Added value of femoral artery atherosclerosis for determining severity of white matter lesion by carotid atherosclerosis: a magnetic resonance imaging study

Abstract

Background

Previous studies reported that single vascular atherosclerosis was an effective indicator for white matter lesions (WMLs).

Purpose

To investigate the added value of femoral atherosclerosis for determining severity of WMLs by carotid atherosclerosis using three-dimensional vessel wall magnetic resonance imaging (MRI).

Material and Methods

Elderly individuals without cardiovascular symptoms within the previous six months were recruited. The plaque features of carotid and femoral arteries were evaluated and compared between individuals with WML score ≤ 3 and those with WML score > 3. Logistic regression and receiver operating characteristic (ROC) analyses were used to determine the value of plaque features in discriminating WMLs with score > 3.

Results

In total, 112 individuals (49 men, mean age 72.0±5.6 years) were included. Participants with a WML score > 3 showed a significantly greater carotid wall area and femoral artery stenosis and higher incidence of carotid calcification and femoral artery calcification and lipid-rich necrotic cores (LRNC) compared to those with a WML score ≤ 3 (all P < 0.05). Carotid artery calcification and femoral artery calcification, LRNC, and stenosis were found to be significantly associated with severe WMLs before and after adjustment for clinical factors (odds ratio 1.51–3.79, all P < 0.05). ROC analysis showed, in discriminating severe WMLs, the area under the curve increased from 0.615 to 0.754 after combining femoral artery LRNC and stenosis with carotid calcification compared to the carotid calcification alone.

Conclusion

Characteristics of femoral artery atherosclerosis determined by vessel wall MRI have added value for carotid atherosclerosis in determining the severity of WMLs.

Keywords

Femoral artery carotid artery atherosclerosis white matter magnetic resonance imaging

Introduction

White matter lesion (WML), the sequela of cerebral small vessel disease, has been demonstrated to be associated with cognitive decline, dementia, and the increased risk of stroke (1). WML is considered to be an imaging marker of cerebral small vessel disease. One of the pathologies of WML is the arteriosclerosis of cerebral small vessels. Previous studies (2,3) have shown that age, hypertension, hyperlipidemia, and smoking were the risk factors for WML due to arteriosclerosis of cerebral small vessels. Apparently, WML shares similar risk factors with large artery atherosclerosis. Increasing evidence has shown that large artery atherosclerotic disease in a single vascular bed is a valuable determinant for severity of WMLs (4,5). Many studies documented that individuals with multiple vascular plaques had higher risk of developing ischemic stroke compared to those with single vascular plaques (6). However, whether combination of information on atherosclerotic diseases in multiple vascular beds has incremental value in discriminating the severity of WMLs than atherosclerosis in single vasculature remains unclear. It is warranted to determine the relationship between atherosclerosis in multiple vascular beds (e.g. carotid and femoral arteries) and the severity of WMLs.

Vessel wall magnetic resonance imaging (MRI) has become a reliable approach in assessing atherosclerosis by providing both morphological and compositional characteristics of atherosclerotic plaques. Recently, investigators proposed three-dimensional (3D) vessel wall MRI techniques with the following advantages: excellent flow suppression; flexible contrast weightings; large coverage; time-efficient imaging; and isotropic high spatial resolution. These 3D vessel wall MRI techniques allow more comprehensive assessment of atherosclerosis and have been largely utilized to evaluate atherosclerotic plaques in multiple vascular beds, such as intracranial artery, carotid artery, aorta, and femoral arteries (7 –9).

The aim of the present study was to investigate the added value of femoral artery atherosclerosis in determining the severity of WMLs by carotid artery atherosclerosis using 3D vessel wall MRI.

Material and Methods

Study population

The study protocol was approved by local Ethics Committee and all participants provided written informed consent. The individuals were recruited from a pilot community study that aimed to determine the risk of cardiovascular disease among elderly asymptomatic adults. The eligible participants of the study were those aged ≥60 years but without cardiovascular symptoms in the previous six months. Individuals who had contraindications to MRI examinations, such as claustrophobia and non-MR-compatible implants, were excluded. The recruited participants underwent brain, carotid, and femoral artery MRI and all MRI examinations were done within one week. The collected medical information of each individual included age, gender, body mass index (BMI), blood pressure, total cholesterol (TC), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglycerides (TG), ankle brachial index (ABI), history of smoking (current or former), hypertension, diabetes, hyperlipidemia, and cardiovascular disease (CVD).

