A predictive model to identify Parkinson disease from administrative claims data

Parkinson's disease and occupational exposure to organic solvents in Finland: a nationwide case-control study

OBJECTIVE

This study aimed to investigate the association between Parkinson's disease (PD) and occupational exposure to organic solvents generally and chlorinated hydrocarbons (CHC) in particular.

METHODS

We assembled a Finland-wide case-control study for birth years 1930-1950 by identifying incident PD cases from the register of Reimbursement of Medical Costs and drawing two controls per case using incidence density sampling from the Population Information System, matched on sex, birth year, and residency in Finland in 1980-2014. Occupation and socioeconomic status (SES) were identified from national censuses. We assessed cumulative occupational exposures via FINJEM job-exposure matrix. Smoking was based on occupation-specific prevalence by sex from national surveys. We estimated confounder-adjusted PD incidence rate ratios (IRR) via logistic regression and evaluated their sensitivity to errors in FINJEM through probabilistic bias analysis (PBA).

RESULTS

Among ever-employed, we identified 17 187 cases (16.0% potentially exposed to CHC) and 35 738 matched controls. Cases were more likely to not smoke and belong to higher SES. Cumulative exposure (CE) to CHC (per 100 ppm-years, 5-year lag) was associated with adjusted IRR 1.235 (95% confidence interval 0.986-1.547), with stronger associations among women and among persons who had more census records. Sensitivity analyses did not reveal notable associations, but stronger effects were seen in the younger birth cohort (1940-1950). PBA produced notably weaker associations, yielding a median IRR 1.097 (95% simulation interval 0.920-1.291) for CHC.

CONCLUSION

Our findings imply that PD is unlikely to be related to typical occupational solvent exposure in Finland, but excess risk cannot be ruled out in some highly exposed occupations.

Fine Particulate Matter and Parkinson Disease Risk Among Medicare Beneficiaries

BACKGROUND AND OBJECTIVES

Numerous studies suggest that environmental exposures play a critical role in Parkinson disease (PD) pathogenesis, and large, population-based studies have the potential to advance substantially the identification of novel PD risk factors. We sought to study the nationwide geographic relationship between PD and air pollution, specifically PM2.5 (particulate matter with a diameter <2.5 micrometers), using population-based US Medicare data.

METHODS

We conducted a population-based geographic study of Medicare beneficiaries aged 66-90 years geocoded to US counties and zip+4. We used integrated nested Laplace approximation to create age, sex, race, smoking, and health care utilization-adjusted relative risk (RR) at the county level for geographic analyses with PM2.5 as the primary exposure of interest. We also performed an individual-level analysis using logistic regression with cases and controls with zip+4 centroid PM2.5. We adjusted a priori for the same covariates and verified no confounding by indicators of socioeconomic status or neurologist density.

RESULTS

Among 21,639,190 Medicare beneficiaries, 89,390 had incident PD in 2009. There was a nationwide association between average annual PM2.5 and PD risk whereby the RR of PD was 56% (95% CI 47%-66%) greater for those exposed to the median level of PM2.5 compared with those with the lowest level of PM2.5. This association was linear up to 13 μg/m3 corresponding to a 4.2% (95% CI 3.7%-4.8%) greater risk of PD for each additional μg/m3 of PM2.5 (p trend < 0.0001). We identified a region with high PD risk in the Mississippi-Ohio River Valley, where the risk of PD was 19% greater compared with the rest of the nation. The strongest association between PM2.5 and PD was found in a region with low PD risk in the Rocky Mountains. PM2.5 was also associated with PD in the Mississippi-Ohio River Valley where the association was relatively weaker, due to a possible ceiling effect at average annual PM2.5 levels of ∼13 μg/m3.

DISCUSSION

State-of-the-art geographic analytic techniques revealed an association between PM2.5 and PD that varied in strength by region. A deeper investigation into the specific subfractions of PM2.5 may provide additional insight into regional variability in the PM2.5-PD association.

A genome-wide association study coupled with machine learning approaches to identify influential demographic and genomic factors underlying Parkinson's disease

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson's disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson's disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. We first applied correlation and GWAS analyses to find the top demographic and genomic factors associated with PD, respectively. We further developed and compared a variety of machine learning (ML) models for predicting PD. From the developed ML models, we performed feature importance analysis to reveal the predictability of each demographic or the genomic input feature for PD. Finally, we performed gene set enrichment analysis on our GWAS results to identify PD-associated pathways. Results: In our study, we identified both novel and well-known demographic and genetic factors (along with the enriched pathways) related to PD. In addition, we developed predictive models that performed robustly, with AUC = 0.89 for demographic data and AUC = 0.74 for genomic data. Our GWAS analysis identified several novel and significant variants and gene loci, including three intron variants in LMNA (p-values smaller than 4.0e-21) and one missense variant in SEMA4A (p-value = 1.11e-26). Our feature importance analysis from the PD-predictive ML models highlighted some significant and novel variants from our GWAS analysis (e.g., the intron variant rs1749409 in the RIT1 gene) and helped identify potentially causative variants that were missed by GWAS, such as rs11264300, a missense variant in the gene DCST1, and rs11584630, an intron variant in the gene KCNN3. Conclusion: In summary, by combining a GWAS with advanced machine learning models, we identified both known and novel demographic and genomic factors as well as built well-performing ML models for predicting Parkinson's disease.

Markov-modulated marked Poisson processes for modeling disease dynamics based on medical claims data

We explore Markov-modulated marked Poisson processes (MMMPPs) as a natural framework for modeling patients' disease dynamics over time based on medical claims data. In claims data, observations do not only occur at random points in time but are also informative, that is, driven by unobserved disease levels, as poor health conditions usually lead to more frequent interactions with the health care system. Therefore, we model the observation process as a Markov-modulated Poisson process, where the rate of health care interactions is governed by a continuous-time Markov chain. Its states serve as proxies for the patients' latent disease levels and further determine the distribution of additional data collected at each observation time, the so-called marks. Overall, MMMPPs jointly model observations and their informative time points by comprising two state-dependent processes: the observation process (corresponding to the event times) and the mark process (corresponding to event-specific information), which both depend on the underlying states. The approach is illustrated using claims data from patients diagnosed with chronic obstructive pulmonary disease by modeling their drug use and the interval lengths between consecutive physician consultations. The results indicate that MMMPPs are able to detect distinct patterns of health care utilization related to disease processes and reveal interindividual differences in the state-switching dynamics.

A predictive algorithm to identify ever smoking in medical claims-based epidemiologic studies

PURPOSE

To develop and validate an algorithm to estimate probability of ever smoking using administrative claims.

