Plasma extracellular vesicle synaptic proteins as biomarkers of clinical progression in patients with Parkinson's disease.

Synaptic dysfunction plays a key role in Parkinson's disease (PD), and plasma extracellular vesicle (EV) synaptic proteins are emerging as biomarkers for neurodegenerative diseases. Assessment of plasma EV synaptic proteins for their efficacy as biomarkers in PD and their relationship with disease progression was conducted. In total, 144 participants were enrolled, including 101 people with PD (PwP) and 43 healthy controls (HCs). The changes in plasma EV synaptic protein levels between baseline and 1-year follow-up did not differ significantly in both PwP and HCs. In PwP, the changes in plasma EV synaptic protein levels were significantly associated with the changes in Unified Parkinson's Disease Rating Scale (UPDRS)-II and III scores. Moreover, PwP with elevated levels (first quartile) of any one plasma EV synaptic proteins (synaptosome-associated protein 25, growth-associated protein 43 or synaptotagmin-1) had significantly greater disease progression in UPDRS-II score and the postural instability and gait disturbance subscore in UPDRS-III than did the other PwP after adjustment for age, sex, and disease duration. The promising potential of plasma EV synaptic proteins as clinical biomarkers of disease progression in PD was suggested. However, a longer follow-up period is warranted to confirm their role as prognostic biomarkers.

Beyond diagnosis: Leveraging routine blood and urine biomarkers to predict severity and functional outcome in acute ischemic stroke.

Background: The initial severity of acute ischemic stroke (AIS) is a crucial predictor of the disease outcome. In this study, blood and urine biomarkers from patients with AIS were measured to estimate stroke severity and predict long-term stroke outcomes. Methods: The medical records of patients with AIS between October 2016 and May 2020 were retrospectively analyzed. The relationships of blood and urine biomarkers with stroke severity at admission were evaluated in patients with AIS. Predictive models for initial stroke severity and long-term prognosis were then developed using a panel of identified biomarkers. Results: A total of 2229 patients were enrolled. Univariate analysis revealed 12 biomarkers associated with the National Institutes of Health Stroke Scale scores at admission. The area under the curve values for predicting initial stroke severity and long-term prognosis on the basis of these biomarkers were 0.7465, 0.7470, and 0.8061, respectively. Among multiple tested machine-learning, eXtreme gradient boosting exhibited the highest effectiveness in predicting 90-day modified Rankin Scale scores. SHapley Additive exPlanations revealed fasting glucose, albumin, hemoglobin, prothrombin time, and urine-specific gravity to be the top five most crucial biomarkers. Conclusion: These findings demonstrate that clinically available blood and urine biomarkers can effectively estimate initial stroke severity and predict long-term prognosis in patients with AIS. Our results provide a scientific basis for developing tailored clinical treatment and management strategies for AIS, through incorporating liquid biomarkers into stroke risk assessment and patient care protocols for patients with AIS.

Intra-Arterial Thrombectomy for Acute Ischemic Stroke Related to the Procoagulant Effect of Warfarin in A Patient with Atrial Fibrillation and Bioprosthetic Valve Replacement.

PURPOSE: Warfarin is associated with paradoxical procoagulant effect that leads to a transient hypercoagulable state and acute ischemic stroke (AIS). This clinical dilemma is further confounded when the patient has multiple comorbidities and the optimal treatment strategies are unclear. CASE REPORT: We report a 78-year-old male with valvular heart disease, congestive heart failure, and atrial fibrillation, who received bioprosthetic valve replacement and developed AIS related to the paradoxical procoagulant effect of warfarin. Emergent cerebral angiography with mechanical thrombectomy was performed, and recanalization was successfully achieved. After shifting warfarin to nonvitamin K oral anticoagulant (NOAC), the paradoxical procoagulant effect ameliorated. CONCLUSION: This report describes the roles of endovascular therapy and NOAC in patients with similar highly complex conditions and has clinical relevance for therapeutic plans in the clinical setting.

