Association between pericarotid fat attenuation index and clinical outcomes of symptomatic cerebral small vessel disease

Introduction

Cerebral small vessel disease (CSVD) refers to a spectrum of clinical and imaging findings originating from various pathological processes affecting arterioles, venules and capillaries (1). Based on a cross-sectional study, CSVD is prevalent in the elderly and contributes to around 25% of ischemic strokes (2). Typical imaging features of CSVD visible on magnetic resonance imaging (MRI) include recent small subcortical infarct (RSSI), lacune, white matter hyperintensity (WMH), enlarged perivascular space (EPVS), cerebral microbleed (CMB) and brain atrophy (3). Amongst multiple subtypes, symptomatic CSVD is a clinical presentation of lacunar syndromes confirmed by anatomically corresponding lacunar infarct on brain MRI (4).

Characterized by complicated manifestations, symptomatic CSVD shows diverse clinical outcomes (5,6). Hence, the determinants of positive or negative outcomes in symptomatic CSVD play crucial roles in clinical practice. Inflammation is increasingly implicated as a risk factor for CSVD (7). However, systemic markers like C-reactive protein cannot pinpoint vascular inflammation sites (8). Previous studies suggested that increased density of perivascular adipose tissue (PVAT) was associated with histopathological markers of vascular inflammation including increased pro-inflammatory cytokines, elevated reactive oxygen species production and macrophage activation, which could lead to endothelial dysfunction and vascular remodeling (9,10). It implied that PVAT could reflect localized inflammation and assist in interventions of cerebrovascular ischemic events. Recently, a novel and reliable imaging marker named perivascular fat attenuation index (FAI) has been gradually employed in coronary artery (11-13) and carotid artery (14,15). FAI can be detected through routine computed tomography angiography (CTA) images and serve as an imaging indicator of localized vascular inflammation (9).

Previous reports indicated that carotid plaque characteristics were independent risk factors for poor prognosis of stroke (16,17); nevertheless, identifying vulnerable features in multiple plaques is still challenging. Currently, pericarotid fat density (PFD) obtained via a semi-automated approach through CTA was considered to be related to vulnerable carotid atherosclerotic plaques and cerebrovascular ischemic events (15,18). Prior research reported that increased PFD was significantly associated with CSVD imaging markers (19), and other studies found that CSVD imaging burden was associated with post-stroke outcomes (20,21). However, the correlation between PFD and outcomes of symptomatic CSVD remains elusive.

This study aimed to explore how carotid inflammation, as measured by pericarotid FAI on CTA, correlated with functional outcomes of symptomatic CSVD. We hypothesized that FAI could provide additional predictive value beyond CSVD burden and other CTA variables in predicting clinical outcomes of symptomatic CSVD. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-746/rc).

Methods

Patients and criteria

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by Ethical Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University (SWYX: No. 2024-072), and informed consent was waived due to the retrospective nature of the study. We retrospectively collected 297 symptomatic CSVD patients undergoing carotid CTA and brain MRI from January 2016 to January 2024. The inclusion criteria were: (I) symptomatic stroke with clinical lacunar syndromes and anatomically corresponding lacunar infarct confirmed on MRI; (II) typical CSVD imaging features on MRI, comprising lacune, WMH, EPVS, CMB and brain atrophy; (III) less than 2 weeks between carotid CTA and MRI scans; (IV) age ≥18 years. The exclusion criteria were: (I) extracranial or intracranial macrovascular stenosis >70%; (II) cardiogenic infarct; (III) without acute infarct or acute infarct with diameter >20 mm; (IV) intracranial hemorrhage; (V) non-CSVD-related WMH, such as multiple sclerosis; (VI) comorbid with other neurological disorders, such as Parkinson’s disease; (VII) comorbid with systemic disorders, such as cancer; (VIII) history of carotid endarterectomy or stenting; (IX) poor image quality. The patient selection flow chart is presented in Figure 1.

Figure 1 The study flow chart. CTA, computed tomography angiography; MRI, magnetic resonance imaging; mRS, modified Rankin scale.