METHODS

Using population-based samples of Medicare-aged individuals (121,278 Behavioral Risk Factor Surveillance System survey respondents and 207,885 Medicare beneficiaries), we developed a logistic regression model to predict probability of ever smoking from demographic and claims data. We applied the model in 1,657,266 additional Medicare beneficiaries and calculated area under the receiver operating characteristic curve (AUC) using presence or absence of a tobacco-specific diagnosis or procedure code as our "gold standard." We used these "gold standard" and lung/laryngeal cancer codes to over-ride predicted probability as 100%. We calculated Spearman's rho between probability from this full algorithm and smoking assessed in prior Parkinson disease studies, by substituting our observed and prior ("true") smoking-Parkinson disease odds ratios into the attenuation equation.

RESULTS

The predictive model contained 23 variables, including basic demographics, high alcohol consumption, asthma, cardiovascular disease and associated risk factors, selected cancers, and indicators of routine medical usage. The AUC was 67.6% (95% confidence interval 67.5%-67.7%) comparing smoking probability to tobacco-specific diagnosis or procedure codes. Spearman's rho for the full algorithm was 0.82.

CONCLUSIONS

Ever smoking might be approximated in administrative data for use as a continuous, probabilistic variable in epidemiologic analyses.

Sociodemographic and Geographic Disparities in End-of-Life Health Care Intensity Among Medicare Beneficiaries With Parkinson Disease

Background and Objective

Current studies of end-of-life care in Parkinson disease (PD) do not focus on diverse patient samples or provide national views of end-of-life resource utilization. We determined sociodemographic and geographic differences in end-of-life inpatient care intensity among persons with PD in the United States (US).

Methods

This retrospective cohort study included Medicare Part A and Part B beneficiaries 65 years and older with a qualifying PD diagnosis who died between January 1, 2017, and December 31, 2017. Medicare Advantage beneficiaries and those with atypical or secondary parkinsonism were excluded. Primary outcomes included rates of hospitalization, intensive care unit (ICU) admission, in-hospital death, and hospice discharge in the last 6 months of life. Descriptive analyses and multivariable logistic regression models compared differences in end-of-life resource utilization and treatment intensity. Adjusted models included demographic and geographic variables, Charlson Comorbidity Index score, and Social Deprivation Index score. The national distribution of primary outcomes was mapped and compared by hospital referral region using Moran I.

Results

Of 400,791 Medicare beneficiaries with PD in 2017, 53,279 (13.3%) died. Of decedents, 33,107 (62.1%) were hospitalized in the last 6 months of life. In covariate-adjusted regression models using White male decedents as the reference category, odds of hospitalization was greater for Asian (AOR 1.38; CI 1.11-1.71) and Black (AOR 1.23; CI 1.08-1.39) male decedents and lower for White female decedents (AOR 0.80; CI 0.76-0.83). ICU admissions were less likely in female decedents and more likely in Asian, Black, and Hispanic decedents. Odds of in-hospital death was greater among Asian (AOR 2.49, CI 2.10-2.96), Black (AOR 1.11, CI 1.00-1.24), Hispanic (AOR 1.59; CI 1.33-1.91), and Native American (AOR 1.49; CI 1.05-2.10) decedents. Asian and Hispanic male decedents were less likely to be discharged to hospice. In geographical analyses, rural-dwelling decedents had lower odds of ICU admission (AOR 0.77; CI 0.73-0.81) and hospice discharge (AOR 0.69; CI 0.65-0.73) than urban-dwelling decedents. Nonrandom clusters of primary outcomes were observed across the US, with highest rates of hospitalization in the South and Midwest (Moran I = 0.134; p < 0.001).

Discussion

Most persons with PD in the US are hospitalized in the last 6 months of life, and treatment intensity varies by sex, race, ethnicity, and geographic location. These group differences emphasize the importance of exploring end-of-life care preferences, service availability, and care quality among diverse populations with PD and may inform new approaches to advance care planning.

Early detection of Parkinson's disease through enriching the electronic health record using a biomedical knowledge graph

Introduction

Early diagnosis of Parkinson's disease (PD) is important to identify treatments to slow neurodegeneration. People who develop PD often have symptoms before the disease manifests and may be coded as diagnoses in the electronic health record (EHR).

Methods

To predict PD diagnosis, we embedded EHR data of patients onto a biomedical knowledge graph called Scalable Precision medicine Open Knowledge Engine (SPOKE) and created patient embedding vectors. We trained and validated a classifier using these vectors from 3,004 PD patients, restricting records to 1, 3, and 5 years before diagnosis, and 457,197 non-PD group.

Results

The classifier predicted PD diagnosis with moderate accuracy (AUC = 0.77 ± 0.06, 0.74 ± 0.05, 0.72 ± 0.05 at 1, 3, and 5 years) and performed better than other benchmark methods. Nodes in the SPOKE graph, among cases, revealed novel associations, while SPOKE patient vectors revealed the basis for individual risk classification.

Discussion

The proposed method was able to explain the clinical predictions using the knowledge graph, thereby making the predictions clinically interpretable. Through enriching EHR data with biomedical associations, SPOKE may be a cost-efficient and personalized way to predict PD diagnosis years before its occurrence.

Predictive models for the incidence of Parkinson's disease: systematic review and critical appraisal

Numerous predictive models for Parkinson's disease (PD) incidence have been published recently. However, the model performance and methodological quality of those available models are yet needed to be summarized and assessed systematically. In this systematic review, we systematically reviewed the published predictive models for PD incidence and assessed their risk of bias and applicability. Three international databases were searched. Cohort or nested case-control studies that aimed to develop or validate a predictive model for PD incidence were considered eligible. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used for risk of bias and applicability assessment. Ten studies covering 10 predictive models were included. Among them, four studies focused on model development, covering eight models, while the remaining six studies focused on model external validation, covering two models. The discrimination of the eight new development models was generally poor, with only one model reported C index > 0.70. Four out of the six external validation studies showed excellent or outstanding discrimination. All included studies had high risk of bias. Three predictive models (the International Parkinson and Movement Disorder Society [MDS] prodromal PD criteria, the model developed by Karabayir et al. and models validated by Faust et al.) are recommended for clinical application by considering model performance and resource-demanding. In conclusion, the performance and methodological quality of most of the identified predictive models for PD incidence were unsatisfactory. The MDS prodromal PD criteria, model developed by Karabayir et al. and model validated by Faust et al. may be considered for clinical use.

Protective Effects of Lovastatin in a Population-Based ALS Study and Mouse Model

OBJECTIVE

The objective of this study was to use a novel combined pharmacoepidemiologic and amyotrophic lateral sclerosis (ALS) mouse model approach to identify potential motor neuron protective medications.

METHODS

We constructed a large, population-based case-control study to investigate motor neuron disease (MND) among US Medicare beneficiaries aged 66 to 90 in 2009. We included 1,128 incident MND cases and 56,400 age, sex, race, and ethnicity matched controls. We calculated MND relative risk for >1,000 active ingredients represented in Part D (pharmacy) claims in 2006 to 2007 (>1 year before diagnosis/reference). We then applied a comprehensive screening approach to select medications for testing in SOD1^G93A mice: sulfasalazine, telmisartan, and lovastatin. We treated mice with the human dose equivalent of the medication or vehicle via subcutaneous osmotic pump before onset of weakness. We then assessed weight, gait, and survival. In additional mice, we conducted histological studies.