Therapeutic hypothermia in patients after cardiac arrest: A systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: Targeted temperature management (TTM) with therapeutic hypothermia (TH) has been used to improve neurological outcomes in patients after cardiac arrest; however, several trials have reported conflicting results regarding its effectiveness. This systematic review and meta-analysis assessed whether TH was associated with better survival and neurological outcomes after cardiac arrest. METHOD: We searched online databases for relevant studies published before May 2023. Randomized controlled trials (RCTs) comparing TH and normothermia in post-cardiac-arrest patients were selected. Neurological outcomes and all-cause mortality were assessed as the primary and secondary outcomes, respectively. A subgroup analysis according to initial electrocardiography (ECG) rhythm was performed. RESULT: Nine RCTs (4058 patients) were included. The neurological prognosis was significantly better in patients with an initial shockable rhythm after cardiac arrest (RR = 0.87, 95% confidence interval [CI] = 0.76-0.99, P = 0.04), especially in those with earlier TH initiation (<120 min) and prolonged TH duration (≥24 h). However, the mortality rate after TH was not lower than that after normothermia (RR = 0.91, 95% CI = 0.79-1.05). In patients with an initial nonshockable rhythm, TH did not provide significantly more neurological or survival benefits (RR = 0.98, 95% CI = 0.93-1.03 and RR = 1.00, 95% CI = 0.95-1.05, respectively). CONCLUSION: Current evidence with a moderate level of certainty suggests that TH has potential neurological benefits for patients with an initial shockable rhythm after cardiac arrest, especially in those with faster TH initiation and longer TH maintenance.

Cytokine profiles of plasma extracellular vesicles as progression biomarkers in Parkinson's disease.

BACKGROUND: Inflammation contributes substantially to the pathogenesis of Parkinson's disease (PD). Plasma extracellular vesicle (EV)-derived cytokines are emerging biomarkers of inflammation. We conducted a longitudinal study of the plasma EV-derived cytokine profiles of people with PD (PwP). METHODS: A total of 101 people with mild to moderate PD and 45 healthy controls (HCs) were recruited, and they completed motor assessments (Unified Parkinson Disease Rating Scale [UPDRS]) and cognitive tests at baseline and 1-year follow-up. We isolated the participants' plasma EVs and analyzed their levels of cytokines, including interleukin (IL)-1ß, IL-6, IL-10, tumor necrosis factor (TNF)-α, and transforming growth factor (TGF)-ß. RESULTS: We noted no significant changes in the plasma EV-derived cytokine profiles of the PwPs and HCs between baseline and the 1-year follow-up. Among the PwP, changes in plasma EV-derived IL-1ß, TNF-α and IL-6 levels were significantly associated with changes in the severity of postural instability and gait disturbance (PIGD) and cognition. Baseline plasma EV-derived IL-1ß, TNF-α, IL-6, and IL-10 levels were significantly associated with the severity of PIGD and cognitive symptoms at follow-up, and PwP with elevated IL-1ß and IL-6 levels exhibited significant progression of PIGD over the study period. CONCLUSION: These results suggested the role of inflammation in PD progression. In addition, baseline levels of plasma EV-derived proinflammatory cytokines can be used to predict the progression of PIGD, the most severe motor symptom of PD. Additional studies with longer follow-up periods are necessary, and plasma EV-derived cytokines may serve as effective biomarkers of PD progression.

XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke.

An all-inclusive and accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. This study developed extreme gradient boosting (XGBoost)-based models using three simple factors-age, fasting glucose, and National Institutes of Health Stroke Scale (NIHSS) scores-to predict the three-month functional outcomes after AIS. We retrieved the medical records of 1848 patients diagnosed with AIS and managed at a single medical center between 2016 and 2020. We developed and validated the predictions and ranked the importance of each variable. The XGBoost model achieved notable performance, with an area under the curve of 0.8595. As predicted by the model, the patients with initial NIHSS score > 5, aged over 64 years, and fasting blood glucose > 86 mg/dL were associated with unfavorable prognoses. For patients receiving endovascular therapy, fasting glucose was the most important predictor. The NIHSS score at admission was the most significant predictor for those who received other treatments. Our proposed XGBoost model showed a reliable predictive power of AIS outcomes using readily available and simple predictors and also demonstrated the validity of the model for application in patients receiving different AIS treatments, providing clinical evidence for future optimization of AIS treatment strategies.

