Association between Parkinson's disease and cardiovascular disease mortality: a prospective population-based study from NHANES.

BACKGROUND AND AIM: Conflicting results have been reported on the association between Parkinson's disease (PD) and cardiovascular disease (CVD) mortality in different populations. Therefore, studying the relationship between PD and CVD mortality is crucial to reduce mortality caused by the former. METHODS: In this cohort investigation, we enrolled 28,242 participants from the National Health and Nutrition Examination Survey spanning from 2003 to 2018. The 380 cases of PD in the cohort were identified by documenting 'ANTIPARKINSON AGENTS' in their reported prescription medications. Mortality outcomes were ascertained by cross-referencing the cohort database with the National Death Index, which was last updated on 31 December 2019. Cardiovascular disease mortality was categorised according to the 10th revision of the International Classification of Diseases by using a spectrum of diagnostic codes. Weighted multivariable Cox regression analysis was used to examine the association between PD and the risk of CVD mortality. RESULTS: A total of 28,242 adults were included in the study [mean age, 60.156 (12.55) years, 13,766 men (48.74%)], and the median follow-up period was 89 months. Individuals with PD had an adjusted HR of 1.82 (95% CI, 1.24-2.69; p = 0.002) for CVD mortality and 1.84 (95% CI, 1.44-2.33; p < 0.001) for all-cause mortality compared with those without PD. The association between PD and CVD mortality was robust in sensitivity analyses, after excluding participants who died within 2 years of follow-up and those with a history of cancer at baseline [HR,1.82 (95% CI, 1.20-2.75; p = 0.005)]. CONCLUSIONS: PD was associated with a high long-term CVD mortality rate in the US population.

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures.

BACKGROUND AND OBJECTIVES: Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables. METHODS: Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB. RESULTS: A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD. DISCUSSION: Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Association of Body Mass Index and Parkinson Disease: A Bidirectional Mendelian Randomization Study.

BACKGROUND AND OBJECTIVES: The role of body mass index (BMI) in Parkinson disease (PD) is unclear. Based on the Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in PD (Courage-PD) consortium, we used 2-sample Mendelian randomization (MR) to replicate a previously reported inverse association of genetically predicted BMI with PD and investigated whether findings were robust in analyses addressing the potential for survival and incidence-prevalence biases. We also examined whether the BMI-PD relation is bidirectional by performing a reverse MR. METHODS: We used summary statistics from a genome-wide association study (GWAS) to extract the association of 501 single-nucleotide polymorphisms (SNPs) with BMI and from the Courage-PD and international Parkinson Disease Genomics Consortium (iPDGC) to estimate their association with PD. Analyses are based on participants of European ancestry. We used the inverse-weighted method to compute odds ratios (ORIVW per 4.8 kg/m2 [95% CI]) of PD and additional pleiotropy robust methods. We performed analyses stratified by age, disease duration, and sex. For reverse MR, we used SNPs associated with PD from 2 iPDGC GWAS to assess the effect of genetic liability toward PD on BMI. RESULTS: Summary statistics for BMI are based on 806,834 participants (54% women). Summary statistics for PD are based on 8,919 (40% women) cases and 7,600 (55% women) controls from Courage-PD, and 19,438 (38% women) cases and 24,388 (51% women) controls from iPDGC. In Courage-PD, we found an inverse association between genetically predicted BMI and PD (ORIVW 0.82 [0.70-0.97], p = 0.012) without evidence for pleiotropy. This association tended to be stronger in younger participants (≤67 years, ORIVW 0.71 [0.55-0.92]) and cases with shorter disease duration (≤7 years, ORIVW 0.75 [0.62-0.91]). In pooled Courage-PD + iPDGC analyses, the association was stronger in women (ORIVW 0.85 [0.74-0.99], p = 0.032) than men (ORIVW 0.92 [0.80-1.04], p = 0.18), but the interaction was not statistically significant (p-interaction = 0.48). In reverse MR, there was evidence for pleiotropy, but pleiotropy robust methods showed a significant inverse association. DISCUSSION: Using an independent data set (Courage-PD), we replicate an inverse association of genetically predicted BMI with PD, not explained by survival or incidence-prevalence biases. Moreover, reverse MR analyses support an inverse association between genetic liability toward PD and BMI, in favor of a bidirectional relation.