RESULTS

We observed previously established medical associations for MND and an inverse dose-response association between lovastatin and MND, with 28% reduced risk at 40 mg/day. In SOD1^G93A mouse studies, sulfasalazine and telmisartan conferred no benefit, whereas lovastatin treatment delayed onset and prolonged survival. Lovastatin treated mice also had less microgliosis, misfolded SOD1, and spinal motor neuron loss in the ventral horn.

INTERPRETATION

Lovastatin reduced the risk of ALS in humans, which was confirmed in an ALS mouse model by delayed symptom onset, prolonged survival, and preservation of motor neurons. Although further studies to understand the mechanism are required, lovastatin may represent a potential neuroprotective therapy for patients with ALS. These data demonstrate the utility of a combined pharmacoepidemiologic and mouse model approach. ANN NEUROL 2023.

Long Short-Term Memory-Based Deep Learning Models for Screening Parkinson's Disease Using Sequential Diagnostic Codes

BACKGROUND AND PURPOSE

It is challenging to detect Parkinson's disease (PD) in its early stages, which has prompted researchers to develop techniques based on machine learning methods for detecting PD. However, previous studies did not fully incorporate the slow progression of PD over a long period of time nor consider that its symptoms occur in a time-sequential manner. Contributing to the literature on PD, which has relied heavily on cross-sectional data, this study aimed to develop a method for detecting PD early that can process time-series information using the long short-term memory (LSTM) algorithm.

METHODS

We sampled 926 patients with PD and 9,260 subjects without PD using medical-claims data. The LSTM algorithm was tested using diagnostic histories, which contained the diagnostic codes and their respective time information. We compared the prediction power of the 12-month diagnostic codes under two different settings over the 4 years prior to the first PD diagnosis.

RESULTS

The model that was trained using the most-recent 12-month diagnostic codes had the best performance, with an accuracy of 94.25%, a sensitivity of 82.91%, and a specificity of 95.26%. The other three models (12-month codes from 2, 3, and 4 years prior) were found to have comparable performances, with accuracies of 92.27%, 91.86%, and 91.81%, respectively. The areas under the curve from our data settings ranged from 0.839 to 0.923.

CONCLUSIONS

We explored the possibility that PD specialists could benefit from our proposed machine learning method as an early detection method for PD.

Biologic targets of prescription medications and risk of neurodegenerative disease in United States Medicare beneficiaries

OBJECTIVE

To identify prescription medications associated with a lower risk of three neurodegenerative diseases: Parkinson disease, Alzheimer disease, and amyotrophic lateral sclerosis.

METHODS

We conducted a population-based, case-control study of U.S. Medicare beneficiaries in 2009 (42,885 incident neurodegenerative disease cases, 334,387 randomly selected controls). Using medication data from 2006-2007, we categorized all filled medications according to their biological targets and mechanisms of action on those targets. We used multinomial logistic regression models, while accounting for demographics, indicators of smoking, and health care utilization, to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for 141 target-action pairs and each neurodegenerative disease. For target-action pairs inversely associated with all three diseases, we attempted replication in a cohort study that included an active comparator group. We constructed the cohort by following controls forward for incident neurodegenerative disease from the beginning of 2010 until death or end of 2014, i.e., up to five years after the two-year exposure lag. We used Cox proportional hazards regression while accounting for the same covariates.

RESULTS

The most consistent inverse association across both studies and all three neurodegenerative diseases was for xanthine dehydrogenase/oxidase blockers, represented by the gout medication, allopurinol. Allopurinol was associated with a 13-34% lower risk for each neurodegenerative disease group in multinomial regression, and a mean reduction of 23% overall, as compared to individuals who did not use allopurinol. In the replication cohort we observed a significant 23% reduction for neurodegenerative disease in the fifth year of follow-up, when comparing allopurinol users to non-users, and more marked associations with an active comparator group. We observed parallel associations for a related target-action pair unique to carvedilol.

DISCUSSION/CONCLUSION

Xanthine dehydrogenase/oxidase blockade might reduce risk of neurodegenerative disease. However, further research will be necessary to confirm that the associations related to this pathway are causal or to examine whether this mechanism slows progression.

Machine learning based risk prediction for Parkinson's disease with nationwide health screening data

Although many studies have been conducted on machine learning (ML) models for Parkinson's disease (PD) prediction using neuroimaging and movement analyses, studies with large population-based datasets are limited. We aimed to propose PD prediction models using ML algorithms based on the National Health Insurance Service-Health Screening datasets. We selected individuals who participated in national health-screening programs > 5 times between 2002 and 2015. PD was defined based on the ICD-code (G20), and a matched cohort of individuals without PD was selected using a 1:1 random sampling method. Various ML algorithms were applied for PD prediction, and the performance of the prediction models was compared. Neural networks, gradient boosting machines, and random forest algorithms exhibited the best average prediction accuracy (average area under the receiver operating characteristic curve (AUC): 0.779, 0.766, and 0.731, respectively) among the algorithms validated in this study. The overall model performance metrics were higher in men than in women (AUC: 0.742 and 0.729, respectively). The most important factor for predicting PD occurrence was body mass index, followed by total cholesterol, glucose, hemoglobin, and blood pressure levels. Smoking and alcohol consumption (in men) and socioeconomic status, physical activity, and diabetes mellitus (in women) were highly correlated with the occurrence of PD. The proposed health-screening dataset-based PD prediction model using ML algorithms is readily applicable, produces validated results, and could be a useful option for PD prediction models.

New-Onset Diabetes Is a Potential Marker for the Malignant Transformation of Pancreatic Cysts: A Real-World Population Cohort Study

OBJECTIVES

New-onset diabetes mellitus has been shown to be associated with pancreatic cancer (PC) in the general population. Our objective was to leverage real-world data to assess the association of new-onset diabetes (NODM) with malignant transformation in a large longitudinal cohort of pancreatic cyst patients.

METHODS

A retrospective longitudinal cohort study was conducted using IBM's MarketScan claims databases from 2009 to 2017. From 200 million database subjects, we selected patients with newly diagnosed cysts without prior pancreatic pathology.

RESULTS

Of the 137,970 patients with a pancreatic cyst, 14,279 had a new diagnosis. Median follow-up was 41.6 months. Patients with NODM progressed to PC at nearly 3 times the rate of patients without a diabetes history (hazard ratio, 2.80; 95% confidence interval, 2.05-3.83) and at a significantly higher rate than patients with preexisting diabetes (hazard ratio, 1.59; 95% confidence interval, 1.14-2.21). The mean interval between NODM and cancer diagnosis was 7.5 months.