Plasma extracellular vesicle tau, ß-amyloid, and α-synuclein and the progression of Parkinson's disease: a follow-up study.

Background: Plasma extracellular vesicle (EV) contents are promising biomarkers of Parkinson's disease (PD). The pathognomonic proteins of PD, including α-synuclein, tau, and ß-amyloid, are altered in people with PD (PwP) and are associated with clinical presentation in previous cross-sectional studies. However, the dynamic changes in these plasma EV proteins in PwP and their correlation with clinical progression remain unclear. Objective: We investigated the dynamic changes in plasma EV α-synuclein, tau, and ß-amyloid and their correlation with/prediction of clinical progression in PwP. Design: A cohort study. Methods: In total, 103 PwP and 37 healthy controls (HCs) completed baseline assessment and 1-year follow-up. Clinical assessments included Unified Parkinson's Disease Rating Scale (UPDRS) parts II and III, Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). Plasma EVs were isolated, and immunomagnetic reduction-based immunoassay was used to assess α-synuclein, tau, and ß-amyloid 1-42 (Aß1-42) levels within the EVs. Results: Compared with HCs, significant differences were noted in the annual changes in all three EV pathognomonic proteins in PwP. Although the absolute changes in plasma EV pathognomonic proteins did not significantly correlate with clinical changes, PwP with elevated baseline plasma EV tau (upper-half) levels demonstrated significantly greater decline in motor and cognition, and increased plasma EV α-synuclein levels were associated with postural instability and the gait disturbance motor subtype. For PwP with elevated levels of all three biomarkers, clinical deterioration was significant, as indicated by UPDRS-II scores, postural instability and gait disturbance subscores of UPDRS-III, and MMSE score. Conclusion: The combination of plasma EV α-synuclein, tau, and Aß1-42 may identify PwP with a high risk of deterioration. Our findings can elucidate the interaction between these pathognomonic proteins, and they may serve as treatment response markers and can be applied in treatment approaches for disease modification.

Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks.

Background: Accurate estimation of prolonged length of hospital stay after acute ischemic stroke provides crucial information on medical expenditure and subsequent disposition. This study used artificial neural networks to identify risk factors and build prediction models for a prolonged length of stay based on parameters at the time of hospitalization. Methods: We retrieved the medical records of patients who received acute ischemic stroke diagnoses and were treated at a stroke center between January 2016 and June 2020, and a retrospective analysis of these data was performed. Prolonged length of stay was defined as a hospital stay longer than the median number of days. We applied artificial neural networks to derive prediction models using parameters associated with the length of stay that was collected at admission, and a sensitivity analysis was performed to assess the effect of each predictor. We applied 5-fold cross-validation and used the validation set to evaluate the classification performance of the artificial neural network models. Results: Overall, 2,240 patients were enrolled in this study. The median length of hospital stay was 9 days. A total of 1,101 patients (49.2%) had a prolonged hospital stay. A prolonged length of stay is associated with worse neurological outcomes at discharge. Univariate analysis identified 14 baseline parameters associated with prolonged length of stay, and with these parameters as input, the artificial neural network model achieved training and validation areas under the curve of 0.808 and 0.788, respectively. The mean accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of prediction models were 74.5, 74.9, 74.2, 75.2, and 73.9%, respectively. The key factors associated with prolonged length of stay were National Institutes of Health Stroke Scale scores at admission, atrial fibrillation, receiving thrombolytic therapy, history of hypertension, diabetes, and previous stroke. Conclusion: The artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic stroke and identified crucial factors associated with a prolonged hospital stay. The proposed model can assist in clinically assessing the risk of prolonged hospitalization, informing decision-making, and developing individualized medical care plans for patients with acute ischemic stroke.

Application of machine learning-based models to boost the predictive power of the SPAN index.

