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Elevated mean platelet volume is associated with silent cerebral infarction B. Li,1 X. Liu,2 Z.-G. Cao,3 Y. Li,2,4 T.-M. Liu5 and R.-T. Wang2 Departments of 1Neurology, 2Geriatrics, 3Medical Imaging and 4International Physical Examination and Healthy Center, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China, and 5Division of Hypothalamic Research, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
Key words silent cerebral infarction, mean platelet volume, atherosclerosis, platelet activation, risk factor. Correspondence Rui-tao Wang, Department of Geriatrics, The Second Affiliated Hospital, Harbin Medical University, NO. 246 Xuefu ST, Nangang District, Harbin, Heilongjiang 150086, China. Email: [email protected] Received 14 February 2014; accepted 4 April 2014. doi:10.1111/imj.12454
Abstract Background: The presence of silent cerebral infarction (SCI) increases the risk of transient ischaemia attack, symptomatic stroke, cardiovascular disease and dementia. Mean platelet volume (MPV) is a surrogate marker of activated platelets and is considered a link between inflammation and thrombosis. In addition, MPV is a risk predictor for cardiovascular disease, stroke and overall vascular mortality. Aims: The purpose of the study was to assess the MPV levels in SCI patients. Methods: A cross-sectional study was conducted to evaluate the association between MPV and SCI in 2215 subjects (1385 men and 830 women). Results: The participants with SCI had higher MPV levels than those without SCI (10.4 ± 1.3 fL vs. 9.2 ± 1.2 fL; P < 0.001). Moreover, the subjects with a high MPV had a higher prevalence of SCI. Multivariate logistic regression analyses revealed that the odds ratios and 95% confidence intervals for SCI according to MPV quartiles were 1.000, 2.131 (1.319–3.444), 3.015 (1.896–4.794), 7.822 (4.874–12.554) respectively (P < 0.001). Conclusion: MPV is a novel index for SCI regardless of classical cardiovascular risk factors.
Introduction Silent cerebral infarction (SCI) is a radiologic marker that implies the presence of cerebrovascular disease that does not show any clinical symptom. In most cases, SCI is found as a lacunar infarction caused by occlusion of small penetrating cerebral arteries. Moreover, some studies have demonstrated that the presence of SCI could be an indicator of transient ischaemia attack, clinically overt stroke, cardiovascular disease and dementia.1–3 Activated platelets play a key role in atherosclerosis. Elevated activity of platelet aggregation was observed in SCI patients.4 Moreover, treatment with anti-platelet drugs has been associated with reduced risk of developing SCI.5,6 Mean platelet volume (MPV), the most commonly used measure of platelet size, is an index of activated platelets and is available in clinical practice. Some studies have reported that MPV is a predictor of cardiovascular risk, stroke risk and overall vascular mortality.7–9 In addi-
Funding: This study was supported by the China Postdoctoral Science Foundation (2013M541409) and Technology Foundation for Selected Overseas Chinese Scholar, Ministry of Personnel of China (No. 2013578). Conflict of interest: None. © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
tion, MPV is associated with worse clinical prognosis in acute myocardial infarction and acute stroke.10,11 Little research has been conducted to investigate the relationship between MPV levels and SCI. We, therefore, conducted this study to assess MPV levels in SCI patients.
Methods Study subjects We studied 2600 subjects who visited International Physical Examination and Healthy Center, Harbin, China, from January 2009 through December 2010 and who underwent magnetic resonance imaging (MRI) of the brain as part of their routine health check. There were 357 subjects who met the exclusion criteria and were excluded. There were 28 subjects who were excluded for having missing data for smoking status and alcohol intake. Therefore, the study population was finalised as 2215 participants. Exclusion criteria for this study included tumour, haematological disorders, autoimmune diseases, rheumatoid arthritis, infection, atrial fibrillation, transient ischaemic attacks, stroke and medical treatment with lipid-lowering agents, and anti-platelet medication. We obtained informed consent from all sub653
Li et al.
jects. The study protocol was approved by the Ethics Committee of the Second Hospital of Harbin Medical University, China.