CONCLUSIONS

Cyst patients who developed NODM progressed to PC at 3 times the rate of nondiabetics and at a greater rate than preexisting diabetics. The diagnosis of NODM preceded cancer detection by several months. These results support the inclusion of diabetes mellitus screening in cyst surveillance algorithms.

Multi-predictor modeling for predicting early Parkinson’s disease and non-motor symptoms progression

Background

Identifying individuals with high-risk Parkinson’s disease (PD) at earlier stages is an urgent priority to delay disease onset and progression. In the present study, we aimed to develop and validate clinical risk models using non-motor predictors to distinguish between early PD and healthy individuals. In addition, we constructed prognostic models for predicting the progression of non-motor symptoms [cognitive impairment, Rapid-eye-movement sleep Behavior Disorder (RBD), and depression] in de novo PD patients at 5 years of follow-up.

Methods

We retrieved the data from the Parkinson’s Progression Markers Initiative (PPMI) database. After a backward variable selection approach to identify predictors, logistic regression analyses were applied for diagnosis model construction, and cox proportional-hazards models were used to predict non-motor symptom progression. The predictive models were internally validated by correcting measures of predictive performance for “optimism” or overfitting with the bootstrap resampling approach.

Results

For constructing diagnostic models, the final model reached a high accuracy with an area under the curve (AUC) of 0.93 (95% CI: 0.91–0.96), which included eight variables (age, gender, family history, University of Pennsylvania Smell Inventory Test score, Montreal Cognitive Assessment score, RBD Screening Questionnaire score, levels of cerebrospinal fluid α-synuclein, and SNCA rs356181 polymorphism). For the construction of prognostic models, our results showed that the AUC of the three prognostic models improved slightly with increasing follow-up time. The overall AUCs fluctuated around 0.70. The model validation established good discrimination and calibration for predicting PD onset and progression of non-motor symptoms.

Conclusion

The findings of our study facilitate predicting the individual risk at an early stage based on the predictors derived from these models. These predictive models provide relatively reliable information to prevent PD onset and progression. However, future validation analysis is still needed to clarify these findings and provide more insight into the predictive models over more extended periods of disease progression in more diverse samples.

Who to Enroll in Parkinson Disease Prevention Trials? The Case for Composite Prodromal Cohorts

Significant progress has been made in expanding our understanding of prodromal Parkinson disease (PD), particularly for recognition of early motor and nonmotor signs and symptoms. Although identification of these prodromal features may improve our understanding of the earliest stages of PD, they are individually insufficient for early disease detection and enrollment of participants in prevention trials in most cases because of low sensitivity, specificity, and positive predictive value. Composite cohorts, composed of individuals with multiple co-occurring prodromal features, are an important resource for conducting prodromal PD research and eventual prevention trials because they are more representative of the population at risk for PD, allow investigators to evaluate the efficacy of an intervention across individuals with varying prodromal feature patterns, are able to produce larger sample sizes, and capture individuals at different stages of prodromal PD. A key challenge in identifying individuals with prodromal disease for composite cohorts and prevention trial participation is that we know little about the natural history of prodromal PD. To move toward prevention trials, it is critical that we better understand common prodromal feature patterns and be able to predict the probability of progression and phenoconversion. Ongoing research in cohort studies and administrative databases is beginning to address these questions, but further longitudinal analyses in a large population-based sample are necessary to provide a convincing and definitive strategy for identifying individuals to be enrolled in a prevention trial.

An Early Disturbance in Serotonergic Neurotransmission Contributes to the Onset of Parkinsonian Phenotypes in Drosophila melanogaster

Parkinson’s disease (PD) is a neurodegenerative disease characterized by motor symptoms and dopaminergic cell loss. A pre-symptomatic phase characterized by non-motor symptoms precedes the onset of motor alterations. Two recent PET studies in human carriers of mutations associated with familial PD demonstrate an early serotonergic commitment—alteration in SERT binding—before any dopaminergic or motor dysfunction, that is, at putative PD pre-symptomatic stages. These findings support the hypothesis that early alterations in the serotonergic system could contribute to the progression of PD, an idea difficult to be tested in humans. Here, we study some components of the serotonergic system during the pre-symptomatic phase in a well-characterized Drosophila PD model, Pink1B9 mutant flies. We detected lower brain serotonin content in Pink1B9 flies, accompanied by reduced activity of SERT before the onset of motor dysfunctions. We also explored the consequences of a brief early manipulation of the serotonergic system in the development of motor symptoms later in aged animals. Feeding young Pink1B9 flies with fluoxetine, a SERT blocker, prevents the loss of dopaminergic neurons and ameliorates motor impairment observed in aged mutant flies. Surprisingly, the same pharmacological manipulation in young control flies results in aged animals exhibiting a PD-like phenotype. Our findings support that an early dysfunction in the serotonergic system precedes and contributes to the onset of the Parkinsonian phenotype in Drosophila.

Comparative efficacy of surgical approaches to disease modification in Parkinson disease

Parkinson’s disease (PD) may optimally be treated with a disease-modifying therapy to slow progression. We compare data underlying surgical approaches proposed to impart disease modification in PD: (1) cell transplantation therapy with stem cell-derived dopaminergic neurons to replace damaged cells; (2) clinical trials of growth factors to promote survival of existing dopaminergic neurons; (3) subthalamic nucleus deep brain stimulation early in the course of PD; and (4) abdominal vagotomy to lower risk of potential disease spread from gut to brain. Though targeted to engage potential mechanisms of PD these surgical approaches remain experimental, indicating the difficulty in translating therapeutic concepts into clinical practice. The choice of outcome measures to assess disease modification separate from the symptomatic benefit will be critical to evaluate the effect of the disease-modifying intervention on long-term disease burden, including imaging studies and clinical rating scales, i.e., Unified Parkinson Disease Rating Scale. Therapeutic interventions will require long follow-up times (i.e., 5–10 years) to analyze disease modification compared to symptomatic treatments. The promise of invasive, surgical treatments to achieve disease modification through mechanistic approaches has been constrained by the reality of translating these concepts into effective clinical trials.

A comparison of prediction approaches for identifying prodromal Parkinson disease

Identifying people with Parkinson disease during the prodromal period, including via algorithms in administrative claims data, is an important research and clinical priority. We sought to improve upon an existing penalized logistic regression model, based on diagnosis and procedure codes, by adding prescription medication data or using machine learning. Using Medicare Part D beneficiaries age 66–90 from a population-based case-control study of incident Parkinson disease, we fit a penalized logistic regression both with and without Part D data. We also built a predictive algorithm using a random forest classifier for comparison. In a combined approach, we introduced the probability of Parkinson disease from the random forest, as a predictor in the penalized regression model. We calculated the receiver operator characteristic area under the curve (AUC) for each model. All models performed well, with AUCs ranging from 0.824 (simplest model) to 0.835 (combined approach). We conclude that medication data and random forests improve Parkinson disease prediction, but are not essential.

