IMPORTANCEThe development of Parkinson disease (PD) may be promoted by exposure to air pollution. OBJECTIVE To investigate the potential association between exposure to particulate matters (PM 2.5 and PM 10 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), sulfur dioxide (SO 2 ), and carbon monoxide (CO) and the risk of incident PD. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study used data from the Korean National Health Insurance Service. Among the 1 021 208 Korean individuals in the database, those who had lived in Seoul from January 2002 to December 2006 (n = 176 875) were screened for eligibility. A total of 78 830 adults older than 40 years without PD and who lived in Seoul between January 2002 and December 2006 were included in this study. Individuals diagnosed with PD before 2006 (n = 159) and individuals 40 years or younger (n = 97 886) were excluded. Each participant was followed up with annually from January 2007 to December 2015, thereby adding up to 757 704 total person-years of follow-up. Data were analyzed from January to September 2020.EXPOSURES Individual exposure levels to PM 2.5 , PM 10 , NO 2 , O 3 , SO 2 , and CO were estimated based on the participants' residential address at the district level. To evaluate long-term exposure to air pollution, time-varying 5-year mean air pollutant exposure was calculated for each participant. MAIN OUTCOMES AND MEASURESThe outcome measure was the association between air pollution and the risk of incident PD measured as hazard ratios after adjusting for demographic factors, socioeconomic factors, and medical comorbidities.RESULTS At baseline, the mean (SD) age of the 78 830 participants was 54.4 (10.7) years, and 41 070 (52.1%) were female. A total of 338 individuals with newly diagnosed PD were identified during the study period. Exposure to NO 2 was associated with an increase in risk of PD (hazard ratio for highest vs lowest quartile, 1.41; 95% CI, 1.02-1.95; P for trend = .045). No statistically significant associations between exposure to PM 2.5, PM 10 , O 3 , SO 2 , or CO and PD incidence were found. CONCLUSIONS AND RELEVANCEIn this large cohort study, a statistically significant association between NO 2 exposure and PD risk was identified. This finding suggests the role of air pollutants in PD development, advocating for the need to implement a targeted public health policy.

Background and Purpose Decreasing the time delay for thrombolysis, including intravenous thrombolysis (IVT) with tissue plasminogen activator and intra-arterial thrombectomy (IAT), is critical for decreasing the morbidity and mortality of patients experiencing acute stroke. We aimed to decrease the in-hospital delay for both IVT and IAT through a multidisciplinary approach that is feasible 24 h/day. Methods We implemented the Stroke Alert Team (SAT) on May 2, 2016, which introduced hospital-initiated ambulance prenotification and reorganized in-hospital processes. We compared the patient characteristics, time for each step of the evaluation and thrombolysis, thrombolysis rate, and post-thrombolysis intracranial hemorrhage from January 2014 to August 2016. Results A total of 245 patients received thrombolysis (198 before SAT; 47 after SAT). The median door-to-CT, door-to-MRI, and door-to-laboratory times decreased to 13 min, 37.5 min, and 8 min, respectively, after SAT implementation (P<0.001). The median door-to-IVT time decreased from 46 min (interquartile range [IQR] 36–57 min) to 20.5 min (IQR 15.8–32.5 min; P<0.001). The median door-to-IAT time decreased from 156 min (IQR 124.5–212.5 min) to 86.5 min (IQR 67.5–102.3 min; P<0.001). The thrombolysis rate increased from 9.8% (198/2,012) to 15.8% (47/297; P=0.002), and the post-thrombolysis radiological intracranial hemorrhage rate decreased from 12.6% (25/198) to 2.1% (1/47; P=0.035). Conclusions SAT significantly decreased the in-hospital delay for thrombolysis, increased thrombolysis rate, and decreased post-thrombolysis intracranial hemorrhage. Time benefits of SAT were observed for both IVT and IAT and during office hours and after-hours.

No abstract

ObjectiveWe developed and investigated the feasibility of a machine learning–based automated rating for the 2 cardinal symptoms of Parkinson disease (PD): resting tremor and bradykinesia.MethodsUsing OpenPose, a deep learning–based human pose estimation program, we analyzed video clips for resting tremor and finger tapping of the bilateral upper limbs of 55 patients with PD (110 arms). Key motion parameters, including resting tremor amplitude and finger tapping speed, amplitude, and fatigue, were extracted to develop a machine learning–based automatic Unified Parkinson's Disease Rating Scale (UPDRS) rating using support vector machine (SVM) method. To evaluate the performance of this model, we calculated weighted κ and intraclass correlation coefficients (ICCs) between the model and the gold standard rating by a movement disorder specialist who is trained and certified by the Movement Disorder Society for UPDRS rating. These values were compared to weighted κ and ICC between a nontrained human rater and the gold standard rating.ResultsFor resting tremors, the SVM model showed a very good to excellent reliability range with the gold standard rating (κ 0.791; ICC 0.927), with both values higher than that of nontrained human rater (κ 0.662; ICC 0.861). For finger tapping, the SVM model showed a very good reliability range with the gold standard rating (κ 0.700 and ICC 0.793), which was comparable to that for nontrained human raters (κ 0.627; ICC 0.797).ConclusionMachine learning–based algorithms that automatically rate PD cardinal symptoms are feasible, with more accurate results than nontrained human ratings.Classification of EvidenceThis study provides Class II evidence that machine learning–based automated rating of resting tremor and bradykinesia in people with PD has very good reliability compared to a rating by a movement disorder specialist.

