http://informahealthcare.com/plt ISSN: 0953-7104 (print), 1369-1635 (electronic) Platelets, 2014; 25(6): 433–438 ! 2014 Informa UK Ltd. DOI: 10.3109/09537104.2013.831064
ORIGINAL ARTICLE
Mean platelet volume and platelet distribution width in vascular dementia and Alzheimer’s disease Qing-Cheng Liang1, Di Jin2, Ying Li2,3, & Rui-Tao Wang2 Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China, 2Department of Geriatrics, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China, and 3International Physical Examination and Healthy Center, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China
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Abstract
Keywords
Activated platelets play a substantial role in Alzheimer’s disease (AD) and atherothrombosis. Mean platelet volume (MPV) is an early marker of platelet activation, which is linked to a variety of pro-thrombotic and pro-inflammatory diseases. This study is to examine the association between platelet indices and vascular dementia (VaD) and AD. In this cross-sectional study, we investigated the levels of platelet count, MPV, and platelet distribution width (PDW) in 150 VaD patients, 110 AD patients, and 150 non-demented controls. MPV and PDW were significantly lower in patients with VaD and AD as compared with controls. The decrease in PDW for AD patients as compared with VaD patients was also significant (p50.001). In addition, there was a positive correlation between Mini-Mental State Examination (MMSE) and MPV and PDW, after adjusting confounding factors (r ¼ 0.532 for MPV and r ¼ 0.425 for PDW, p50.001 for both). Multivariate regression analysis showed that MPV and PDW were significantly associated with MMSE ( ¼ 0.366 for MPV and ¼ 0.273 for PDW, p50.001 for both). In conclusion, MPV and PDW were both decreased in VaD and AD. PDW levels were significantly lower in AD as compared to those in VaD. Our findings suggest that PDW in combination with MMSE scores could be potential indicators for distinguishing VaD from AD.
Alzheimer’s disease, vascular dementia, mean platelet volume, platelet distribution width
Introduction The distinction between Alzheimer’s disease (AD) and vascular dementia (VaD) has always been the subject of intense investigation. The coexistence of multiple vascular risk factors significantly enhances the probability of late onset AD and aggravates the progression of dementia [1]. Moreover, treatment of vascular risk factors decreases the risk for developing AD [2]. Amyloid precursor protein (APP) expresses at similar levels in platelets and brain. Moreover, platelets contain all the enzymatic machinery to its processing and contribute to more than 90% of circulating APP. Some studies have reported that platelet APP and platelet Tau have been identified as new diagnostic indicators for preclinical AD [3, 4]. In addition, platelet beta-secretase activity and platelet GSK-3b activity are also increased in AD patients and correlated with markers of the intracerebral pathology [5–7]. Therefore, platelet is an interesting model to study AD pathogenic mechanisms. The roles of platelet activation in AD and atherogenesis have been well documented [8, 9]. Some reports demonstrated that cerebral microemboli could cause progressive brain damage contributing to the development of dementia [10]. A prospective study found that asymptomatic spontaneous cerebral emboli are associated with an accelerated cognitive and functional decline in both AD and VaD patients [11]. Moreover, recently a study observed that antiplatelet agent has a Correspondence: Rui-Tao Wang, MD, PhD, Department of Geriatrics, The Second Affiliated Hospital, Harbin Medical University, No. 246 Xuefu ST, Nangang District, Harbin, 150086, China. Tel: 86-45186605721. Fax: 86-451-86605725. E-mail: [email protected]
History Received 24 June 2013 Revised 30 July 2013 Accepted 30 July 2013 Published online 7 October 2013
preventive effect on cognitive decline in patients with AD and VaD [12]. Mean platelet volume (MPV) is a marker of activated platelets and is associated with different inflammatory conditions. Enhanced MPV levels are observed in diabetes mellitus, cardiovascular disease, peripheral artery disease, and cerebrovascular disease and reduced MPV are documented in rheumatoid arthritis and ulcerative colitis [13]. Platelet distribution width (PDW), another platelet parameter, indicates variation in platelet size and differentially diagnoses thrombocytosis [14]. Additionally, both MPV and PDW are available and widely used in clinical practice. Therefore, in this study, we evaluated the changes in platelet count, MPV and PDW in VaD, AD patients and control subjects. We also examined these indicators in combination with MiniMental State Examination (MMSE) results as novel diagnostic indicators for VaD and AD.
Methods Study population The study involved 150 patients with VaD (mean age 72.2 3.5 years), 110 patients with AD (mean age 73.4 4.0 years), and 150 non-demented controls (mean age 72.7 3.9 years) from September 2009 to March 2011. All participants were recruited from our check-up center in our hospital. We selected the age and gender matched controls with similar educational levels. All subjects gave the informed consent. The study was approved by the Ethical Committee of the Second Hospital of Harbin Medical University, China.
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Clinical examination
Statistical analysis
All subjects underwent clinical investigation including medical history, and physical, neurological, and psychiatric examinations, laboratory tests and MRI scan of the brain. Blood pressure was determined using a mercury-gravity sphygmomanometer in a sitting position after a 15-min rest. 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).