A Rapid Motor Task-Based Screening Tool for Parkinsonism in Community-Based Studies

Background: The prevalence of parkinsonism in developing countries is largely unknown due to difficulty in ascertainment because access to neurologists is often limited.

Objective: Develop and validate a parkinsonism screening tool using objective motor task-based tests that can be administered by non-clinicians.

Methods: In a cross-sectional population-based sample from South Africa, we evaluated 315 adults, age >40, from an Mn-exposed (smelter) community, using the Unified Parkinson Disease Rating Scale motor subsection 3 (UPDRS3), Purdue grooved pegboard, and kinematic-UPDRS3-based motor tasks. In 275 participants (training dataset), we constructed a linear regression model to predict UPDRS3. We selected motor task summary measures independently associated with UPDRS3 (p < 0.05). We validated the model internally in the remaining 40 participants from the manganese-exposed community (test dataset) using the area under the receiver operating characteristic curve (AUC), and externally in another population-based sample of 90 participants from another South African community with only background levels of environmental Mn exposure.

Results: The mean UPDRS3 score in participants from the Mn-exposed community was 9.1 in both the training and test datasets (standard deviation = 6.4 and 6.1, respectively). Together, 57 (18.1%) participants in this community had a UPDRS3 ≥ 15, including three with Parkinson's disease. In the non-exposed community, the mean UPDRS3 was 3.9 (standard deviation = 4.3). Three (3.3%) had a UPDRS3 ≥ 15. Grooved pegboard time and mean velocity for hand rotation and finger tapping tasks were strongly associated with UPDRS3. Using these motor task summary measures and age, the UPDRS3 predictive model performed very well. In the test dataset, AUCs were 0.81 (95% CI 0.68, 0.94) and 0.91 (95% CI 0.81, 1.00) for cut points for neurologist-assessed UPDRS3 ≥ 10 and UPDRS3 ≥ 15, respectively. In the external validation dataset, the AUC was 0.85 (95% CI 0.73, 0.97) for UPDRS3 ≥ 10. AUCs were 0.76–0.82 when excluding age.

Conclusion: A predictive model based on a series of objective motor tasks performs very well in assessing severity of parkinsonism in both Mn-exposed and non-exposed population-based cohorts.

Solvent exposed occupations and risk of Parkinson disease in Finland

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Finnish register-based case-control Parkinson disease (PD) studies are feasible.

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Some occupations with potential for exposure to solvents are associated with PD.

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Chlorinated hydrocarbon solvents in particular might increase PD risk modestly.

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Some occupational co-exposures might enhance PD-solvent associations in PD studies.

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Studies without smoking/education data will underestimate PD-solvent associations.

Introduction

Epidemiologic and toxicology studies suggest that exposure to various solvents, especially chlorinated hydrocarbon solvents, might increase Parkinson disease (PD) risk.

Methods

In a population-based case-control study in Finland, we examined whether occupations with potential for solvent exposures were associated with PD. We identified newly diagnosed cases age 45–84 from a nationwide medication reimbursement register in 1995–2014. From the population register, we randomly selected non-PD controls matched on sex, along with birth and diagnosis years (age). We included 11,757 cases and 23,236 controls with an occupation in the 1990 census, corresponding to age 40–60. We focused on 28 occupations with ≥ 5% probability of solvent exposure according to the Finnish Job Exposure Matrix. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) by logistic regression modeling, adjusting for age, sex, socioeconomic status, and smoking probability.

Results

Similar proportions of cases (5.5%) and controls (5.6%) had an occupation with potential exposure to any solvents. However, all occupations with a point estimate above one, and all significantly or marginally significantly associated with PD (electronic/telecommunications worker [OR = 1.63, 95% CI 1.05–2.50], laboratory assistant [OR = 1.40, 95% CI 0.98–1.99], and machine/engine mechanic [OR = 1.23, 95% CI 0.99–1.52]) entailed potential for exposure to chlorinated hydrocarbon solvents, specifically. Secondary analyses indicated exposure to polycyclic aromatic hydrocarbons and some metals might contribute to the association for mechanics.

Conclusion

PD risk might be slightly increased in occupations with potential exposure to chlorinated hydrocarbon solvents. Confirmation is required in additional studies that adjust for other occupational exposures and smoking.

Herpesvirus Infections and Risk of Parkinson's Disease

INTRODUCTION

Herpesviruses might play a role in the pathogenesis of neurodegenerative disorders. We sought to examine a possible association between alpha herpesvirus infections and Parkinson's disease.

METHODS

We conducted a population-based case-control study of incident Parkinson's disease in 2009 Medicare beneficiaries age 66-90 years (89,790 cases, 118,095 randomly selected comparable controls). We classified beneficiaries with any diagnosis code for "herpes simplex" and/or "herpes zoster" in the previous 5 years as having had the respective alpha herpesviruses. In beneficiaries with Part D prescription coverage, we also identified those prescribed anti-herpetic medications. We calculated odds ratios (OR) and 95% CI between alpha herpesvirus diagnosis/treatment and Parkinson's disease with logistic regression, with adjustment for age, sex, race/ethnicity, smoking, and use of medical care.

RESULTS

Parkinson's disease risk was inversely associated with herpes simplex (OR 0.79, 95% CI 0.74-0.84), herpes zoster (OR 0.88, 95% CI 0.85-0.91), and anti-herpetic medications (OR 0.87, 95% CI 0.80-0.96).

CONCLUSION

Herpesvirus infection or treatment might reduce risk of Parkinson's disease, but future studies will be required to explore whether this inverse association is causal.

Smoking Cessation, Weight Change, and Risk of Parkinson's Disease: Analysis of National Cohort Data

BACKGROUND AND PURPOSE

To determine whether the postcessation weight gain modifies the protective effect of smoking on the development of Parkinson's disease (PD).

METHODS

This nationwide cohort study included 3,908,687 Korean males aged ≥40 years who underwent at least 2 health checkups biennially between 2009 and 2015. They were grouped into current smokers; quitters with body mass index (BMI) increase, maintenance, and decrease; and never smokers. The occurrence of incident PD was tracked, and Cox proportional-hazard models were used to adjust for potential confounding factors. We also analyzed the impact of weight change regardless of smoking status in the study population.

RESULTS

There were 6,871 incident PD cases observed during the 13,059,208 person-years of follow-up. The overall risk of PD was significantly lower in quitters than in never smokers [hazard ratio (HR)=0.78, 95% confidence interval (CI)=0.70-0.86]. The risk of PD was still lower in quitters with BMI increase (HR=0.80, 95% CI=0.65-0.98) and in those with BMI maintenance (HR=0.77, 95% CI=0.68-0.87). This tendency was also observed in quitters with BMI decrease (HR=0.76, 95% CI=0.55-1.06), although it was not as robust as in the other two groups. With respect to weight change alone, BMI increase (HR=1.10, 95% CI=1.02-1.18) but not BMI decrease (HR=1.06, 95% CI=0.98-1.14) significantly increased the PD risk compared to BMI maintenance.

