Associations between carotid atherosclerotic plaque characteristics determined by magnetic resonance imaging and improvement of cognition in patients undergoing carotid endarterectomy
Introduction
Stroke is one of the leading causes of death worldwide (1,2). Ischemic stroke accounts for 80% of strokes, of which 25–30% are attributed to carotid atherosclerotic stenosis (AS) (3). Carotid stenosis or occlusion is significantly associated with decreased cerebral blood flow (CBF). Previous studies have shown that a reduction of CBF by 40–50% causes ischemic damage, including degeneration of neurons, increase in cerebral oxygen uptake fraction, and damage to the cerebral vascular reserve (4-6). Persistent chronic cerebral ischemia caused by carotid AS stenosis will eventually lead to vascular-related cognitive impairment (7-9).
Vascular-related cognitive impairments exist as a dynamic continuum involving 3 stages: brain-at-risk (intact cognitive function), cognitive impairment-without dementia, and vascular dementia (10). Therefore, early intervention for vascular-related cognitive impairment is critical. Timely and effective treatment of carotid AS might delay or even curb the progression of cognitive impairment to a certain extent. Carotid endarterectomy (CEA) has been shown to be an effective treatment for plaque removal and recanalization (9,11). Previous studies have demonstrated the associations between plaque characteristics, such as morphological and compositional features, and cognitive function in patients with carotid AS stenosis (12-14). A clinical study including 99 patients with carotid AS stenosis showed that carotid intraplaque hemorrhage (IPH) increased the risk of cerebral infarction and cognitive impairment (15). Another study reported that carotid calcification was negatively correlated with cognitive function score, which might be an effective predictor for early cognitive impairment (13,16). However, evidence is scarce regarding the association between carotid plaque characteristics and the improvement of cognitive function after CEA. A better understanding of the relationship between carotid plaque characteristics and cognitive improvement after CEA will facilitate precise prediction of the cognition-related prognosis in patients referred to CEA, and thus refine treatment strategy formulation.
The high resolution vessel wall magnetic resonance imaging (MRI) technique has been largely used to accurately evaluate the morphological and compositional characteristics of carotid AS plaques (17). The aim of this study was to determine the predictive value of carotid plaque characteristics for the improvement of cognition in patients with moderate-to-severe carotid stenosis after CEA, using vessel wall MRI.
We present the following article in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-21-981/rc).
Methods
Study sample
Patients with unilateral symptomatic or asymptomatic moderate-to-severe carotid stenosis (50–99%) determined by computed tomography angiography (CTA) who were >40 years old and referred to CEA at the Department of Neurosurgery of the Peking University Third Hospital were prospectively enrolled from May 2019 to December 2020. All participants underwent carotid artery vessel wall MRI within 1 week before CEA. The assessment of cognition was performed within 1 week before and 3–4 days after CEA.
The exclusion criteria were as follows: (I) large artery atherosclerotic stroke and cardio-embolic stroke; (II) hemorrhagic stroke; (III) cerebral neoplasms; (IV) history of vascular intervention treatment, including CEA, carotid stenting, clips or coils of aneurysms; (V) intracranial vasculature (intracranial segment of internal carotid artery or middle cerebral artery) stenosis ≥50% on CTA; (VI) any contraindications to MRI examination, such as claustrophobia; (VII) previous psychosis; (VIII) dementia or difficulties in complying with cognitive assessment, such as severe hearing or language impairment; and (IX) refusal to sign informed consent. Clinical information, including age, gender, education attainment, height, weight, history of hypertension, hyperlipidemia, diabetes, smoking, drinking, stroke, transient ischemic attack, and coronary heart disease and medication was collected. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study protocol was approved by the Medical Ethics Committee of Peking University Third Hospital. All patients provided written informed consent before participating in this study.
Surgical procedures
All participants underwent CEA under general anesthesia conducted by the same senior neurosurgeon (T Wang) with 30 years’ experience in standardized surgical procedures. Manipulation of the vessels was performed under microscope. The vital signs and neurological function of all participants were closely monitored, and blood pressure and heart rate were strictly controlled.
MRI protocol
All participants underwent carotid vessel wall MRI on a 3.0-T MR scanner (uMR780, United Imaging Healthcare, Shanghai, China) with an 8-channel dedicated carotid coil. The imaging protocol and parameters were as follows: 3D time-of-flight (TOF) image using gradient echo (GRE) with repeat time (TR)/echo time (TE) of 17.6/6.7 ms, flip angle of 8°, and slice thickness of 2 mm; 2D T1-weighted (T1W) image using MATRIX (Modulated flip Angle Technique in Refocused Imaging with eXtended echo train) and fast spin echo (FSE) with TR/TE of 850/13.44 ms and slice thickness of 2 mm; 2D T2-weighted image using MATRIX and FSE with TR/TE of 2,000/96.6 ms and slice thickness of 2 mm; and simultaneous non-contrast angiography intraplaque hemorrhage (SNAP) imaging using GRE with TR/TE of 9.6/4.0 ms, flip angle of 12°, and slice thickness of 1 mm. All of the above imaging sequences were acquired with an identical field of view (140×140 mm2), in-plane spatial resolution (0.55×0.55 mm2), and longitudinal coverage (32 mm). The carotid vessel wall MRI was centered on the index-side of the plaque, which was defined as the carotid artery with moderate-to-severe stenosis referred to CEA.
All participants underwent brain MRI on a 3.0-T MR scanner (Discovery 750, General Electric, Milwaukee, WI, USA) with an 8-channel dedicated brain coil within the 48 hours before the CEA. Table S1 provides the imaging protocol and parameters.
