Clinical Feature, Risk Factor And MRI Characteristics Of Subcortical Vascular Congnitive Impairment

Vascular dementia (VD) is the second most common cause of dementia in elderly after Alzheimer's disease (AD). It results from ischaemic, ischemic-hypoxic, or haemorrhagic brain injury that cuased by cerebrovascular disease, manifests a series of cognitive impairment syndromes. With the deeper development of cognitive impairment research, the criteria for VD based on AD presents its shortcomings. Based on the model of AD, the old criteria for VD emphasizes on new knowledge learning disability and memory loss, but not on psychomotor slowing and impairment of executive functioning which is the clinical features of the typical VD form. It also defines dementia as the level of cognitive impairment at which normal daily functions are impaired, therefore only late cases will be identified, so underestimating the prevalence of cognitive impairment due to vascular disease and denying early cases the benefit of timely preventative treatment. Vascular cognitive impairment (VCI), a new concept covering that of VD suggested by Hachinski and Bowler in 1993, is now widely accepted as a more appropriate alternative terminology than VD.Cognitive impairment that is caused by or associated with vascular factors has been termed as VCI. It covers a wide spectrum of cognitive dysfunction associated with and presumed to be caused by cerebrovascular disease, what most important is that it included subtle and clinically often undetected cognitive impairment. VCI presents a clinical syndrome. The main subtypes of VCI included in current classification are cortical VCI, strategic infarct VCI and subcortical VCI. The clinical manifest and treatment of the subtype are different from each other for its difference at etiological aspects and pathogenesis. Because vascular risk factors are treatable, it should be possible to prevent, postpone, or mitigate VCI. However, progress in VCI research has been hindered by lack of unified and satisfactory diagnostic criteria for the condition. Research emphasis of VCI gradually turned to the relatively homogeneous subcortical VCI.Subcortical ischemic vascular disease (SIVD) is the most common cause of subcortical VCI. It is accepted now that the pathogenesis of SIVD related to cerebral small vessel disease caused by multifactor. Two paths may be involved. First, occlusion of the arteriolar lumen due to arteriolosclerosis leads to the formation of lacunar infarcts (LI); second or critical stenosis and hypoperfusion of multiple medullary arterioles causes widespread incomplete infarction of deep white matter. Symptoms include motor and cognitive dysexecutive slowing, forgetfulness, dysarthria, mood changes, urinary symptoms, and short-stepped gait. Hypertension serves as the main risk factor of SIVD. Other vascular risk factor of cerebrovascular disease, such as diabetes millitus, hyperlipidemia, heart disease, anemia, smoking and excessive drinking, rise the incidence of SIVD. VCI can not be diagnosed to all patients with SIVD. XU et al reported that total of 44.5% patients with SIVD presented cognitive impairment at a successive clinical research. There were few reports considering the relationship between cognitive impairment and risk factor of SIVD. It is benefit to VCI patients'timely preventively treatment to further study on the risk factor of subcortical VCI and identify the relationships between cognitive impairment and vascular risk factors of SIVD.SIVD is characterised by extensive cerebral white matter lesions (WML) and lacunar infarcts in deep grey and white matter structures. WML, which be called"leukoaraiosis"in the past, shows "white matter hyperintensities (WMH) on T2 and fliud attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) sequence. Researches of the relationship between cognitive impairment and the degree or type of lesions on MRI had been going on for the past many years, but no definite consensus had been to reach. There were not significant difference between leukoaraiosis and cognitive impairment at initial research reports. Those differet results some extent due to the qualitative analysis used at their research. With the progress of technique on automated image segmentation to quantitation and location, researches on relationships between cognitive impairment and neuroimaging changes are stepping into a new era. Bowler reviewed reports considering WML and cognitive impairment in the past few years, and mentioned that"attempts to correlate closely infarct volumes with cognitive change routinely fail because of the overriding importance of lesion location". Combinated quantitation with qualitation analysis, it is possible to gain reliable data which is benefit to early cases diagnosis of cognitive impairtment in SIVD, and even to other types of VD.The following 4 parts were designed to study on the risk factor, relationship between LI, WML and cognitive impairtment in SIVD.Part 1: Clinical Feature and Risk Factor of Cognitive Impairment in Patients with Subcortical Ischemic Vascular DiseaseObjectives:To investigate the clinical feature, the risk factor for cognitive impairment of SIVD, and the risk factor for its subtypes: white matter lesions type (WML-SIVD) and lacunar infarct type (LI-SIVD).Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were divided into no cognitive impairment(NCI) and vascular cognitive impairment(VCI) group. Symptomes and signs, vascular risk factor profiles were recorded by interviewers and examiners, then risk factor profiles for cognitive impairment of SIVD or its subtypes were compared using univariate and multivariate logistic regression analysis.Results: The subjects consisted of 33 males and 37 females; the mean age was 66.89±7.03 years, the mean educational attainment was 2.81±2.37 years. Mini-mental status examation (MMSE) scores of VCI group (19.81±2.56) was significant lower than that of NCI group (24.45±2.95). Of the 70 SIVD patients, 81.43%(57) patients complainted of limbs weakness, 40% (28) of instability walking, 20%(14) of mild dysarthria, and 14.29%(10) of difficulty with drinding or urinary incontinence. On nervous system examination, 71.4% patients with upper motor neuron damaged signs, 14.9% with pseudobulbar palsy signs and 10% with extrapyramidal signs were detected, while only 7.14% patients with ataxia gait。On comparison the subtype of SIVD, univariate analysis demonstrated significant associations between WM and hypertension (P<0.05), and between LI and diabetes mellitus, hyperlipidemia, coronary artery disease (P<0.05), but no significant associations with anemia, smoking, alcohol drinking (P>0.05). These associations persisted after multivariate stepwise Logistic analysis, Hypertension was more frequent in WML-SIVD than LI-SIVD [odds ratio (OR) 8.531 (1.676 to 43.41); P=0.008], whereas diabetes mellitus [OR 0.082 (0.016 to 0.436); P=0.003], hyperlipidemia [OR 0.158 (0.035 to 0.720) ; P=0.019], coronary artery disease [OR 0.005 (0.007 to 0.336); P=0.002] more frequent in LI-SIVD. As to the risk factor for cognitive impairment, hypertension [OR 5.265 (1.563 to 17.731); P=0.007], diabetes mellitus [OR 3.445 (1.008 to 11.772); P=0.049] and hyperlipidemia [OR3.649 (0.974 to 11.466); P=0.027] were associated with VCI patients, even in the multivariate stepwise Logistic analysis after controling for the effects of other variables, but no correlations was found between coronary artery disease, anemia, smoking, alcohol drinking and cognitive impairment.Conclusions: Limbs weakness, instability walking, mild dysarthria, urinary incontinence, difficult with drinking, upper motor neuron damaged signs and pseudobulbar palsy are common clinical manifestation of subcortical ischemic vascular disease, although extrapyramidal signs may be detected. Hypertension associates with WML of SIVD, while diabetes mellitus, hyperlipidemia, coronary artery disease associates with LI-SIVD. Hypertension and diabetes mellitus are the main risk factors for cognitive impairment of SIVD, and the risk increase 5.265 and 3.445 times, respectively. The relationship between hyperlipidemia and cognitive impairment couldn't be established from our data. It is likely that age is not correlated with cognitive impairment of SIVD patients.Part 2: Contributions of White Matter Lesions Volume to Subcortical Vascular CognitiveObjectives:To evaluate contributions of WML to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD).Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into NCI and VCI groups. The volume of WML, which shows white matter hyperintensities (WMH) on MRI fluid-attenuated inversion recovery images, was measured using a semi-automated quantitative method. Finally, correlation analysis and hierarchical multiple regression analysis was used to examine the relationship between general cognitive function and the volume of WMH.Results: The mean WMH volume of all SIVD patients was (29.56±15.58)cm3, ranged from 4.20cm3 to 86.01cm3. The mean WMH volume of VCI group was (34.84±17.50) cm3, increased remarkedly than that of NCI group (23.63±10.52) cm3, there was significant difference between groups(Z=-2.83, P=0.005). MMSE score was positively correlated with educational attainment(r=0.49, P<0.001), but negatively correlated with age(r=-0.235, P=0.025) and WMH volume(r=-0.398, P<0.001) in a univariate correlation analysis. And age was positively correlated with WMH volume(r=0.279, P=0.01), but negatively with educational attainment. In the hierarchical multiple regression analysis, age, gender and educational attainment was entered at the first step, but only the educational attainment was related to MMSE score(P<0.001). After the WMH volume entered at the second step, the relationship remain significant between MMSE score and eductional attainment(P<0.001). It was revealed that WMH volume was negatively correlated with MMSE score(B=-0.077, t=-3.243, P=0.002) after controlling for the effects of age, gender and educational attainment, WMH volume explained an additional 10.2% of the variance in MMSE.Conclusions: It is convenient to measure and count the volume of white matter hyperintensities using the semi-automated quantitative method which is based on ImageJ and its Voxel Counter Plugins. The result is accurary and reliable, the method is time-saving and easy to manipulate. There is a highly significant negative correlation between MMSE score and WMH volume, but WMH volume explains only an additional 10.2% of the variance in MMSE independently.Part 3: Contributions of White Matter Lesions Location to Subcortical Vascular Cognitive ImpairmentObjectives:To evaluate contributions of WML location to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD), and to evaluate the correlation of volumetric quantitation and qualitation of WMH .Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and magnetic resonance imaging (MRI) scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into no cognitive impairment(NCI) and vascular cognitive impairment(VCI) group. Age-related white matter change rating scale, which is a visual rating scale developed by Wahlund, was used to qualitative measure and locate the WML. Integrated with the data of part 2, using correlation analysis and hierarchical multiple regression analysis to examine the relationship between general cognitive function and the location of WMH, and the correlation of volumetric quantitation and qualitation.Results: The mean WMH rating scores of all the SIVD patients was (12.37±3.93), ranged from 3 to 20. There was a highly significant positively correlation between total WMH volume and total WMH score(r=0.879, P<0.001), scatter diagram using WMH volume as independent variables reveal a curvilinear relationship between total WMH volume and total WMH score. The mean scores of WMH in all regions of VCI group was (13.65±3.51), higher than that of NCI group(t=-3.043,P=0.003), which was (10.94±3.94). As to each regions, the VCI group scores of frontal area, parieto-occipital area and basal ganglia area were remarkedly increased than that of NCI group(P<0.05), but no difference were noted at temporal area and infratentorial area. In the hierarchical multiple regression analysis, The result of first step was same to part 2. After the score of total WMH entered at the second step, the relationship remained significantly between MMSE score and eductional attainment(P<0.001). It was revealed that the score of total WMH was negatively correlated with MMSE score(B=-0.270,t=-2.842, P=0.006) after controlling for the effects of age, gender and educational attainment. The score of total WMH explained an additional 8.1% of the variance in MMSE,lower than the 10.2% of WMH volume. Moreover, when all the five regions enter instead of the score of total WMH at the second step, the R2 change was 19.3%. That is, all the five regions explained an additional 19.3% of the variance in MMSE,much higher than the effects of WMH volume and the score of total WMH. And the scores of frontal area and of basal ganglia area were the mainly variables(P<0.05), but no the scores of parieto-occipital area, infratentorial area and temporal area(P>0.05).Conclusions: The volumetric quantitation of WMH was positively correlated with its volumetric qualitation. Each of the methods can be used to tract any changes of WML, except that the volumetric quantitation is more sensitivity. WML located at frontal area and basal ganglia area predicts cognitive decline, but not suits for WML located at parieto-occipital area, infratentorial area and temporal area.Part 4: Contributions of Lacunar Infarcts to Subcortical Vascular Cognitive Impairment Objectives:To explore contributions of LI to cognitive impairment of patients with subcortical ischemic vascular disease (SIVD), and to evaluate the relationship of LI and WML and effects of each other.Methods: Patients who had ever suffered at least an acute ischemic stroke for more than 3 months were invited to the pre-screening. After comprehensive clinical, neuropsychological profiles evaluation and MRI scanning, 70 patients with SIVD diagnosed according to the MRI criteria of Erkinjuntti and neuropsychological profiles were included and divided into NCI and VCI groups. The number of LI was counted and the location of it was recorded according to the method using at part 3. Integrated with the data of ahead, using correlation analysis and hierarchical multiple regression analysis to examine the relationship between general cognitive function and the number and the location of LI, and the relationship of LI and WML.Results: The mean number of LI in VCI group was (4.38±1.82), higher than that of NCI group(Z=-2.202,P=0.028), which was (3.39±1.71). As to each regions, the VCI group number of LI in frontal area and basal ganglia area were remarkedly increased than that of NCI group(P<0.05), but no difference was noted at temporal area, parieto-occipital area and infratentorial area. In the hierarchical multiple regression analysis, considered MMSE scores as dependent variables, the first step is same as part 2 and part 3. After the number of total LI entered at the second step, the relationship remain significant between MMSE score and eductional attainment(P<0.001). It was revealed that the number of total LI was negatively correlated with MMSE score(B=-0.270,t=-2.842, P=0.006) after controlling for the effects of age, gender and educational attainment, the number of total LI explained an additional 9.1% of the variance in MMSE. Another analysis then performed, all of the five regions enter instead of the number of total LI at the second step, the R2 change was 25.7%. That is, all of the five regions explained an additional 25.7% of the variance in MMSE,much higher than the effects of the number of total LI. And the number of LI at frontal area and basal ganglia area were the mainly negative variables(P<0.05) to predict cognitive decline of patients with SIVD, but no relationship was noted between MMSE and the number of LI at parieto-occipital area, infratentorial area or temporal area (P>0.05). The third hierarchical multiple regession analysis followed, at the first step, age, gender and education were entered; then WMH volume were entered at the second step, the result was same as part 2, and WMH volume make the model R2 change 10.2%; the number of total LI enter after WMH volume at step 3, an additional R2 change was 0.112, there was a negatively correlation between MMSE and WMH volume, MMSE and the number of LI, independently. At last, the fourth analysis performed, the location of WML and LI were entered the model at the second step after age, gender and education, it revealed that only WML located at frontal area and LI located at basal ganglia area predicted cognitive decline of patients with SIVD, independently.Conclusions: The number and location of LI were related to cognitive impairment, the effect of LI location is greater than than of its number. The number of LI and WMH volume both are predictors of cognitive impairment in patients with SIVD, independently; and LI located at basal ganglia area and WML at frontal area are the best predictors.