CONCLUSIONS

Postcessation weight gain in males did not offset the protective impact of smoking on PD development, although overall weight gain predicted an increased risk of PD.

Fractures in the prodromal period of Parkinson disease

OBJECTIVE

To examine the association between fractures and Parkinson disease (PD) during the 5-year prodromal phase as compared to controls.

METHODS

We performed a population-based case-control study of Medicare beneficiaries in the United States from 2004 to 2009. We identified 89,632 incident PD cases and 117,760 comparable controls 66-90 years of age in 2009. PD case status was the outcome, and noncranial fracture the independent variable. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for association between fracture and PD in yearly time intervals prior to PD diagnosis/control reference date, after adjusting for covariates.

RESULTS

There were 39,606 total fractures (25.4% cases, 14.3% controls) over the 5 years prior to the PD diagnosis/control reference date. PD was positively associated with fractures even after adjusting for age, sex, race/ethnicity, Charlson comorbidity index, alcohol use, tobacco use, and osteoporosis. The association between PD and fracture was evident at yearly time windows prior to PD diagnosis/control reference date. The association between PD and each type of fracture strengthened as the PD diagnosis/control reference date approached (all time interaction p values ≤0.02). Among beneficiaries with a mechanism of injury, the majority were attributed to falls (74.6% cases, 72.8% controls).

CONCLUSION

Fractures occur more commonly during the prodromal period of PD compared to controls, especially as diagnosis date approached, suggesting that patients with PD may experience unrecognized motor and nonmotor symptoms.

Artificial intelligence in sleep medicine: background and implications for clinicians

None

Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources of health data, AI is expected to provide new insights to inform the clinical care of sleep disorders and advance our understanding of the integral role sleep plays in human health. Additionally, AI has the potential to streamline day-to-day operations and therefore optimize direct patient care by the sleep disorders team. However, clinicians, scientists, and other stakeholders must develop best practices to integrate this rapidly evolving technology into our daily work while maintaining the highest degree of quality and transparency in health care and research. Ultimately, when harnessed appropriately in conjunction with human expertise, AI will improve the practice of sleep medicine and further sleep science for the health and well-being of our patients.

Validation of a Parkinson Disease Predictive Model in a Population-Based Study

Parkinson disease (PD) has a relatively long prodromal period that may permit early identification to reduce diagnostic testing for other conditions when patients are simply presenting with early PD symptoms, as well as to reduce morbidity from fall-related trauma. Earlier identification also could prove critical to the development of neuroprotective therapies. We previously developed a PD predictive model using demographic and Medicare claims data in a population-based case-control study. The area under the receiver-operating characteristic curve (AUC) indicated good performance. We sought to further validate this PD predictive model. In a randomly selected, population-based cohort of 115,492 Medicare beneficiaries aged 66-90 and without PD in 2009, we applied the predictive model to claims data from the prior five years to estimate the probability of future PD diagnosis. During five years of follow-up, we used 2010-2014 Medicare data to determine PD and vital status and then Cox regression to investigate whether PD probability at baseline was associated with time to PD diagnosis. Within a nested case-control sample, we calculated the AUC, sensitivity, and specificity. A total of 2,326 beneficiaries developed PD. Probability of PD was associated with time to PD diagnosis (p < 0.001, hazard ratio = 13.5, 95% confidence interval (CI) 10.6-17.3 for the highest vs. lowest decile of probability). The AUC was 83.3% (95% CI 82.5%-84.1%). At the cut point that balanced sensitivity and specificity, sensitivity was 76.7% and specificity was 76.2%. In an independent sample of additional Medicare beneficiaries, we again applied the model and observed good performance (AUC = 82.2%, 95% CI 81.1%-83.3%). Administrative claims data can facilitate PD identification within Medicare and Medicare-aged samples.

β-adrenoreceptors and the risk of Parkinson's disease

BACKGROUND

β-adrenoceptors are widely expressed in different human organs, mediate important body functions and are targeted by medications for various diseases (such as coronary heart disease and heart attack) and many β-adrenoceptor acting drugs are listed on the WHO Model List of Essential Medicines. β-adrenoceptor antagonists are used by billions of patients with neurological disorders, primarily for the treatment of migraine and action tremor (mainly essential tremor), worldwide.

RECENT DEVELOPMENTS

An observational study reported a link between the chronic use of the β-adrenoceptor antagonist propranolol and an increased risk of Parkinson's disease, while the chronic use of the β-adrenoceptor agonists was associated with a decreased risk. Further support of this association was provided by a dose-dependent decrease in the risk of Parkinson's disease with chronic β-adrenoceptor agonist (eg, salbutamol) use, and by functional data indicating a possible underlying molecular mechanism. Five additional epidemiological studies have examined the modulation of the risk of Parkinson's disease as a result of the use of β-adrenoceptor-acting drugs in different populations. Overall, similar estimates but different interpretations of the associations were provided. Several findings suggest that the increase in risk of Parkinson's disease associated with β-adrenoceptor antagonists use can be explained by reverse causation because prodromal Parkinson's disease is often associated with non-specific action tremor, which is usually treated with propranolol. The lower risk of Parkinson's disease seen in patients receiving β-adrenoceptor agonists is likely to be indirectly mediated by smoking because smoking has a strong inverse association with Parkinson's disease (people that smoke have a reduced risk of developing Parkinson's disease). Smoking also causes chronic obstructive pulmonary disease, which is treated with β-adrenoceptor-agonist medications. Even if causal, the effect of β-adrenoceptor antagonists on the risk of Parkinson's disease would be small compared with other Parkinson's disease risk factors and would be similar to the risk evoked by pesticide exposure. The estimated risk of Parkinson's disease because of β-adrenoceptor antagonists use corresponds to one case in 10 000 patients after 5 years of propranolol use, and would be considered a very rare adverse effect. Thus, not using β-adrenoceptor antagonists would severely harm patients with recommended indications, such as heart disease or migraine. Similarly, 50 000 people would have to be treated for 5 years with salbutamol to prevent Parkinson's disease in one patient, suggesting that primary preventive therapy studies on disease modification are not warranted. WHERE NEXT?: Epidemiological evidence for a causal relationship between use of β2-adrenoceptor antagonists and the increased risk of Parkinson's disease is weak, with other explanations for the association being more probable. Future observational studies are warranted to clarify this association. However, given the very low risk associated with propranolol, most clinicians are unlikely to change their treatment approach.

Well Water and Parkinson's Disease in Medicare Beneficiaries: A Nationwide Case-Control Study

BACKGROUND

Well water frequently is considered a risk factor for Parkinson's disease (PD), but few studies were designed appropriately to test whether geographic factors affect PD risk.

OBJECTIVE

To determine the risk of PD in relation to residential use of private well water.

