Diagnostic Value of Carotid Intima-Media Thickness Combined with Periventricular White Matter Hyperintensities for Mild Cognitive Impairment in Parkinson’s Disease

Introduction

Parkinson’s disease (PD), the second most common neurodegenerative disorder after Alzheimer’s disease (AD), affects > 1% of adults aged ≥ 65 years and is characterized by motor symptoms (eg, bradykinesia, rigidity) and non-motor manifestations.¹ Among non-motor manifestations, cognitive impairment is common, potentially occurring at any disease stage. Mild cognitive impairment (MCI) represents a critical intermediate stage between normal cognition and dementia in PD, with a pooled prevalence of ≥ 40%.^2,3 Mild cognitive impairment in Parkinson’s disease (PD-MCI) confers a substantially elevated risk of progression to Parkinson’s disease dementia (PDD),⁴ with individuals six times more likely to develop dementia than age-matched controls and a long-term PDD prevalence reaching 80% after 15–20 years of disease duration.⁵ This progression severely compromises patients’ quality of life, functional independence, and survival,^6,7 underscoring the urgent need for early biomarkers to predict and intercept cognitive decline.

Carotid intima-media thickness (CIMT), a surrogate marker of subclinical atherosclerosis measured via ultrasonography, is an independent predictor of cognitive decline in middle-aged and elderly populations.⁸ In PD, CIMT correlates with cognitive dysfunction and disease progression,^9,10 reflecting an underlying vascular pathology. Concurrently, periventricular white matter hyperintensities (PWMHs), visualized on T2-weighted or FLAIR MRI sequences, are indicative of cerebral small vessel disease and chronic hypoperfusion.¹¹ The vascular pathology represented by CIMT may thus synergize with the cerebral small vessel disease evident as PWMHs, collectively exacerbating cognitive decline in PD. Indeed, PWMHs are highly prevalent in PD and are strongly associated with MCI,¹² with particular detrimental effects on executive function and processing speed.^13,14 Recent studies continue to underscore the role of WMHs in PD-related cognitive and motor outcomes.^15–17 While CIMT and PWMHs individually demonstrate associations with PD-MCI, their combined diagnostic utility remains unexplored. No study has yet integrated these vascular and neuroimaging markers into a unified model to discriminate PD-MCI patients from those with normal cognition in a cross-sectional setting.

In this study, PD-MCI was diagnosed based on a comprehensive neuropsychological assessment according to the criteria described in the Methods section, aiming to bridge this gap by evaluating the synergistic potential of CIMT and PWMHs in diagnosing PD-MCI. We hypothesize that their combination will enhance diagnostic accuracy beyond either marker alone, offering a clinically accessible tool for early risk stratification and intervention.

Patients and Methods

Participants

Between January 2021 and March 2024, we prospectively enrolled a consecutive cohort of PD patients from Chongqing University Jiangjin Hospital. This study was approved by the Chongqing University Jiangjin Hospital Ethics Committee (JJ2021017036) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent.

Inclusion criteria comprised: ① Newly diagnosed PD meeting the UK Parkinson’s Disease Society Brain Bank clinical diagnostic criteria;¹⁸ ② De novo status, defined as cumulative levodopa exposure ≤2 weeks and no use within 4 weeks prior to recruitment; ③ Age 45–80 years; ④ Ability to complete neuropsychological evaluation.

Exclusion criteria included: ① Medication-induced parkinsonism; ② Features suggestive of progressive supranuclear palsy or multiple system atrophy per consensus criteria;^19,20 ③ Documented severe normal-pressure hydrocephalus (NPH) or vascular encephalopathy on MRI; ④ History of deep brain stimulation (DBS); ⑤ Significant renal/hepatic impairment, or severe cardiovascular, respiratory, hematologic, wasting, or metabolic disorders; ⑥ Prior carotid endarterectomy (CEA) or carotid artery stenting (CAS) for carotid stenosis; ⑦ Current use of medications with potential cognitive adverse effects.

All participants underwent baseline MRI and cognitive assessment. Patients with Clinical Dementia Rating (CDR) ≥ 0.5, indicating possible dementia, were excluded to enhance the specificity of the PD-MCI diagnosis.

Data Collection

Clinical, demographic, and laboratory data were collected for all participants. These included age, gender, body mass index (BMI), hypertension status, diabetes status, hyperlipidemia status, smoking history, disease duration, educational duration, current non-PD and PD medications, homocysteine (Hcy), total cholesterol (TC), triglycerides (TG), cystatin C (Cys C), apolipoprotein B (Apo B), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Trained research personnel performed clinical evaluations, including the Hoehn and Yahr (H&Y) stage and the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS).