The SPSS statistical software package version 17.0 (SPSS Inc., Chicago, IL) was used for all statistical analyses. All data were expressed as means SD or median (IQR) or percentage. The Chi-square test or Fisher’s exact test was used for all categorical variables, while one-way ANOVA or Kruskal–Wallis H test was used for all continuous variables. Post hoc analyses using twotailed Tukey’s HSD were conducted to compare the differences between the groups. General linear model/univariate was performed to examine the impact on MPV and PDW levels of the variables where significance was indicated. Correlations between MMSE and platelet indices were tested. Multivariate analysis was performed using a linear regression model to determine the relationships between MMSE and various clinical variables. Receiver operating characteristic (ROC) curve analysis was used to show the utility of combined MMSE scores and PDW for differentiating of VaD and AD. TG, HDL-C, and FPG were logarithmically transformed before statistical analysis to approximate normal distribution. All results were considered significant at p50.05.
Biochemical measurements
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Clinical data including medical history, smoking status, and medication use were recorded for each participant. Fasting venous blood samples were collected in the morning after an 8-hour fast. The values included serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting plasma glucose (FPG). 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, Germany) using standard methods. Platelet count, MPV, and PDW were determined with an autoanalyzer (Sysmex XE-2100, Kobe, Japan). The whole blood samples were collected in EDTA-containing tubes and all samples were processed within 30 min after blood collection [15]. Diagnostic criteria Global cognitive function was assessed by the MMSE. General inclusion/exclusion criteria were as follows: (1) Control subjects: these individuals were fully independent in the activities of daily living (ADL) and instrumental activities of daily living (IADLs); (2) Probable VaD: meet the National Institute of Neurologial Disorders and Stroke-Association Internationale pour le Recherche et l’Enseignement en Neurosciences (NINDS AIREN) Criteria; (3) Probable AD: fulfill the National Institute of Neurological and Communicative Disorders and Stroke/ Alzheimer’s Disease and Related Disorders Association (NINCDS/ADRDA) criteria. Diagnosis of type 2 diabetes (DM) was based on American Diabetes Association criteria such as fasting plasma glucose 7.0 mmol/L, current treatment with a hypoglycemic agent, or casual glucose 11.1 mmol/L. For the controls or the patients with impaired fasting glucose, DM was diagnosed if a 2-hour post-glucose level after a 75-g oral glucose tolerance test 11.1 mmol/L. Hypertension was diagnosed if systolic blood pressure 140 mmHg and diastolic pressure 90 mmHg, or as antihypertensive treatment. Two readings were taken, with a 5-minute interval between measurements. The mean of the two readings was recorded. CAD (coronary atherosclerotic heart disease) was defined as the occurrence of a non-fatal myocardial infarction, a percutaneous coronary angioplasty, or other forms of acute or chronic ischemic heart disease. Exclusion criteria Exclusion criteria for this study included other psychiatric diseases, chronic alcoholism, tumor, infection, hematological disorders, atrial fibrillation, chronic liver, and kidney diseases, abnormal vitamin B12 or thyroid function tests, depression (as indicated by a score 410 on the Geriatric Depression Scale), Parkinson’s disease, therapeutic use of anticoagulant for various medical conditions, and dementia treatment.
Results The clinical characteristics of the control, VaD, and AD subjects are showed in Table 1. There were no significant differences in age, gender, smoking status, platelet count, and use of statins between the three groups, but there were significant differences in BMI, MPV, PDW, MMSE, use of anti-platelet, presence of diabetes, hypertension, and CAD between the three groups. Post hoc analyses using two-tailed Tukey’s HSD were used to compare the differences between the groups. The means of MPV and PDW decreased as cognition levels impaired (p50.001). The correlation coefficients between MMSE and platelet indices were presented in Table 2. MMSE score was associated with age and levels of education (r ¼ 0.105, p ¼ 0.034 for age and r ¼ 0.111, p ¼ 0.025 for levels of education, respectively). After adjusting for age, sex, body mass index, education, LDL-C, TG, TC, FPG, SBP, anti-platelet medication, CAD, hypertension and diabetes, the partial correlation coefficients between MMSE and platelet number, MPV, and PDW were 0.073 (p40.05), 0.532 (p50.001), 0.425 (p50.001), respectively (see Figure 1A and B). A two-sided Pearson’s Chi-square test was used to analyze the MPV and PDW levels as a correlate of control subjects, VaD, and AD patients (see Table 3). The results showed a significant difference of MPV levels in different group (for control group vs. VaD group, 2 ¼ 107.3, p50.001; for control group vs. AD group, 2 ¼ 92.6, p50.001; for VaD group vs. AD group, 2 ¼ 36.9, p50.001). A similar result was found according to the PDW levels (for control group vs. VaD group, 2 ¼ 76.2, p50.001; for control group vs. AD group, 2 ¼ 111.6, p50.001; for VaD group vs. AD group, 2 ¼ 20.8, p50.001). Multivariate linear regression analysis was performed to assess the relationship between MMSE and clinical variables in Table 4. Twelve variables including BMI, LDL-C, TG, TC, FPG, MPV, PDW, SBP, anti-platelet, CAD, hypertension, and diabetes entered into the original model. The results revealed that MPV and PDW were significant factors in the multivariate model with MMSE. MPV and PDW were found to be significant factors for decreased MMSE ( ¼ 0.366; p50.001 for MPV; ¼ 0.273; p50.001 for PDW, respectively). We performed general linear model/univariate analysis with MPV and PDW levels as the dependent variables and all variables that showed different in Table 1 as independent variables (Table 5). The main significant effect was observed only for the group (for MPV, F ¼ 83.25, p50.001; for PDW, F ¼ 64.88,
MPV and PDW in VaD and AD
DOI: 10.3109/09537104.2013.831064
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Table 1. Clinical characteristics of the analyzed participants.
