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DOI:10.2214/AJR.07.3566
AJR 2009; 192:W63-W70
© American Roentgen Ray Society

Original Research

Carotid Artery Abnormalities and Leukoaraiosis in Elderly Patients: Evaluation with MDCT

Luca Saba1, Roberto Sanfilippo2, Luigi Pascalis3, Roberto Montisci2 and Giorgio Mallarini1,4

1 Department of Radiology, Policlinico Universitario, University of Cagliari, Cagliari, Italy.
2 Department of Vascular Surgery, Policlinico Universitario, Cagliari, Italy.
3 Division of Internal Medicine, Ospedale San Giovanni di Dio, Cagliari, Italy.
4 Institute of Radiology, Ospedale San Giovanni di Dio, Via Tola 7, Cagliari 09128, Italy.

Received December 19, 2007; accepted after revision August 21, 2008.

 
Address correspondence to L. Saba (lucasaba{at}tiscali.it).

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Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
OBJECTIVE. Several studies have shown that leukoaraiosis is a clinically relevant condition. Patients with leukoaraiosis have a negative prognosis in terms of death, stroke, and myocardial infarction. The aim of this study was to evaluate whether the presence and severity of leukoaraiosis correlate with degree of carotid stenosis and the presence of specific types of plaque (fatty, mixed, calcified) in a group of elderly patients with clinical indications for MDCT.

MATERIALS AND METHODS. From January 2004 to March 2007, 147 consecutively registered patients 65 years and older underwent MDCT. All patients enrolled in the study cohort were assessed for the presence and severity of leukoaraiosis. Degree of carotid artery stenosis according to the North American Symptomatic Carotid Endarterectomy Trial criteria and type of plaque were evaluated. Statistical analysis was performed to determine whether an independent interaction existed among the presence of leukoaraiosis, severity of leukoaraiosis, and degree of carotid artery stenosis associated with plaque type.

RESULTS. A correlation was observed between the presence of leukoaraiosis and degree of carotid stenosis (Pearson correlation, 0.23; p < 0.001). A statistically significant correlation between advanced patient age and presence of leukoaraiosis (Pearson correlation, 0.32; p < 0.0001) and severity of leukoaraiosis (Pearson correlation, 0.55; p < 0.0001) was recorded. The data obtained showed a trend toward increased risk of development of leukoaraiosis (p = 0.08) in carotid arteries with fatty plaques.

CONCLUSION. The results of this study showed a statistically significant correlation between the presence and severity of leukoaraiosis and degree of carotid stenosis. A trend toward increased risk of development of leukoaraiosis in carotids with fatty plaques also was observed. The data confirmed that the development of leukoaraiosis is strongly correlated with age.

Keywords: carotid artery • leukoaraiosis • MDCT angiography • stroke


Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
The term "leukoaraiosis"—from the Greek leuko, or white, and araiosis, or rarefaction—was first used by Hachinski et al. [1] to describe areas of low attenuation in the periventricular white matter. The radiologic findings of leukoaraiosis probably are caused by chronic cerebral ischemia, but the pathogenesis and clinical significance are incompletely understood [2]. Histologically, leukoaraiosis is characterized by demyelination, loss of glial cells, and spongiosis [3]. The condition is common, visualized on more than one half of routine CT scans of elderly patients. Epidemiologic studies have shown a high prevalence of leukoaraiosis among persons older than 65 years [4–7]. In some persons, leukoaraiosis remains asymptomatic for prolonged periods, but others experience disability, depression, gait disturbance, mood disorders, and dementia [8–14]. Both CT [15–17] and MRI [18–20] studies have shown that the presence of leukoaraiosis is an important predictor of future stroke risk that is independent from traditional stroke risk factors. Results of more recent studies [6, 21, 22] emphasize the relevance of this common pathologic condition.