BACKGROUND: This study re-explored the predictive validity of Stroke Prognostication using Age and National Institutes of Health Stroke Scale (SPAN) index in patients who received different treatments for acute ischemic stroke (AIS) and developed machine learning-boosted outcome prediction models. METHODS: We evaluated the prognostic relevance of SPAN index in patients with AIS who received intravenous tissue-type plasminogen activator (IV-tPA), intra-arterial thrombolysis (IAT) or non-thrombolytic treatments (non-tPA), and applied machine learning algorithms to develop SPAN-based outcome prediction models in a cohort of 2145 hospitalized AIS patients. The performance of the models was assessed and compared using the area under the receiver operating characteristic curves (AUCs). RESULTS: SPAN index ≥100 was associated with higher mortality rate and higher modified Rankin Scale at discharge in AIS patients who received the different treatments. Compared to the lower AUCs for the SPAN-alone model across all groups, the AUCs of the logistic regression-boosted model were 0.838, 0.857, 0.766 and 0.875 for the whole cohort, non-tPA, IV-tPA and IAT groups, respectively. Similarly, the AUCs of the generated artificial neural network were 0.846, 0.858, 0.785 and 0.859 for the whole cohort, non-tPA, IV-tPA and IAT groups, respectively, while for gradient boosting decision tree model, we computed 0.850, 0.863, 0.779 and 0.815. CONCLUSIONS: SPAN index has prognostic relevance in patients with AIS who received different treatments. The generated machine learning-based models exhibit good performance for predicting the functional recovery of AIS; thus, their proposed clinical application to aid outcome prediction and decision-making for the patients with AIS.

Logistic regression and artificial neural network-based simple predicting models for obstructive sleep apnea by age, sex, and body mass index.

Age, sex, and body mass index (BMI) were associated with obstructive sleep apnea (OSA). Although various methods have been used in OSA prediction, this study aimed to develop predictions using simple and general predictors incorporating machine learning algorithms. This single-center, retrospective observational study assessed the diagnostic relevance of age, sex, and BMI for OSA in a cohort of 9, 422 patients who had undergone polysomnography (PSG) between 2015 and 2020. The participants were randomly divided into training, testing, and independent validation groups. Multivariable logistic regression (LR) and artificial neural network (ANN) algorithms used age, sex, and BMI as predictors to develop risk-predicting models for moderate-and-severe OSA. The training-testing dataset was used to assess the model generalizability through five-fold cross-validation. We calculated the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the independent validation set to assess the performance of the model. The results showed that age, sex, and BMI were significantly associated with OSA. The validation AUCs of the generated LR and ANN models were 0.806 and 0.807, respectively. The independent validation set's accuracy, sensitivity, specificity, PPV, and NPV were 76.3%, 87.5%, 57.0%, 77.7%, and 72.7% for the LR model, and 76.4%, 87.7%, 56.9%, 77.7%, and 73.0% respectively, for the ANN model. The LR- and ANN-boosted models with the three simple parameters effectively predicted OSA in patients referred for PSG examination and improved insight into risk stratification for OSA diagnosis.

Poststroke Cognitive Impairment: A Longitudinal Follow-Up and Pre/Poststroke Mini-Mental State Examination Comparison.

BACKGROUND: Poststroke cognitive impairment (PSCI) is a prevalent cause of disability in people with stroke. PSCI results from either lesion-dependent loss of cognitive function or augmentation of Alzheimer's pathology due to vascular insufficiency. The lack of prestroke cognitive assessments limits the clear understanding of the impact of PSCI on cognition. OBJECTIVE: The present study aims to make a direct comparison of longitudinal cognitive assessment results to clarify the impact of ischemic stroke on PSCI and assess the cognitive decline in PSCI compared to people with Alzheimer's disease (AD). METHODS: All study participants had their Mini-Mental State Examination (MMSE) at the chronic poststroke stage (≥6 months after stroke), which was compared with prestroke or acute poststroke (<6 months after stroke) MMSE to investigate the two aspects of PSCI. A group of patients with AD was used to reference the speed of neurodegenerative cognitive deterioration. Repeated measures analysis of variance was used to compare the longitudinal change of MMSE. RESULTS: MMSE score between acute and chronic poststroke revealed a 1.8 ± 6.49 decline per year (n=76), which was not significantly different from the AD patients who underwent cholinesterase inhibitors treatment (-1.11 ± 2.61, p=0.35, n=232). MMSE score between prestroke and chronic poststroke (n=33) revealed a significant decline (-6.52 ± 6.86, p < 0.001). In addition, their cognitive deterioration was significantly associated with sex, age, and stroke over the white matter or basal ganglia. CONCLUSION: Ischemic stroke substantially affects cognition with an average six-point drop in MMSE. The rate of cognitive decline in PSCI was similar to AD, and those with white matter or basal ganglia infarct were at greater risk of PSCI.