MRI protocol

MRI was performed for all individuals on a 3.0-T MR scanner (Achieva TX, Philips Healthcare, Best, The Netherlands) with 36-channel custom-designed neurovascular and 32-channel cardiac coils. Structural MR images of the brain were acquired by scanning the T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) and T1-weighted (T1W) sequences with routine parameters: T2-FLAIR: turbo spin echo (TSE), repeat time (TR)/echo time (TE) = 7000/140 ms, field of view (FOV) 23 cm × 23 cm, matrix size = 256 × 256, and slice thickness = 5.0 mm; T1W: TSE, TR/TE = 190/1.9 ms, FOV = 23 cm × 23 cm, matrix size = 256 × 256, and slice thickness = 5 mm. The vessel wall of carotid and femoral arteries was imaged by acquiring 3D time-of-flight (TOF), motion-sensitized driven equilibrium prepared rapid gradient echo (MERGE), T2-weighted volumetric isotropic turbo spin echo acquisition (T2-VISTA), and simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP) sequences. To facilitate the vessel wall MRI for the full coverage from the common femoral artery to the popliteal artery, two stacks of MRI were conducted with 40% longitudinal overlap. The parameters for the imaging sequences are shown in Table 1.

Table 1.

Parameters of carotid and femoral artery vessel wall MRI.

	Carotid artery				Femoral artery
	TOF	MERGE	T2-VISTA	SNAP	MERGE	T2-VISTA	SNAP
Sequence	FFE	FFE	TSE	FFE	FFE	TSE	FFE
Repeat time (ms)	20	9	2500	10	9.1	2000	8.5
Echo time (ms)	4.9	4.2	267	4.8	4.2	210	4.6
Flip angle (°)	20	6	VFA	11/5	6	VFA	11/5
Field of view (cm)	25 × 16 × 4				30 × 40 × 6
Spatial resolution (mm)	0.8 × 0.8 × 0.8				0.8 × 0.8 × 0.8
Matrix	512 × 512				512 × 512
Orientation	Coronal	Coronal	Coronal	Coronal	Coronal	Coronal	Coronal

FFE, fast field echo; MERGE, motion-sensitized driven equilibrium prepared rapid gradient echo; SNAP, simultaneous non-contrast angiography and intraplaque hemorrhage; TOF, time of flight; TSE, turbo spin echo; VFA, variable flip angle; VISTA, volumetric isotropic turbo spin echo acquisition.

MRI analysis

The MR images were reviewed by experienced radiologists (>5 years of experience in neuroradiology) with consensus. The WML defined as lesions with hyperintense on T2-FLAIR images but iso-intense on T1W images was evaluated by two observers (Y.L. and Y.H.) blinded to carotid and femoral artery MR images. The WMLs were scored and categorized into 10 grades (grades 0–9) according to the published criteria (10). Severe WML is defined as WML score > 3. All the carotid arteries were scanned coronally with the longitudinal coverage from proximal common carotid artery to distal internal carotid artery. The segments of carotid arteries include common carotid artery, carotid bulb, and internal carotid artery. The longitudinal coverage of the femoral arteries was from the middle section of the external iliac artery to the trifurcation level of the popliteal artery. The segments of femoral artery include common femoral artery, the proximal of superficial femoral artery, adductor canal, and popliteal artery. The MR images of carotid artery (Observers: M.G. and Y.C. who were blinded to femoral artery and brain images) and femoral artery (Observers: Z.Z. and X.Z. who were blinded to carotid artery and brain images) were interpreted using 3D CASCADE software (Tsinghua University, Beijing, PR China). The software has been validated and widely utilized for clinical purposes in previous studies (8,11,12). This software has not been implemented in the PACS workstation. Currently, we analyzed the MR images with this software offline. In measuring the morphology of carotid and femoral arteries, the boundaries of lumen and outer wall were traced semi-automatically. The boundaries were adjusted manually on the cross-sectional view of each artery, which is perpendicular to the centerline of the artery. The morphological measurements at each axial location included lumen area (LA), wall area (WA), total vessel area (TVA = LA + WA), maximum wall thickness (max WT), and normalized wall index (NWI = WA/TVA × 100%). For each individual, the max WT was taken from the maximum value and the other morphological measurements were taken from the mean values of each corresponding measurement for each vascular bed. The stenosis of carotid artery was measured using NASCET criteria (percent stenosis = 100%× [1-luminal diameter at the point of maximal narrowing/the diameter of the normal distal internal artery]) on the 3D TOF-MRA (magnetic resonance angiography) images. The stenosis of femoral arteries was measured on 3D MERGE images. As demonstrated in previous studies, the vessel wall images of 3D MERGE can be used to accurately measure arterial stenosis validated by digital subtraction angiography (13). The presence or absence of atherosclerotic plaque, which is defined as eccentric wall thickening, was determined in each vascular bed. Fig. 1 represents the examples for identification of plaque compositional features in carotid and femoral arteries, including lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), and calcification. The LRNC without hemorrhage shows iso-intense or slight hypointense on MERGE images and slight hypointense on VISTA images, calcification shows hypointense on both MERGE and VISTA images, and IPH shows hyperintense on all images, especially on SNAP images, respectively (7,14,15). Good to excellent intra-observer and inter-observer agreements in assessing atherosclerotic plaques on 3D vessel wall MR images have been reported previously (11,12).