Age (year) Gender (male, %) Education (years) BMI (kg/m2) FPG (mmol/L) TC (mmol/L) TG (mmol/L) HDL-C (mmol/L) LDL-C (mmol/L) SBP (mmHg) DBP (mmHg) Platelet (109/L) MPV (fl) PDW (%) MMSE score Anti-platelet (%) Stations (%) Smoking (%) Diabetes (%) Hypertension (%) CAD (%)
Control (n ¼ 150)
VaD (n ¼ 150)
AD (n ¼ 110)
p
72.7 (3.9) 51 10.9 (2.2) 22.8 (2.4) 5.16 (4.88–5.72) 4.55 (0.81) 1.29 (0.99–1.59) 1.50 (1.26–1.73) 2.18 (0.66) 140.3 (13.9) 80.5 (10.3) 218.4 (69.1) 10.8 (1.2) 12.6 (1.8) 27.8 (1.6) 43 39 19 49 54 51
72.2 (3.5) 57 10.4 (2.7) 23.0 (2.2) 5.97 (5.79–6.42) 5.81 (1.22) 1.87 (1.48–2.45) 1.42 (1.22–1.66) 2.73 (1.18) 143.8 (14.6) 79.2 (8.4) 215.3 (56.4) 9.4 (0.9) 10.7 (2.6) 17.2 (3.6) 59 51 22 65 60 56
73.4 (4.0) 47 10.5 (2.3) 21.7 (1.6) 5.49 (5.13–5.89) 4.71 (1.11) 1.25 (1.02–1.50) 1.45 (1.21–1.66) 2.48 (0.92) 138.8 (9.1) 78.7 (8.6) 223.6 (62.9) 9.3 (1.0) 9.5 (1.7) 15.2 (3.1) 39 40 16 58 38 34
0.061 0.247 0.110 50.001 50.001 50.001 50.001 0.277 50.001 0.004 0.253 0.578 50.001 50.001 50.001 0.002 0.070 0.525 0.019 0.002 0.001
BMI, body mass index; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; MMSE, Mini-Mental State Examination; CAD, coronary atherosclerotic heart disease. Values are shown as mean (standard deviation) or median (IQR) or percentage. p Value was calculated by one-way ANOVA test or Kruskal–Wallis H test or Chi-square test. Table 2. Correlation of platelet indices with MMSE.
a
Model 1 Model 2b Model 3c
MMSE score MMSE score MMSE score
Platelet
MPV
PDW
0.049* 0.074* 0.075*
0.480** 0.445** 0.434**
0.430** 0.417** 0.405**
MMSE, Mini-Mental State Examination; MPV, mean platelet volume; PDW, platelet distribution width. Model 1a: adjusted for age, sex, body mass index, and education. Model 2b: adjusted for age, sex, body mass index, education, LDL-C, TG, TC, FPG, and SBP. Model 3c: adjusted for age, sex, body mass index, education, LDL-C, TG, TC, FPG, SBP, anti-platelet medication, CAD, hypertension, and diabetes. Variables TG and FPG were log-transformed before statistical analysis. *p40.05, **p50.001.
p50.001, respectively). No significant main effects or significant interactions for the other variables were found. ROC curve analysis was used to show the utility of combined MMSE scores and PDW for differentiating of VaD and AD (Figure 2). The area under the ROC curve was 0.729 (95% CI, 0.667–0.791). The sensitivity and the specificity using combined MMSE scores and PDW were 0.818 and 0.547, respectively.
Discussion In this study, we examined MPV and PDW levels in VaD and AD patients. We found that, first, MPV and PDW levels significantly reduced as cognitive levels declined. Second, there was a correlation between MMSE and MPV, and PDW levels. Third, PDW values were significantly lower in AD as compared to those in VaD. Platelet is a good peripheral model of neurons because of similar enzymes, receptors and cell products. Platelet activation may be implicated in VaD and AD through an altered metabolism of APP [16]. Coated-platelet, a subset of activated platelet, is increased in patients with AD [17]. Coated-platelet influenced APP metabolism by retention and derivatization of full-length APP on the surface of these activated cells [18].