Cardiovascular history and risk of idiopathic Parkinson's disease: a cross-sectional observational study.

BACKGROUND: Parkinson's disease (PD), while often associated with its distinctive motor symptoms, can also exert a notable impact on the cardiovascular system due to the development of severe autonomic dysfunction. One of the initial indicators of PD is the appearance of cardiovascular dysautonomia. As such, it is vital to monitor and manage cardiovascular health of individuals with PD, as it may have clinical implications in the development of commonly recognized motor and non-motor aspects of the disease. To study the association of history of cardiovascular disease (CVD) with occurrence and severity of PD, here, we lend data on the association of CVD history with the frequency and the occurrence of idiopathic PD (iPD) using data from the Luxembourg Parkinson's study (iPD n = 676 patients and non-PD n = 874 controls). RESULTS: We report that patients with a history of CVD are at high risk of developing iPD (odds ratio; OR = 1.56, 95% confidence interval; CI 1.09-2.08). This risk is stronger in males and remains significant after adjustment with confounders (OR 1.55, 95% CI 1.05-2.30). This increased susceptibility to iPD is linked to the severity of iPD symptoms mainly the non-motor symptoms of daily living (MDS-UPDRS I) and motor complications (MDS-UPDRS IV) in the affected individuals. CONCLUSION: Individuals with history of CVD have a high risk of developing severe forms of iPD. This observation suggests that careful monitoring and management of patients with a history of cardiac problems may reduce the burden of iPD.

COVID-19 and Parkinson's disease: a single-center study and Mendelian randomization study.

To investigate the association between COVID-19 and Parkinson's disease (PD) via a single-center study and a Mendelian randomization (MR) study. A questionnaire-based survey was conducted among PD patients at a single center from December 7, 2022, to March 10, 2023. Logistic regression analysis was performed to identify the infection-related risk factors. Subsequently, bidirectional two-sample Mendelian randomization was employed to explore the association between COVID-19 and PD. In the cross-sectional analysis, it was found that the prevalence of COVID-19 infection in PD patients was 65.7%. Forty-eight (35.3%) PD patients experienced exacerbation of motor symptoms following COVID-19 infection. Long PD disease duration (≥ 10 years) (OR: 3.327, P = 0.045) and long time since last vaccination (> 12 m) (OR: 4.916, P = 0.035) were identified as significant risk factors related to infection. The MR analysis results supported that PD increases the COVID-19 susceptibility (ß = 0.081, OR = 1.084, P = 0.006). However, the MR analysis showed that PD did not increases the COVID-19 severity and hospitalization, and no significant association of COVID-19 on PD was observed. The findings from this cross-sectional study suggest that individuals with PD may experience worsened motor symptoms following COVID-19 infection. Long disease duration (≥10 years) and long time since last vaccination (> 12 m) are identified as important risk factors for infection in these patients. Furthermore, our MR study provides evidence supporting an association between PD and COVID-19 susceptibility.

Temporal trends in the prevalence of Parkinson's disease from 1980 to 2023: a systematic review and meta-analysis.