Fig. 1.

Examples of plaque compositions in carotid and femoral arteries. (a) Reconstructed axial image of carotid artery MERGE imaging and calcification (remarkable hypointense, arrowhead) and lipid-rich necrotic core (slightly low signal intensity within plaque, arrow) can be seen. Intraplaque hemorrhage (hyperintense, hollow arrow) was detected on reconstructed axial image of SNAP imaging (b). (c) Calcification (remarkable hypointense, arrowhead), lipid-rich necrotic core (slightly low signal intensity within plaque, arrow), and intraplaque hemorrhage (hyperintense, hollow arrow) in the femoral artery. MERGE, motion-sensitized driven equilibrium prepared rapid gradient echo; SNAP, simultaneous non-contrast angiography and intraplaque hemorrhage

Statistical analysis

The continuous normal variables were presented as mean ± SD, abnormally distributed continuous variables were expressed in terms of median (interquartile range), and the discrete variables were described as percentage. The demographic clinical characteristics, and plaque features of the two vascular beds were compared between individuals with WML score ≤ 3 and those with WML score > 3 using independent-sample t test, Mann–Whitney U test, and Chi-square test or Fisher’s test as appropriate. The odds ratio (OR) and corresponding 95% confidence interval (CI) of plaque features in discriminating WML score > 3 were calculated using univariate and multivariate logistic regression before and after adjusted for confounding factors including age, sex, BMI, hypertension, hyperlipidemia, diabetes, smoking, and history of CVD. The receiver operating characteristic (ROC) curve analysis was conducted to calculate the area under the curve (AUC) of plaque features in discriminating WML score > 3. P values < 0.05 were considered statistically significant. All statistical analyses were performed using SPSS 16.0 (SSPS Inc. Chicago, IL, USA).

Results

Population characteristics

A total of 119 participants who completed the brain, carotid, and femoral artery MRI examinations were included in the present study. Of the 119 individuals, seven were excluded due to poor image quality. Of the remaining 112 participants (49 men; mean age = 72.0 ± 5.6 years), 31 (27.7%) had a WML score > 3. Compared to individuals with a WML score ≤ 3, those with a WML score > 3 showed a significantly higher prevalence of hypertension (67.7% vs. 44.4%; P = 0.028) and lower prevalence of hyperlipidemia (54.8% vs. 75.3%; P = 0.036). No statistically significant differences were found in other clinical characteristics between individuals with WML score ≤ 3 and WML score > 3 (all P > 0.05). The demographic and clinical characteristics between the two groups are shown in Table 2.

Table 2.

Clinical characteristics of the study population (n = 112).

