Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram

Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months-1 year, 6 months-3 years, and 6 months-5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

Risk of Parkinson Disease Among Service Members at Marine Corps Base Camp Lejeune

Importance

An increased risk of Parkinson disease (PD) has been associated with exposure to the solvent trichloroethylene (TCE), but data are limited. Millions of people in the US and worldwide are exposed to TCE in air, food, and water.

Objective

To test whether the risk of PD is higher in veterans who served at Marine Corps Base Camp Lejeune, whose water supply was contaminated with TCE and other volatile organic compounds (VOCs), compared with veterans who did not serve on that base.

Design, Setting, and Participants

This population-based cohort study examined the risk for PD among all Marines and Navy personnel who resided at Camp Lejeune, North Carolina (contaminated water) (n = 172 128), or Camp Pendleton, California (uncontaminated water) (n = 168 361), for at least 3 months between 1975 and 1985, with follow-up from January 1, 1997, until February 17, 2021. Veterans Health Administration and Medicare databases were searched for International Classification of Diseases diagnostic codes for PD or other forms of parkinsonism and related medications and for diagnostic codes indicative of prodromal disease. Parkinson disease diagnoses were confirmed by medical record review.

Exposures

Water supplies at Camp Lejeune were contaminated with several VOCs. Levels were highest for TCE, with monthly median values greater than 70-fold the permissible amount.

Main Outcome and Measures

Risk of PD in former residents of Camp Lejeune relative to residents of Camp Pendleton. In those without PD or another form of parkinsonism, the risk of being diagnosed with features of prodromal PD were assessed individually and cumulatively using likelihood ratio tests.

Results

Health data were available for 158 122 veterans (46.4%). Demographic characteristics were similar between Camp Lejeune (5.3% women, 94.7% men; mean [SD] attained age of 59.64 [4.43] years; 29.7% Black, 6.0% Hispanic, 67.6% White; and 2.7% other race and ethnicity) and Camp Pendleton (3.8% women, 96.2% men; mean [SD] age, 59.80 [4.62] years; 23.4% Black, 9.4% Hispanic, 71.1% White, and 5.5% other race and ethnicity). A total of 430 veterans had PD, with 279 from Camp Lejeune (prevalence, 0.33%) and 151 from Camp Pendleton (prevalence, 0.21%). In multivariable models, Camp Lejeune veterans had a 70% higher risk of PD (odds ratio, 1.70; 95% CI, 1.39-2.07; P < .001). No excess risk was found for other forms of neurodegenerative parkinsonism. Camp Lejeune veterans also had a significantly increased risk of prodromal PD diagnoses, including tremor, anxiety, and erectile dysfunction, and higher cumulative prodromal risk scores.

Conclusions and Relevance

The study's findings suggest that the risk of PD is higher in persons exposed to TCE and other VOCs in water 4 decades ago. Millions worldwide have been and continue to be exposed to this ubiquitous environmental contaminant.

Variable abnormality of the melanopsin-derived portion of the pupillary light reflex (PLR) in patients with Parkinson's disease (PD) and parkinsonism features

OBJECTIVES

Ascertain and quantify abnormality of the melanopsin-derived portion of the pupillary light reflex (PLR) in patients with Parkinson's disease (PD) and parkinsonism features based on a statistical predictive modeling strategy for PLR classification.

METHODS

Exploratory cohort analysis of pupillary kinetics in non-disease controls, PD subjects, and subjects with parkinsonism features using chromatic pupillometry. Receiver operating characteristic (ROC) curve interpretation of pupillary changes consistent with abnormality of intrinsically photosensitive retinal ganglion cells (ipRGCs) was employed using a thresholding algorithm to discriminate pupillary abnormality between study groups.

RESULTS

Twenty-eight subjects were enrolled, including 17 PD subjects (age range 64-85, mean 70.65) and nine controls (age range 48-95, mean 63.89). Two subjects were described as demonstrating parkinsonism symptoms due to presumed Lewy body dementia and motor system atrophy (MSA) respectively. On aggregate analysis, PD subjects demonstrated abnormal but variable pupillary dynamics suggestive of ipRGC abnormality. Subjects with parkinsonism features did not demonstrate pupillary changes consistent with ipRGC abnormality. There was no relationship between levodopa equivalent dosage or PD severity and ipRGC abnormality. The pupillary test sensitivity in predicting PD was 0.75 and likelihood ratio was 1.2.

CONCLUSIONS

ipRGC deficit is demonstrated in PD subjects; however, the degree and constancy of abnormality appear variable.

Detection of Parkinson's Disease Through Automated Pupil Tracking of the Post-illumination Pupillary Response

Parkinson's disease (PD) is one of the most common neurodegenerative disorders, but it is often diagnosed after the majority of dopaminergic cells are already damaged. It is critical to develop biomarkers to identify the disease as early as possible for early intervention. PD patients appear to have an altered pupillary response consistent with an abnormality in photoreceptive retinal ganglion cells. Tracking the pupil size manually is a tedious process and offline automated systems can be prone to errors that may require intervention; for this reason in this work we describe a system for pupil size estimation with a user interface to allow rapid adjustment of parameters and extraction of pupil parameters of interest for the present study. We implemented a user-friendly system designed for clinicians to automate the process of tracking the pupil diameter to measure the post-illumination pupillary response (PIPR), permit manual corrections when needed, and continue automation after correction. Tracking was automated using a Kalman filter estimating the pupil center and diameter over time. The resulting system was tested on a PD classification task in which PD subjects are known to have similar responses for two wavelengths of light. The pupillary response is measured in the contralateral eye to two different light stimuli (470 and 610 nm) for 19 PD and 10 control subjects. The measured Net PIPR indicating different responsiveness to the wavelengths was 0.13 mm for PD subjects and 0.61 mm for control subjects, demonstrating a highly significant difference (p < 0.001). Net PIPR has the potential to be a biomarker for PD, suggesting further study to determine clinical validity.

An angiogenic inhibitor, cyclic RGDfV, attenuates MPTP-induced dopamine neuron toxicity

We previously demonstrated that several dopamine (DA) neurotoxins produced punctate areas of FITC-labeled albumin (FITC-LA) leakage in the substantia nigra and striatum suggesting blood brain barrier (BBB) dysfunction. Further, this leakage was co-localized with αvβ3 integrin up-regulation, a marker for angiogenesis. This suggested that the FITC-LA leakage might have been a result of angiogenesis. To assess the possible role of angiogenesis in DA neuron loss, we treated mice with 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine (MPTP) and on the following day treated with cyRGDfV, a cyclic peptide that binds to integrin αvβ3 and prevents angiogenesis. Post-treatment for 3 days (b.i.d.) with cyRGDfV blocked the MPTP-induced upregulation of integrin β3 immunoreactivity (a marker for angiogenesis), leakage of FITC-LA into brain parenchyma (a marker for BBB disruption) as well as the down regulation of Zona Occludin-1 (ZO-1; a marker for tight junction integrity). In addition, cyRGDfV also completely prevented tyrosine hydroxylase immunoreactive cell loss (a marker for DA neurons) and markedly attenuated the up-regulation of activated microglia (Iba1 cell counts and morphology). These data suggest that cyRGDfV, and perhaps other anti-angiogenic drugs, are neuroprotective following acute MPTP treatment and may suggest that compensatory angiogenesis and BBB dysfunction may contribute to inflammation and DA neuron loss.