BACKGROUND: Parkinson's disease is the second most common neurodegenerative disorder, exhibiting an upward trend in prevalence. We aimed to investigate the prevalence of Parkinson's disease, temporal trends between 1980 and 2023, and variations in prevalence by location, age, sex, survey period, sociodemographic index (SDI), human development index (HDI), and study characteristics (sample size, diagnostic criteria, and data source). METHODS: In this systematic review and meta-analysis we searched PubMed, Cochrane, Web of Science, Embase, Scopus, and Global Health for observational studies that reported Parkinson's disease prevalence in the general population from database inception to Nov 1, 2023. We included studies if they were original observational investigations, had participants from the general population or community-based datasets, and provided numerical data on the prevalence of Parkinson's disease either with 95% CIs or with sufficient information to calculate 95% CIs. Studies were excluded if they were conducted in a specific population, had a sample size smaller than 1000, or were review articles, case reports, protocols, meeting abstracts, letters, comments, short communications, posters, and reports. The publication characteristics (first author and publication year), study location (countries, WHO regions, SDI, and HDI), survey period, study design, diagnostic criteria, data source, participant information, and prevalence data were extracted from articles using a standard form. Two authors independently evaluated eligibility, and discrepancies were resolved through discussion with the third author. We used random effect models to pool estimates with 95% CIs. Estimated annual percentage change (EAPC) was calculated to assess the temporal trend in prevalence of Parkinson's disease. The study was registered with PROSPERO, CRD42022364417. FINDINGS: 83 studies from 37 countries were eligible for analysis, with 56 studies providing all-age prevalence, 53 studies reporting age-specific prevalence, and 26 studies providing both all-age and age-specific prevalence. Global pooled prevalence of Parkinson's disease was 1·51 cases per 1000 (95% CI 1·19-1·88), which was higher in males (1·54 cases per 1000 [1·17-1·96]) than in females (1·49 cases per 1000 [1·12-1·92], p=0·030). During different survey periods, the prevalence of Parkinson's disease was 0·90 cases per 1000 (0·48-1·44; 1980-89), 1·38 cases per 1000 (1·17-1·61; 1990-99), 1·18 cases per 1000 (0·77-1·67; 2000-09), and 3·81 cases per 1000 (2·67-5·14; 2010-23). The EAPC of Parkinson's disease prevalence was significantly higher in the period of 2004-23 (EAPC 16·32% [95% CI 6·07-26·58], p=0·0040) than in the period of 1980-2003 (5·30% [0·82-9·79], p=0·022). Statistically significant disparities in prevalence were observed across six WHO regions. Prevalence increased with HDI or SDI. Considerable variations were observed in the pooled prevalence of Parkinson's disease based on different sample sizes or diagnostic criteria. Prevalence also increased with age, reaching 9·34 cases per 1000 (7·26-11·67) among individuals older than 60 years. INTERPRETATION: The global prevalence of Parkinson's disease has been increasing since the 1980s, with a more pronounced rise in the past two decades. The prevalence of Parkinson's disease is higher in countries with higher HDI or SDI. It is necessary to conduct more high-quality epidemiological studies on Parkinson's disease, especially in low SDI countries. FUNDING: National Nature Science Foundation of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Incidence of Parkinson's disease in Germany based on prevalence data from 70 million patients of the statutory health insurance.

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and nonmotor system manifestations and psychiatric symptoms. The aim of this study was to estimate the age- and sex-specific incidence of PD in Germany using an illness-death model and a corresponding partial differential equation (PDE) based on prevalence and mortality data. METHODS: Based on a PDE that describes the dynamics in an illness-death model, the age- and sex-specific incidence of PD in Germany was estimated using published prevalence and mortality rates. Prevalence rates were provided by the Central Institute for Statutory Health Insurance (Zi) for the period from 2010 to 2019. Parkinson's related mortality was estimated based on comparable population data from Norway. Bootstrapping was used for incidence estimation (median of 5000 samples) and to obtain 95% confidence intervals to interpret the accuracy of the incidence estimation. RESULTS: Men had higher incidences of PD than women at all ages. The highest incidences (median of 5000 bootstrap samples) for both groups were estimated for the age of 85 years with an incidence of 538.49 per 100,000 person-years (py) in men and 284.09 per 100,000 py in women, with an increasing width of bootstrapping 95% CIs showing greater uncertainty in the estimation at older ages. CONCLUSION: The illness-death model and the corresponding PDE, which describes changes in prevalence as a function of mortality and incidence, can be used to estimate the incidence of PD as a chronic disease. As overestimation of incidence is less likely with this method, we found incidence rates of Parkinson's disease that are suitable for further analyses with a lower risk of bias.

21st century accelerating neurological deaths in UK and major Western countries: - Demographic and/or multiple-interactive-environmental causes?