	WML score ≤ 3 (n = 81)	WML score > 3 (n = 31)	P
Age (years)	71.7 ± 5.7	73.0 ± 5.4	0.214
Male sex	33 (40.7)	16 (51.6)	0.298
BMI (kg/m²)	23.9 ± 2.9	23.7 ± 2.6	0.850
Smoking	7 (8.6)	5 (16.1)	0.308
Hypertension	36 (44.4)	21 (67.7)	0.027
SBP (mmHg)	129.9 ± 15.6	136.9 ± 12.3	0.011
DBP (mmHg)	70.4 ± 8.9	74.3 ± 9.0	0.049
Hyperlipidemia	61 (75.3)	17 (54.8)	0.035
HDL (mmol/L)	1.5 ± 0.4	1.5 ± 0.4	0.950
LDL (mmol/L)	3.0 ± 0.9	2.7 ± 0.9	0.205
TG (mmol/L)	1.5 ± 0.8	1.5 ± 1.4	0.288
TC (mmol/L)	5.0 ± 0.9	4.8 ± 0.9	0.442
Diabetes	21 (25.9)	4 (12.9)	0.139
Glucose (mmol/L)	5.7 ± 1.0	5.8 ± 1.2	0.570
History of CVD	14 (17.3)	6 (19.4)	0.798
ABI	1.1 ± 0.1	1.1 ± 0.1	0.144

Values are given as n (%) or mean ± SD.

ABI, ankle branchial index; BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; WML, white matter lesion.

Characteristics of carotid and femoral artery atherosclerosis

Of the 112 participants, 70 (62.5%) had carotid atherosclerotic plaque and the prevalence of calcification, LRNC, and IPH was 28.6%, 49.1%, and 7.1%, respectively. Of the 70 individuals with carotid atherosclerosis, 30 (42.9%), 57 (81.4%), and 30 (42.9%) had atherosclerotic plaques in the common carotid artery, carotid bulb, and internal carotid artery, respectively. The morphological and compositional characteristics of carotid arteries are detailed in Table 3. Compared to the individuals with WML scores ≤ 3, those with WML score > 3 showed significantly greater carotid WA (26.6 ± 3.0 mm² vs. 25.4 ± 2.8 mm²; P = 0.047). There were no significant differences in other morphological measurements between the two groups (all P > 0.05). For the plaque compositions, participants with WML score > 3 were found to have a higher prevalence of carotid calcification compared to those with WML score ≤ 3 (45.2% vs. 22.2%; P = 0.017). No significant differences were found in other compositional features between the two groups (all P > 0.05).

Table 3.

Comparison of plaque features between individuals with without WML score > 3.

	WML score ≤ 3 (n = 81)	WML score > 3 (n = 31)	P
Carotid artery
Lumen area (mm)	25.8 (23.6–30.0)	27.4 (25.3–32.2)	0.122
Wall area (mm)	25.4 ± 2.8	26.6 ± 3.0	0.047
Total vessel area (mm)	52.3 ± 7.3	55.0 ± 8.0	0.092
Maximum wall thickness (mm)	2.8 (2.4, 3.8)	3.4 (2.6, 4.0)	0.275
Normalized wall index (%)	50.2 ± 2.9	50.0 ± 3.1	0.816
Presence of stenosis	10 (12.3)	3 (9.7)	0.948
Presence of plaque	48 (59.3)	22 (71.0)	0.254
Presence of calcification	18 (22.2)	14 (45.2)	0.017
Presence of LRNC	37 (45.7)	18 (58.1)	0.243
Presence of IPH	5 (6.2)	3 (9.7)	0.521
Femoral artery
Lumen area (mm)	30.1 ± 8.1	29.9 ± 6.0	0.857
Wall area (mm)	31.8 ± 5.6	33.9 ± 5.1	0.077
Total vessel area (mm)	62.0 ± 13.0	63.7 ± 10.1	0.497
Maximum wall thickness (mm)	3.1 (2.1–3.9)	3.6 (2.2–4.3)	0.136
Normalized wall index (%)	52.5 ± 3.9	54.0 ± 3.7	0.063
Presence of stenosis	25 (30.9)	19 (61.3)	0.003
Stenosis* (%)	19.0 (14.0–26.0)	21.0 (15.0–30.0)	0.553
Presence of plaque	52 (64.2)	21 (67.7)	0.726
Presence of calcification	19 (23.5)	15 (48.8)	0.011
Presence of LRNC	23 (28.4)	17 (54.8)	0.009
Presence of IPH	6 (7.4)	3 (10.0)	0.658

Values are given as n (%), mean ± SD, or median (IQR).

*Only individuals with stenosis >0 were included in this comparison.