Assessment of cognition
All participants underwent neuropsychological testing with the Beijing version of the Montreal Cognitive Assessment (MoCA) for evaluating cognitive function, which was applied by the same trained observer (R Huo) with more than 2 years’ experience who was blinded to the patients’ clinical information and findings of carotid MRI. Educational level was assessed according to the maximum educational time and categorized into 2 levels: a lower educational level comprising ≤12 years and a higher educational level comprising >12 years. The final total MoCA score of patients with the lower educational level was corrected to the actual score plus 1 point. The improvement of cognition was defined as a >10% increase in total MoCA score after CEA compared to that at baseline (18). A follow-up assessment of cognition was performed at 3–4 days after CEA.
MR data analysis
All the carotid vessel wall MR images were independently interpreted by another 2 trained radiologists (Y Liu and H Xu) with more than 5 years’ experience in cerebrovascular imaging, using a commercialized software (Vessel Explorer 2, TSimaging Healthcare, Beijing, China). In the case of inconsistency in the interpretation of results between the 2 observers, another senior radiologist (X Zhao) with more than 10 years’ experience in neurovascular imaging was invited to arbitrate. All observers were blinded to the participants’ clinical information and results of cognitive assessment. The image quality (IQ) was assessed using a 4-point scale (19): 1 = poor, low signal-to-noise ratio (SNR) with unidentifiable arterial wall and vessel margins; 2 = marginal, marginal SNR with identifiable wall structure but more than half of the lumen and outer boundaries indistinct; 3 = good, high SNR with minimal artifacts, well-defined vessel wall, lumen and adventitial boundary; and 4 = excellent, high SNR without artifacts, clear wall architecture, lumen and adventitial boundary. Only images with an IQ ≥2 were included in further analysis. The lumen, wall, and plaque component boundaries at each axial MR image of carotid artery on the index side were outlined manually. The plaque burden, including the mean values of wall area (WA), lumen area (LA), total vessel area (TVA), normalized wall index (NWI = WA/TVA ×100%) and maximal wall thickness (Max WT), was measured. The presence of plaque components, including calcification, lipid-rich necrotic core (LRNC), IPH, ulcer, and fibrous cap rupture (FCR), was evaluated using published criteria (17,20,21). Briefly, calcification within the plaque was defined as low signal intensity (SI) on all multi-contrast carotid vessel wall images. The LRNC was identified as iso-SI on both T1W and TOF images and lower SI on T2W images within the plaque. The IPH was determined when there was a region appearing as high SI on T1W, TOF, and SNAP images within the plaque. Ulceration showed a disrupted luminal surface on T1W images and the formation of a niche on TOF images. A FCR had 2 manifestations: ulcer and minor FCR which is defined when the hyperintensity of an IPH extended into the lumen and the hypointensity of a band between the high signal of the lumen and juxta-luminal hemorrhage was absent on TOF images. The volume and cumulative slice of each plaque component, and maximum percentage of each plaque component area were also measured.
The white matter hyperintensity characteristics of all the brain MR images were independently identified by another 2 trained radiologists (J Li and R Xin) with more than 3 years’ experience in brain imaging. The total Fazekas scale was evaluated using published criteria (22,23).
Statistical analysis
Continuous variables with normal distribution were presented as mean and standard deviation, whereas those with abnormal distribution were expressed as median and interquartile range (IQR). Categorical variables were described as count and percentage. Clinical variables and plaque characteristics were compared between patients with and without improvement of cognition using the independent t-test, Mann-Whitney U test, or chi-square test. The predictive value of baseline carotid plaque characteristics for the improvement of cognitive function after CEA was analyzed using a logistic regression model. During univariate and multivariate logistic regression analyses, the odds ratio (OR), risk difference (RD), adjusted risk difference (ARD), and corresponding 95% confidence interval (CI) of plaque characteristics were calculated in predicting the improvement of cognition. In multivariate logistic regression model 1, we adjusted for age (24-26), gender (26,27), educational level (24), and Fazekas score (28). In multivariate logistic regression model 2, we adjusted for age, gender, educational level, Fazekas score, and clinical variables with P<0.100 in the comparison analysis (Table 1), including luminal stenosis, coronary heart disease, and antihypertensive treatment. These clinical variables had been demonstrated to be associated with cognitive function in previous studies (24,29,30). A P value <0.05 (two-tailed) was considered statistically significant. All statistical analyses were performed using the software SPSS 26.0 (SPSS, Inc., IBM Corp., Chicago, IL, USA) and Stata SE 15.0. (StataCorp, College Station, TX, USA).