Results of three studies with large cohorts—the North American Symptomatic Carotid Endarterectomy Trial (NASCET), the European Carotid Surgery Trial, and the Asymptomatic Carotid Atherosclerosis Group study—provide cutoff values for degree of stenosis that suggest possible benefits of carotid endarterectomy [23–25]. In particular, NASCET [9] proved the benefits of endarterectomy in the care of patients with symptomatic high-grade stenosis (70–99%) by showing how the presence of a carotid plaque with a severe degree of stenosis can be highly predictive of an increased incidence of cerebrovascular events. In more recent studies [26–32], however, numerous features of carotid plaque other than degree of stenosis were identified that are associated with the occurrence of cerebrovascular lesions. These features include plaque ulceration, fissured fibrous cap, intraplaque hemorrhage, and composition of plaque (fatty, mixed, or calcified). In this study we aimed to evaluate whether the presence and severity of leukoaraiosis correlate with degree of carotid stenosis and with the presence of specific types of plaque in elderly patients with clinical indications for MDCT angiography.


Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Demographic Data
CT scans of the carotid arteries and brains of 147 patients (102 men, 45 women; mean age, 74 years; range, 65–85 years) were retrospectively reviewed. Only subjects 65 years and older who underwent MDCT of the brain and carotid arteries in the same session were included in the cohort. The inclusion criterion for MDCT angiography was a clinical indication for CT angiography of the supraaortic vessels confirmed with extracranial Doppler sonography, when available, as determined by the referring physician and established by the attending radiologist. In particular, patients had been referred for MDCT after undergoing a sonographic examination that showed pathologic stenosis, plaque alteration, or both, or when sonography did not provide sufficient information about the degree of stenosis—for example, in the case of findings of large calcified plaques with acoustic shadowing or high carotid bifurcation or of dif ficulty performing neck studies, as in the presence of edema or obesity.

At sonographic examination, we considered the following findings to be plaque alteration: heterogeneous plaque, plaque surface irregularity, intraplaque hemorrhage, and plaque ulceration. Exclusion criteria were contraindications to administration of iodinated contrast medium, such as a known allergy to iodinated contrast material, and elevated results of renal function tests. Patients with restenosis after carotid endarterectomy were excluded from this retrospective analysis. Exclusion criteria on chart review were other sources of white matter hypo attenuation, such as acute disseminated encephalomyelitis, multiple sclerosis, vasculitis, and connective tissue diseases [33].

From January 2004 to March 2007, 147 patients at our university hospital met the inclusion criterion and were selected for the study. Demo graphic details, including age, sex, and risk factors (hypertension, diabetes, coronary artery disease, dyslipidemia, tobacco use) were recorded. Because this study was retrospective and imaging was not additional to that performed routinely on this group of patients, our divisional research committee did not require specific ethical approval. Some of the patients had been recruited for previous studies [34–37].

MDCT Angiography
MDCT of the carotid arteries was performed (MX8000 scanner, Philips Healthcare). Arterial enhancement was obtained by IV administration of 90–110 mL of nonionic iodinated contrast material (iopromide, Ultravist 370, Bayer HealthCare; iomeprol, Iomeron 350, Bracco) at an injection speed of 4–6 mL/s through a power injector and an 18- to 20-gauge IV catheter in the antecubital vein. A delay variable of 12–18 seconds was used. CT technical parameters were matrix, 512 x 512; field of view, 11–19 cm; 180–200 mAs; 120–140 kV; section thickness, 3.2 mm; increment, 1.6 mm. The window level was preset to 200 HU with a width of 750 HU. Images were processed with our workstations with multiplanar reconstruction, maximum intensity projection, and volume-rendering algorithms.

Each area of carotid stenosis was graded according to NASCET criteria: 1, normal; 2, mild stenosis (1–39%); 3, moderate stenosis (40–69%); 4, severe stenosis (70–99%); and 5, occlusion. Degree of stenosis was calculated by selection of a plane perpendicular to the lumen centerline [38]. The degree of stenosis was the ratio of the diameter of the stenosed segment to that of the more distal segment in which no stenosis was present. When stenosis or near occlusion was detected, the ratio method was not used, and the carotid arteries were immediately included in the NASCET grade 4 group.