OBJECTIVE: We examine whether the rise in neurological death rates over the 21st century are solely explained by the Gompertzian hypothesis. STUDY DESIGN: We examine two data-sets. First, Office of National Statistics (ONS, 2022) for nineteen mortality categories in England/Wales, including Alzheimer's, Dementias and Parkinson's Disease. Secondly, WHO (2020) Combined Neurological Mortality (CNM), from WHO Global mortality categories, Nervous Disease Deaths, and Alzheimer's & Other Dementias. METHODS: Based on ONS data we investigate trends in Age-Standardised Mortality Rates (ASMR) of CNM 2000-2022. Based on WHO data we examine rates of Early Deaths (55-74) and ASMR, for CNM between 2000 and 2015 in the ten Major 'Western' economies: Australia, Canada, France, Germany, Italy, Japan, Netherlands, Spain, UK, and the USA. RESULTS: In England & Wales death rates have increased 348% for Alzheimer's, 235% for Dementias, and 105% for Parkinson's Disease in contrast with falls in most other cause mortality. Early Adults Deaths CNM rates increased in eight countries, an average of 19%. Neurological ASMR rose in every country, averaging 43%, the highest was the UK 95%. CONCLUSION: We reject the Gompertzian hypothesis as an all-encompassing explanation for these marked increases in ASMR. Increases in early adult neurological deaths suggests this cannot be solely explained by an aging population. Furthermore, increases in mortality could be related to an increased prevalence of neurological conditions in this age group. Action is urgently needed to investigate factors - whether environmental, lifestyle or health systems - that could explain these findings.

The combined effect of lifestyle factors and polygenic scores on age at onset in Parkinson's disease.

The objective of this study was to investigate the association between a Parkinson's disease (PD)-specific polygenic score (PGS) and protective lifestyle factors on age at onset (AAO) in PD. We included data from 4367 patients with idiopathic PD, 159 patients with GBA1-PD, and 3090 healthy controls of European ancestry from AMP-PD, PPMI, and Fox Insight cohorts. The association between PGS and lifestyle factors on AAO was assessed with linear and Cox proportional hazards models. The PGS showed a negative association with AAO (ß = - 1.07, p = 6 × 10-7) in patients with idiopathic PD. The use of one, two, or three of the protective lifestyle factors showed a reduction in the hazard ratio by 21% (p = 0.0001), 44% (p < 2 × 10-16), and 55% (p < 2 × 10-16), compared to no use. An additive effect of aspirin (ß = 7.62, p = 9 × 10-7) and PGS (ß = - 1.58, p = 0.0149) was found for AAO without an interaction (p = 0.9993) in the linear regressions, and similar effects were seen for tobacco. In contrast, no association between aspirin intake and AAO was found in GBA1-PD (p > 0.05). In our cohort, coffee, tobacco, aspirin, and PGS are independent predictors of PD AAO. Additionally, lifestyle factors seem to have a greater influence on AAO than common genetic risk variants with aspirin presenting the largest effect.

Unraveling sex differences in Parkinson's disease through explainable machine learning.

Sex differences affect Parkinson's disease (PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions. Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis. However, underlying aspects could make a feature significant for predicting PD, despite its score. Interactions between features require consideration, as do distinctions between scoring disparities and actual feature importance. For instance, a higher score in males for a certain feature doesn't necessarily mean it's less important for characterizing PD in females. This article proposes an explainable Machine Learning (ML) model to elucidate these underlying factors, emphasizing the importance of features. This insight could be critical for personalized medicine, suggesting the need to tailor data collection and analysis for males and females. The model identifies sex-specific differences in PD, aiding in predicting outcomes as "Healthy" or "Pathological". It adopts a system-level approach, integrating heterogeneous data - clinical, imaging, genetics, and demographics - to study new biomarkers for diagnosis. The explainable ML approach aids non-ML experts in understanding model decisions, fostering trust and facilitating interpretation of complex ML outcomes, thus enhancing usability and translational research. The ML model identifies muscle rigidity, autonomic and cognitive assessments, and family history as key contributors to PD diagnosis, with sex differences noted. The genetic variant SNCA-rs356181 may be more significant in characterizing PD in males. Interaction analysis reveals a greater occurrence of feature interplay among males compared to females. These disparities offer insights into PD pathophysiology and could guide the development of sex-specific diagnostic and therapeutic approaches.