Neuropsychological Evaluation

Participants underwent a comprehensive neuropsychological assessment comprising 10 tests evaluating five cognitive domains: memory, attention/working memory, visuospatial function, executive function, and language. Two tests assessed each domain:

① Memory: Rey-Osterrieth Complex Figure (ROCF) delayed recall and Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) word list learning with delayed recall;

② Attention/Working Memory: Wechsler Memory Scale-Fourth Edition (WMS-IV) Symbol Span and Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) Digit Span Backward;

③ Visuospatial Function: Benton Judgment of Line Orientation (BJLOT) and Rey-Osterrieth Complex Figure (ROCF) copying test;

④ Executive Function: Frontal Assessment Battery (FAB) and Fruit Fluency Test (FFT);

⑤ Language: WAIS-IV Similarities and Boston Naming Test (BNT).

MCI in PD patients was defined by abnormal performance on at least two tests, meeting either of the following criteria: ① one impaired test in each of two different cognitive domains, or ② two impaired tests within a single cognitive domain. A test score falling 1.5 standard deviations below the normative mean, adjusted for age and education where available, was considered impaired.

Quantification of WMHs

MRI was performed using a 3T Skyra system (Siemens Healthineers, Erlangen, Germany). The acquisition parameters for each sequence were as follows: ① T1-weighted Magnetization Prepared-Rapid Gradient Echo (MP RAGE): 1900 / 2.44 ms of repetition time (TR)/echo time (TE), 256×253 of matrix size, 900 ms of inversion time (TI), 250×250 of field of view (FOV), and 1 mm of slice thickness; ② Fluid-Attenuated Inversion Recovery (FLAIR): 7000 / 132 ms of TR/TE, 256×256 of matrix size, 2.2102e + 03 ms of TI, 220×220 of field of view, 150°of flip angle and 4 mm of slice thickness.

Image preprocessing was implemented using custom scripts in MATLAB R2017b (The MathWorks, Natick, MA, USA) and SPM8 (https://www.fil.ion.ucl.ac.uk/spm/). The processing pipeline consisted of: ① Segmentation & Coregistration: T1-weighted images were segmented into tissue classes using a priori SPM8 tissue probability maps. The resulting tissue segments were then coregistered to the corresponding FLAIR images; ② Intensity Correction & White Matter Segmentation: Non-uniformity intensity correction and segmentation of white matter tissue on FLAIR images were performed using the “new segment” function within SPM8;²¹ ③ WMH Quantification: White matter hyperintensity (WMH) volumes for the total brain and periventricular region were calculated based on the segmented FLAIR images.

Carotid Artery Ultrasound Examination

Carotid artery ultrasound examination was performed using a GE LOGIQ E9 color Doppler ultrasound system (GE Healthcare, USA). CIMT was defined as the distance between the media-adventitia interface and the lumen-intima interface. CIMT was measured bilaterally at the following sites: the common carotid artery, carotid bulb, and the proximal and distal walls of the internal carotid artery (ICA). The mean CIMT value was computed from these twelve measurement points.

Statistical Analysis

Statistical analyses were performed using IBM SPSS Statistics (version 20.0; SPSS Inc., Chicago, IL, USA). A two-sided P value < 0.05 was considered statistically significant. Normality of continuous variables was assessed using the Kolmogorov–Smirnov test. Normally distributed data are presented as mean ± standard deviation (SD) and were compared between groups using the independent samples t test. Non-normally distributed data are presented as median (M) and interquartile range (IQR) and were compared between groups using the Mann–Whitney U-test. Categorical data are presented as percentages (%) and were compared between groups using the chi-square test (χ² test). Multicollinearity was assessed using Variance Inflation Factors (VIF), and a VIF value of ≥ 5 was considered indicative of significant collinearity.

Variables yielding a two-sided P value < 0.10 in univariate analysis were entered into a binary logistic regression model to identify independent risk factors for MCI in patients with PD. Receiver operating characteristic (ROC) curve analysis was employed to evaluate predictive values. The DeLong test (Z test for AUC comparison) was used to compare the areas under the ROC curves (AUCs).