Moreover, enhanced coated-platelet synthesis is not found in frontotemporal dementia (FTD) without neuropathologic changes that involve an abnormality in the amyloid-b peptide pathway [19]. Recent studies demonstrated that there is positive correlation between coated-platelet levels and the rate of AD progression [20, 21]. Chronic inflammation and endothelial damage also play key roles in vascular abnormalities and AD pathogenesis [22]. The complicated interactions between platelets, leukocytes and endothelial cells stimulate proinflammatory cytokines production and aggravate vascular injury. Phospholipase A2 (PLA2) is released by activated platelets and is associated with inflammatory reactions. Furthermore, PLA2 is expressed both in brain tissue and in platelets [23]. Recent studies demonstrate significant changes of PLA2 in VaD [24]. PLA2 activation also contributes to AD by increasing secretion of APP and Ab [25]. MPV is an early indicator of activated platelets and is available in any clinical practice. PDW has been reported to have discriminative value for myelosuppression-related and immunemediated thrombocytopenia [26]. We observe that MPV and PDW are reduced in VaD and AD patients. The mechanism is unclear. Bone marrow cells (including megakaryocytes) dys-regulation may contribute to decreased MPV and PDW. Microglia mainly arises from hematopoeitic precursor cells and plays a crucial role in the regulation of immune responses in the central nervous system [27]. Bone marrow-derived microglia is capable of internalizing and clearing Ab by a cell-specific phagocytic mechanism [28]. Recent studies implicate disruption within the hematopoietic system in the initiation and/or progression of AD. For example, megakaryocytic maturation, platelet production and platelet size could be modulated by cytokines, such as interleukin3 (IL-3), interleukin-6 (IL-6), granulocytes colony stimulating factor (G-CSF) and macrophage colony stimulating factor (M-CSF) [29]. IL-1a, IL-3, TNF-a, and G-CSF have been found to predict the progression from mild cognitive impairment to AD with 96% accuracy [30]. In addition, there is accumulating evidence demonstrating the beneficial effects of G-CSF and M-CSF on the innate immune system in AD [31].
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Figure 1. The partial correlation coefficients of MMSE score with MPV and PDW are presented in Figure 2 after adjusting for age, sex, body mass index, education, LDL-C, TG, TC, FPG, SBP, anti-platelet medication, CAD, hypertension, and diabetes. (A) Partial correlation coefficient of MPV with MMSE score. (B) Partial correlation coefficient of PDW with MMSE score.
In our study, PDW is higher in VaD group compared to that in AD group, while PDW is lower in VaD group compared to the control. Our previous data reveal that MPV could be a potential indicator predicting the progressing from mild cognitive impairment to AD [32]. Recent studies show that PDW is a more
specific marker of platelet activation and is associated with vascular damage [33]. This study has limitations: (1) the sample size is small. It is necessary to validate the findings in a larger study. (2) The study is cross-sectional and it is difficult to study the
MPV and PDW in VaD and AD
DOI: 10.3109/09537104.2013.831064
Table 3. The analyzed participants distributed according to MPV and PDW quartiles.
MPV (fL) Control (n) VaD (n) AD (n) Total (n) PDW (%) Control (n) VaD (n) AD (n) Total (n)
Q1
Q2
Q3
Q4
9.0 10 38 49 97 9.1 5 38 58 101
9.1–9.5 18 46 36 100 9.2–10.4 18 54 25 97
9.6–10.5 34 60 10 104 10.5–12.6 52 37 19 108
10.6 88 6 15 109 12.7 75 21 8 104
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MPV, mean platelet volume; PDW, platelet distribution width; VaD, vascular disease; AD, Alzheimer’s disease.
Table 4. Stepwise multivariate linear regression analysis with MMSE as the dependent variable. Variables MPV (fL) FPG (mmol/L) PDW (%) TC (mmol/L)
0.320 0.149 0.290 0.142
95% CI 1.203, 19.168, 0.530, 1.227,
2.103 5.602 0.978 0.333
p Value 50.001 50.001 50.001 0.001
MMSE, Mini-Mental State Examination; MPV, mean platelet volume; PDW, platelet distribution width. , standardized regression coefficients; CI, confidence intervals. FPG and TG were log-transformed for analysis. The p value for entry was set at 0.05, and the p value for removal was set at 0.10. Adjusted R2 ¼ 0.353, p50.001.
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Table 5. Statistical analyses of variables potentially affecting MPV and PDW levels. MPV Variables Groups (control/VaD/AD) Anti-platelet (yes/no) Diabetes (yes/no) Hypertension (yes/no) CAD (yes/no)
PDW
F
p
F
p
83.25 0.01 0.01 0.17 2.48
50.001* 0.94 0.95 0.68 0.12
64.88 0.19 0.00 0.12 0.01
50.001* 0.67 0.96 0.73 0.91
MPV, mean platelet volume; PDW, platelet distribution width; VaD, vascular disease; AD, Alzheimer’s disease; CAD, coronary atherosclerotic heart disease. General linear model/univariate was performed to examine the impact on MPV and PDW levels of the variables that differed between the three groups. Adjusted R2 ¼ 0.293 for MPV and adjusted R2 ¼ 0.247 for PDW, respectively. *p50.001.
direction of causality. A prospective study is needed to clarify this point.
Conclusion To conclude, MPV and PDW are decreased both in VaD and AD. PDW is lower in AD than that in VaD. Early detection of abnormality of MPV and PDW in VaD and AD patients could be helpful to evaluate platelet activation. In addition, prospective studies are needed to clarify whether the reduced PDW in combination with MMSE scores may be indicators in discriminating VaD from AD.
Authors’ contributions QC.L. participated in manuscript preparation, data analysis and editing. D.J. participated in manuscript revision, data collection, and data analysis. Y.L. participated in data collection and data analysis. RT.W. participated in study design, data analysis, and manuscript preparation. All authors read and approved the final manuscript.
Acknowledgements We thank Philip T.T. Ly for critical reading of the manuscript prior to submission.
Declaration of interest The authors declare no conflict of interest.