Demographic and clinical data, including age, sex, vascular risk factors, prescribed medications and National Institutes of Health Stroke Scale (NIHSS) at admission, were collected. Vascular risk factors included hypertension, diabetes mellitus, coronary heart disease, hyperlipidemia, smoking and alcohol consumption. Functional outcomes were evaluated at 90 days after stroke using modified Rankin scale (mRS), which is a global functional scale focusing on motor recovery with scores ranging from 0 (no symptoms) to 6 (death) (22). A good clinical outcome was defined as mRS ≤2, and mRS >2 was regarded as a poor outcome (23,24).

Neuroimaging acquisition and processing

Carotid CTA was performed utilizing a third-generation dual-source CT scanner (SOMATOM Force, Siemens Healthcare, Erlangen, Germany). Examination parameters were set as follows: tube voltage, 80 kV for individuals with BMI ≤25 kg/m² and 100 kV for those with BMI >25 kg/m²; pitch, 1.0, 0.35 s rotation time; reconstructed slice thickness (ST), 1.5 mm; increment, 1.0 mm. A 60–80 mL volume of contrast media (Omnipaque-350; GE Healthcare, Shanghai, China) was injected at speed of 4 mL/s, then 40 mL saline solution was administered at the same rate. Bolus tracking was used to start acquiring images with a 5 s-delay after reaching 100 Hounsfield Unit (HU) attenuation threshold in the aortic arch.

Brain MRI was performed using a 3.0T scanner (Magnetom Prisma, Siemens Healthineers, Erlangen, Germany). MRI images were acquired following axial scanning and parameters for different sequences included: T1WI—time repetition (TR) =2,000 ms, time echo (TE) =7.4 ms, ST =5.0 mm; T2WI—TR/TE =4,300 ms/109 ms, ST =5.0 mm; FLAIR: TR/TE =8,000 ms/81 ms, ST =5.0 mm; DWI: TR/TE =2,680 ms/57 ms, ST =1.5 mm; SWI: TR/TE =27 ms/20 ms, ST =1.5 mm.

Imaging assessment

Two radiologists experienced in vascular imaging (J.Z., S.Z.) assessed all images independently. They were blinded to patient-specific details and discrepancies were resolved by another senior radiologist (X.W.).

Assessment of CSVD markers

The neuroimaging markers of CSVD were evaluated based on consensus criteria outlined in Standards for Reporting Vascular Changes on Neuroimaging (STRIVE) (3) and were totaled across both hemispheres. The following individual CSVD markers were evaluated: (I) RSSI, high-signal lesion on DWI with diameter <20 mm (axial position). (II) Lacune, a subcortical ovoid or round cavity ranging from 3–15 mm in diameter. On T1WI and T2WI, its signal resembles cerebrospinal fluid; on FLAIR, it exhibits hyperintensity surrounding rims (25); (III) WMH, isointensity or hypointensity on T1WI and hyperintensity on FLAIR. WMH in periventricular and deep regions is graded according to the Fazekas scale ranging from 0 to 3 (26); (IV) EPVS, round, ovoid or linear shapes with cerebrospinal fluid-like signal on T2WI and diameter <3 mm. EPVS is typically observed in the basal ganglia and centrum semiovale and can be assessed using a validated four-point scale (0 point, absence; 1 point, less than 11; 2 points, 11–20; 3 points, 21–40; 4 points, more than 40) (27); (V) CMB, small ovoid or round lesion varying from 2 to 10 mm in diameter and with hypointensive signal on SWI sequence (28).

We quantified overall burden of CSVD by utilizing a validated scoring system ranging from 0 to 4 (29). One point was assigned for the presence of following manifestations: (I) at least one lacune; (II) Fazekas grade 3 for periventricular WMH or Fazekas grades 2 and 3 for deep WMH; (III) at least one CMB; (IV) moderate to severe EPVS (at least 2 points) in the basal ganglia.