Prevalence of Thai patients with advanced Parkinson's disease and external validation of the 5-2-1 Criteria and the CDEPA Questionnaire: A single-centered, cross-sectional study.

BACKGROUND: Advanced stage of Parkinson's disease (APD) diagnosis is challenging for general neurologists. The 5-2-1 Criteria and the Cuestionario De Enfermedad de Parkinson Avanzada (CDEPA) have been validated for screening for APD. OBJECTIVE: This article reports the period-prevalence of APD defined by a movement disorder expert, the 5-2-1 Criteria, and CDEPA and to improve the screening performance of the 5-2-1 Criteria METHODS: A cross-sectional retrospective study at the Parkinson's disease (PD) clinic of a tertiary hospital in Bangkok, Thailand amongst all PD patients aged ≥ 18 years was performed from January 2016 to January 2020. We compared the characteristics of APD and non-APD patients. We externally validated the 5-2-1 Criteria and CDEPA. We explored improving the 5-2-1 Criteria. RESULTS: Of 480 PD patients with complete data, the period-prevalence of APD by the movement disorder expert, the 5-2-1 Criteria and CDEPA were 37.1 %, 48.5 %, and 27.5 %, respectively. Adding requiring help with an activity of daily living and freezing of gait to the original 5-2-1 Criteria enhanced the sensitivity from 86.5 % (95 %CI 80.6, 91.2) to 94.9 % (95 %CI 90.6, 97.7) and negative predictive value (NPV) from 90.3 % (95 %CI 85.9, 93.7) to 96 % (95 %CI 92.6, 98.2). However, the CDEPA had a sensitivity of 62.9 % (95 %CI 55.4, 70) and NPV of 81.0 (95 %CI 76.5, 85). CONCLUSION: The 5-2-1 Criteria had a good screening tool performance for general neurologists to refer APD patients for optimal treatments. The modified 5-2-1 Criteria had better performance than the original one. External validation is needed.

Association of metabolic syndrome and its components with Parkinson's disease: a cross-sectional study.

BACKGROUND: The interrelation between metabolic syndrome (MetS) and Parkinson's disease (PD) likely arises from shared pathological mechanisms. This study thus aims to examine the impact of MetS and its components on PD. METHODS: This study utilized data extracted from the National Health and Nutrition Examination Survey database spanning 1999 to 2020. The random forest algorithm was applied to fill in the missing data. Propensity score optimal full matching was conducted. The data were adjusted by total weights derived from both sampling and matching weights. The weighted data were utilized to create multifactor logistic regression models. Odds ratios (ORs) and average marginal effects, along with their corresponding 95% confidence intervals (CIs), were calculated. RESULTS: MetS did not significantly affect the risk of PD (OR: 1.01; 95% CI: 0.77, 1.34; P = 0.92). Hypertension elevated the risk of PD (OR: 1.33; 95% CI: 1.01, 1.76; P = 0.045), accompanied by a 0.26% increased probability of PD occurrence (95% CI: 0.01%, 0.52%; P = 0.04). Diabetes mellitus (DM) had a 1.38 times greater likelihood of developing PD (OR:1.38; 95% CI: 1.004, 1.89; P = 0.046), corresponding to a 0.32% increased probability of PD occurrence (95% CI: -0.03%, 0.67%; P = 0.07). Nevertheless, no correlation was observed between hyperlipidemia, waist circumference and PD. CONCLUSION: MetS does not affect PD; however, hypertension and DM significantly increase the risk of PD.

Global, regional, and national health inequalities of Alzheimer's disease and Parkinson's disease in 204 countries, 1990-2019.