IPH, intraplaque hemorrhage; LRNC, lipid-rich necrotic core; WML, white matter lesion.

In femoral arteries, 73 (65.2%) participants were found to have atherosclerotic plaques and the prevalence of calcification, LRCN, and IPH was 30.4%, 35.7%, and 8.0%, respectively. The prevalence of atherosclerotic plaques in the common femoral artery, proximal superficial femoral artery, adductor canal, and popliteal artery was 41.1%, 32.1%, 23.2%, and 41.1%, respectively. Table 3 presents the results on the comparison of femoral atherosclerotic plaque features between participants with WML score ≤ 3 and those with WML score > 3. Compared to individuals with WML score ≤ 3, those with WML score > 3 had a significantly higher prevalence of stenosis (30.9% vs. 61.3%; P = 0.003), calcification (48.8% vs. 23.5%; P = 0.009), and LRNC (54.8% vs. 28.4%; P = 0.011) in the femoral arteries. There were no statistically differences in the prevalence of IPH and luminal stenosis (stenosis > 0) in the femoral artery between the two groups (all P > 0.05).

Association between atherosclerotic plaque features and WMLs

Logistic regression analysis revealed that the presence of calcification in carotid artery plaques (OR = 2.882, 95% CI = 1.195–6.950; P = 0.018), presence of calcification (OR = 3.059, 95% CI = 1.279–7.316; P = 0.012) and LRNC (OR = 3.062, 95% CI = 1.300–7.211; P = 0.010) in femoral artery plaques, and luminal stenosis of femoral arteries (increment of 10%: OR = 1.512, 95% CI = 1.101–2.078; P = 0.011) were significantly associated with severe WMLs. After adjusting for confounding factors, the above associations remained statistically significant (all P < 0.05, Table 4). In particular, the NWI of the femoral artery was marginally associated with severe WMLs (OR = 1.107, 95% CI = 0.993–1.232; P = 0.066), but this association was statistically significant after adjusted for clinical factors (OR = 1.172, 95% CI = 1.024–1.342; P = 0.021). No significant associations were found between other plaque features of carotid and femoral artery and severe WMLs (all P > 0.05). ROC analysis showed that, in discriminating WML score > 3, the AUC of carotid calcification, femoral artery LRNC, and femoral artery stenosis was 0.615 (95% CI = 0.494–0.736), 0.632 (95% CI = 0.514–0.750), and 0.662 (95% CI = 0.546–0.779), respectively. The AUC increased to 0.701 after combined femoral artery LRNC and stenosis with carotid calcification. After adjustment for hypertension and hyperlipidemia, the AUC of the combination of carotid and femoral artery plaque features further increased to 0.754 (Fig. 2). Fig. 3 shows a patient with carotid and femoral artery plaques who had a WML score > 3.

Table 4.

Association between atherosclerotic plaque features and severe WMLs.

	Presence of WML score > 3
	Univariate regression			Multivariate regression*
	OR	95% CI	P	OR	95% CI	P
Carotid artery
LA (mm)	1.334	0.884–2.012	0.170	1.135	0.709–1.816	0.599
WA (mm)	1.534	1.000–2.354	0.050	1.427	0.846–2.409	0.183
TVA (mm)	1.423	0.941–2.154	0.095	1.234	0.760–2.004	0.396
Max WT (mm)	1.099	0.761–1.588	0.615	1.098	0.696–1.735	0.687
NWI (%)	0.983	0.851–1.135	0.814	1.024	0.871–1.204	0.772
Stenosis (%)	0.898	0.547–1.473	0.670	0.866	0.476–1.575	0.638
Presence of plaque	1.681	0.688–4.106	0.255	1.480	0.499–4.389	0.480
Presence of calcification	2.882	1.195–6.950	0.018	3.081	1.021–9.298	0.046
Presence of LRNC	1.647	0.713–3.801	0.243	1.406	0.488–4.056	0.528
Presence of IPH	1.629	0.365–7.266	0.523	1.180	0.204–6.820	0.854
Femoral artery
LA (mm)	0.962	0.632–1.464	0.856	0.713	0.392–1.296	0.267
WA (mm)	1.454	0.956–2.214	0.081	1.331	0.791–2.242	0.282
TVA (mm)	1.153	0.766–1.736	0.494	0.957	0.554–1.652	0.874
Max WT (mm	1.148	0.858–1.536	0.354	1.136	0.813–1.586	0.455
NWI (%)	1.107	0.993–1.232	0.066	1.172	1.024–1.342	0.021
Stenosis (%)	1.512	1.101–2.078	0.011	1.644	1.115–2.423	0.012
Presence of plaque	1.171	0.486–2.822	0.725	1.007	0.367–8.760	0.990
Presence of calcification	3.059	1.279–7.316	0.012	3.791	1.275–11.273	0.017
Presence of LRNC	3.062	1.300–7.211	0.010	3.380	1.180–9.684	0.023
Presence of IPH	1.389	0.324–5.945	0.658	2.045	0.351–11.920	0.427