Safety and efficacy of endovascular thrombectomy in acute ischemic stroke treated with anticoagulants: a systematic review and meta-analysis.

BACKGROUND: Endovascular thrombectomy (EVT) is an effective therapy in acute ischemic stroke (AIS) with large vessel occlusion, especially for those who are unsuitable for intravenous thrombolysis. However, the safety and efficacy of EVT in AIS patients who receiving oral anticoagulants (OACs) is unclear, especially for the risk of symptomatic intracranial hemorrhage (sICH).  METHODS: Database of PubMed, Embase, and Cochrane Library were searched from Jan 1, 2000, through the final search date of Jun 2, 2021. Eligible studies for enrollment required outcomes reported for events of sICH, mortality, functional status, and successful reperfusion. Meta-analysis was conducted to compare the outcomes difference after EVT between AIS patients with or without OACs use. The primary safety outcome was sICH after EVT, and the primary efficacy outcome was functional status at 3 months.  RESULTS: One thousand nine hundred forty studies were screened for eligibility and 15 of them were included in the meta-analysis. Compared the OACs group to control arm, vitamin K antagonists (VKAs) was associated with higher risk of sICH (OR 1.49, 95% CI 1.10-2.02) and mortality (OR 1.67, 95% CI 1.35-2.06). Poor functional outcomes were noted both in the VKAs and direct oral anticoagulants (DOACs) groups (OR 0.62, 95% CI 0.54-0.71 and OR 0.61, 95% CI 0.53-0.71, respectively). No differences in successful reperfusion were observed. CONCLUSIONS: Comparing with DOACs, VKAs use was associated with a higher risk of sICH and mortality after EVT. Patients who did not receive OACs exhibited more favorable outcomes. The successful reperfusion did not differ between groups. However, results for mortality and functional outcomes have to be interpreted with caution since they are based on non-randomized data and unadjusted proportions.

Artificial neural network-boosted Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management.

Existing prognostic models to predict the neurological recovery in patients with cardiac arrest receiving targeted temperature management (TTM) either exhibit moderate accuracy or are too complicated for clinical application. This necessitates the development of a simple and generalizable prediction model to inform clinical decision-making for patients receiving TTM. The present study explores the predictive validity of the Cardiac Arrest Survival Post-resuscitation In-hospital (CASPRI) score in cardiac arrest patients receiving TTM, regardless of cardiac event location, and uses artificial neural network (ANN) algorithms to boost the prediction performance. This retrospective observational study evaluated the prognostic relevance of the CASPRI score and applied ANN to develop outcome prediction models in a cohort of 570 patients with cardiac arrest and treated with TTM between 2014 and 2019 in a nationwide multicenter registry in Taiwan. In univariate logistic regression analysis, the CASPRI score was significantly associated with neurological outcome, with the area under the receiver operating characteristics curve (AUC) of 0.811. The generated ANN model, based on 10 items of the CASPRI score, achieved a training AUC of 0.976 and validation AUC of 0.921, with the accuracy, precision, sensitivity, and specificity of 89.2%, 91.6%, 87.6%, and 91.2%, respectively, for the validation set. CASPRI score has prognostic relevance in patients who received TTM after cardiac arrest. The generated ANN-boosted, CASPRI-based model exhibited good performance for predicting TTM neurological outcome, thus, we propose its clinical application to improve outcome prediction, facilitate decision-making, and formulate individualized therapeutic plans for patients receiving TTM.

Efficacy and safety of botulinum toxin for treating sialorrhea: A systematic review and meta-analysis.