Types of plaque were characterized according to a previously described classification [35, 36, 38]. Fatty (soft) plaques had an attenuation less than 50 HU; mixed (intermediate) plaques, attenuation of 50–119 HU; and calcified plaque, attenuation greater than 120 HU. For measurement of attenuation, a circular or elliptic region of interest cursor was placed in the plaque-predominant area. Areas exhibiting artifact from contrast material or beam hardening were carefully avoided.

MDCT of the Brain
CT of the brain was performed with the scanner used for carotid imaging (MX8000, Philips Healthcare). Acquisition was performed before and after injection of contrast material from the base of the skull to the vertex in the plane parallel to the canthomeatal line [39]. For 115 patients a 5-mm section thickness was used; a 3.2-mm section thickness was used for the other 32 patients. The tube voltage was 120 kV, and the tube current–time product ranged from 240 to 300 mAs. The mean field of view was 24.68 cm. In 110 patients, the field of view was 25 cm; in 27 patients, 24 cm; and in 10 patients, 23 cm.

CT images of the brain were analyzed with the reviewers blinded to the demographic details. In this study, the hemispheric white matter score and corresponding ipsilateral carotid characteristics—degree of stenosis and type of plaque—were considered a unique unit of analysis, as indicated by Fanning and colleagues [33]. Therefore, 294 carotid artery–hemisphere units were analyzed.

For white matter assessment, a visual scale of severity of change in white matter was based on the European Task Force on Age-Related White Matter Changes [40]. The following 4-point scale was used (Fig. 1A, 1B, 1C, 1D, 1E, 1F): 0, no lesions; 1, focal lesions larger than 5 mm in diameter; 2, early confluent lesions; 3, diffuse involvement of an entire brain region. For all patients, the highest score for each hemi sphere was used in the analysis. Two ex perienced radiologists quantified white matter scores, and differences were resolved by consensus.


Figure 1
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  		Fig. 1A —Examples of leukoaraiosis grades according to North American Symptomatic Carotid Endarterectomy Trial criteria [23]. 69-year-old man with grade 1 leukoaraiosis. Axial CT angiographic image (A) shows focal ill-defined hypoattenuation, and maximum-intensity-projection postprocessed image (D) of right internal carotid artery shows 70% stenosis (arrowhead, A and arrow, D).

 

Figure 2
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  		Fig. 1B —Examples of leukoaraiosis grades according to North American Symptomatic Carotid Endarterectomy Trial criteria [23]. 74-year-old man with grade 2 leukoaraiosis. CT angiographic image (B) shows beginning confluence of lesions (arrowheads), and maximum-intensity-projection postprocessed image (E) of right internal carotid artery shows 85% stenosis (arrowheads, B and arrow, E).

 

Figure 3
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  		Fig. 1C —Examples of leukoaraiosis grades according to North American Symptomatic Carotid Endarterectomy Trial criteria [23]. 78-year-old man with grade 3 leukoaraiosis. CT angiographic image (C) shows diffuse involvement (arrowheads), and maximum-intensity-projection postprocessed image (F) of right internal carotid artery shows 95% stenosis (arrowheads, C and arrow, F).

 

Figure 4
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  		Fig. 1D —Examples of leukoaraiosis grades according to North American Symptomatic Carotid Endarterectomy Trial criteria [23]. 69-year-old man with grade 1 leukoaraiosis. Axial CT angiographic image (A) shows focal ill-defined hypoattenuation, and maximum-intensity-projection postprocessed image (D) of right internal carotid artery shows 70% stenosis (arrowhead, A and arrow, D).