Table 1
Characteristics | Mean ± SD or n (%) | P value | ||
---|---|---|---|---|
All patients (n=105) | Cognitive unimprovement (n=46) | Cognitive improvement (n=59) | ||
Age, years | 65.5±8.4 | 66.5±8.8 | 64.6±8.1 | 0.347 |
Gender, male | 91 (86.7) | 43 (93.5) | 48 (81.4) | 0.070 |
BMI, kg/m2 | 24.9±2.7 | 25.0±2.7 | 24.9±2.8 | 0.804 |
Education level, >12 years | 35 (33.3) | 17 (37.0) | 18 (30.5) | 0.487 |
Hypertension | 76 (72.4) | 35 (76.1) | 41 (69.5) | 0.453 |
SBP, mmHg | 134.0±16.2 | 132.2±17.1 | 135.3±15.5 | 0.179 |
DBP, mmHg | 79.4±9.8 | 78.6±10.1 | 80.0±9.6 | 0.245 |
BPD, mmHg | 54.6±13.9 | 53.7±15.0 | 55.3±13.2 | 0.355 |
Hyperlipidemia | 59 (56.2) | 27 (58.7) | 32 (54.2) | 0.648 |
HDL, mmol/L | 1.0±0.2 | 1.04±0.25 | 0.99±0.24 | 0.292 |
LDL, mmol/L | 2.1±0.8 | 2.2±0.7 | 2.1±0.8 | 0.744 |
TC, mmol/L | 3.7±0.9 | 3.7±0.8 | 3.7±1.0 | 0.539 |
TG, mmol/L | 1.6±0.8 | 1.6±0.8 | 1.5±0.7 | 0.366 |
Diabetes | 42 (40.0) | 19 (41.3) | 23 (39.0) | 0.810 |
Glu, mmol/L | 6.2±1.6 | 6.4±1.9 | 6.0±1.4 | 0.446 |
Smoke | 69 (65.7) | 30 (65.2) | 39 (66.1) | 0.925 |
Alcohol | 70 (66.7) | 32 (69.6) | 38 (64.4) | 0.578 |
Coronary heart disease | 19 (18.1) | 12 (26.1) | 7 (11.9) | 0.060 |
Stroke | 31 (29.5) | 13 (28.3) | 18 (30.5) | 0.802 |
TIA | 48 (45.7) | 20 (43.5) | 28 (47.5) | 0.685 |
Antiplatelet | 86 (81.9) | 38 (82.6) | 48 (81.4) | 0.869 |
Anticoagulation | 2 (1.9) | 1 (2.2) | 1 (1.7) | 0.687 |
Statin use | 86 (81.9) | 38 (82.6) | 48 (81.4) | 0.869 |
Hypoglycemic treatment | 34 (32.4) | 18 (39.1) | 16 (27.1) | 0.192 |
Antihypertensive treatment | 62 (59.0) | 32 (69.6) | 30 (50.8) | 0.053 |
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; BPD: blood pressure difference; HDL, high density lipoprotein; LDL, low density lipoprotein; TC, total cholesterol; TG, triglyceride; Glu, glucose; TIA, transient ischemic attack; SD, standard deviation.
Results
Clinical characteristics of the study population
A total of 126 patients were enrolled in this study from May 2019 to December 2020. Of the 126 patients, 21 were excluded from the analysis due to the following reasons (Figure 1): (I) loss to cognitive follow-up (n=9); (II) moderate or severe stenosis or occlusion of the intracranial artery (n=5); (III) poor carotid MR IQ due to motion (IQ <2; n=2); and (IV) history of vascular intervention treatment (n=5). Of the remaining 105 patients (65.5±8.4 years old), 91 (86.7%) were male, 92 (87.6%) had symptomatic stenosis, and 35 (33.3%) had a high educational level. Demographic and clinical characteristics of this study population are summarized in Table 1. There were no significant differences in age (65.5±8.4 vs. 64.7±8.3 years; P=0.529), male gender (86.7% vs. 90.5%; P=0.905), and educational level (>12 years, 33.3% vs. 26.8%; P=0.671) between the included and excluded patients (Table S2).
In the present study, cognitive improvement was observed in 56.2% (59/105) of participants {MoCA score: before CEA, 18 [15–21]; after CEA, 23 [20–26]}. A total of 46 (43.8%) participants did not have cognitive improvement {MoCA score: before CEA, 20 [17–23]; after CEA, 21 [18–24]}. Domain scores of the MoCA for each group in the study population are shown in Table S3.
Comparison of clinical and carotid plaque characteristics
The mean cognitive follow-up time was 3.4±0.5 days. Of the 105 participants, 59 (56.2%) had improved cognition after CEA. No significant differences were found in clinical characteristics between participants with and without improvement of cognition (all P>0.05).
The results for the comparison of carotid plaque characteristics between participants with and without improvement of cognition are shown in Table 2. Compared to participants with improvement of cognition, those without improvement of cognition showed significantly larger calcification volume [48.0 (IQR, 21.0 to 91.6) vs. 16.3 (IQR, 8.1 to 53.1) mm3; P=0.005], larger maximum percentage of calcification area [13.1% (IQR, 6.0% to 19.8%) vs. 6.2% (IQR, 3.7% to 10.8%); P=0.004], and more cumulative slices of calcification [4.0 (3.0 to 7.0) vs. 3.0 (2.0 to 5.0); P=0.019]. Other carotid plaque components and morphological measurements did not significantly differ between the 2 groups (all P>0.05).