BACKGROUND: Alzheimer's disease and related dementias (ADRD) and Parkinson's disease (PD), pose growing global health challenges. Socio-demographic and economic development acts paradoxically, complicating the process that determines how governments worldwide designate policies and allocate resources for healthcare. METHODS: We extracted data on ADRD and PD in 204 countries from the Global Burden of Disease 2019 database. Health disparities were estimated using the slope index of inequality (SII), and concentration index (CIX) based on the socio-demographic index. Estimated annual percentage changes (EAPCs) were employed to evaluate temporal trends. RESULTS: Globally, the SII increased from 255.4 [95% confidence interval (CI), 215.2 to 295.5)] in 1990 to 559.3 (95% CI, 497.2 to 621.3) in 2019 for ADRD, and grew from 66.0 (95% CI, 54.9 to 77.2) in 1990 to 132.5 (95% CI, 118.1 to 147.0) in 2019 for PD; CIX rose from 33.7 (95% CI, 25.8 to 41.6) in 1990 to 36.9 (95% CI, 27.8 to 46.1) in 2019 for ADRD, and expanded from 22.2 (95% CI, 21.3 to 23.0) in 1990 to 29.0 (95% CI, 27.8 to 30.3) in 2019 for PD. Age-standardized disability-adjusted life years displayed considerable upward trends for ADRD [EAPC = 0.43 (95% CI, 0.27 to 0.59)] and PD [0.34 (95% CI, 0.29 to 0.38)]. CONCLUSIONS: Globally, the burden of ADRD and PD continues to increase with growing health disparities. Variations in health inequalities and the impact of socioeconomic development on disease trends underscored the need for targeted policies and strategies, with heightened awareness, preventive measures, and active management of risk factors.

Patterns and determinants of health care utilization among people with Parkinson's disease: A population-based analysis in Ontario, Canada.

In Ontario, despite the increasing prevalence of Parkinson's disease (PD), barriers to access-to-care for people with Parkinson's disease (PwP) and their caregivers are not well understood. The objective of this study is to examine spatial patterns of health care utilization among PwP and identify factors associated with PD-related health care utilization of individuals in Ontario. We employed a retrospective, population-based study design involving administrative health data to identify PwP as of March 31, 2018 (N = 35,482) using a previously validated case definition. An enhanced 2-step floating catchment area method was used to measure spatial accessibility to PD care and a descriptive spatial analysis was conducted to describe health service utilization by geographic area and specialty type. Negative binomial regression models were then conducted to identify associated geographic, socioeconomic, comorbidity and demographic factors. There was marked spatial variability in PD-related service utilization, with neurology and all provider visits being significantly higher in urban areas (CMF>1.20; p<0.05) and family physician visits being significantly higher (CMF >1.20; p<0.05) in more rural areas and remote areas. More frequent visits to family physicians were associated with living in rural areas, while less frequent visitation was associated with living in areas of low spatial accessibility with high ethnic concentration. Visits to neurologists were positively associated with living in areas of high spatial accessibility and with high ethnic concentration. Visits to all providers were also positively associated with areas of high spatial accessibility. For all outcomes, less frequent visits were found in women, older people, and those living in more deprived areas as years living with PD increased. This study demonstrates the importance of geographic, socioeconomic and individual factors in determining PwP's likelihood of accessing care and type of care provided. Our results can be expected to inform the development of policies and patient care models aimed at improving accessibility among diverse populations of PwP.

Role of PON1 L55M Gene Polymorphism in Parkinson's Disease among North Indian Population.