Clinical assessment All the subjects underwent a clinical investigation including medical history, smoking status, alcohol intake, and physical examinations, laboratory tests and an MRI scan of the brain. Body weight was measured in light clothing, without shoes, to the nearest 0.5 kg. Height was measured to the nearest 0.5 cm. Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Blood pressure was determined using a mercury-gravity sphygmomanometer in a sitting position after a 15-min rest. Two readings were taken, with a 5-min interval between measurements. The mean of the two readings was recorded. Hypertension was diagnosed if systolic blood pressure ≥ 140 mmHg and diastolic pressure ≥ 90 mmHg, or as antihypertensive treatment.
Blood investigations Fasting venous blood samples were drawn in the morning after an 8-h fast. The values included total serum total cholesterol (TC), triglyceride (TG), highdensity lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and fasting plasma glucose (FPG). Diabetes mellitus (DM) was defined as fasting serum glucose was ≥ 7.0 mmol/L or non-fasting serum glucose was ≥ 11.1 mmol/L or as taking prescription medications. For the controls or the patients with impaired fasting glucose, DM was diagnosed if a 2-h post-glucose level after a 75-g oral glucose tolerance test ≥ 11.1 mmol/L. The Modification of Diet in Renal Disease (MDRD) equation was used to estimate glomerular filtration rate (eGFR). MDRD equation was: eGFR = 186.3 × (serum creatinine)−1.154 × (age)−0.203 (×0.742 if female). The assays were performed at the Laboratory of Analytical Biochemistry at the Second Hospital of Harbin Medical University, Harbin, using a biochemical analyzer (MODULAR ANALYTICS, Roche, Mannheim, German) using standard methods. Platelet count, MPV and platelet distribution width were determined with an autoanalyser (Sysmex XE-2100, Kobe, Japan). The whole blood samples were collected in ethylenediaminetetraacetic acid-containing tubes, and all samples were processed within 30 min after blood collection.
Cerebral MRI Brain MRI examination was performed on a 1.5-T magnetic resonance system (Achieva 1.5T, Philips, Best, The Netherlands). The scan protocol consisted of transverse relaxation time (T2)-weighted spin echo (repetition time 654
(TR): 4800 ms, echo time (TE): 100 ms), longitudinal relaxation time (T1)-weighted spin echo (TR: 520 ms, TE: 14 ms) and fluid-attenuated inversion recovery (FLAIR; TR: 8500 ms, TE: 120 ms, inversion time: 2000 ms) imaging in axial planes at 5-mm thick slices with an interslice gap of 1.5 mm. The criteria for SCI were as follows: (i) a focal high intensity lesion (3–15 mm) in the T2 and FLAIR and low intensity in the T1 image; (ii) no corresponding symptoms in the clinical history of the patient that could be attributed to the lesion; and (iii) no history of clinical stroke. Periventricular white matter lesions were distinguished from SCI based on the highsignal intensity on FLAIR. Dilated perivascular spaces were differentiated from SCI based on their locations (along perforating or medullary arteries, often bilaterally symmetrical, usually in the lower third of the basal ganglia) and by absence of gliosis.12 The diagnoses were made by two independent experienced radiologists blinded to subject history and clinical status. The kappa value of agreement for SCI was 0.84. A consensus on inconsistent readings was reached through discussions.