References
Figure 2. ROC curves for the utility of combined MMSE scores and PDW for discrimination of VaD from AD. PDW, platelet distribution width; VaD, vascular disease; AD, Alzheimer’s disease.
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ORIGINAL ARTICLE
Mean platelet volume and platelet distribution width in vascular dementia and Alzheimer’s disease Qing-Cheng Liang1, Di Jin2, Ying Li2,3, & Rui-Tao Wang2 Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China, 2Department of Geriatrics, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China, and 3International Physical Examination and Healthy Center, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China
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1
Abstract
Keywords
Activated platelets play a substantial role in Alzheimer’s disease (AD) and atherothrombosis. Mean platelet volume (MPV) is an early marker of platelet activation, which is linked to a variety of pro-thrombotic and pro-inflammatory diseases. This study is to examine the association between platelet indices and vascular dementia (VaD) and AD. In this cross-sectional study, we investigated the levels of platelet count, MPV, and platelet distribution width (PDW) in 150 VaD patients, 110 AD patients, and 150 non-demented controls. MPV and PDW were significantly lower in patients with VaD and AD as compared with controls. The decrease in PDW for AD patients as compared with VaD patients was also significant (p50.001). In addition, there was a positive correlation between Mini-Mental State Examination (MMSE) and MPV and PDW, after adjusting confounding factors (r ¼ 0.532 for MPV and r ¼ 0.425 for PDW, p50.001 for both). Multivariate regression analysis showed that MPV and PDW were significantly associated with MMSE ( ¼ 0.366 for MPV and ¼ 0.273 for PDW, p50.001 for both). In conclusion, MPV and PDW were both decreased in VaD and AD. PDW levels were significantly lower in AD as compared to those in VaD. Our findings suggest that PDW in combination with MMSE scores could be potential indicators for distinguishing VaD from AD.
Alzheimer’s disease, vascular dementia, mean platelet volume, platelet distribution width
Introduction The distinction between Alzheimer’s disease (AD) and vascular dementia (VaD) has always been the subject of intense investigation. The coexistence of multiple vascular risk factors significantly enhances the probability of late onset AD and aggravates the progression of dementia [1]. Moreover, treatment of vascular risk factors decreases the risk for developing AD [2]. Amyloid precursor protein (APP) expresses at similar levels in platelets and brain. Moreover, platelets contain all the enzymatic machinery to its processing and contribute to more than 90% of circulating APP. Some studies have reported that platelet APP and platelet Tau have been identified as new diagnostic indicators for preclinical AD [3, 4]. In addition, platelet beta-secretase activity and platelet GSK-3b activity are also increased in AD patients and correlated with markers of the intracerebral pathology [5–7]. Therefore, platelet is an interesting model to study AD pathogenic mechanisms. The roles of platelet activation in AD and atherogenesis have been well documented [8, 9]. Some reports demonstrated that cerebral microemboli could cause progressive brain damage contributing to the development of dementia [10]. A prospective study found that asymptomatic spontaneous cerebral emboli are associated with an accelerated cognitive and functional decline in both AD and VaD patients [11]. Moreover, recently a study observed that antiplatelet agent has a Correspondence: Rui-Tao Wang, MD, PhD, Department of Geriatrics, The Second Affiliated Hospital, Harbin Medical University, No. 246 Xuefu ST, Nangang District, Harbin, 150086, China. Tel: 86-45186605721. Fax: 86-451-86605725. E-mail: [email protected]
History Received 24 June 2013 Revised 30 July 2013 Accepted 30 July 2013 Published online 7 October 2013
preventive effect on cognitive decline in patients with AD and VaD [12]. Mean platelet volume (MPV) is a marker of activated platelets and is associated with different inflammatory conditions. Enhanced MPV levels are observed in diabetes mellitus, cardiovascular disease, peripheral artery disease, and cerebrovascular disease and reduced MPV are documented in rheumatoid arthritis and ulcerative colitis [13]. Platelet distribution width (PDW), another platelet parameter, indicates variation in platelet size and differentially diagnoses thrombocytosis [14]. Additionally, both MPV and PDW are available and widely used in clinical practice. Therefore, in this study, we evaluated the changes in platelet count, MPV and PDW in VaD, AD patients and control subjects. We also examined these indicators in combination with MiniMental State Examination (MMSE) results as novel diagnostic indicators for VaD and AD.
Methods Study population The study involved 150 patients with VaD (mean age 72.2 3.5 years), 110 patients with AD (mean age 73.4 4.0 years), and 150 non-demented controls (mean age 72.7 3.9 years) from September 2009 to March 2011. All participants were recruited from our check-up center in our hospital. We selected the age and gender matched controls with similar educational levels. All subjects gave the informed consent. The study was approved by the Ethical Committee of the Second Hospital of Harbin Medical University, China.
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Clinical examination
Statistical analysis
All subjects underwent clinical investigation including medical history, and physical, neurological, and psychiatric examinations, laboratory tests and MRI scan of the brain. Blood pressure was determined using a mercury-gravity sphygmomanometer in a sitting position after a 15-min rest. 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).