Carotid CTA

CTA variables, including degree of extracranial carotid luminal stenosis, plaque type and maximum plaque thickness, were measured using a post-processing workstation (Syngovia, Siemens Force, Erlangen, Germany). The degree of carotid stenosis, including common carotid artery, external carotid artery and internal carotid artery (ICA), was assessed using criteria established by North American Symptomatic Carotid Endarterectomy Trial (30). Patients were initially classified into four groups based on severity of stenosis: (I) non-stenotic; (II) 30% stenosis; (III) 30–69% stenosis; (IV) 70–99% stenosis; the last group was excluded due to severe stenosis. High-risk plaques included soft plaque (low-density plaque, with an estimated range of 40–50 HU), plaque ulceration (contrast media protrusion into the plaque, measuring at least 2 mm) (31), plaque neovascularization (plaque enhancement on angiography imaging) (32) and plaque thickness ≥3 mm.

Pericarotid FAI analysis

A dedicated and validated software (Perivascular Fat Analysis Tool, Shunkun Technology, Beijing, China) was used to quantify PVAT attenuation. The adipose tissue surrounding carotid artery is located at a radial distance from outer vessel wall that matches the vessel diameter (15). To measure FAI surrounding both sides of extracranial ICA, we utilized a well-established method described in coronary artery (33). The FAI value was measured using a semi-automated approach; voxels with attenuation between −190 and −30 HU and proximal 40 mm of the vessel were analyzed. Subsequently, the mean FAI and maximum FAI of bilateral extracranial ICA were calculated. The details of pericarotid FAI analysis are depicted in Figure 2.

Figure 2 Analysis of pericarotid FAI in patients with good or poor clinical outcomes. (A-C) A 68-year-old female patient with poor outcome. (D-F) A 71-year-old female patient with good outcome. (A,D) Presentations of PVAT (white arrows) surrounding extracranial ICA, as shown by red pixels on CTA. (B,E) The histograms of pericarotid FAI. FAI values surrounding extracranial ICA were –45.62 HU (poor outcome) and –90.46 HU (good outcome). (C,F) The pixel diagrams of pericarotid FAI from patients with poor and good clinical outcomes, respectively. CT, computed tomography; CTA, computed tomography angiography; FAI, fat attenuation index; HU, Hounsfield Unit; PVAT, perivascular adipose tissue.

Statistical analysis

The Kolmogorov-Smirnov test was used to determine data distributions. Continuous variables were presented as mean ± standard deviation or median (interquartile range) and were analyzed by Student’s t-test or Mann-Whitney U test. Categorical data were reported as frequency (percentage) and were compared with χ² test or Fisher’s exact test. Bonferroni correction was used for post-hoc analyses of significant results. Univariate and multivariate logistic regressions were used to assess correlations between baseline characteristics, FAI, CSVD markers, CTA variables (including stenosis degree and high-risk plaque) and risk of poor outcomes (Figure 3) (34). Univariate analysis was conducted for all variables, then significant predictors (P<0.05) and other covariates (0.05<P<0.2) were included in multivariable analysis. Six prediction models were constructed to evaluate additional predictive value of pericarotid FAI in poor outcomes compared to total CSVD burden and CTA variables. The models were as follows: Model A, total CSVD burden; Model B, stenosis degree; Model C, FAI; Model D, total CSVD burden + stenosis degree; Model E, total CSVD burden + FAI; Model F, total CSVD burden + stenosis degree + FAI. Receiver operating characteristic (ROC) curve was employed to evaluate predictive role of FAI in symptomatic CSVD, with comparison of the respective areas under the curve (AUCs). The optimal cut-off value in ROC curve analysis was determined via Youden’s index to attain best discrimination. The calibration ability and clinical benefit/practicality of prediction models were assessed through calibration curve plots and decision curve analysis (DCA) respectively. To ensure the reliability and consistency of constructed models, we calculated inter-reader reproducibility of mean FAI and max FAI using intraclass correlation coefficient; CSVD markers and CTA data were assessed by Kappa test, with values greater than 0.75 considered excellent reproducibility. SPSS software 26.0 (IBM Inc.) was used for all statistical analyses and two-sided P<0.05 was considered significant.