METHODS

In a nationwide, population-based case-control study, we identified all incident PD cases (N = 89,790) and all comparable controls (N = 21,549,400) age 66-90 who solely relied on Medicare coverage in the U.S. in 2009. We estimated the probability of use of private well water using zip code of residence at diagnosis/reference and U.S. Census data on household water source. We modeled this exposure linearly in logistic regression to calculate the odds ratio (OR) and 95% confidence interval (CI) of PD risk in relation to well water use. We adjusted for age, sex and race/ethnicity, and verified that smoking and use of medical care did not confound results. We repeated analyses with a 2-year exposure lag and separately within each U.S. state.

RESULTS

Use of well water was inversely associated with PD risk (OR = 0.87, 95% CI 0.85-0.89). We confirmed this association in a Cox survival analysis in which we followed controls for 5 years, death or PD diagnosis. There was little evidence that well water use increased risk of PD in any individual state.

CONCLUSIONS

Although it remains possible that exposures in well water in more narrow geographic regions increase PD risk, in general these results suggest that exposures more common in urban/suburban areas might also be relevant.

Using Medical Claims Analyses to Understand Interventions for Parkinson Patients

The scientific evidence to support the value of a range of non-pharmacological interventions for people with Parkinson’s disease (PD) is increasing. However, showing unequivocally that specific interventions are better than usual care is not straightforward because of generic drawbacks of clinical trials. Here, we address these challenges, specifically related to the context of evaluating complex non-pharmacological interventions for people with PD. Moreover, we discuss the potential merits of undertaking “real world” analyses using medical claims data. We illustrate this approach by discussing an interesting recent publication in The Lancet Neurology, which used such an approach to demonstrate the value of specialized physiotherapy for PD patients, over and above usual care physiotherapy.

Treatment Patterns in Patients with Incident Parkinson's Disease in the United States

BACKGROUND

Treatment patterns in Parkinson's disease (PD) have not been extensively studied for nearly two decades. Insurance claims are appropriate for such analysis.

OBJECTIVE

To understand the standard of care use of symptomatic treatments in new cases of PD and factors associated with treatment choice.

METHODS

Retrospective cohort study using claims data from the United States between 2008 and 2016. We used Kaplan-Meier methodology to estimate time to treatment start and switch or add-on therapy and Cox proportional hazards models to identify predictors.

RESULTS

We identified 68,532 patients eligible for treatment pattern analyses. Median time from diagnosis until first treatment was 37 days (95% confidence interval: 36-38). Two distinct patterns of treatment initiation were identified: fast initiators and patients with delayed treatment start (or no recorded treatment). Levodopa therapies were the most commonly prescribed treatment class (52.6%). Increased age was associated with shorter time to start of treatment with levodopa. Younger age was associated with shorter time to initiation of dopamine agonists and other symptomatic treatments. Patients that initiated treatment with levodopa/combinations had the fewest switches/add-ons [30.4%; median time 7.29 (6.71, 8.13) years]. Older patients had fewer switch/add-on therapies, but only in the group that started with levodopa/combination therapy.

CONCLUSIONS

Time from diagnosis to treatment start was relatively short, suggesting that PD diagnosis, as reflected in the database, is closely linked to start of symptomatic treatment. Levodopa treatment remains the most common treatment, especially for older patients. Delayed treatment start was associated with increased age and comorbidity.

Early and Accurate Identification of Parkinson Disease Among US Veterans

Early and accurate identification and management of veterans at risk for Parkinson disease is an important priority area for the US Department of Veterans Affairs because of the substantial impact on quality of life and disability-adjusted life years.

The Parkinson's phenome-traits associated with Parkinson's disease in a broadly phenotyped cohort

In order to systematically describe the Parkinson's disease phenome, we performed a series of 832 cross-sectional case-control analyses in a large database. Responses to 832 online survey-based phenotypes including diseases, medications, and environmental exposures were analyzed in 23andMe research participants. For each phenotype, survey respondents were used to construct a cohort of Parkinson's disease cases and age-matched and sex-matched controls, and an association test was performed using logistic regression. Cohorts included a median of 3899 Parkinson's disease cases and 49,808 controls, all of European ancestry. Highly correlated phenotypes were removed and the novelty of each significant association was systematically assessed (assigned to one of four categories: known, likely, unclear, or novel). Parkinson's disease diagnosis was associated with 122 phenotypes. We replicated 27 known associations and found 23 associations with a strong a priori link to a known association. We discovered 42 associations that have not previously been reported. Migraine, obsessive-compulsive disorder, and seasonal allergies were associated with Parkinson's disease and tend to occur decades before the typical age of diagnosis for Parkinson's disease. The phenotypes that currently comprise the Parkinson's disease phenome have mostly been explored in relatively small purpose-built studies. Using a single large dataset, we have successfully reproduced many of these established associations and have extended the Parkinson's disease phenome by discovering novel associations. Our work paves the way for studies of these associated phenotypes that explore shared molecular mechanisms with Parkinson's disease, infer causal relationships, and improve our ability to identify individuals at high-risk of Parkinson's disease.

Transplant and risk of Parkinson disease

INTRODUCTION

The pathophysiology of Parkinson's disease (PD) remains unclear, but growing evidence supports a role of neuroinflammation. The purpose of this study was to investigate the association between tissue transplantation and PD risk, given the importance of immunosuppressants in post-transplant management.

METHODS

We performed a case-control study among Medicare beneficiaries age 66-90 using claims from 2004 to 2009. We used International Classification of Diseases, Ninth Edition (ICD-9) and Current Procedural Terminology (CPT) codes to identify PD (89,790 incident cases, 118,095 population-based controls) and history of tissue transplant (kidney, heart, liver, lung, and bone marrow). We investigated risk of PD in relation to tissue transplant in logistic regression models, adjusting for age, sex, race, smoking, and overall use of medical care.

RESULTS

Beneficiaries who had received a tissue transplant at least five years prior to PD diagnosis or reference had a lower risk of PD (odds ratio [OR] 0.63, 95% confidence interval [CI] 0.53, 0.75) than those without tissue transplant. This inverse association was observed for kidney (OR 0.63, 95% CI 0.47, 0.84), heart (OR 0.58, 95% CI 0.40, 0.83), lung (OR 0.41, 95% CI 0.21, 0.77), and bone marrow (OR 0.57, 95% 0.38, 0.85) transplants. Associations were attenuated, but remained, following adjustment for indications for the respective type of transplant. Liver transplant was not associated with PD risk.

CONCLUSIONS

Patients undergoing tissue transplant may have a lower risk of developing PD than the general population. Further studies are needed to determine if this association is causal and if immunosuppressants mediate this association.

β2-adrenoreceptor medications and risk of Parkinson disease

OBJECTIVE

A recent study observed a 2-fold greater risk of Parkinson disease (PD) in relation to the β2-adrenoreceptor antagonist propranolol and a markedly lower risk of PD for the β2-adrenoreceptor agonist salbutamol. We examined whether confounding by clinical indication for these medications, that is, tremor and smoking-related pulmonary conditions, explained these associations.

