Third ventricular width by transcranial sonography is associated with cognitive impairment in Parkinson's disease

Association between third ventricular width assessed by transcranial sonography and plasma homocysteine in Parkinson's disease with cognitive impairment and their potential to predict conversion to dementia

Background

Cognitive impairment (CI) is a prevalent non-motor symptom in Parkinson's disease (PD) that often leads to disability. This study aimed to explore the association between plasma homocysteine (Hcy) and the width of third ventricle (V3) in PD patients with CI and their potential to predict dementia conversion.

Methods

Totals of 118 PD patients with normal cognition (PD-NC), 81 PD patients with mild CI (PD-MCI), 58 PD patients with dementia (PDD), and 35 healthy controls (HCs) were retrospectively recruited. V3 width was measured using transcranial sonography (TCS), plasma Hcy level was quantified using cyclase assay, and cognitive function was analyzed using the Montreal Cognitive Assessment (MoCA).

Results

Both V3 width and plasma Hcy concentration were negatively correlated with MoCA scores in PD (r=-0.358, P<0.001; r=-0.187, P=0.013, respectively). Receiver operating characteristic (ROC) analysis suggested that V3 width and Hcy were able to discriminate PDD and PD without dementia [area under the curve (AUC) =0.767 and 0.628, respectively], PD-NC and PD with cognitive decline (AUC =0.735 and 0.657, respectively), and PD-NC and PD-MCI (AUC =0.683 and 0.664, respectively). Following an average follow-up period of 31.04±18.84 months, PD-MCI patients with a V3 width ≥6.55 mm were at a 4.085 times higher risk of developing dementia, whereas PD-NC patients with a V3 width ≥5.75 mm had nearly double the risk of progressing to MCI. However, baseline plasma Hcy levels were unsuitable to predict cognition alternation over time.

Conclusions

Plasma Hcy level and V3 width are associated with cognition function severity in PD patients, a V3 width is an independent predictor of MCI-dementia conversion in PD.

A nomogram based on neuron-specific enolase and substantia nigra hyperechogenicity for identifying cognitive impairment in Parkinson's disease

Background

One in four individuals with Parkinson's disease (PD) experience cognitive impairment (CI). However, few practical models integrating clinical and neuroimaging biomarkers have been developed to address CI in PD. This study aimed to evaluate the correlation between circulating neuron-specific enolase (NSE) levels, substantia nigra hyperechogenicity (SNH), and cognitive function in PD and to develop a nomogram based on clinical and neuroimaging biomarkers for predicting CI in patients with PD.

Methods

A total of 385 patients with PD who underwent transcranial sonography (TCS) from January 2021 to December 2022 at Beijing Tiantan Hospital, Capital Medical University, were recruited as the training cohort. For validation, 165 patients with PD treated from January 2023 to December 2023 were enrolled. Data for SNH, plasma NSE, and other clinical measures were collected, and cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Logistic regression analysis was employed to select potential risk factors and establish a nomogram. The receiver operating characteristic curve and calibration curve were generated to evaluate the performance of the nomogram.

Results

Patients with PD exhibiting CI displayed advanced age, elevated Unified PD Rating Scale-III (UPDRS-III) score, an increased percentage of SNH, higher levels of plasma NSE and homocysteine (Hcy), a larger SNH area, and lower education levels compared to PD patients without CI. Gender [odds ratio (OR) =0.561, 95% confidence interval (CI): 0.330-0.954, P=0.03], age (OR =1.039; 95% CI: 1.011-1.066; P=0.005), education level (OR =0.892; 95% CI: 0.842-0.954; P<0.001), UPDRS-III scores (OR =1.026; 95% CI: 1.009-1.043; P=0.003), plasma NSE concentration (OR =1.562; 95% CI: 1.374-1.776; P<0.001), and SNH (OR =0.545; 95% CI: 0.330-0.902; P=0.02) were independent predictors of CI in patients with PD. A nomogram developed using these six factors yielded a moderate discrimination performance with an area under the curve (AUC) of 0.823 (95% CI 0.781-0.864; P<0.001). The calibration curve demonstrated acceptable agreement between predicted outcomes and actual values. Validation further confirmed the reliability of the nomogram, with an AUC of 0.864 (95% CI: 0.805-0.922; P<0.001).

Conclusions

The level of NSE in plasma and the SNH assessed by TCS are associated with CI in patients with PD. The proposed nomogram has the potential to facilitate the detection of cognitive decline in individuals with PD.

The severity assessment of Parkinson's disease based on plasma inflammatory factors and third ventricle width by transcranial sonography

BACKGROUND

Predicting Parkinson's disease (PD) can provide patients with targeted therapies. However, disease severity can be roughly evaluated in clinical practice based on the patient's symptoms and signs.

OBJECTIVE

The current study attempted to explore the factors linked with PD severity and construct a predictive model.

METHOD

The PD patients and healthy controls were recruited from our study center while recording their basic demographic information. The serum inflammatory markers levels, such as Cystatin C (Cys C), C-reactive protein (CRP), RANTES (regulated on activation, normal T cell expressed and secreted), Interleukin-10 (IL-10), and Interleukin-6 (IL-6) were determined for all the participants. PD patients were categorized into early and mid-advanced groups based on the Hoehn and Yahr (H-Y) scale and evaluated using PD-related scales. LASSO logistic regression analysis (Model C) helped select variables based on clinical scale evaluations, serum inflammatory factor levels, and transcranial sonography measurements. The optimal harmonious model coefficient λ was determined via 10-fold cross-validation. Moreover, Model C was compared with multivariate (Model A) and stepwise (Model B) logistic regression. The area under the curve (AUC) of a receiver operator characteristic (ROC), brier score, calibration curve, and decision curve analysis (DCA) helped determine the discrimination and calibration of the predictive model, followed by configuring a forest plot and column chart.

RESULTS

The study included 113 healthy individuals and 102 PD patients, with 26 early and 76 mid-advanced patients. Univariate analysis of variance screened out statistically significant differences among inflammatory markers Cys C and RANTES. The average Cys C level in the mid-advanced stage was significantly higher than in the early stage (p < 0.001) but not for RANTES (p = 0.740). The LASSO logistic regression model (λ.1se = 0.061) associated with UPDRS-I, UPDRS-II, UPDRS-III, HAMA, PDQ-39, and Cys C as the included independent variables revealed that the Model C discrimination and calibration (AUC = 0.968, Brier = 0.049) were superior to Model A (AUC = 0.926, Brier = 0.079) and Model B (AUC = 0.929, Brier = 0.071) models.

CONCLUSION

The study results show multiple factors are linked with PD assessment. Moreover, the inflammatory marker Cys C and transcranial sonography measurement could objectively predict PD symptom severity, helping doctors monitor PD evolution in patients while targeting interventions.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.