The SPSS statistical software package version 17.0 (SPSS Inc., Chicago, IL) was used for all statistical analyses. All data were expressed as means SD or median (IQR) or percentage. The Chi-square test or Fisher’s exact test was used for all categorical variables, while one-way ANOVA or Kruskal–Wallis H test was used for all continuous variables. Post hoc analyses using twotailed Tukey’s HSD were conducted to compare the differences between the groups. General linear model/univariate was performed to examine the impact on MPV and PDW levels of the variables where significance was indicated. Correlations between MMSE and platelet indices were tested. Multivariate analysis was performed using a linear regression model to determine the relationships between MMSE and various clinical variables. Receiver operating characteristic (ROC) curve analysis was used to show the utility of combined MMSE scores and PDW for differentiating of VaD and AD. TG, HDL-C, and FPG were logarithmically transformed before statistical analysis to approximate normal distribution. All results were considered significant at p50.05.
Biochemical measurements
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Clinical data including medical history, smoking status, and medication use were recorded for each participant. Fasting venous blood samples were collected in the morning after an 8-hour fast. The values included serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting plasma glucose (FPG). 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, Germany) using standard methods. Platelet count, MPV, and PDW were determined with an autoanalyzer (Sysmex XE-2100, Kobe, Japan). The whole blood samples were collected in EDTA-containing tubes and all samples were processed within 30 min after blood collection [15]. Diagnostic criteria Global cognitive function was assessed by the MMSE. General inclusion/exclusion criteria were as follows: (1) Control subjects: these individuals were fully independent in the activities of daily living (ADL) and instrumental activities of daily living (IADLs); (2) Probable VaD: meet the National Institute of Neurologial Disorders and Stroke-Association Internationale pour le Recherche et l’Enseignement en Neurosciences (NINDS AIREN) Criteria; (3) Probable AD: fulfill the National Institute of Neurological and Communicative Disorders and Stroke/ Alzheimer’s Disease and Related Disorders Association (NINCDS/ADRDA) criteria. Diagnosis of type 2 diabetes (DM) was based on American Diabetes Association criteria such as fasting plasma glucose 7.0 mmol/L, current treatment with a hypoglycemic agent, or casual glucose 11.1 mmol/L. For the controls or the patients with impaired fasting glucose, DM was diagnosed if a 2-hour post-glucose level after a 75-g oral glucose tolerance test 11.1 mmol/L. Hypertension was diagnosed if systolic blood pressure 140 mmHg and diastolic pressure 90 mmHg, or as antihypertensive treatment. Two readings were taken, with a 5-minute interval between measurements. The mean of the two readings was recorded. CAD (coronary atherosclerotic heart disease) was defined as the occurrence of a non-fatal myocardial infarction, a percutaneous coronary angioplasty, or other forms of acute or chronic ischemic heart disease. Exclusion criteria Exclusion criteria for this study included other psychiatric diseases, chronic alcoholism, tumor, infection, hematological disorders, atrial fibrillation, chronic liver, and kidney diseases, abnormal vitamin B12 or thyroid function tests, depression (as indicated by a score 410 on the Geriatric Depression Scale), Parkinson’s disease, therapeutic use of anticoagulant for various medical conditions, and dementia treatment.
Results The clinical characteristics of the control, VaD, and AD subjects are showed in Table 1. There were no significant differences in age, gender, smoking status, platelet count, and use of statins between the three groups, but there were significant differences in BMI, MPV, PDW, MMSE, use of anti-platelet, presence of diabetes, hypertension, and CAD between the three groups. Post hoc analyses using two-tailed Tukey’s HSD were used to compare the differences between the groups. The means of MPV and PDW decreased as cognition levels impaired (p50.001). The correlation coefficients between MMSE and platelet indices were presented in Table 2. MMSE score was associated with age and levels of education (r ¼ 0.105, p ¼ 0.034 for age and r ¼ 0.111, p ¼ 0.025 for levels of education, respectively). After adjusting for age, sex, body mass index, education, LDL-C, TG, TC, FPG, SBP, anti-platelet medication, CAD, hypertension and diabetes, the partial correlation coefficients between MMSE and platelet number, MPV, and PDW were 0.073 (p40.05), 0.532 (p50.001), 0.425 (p50.001), respectively (see Figure 1A and B). A two-sided Pearson’s Chi-square test was used to analyze the MPV and PDW levels as a correlate of control subjects, VaD, and AD patients (see Table 3). The results showed a significant difference of MPV levels in different group (for control group vs. VaD group, 2 ¼ 107.3, p50.001; for control group vs. AD group, 2 ¼ 92.6, p50.001; for VaD group vs. AD group, 2 ¼ 36.9, p50.001). A similar result was found according to the PDW levels (for control group vs. VaD group, 2 ¼ 76.2, p50.001; for control group vs. AD group, 2 ¼ 111.6, p50.001; for VaD group vs. AD group, 2 ¼ 20.8, p50.001). Multivariate linear regression analysis was performed to assess the relationship between MMSE and clinical variables in Table 4. Twelve variables including BMI, LDL-C, TG, TC, FPG, MPV, PDW, SBP, anti-platelet, CAD, hypertension, and diabetes entered into the original model. The results revealed that MPV and PDW were significant factors in the multivariate model with MMSE. MPV and PDW were found to be significant factors for decreased MMSE ( ¼ 0.366; p50.001 for MPV; ¼ 0.273; p50.001 for PDW, respectively). We performed general linear model/univariate analysis with MPV and PDW levels as the dependent variables and all variables that showed different in Table 1 as independent variables (Table 5). The main significant effect was observed only for the group (for MPV, F ¼ 83.25, p50.001; for PDW, F ¼ 64.88,
MPV and PDW in VaD and AD
DOI: 10.3109/09537104.2013.831064
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Table 1. Clinical characteristics of the analyzed participants.