*Adjusted for age, sex, body mass index, hypertension, hyperlipidemia, diabetes, smoking, and history of cardiovascular disease. The increment for LA, WA, and TVA was 1 SD. The increment for stenosis was 10%.

CI, confidence interval; IPH, intraplaque hemorrhage; LA, lumen area; LRNC, lipid-rich necrotic core; Max WT, maximum wall thickness; NWI, normal wall index; OR, odds ratio; TVA, total vessel area; WA, wall area; WML, white matter lesion.

Fig. 2.

The receiver operating characteristic curves of plaque features of carotid and femoral arteries in discriminating severe white matter lesions.

Fig. 3.

An individual with severe WMLs and co-existing carotid and femoral artery atherosclerosis. (c) Vessel wall MR images of left femoral artery (curved reconstruction and cross-sectional reconstruction). (b) Vessel wall MR images of bilateral carotid arteries. Calcification (white arrows) and lipid-rich necrotic core (hollow arrows) can be seen in both femoral and carotid arteries. (c) Brain FLAIR images and severe WMLs can be found. FLAIR, fluid-attenuated inversion recovery; MR, magnetic resonance; WML, white matter lesion.

Discussion

The present study is one of the first to investigate the incremental value of femoral artery atherosclerosis for carotid atherosclerotic disease in determining severity of WMLs using 3D multicontrast vessel wall MRI. We found that both carotid and femoral plaque features were associated with severity of WMLs and the combination of femoral and carotid artery plaque features had a stronger predictive value for severe WMLs compared to carotid plaque features alone. Our findings suggest that femoral artery atherosclerosis had added value for carotid artery atherosclerotic disease in determining severity of WMLs.

We found that plaque burden measurements in the carotid and femoral arteries were associated with severity of WMLs. Our findings are in line with literature reports. The Cardiovascular Health Study showed that white matter score was strongly related to carotid intimal-medial thickness measured by ultrasound (4). In a longitudinal follow-up study (16), carotid plaque burden characterized by total plaque volume was found to be capable of predicting severity of WMLs. Our results further compel the evidence that carotid plaque burden is an effective indicator for WMLs. In the present study, the association between plaque burden and WMLs was also observed in the femoral artery. Similar findings have been documented in previous studies on peripheral artery disease (PAD) (5,17). In the present study, the measurements of plaque burden for both the carotid and femoral arterial walls were derived from the 3D vessel wall MR images with large longitudinal coverage which may be more objective and representative than the results of previous studies. It is evidenced that 3D vessel wall MRI with large coverage can detect 14.7% more carotid plaques in more distal and proximal arterial segments which might not be captured by traditional 2D vessel wall imaging protocol (11).

Besides plaque burden, we found the compositional features of carotid and femoral atherosclerosis were effective indicators for severity of WMLs. Atherosclerotic calcification can occur in multiple vascular beds which may represent as a systemic burden of atherosclerotic disease. Previous studies have shown that WMLs were associated with calcifications in the carotid artery, intracranial artery, coronary artery, and aorta. A Rotterdam Study (18) demonstrated that calcification in carotid plaques determined by MRI was related to WMLs. Chung et al. (19) found that calcification in intracranial arteries evaluated by CT angiography was an indicator for WMLs. Such relationships were also reported in the coronary artery and aortic arch (20,21). The potential mechanism on the association between WMLs and atherosclerotic calcification might be that these two disorders may share the similar risk factors, such as hypertension and aging (22,23).

