Q ( ) ( ) ( ).640 P Value Abbreviation: Q, quartile. Model 1 was adjusted for age and gender. Model 2 was adjusted for age, gender, diabetic duration, BMI, smoking, and HbA1c. Model 3 was adjusted for age, gender, diabetic duration, BMI, smoking, HbA1c, hyperlipidemia, hypertension, history of CVD, use of statin, and egfr. a Defined as the highest quartile of values among the subjects ( 1743 cm/sec). b Defined as carotid IMT greater than 0.8 mm or the presence of plaques. c Defined as 50% or greater of luminal narrowing (plaque score 2). perlipidemia, and HOMA-IR in the present study provides further support that MPV is not a marker of cardiovascular risk in diabetic patients. Our results imply that MPV cannot discriminate cardiovascular risk in diabetic patients. Contrary to previous studies, the graded association of MPV with poor glycemic control might mask the clinical implications of MPV in increased atherothrombotic potential as a confounder rather than indirectly indicating greater cardiovascular risks. Further studies are needed to validate the significance of MPV as a biomarker for CVD in diabetic patients and to investigate the biological mechanisms of increased MPV with the degree of glycemic status. This study had some limitations. Because of its crosssectional nature, we could not infer any causal or temporal relations between MPV and the atherosclerotic vascular burden. Second, we measured bapwv rather than the preferred measurement of aortic stiffness. Third, only single MPV measurements were available, which are not as desirable as using the mean of several measurements. Fourth, we used EDTA and not heparin, which made our results unreliable after 4 hours because MPV increases as the storage time in EDTA increases (33, 34). However, we performed MPV measurements within 2 hours of sampling, therefore attenuating the possibility of storage-related errors. In conclusion, MPV was strongly associated with the severity of glycemic control but was not significantly associated with early and late stages of atherosclerotic vascular changes in T2DM patients. Further studies should be performed to ascertain whether MPV could be a reliable marker for increased cardiovascular risk in the diabetic population. Acknowledgments We are grateful to Dr Yong Gyu Park of the Department of Biostatistics, College of Medicine, Catholic University, for his statistical advice. Address all correspondence and requests for reprints to: Je Ho Han, MD, PhD, Division of Endocrinology and Metabolism, Department of Internal Medicine, Incheon St Mary s Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul , Republic of Korea. jehohan@gmail.com; or Sung Dae Moon, MD, PhD, Division of Endocrinology and Metabolism, Department of Internal Medicine, Incheon St Mary s Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul , Republic of Korea. sungdaem@gmail.com. This study was supported by the National Research Foundation of Korea supported by the Korea Government (MSIP, Grant 2014R1A1A ).

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