Parkinson’s disease (PD) is recognized as the most prevalent movement disorder worldwide and as the second most common neurodegenerative disease after Alzheimer’s disease (AD) [1, 2]. Beyond the cardinal motor symptoms (bradykinesia, rigidity, resting tremor), PD is characterized by heterogeneous non-motor sequelae, and cognitive impairment (CI) shows a sixfold higher prevalence than in age-matched controls [2]. CI substantially diminishes functional independence and quality-of-life measures in patients with PD, imposing socioeconomic burdens that exceed those of motor disability even during prodromal phases [3, 4]. Accordingly, early diagnostic and identification of CI are essential for PD management.

Neuroinflammation is recognized as a core pathological mechanism in PD. It affects not only the substantia nigra (SN) and striatum but also cognition-related regions such as the frontal lobe and hippocampus, thereby contributing to CI [5, 6, 7]. This process is amplified by peripheral metabolic disturbances, in which homocysteine (Hcy) accumulation—mediated by deficiencies of the cofactors folate (FA) and vitamin B12 (VB12)—induces excitotoxicity via N-methyl-D-aspartate (NMDA) receptor overactivation and impairs DNA repair in cognitive circuits [8, 9, 10]. Concurrently, iron dyshomeostasis, reflected by elevated ferritin (Fer), potentiates microglial activation and lipid peroxidation, thereby exacerbating neuronal vulnerability through ferroptotic pathways. The resulting oxidative milieu depletes endogenous antioxidants such as uric acid (UA), further diminishing neuroprotection and accelerating cognitive decline [11, 12, 13]. Systemic inflammation is evidenced by elevated C-reactive protein (CRP), which correlates with blood-brain barrier disruption and microglial priming [14], whereas neuron-specific enolase (NSE) serves as a surrogate marker of neuronal damage in cortico-limbic networks [15, 16]. These peripheral biomarkers appear mechanistically interlinked: elevations in Hcy (due to FA/VB12 deficiency) synergize with iron overload (high Fer) to amplify mitochondrial reactive oxygen species (ROS) generation, which in turn depletes UA and upregulates CRP, creating a pathological feed-forward loop that drives neuroinflammation and CI. Therefore, neuroinflammation-related peripheral biomarkers may help in the diagnosis of CI in PD.

Transcranial sonography (TCS) has emerged as a noninvasive neuroimaging tool, and substantia nigra hyperechogenicity (SNH) has been validated as an imaging biomarker of nigrostriatal pathology in PD [17]. SNH can be detected in prodromal PD and persists throughout disease progression. Although current TCS research predominantly examines associations between SNH and measures of PD duration or severity, its utility for predicting cognitive impairment has been underexplored [18, 19].

Given the neuroinflammatory heterogeneity underlying CI in PD and the diagnostic advantages of TCS-identified high echogenicity in the SN, SNH, and multisource inflammation markers were integrated to identify individuals at increased risk of early, rapid cognitive decline. This approach is intended to enhance diagnostic stratification and to support the implementation of precision-prevention protocols.

This study developed a diagnostic classification model by combining SNH with serum inflammation-associated biomarkers CRP and Hcy, significantly improving the diagnostic performance of PD-CI risk stratification. This diagnostic classification model not only provides clinical targets for individualized intervention but also optimizes CI risk stratification and clinical pathways. Additionally, this study provides some research support for the mechanism gap in ultrasound technology for PD-CI diagnosis, as SNH-reflected substantia nigra iron deposition and abnormal inflammatory markers may support the hypothesis of the “iron deposition-neuroinflammation-CI” circuit.

This study indicates that patients 50 years had 83% lower odds of CI than those $\ge$75 years. Neurodegenerative processes in PD are thought to accelerate brain aging and alter cognitive trajectories, with older adults exhibiting more pronounced cognitive decline [25]. A cohort of patients with Parkinson’s disease in Spain cohort study reported predicted age differences of 7.08 years in gray matter and 8.82 years in white matter among patients with PD-CI [26]. This study corroborates the aforementioned research, emphasizing the heterogeneous impact of aging on cognitive decline in PD. However, a key distinction is that the cognitive protective association in early-onset PD (50 years) was not moderated by years of education or the measured biomarkers, suggesting that age 50 years itself may confer an independent neuroprotective effect.