BACKGROUND AND OBJECTIVES: The role of various genetic markers including alpha synuclein, Parkin, etc., is known in the pathogenesis of Parkinson's disease (PD). Novel genetic markers including paraoxonase 1 (PON1) have also been linked to PD pathogenesis in recent studies. The PON1 L55M allele carriers may have defective clearance of environmental toxins and may result in increased susceptibility to PD. Hence, we studied the role of PON1 L55M polymorphism in PD among a North Indian population. MATERIALS AND METHOD: Seventy-four PD patients and 74 age- and sex-matched controls were recruited in this hospital-based case-control study. Baseline characteristics were recorded using structured questionnaire. DNA was extracted from 3-4 ml of venous blood, followed by PCR and restriction digestion. PON1 L55M genotypes were visualized as bands: LL (177 bp), LM (177, 140 bp) and MM (140,44 bp) on 3% agarose gel. Mann-Whitney U test and Chi-squared test were used for comparing two groups of skewed and categorical variables, respectively. Measures of strength of association were calculated by binary regression analysis. P value < 0.05 was considered as significant. RESULTS: Parkinson's disease patients had significantly higher exposure to pesticides (12.2%; P (organophosphate exposure) < 0.001) and well water drinking (28.4%; P = 0.006) compared to controls. Frequency distribution of LL, LM, MM genotypes was 67.5% (50/74), 28.4% (21/74), and 4.1% (3/74), respectively, for cases and 72.6% (54/74), 26% (19/74) and 1.4% (1/74), respectively, for controls. PON1 L55M genotype distribution between Parkinson's disease cases and controls was not significant (P = 0.53). PON1 L55M polymorphism was not associated with PD after adjusting for confounders by binary regression analysis. CONCLUSION: There was no significant association between PON1 L55M polymorphism and PD. Larger population-based studies would be required from India before drawing any definite conclusions.

Incidence and predictors of postural abnormalities in Parkinson's disease: a PPMI cohort study.

BACKGROUND: Axial postural abnormalities (PA) are invalidating symptoms of Parkinson's disease (PD). Risk factors for PA are unknown. OBJECTIVES: We sought to evaluate PA incidence and risk factors over the first 4-6 years of PD. METHODS: We included 441 PD patients from the Parkinson's Progression Markers Initiative (PPMI) cohort with data at diagnosis and after 4-year follow-up. PA was defined according to a posture item ≥ 2 at the Movement Disorder Society-sponsored-revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) in Off therapeutic condition. The Kruskal-Wallis test was used to compare characteristics of patients without PA ('no-PA'), with PA at disease onset ('baseline-PA'), and PA developed during follow-up ('develop-PA'). To identify predictors of PA development, univariate and multivariate Cox regression analyses were performed considering demographic, clinical and therapeutic variables. RESULTS: 10.9% of patients showed PA at baseline and 23.7% developed PA within the first 4-6 years since diagnosis. Older age, malignant phenotype, higher MDS-UPDRS part III, Hoehn & Yahr, and dysautonomia (SCOPA-AUT) score, and lower levels of physical activity were predictors of PA development at the univariate analysis. Older age (Hazard ratio [HR] per year: 1.041) and higher MDS-UPDRS part III score (HR per point: 1.035) survived as PA development predictors in the multivariate analysis. CONCLUSIONS: PPMI cohort data show that > 30% of PD patients present PA within the first 4-6 years of disease. Older age at onset and higher motor burden are associated with a higher risk for PA development. The protective role of physical activity merits to be further investigated.

Enhancing recruitment of individuals living with frailty, multimorbidity and cognitive impairment to Parkinson's research: experiences from the PRIME-UK cross-sectional study.

BACKGROUND AND OBJECTIVES: People with parkinsonism who are older, living in a care home, with frailty, multimorbidity or impaired capacity to consent are under-represented in research, limiting its generalisability. We aimed to evaluate more inclusive recruitment strategies. METHODS: From one UK centre, we invited people with parkinsonism to participate in a cross-sectional study. Postal invitations were followed by telephone reminders and additional support to facilitate participation. Personal consultees provided information on the views regarding research participation of adults with impaired capacity. These approaches were evaluated: (i) using external data from the Parkinson's Real World Impact assesSMent (PRISM) study and Clinical Practice Research Datalink (CPRD), a sample of all cases in UK primary care, and (ii) comparing those recruited with or without intensive engagement. RESULTS: We approached 1,032 eligible patients, of whom 542 (53%) consented and 477 (46%) returned questionnaires. The gender ratio in PRIME-UK (65% male) closely matched CPRD (61% male), unlike in the PRISM sample (46%). Mean age of PRIME participants was 75.9 (SD 8.5) years, compared to 75.3 (9.5) and 65.4 (8.9) years for CPRD and PRISM, respectively. More intensive engagement enhanced recruitment of women (13.3%; 95% CI 3.8, 22.9%; P = 0.005), care home residents (6.2%; 1.1, 11.2%; P = 0.004), patients diagnosed with atypical parkinsonism (13.7%; 5.4, 19.9%; P < 0.001), and those with a higher frailty score (mean score 0.2, 0.1, 0.2; P < 0.001). CONCLUSIONS: These recruitment strategies resulted in a less biased and more representative sample, with greater inclusion of older people with more complex parkinsonism.