BACKGROUND AND PURPOSE: Sialorrhea often happens in patients with neurologic disorders, and botulinum toxin (BoNT), which inhibits acetylcholine activation, may be an effective treatment for drooling. This systematic review and meta-analysis of randomized control trials aims to evaluate the efficacy and safety of BoNT in adults and children with sialorrhea due to neurological disorders. METHODS: The PubMed, Embase, and Cochrane databases were searched for relevant studies published before August 2021. The pooled estimate of outcomes was calculated using a random effect model. RESULTS: The review included 17 studies involving 981 patients. Compared with placebo, both BoNT type A (BoNT-A) and BoNT type B (BoNT-B) alleviated drooling frequency and severity (mean difference, 95% CI; BoNT-A: -1.20, -1.89 to -0.51; BoNT-B: -1.62, -2.07 to -1.17), reduced saliva weight (BoNT-A: -1.70, -2.30 to -1.10; BoNT-B: -1.12, -1.97 to -0.27), and improved global impression of change (BoNT-A: -1.30, -1.73 to -0.86; BoNT-B: -1.58, -1.95 to -1.21) in adults 4 weeks postinjection. BoNT-B remained effective at 12 weeks. In children, BoNT-A and BoNT-B alleviated sialorrhea symptoms (BoNT-A: -1.63, -2.42 to -0.85; BoNT-B: -5.20, -6.03 to -4.37) and BoNT-A reduced saliva weight (-0.77, -1.54 to 0.00) at 4 weeks postinjection. After 12 weeks, BoNT-B remained efficacious. Most adverse effects (AEs) were mild to moderate and self-limited. CONCLUSIONS: There is moderate certainty of evidence (COE) that either BoNT-A or BoNT-B could relieve sialorrhea after 4 and 12 weeks of follow-up without significantly more severe AEs in adults. However, the COE is very low to low in children.

Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks.

BACKGROUND: To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN). METHODS: The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome. ANN were applied for developing outcome prediction models. The generalizability of the models was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: The parameters associated with survival were age, duration of cardiopulmonary resuscitation, history of diabetes mellitus (DM), heart failure, end-stage renal disease (ESRD), systolic blood pressure (BP), diastolic BP, body temperature, motor response after ROSC, emergent coronary angiography or percutaneous coronary intervention (PCI), and the cooling methods. The parameters associated with the favorable neurologic outcomes were age, sex, DM, chronic obstructive pulmonary disease, ESRD, stroke, pre-arrest cerebral-performance category, BP, body temperature, motor response after ROSC, emergent coronary angiography or PCI, and cooling methods. After adequate training, ANN Model 1 to predict survival achieved an AUC of 0.80. Accuracy, sensitivity, and specificity were 75.9%, 71.6%, and 79.3%, respectively. ANN Model 4 to predict the favorable neurologic outcome achieved an AUC of 0.87, with accuracy, sensitivity, and specificity of 86.7%, 77.7%, and 88.0%, respectively. CONCLUSION: The ANN-based models achieved good performance to predict the survival and favorable neurologic outcomes after TTM. The models proposed have clinical value to assist in decision-making.

Blood Neurofilament Light Chain in Parkinson's Disease: Comparability between Parkinson's Progression Markers Initiative (PPMI) and Asian Cohorts.