Figure 3 Association between pericarotid FAI and poor outcomes in symptomatic CSVD. (A) Univariable logistic regression results. Only variables with P<0.2 are displayed; (B) multivariate logistic regression results. ^a, the results were adjusted for influencing factors that were selected based on univariate analyses (including significant predictors in univariable analysis combined with other covariates with P<0.2); ^b, significant after adjusting for influencing factors. CMB, cerebral microbleeds; CI, confidence interval; CSVD, cerebral small vessel disease; EPVS, enlarged perivascular space; FAI, fat attenuation index; OR, odds ratio; WMH, white matter hyperintensity.

Results

Patient characteristics

A total of 202 patients were included in this study, comprising 123 with poor outcomes and 79 with good outcomes. The clinical and imaging data of two groups are shown in Tables 1,2. Patients with poor outcomes were older (P=0.001) and had higher NIHSS scores at admission compared to those with good outcomes (P<0.001). We found that poor outcome patients were more likely to develop lacune (P<0.001), severe periventricular and deep WMH (P<0.001), CMB (P=0.005) and higher total CSVD burden (P<0.001). As for CTA variables, severe carotid stenosis (P<0.001) and high-risk plaque (P=0.005) were frequently observed in poor outcome group. The analyses regarding other parameters did not reach significant level (all P>0.05).

No significant difference was observed in bilateral FAI (Table S1). In addition, poor outcome group showed significantly higher mean and maximum FAI values than good outcome group (mean FAI: −64.47±6.86 vs. −70.86±6.74, P<0.001; maximum FAI: −61.60±7.00 vs. −67.96±7.01, P<0.001) (Figure 4). The intraclass correlation coefficient values of mean and maximum FAI were determined to be 0.988 (0.972, 0.995) and 0.979 (0.947, 0.991), indicating excellent inter-reader reliability; satisfying inter-reader agreements were obtained in CSVD markers and CTA variables as well (lacune: k=0.891; periventricular WMH: k=0.804; deep WMH: k=0.850; CMB: k=0.891; EPVS: k=0.805; CSVD total score: k=0.854; carotid stenosis: k=0.836; high-risk plaque: k=0.817).

Figure 4 The difference in pericarotid FAI between patients with poor and good outcomes. **, P<0.01. FAI, fat attenuation index; HU, Hounsfield Unit.

Logistic regression analyses for poor clinical outcomes

In univariate logistic regression analyses, FAI [odds ratio (OR) =1.144], lacune (OR =4.335), periventricular WMH (OR =2.814), deep WMH (OR =2.969), CMB (OR =4.312), total CSVD score (OR =3.426), carotid stenosis degree (OR =5.098), high-risk plaque (OR =2.270), age (OR =1.044) and coronary heart disease (OR =2.494) all showed significant associations with poor outcomes in symptomatic CSVD (Figure 3A). After adjustment for covariates selected based on univariate analyses (P<0.2), multivariate logistic regression analysis suggested that FAI (OR =1.156), total CSVD score (OR =3.434) and carotid stenosis degree (OR =3.247) were considered as independent risk factors for adverse outcomes at 90 days after stroke (Figure 3B).

Predictive value of pericarotid FAI in clinical outcomes

As shown in Figure 5, a total of six prediction models including variables of pericarotid FAI, stenosis degree and total CSVD burden were constructed. Among univariate models, we found that FAI alone (Model C) presented higher predictive performance (AUC =0.760, P<0.001) compared with total CSVD burden (Model A, AUC =0.746) and stenosis degree (Model B, AUC =0.694). The addition of stenosis degree (Model D: AUC =0.801, P<0.001) or pericarotid FAI (Model E: AUC =0.849, P<0.001) dramatically improved predictive performance relative to total CSVD burden alone. Moreover, Model D had higher sensitivity (78.86% vs. 61.79%) but lower specificity (68.35% vs. 93.67%) compared to Model E (Table 3). The AUC for Model F was 0.863, which exhibited best predictive performance (sensitivity 65.85%, specificity 93.67%). As shown in Figure S1, models combining FAI with CSVD and stenosis degree (Models E and F) exhibited excellent consistency between predicted and actual risks. Moreover, DCA curves suggested that Model E and Model F could provide more favorable clinical benefits than other models (Figure S2).