METHODS

In a large, population-based case-control study of United States Medicare beneficiaries in 2009 with diagnosis codes, procedure codes, and prescription data (48,295 incident PD cases, 52,324 controls), we examined the risk of PD in relation to use of selected β antagonists (propranolol, carvedilol, metoprolol), the β2 agonist salbutamol, and other medications used for the same clinical indications (primidone, inhaled corticosteroids). We adjusted for demographics, smoking, and overall use of medical care. We then examined the effect of also adjusting for clinical indication and applying medication exposure lagging.

RESULTS

Propranolol appeared to increase PD risk (odds ratio [OR] = 3.62, 95% confidence interval [CI] = 3.31-3.96). When we adjusted for tremor or abnormal involuntary movement prior to the PD diagnosis/reference date and lagged propranolol exposure, the association was 0.97 (95% CI = 0.80-1.18). Primidone, also used for tremor, was similarly sensitive to this adjustment and lagging. β Antagonists not indicated for tremor appeared to reduce PD risk (carvedilol: OR = 0.77, 95% CI = 0.73-0.81; metoprolol: OR = 0.94, 95% CI = 0.91-0.97) and were insensitive to adjustment for indications and lagging. Neither salbutamol nor inhaled corticosteroids were consistently associated with PD risk.

INTERPRETATION

β2-adrenoreceptor agonists and antagonists do not appear to alter PD risk. Ann Neurol 2018;84:691-701.

Immunosuppressants and risk of Parkinson disease

We performed a population‐based case–control study of United States Medicare beneficiaries age 60–90 in 2009 with prescription data (48,295 incident Parkinson disease cases and 52,324 controls) to examine the risk of Parkinson disease in relation to use of immunosuppressants. Inosine monophosphate dehydrogenase inhibitors (relative risk = 0.64; 95% confidence interval 0.51–0.79) and corticosteroids (relative risk = 0.80; 95% confidence interval 0.77–0.83) were both associated with a lower risk of Parkinson disease. Inverse associations for both remained after applying a 12‐month exposure lag. Overall, this study provides evidence that use of corticosteroids and inosine monophosphate dehydrogenase inhibitors might lower the risk of Parkinson disease.

Use of medical care biases associations between Parkinson disease and other medical conditions

OBJECTIVE

To examine how use of medical care biases the well-established associations between Parkinson disease (PD) and smoking, smoking-related cancers, and selected positively associated comorbidities.

METHODS

We conducted a population-based, case-control study of 89,790 incident PD cases and 118,095 randomly selected controls, all Medicare beneficiaries aged 66 to 90 years. We ascertained PD and other medical conditions using ICD-9-CM codes from comprehensive claims data for the 5 years before PD diagnosis/reference. We used logistic regression to estimate age-, sex-, and race-adjusted odds ratios (ORs) between PD and each other medical condition of interest. We then examined the effect of also adjusting for selected geographic- or individual-level indicators of use of care.

RESULTS

Models without adjustment for use of care and those that adjusted for geographic-level indicators produced similar ORs. However, adjustment for individual-level indicators consistently decreased ORs: Relative to ORs without adjustment for use of care, all ORs were between 8% and 58% lower, depending on the medical condition and the individual-level indicator of use of care added to the model. ORs decreased regardless of whether the established association is known to be positive or inverse. Most notably, smoking and smoking-related cancers were positively associated with PD without adjustment for use of care, but appropriately became inversely associated with PD with adjustment for use of care.

CONCLUSION

Use of care should be considered when evaluating associations between PD and other medical conditions to ensure that positive associations are not attributable to bias and that inverse associations are not masked.

Inflammatory bowel disease and risk of Parkinson's disease in Medicare beneficiaries

INTRODUCTION

Gastrointestinal (GI) dysfunction precedes the motor symptoms of Parkinson's disease (PD) by several years. PD patients have abnormal aggregation of intestinal α-synuclein, the accumulation of which may be promoted by inflammation. The relationship between intestinal α-synuclein aggregates and central nervous system neuropathology is unknown. Recently, we observed a possible inverse association between inflammatory bowel disease (IBD) and PD as part of a predictive model of PD. Therefore, the objective of this study was to examine the relationship between PD risk and IBD and IBD-associated conditions and treatment.

METHODS

Using a case-control design, we identified 89,790 newly diagnosed PD cases and 118,095 population-based controls >65 years of age using comprehensive Medicare data from 2004-2009 including detailed claims data. We classified IBD using International Classification of Diseases version 9 (ICD-9) diagnosis codes. We used logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the association between PD and IBD. Covariates included age, sex, race/ethnicity, smoking, Elixhauser comorbidities, and health care use.

RESULTS

PD was inversely associated with IBD overall (OR = 0.85, 95% CI 0.80-0.91) and with both Crohn's disease (OR = 0.83, 95% CI 0.74-0.93) and ulcerative colitis (OR = 0.88, 95% CI 0.82-0.96). Among beneficiaries with ≥2 ICD-9 codes for IBD, there was an inverse dose-response association between number of IBD ICD-9 codes, as a potential proxy for IBD severity, and PD (p-for-trend = 0.006).

CONCLUSION

IBD is associated with a lower risk of developing PD.

Traumatic brain injury in the prodromal period of Parkinson's disease: A large epidemiological study using medicare data

OBJECTIVE

Studies suggest a greater risk of Parkinson's disease (PD) after traumatic brain injury (TBI), but it is possible that the risk of TBI is greater in the prodromal period of PD. We aimed to examine the time-to-TBI in PD patients in their prodromal period compared to population-based controls.

METHODS

We identified 89,790 incident PD cases and 118,095 comparable controls aged > 65 years in 2009 using Medicare claims data. Using data from the preceding 5 years, we compared time-to-TBI in PD patients in their prodromal period to controls. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for TBI in a Cox regression, while adjusting for age, sex, race/ethnicity, modified Charlson comorbidity index, smoking, and alcohol use.

RESULTS

Risk of TBI was greater in PD patients in their prodromal period across all age and sex groups, with HRs consistently increasing with proximity to PD diagnosis. HRs ranged from 1.64 (95% CI, 1.52, 1.77) 5 years preceding diagnosis to 3.93 (95% CI, 3.74, 4.13) in the year before. The interaction between PD, TBI, and time was primarily observed for TBI attributed to falls. Motor dysfunction and cognitive impairment, suggested by corresponding International Classification of Diseases, Ninth Revision codes, partially mediated the PD-TBI association.

INTERPRETATION

There is a strong association between PD and a recent TBI in the prodromal period of PD. This association strengthens as PD diagnosis approaches and may be a result of undetected nonmotor and motor symptoms, but confirmation will be required. Ann Neurol 2017;82:744-754.