Table 2
Characteristics | All patients (n=105) | Unimprovement (n=46) | Improvement (n=59) | P value |
---|---|---|---|---|
Morphology, median (IQR) | ||||
Mean lumen area, mm2 | 26.1 (21.6, 32.0) | 26.7 (21.7, 32.3) | 26.0 (21.5, 31.6) | 0.675 |
Mean wall area, mm2 | 40.7 (32.2, 52.0) | 41.5 (32.8, 54.2) | 40.4 (30.7, 47.1) | 0.568 |
Mean total vessel area, mm2 | 67.4 (57.8, 79.6) | 68.0 (57.8, 85.1) | 66.7 (57.2, 77.6) | 0.633 |
Maximum wall thickness, mm | 6.0 (4.9, 7.4) | 6.1 (4.9, 7.2) | 5.9 (4.8, 7.4) | 0.689 |
Mean normalized wall index, % | 60.6 (54.6, 66.3) | 61.0 (56.5, 66.3) | 59.6 (51.9, 66.4) | 0.354 |
Stenosis, % | 75.5 (69.7, 83.5) | 74.2 (69.9, 81.4) | 75.7 (69.5, 85.5) | 0.342 |
Presence of plaque components, n (%) | ||||
LRNC | 96 (91.4) | 41 (89.1) | 55 (93.2) | 0.695 |
IPH | 56 (53.3) | 25 (54.3) | 31 (52.5) | 0.854 |
Calcification | 87 (82.9) | 38 (82.6) | 49 (83.1) | 0.952 |
Ulcer | 29 (27.6) | 14 (30.4) | 15 (25.4) | 0.569 |
FCR | 59 (56.2) | 26 (56.5) | 33 (55.9) | 0.952 |
Volume of plaque components*, median (IQR) | ||||
LRNC, mm3 | 267.6 (95.8, 541.6) | 274.6 (104.9, 564.4) | 217.1 (87.8, 538.6) | 0.349 |
IPH, mm3 | 193.1 (45.0, 384.4) | 159.5 (22.3, 408.3) | 210.4 (82.5, 383.7) | 0.387 |
Calcification, mm3 | 28.1 (11.4, 75.8) | 48.0 (21.0, 91.6) | 16.3 (8.1, 53.1) | 0.005 |
Ulcer, mm3 | 21.0 (9.6, 55.2) | 26.8 (7.2, 64.7) | 20.6 (14.7, 44.3) | 0.747 |
Maximum percentage of plaque component area*, median (IQR) | ||||
LRNC, % | 62.2 (40.6, 73.8) | 60.6 (36.6, 75.4) | 62.5 (41.4, 73.3) | 0.731 |
IPH, % | 47.2 (19.0, 61.8) | 47.0 (14.6, 60.1) | 50.4 (20.6, 61.9) | 0.644 |
Calcification, % | 8.0 (4.3, 15.8) | 13.1 (6.0, 19.8) | 6.2 (3.7, 10.8) | 0.004 |
Ulcer, % | 9.8 (4.1, 18.7) | 13.1 (4.1, 19.9) | 8.9 (3.9, 17.9) | 0.683 |
Cumulative slice of plaque components*, median (IQR) | ||||
LRNC | 8.0 (5.0, 10.0) | 8.0 (5.50, 9.50) | 8.0 (4.00, 10.00) | 0.958 |
IPH | 6.0 (4.0, 8.8) | 6.0 (3.0, 8.5) | 6.0 (4.0, 9.0) | 0.829 |
Calcification | 4.0 (2.0, 6.0) | 4. 0 (3.0, 7.0) | 3.0 (2.0, 5.0) | 0.019 |
Ulcer | 3.0 (2.0, 3.0) | 2.50 (1.0, 3.0) | 3.0 (2.0, 3.0) | 0.451 |
*, only patients with the corresponding component present were included in the comparison. LRNC, lipid-rich necrotic core; IQR, interquartile range; IPH, intraplaque hemorrhage; FCR, fibrous cap rupture.
Carotid plaque characteristics predict the improvement of cognition
Table 3 shows the results of univariate and multivariate regression analyses. In the univariate logistic regression analysis, significant associations between improvement of cognition and calcification volume (OR =0.994; 95% CI: 0.989 to 1.000; P=0.043), maximum percentage of calcification area (OR =0.949; 95% CI: 0.909 to 0.991; P=0.018), and cumulative slices of calcification (OR =0.823; 95% CI: 0.698 to 0.970; P=0.020) were detected (Figure 2). After adjusting for confounding factors of age, gender, educational level and Fazekas score, the associations remained statistically significant (calcification volume: OR =0.994; 95% CI: 0.988 to 1.000; P=0.043; maximum percentage of calcification area: OR =0.945; 95% CI: 0.901 to 0.990; P=0.017; and cumulative slice of calcification: OR =0.802; 95% CI: 0.670 to 0.961; P=0.017). After further adjusting for the above confounding factors and stenosis, history of coronary heart disease, and anti-hypertension treatment, the associations of cognitive function with maximum percentage of the calcification area (OR =0.937; 95% CI: 0.890 to 0.987; P=0.014) and cumulative slice (OR =0.791; 95% CI: 0.646 to 0.967; P=0.022) of calcification remained statistically significant, but the volume of calcification was no longer significantly associated with cognitive function (OR =0.994; 95% CI: 0.988 to 1.000; P=0.057). No significant associations were found between other carotid plaque characteristics and improvement of cognition (all P>0.05).
Table 3
Characteristics | Univariate regression | Multivariate regression (model 1) | Multivariate regression (model 2) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
OR | 95% CI | P value | OR | 95% CI | P value | OR | 95% CI | P value | |||
Morphology | |||||||||||
Mean lumen area, mm2 | 1.006 | 0.970, 1.043 | 0.762 | 1.005 | 0.967, 1.044 | 0.792 | 1.008 | 0.967, 1.050 | 0.706 | ||
Mean wall area, mm2 | 0.999 | 0.977, 1.022 | 0.935 | 1.007 | 0.982, 1.032 | 0.591 | 1.005 | 0.980, 1.031 | 0.684 | ||
Mean total vessel area, mm2 | 1.001 | 0.984, 1.018 | 0.935 | 1.005 | 0.987, 1.024 | 0.595 | 1.005 | 0.986, 1.024 | 0.629 | ||
Maximum wall thickness, mm | 0.954 | 0.777, 1.170 | 0.649 | 0.995 | 0.803, 1.233 | 0.962 | 0.996 | 0.792, 1.253 | 0.974 | ||
Mean normalized wall index, % | 0.988 | 0.949, 1.030 | 0.575 | 1.005 | 0.961, 1.052 | 0.814 | 0.993 | 0.945, 1.043 | 0.775 | ||
Stenosis, % | 1.015 | 0.982, 1.050 | 0.376 | 1.020 | 0.984,1.058 | 0.271 | – | – | – | ||
Presence of plaque components | |||||||||||
LRNC | 1.677 | 0.424, 6.636 | 0.461 | 2.800 | 0.619, 12.665 | 0.181 | 3.674 | 0.738, 18.288 | 0.112 | ||
IPH | 0.930 | 0.429, 2.015 | 0.854 | 1.151 | 0.510, 2.598 | 0.735 | 1.363 | 0.568, 3.275 | 0.488 | ||
Calcification | 1.032 | 0.371, 2.866 | 0.952 | 1.348 | 0.450, 4.033 | 0.593 | 1.263 | 0.387, 4.125 | 0.699 | ||