Figure 5 ROC curves for prediction of poor outcomes at 90 days after stroke. Model A: total CSVD burden; Model B: stenosis degree; Model C: FAI; Model D: total CSVD burden + stenosis degree; Model E: total CSVD burden + FAI; Model F: total CSVD burden + stenosis degree + FAI. AUC, area under the curve; CSVD, cerebral small vessel disease; FAI, fat attenuation index; ROC, receiver operating characteristics.

Discussion

In this study, by using logistic regressions and constructing predictive models, we found a significant correlation between higher FAI values and poor outcomes in a cohort of symptomatic CSVD patients. It is implicated that pericarotid FAI may serve as an auxiliary indicator of adverse outcomes in symptomatic CSVD and provide additional predictive value beyond total CSVD score and carotid stenosis degree.

Although associations between extracranial or intracranial carotid arteriosclerosis and CSVD markers were gradually identified (35,36), the pathophysiological mechanisms underlying correlations between pericarotid fat and CSVD remained unclear. Our study revealed that pericarotid FAI was significantly correlated with poor outcomes in symptomatic CSVD at 90 days after stroke. To our knowledge, CSVD primarily develops through mechanisms of atherosclerosis, hemodynamic disturbance and blood-brain barrier (BBB) dysfunction (1,5), hence two hypotheses were proposed to explain the results. One possible explanation is that alterations in structure and function of large upstream arteries exert an impact on hemodynamics of small downstream vessels in the brain, disrupt BBB and promote vascular remodeling (37). These secondary alterations of small vessels may accelerate progression of CSVD lesions in cascades. Another probable explanation is that bioactive inflammatory factors [e.g., interleukin-1 beta (IL-1β), tumor necrosis factor-alpha (TNF-α)] derived from PVAT can diffuse into vascular walls and nearby tissues via paracrine mechanisms and deteriorate vascular endothelial dysfunction, oxidative stress and inflammation, which might lead to disrupted small vessels (10). As reflected by pericarotid FAI values, vascular lesions potentially originating from PVAT effect (e.g., vascular endothelial dysfunction, vascular remodeling, plaque instability, vascular smooth muscle cell proliferation and migration) may result in reduced blood supply to small perforating arteries, chronic hypoperfusion of brain and parenchymal damage eventually. Taken together, the aforementioned pathophysiological changes might contribute to worse clinical outcomes of CSVD patients. However, the exact roles of FAI in CSVD pathogenesis remain unclear and deserve further exploration through future animal studies.

In addition to FAI values, we observed that total CSVD burden and carotid stenosis degree could serve as predictors for clinical outcomes in symptomatic CSVD. Similar to our findings, previous studies demonstrated that higher total CSVD score or severe carotid artery stenosis were correlated with poor functional or cognitive outcomes in stroke patients (20,21,38). With individual neuroimaging markers included, total CSVD burden reliably reflects brain tissue affected by ischemia or other damage and severity level; chronic ischemia may result in stroke, gait impairment and dementia, which can be explained by lower neural reserve capacity (39). Carotid atherosclerosis aggravates oxidative stress, activates neuroinflammation and then promotes vascular aging in large vessels and microvessels consecutively, which results in disruption of BBB integrity, CMB development and white matter damage (40,41). Carotid artery stenosis also decreases cerebral blood perfusion by changing local hemodynamics and producing microthrombus (42). The aforementioned pathophysiological changes may lead to the occurrence of cerebrovascular accidents and poor outcomes.