Results

General Data

During the study period, 601 patients with PD were recruited. Of these, 60 patients were excluded: 30 for declining study participation, 17 due to signs or symptoms suggestive of other (atypical) disorders, 6 because of prior PD medication use, 4 due to the occurrence of NPH, and 3 for exhibiting predominant postural tremor and polyneuropathy. Consequently, 541 patients were included in the final analysis.

The study cohort comprised 306 males (56.6%) and 235 females (43.4%), with a mean age of 64.07 ± 9.15 years. The mean disease duration was 14.28 ± 8.16 months. MCI was identified in 246 PD patients, yielding a prevalence of 45.5%.

Univariate Analysis

Univariate analysis was performed comparing PD patients with MCI to those without MCI. The results (Table 1) demonstrated statistically significant differences (P < 0.05) in hypertension, disease duration, duration of education, MDS-UPDRS part III score, Hoehn and Yahr stage, total brain WMHs, PWMHs, CIMT, Hcy levels, and Cys C levels. No significant differences (P > 0.05) were observed for the remaining variables, although age and hyperlipidemia prevalence exhibited trends towards significance (P < 0.10).

Table 1 Results of Univariate Analysis Between MCI Group and Non-MCI Group in PD Patients

Multivariate Analysis

To identify risk factors for MCI in PD patients, variables including hypertension, disease duration, education duration, MDS-UPDRS part III score, Hoehn and Yahr stage, total brain WMHs, PWMHs, CIMT, Hcy levels, Cys C levels, age, and hyperlipidemia were included in a binary logistic regression model for multivariate analysis. After adjustment for confounders (education duration, Cys C levels, disease duration, total brain WMHs, hypertension, hyperlipidemia, and age; all P > 0.05), multivariate analysis revealed that PWMHs [odds ratio (OR): 2.114, 95% confidence interval (CI): 1.391–4.586, P = 0.008], CIMT (OR: 2.027, 95% CI: 1.485–4.281, P = 0.005), Hcy levels (OR: 1.551, 95% CI: 1.172–2.680, P = 0.019), Hoehn and Yahr stage (OR: 2.243, 95% CI: 1.534–4.736, P = 0.002), and MDS-UPDRS part III score (OR: 1.416, 95% CI: 1.158–2.465, P = 0.032) were independently associated with MCI in PD patients, as shown in Table 2.

Table 2 Results of Multivariate Analysis Between MCI Group and Non-MCI Group in PD Patients

Predictive Values

The ROC curve was used to assess the predictive value of PWMHs, CIMT, and their combination for MCI in PD patients. The results (Figure 1) demonstrated that the AUCs were 0.705 [standard error (SE): 0.023, 95% confidence interval (CI): 0.661–0.750, P < 0.001) for PWMHs, 0.722 (SE: 0.022, 95% CI: 0.678–0.765, P < 0.001) for CIMT, and 0.908 (SE: 0.013, 95% CI: 0.882–0.934, P < 0.001) for the combination. A Z test revealed that the AUC of the combination prediction was significantly higher than those of the individual predictions (combination vs PWMHs: 0.908 vs 0.705, Z = 7.684, P < 0.001; combination vs CIMT: 0.908 vs 0.722, Z = 7.279, P < 0.001). The ROC curve for the combination prediction was based on probabilities derived from the logistic regression model.

Figure 1 Receiver operating characteristic (ROC) curves for PWMHs, CIMT, and their combination in discriminating PD-MCI. The AUC (95% CI) for the combination model was 0.908 (0.882–0.934). The combination curve is based on the probabilities derived from the logistic regression model including PWMHs and CIMT.

Discussion

This study demonstrated, for the first time, the synergistic diagnostic value of CIMT and PWMHs for discriminating PD-MCI. Our key finding-that the combination of CIMT (AUC = 0.722) and PWMHs (AUC = 0.705) yielded a significantly superior diagnostic accuracy (AUC = 0.908)-suggested these biomarkers capture complementary aspects of PD-related cognitive vulnerability rooted in intersecting vascular and neurodegenerative pathways.