Ulcer | 0.799 | 0.330, 1.840 | 0.569 | 0.891 | 0.363, 2.183 | 0.800 | 1.160 | 0.438, 3.076 | 0.765 | ||
FCR | 0.976 | 0.449, 2.124 | 0.952 | 1.210 | 0.533, 2.746 | 0.648 | 1.553 | 0.638, 3.780 | 0.332 | ||
Volume of plaque components* | |||||||||||
LRNC, mm3 | 0.999 | 0.998, 1.001 | 0.344 | 0.999 | 0.998, 1.001 | 0.462 | 1.000 | 0.998, 1.001 | 0.802 | ||
IPH, mm3 | 1.001 | 0.999, 1.003 | 0.473 | 1.001 | 0.999, 1.003 | 0.435 | 1.002 | 0.999, 1.004 | 0.214 | ||
Calcification, mm3 | 0.994 | 0.989, 1.000 | 0.043 | 0.994 | 0.988, 1.000 | 0.043 | 0.994 | 0.988, 1.000 | 0.057 | ||
Ulcer, mm3 | 0.993 | 0.971, 1.015 | 0.528 | 0.995 | 0.971, 1.020 | 0.688 | 0.996 | 0.969, 1.023 | 0.748 | ||
Maximum percentage of plaque component area* | |||||||||||
LRNC, % | 1.005 | 0.985, 1.025 | 0.643 | 1.007 | 0.986, 1.028 | 0.532 | 1.006 | 0.982, 1.030 | 0.645 | ||
IPH, % | 1.007 | 0.983, 1.031 | 0.576 | 1.008 | 0.982, 1.033 | 0.565 | 1.014 | 0.985, 1.044 | 0.353 | ||
Calcification, % | 0.949 | 0.909, 0.991 | 0.018 | 0.945 | 0.901, 0.990 | 0.017 | 0.937 | 0.890, 0.987 | 0.014 | ||
Ulcer, % | 0.965 | 0.892, 1.044 | 0.377 | 0.966 | 0.879, 1.061 | 0.467 | 0.972 | 0.882, 1.071 | 0.561 | ||
Cumulative slice of plaque components* | |||||||||||
LRNC | 1.010 | 0.905, 1.127 | 0.858 | 1.038 | 0.925, 1.164 | 0.528 | 1.000 | 0.881, 1.135 | 1.000 | ||
IPH | 1.053 | 0.901, 1.230 | 0.515 | 1.067 | 0.898, 1.268 | 0.459 | 1.119 | 0.909, 1.376 | 0.288 | ||
Calcification | 0.823 | 0.698, 0.970 | 0.020 | 0.802 | 0.670, 0.961 | 0.017 | 0.791 | 0.646, 0.967 | 0.022 | ||
Ulcer | 1.440 | 0.691, 3.000 | 0.330 | 1.678 | 0.741, 3.798 | 0.215 | 1.947 | 0.763, 4.968 | 0.163 |
*, only patients with the corresponding component present were included in the comparison. Model 1: adjusted for age, gender, educational level and Fazekas score; Model 2: adjusted for age, gender, educational level, Fazekas score, stenosis, coronary heart disease and antihypertensive treatment. MoCA, Montreal Cognitive Assessment; LRNC, lipid-rich necrotic core; IPH, intraplaque hemorrhage; FCR, fibrous cap rupture; CI, confidence interval; OR, odds ratio.
The values for absolute risk of the measurements of carotid morphology and plaque characteristics on MRI in predicting improvement of cognitive function are shown in Table S4.
Discussion
This study investigated the relationships between carotid plaque characteristics determined by vessel wall MRI and the improvement of cognition in patients with moderate-to-severe unilateral carotid stenosis after CEA. We found that patients who experienced improvement of cognitive function after CEA had a smaller size of carotid calcification. Our findings suggest that for patients with a smaller size of calcification in a carotid atherosclerotic plaque, CEA may provide a benefit for their cognitive function.
This study demonstrated that the size of carotid calcification was negatively associated with improvement of cognitive function in patients with moderate-to-severe unilateral carotid stenosis after CEA. This finding suggests that patients with calcified plaque in a carotid artery benefit less from CEA with respect to restoration of cognitive function. Calcium deposition occurs in the advanced stage of AS (31). A larger size of calcification indicates the long-term progression of a carotid plaque, in which various compensatory mechanisms, including the redistribution of CBF via the circle of Willis and collateral vessels, gradually mature. Improvement of cognitive function might be bolstered by the recovery of CBF at the unilateral side of moderate-to-severe carotid stenosis after CEA (18,32). However, the compensatory process of CBF is initiated when chronic carotid stenosis occurs. In cases with chronic carotid stenosis and sufficient blood flow compensation, cognitive function can be sustained (33). In addition, neurodegeneration during cerebral gliosis mediated by oxidative stress in patients with long-term carotid stenosis may partially offset the improvement of cognitive function after CEA. An animal study demonstrated an increase in the superoxide anion and the activation of microglia and astrocytes in the hippocampus in mice with carotid AS (34). The activation of microglia and astrogliosis are considered key mechanisms contributing to neurodegeneration and leading to cognitive impairment (35,36). Coexisting cerebral lacunar infarction and white matter lesions in carotid stenosis patients with calcification might partially account for lesser improvement of cognitive function after removing the plaques by CEA. Previous studies have shown that the incidence of small vessel disease in patients with carotid AS stenosis significantly increases, and is one of the important risk factors of dementia (37,38). A community-dwelling population study showed that calcification in intracranial and extracranial arteries is associated with the increase of white matter lesion volume (39). Finally, arterial calcification has been shown to increase artery wall shear stress, which is a risk factor for cognitive decline and dementia (14,40). Briefly, cerebral damage that has already occurred due to the long-term presence of plaques with more calcium may account for reduced safeguard in the future. However, the safeguard in patients with softer plaques without calcium may be effective.