 

Figure 5
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  		Fig. 1E —Examples of leukoaraiosis grades according to North American Symptomatic Carotid Endarterectomy Trial criteria [23]. 74-year-old man with grade 2 leukoaraiosis. CT angiographic image (B) shows beginning confluence of lesions (arrowheads), and maximum-intensity-projection postprocessed image (E) of right internal carotid artery shows 85% stenosis (arrowheads, B and arrow, E).

 

Figure 6
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  		Fig. 1F —Examples of leukoaraiosis grades according to North American Symptomatic Carotid Endarterectomy Trial criteria [23]. 78-year-old man with grade 3 leukoaraiosis. CT angiographic image (C) shows diffuse involvement (arrowheads), and maximum-intensity-projection postprocessed image (F) of right internal carotid artery shows 95% stenosis (arrowheads, C and arrow, F).

 
Statistical Analysis
The following descriptive statistics were calculated: number of patients with leukoaraiosis and degree of severity of leukoaraiosis; number of patients with carotid stenosis and degree of stenosis; and number of patients with fatty, mixed, and calcified plaques. Tobacco use, hypertension, dyslipidemia, and diabetes mellitus also were analyzed. Continuous data were described as mean ± SD. Normality of data distribution was assessed with the Kolmogorov-Smirnov test. Comparison between groups was performed with the Mann-Whitney test because normality of the variable was rejected.

Box plots were made of age in relation to degree of severity of leukoaraiosis and age in relation to degree of carotid stenosis. The correlation coefficient between degree of carotid stenosis and leukoaraiosis severity was calculated with the Pearson statistic. A pyramid plot of presence or absence of leukoaraiosis in relation to age was made to compare age frequency among patients with and those without leukoaraiosis.

Simple logistic regression analysis was performed to examine the relation between the dichotomous variable leukoaraiosis and independent variables such as age, class of carotid artery stenosis, sex, tobacco use, hypertension, dyslipidemia, and diabetes mellitus. A chi-square test with Yates correction was used to investigate associations between type of plaque and presence of leukoaraiosis. The Cohen kappa test was used to assess the level of interobserver and intra observer agreement for ordinal categoric data (white matter scores). A kappa value of 0.20 or less implied poor agreement; 0.21–0.40, fair agreement; 0.41–0.60, moderate agreement; 0.61–0.80, substantial agreement; and 0.81–1.0, almost perfect agreement [41]. R software (R Project) was used. A value of p < 0.05 was considered to indicate statistical significance.


Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
The clinical features of the study group are summarized in Table 1. The leukoaraiosis severity scores are summarized in Table 2 and class of carotid artery stenosis in Table 3. Figure 2A, 2B shows box plots of age in relation to degree of severity of leukoaraiosis and degree of carotid stenosis. Pyramid plots of the presence of leukoaraiosis in relation to age are shown in Figure 3. The results show a strong correlation between advanced age and a higher rate of occurrence of leukoaraiosis. Statistically significant correlations were observed between advanced patient age and presence (Pearson correlation, 0.32) and severity (Pearson correlation, 0.55) of leukoaraiosis (both p < 0.0001). A statistically significant correlation between advanced age and severity of stenosis (Pearson correlation, 0.18; p = 0.002) also was recorded.


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  		TABLE 1: Clinical Characteristics of Study Population

 

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  		TABLE 2: Summary of Leukoaraiosis Severity Scores

 

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  		TABLE 3: Summary of Degree of Severity of Stenosis of Carotid Artery

 

Figure 7
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  		Fig. 2A —Box plots show grade of leukoaraiosis (A) and class of carotid stenosis (B) in relation to patient age.

 

Figure 8
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  		Fig. 2B —Box plots show grade of leukoaraiosis (A) and class of carotid stenosis (B) in relation to patient age.

 

Figure 9
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  		Fig. 3 —Pyramid plot shows relation between frequency of leukoaraiosis and age.