Statistical analyses The SPSS statistical software package version 17.0 (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses. All participants were classified into quartiles by their MPV levels. Quartile 1 (Q1) was MPV ≤ 8.2 fL, quartile 2 (Q2) was MPV 8.3–9.5 fL, quartile 3 (Q3) was MPV 9.6–10.3 fL, and quartile 4 (Q4) was MPV ≥ 10.4 fL. The data were expressed as means ± standard deviation or medians (with interquartile ranges) for continuous variables or percentage for categorical variables. The Chi-squared test was used for all categorical variables. The differences of continuous variables between SCI group and non-SCI group were determined using the Student’s t-test or Mann– Whitney U-test. The differences of continuous variables according to MPV quartiles were determined using oneway analysis of variance or Kruskal–Wallis H-test. The odds ratios (OR) and 95% confidence intervals (95% CI) for SCI were calculated after adjusting for confounding variables across MPV quartiles using multivariate logistic regression analysis. A P-value of
Elevated mean platelet volume is associated with silent cerebral infarction B. Li,1 X. Liu,2 Z.-G. Cao,3 Y. Li,2,4 T.-M. Liu5 and R.-T. Wang2 Departments of 1Neurology, 2Geriatrics, 3Medical Imaging and 4International Physical Examination and Healthy Center, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China, and 5Division of Hypothalamic Research, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
Key words silent cerebral infarction, mean platelet volume, atherosclerosis, platelet activation, risk factor. Correspondence Rui-tao Wang, Department of Geriatrics, The Second Affiliated Hospital, Harbin Medical University, NO. 246 Xuefu ST, Nangang District, Harbin, Heilongjiang 150086, China. Email: [email protected] Received 14 February 2014; accepted 4 April 2014. doi:10.1111/imj.12454
Abstract Background: The presence of silent cerebral infarction (SCI) increases the risk of transient ischaemia attack, symptomatic stroke, cardiovascular disease and dementia. Mean platelet volume (MPV) is a surrogate marker of activated platelets and is considered a link between inflammation and thrombosis. In addition, MPV is a risk predictor for cardiovascular disease, stroke and overall vascular mortality. Aims: The purpose of the study was to assess the MPV levels in SCI patients. Methods: A cross-sectional study was conducted to evaluate the association between MPV and SCI in 2215 subjects (1385 men and 830 women). Results: The participants with SCI had higher MPV levels than those without SCI (10.4 ± 1.3 fL vs. 9.2 ± 1.2 fL; P < 0.001). Moreover, the subjects with a high MPV had a higher prevalence of SCI. Multivariate logistic regression analyses revealed that the odds ratios and 95% confidence intervals for SCI according to MPV quartiles were 1.000, 2.131 (1.319–3.444), 3.015 (1.896–4.794), 7.822 (4.874–12.554) respectively (P < 0.001). Conclusion: MPV is a novel index for SCI regardless of classical cardiovascular risk factors.
Introduction Silent cerebral infarction (SCI) is a radiologic marker that implies the presence of cerebrovascular disease that does not show any clinical symptom. In most cases, SCI is found as a lacunar infarction caused by occlusion of small penetrating cerebral arteries. Moreover, some studies have demonstrated that the presence of SCI could be an indicator of transient ischaemia attack, clinically overt stroke, cardiovascular disease and dementia.1–3 Activated platelets play a key role in atherosclerosis. Elevated activity of platelet aggregation was observed in SCI patients.4 Moreover, treatment with anti-platelet drugs has been associated with reduced risk of developing SCI.5,6 Mean platelet volume (MPV), the most commonly used measure of platelet size, is an index of activated platelets and is available in clinical practice. Some studies have reported that MPV is a predictor of cardiovascular risk, stroke risk and overall vascular mortality.7–9 In addi-
Funding: This study was supported by the China Postdoctoral Science Foundation (2013M541409) and Technology Foundation for Selected Overseas Chinese Scholar, Ministry of Personnel of China (No. 2013578). Conflict of interest: None. © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
tion, MPV is associated with worse clinical prognosis in acute myocardial infarction and acute stroke.10,11 Little research has been conducted to investigate the relationship between MPV levels and SCI. We, therefore, conducted this study to assess MPV levels in SCI patients.
Methods Study subjects We studied 2600 subjects who visited International Physical Examination and Healthy Center, Harbin, China, from January 2009 through December 2010 and who underwent magnetic resonance imaging (MRI) of the brain as part of their routine health check. There were 357 subjects who met the exclusion criteria and were excluded. There were 28 subjects who were excluded for having missing data for smoking status and alcohol intake. Therefore, the study population was finalised as 2215 participants. Exclusion criteria for this study included tumour, haematological disorders, autoimmune diseases, rheumatoid arthritis, infection, atrial fibrillation, transient ischaemic attacks, stroke and medical treatment with lipid-lowering agents, and anti-platelet medication. We obtained informed consent from all sub653
Li et al.
jects. The study protocol was approved by the Ethics Committee of the Second Hospital of Harbin Medical University, China.