Each additional year of formal education was associated with a 12.2% reduction in the odds of PD-CI. These results are consistent with a prior study indicating that higher educational attainment is associated with better cognitive function and reduced dementia risk, even in the presence of neurodegenerative disease [27]. Educational attainment influences cognitive trajectories, with higher levels associated with better working memory and subcortical task performance, greater compensatory capacity of the frontostriatal circuit, enhanced dopaminergic efficiency, and greater cognitive reserve [26]. In the Proceedings of the National Academy of Sciences, Schwarzacher and colleagues showed that repetitive neural activity induced by learning expands synapses on dendritic spines, strengthening neuronal connections. This synaptic plasticity helps stabilize neuronal activity and supports cognitive function [28].

Additionally, each one-point increase in the UPDRS-III score corresponded to a 2.4% increase in the odds of PD-CI. From an effect-size perspective, the impact may be modest at low UPDRS-III scores, but cumulative effects at higher scores can meaningfully increase the odds of PD-CI (e.g., ~48% higher odds for a 20-point increase from 30 to 50). These cumulative effects have clear implications for clinical intervention. Furthermore, as the gold standard for assessing motor symptoms, an increase in UPDRS-III scores reflects worsening degeneration of the cortical-basal ganglia-thalamic circuit (CBTC) [29]. The observed association between PD-CI and UPDRS-III deepens understanding of the scale’s diagnostic performance, as score changes mark motor deterioration and may also indicate cognitive decline via motor-cognitive neural circuits [30].

SNH serves as an imaging marker for SN iron deposition, with a sensitivity of 60.56% and specificity of 66.67%, consistent with a previous TCS study [15]. The high specificity of CRP and Hcy (88.10% and 83.33%, respectively) further supports the significant role of neuroinflammation in PD-CI [31, 32, 33]. Additionally, the SNH+CRP+Hcy diagnostic classification model demonstrated improved AUCs (PD-CI: 0.783; PD-MCI: 0.729; PD-dementia: 0.823), with the most significant gain observed for PD-dementia. This pattern may reflect synergistic effects of multiple pathway biomarkers amplified by widespread corticolimbic degeneration [34]. This mechanism aligns with proteomics findings of abnormal plasma H3C15 (histone-associated neuroinflammation) and PSAP (lysosomal dysfunction) in PD dementia patients, suggesting that multimodal biomarkers reflect interplay between neurodegenerative and inflammatory pathways [35].

Notably, SNH, CRP, and Hcy were independent risk factors for PD-CI, with a gradient in effect sizes: SNH-positive cases had 2.55-fold higher odds (OR = 2.550), elevated CRP conferred 2.04-fold higher odds (OR = 2.038), and elevated Hcy had the strongest association (OR = 2.830; 2.83-fold higher odds). These factors may collectively participate in an “iron deposition-neuroinflammation-CI” circuit: SNH reflects iron deposition in the SN, which activates microglia and promotes release of inflammatory mediators (e.g., IL-1$\beta$), CRP, as a systemic marker, is linked to blood-brain barrier disruption and neurodegeneration, and Hcy synergistically promotes cognitive decline via oxidative stress and tau hyperphosphorylation [36, 37, 38]. SNH serves as a structural marker, while CRP and Hcy are dynamic inflammation-associated indicators, and the three factors synergistically amplify the risk of cognitive decline in PD.

Quantitative susceptibility mapping (QSM) on MRI provides supporting evidence for links between iron deposition and CI-related circuits. Voxel-based QSM study has shown that, relative to PD with normal cognition, PD-MCI exhibits higher susceptibility in the cuneus, precuneus, caudate head, fusiform gyrus, and orbitofrontal cortex, and susceptibility in these regions is inversely associated with MoCA scores, indicating structural and functional links between iron burden and cognitive deficits [39]. A subsequent study has linked QSM-derived iron metrics to the severity and progression of cognitive impairment in PD, suggesting a pathological connection between iron dyshomeostasis and dysfunction of corticostriatal networks [40]. These observations align with the present framework: SNH primarily reflects nigral iron deposition and glial activation, providing a PD-specific anchor for peripheral inflammation-related signals, whereas QSM quantifies iron burden in key extranigral circuits (including cortical and striatal regions), complementing ultrasound by addressing limitations in spatial resolution and quantification for mechanistic interpretation.