 

Analysis of the relation between severity of carotid stenosis and leukoaraiosis showed statistically significant correlation between the presence (Pearson correlation, 0.23) and severity (Pearson correlation, 0.33) of leukoaraiosis and the class of carotid stenosis (both p < 0.001). Pearson statistic results are summarized in Table 4. As a further step, simple logistic regression was performed to improve understanding of the relations among degree of carotid stenosis, age of patients with leukoaraiosis, and presence of risk factors such as sex, tobacco use, and coexistence of hypertension, dyslipidemia, or diabetes mellitus (Table 5). Our data definitely showed a statistically significant association between leukoaraiosis and patient age (p > 0.0001) and leukoaraiosis and class of carotid stenosis (p = 0.0113).


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  		TABLE 4: Pearson Correlation Scores

 

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  		TABLE 5: Logistic Regression of Presence of Leukoaraiosis and Variables

 

In analysis of associations between plaque type and presence of leukoaraiosis, a trend toward increased risk of development of leukoaraiosis (p = 0.08) in carotid arteries with fatty plaque was observed. The data indicated that fatty plaques in a carotid artery are more likely to influence the development of leukoaraiosis than are nonfatty plaques. The presence of calcified and mixed plaques was not directly associated with the occurrence of leukoaraiosis (Table 6). Interobserver agreement in measurement of severity of leukoaraiosis was optimal, with a kappa value of 0.856 and weighted kappa value of 0.893.


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  		TABLE 6: Chi-Square Test with Yates Correction Scores

 


Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Leukoaraiosis is believed to be caused by insufficient blood supply to the cerebral white matter resulting from pathologic vascular changes [2, 42–44], but the pathogenesis is unclear and controversial. Studies [45, 46] have shown significantly lower vessel density in the deep white matter of patients with leukoaraiosis than that of healthy subjects (p = 0.018). Evidence has been gathered to document that the presence of leukoaraiosis is associated with a history of stroke and that it is predictive of the occurrence of ischemic and hemorrhagic strokes [47–52]. Persons with leukoaraiosis may have disability, depression, gait disturbance, mood disorders, and dementia [8, 9, 11, 13, 14]. Therefore, it is important to identify risk factors associated with leukoaraiosis.

Stenosis of the extracranial carotid arteries is believed to decrease cerebral blood flow [53]. This alteration in cerebral blood flow may lead to the development of leukoaraiosis [54–57]. Our data in this study confirmed a statistically significant association among presence of leukoaraiosis, severity of leukoaraiosis, and degree of carotid artery stenosis. The association we detected supports the hypothesis of Inzitari [58] that leukoaraiosis is a marker of stroke risk. Persons with leukoaraiosis have diffuse demyelinization of the white matter that is strongly associated with atherosclerosis. Detection of changes in the white matter may suggest an advanced stage of generalized atherosclerosis.

Results of several studies [59] support the hypothesis that leukoaraiosis is associated with an increase in cardiac, peripheral arterial, and carotid arterial atherosclerosis. Our results confirmed that one of the most important determinants of atherosclerosis in the presence of carotid artery plaque and of degree of stenosis is the presence of leukoaraiosis. Leukoaraiosis may have an ischemic origin, as suggested by Fazekas et al. [60], who described the link between the presence of irregular lesions exhibiting microcystic infarcts and patchy rarefaction of myelin.

In regard to associated risk factors predictive of the occurrence of leukoaraiosis, our evidence shows that the presence (Pearson correlation, 0.32) and severity (Pearson correlation, 0.55) of leukoaraiosis are strongly correlated with age (both p < 0.0001). These data have been confirmed in other studies [4–7]. We included in our retrospective analysis patients 65 years and older to minimize the age effect on leukoaraiosis. Nevertheless, among these patients 65–85 years old we observed a significant association between age and the presence and severity of leukoaraiosis. We therefore agree with Adachi and colleagues [61] that it is conceivable that age-related brain atrophy may contribute to the severity of leukoaraiosis through anterograde wallerian degeneration, as indicated by other authors [55, 56, 62]. In a comparison of the age data in our study and a study by Fanning and colleagues [33], we observed a higher frequency of leukoaraiosis (70% vs 51.2%). This result can be easily explained by the higher mean age of the patients in our study (74 years).