In the present study, the compositional features of IPH, LRNC, and ulcer were not found to be associated with changes of cognitive function. Histologically, the IPH, large LRNC, and ulcer of a carotid plaque are key risk features of a vulnerable plaque (41). An autopsy study suggested that presence of IPH is a potent atherogenic stimulus via promotion of plaque progression, including cholesterol deposition, macrophage infiltration, and the expansion of the necrotic core (42). Takaya et al. (43) found that repeated intraplaque bleeding accelerated lipid core volume in a longitudinal clinical study conducted over an 18-month period. In addition, Cui et al. (21) reported that the size of fresh IPH was independently associated with minor fibrous cap disruption in patients with carotid plaque. Minor disruption of vulnerable plaques with IPH, large LRNC, or ulcer may occur asymptomatically but will contribute to obstruction of the micro-circulation by episodic micro-emboli. Previous studies have shown that cerebral perfusion is correlated with cognitive function in patients with carotid plaques (18,33). As such, we speculate that cognitive impairment may not be subsequently improved after removing the plaque due to the existing obstruction of the micro-circulation.
We also found that there was no significant association between carotid AS stenosis and cognitive improvement after CEA. The present results might be attributed to the compensatory mechanisms from contralateral vessels accompanied by progression of a carotid plaque with a certain degree of stenosis. Collateral circulation has been understood to play a key role in maintaining CBF in patients with severe extracranial carotid artery stenosis (3,44,45). In carotid AS stenotic patients, decreased CBF on the ipsilateral side is compensated via the circle of Willis (46-48). Our findings suggest that the improvement of cognitive function in patients with carotid AS stenosis after CEA might be independent of the degree of stenosis.
This study had several limitations. First, patients with a carotid occlusion were not included in the present study. Carotid occlusion is one of the main factors related to vascular-related cognitive impairment due to reduction of CBF, damaged cerebral vascular reserve, as well as carotid stenosis. Second, only short-term cognitive function was analyzed. A previous study reported that there was a fluctuation in cognitive function recovery between short-term (3 days after CEA) and long-term (3 months) periods (49). Therefore, long-term evaluation of postoperative cognitive function after CEA is warranted in future studies. Third, in the present study, only 32 mm of extracranial carotid artery centered to the bifurcation was analyzed. Lesions in more proximal or distal segments of the extracranial carotid artery were not assessed. This may have led to underestimation of the calcification volume in some cases. Fourth, the lesion of the intracranial segment of the internal carotid artery and middle cerebral artery was not evaluated due to the unavailability of the corresponding vessel wall imaging data. Intracranial artery disease with lower grade stenosis may play a role in the recovery of cognitive function after CEA. Fifth, we did not adjust for the information of lacunar and cortical infarcts and cerebral microbleeds in the multivariate regression analysis, which might be associated with cognitive function.
Conclusions
More than half of the patients with moderate-to-severe unilateral carotid AS exhibited cognitive improvement. Calcification size might be an effective indicator for cognitive improvement after CEA.
Acknowledgments
The authors extend thanks to Dr. Xiaoli Liu from Beijing Chao-Yang Hospital for the assistance with statistical analysis.
Funding: This work was supported by National Natural Science Foundation of China (82071308), Beijing Natural Science Foundation (7192219), National Natural Science Foundation of China (81771825), and the National Key R&D Program of China (2017YFC1307900, 2017YFC1307904).
Footnote
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-21-981/rc
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-21-981/coif). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study protocol was supported by the Medical Ethics Committee of Peking University Third Hospital, and all participants provided written informed consent.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
References
- Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019;394:1145-58. [Crossref] [PubMed]
- Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2095-128. [Crossref] [PubMed]
- Hartkamp NS, Petersen ET, Chappell MA, Okell TW, Uyttenboogaart M, Zeebregts CJ, Bokkers RP. Relationship between haemodynamic impairment and collateral blood flow in carotid artery disease. J Cereb Blood Flow Metab 2018;38:2021-32. [Crossref] [PubMed]
- Haratz S, Weinstein G, Molshazki N, Beeri MS, Ravona-Springer R, Marzeliak O, Goldbourt U, Tanne D. Impaired Cerebral Hemodynamics and Cognitive Performance in Patients with Atherothrombotic Disease. J Alzheimers Dis 2015;46:137-44. [Crossref] [PubMed]