Among univariate models, we found that FAI alone (Model C) showed highest predictive performance (AUC =0.760). In combined models, we observed that incorporating FAI into predictive models obviously improved diagnostic specificity. The Model E (total CSVD score + FAI) and Model F (total CSVD score + stenosis degree + FAI) achieved 93.67% specificity, which were higher compared with Model A (total CSVD score) and Model D (total CSVD score + stenosis degree). It implied that models including FAI might possess lower misdiagnosis rates; symptomatic CSVD patients with increased pericarotid FAI values were more likely to progress into poor clinical outcomes, and those who were at lower risk might show decreased FAI values. The performance of these models was further supported by calibration and DCA curves, among which Model E and Model F showed satisfying calibration ability and clinical benefit, implying their potential for utility in real-world settings to guide treatment. Therefore, rigorous controlling of vascular risk factors, prompt drug treatment and tailored rehabilitation needed to be adopted for the subset of individuals to prevent motor impairment, other sequalae or even recurrence of stroke. In clinical practice, patients with higher FAI values might demand strict control over blood pressure (<130/80 mmHg), glucose (<6 mmol/L) and low-density lipoprotein levels (<1.4 mmol/L); strategies that might target PVAT, including double antiplatelets (e.g., aspirin combined with clopidogrel), anti-inflammation (e.g., edaravone), vascular protection (e.g., evolocumab) and rehabilitation therapy if necessary, should be performed to facilitate prognosis (43).

Previous study has reported that PVAT density obtained from CTA is comparable to PET imaging (44), which is considered as gold standard for detecting PVAT. In contrast to manual and single-level measurements (19), a well-designed and validated semi-automated approach was used to measure pericarotid FAI; the results exhibited minor inter-reader discrepancy and excellent reproducibility. In symptomatic CSVD patients, we comprehensively explored correlation between FAI values and functional outcomes, which were crucial for recovery and quality of life. Moreover, multiple models were constructed to elucidate predictive efficacy of FAI values and compare it to traditional total CSVD burden; these analyzes made our findings more convincing.

Nevertheless, this study has several limitations. Firstly, this is a single-center, retrospectively designed study and thus external cohort validations were not performed. Moreover, only functional outcomes were correlated with FAI. To better resolve these issues, we are conducting a multi-center, prospective investigation with large cohort at present, and cognitive outcomes of symptomatic CSVD are determined by neuropsychological (e.g., Montreal Cognitive Assessment) combined with neurophysiological (e.g., event-related potential) means as described in prior study (45). Secondly, this study did not identify a clinically validated cutoff for FAI, hence future investigation is necessary to determine specific FAI thresholds. Thirdly, PVAT density was reflected by FAI values in CSVD patients, whereas inflammatory changes within PVAT needed to be further validated in histopathological detections, perhaps using CSVD animal models (e.g., stroke-prone spontaneously hypertensive rats) (46). Finally, generally-used dichotomization at mRS >2 might oversimplify the continuum of recovery in CSVD patients, and ordinal regression or other stratifications could also be considered to analyze functional outcomes (47). The clinical outcomes were only followed up 90 days after stroke, and longer-term outcomes (e.g., 1 year) should be dynamically traced.

Conclusions

In summary, combined with CSVD burden and carotid stenosis degree, increased pericarotid FAI values assessed in carotid CTA can serve as reliable predictors of poor clinical outcomes in symptomatic CSVD. Integrating pericarotid FAI into existing risk models has the potential to improve predictive efficacy compared with CSVD neuroimaging markers alone. The evaluation of FAI may assist in risk stratification, individualized therapy in multiple facets and prognosis judgment.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-746/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-746/dss

Funding: This study was supported by the National Natural Science Foundation of China (grant Nos. 8187354, 81571672, and 82301474); the Natural Science Foundation of Shandong Province (grant Nos. ZR2022QH275 and ZR2021QH003); and the Foundation of Youth Talent of Shandong Provincial Hospital.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-746/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 and its subsequent amendments. This study was approved by Ethical Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University (SWYX: No. 2024-072), and informed consent was waived due to the retrospective nature of the study.

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/.

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