Several investigators of leukoaraiosis [5, 48, 57, 63–73] have described a relation between occurrence of leukoaraiosis and presence of associated risk factors for cerebrovascular disease, such as hypertension, diabetes, dyslipidemia, and tobacco use. By analyzing the features of our study group with the Mann-Whitney statistic, we detected a significant statistical association between the presence of leukoaraiosis and advanced age, female sex, and hypertension (Table 1). These results, however, should be critically evaluated because most of these factors can be reciprocally linked. More detailed logistic regression analysis is needed. Applying logistic regression, we observed that the only significant associations were between presence of leukoaraiosis and age and between presence of leukoaraiosis and stenosis class (Table 5).

In addition to the already mentioned risk factors predictive of leukoaraiosis occurrence, the most significant result we observed in our study was a trend toward increased risk of development of leukoaraiosis (p = 0.08) in carotid arteries containing fatty plaque. Our data suggested that the presence of fatty plaques in carotid arteries is more likely to be associated with leukoaraiosis than is the presence of nonfatty plaques. The presence of calcified or mixed plaques was not associated with leukoaraiosis. We did not find a statistically significant association between type of plaque and presence of leukoaraiosis, but the trend produced by fatty plaques (p = 0.08) is suggestive. It is possible that a study with a larger number of patients can validate the association.

Our results on the correlation between the presence of fatty plaque and occurrence of leukoaraiosis are in accordance with those of Altaf et al. [74]. Those authors found an association between unstable carotid plaques and number of white matter lesions. The findings suggest that plaque activity may contribute to the development of leukoaraiosis. It is well known that the composition of atherosclerotic plaque can play a role in the origin of cerebrovascular events [75]. Tegos et al. [76] described the relation between plaque characteristics by using sonography and CT of brain lesions, whereas Ouhlous et al. [75] used MRI.

Our data support the theory that leukoaraiosis may be an intermediate surrogate of stroke. In a cohort of 141 patients, Serfaty et al. [30] identified a statistically significant association between decreased plaque density and occurrence of cerebrovascular events. By analyzing the relation between leukoaraiosis and calcified plaque, Fanning and colleagues [33] found no correlation between CT carotid calcium scores and severity of white matter disease. Interobserver agreement on measurements of leukoaraiosis severity was excellent, with a kappa value of 0.856 and weighted kappa value of 0.893. These values indicate optimal reproducibility of the visual scale of white matter severity change based on the European Task Force on Age-Related White Matter Changes [40].

We are aware that our work had important limitations deriving from its retrospective nature. A prospective longitudinal study would probably provide even more accurate results. Another bias was that the method used for patient selection to determine the need for CT angiography was a previous clinical indication for CT angiography of the supraaortic vessels. When the technique was available, the findings determined by the referring physician and established by the attending radiologist were confirmed with extracranial Doppler sonography. As a consequence, a large number of patients with no evidence of pathologic changes were excluded from the study. Third, we did not analyze the effect of posterior circulation. This missing information might have introduced bias in the data analysis because of the potential effect of the vertebrobasilar system. Finally, we studied only a subset of the patient population older than 65 years with neurologic symptoms severe enough to necessitate an additional CT angiographic examination. This group is very different demographically from the general population in the same age class. This bias should be kept in mind for better understanding of the study results.

In conclusion, we saw a statistically significant association between presence and severity of leukoaraiosis and degree of stenosis involving the extracranial common and internal carotid arteries. There is a statistical trend toward increased risk of development of leukoaraiosis in patients with predominantly fatty carotid plaque. The presence and severity of leukoaraiosis increase with patient age.


Acknowledgments
 
The authors are grateful to Giancarlo Caddeo for advice and to Tiziana Langella for assistance in linguistic review of this manuscript. We also thank the anonymous reviewers for their helpful and constructive critique.


References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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