- Cechetti F, Pagnussat AS, Worm PV, Elsner VR, Ben J, da Costa MS, Mestriner R, Weis SN, Netto CA. Chronic brain hypoperfusion causes early glial activation and neuronal death, and subsequent long-term memory impairment. Brain Res Bull 2012;87:109-16. [Crossref] [PubMed]
- Kim JH, Ko PW, Lee HW, Jeong JY, Lee MG, Kim JH, Lee WH, Yu R, Oh WJ, Suk K. Astrocyte-derived lipocalin-2 mediates hippocampal damage and cognitive deficits in experimental models of vascular dementia. Glia 2017;65:1471-90. [Crossref] [PubMed]
- Wendell CR, Waldstein SR, Evans MK, Zonderman AB. Subclinical carotid atherosclerosis and neurocognitive function in an urban population. Atherosclerosis 2016;249:125-31. [Crossref] [PubMed]
- Gottesman RF, Albert MS, Alonso A, Coker LH, Coresh J, Davis SM, Deal JA, McKhann GM, Mosley TH, Sharrett AR, Schneider ALC, Windham BG, Wruck LM, Knopman DS. Associations Between Midlife Vascular Risk Factors and 25-Year Incident Dementia in the Atherosclerosis Risk in Communities (ARIC) Cohort. JAMA Neurol 2017;74:1246-54. [Crossref] [PubMed]
- Halliday A, Mansfield A, Marro J, Peto C, Peto R, Potter J, Thomas DMRC Asymptomatic Carotid Surgery Trial (ACST) Collaborative Group. Prevention of disabling and fatal strokes by successful carotid endarterectomy in patients without recent neurological symptoms: randomised controlled trial. Lancet 2004;363:1491-502. [Crossref] [PubMed]
- Garrett KD, Browndyke JN, Whelihan W, Paul RH, DiCarlo M, Moser DJ, Cohen RA, Ott BR. The neuropsychological profile of vascular cognitive impairment--no dementia: comparisons to patients at risk for cerebrovascular disease and vascular dementia. Arch Clin Neuropsychol 2004;19:745-57. [Crossref] [PubMed]
- Luebke T, Aleksic M, Brunkwall J. Meta-analysis of randomized trials comparing carotid endarterectomy and endovascular treatment. Eur J Vasc Endovasc Surg 2007;34:470-9. [Crossref] [PubMed]
- Gardener H, Caunca MR, Dong C, Cheung YK, Elkind MSV, Sacco RL, Rundek T, Wright CB. Ultrasound Markers of Carotid Atherosclerosis and Cognition: The Northern Manhattan Study. Stroke 2017;48:1855-61. [Crossref] [PubMed]
- Di Daniele N, Celotto R, Alunni Fegatelli D, Gabriele M, Rovella V, Scuteri A. Common Carotid Artery Calcification Impacts on Cognitive Function in Older Patients. High Blood Press Cardiovasc Prev 2019;26:127-34. [Crossref] [PubMed]
- Pase MP, Herbert A, Grima NA, Pipingas A, O'Rourke MF. Arterial stiffness as a cause of cognitive decline and dementia: a systematic review and meta-analysis. Intern Med J 2012;42:808-15. [Crossref] [PubMed]
- Takasugi J, Miwa K, Watanabe Y, Okazaki S, Todo K, Sasaki T, Sakaguchi M, Mochizuki H. Cortical Cerebral Microinfarcts on 3T Magnetic Resonance Imaging in Patients With Carotid Artery Stenosis. Stroke 2019;50:639-44. [Crossref] [PubMed]
- Chu Z, Cheng L, Tong Q. Carotid artery calcification score and its association with cognitive impairment. Clin Interv Aging 2019;14:167-77. [Crossref] [PubMed]
- Saam T, Ferguson MS, Yarnykh VL, Takaya N, Xu D, Polissar NL, Hatsukami TS, Yuan C. Quantitative evaluation of carotid plaque composition by in vivo MRI. Arterioscler Thromb Vasc Biol 2005;25:234-9. [Crossref] [PubMed]
- Ghogawala Z, Amin-Hanjani S, Curran J, Ciarleglio M, Berenstein A, Stabile L, Westerveld M. The effect of carotid endarterectomy on cerebral blood flow and cognitive function. J Stroke Cerebrovasc Dis 2013;22:1029-37. [Crossref] [PubMed]
- Wei H, Zhang M, Li Y, Zhao X, Canton G, Sun J, Xu D, Zhou Z, Chen S, Ferguson MS, Hatsukami TS, Li R, Yuan C. Evaluation of 3D multi-contrast carotid vessel wall MRI: a comparative study. Quant Imaging Med Surg 2020;10:269-82. [Crossref] [PubMed]
- Zhao X, Hippe DS, Li R, Canton GM, Sui B, Song Y, Li F, Xue Y, Sun J, Yamada K, Hatsukami TS, Xu D, Wang M, Yuan CCARE‐II Study Collaborators. Prevalence and Characteristics of Carotid Artery High-Risk Atherosclerotic Plaques in Chinese Patients With Cerebrovascular Symptoms: A Chinese Atherosclerosis Risk Evaluation II Study. J Am Heart Assoc 2017;6:005831. [Crossref] [PubMed]
- Cui Y, Qiao H, Ma L, Lu M, Yang J, Yao G, Cai J, Zhao X. Association of Age and Size of Carotid Artery Intraplaque Hemorrhage and Minor Fibrous Cap Disruption: A High Resolution Magnetic Resonance Imaging Study. J Atheroscler Thromb 2018;25:1222-30. [Crossref] [PubMed]
- Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol 1987;149:351-6. [Crossref] [PubMed]
- Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822-38. [Crossref] [PubMed]
- Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA 2010;304:1787-94. [Crossref] [PubMed]
- Petersen RC. Clinical practice. Mild cognitive impairment. N Engl J Med 2011;364:2227-34. [Crossref] [PubMed]
- Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, Burke JR, Hurd MD, Potter GG, Rodgers WL, Steffens DC, McArdle JJ, Willis RJ, Wallace RB. Prevalence of cognitive impairment without dementia in the United States. Ann Intern Med 2008;148:427-34. [Crossref] [PubMed]