Age (year) Gender (male, %) Education (years) BMI (kg/m2) FPG (mmol/L) TC (mmol/L) TG (mmol/L) HDL-C (mmol/L) LDL-C (mmol/L) SBP (mmHg) DBP (mmHg) Platelet (109/L) MPV (fl) PDW (%) MMSE score Anti-platelet (%) Stations (%) Smoking (%) Diabetes (%) Hypertension (%) CAD (%)
Control (n ¼ 150)
VaD (n ¼ 150)
AD (n ¼ 110)
p
72.7 (3.9) 51 10.9 (2.2) 22.8 (2.4) 5.16 (4.88–5.72) 4.55 (0.81) 1.29 (0.99–1.59) 1.50 (1.26–1.73) 2.18 (0.66) 140.3 (13.9) 80.5 (10.3) 218.4 (69.1) 10.8 (1.2) 12.6 (1.8) 27.8 (1.6) 43 39 19 49 54 51
72.2 (3.5) 57 10.4 (2.7) 23.0 (2.2) 5.97 (5.79–6.42) 5.81 (1.22) 1.87 (1.48–2.45) 1.42 (1.22–1.66) 2.73 (1.18) 143.8 (14.6) 79.2 (8.4) 215.3 (56.4) 9.4 (0.9) 10.7 (2.6) 17.2 (3.6) 59 51 22 65 60 56
73.4 (4.0) 47 10.5 (2.3) 21.7 (1.6) 5.49 (5.13–5.89) 4.71 (1.11) 1.25 (1.02–1.50) 1.45 (1.21–1.66) 2.48 (0.92) 138.8 (9.1) 78.7 (8.6) 223.6 (62.9) 9.3 (1.0) 9.5 (1.7) 15.2 (3.1) 39 40 16 58 38 34
0.061 0.247 0.110 50.001 50.001 50.001 50.001 0.277 50.001 0.004 0.253 0.578 50.001 50.001 50.001 0.002 0.070 0.525 0.019 0.002 0.001
BMI, body mass index; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; MMSE, Mini-Mental State Examination; CAD, coronary atherosclerotic heart disease. Values are shown as mean (standard deviation) or median (IQR) or percentage. p Value was calculated by one-way ANOVA test or Kruskal–Wallis H test or Chi-square test. Table 2. Correlation of platelet indices with MMSE.
a
Model 1 Model 2b Model 3c
MMSE score MMSE score MMSE score
Platelet
MPV
PDW
0.049* 0.074* 0.075*
0.480** 0.445** 0.434**
0.430** 0.417** 0.405**
MMSE, Mini-Mental State Examination; MPV, mean platelet volume; PDW, platelet distribution width. Model 1a: adjusted for age, sex, body mass index, and education. Model 2b: adjusted for age, sex, body mass index, education, LDL-C, TG, TC, FPG, and SBP. Model 3c: adjusted for age, sex, body mass index, education, LDL-C, TG, TC, FPG, SBP, anti-platelet medication, CAD, hypertension, and diabetes. Variables TG and FPG were log-transformed before statistical analysis. *p40.05, **p50.001.
p50.001, respectively). No significant main effects or significant interactions for the other variables were found. ROC curve analysis was used to show the utility of combined MMSE scores and PDW for differentiating of VaD and AD (Figure 2). The area under the ROC curve was 0.729 (95% CI, 0.667–0.791). The sensitivity and the specificity using combined MMSE scores and PDW were 0.818 and 0.547, respectively.
Discussion In this study, we examined MPV and PDW levels in VaD and AD patients. We found that, first, MPV and PDW levels significantly reduced as cognitive levels declined. Second, there was a correlation between MMSE and MPV, and PDW levels. Third, PDW values were significantly lower in AD as compared to those in VaD. Platelet is a good peripheral model of neurons because of similar enzymes, receptors and cell products. Platelet activation may be implicated in VaD and AD through an altered metabolism of APP [16]. Coated-platelet, a subset of activated platelet, is increased in patients with AD [17]. Coated-platelet influenced APP metabolism by retention and derivatization of full-length APP on the surface of these activated cells [18].