CIMT, a validated marker of subclinical atherosclerosis,²² has been shown to correlate significantly with cognitive decline in both middle-aged/elderly populations and AD patients.^8,23,24 The potential mechanisms linking atherosclerosis to cognitive dysfunction involve four primary pathways: First, atherosclerosis-induced carotid calcification and elevated CIMT promote hippocampal degeneration and atrophy.²⁵ Second, cerebral hypoperfusion and hypoxia secondary to atherosclerosis accelerate β-amyloid (Aβ) production.²⁶ Third, Aβ clearance may be impaired through disruption of the blood-brain barrier (BBB), perivascular pathways, glymphatic drainage, and enzymatic degradation. Fourth, atherosclerosis-mediated neurovascular unit dysfunction triggers oxidative stress and subsequent neuroinflammatory responses.^27,28 PWMHs, visualized on MRI, signify cerebral small vessel disease (SVD) and chronic hypoperfusion.¹¹ They disrupt frontal-subcortical circuits critical for executive function and processing speed-domains frequently impaired in PD-MCI.^13,14 PWMHs may accelerate neurodegeneration by promoting BBB disruption, neuroinflammation, and axonal damage within vulnerable periventricular white matter tracts.²⁹

Prior studies support the individual relevance of the two biomarkers. In PD, Yan et al demonstrated that increased CIMT correlated with longer disease duration and higher Hoehn & Yahr stage,⁹ while Peng et al reported its association with cognitive impairment.¹⁰ Similarly, Huang et al and Mak et al found that PWMH burden independently predicted MCI in early PD, particularly affecting executive functions.^13,14 Our previous study further showed that combining PWMHs with homocysteine discriminated PD-MCI with moderate accuracy (AUC=0.701).¹¹ The current results aligned closely with these findings: CIMT alone achieved an AUC of 0.722 (95% CI: 0.678–0.765) and PWMHs an AUC of 0.705 (95% CI: 0.661–0.750), confirming robust yet imperfect individual diagnostic capacity.

The marked increase in diagnostic power (AUC=0.908) when combining CIMT and PWMHs underscored their complementary roles. While CIMT reflects systemic macrovascular atherosclerosis and associated hemodynamic risks, PWMHs indicate cerebral microvascular damage and localized white matter integrity loss. Their integration likely captures a broader spectrum of vascular contributions to PD-MCI pathogenesis-spanning large-vessel atherosclerosis impacting global perfusion and SVD affecting specific cognitive networks.³⁰

Both CIMT (via carotid ultrasound) and PWMHs (via routine brain MRI) are highly accessible in standard neurology or vascular clinics. Ultrasound is portable, low-cost, and non-invasive,³¹ while brain MRI is already routinely performed in many PD patients for differential diagnosis or monitoring. This contrasts with research-centric biomarkers like PET amyloid imaging or CSF analysis, which are expensive and lack widespread availability.³² Implementing this two-marker panel could facilitate early identification of high-risk PD-MCI patients during routine clinical assessments, enabling timely intervention. Potential interventions include rigorous cardiovascular risk factor management (eg, hypertension control, statin therapy) or cognitive rehabilitation programs, which may be most effective in the pre-symptomatic or early MCI stages. However, the clinical applicability of this combined model is weakened by the lack of model calibration and validation, warranting further investigation.

Our findings resonate with the growing recognition of vascular contributions to PD pathology and cognitive decline. The high long-term prevalence of PDD (80% at 15–20 years)⁵ underscores the need for early, accessible predictors like those identified here. The AUC of 0.908 approaches the diagnostic range for established clinical tools, suggesting genuine utility for risk stratification.

Our study has several limitations. First, the cross-sectional design precludes causal inference and true prediction of incident MCI; longitudinal studies are needed. Second, we reported discrimination using AUC but did not assess model calibration or perform internal validation (eg, bootstrapping), which limits the estimation of the model’s performance in new populations and its immediate clinical utility. Third, although VIF diagnostics did not indicate severe multicollinearity, the inclusion of both PWMHs and total WMHs requires careful interpretation. Fourth, the intra- and inter-rater reliability for CIMT and PWMH measurements were not systematically assessed. Fifth, although we included de novo PD patients, some heterogeneity in disease duration existed. Future studies with larger, more homogeneous cohorts are warranted.

Conclusion

This study demonstrated that the combination of CIMT and PWMHs provided a potential tool for identifying PD-MCI. This integrated model significantly outperformed either biomarker alone. By combining a peripheral vascular marker (CIMT) with a direct neuroimaging indicator of cerebral small vessel disease (PWMHs), it captured the critical interaction between systemic and central vascular pathology underlying cognitive impairment in PD. Given the routine clinical availability of carotid ultrasound and brain MRI, this approach offered a potential strategy for early identification of at-risk individuals. This paved the way for targeted interventions to delay or prevent cognitive decline in PD. However, the clinical application of this model requires further validation in longitudinal settings and external cohorts, with formal assessment of calibration and reliability.