- Petersen RC, Roberts RO, Knopman DS, Geda YE, Cha RH, Pankratz VS, Boeve BF, Tangalos EG, Ivnik RJ, Rocca WA. Prevalence of mild cognitive impairment is higher in men. The Mayo Clinic Study of Aging. Neurology 2010;75:889-97. [Crossref] [PubMed]
- Tubi MA, Feingold FW, Kothapalli D, Hare ET, King KS, Thompson PM, Braskie MNAlzheimer’s Disease Neuroimaging Initiative. White matter hyperintensities and their relationship to cognition: Effects of segmentation algorithm. Neuroimage 2020;206:116327. [Crossref] [PubMed]
- Gray VL, Goldberg AP, Rogers MW, Anthony L, Terrin ML, Guralnik JM, Blackwelder WC, Lam DFH, Sikdar S, Lal BK. Asymptomatic carotid stenosis is associated with mobility and cognitive dysfunction and heightens falls in older adults. J Vasc Surg 2020;71:1930-7. [Crossref] [PubMed]
- Kuller LH, Lopez OL, Mackey RH, Rosano C, Edmundowicz D, Becker JT, Newman AB. Subclinical Cardiovascular Disease and Death, Dementia, and Coronary Heart Disease in Patients 80+ Years. J Am Coll Cardiol 2016;67:1013-22. [Crossref] [PubMed]
- Badimon L, Vilahur G. Thrombosis formation on atherosclerotic lesions and plaque rupture. J Intern Med 2014;276:618-32. [Crossref] [PubMed]
- Lan Y, Lyu J, Ma X, Ma L, Lou X. Longitudinal assessment of cerebral blood flow changes following carotid artery stenting and endarterectomy. Radiol Med 2019;124:636-42. [Crossref] [PubMed]
- Khan AA, Patel J, Desikan S, Chrencik M, Martinez-Delcid J, Caraballo B, Yokemick J, Gray VL, Sorkin JD, Cebral J, Sikdar S, Lal BK. Asymptomatic carotid artery stenosis is associated with cerebral hypoperfusion. J Vasc Surg 2021;73:1611-1621.e2. [Crossref] [PubMed]
- Sadekova N, Iulita MF, Vallerand D, Muhire G, Bourmoum M, Claing A, Girouard H. Arterial stiffness induced by carotid calcification leads to cerebral gliosis mediated by oxidative stress. J Hypertens 2018;36:286-98. [Crossref] [PubMed]
- Zhao X, Underhill HR, Yuan C, Oikawa M, Dong L, Ota H, Hatsukami TS, Wang Q, Ma L, Cai J. Minimization of MR contrast weightings for the comprehensive evaluation of carotid atherosclerotic disease. Invest Radiol 2010;45:36-41. [Crossref] [PubMed]
- Sofroniew MV, Vinters HV. Astrocytes: biology and pathology. Acta Neuropathol 2010;119:7-35. [Crossref] [PubMed]
- Smith CD, Snowdon DA, Wang H, Markesbery WR. White matter volumes and periventricular white matter hyperintensities in aging and dementia. Neurology 2000;54:838-42. [Crossref] [PubMed]
- Kalaria RN, Akinyemi R, Ihara M. Does vascular pathology contribute to Alzheimer changes? J Neurol Sci 2012;322:141-7. [Crossref] [PubMed]
- Bos D, Ikram MA, Elias-Smale SE, Krestin GP, Hofman A, Witteman JC, van der Lugt A, Vernooij MW. Calcification in major vessel beds relates to vascular brain disease. Arterioscler Thromb Vasc Biol 2011;31:2331-7. [Crossref] [PubMed]
- Zhao X, Miller ZE, Yuan C. Atherosclerotic plaque imaging by carotid MRI. Curr Cardiol Rep 2009;11:70-7. [Crossref] [PubMed]
- Chen XY, Fisher M. Pathological Characteristics. Front Neurol Neurosci 2016;40:21-33. [Crossref] [PubMed]
- Zhao XQ, Yuan C, Shah PK. Imaging to Assess the Effect of Anti-Inflammatory Therapy in Aortic and Carotid Atherosclerosis. J Am Coll Cardiol 2016;68:1781-4. [Crossref] [PubMed]
- Takaya N, Yuan C, Chu B, Saam T, Polissar NL, Jarvik GP, Isaac C, McDonough J, Natiello C, Small R, Ferguson MS, Hatsukami TS. Presence of intraplaque hemorrhage stimulates progression of carotid atherosclerotic plaques: a high-resolution magnetic resonance imaging study. Circulation 2005;111:2768-75. [Crossref] [PubMed]
- Wei W, Yi X, Ruan J, Duan X, Luo H, Lv Z. Influence of collateral circulation on cerebral blood flow and frontal lobe cognitive function in patients with severe internal carotid artery stenosis. BMC Neurol 2019;19:151. [Crossref] [PubMed]
- Zarrinkoob L, Wåhlin A, Ambarki K, Birgander R, Eklund A, Malm J. Blood Flow Lateralization and Collateral Compensatory Mechanisms in Patients With Carotid Artery Stenosis. Stroke 2019;50:1081-8. [Crossref] [PubMed]
- Van Laar PJ, Hendrikse J, Mali WP, Moll FL, van der Worp HB, van Osch MJ, van der Grond J. Altered flow territories after carotid stenting and carotid endarterectomy. J Vasc Surg 2007;45:1155-61. [Crossref] [PubMed]
- Fang H, Song B, Cheng B, Wong KS, Xu YM, Ho SS, Chen XY. Compensatory patterns of collateral flow in stroke patients with unilateral and bilateral carotid stenosis. BMC Neurol 2016;16:39. [Crossref] [PubMed]
- Jones CE, Wolf RL, Detre JA, Das B, Saha PK, Wang J, Zhang Y, Song HK, Wright AC, Mohler EM 3rd, Fairman RM, Zager EL, Velazquez OC, Golden MA, Carpenter JP, Wehrli FW. Structural MRI of carotid artery atherosclerotic lesion burden and characterization of hemispheric cerebral blood flow before and after carotid endarterectomy. NMR Biomed 2006;19:198-208. [Crossref] [PubMed]
- Zhang HP, Ma XD, Chen LF, Yang Y, Xu BN, Zhou DB. Cognitive Function After Carotid Endarterectomy: Early Decline and Later Recovery. Turk Neurosurg 2016;26:833-9. [PubMed]