The present study provided the first evidence for the value of LRNC in both carotid and femoral arteries in stratifying the severity of WMLs. Interestingly, we found that severity of WMLs was associated with presence of LRNC in the femoral arteries but not in the carotid arteries. There is evidence that no clear links were found between LRNC of atherosclerosis in the carotid arteries and WMLs (16,18). It has been shown that the major risk factor for arterial atherosclerotic LRNC is the level of serum lipoprotein (24) and lipid-lowering treatment can significantly deplete the lipid component within atherosclerotic plaque (25). A study (26) showed that a higher serum triglyceride level was associated with larger WML volume (β = 0.0936; P = 0.0002) (26). Abraham et al. (3) thought that cholesterol may play an important role in the development of the central nervous system and in the creation and maintenance of new synapses, which may improve response to WMLs. The level of serum lipoprotein or hyperlipidemia may bridge the links between plaque lipid-rich components in femoral arteries and WMLs. The relationship between LRNC and WMLs needs further investigation.

We found that the combination of plaque burden and compositional characteristics in the femoral and carotid arteries had a stronger predictive value for severe WML compared with carotid artery alone. Little evidence on the relationship between multiple vascular atherosclerotic diseases and WMLs has been reported. As a systemic disease, atherosclerosis that involves multiple vascular beds may represent a higher systemic disease burden than a single vascular bed involvement. Previous studies have shown that atherosclerotic diseases in multiple vascular beds had a higher risk of developing ischemic cerebrovascular events compared to single vascular disease alone (6). In the SMART-MR Study (27), after accounting for presence or development of brain infarcts, atherosclerotic diseases at different locations (coronary artery disease, cerebrovascular disease, PAD) do not affect progression of WMLs differently over the four-year follow-up. Our findings suggest that multiple vascular atherosclerotic diseases may have a stronger predictive value for WMLs than single vascular atherosclerosis. It is warranted to pay attention to the detection of WMLs for patients with atherosclerosis in multiple vascular beds, which is helpful for early detection and treatment.

In the present study, atherosclerotic plaques in both the carotid arteries and femoral arteries were measured by vessel wall MRI. We also found that the combination of plaque features in both the carotid and femoral arteries had a stronger predictive value for the severity of WML compared to those features in a single vascular bed. However, vessel wall MRI is not an ideal screening tool due to its high cost and less availability. In clinical settings, ultrasound imaging is a widely used screening tool for both carotid and femoral artery plaques. A recent comparison study between ultrasound and vessel wall MRI showed that moderate to strong correlations can be found in maximum wall thickness, plaque area, and plaque length between 3D vessel wall MRI and ultrasound imaging (28). Although ultrasound imaging has limitations in characterizing the vulnerability of atherosclerotic plaques, particularly plaque compositional feature of intraplaque hemorrhage, this imaging modality can well detect lipid-rich plaques according to the presence of echolucent components (29). As such, most of the carotid and femoral plaque features can be also measured by ultrasound, such as luminal stenosis, wall thickness, and lipid-rich plaque. Utilizing ultrasound imaging for screening for multiple vascular plaques is a cost-effective way in clinical settings.

The present study has several limitations. First, this is a cross-sectional study lacking longitudinal data. It will be interesting to assess the relationship between the progression of atherosclerosis in carotid and femoral arteries and WMLs. Second, the present study only recruited asymptomatic individuals aged > 60 years. Since symptomatic patients may have different vascular and white matter diseases compared with asymptomatic patients, it is warranted to include symptomatic individuals with a broad age range in future studies. Third, the present study focused on the association of WMLs with atherosclerosis in the carotid and femoral arteries. Our study lacks the assessment of intracranial artery atherosclerosis, which might be more related to the brain lesions, due to the unavailability of intracranial vessel wall MRI. This will be investigated in future studies. Whether information on atherosclerosis in more vascular beds will enhance its strength in discriminating severity of WMLs needs further investigation.

In conclusion, the characteristics of femoral artery atherosclerosis determined by vessel wall MRI have added value for carotid atherosclerosis in determining the severity of WMLs.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received the following financial support for the research, authorship, and/or publication of this article: The study is funded by grants from the National Natural Science Foundation of China (81771825), Beijing Municipal Science and Technology Commission (D171100003017003), Ministry of Science and Technology of China (2017YFC1307904), and Philips Healthcare.

ORCID iD

Xihai Zhao

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