Elevated blood neurofilament light chain (NfL), which indicates the loss of neuronal integrity, is increasingly implicated as a diagnostic and outcome-predicting biomarker for neurological diseases. However, its diagnostic implication for Parkinson's disease (PD) remains unclear, with conflicting data reported by several studies. This may result from the demographic heterogeneity of the studied cohorts. The present study investigated the comparability of blood NfL between a domestic, single-centered PD cohort from Shuang Ho Hospital (SHH) in Taiwan, with the large international, multi-center cohort, Parkinson's Progression Markers Initiative (PPMI). In the SHH PD cohort, with 61 people with PD (PwP) and 25 healthy non-PD controls, plasma NfL unexpectedly was significantly higher in the control group than PwP (14.42 ± 13.84 vs. 9.39 ± 6.91 pg/mL, p = 0.05). Interestingly, subgroup analysis revealed a non-significant difference of plasma NfL levels in male PwP compared with controls (8.58 ± 6.21 vs. 7.25 ± 4.43 pg/mL, p =0.575), whereas NfL levels were significantly lower in the female PwP group than in their healthy control peers (10.29 ± 7.62 vs. 17.79 ± 15.52 pg/mL, p = 0.033). Comparative analysis of the SHH and PPMI cohorts revealed a comparable gender-stratified distribution of blood NfL based on approximate theoretical quantiles. After adjusting for age and gender, no apparent difference in NfL value distribution was observed between the SHH and PPMI cohorts' control or PD groups. Significant downregulation of blood NfL levels were positively correlated with a reduced probability of having a PD diagnosis in both cohorts. These results demonstrated that the adjustment for demographic background enhances comparability between cohorts, and may be required to eliminate covariate/confounder-associated conflict in blood NfL results between different PD studies. This experience may be beneficial to other researchers around the world who are saddled with limited study participants, especially as data from small cohort sizes are often at greater risk of being skewed by specific variables.

Plasma extracellular vesicles tau and ß-amyloid as biomarkers of cognitive dysfunction of Parkinson's disease.

The contribution of circulatory tau and ß-amyloid in Parkinson's disease (PD), especially the cognitive function, remains inconclusive. Extracellular vesicles (EVs) cargo these proteins throughout the bloodstream after they are directly secreted from many cells, including neurons. The present study aims to investigate the role of the plasma EV-borne tau and ß-amyloid as biomarkers for cognitive dysfunction in PD by investigating subjects with mild to moderate stage of PD (n = 116) and non-PD controls (n = 46). Plasma EVs were isolated, and immunomagnetic reduction-based immunoassay was used to assess the levels of α-synuclein, tau, and ß-amyloid 1-42 (Aß1-42) within the EVs. Artificial neural network (ANN) models were then applied to predict cognitive dysfunction. We observed no significant difference in plasma EV tau and Aß1-42 between PD patients and controls. Plasma EV tau was significantly associated with cognitive function. Moreover, plasma EV tau and Aß1-42 were significantly elevated in PD patients with cognitive impairment when compared to PD patients with optimal cognition. The ANN model used the plasma EV α-synuclein, tau, and Aß1-42, as well as the patient's age and gender, as predicting factors. The model achieved an accuracy of 91.3% in identifying cognitive dysfunction in PD patients, and plasma EV tau and Aß1-42 are the most valuable factors. In conclusion, plasma EV tau and Aß1-42 are significant markers of cognitive function in PD patients. Combining with the plasma EV α-synuclein, age, and sex, plasma EV tau and Aß1-42 can identify cognitive dysfunction in PD patients. This study corroborates the prognostic roles of plasma EV tau and Aß1-42 in PD.

Retinal Diseases and Parkinson Disease: A Population-Based Study.

INTRODUCTION: Patients with Parkinson disease (PD) tend to have ophthalmic symptoms. Retinal diseases are associated with central nervous system diseases, especially neurodegenerative diseases. Here, we investigated the association of retinal diseases with PD, especially the temporal relationship before and after PD diagnosis. METHODS: Data were obtained from the National Health Insurance Research Database of Taiwan. In total, 21,845 patients with newly diagnosed PD were matched with four controls each on the basis of propensity score. This study was bidirectional. A case-control study evaluated the adjusted odds ratio (aOR) of retinal disease before PD diagnosis by using conditional logistic regression. Furthermore, a cohort study evaluated the adjusted subdistribution hazard ratio (aSHR) for new-onset retinal and optic nerve diseases after PD diagnosis by using competing risk analysis. The association between PD with optic nerve diseases and glaucoma (another common ophthalmic diseases with the consequence of retinal dysfunction) were also analyzed as reference. RESULTS: In the case-control study, PD was found to be significantly comorbid with recent and remote retinal disease [recent: ≤ 5 years, aOR: 1.12, 95% confidence interval (CI): 1.03-1.23; remote: > 5 years, aOR: 1.18, 95% CI: 1.04-1.34]. No similar association was identified between optic nerve disease or glaucoma with PD. In the cohort study, patients with PD were found to have a low risk of retinal disease in short-term (≤ 5 years, aSHR: 0.81, 95% CI: 0.71-0.93) and long-term (> 5 years, aSHR: 0.82, 95% CI: 0.72-0.93) follow-up. CONCLUSION: The study findings demonstrated that patients with prediagnostic PD were at greater risk of retinal disease than non-PD participants, but the risk reversed afterward. Thus, retinal disease may be a premotor manifestation of PD, and there may be some possible effect of dopamine supplements on retina.