Clinical assessment All the subjects underwent a clinical investigation including medical history, smoking status, alcohol intake, and physical examinations, laboratory tests and an MRI scan of the brain. Body weight was measured in light clothing, without shoes, to the nearest 0.5 kg. Height was measured to the nearest 0.5 cm. Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Blood pressure was determined using a mercury-gravity sphygmomanometer in a sitting position after a 15-min rest. Two readings were taken, with a 5-min interval between measurements. The mean of the two readings was recorded. Hypertension was diagnosed if systolic blood pressure ≥ 140 mmHg and diastolic pressure ≥ 90 mmHg, or as antihypertensive treatment.
Blood investigations Fasting venous blood samples were drawn in the morning after an 8-h fast. The values included total serum total cholesterol (TC), triglyceride (TG), highdensity lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and fasting plasma glucose (FPG). Diabetes mellitus (DM) was defined as fasting serum glucose was ≥ 7.0 mmol/L or non-fasting serum glucose was ≥ 11.1 mmol/L or as taking prescription medications. For the controls or the patients with impaired fasting glucose, DM was diagnosed if a 2-h post-glucose level after a 75-g oral glucose tolerance test ≥ 11.1 mmol/L. The Modification of Diet in Renal Disease (MDRD) equation was used to estimate glomerular filtration rate (eGFR). MDRD equation was: eGFR = 186.3 × (serum creatinine)−1.154 × (age)−0.203 (×0.742 if female). The assays were performed at the Laboratory of Analytical Biochemistry at the Second Hospital of Harbin Medical University, Harbin, using a biochemical analyzer (MODULAR ANALYTICS, Roche, Mannheim, German) using standard methods. Platelet count, MPV and platelet distribution width were determined with an autoanalyser (Sysmex XE-2100, Kobe, Japan). The whole blood samples were collected in ethylenediaminetetraacetic acid-containing tubes, and all samples were processed within 30 min after blood collection.
Cerebral MRI Brain MRI examination was performed on a 1.5-T magnetic resonance system (Achieva 1.5T, Philips, Best, The Netherlands). The scan protocol consisted of transverse relaxation time (T2)-weighted spin echo (repetition time 654
(TR): 4800 ms, echo time (TE): 100 ms), longitudinal relaxation time (T1)-weighted spin echo (TR: 520 ms, TE: 14 ms) and fluid-attenuated inversion recovery (FLAIR; TR: 8500 ms, TE: 120 ms, inversion time: 2000 ms) imaging in axial planes at 5-mm thick slices with an interslice gap of 1.5 mm. The criteria for SCI were as follows: (i) a focal high intensity lesion (3–15 mm) in the T2 and FLAIR and low intensity in the T1 image; (ii) no corresponding symptoms in the clinical history of the patient that could be attributed to the lesion; and (iii) no history of clinical stroke. Periventricular white matter lesions were distinguished from SCI based on the highsignal intensity on FLAIR. Dilated perivascular spaces were differentiated from SCI based on their locations (along perforating or medullary arteries, often bilaterally symmetrical, usually in the lower third of the basal ganglia) and by absence of gliosis.12 The diagnoses were made by two independent experienced radiologists blinded to subject history and clinical status. The kappa value of agreement for SCI was 0.84. A consensus on inconsistent readings was reached through discussions.
Statistical analyses The SPSS statistical software package version 17.0 (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses. All participants were classified into quartiles by their MPV levels. Quartile 1 (Q1) was MPV ≤ 8.2 fL, quartile 2 (Q2) was MPV 8.3–9.5 fL, quartile 3 (Q3) was MPV 9.6–10.3 fL, and quartile 4 (Q4) was MPV ≥ 10.4 fL. The data were expressed as means ± standard deviation or medians (with interquartile ranges) for continuous variables or percentage for categorical variables. The Chi-squared test was used for all categorical variables. The differences of continuous variables between SCI group and non-SCI group were determined using the Student’s t-test or Mann– Whitney U-test. The differences of continuous variables according to MPV quartiles were determined using oneway analysis of variance or Kruskal–Wallis H-test. The odds ratios (OR) and 95% confidence intervals (95% CI) for SCI were calculated after adjusting for confounding variables across MPV quartiles using multivariate logistic regression analysis. A P-value of