Background Little is known about newly developed stroke in patients admitted to the intensive care unit (ICU). Objective This study aimed to investigate characteristics and outcomes of newly developed stroke in patients admitted to the non-neurological intensive care units (ICU-onset stroke, IOS). Methods A consecutive series of adult patients who were admitted to the non-neurological ICU were included in this study. We compared neurological profiles, risk factors, and mortality rates between patients with IOS and those without IOS. Results Of 18,604 patients admitted to the ICU for non-neurological illness, 218 (1.2%) developed stroke (ischemic, n = 182; hemorrhagic, n = 36). The most common neurological presentation was altered mental status (n = 149), followed by hemiparesis (n = 55), and seizures (n = 28). The most common etiology of IOS was cardioembolism (50% [91/182]) for ischemic IOS and coagulopathy (67% [24/36]) for hemorrhagic IOS. In multivariable analysis,

Although several studies have identified a distinct gut microbial composition in Parkinson’s disease (PD), few studies have investigated the oral microbiome or functional alteration of the microbiome in PD. We aimed to investigate the connection between the oral and gut microbiome and the functional changes in the PD-specific gut microbiome using shotgun metagenomic sequencing. The taxonomic composition of the oral and gut microbiome was significantly different between PD patients and healthy controls (P = 0.003 and 0.001, respectively). Oral Lactobacillus was more abundant in PD patients and was associated with opportunistic pathogens in the gut (FDR-adjusted P < 0.038). Functional analysis revealed that microbial gene markers for glutamate and arginine biosynthesis were downregulated, while antimicrobial resistance gene markers were upregulated in PD patients than healthy controls (all P < 0.001). We identified a connection between the oral and gut microbiota in PD, which might lead to functional alteration of the microbiome in PD.

Microglial activation is a central player in the pathophysiology of Alzheimer’s disease (AD). The soluble fragment of triggering receptor expressed on myeloid cells 2 (sTREM2) can serve as a marker for microglial activation and has been shown to be overexpressed in AD. However, the relationship of sTREM2 with other AD biomarkers has not been extensively studied. We investigated the relationship between cerebrospinal fluid (CSF) sTREM2 and other AD biomarkers and examined the correlation of plasma sTREM2 with CSF sTREM2 in a cohort of individuals with AD and without AD. Participants were consecutively recruited from Asan Medical Center from 2018 to 2020. Subjects were stratified by their amyloid positivity and clinical status. Along with other AD biomarkers, sTREM2 level was measured in the plasma as well as CSF. In 101 patients with either amyloid-positive or negative status, CSF sTREM2 was closely associated with CSF T-tau and P-tau and not with Abeta42. CSF sTREM2 levels were found to be strongly correlated with CSF neurofilament light chain. The comparison of CSF and plasma sTREM2 levels tended to have an inverse correlation. Plasma sTREM2 and P-tau levels were oppositely influenced by age. Our results suggest that neuroinflammation may be closely associated with tau-induced neurodegeneration.

We aimed to investigate the role of the APOE genotype in cognitive and motor trajectories in Parkinson’s disease (PD). Using PD registry data, we retrospectively investigated a total of 253 patients with PD who underwent the Mini-Mental State Exam (MMSE) two or more times at least 5 years apart, were aged over 40 years, and free of dementia at the time of enrollment. We performed group-based trajectory modeling to identify patterns of cognitive change using the MMSE. Kaplan–Meier survival analysis was used to investigate the role of the APOE genotype in cognitive and motor progression. Trajectory analysis divided patients into four groups: early fast decline, fast decline, gradual decline, and stable groups with annual MMSE scores decline of − 2.8, − 1.8, − 0.6, and − 0.1 points per year, respectively. The frequency of APOE ε4 was higher in patients in the early fast decline and fast decline groups (50.0%) than those in the stable group (20.1%) (p = 0.007). APOE ε4, in addition to older age at onset, depressive mood, and higher H&Y stage, was associated with the cognitive decline rate, but no APOE genotype was associated with motor progression. APOE genotype could be used to predict the cognitive trajectory in PD.