Plasma Extracellular Vesicle α-Synuclein Level in Patients with Parkinson's Disease.

BACKGROUND: The most established pathognomonic protein of Parkinson's disease (PD), α-synuclein, is extensively investigated for disease diagnosis and prognosis; however, investigations into whether the free form of α-synuclein in the blood functions as a PD biomarker have not been fruitful. Extracellular vesicles (EVs) secreted from cells and present in blood transport molecules are novel platforms for biomarker identification. In blood EVs, α-synuclein originates predominantly from the brain without the interference of the blood-brain barrier. The present study investigated the role of plasma EV-borne α-synuclein as a biomarker of PD. METHODS: Patients with mild to moderate stages of PD (n = 116) and individuals without PD (n = 46) were recruited to serve as the PD study group and the control group, respectively. Plasma EVs were isolated, and immunomagnetic reduction-based immunoassay was used to assess EV α-synuclein levels. Conventional statistical analysis was performed using SPSS 25.0, and p < 0.05 was considered significant. RESULTS: Compared with controls, we observed significantly lower plasma EV α-synuclein levels in the patients with PD (PD: 56.0 ± 3.7 fg/mL vs. control: 74.5 ± 4.3 fg/mL, p = 0.009), and the significance remained after adjustment for age and sex. Plasma EV α-synuclein levels in the patients with PD did not correlate with age, disease duration, Part I and II scores of the Unified Parkinson's Disease Rating Scale (UPDRS), or the Mini-Mental State Examination scores. However, such levels were significantly correlated with UPDRS Part III score, which assesses motor dysfunction. Furthermore, the severity of akinetic-rigidity symptoms, but not tremor, was inversely associated with plasma EV α-synuclein level. CONCLUSION: Plasma EV α-synuclein was significantly different between the control and PD group and was associated with akinetic-rigidity symptom severity in patients with PD. This study corroborates the possible diagnostic and subtyping roles of plasma EV α-synuclein in patients with PD, and it further provides a basis for this protein's clinical relevance and feasibility as a PD biomarker.

Cytokine Profile in Plasma Extracellular Vesicles of Parkinson's Disease and the Association with Cognitive Function.

Plasma extracellular vesicles (EVs) containing various molecules, including cytokines, can reflect the intracellular condition and participate in cell-to-cell signaling, thus emerging as biomarkers for Parkinson's disease (PD). Inflammation may be a crucial risk factor for PD development and progression. The present study investigated the role of plasma EV cytokines as the biomarkers of PD. This cross-sectional study recruited 113 patients with PD, with mild to moderate stage disease, and 48 controls. Plasma EVs were isolated, and the levels of cytokines, including pro-interleukin (IL)-1ß, IL-6, IL-10, tumor necrosis factor (TNF)-α, and transforming growth factor (TGF)-ß1, were evaluated. Patients with PD had significantly increased plasma EV pro-IL-1ß and TNF-α levels compared with controls after adjustment for age and sex. Despite the lack of a significant association between plasma EV cytokines and motor symptom severity in patients with PD, cognitive dysfunction severity, assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment, was significantly associated with plasma EV pro-IL-1ß, IL-6, IL-10, and TNF-α levels. This association was PD specific and not found in controls. Furthermore, patients with PD cognitive deficit (MMSE < 26) exhibited a distinguished EV cytokine profile compared to those without cognitive deficit. The findings support the concept of inflammatory pathogenesis in the development and progression of PD and indicate that plasma EV cytokines may serve as PD biomarkers in future.