The core clinical insight of this study is the development of a CI-oriented diagnostic framework that integrates SNH measured by transcranial sonography with the inflammatory biomarkers CRP and Hcy, providing a practical avenue for early screening and more precise intervention in PD. In PD patients at lower risk of CI, the SNH+CRP/Hcy combination achieves a sensitivity of 76.76%–80.28% in the PD-CI group. When all three markers are combined, the AUC increases to 0.783, enabling efficient preliminary screening. For high-risk PD populations (such as those with RBD or olfactory dysfunction), the SNH+CRP/Hcy combination achieves a sensitivity of 84.15% in the PD-dementia group. When all three are combined, the AUC reaches 0.823, maximizing the identification of early cognitive decline signals. In specialized clinical settings, the SNH+CRP+Hcy model demonstrates balanced performance, supporting the accurate identification of PD-dementia. Because transcranial sonography is simple, safe, and cost-effective, combining it with readily available blood markers yields a screening protocol suitable for primary care and may reduce costs compared with strategies relying on cerebrospinal fluid testing or advanced imaging. This panel is positioned as a complement to the Montreal Cognitive Assessment, allowing integration into standardized PD assessment pathways and enabling prioritization of neuropsychological testing and shorter follow-up intervals when MoCA results are normal or borderline but clinical concern persists. This approach may help address limitations of single neuropsychological assessments and enable earlier identification of cognitive impairment in PD, thereby guiding clinical decisions.

This study has limitations. First, follow-up data were not available. However, as a cross-sectional study, the research focused on evaluating the diagnostic performance of the SNH and inflammation-associated biomarker model rather than disease-progression trajectories. Multivariable logistic regression controlled for time-related variables (e.g., disease duration and age), and the model maintained significant diagnostic power across CI stages, supporting the robustness of the conclusions. Future work will employ longitudinal cohorts, combining MoCA subscale scores with dynamic biomarker changes to construct a temporal risk model for cognitive decline. Second, cognitive status was defined using MoCA cutoffs for pragmatic, reproducible multicenter evaluation of model discrimination, rather than diagnostic confirmation by comprehensive neuropsychological assessment. Because MoCA may be influenced by education, we interpret education-related findings as screening-based associations rather than a causal “protective” effect. Third, the sample size was modest, increasing the risk of overfitting. Nevertheless, credibility is supported by a parsimonious, clinically prespecified set of covariates. Effect directions and magnitudes were consistent across cognitive strata. Evidence converged across independent modalities, including SNH and inflammation-related biomarkers. AUC estimates and their confidence intervals were clearly above chance. Findings support the model’s potential to guide early risk stratification and clinical decision-making, while underscoring the need for confirmation in larger prospectively validated cohorts and external validation studies. Future studies will expand sample size through multicenter collaboration to enhance generalizability.

In conclusion, a diagnostic classification model based on ultrasound-derived SNH and the inflammation-associated biomarkers CRP and Hcy was proposed, establishing a framework for stratified diagnosis of CI stage in PD. This framework may provide clinical targets for individualized intervention and help optimize PD risk stratification and clinical pathways.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

WH and WZ conceived and designed the study, provided study materials/patients, and provided administrative support. JZ conceived and designed the study and provided study materials/patients. JZ, YL, SW and LX collected and assembled the data. JZ and CH analyzed and interpreted the data. All authors drafted the manuscript or revised it critically for important intellectual content. All authors read and approved the final version to be published. All authors participated sufficiently in the work and agreed to be accountable for all aspects of the work, ensuring that any questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

The study protocol was approved by the Institutional Board of Beijing Tiantan Hospital, Capital Medical University (approval No. KY2022-015-04). Informed consent was obtained from all participants. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and the Declaration of Helsinki (1964) and its later amendments or comparable ethical standards.

Not applicable.

This work was supported by the National Natural Science Foundation of China (No. 82271995 and 81730050).

The authors declare no conflict of interest.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/JIN46585.