Serum urate levels and neurodegenerative outcomes: a prospective cohort study and mendelian randomization analysis of the UK Biobank.

BACKGROUND: Previous studies on the associations between serum urate levels and neurodegenerative outcomes have yielded inconclusive results, and the causality remains unclear. This study aimed to investigate whether urate levels are associated with the risks of Alzheimer's disease and related dementias (ADRD), Parkinson's disease (PD), and neurodegenerative deaths. METHODS: This prospective study included 382,182 participants (45.7% men) from the UK Biobank cohort. Cox proportional hazards models were used to assess the associations between urate levels and risk of neurodegenerative outcomes. In the Mendelian randomization (MR) analysis, urate-related single-nucleotide polymorphisms were identified through a genome-wide association study. Both linear and non-linear MR approaches were utilized to investigate the potential causal associations. RESULTS: During a median follow-up period of 12 years, we documented 5,400 ADRD cases, 2,553 PD cases, and 1,531 neurodegenerative deaths. Observational data revealed that a higher urate level was associated with a decreased risk of ADRD (hazard ratio [HR]: 0.93, 95% confidence interval [CI]: 0.90, 0.96), PD (HR: 0.87, 95% CI: 0.82, 0.91), and neurodegenerative death (HR: 0.88, 95% CI: 0.83, 0.94). Negative linear associations between urate levels and neurodegenerative events were observed (all P-values for overall < 0.001 and all P-values for non-linearity > 0.05). However, MR analyses yielded no evidence of either linear or non-linear associations between genetically predicted urate levels and the risk of the aforementioned neurodegenerative events. CONCLUSION: Although the prospective cohort study demonstrated that elevated urate levels were associated with a reduced risk of neurodegenerative outcomes, MR analyses found no evidence of causality.

Global prevalence and incidence of Young Onset Parkinson's disease: A systematic review and meta-analysis.

BACKGROUND: There is a lack of enough evidence regarding the epidemiology of Young-onset Parkinson's disease (YOPD) which is needed by clinicians and healthcare policymakers. AIM: Herein, in this systematic review and meta-analysis, we aimed to estimate the global prevalence and incidence rates of YOPD. METHODS: We searched the literature in PubMed, Scopus, and Web of Science in May 2022. We included retrospective, prospective, cross-sectional observational population-based studies that reported the prevalence or incidence of PD in individuals younger than 40 years with known diagnostic criteria. RESULTS: After two-step screening, 50 studies were eligible to be included in our study. The age-standardized prevalence of YOPD was 10.2 per 100,000 persons globally while it was 14.7 per 100,000 population in European countries. Age-standardized prevalence estimates for 5-year age bands showed that the YOPD prevalence estimates varied from 6.1 per 100,000 population in the group aged 20-24 to 16.1 per 100,000 population in the group aged 35-39. Also, the age-standardized incidence of YOPD was 1.3 per 100,000 person-years population worldwide and 1.2 per 100,000 person-years in the European population. CONCLUSION: Based on this systematic review and meta-analysis, the overall prevalence of YOPD is 10.2 per 100,000 population, although estimates of the prevalence and incidence in low-income countries remain scarce. To improve monitoring and certain diagnoses of YOPD, healthcare providers and policymakers should be aware that much more effective tools are required.

Associations of street-view greenspace with Parkinson's disease hospitalizations in an open cohort of elderly US Medicare beneficiaries.

INTRODUCTION: Protective associations of greenspace with Parkinson's disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). METHODS: We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007-2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. RESULTS: There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. CONCLUSION: Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.