Moreover, enhanced coated-platelet synthesis is not found in frontotemporal dementia (FTD) without neuropathologic changes that involve an abnormality in the amyloid-b peptide pathway [19]. Recent studies demonstrated that there is positive correlation between coated-platelet levels and the rate of AD progression [20, 21]. Chronic inflammation and endothelial damage also play key roles in vascular abnormalities and AD pathogenesis [22]. The complicated interactions between platelets, leukocytes and endothelial cells stimulate proinflammatory cytokines production and aggravate vascular injury. Phospholipase A2 (PLA2) is released by activated platelets and is associated with inflammatory reactions. Furthermore, PLA2 is expressed both in brain tissue and in platelets [23]. Recent studies demonstrate significant changes of PLA2 in VaD [24]. PLA2 activation also contributes to AD by increasing secretion of APP and Ab [25]. MPV is an early indicator of activated platelets and is available in any clinical practice. PDW has been reported to have discriminative value for myelosuppression-related and immunemediated thrombocytopenia [26]. We observe that MPV and PDW are reduced in VaD and AD patients. The mechanism is unclear. Bone marrow cells (including megakaryocytes) dys-regulation may contribute to decreased MPV and PDW. Microglia mainly arises from hematopoeitic precursor cells and plays a crucial role in the regulation of immune responses in the central nervous system [27]. Bone marrow-derived microglia is capable of internalizing and clearing Ab by a cell-specific phagocytic mechanism [28]. Recent studies implicate disruption within the hematopoietic system in the initiation and/or progression of AD. For example, megakaryocytic maturation, platelet production and platelet size could be modulated by cytokines, such as interleukin3 (IL-3), interleukin-6 (IL-6), granulocytes colony stimulating factor (G-CSF) and macrophage colony stimulating factor (M-CSF) [29]. IL-1a, IL-3, TNF-a, and G-CSF have been found to predict the progression from mild cognitive impairment to AD with 96% accuracy [30]. In addition, there is accumulating evidence demonstrating the beneficial effects of G-CSF and M-CSF on the innate immune system in AD [31].
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Figure 1. The partial correlation coefficients of MMSE score with MPV and PDW are presented in Figure 2 after adjusting for age, sex, body mass index, education, LDL-C, TG, TC, FPG, SBP, anti-platelet medication, CAD, hypertension, and diabetes. (A) Partial correlation coefficient of MPV with MMSE score. (B) Partial correlation coefficient of PDW with MMSE score.
In our study, PDW is higher in VaD group compared to that in AD group, while PDW is lower in VaD group compared to the control. Our previous data reveal that MPV could be a potential indicator predicting the progressing from mild cognitive impairment to AD [32]. Recent studies show that PDW is a more
specific marker of platelet activation and is associated with vascular damage [33]. This study has limitations: (1) the sample size is small. It is necessary to validate the findings in a larger study. (2) The study is cross-sectional and it is difficult to study the
MPV and PDW in VaD and AD
DOI: 10.3109/09537104.2013.831064
Table 3. The analyzed participants distributed according to MPV and PDW quartiles.
MPV (fL) Control (n) VaD (n) AD (n) Total (n) PDW (%) Control (n) VaD (n) AD (n) Total (n)
Q1
Q2
Q3
Q4
9.0 10 38 49 97 9.1 5 38 58 101
9.1–9.5 18 46 36 100 9.2–10.4 18 54 25 97
9.6–10.5 34 60 10 104 10.5–12.6 52 37 19 108
10.6 88 6 15 109 12.7 75 21 8 104
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MPV, mean platelet volume; PDW, platelet distribution width; VaD, vascular disease; AD, Alzheimer’s disease.
Table 4. Stepwise multivariate linear regression analysis with MMSE as the dependent variable. Variables MPV (fL) FPG (mmol/L) PDW (%) TC (mmol/L)
0.320 0.149 0.290 0.142
95% CI 1.203, 19.168, 0.530, 1.227,
2.103 5.602 0.978 0.333
p Value 50.001 50.001 50.001 0.001
MMSE, Mini-Mental State Examination; MPV, mean platelet volume; PDW, platelet distribution width. , standardized regression coefficients; CI, confidence intervals. FPG and TG were log-transformed for analysis. The p value for entry was set at 0.05, and the p value for removal was set at 0.10. Adjusted R2 ¼ 0.353, p50.001.
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Table 5. Statistical analyses of variables potentially affecting MPV and PDW levels. MPV Variables Groups (control/VaD/AD) Anti-platelet (yes/no) Diabetes (yes/no) Hypertension (yes/no) CAD (yes/no)
PDW
F
p
F
p
83.25 0.01 0.01 0.17 2.48
50.001* 0.94 0.95 0.68 0.12
64.88 0.19 0.00 0.12 0.01
50.001* 0.67 0.96 0.73 0.91
MPV, mean platelet volume; PDW, platelet distribution width; VaD, vascular disease; AD, Alzheimer’s disease; CAD, coronary atherosclerotic heart disease. General linear model/univariate was performed to examine the impact on MPV and PDW levels of the variables that differed between the three groups. Adjusted R2 ¼ 0.293 for MPV and adjusted R2 ¼ 0.247 for PDW, respectively. *p50.001.
direction of causality. A prospective study is needed to clarify this point.
Conclusion To conclude, MPV and PDW are decreased both in VaD and AD. PDW is lower in AD than that in VaD. Early detection of abnormality of MPV and PDW in VaD and AD patients could be helpful to evaluate platelet activation. In addition, prospective studies are needed to clarify whether the reduced PDW in combination with MMSE scores may be indicators in discriminating VaD from AD.
Authors’ contributions QC.L. participated in manuscript preparation, data analysis and editing. D.J. participated in manuscript revision, data collection, and data analysis. Y.L. participated in data collection and data analysis. RT.W. participated in study design, data analysis, and manuscript preparation. All authors read and approved the final manuscript.
Acknowledgements We thank Philip T.T. Ly for critical reading of the manuscript prior to submission.
Declaration of interest The authors declare no conflict of interest.
References
Figure 2. ROC curves for the utility of combined MMSE scores and PDW for discrimination of VaD from AD. PDW, platelet distribution width; VaD, vascular disease; AD, Alzheimer’s disease.
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