Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.
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ABSTRACT:
SUBMITTER: Sieberts SK
PROVIDER: S-EPMC7979931 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.
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Project description:BackgroundImpaired gait plays an important role for quality of life in patients with Huntington's disease (HD). Measuring objective gait parameters in HD might provide an unbiased assessment of motor deficits in order to determine potential beneficial effects of future treatments.ObjectiveTo objectively identify characteristic features of gait in HD patients using sensor-based gait analysis. Particularly, gait parameters were correlated to the Unified Huntington's Disease Rating Scale, total motor score (TMS), and total functional capacity (TFC).MethodsPatients with manifest HD at two German sites (n = 43) were included and clinically assessed during their annual ENROLL-HD visit. In addition, patients with HD and a cohort of age- and gender-matched controls performed a defined gait test (4 × 10 m walk). Gait patterns were recorded by inertial sensors attached to both shoes. Machine learning algorithms were applied to calculate spatio-temporal gait parameters and gait variability expressed as coefficient of variance (CV).ResultsStride length (- 15%) and gait velocity (- 19%) were reduced, while stride (+ 7%) and stance time (+ 2%) were increased in patients with HD. However, parameters reflecting gait variability were substantially altered in HD patients (+ 17% stride length CV up to + 41% stride time CV with largest effect size) and showed strong correlations to TMS and TFC (0.416 ≤ rSp ≤ 0.690). Objective gait variability parameters correlated with disease stage based upon TFC.ConclusionsSensor-based gait variability parameters were identified as clinically most relevant digital biomarker for gait impairment in HD. Altered gait variability represents characteristic irregularity of gait in HD and reflects disease severity.
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Project description:Therapeutic trials of disease-modifying agents in neurodegenerative disease typically require several hundred participants and long durations for clinical endpoints. Trials of this size are not feasible for prion diseases, rare dementia disorders associated with misfolding of prion protein. In this situation, biomarkers are particularly helpful. On diagnostic imaging, prion diseases demonstrate characteristic brain signal abnormalities on diffusion-weighted MRI. The aim of this study was to determine whether cerebral water diffusivity could be a quantitative imaging biomarker of disease severity. We hypothesized that the basal ganglia were most likely to demonstrate functionally relevant changes in diffusivity. Seventy-one subjects (37 patients and 34 controls) of whom 47 underwent serial scanning (23 patients and 24 controls) were recruited as part of the UK National Prion Monitoring Cohort. All patients underwent neurological assessment with the Medical Research Council Scale, a functionally orientated measure of prion disease severity, and diffusion tensor imaging. Voxel-based morphometry, voxel-based analysis of diffusion tensor imaging and regions of interest analyses were performed. A significant voxel-wise correlation of decreased Medical Research Council Scale score and decreased mean, radial and axial diffusivities in the putamen bilaterally was observed (P < 0.01). Significant decrease in putamen mean, radial and axial diffusivities over time was observed for patients compared with controls (P = 0.01), and there was a significant correlation between monthly decrease in putamen mean, radial and axial diffusivities and monthly decrease in Medical Research Council Scale (P < 0.001). Step-wise linear regression analysis, with dependent variable decline in Medical Research Council Scale, and covariates age and disease duration, showed the rate of decrease in putamen radial diffusivity to be the strongest predictor of rate of decrease in Medical Research Council Scale (P < 0.001). Sample size calculations estimated that, for an intervention study, 83 randomized patients would be required to provide 80% power to detect a 75% amelioration of decline in putamen radial diffusivity. Putamen radial diffusivity has potential as a secondary outcome measure biomarker in future therapeutic trials in human prion diseases.
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Project description:BackgroundNeuropsychiatric and affective symptoms are prevalent in prodromal and clinical Parkinson's disease (PD). Some evidence suggests that they may also signify risk for motor complications (motor fluctuations and dyskinesias) of dopamine replacement therapy (DRT).ObjectiveTo determine whether neuropsychiatric symptoms present in de novo PD (ie, before DRT initiation) predict the severity of eventual motor complications of DRT.MethodsWe used clinical, demographic, neurobehavioral, and neuroimaging data from the Parkinson's Progression Markers Initiative (PPMI), a multicenter observational PD study. Participants were unmedicated at enrollment and 361 initiated DRT during PPMI follow-up. We used Cox proportional hazard and multivariate ordinal mixed-effects regression models to evaluate the relationship between baseline neuropsychiatric symptoms and motor complications as measured by the Movement Disorders Society-revised Unified Parkinson's Disease Rating Scale (MDS-UPDRS).ResultsThe cumulative incidences of dyskinesias and motor fluctuations during follow-up (6.0 ± 1.5 years) were 34.3% and 59.9%, respectively. Both apathy and high trait-anxiety (top quartile) conveyed over two-fold increases in hazard for dyskinesia onset and for adverse impact on activities of daily living caused by both dyskinesias and motor fluctuations. The longitudinal severity of motor fluctuations and dyskinesias was significantly predicted by baseline trait-anxiety and apathy, but not depression. Models were adjusted for dimensionally related symptoms (eg autonomic dysfunction) and potential confounding variables (eg DRT dose).ConclusionsApathy and anxiety levels in de novo PD may be neuropsychiatric biomarkers of vulnerability to earlier and more disabling motor complications of DRT.
| S-EPMC7780944 | biostudies-literature
Project description:While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.
| S-EPMC7326699 | biostudies-literature
Project description:We investigated the role of the dopamine system [i.e., subcortical-medial prefrontal cortex (mPFC) network] in dreaming, by studying patients with Parkinson's Disease (PD) as a model of altered dopaminergic transmission. Subcortical volumes and cortical thickness were extracted by 3T-MR images of 27 PD patients and 27 age-matched controls, who were asked to fill out a dream diary upon morning awakening for one week. PD patients do not substantially differ from healthy controls with respect to the sleep, dream, and neuroanatomical measures. Multivariate correlational analyses in PD patients show that dopamine agonist dosage is associated to qualitatively impoverished dreams, as expressed by lower bizarreness and lower emotional load values. Visual vividness (VV) of their dream reports positively correlates with volumes of both the amygdalae and with thickness of the left mPFC. Emotional load also positively correlates with hippocampal volume. Beside the replication of our previous finding on the role of subcortical nuclei in dreaming experience of healthy subjects, this represents the first evidence of a specific role of the amygdala-mPFC dopaminergic network system in dream recall. The association in PD patients between higher dopamine agonist dosages and impoverished dream reports, however, and the significant correlations between VV and mesolimbic regions, however, provide an empirical support to the hypothesis that a dopamine network plays a key role in dream generation. The causal relation is however precluded by the intrinsic limitation of assuming the dopamine agonist dosage as a measure of the hypodopaminergic state in PD. Periodicals, Inc.
| S-EPMC6867535 | biostudies-literature
Project description:Hyposmia, identified as reduced sensitivity to odor, is a common non-motor symptom of Parkinson's disease (PD) that antedates the typical motor symptoms by several years. It occurs in ∼90% of early-stage cases of PD. In addition to the high prevalence, the occurrence of hyposmia may also predict a higher risk of PD. Investigations into hyposmia and its relationship with PD may help elucidate the underlying pathogenic mechanisms. This review provides an update of olfactory dysfunction in PD and its potential as a biomarker for this devastating disease.
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Project description:BACKGROUND:Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria (HD). However, the brain morphological changes associated with HD have not been investigated. Moreover, no reliable model for predicting the severity of HD based on neuroimaging has yet been developed. METHODS:A total of 134 PD patients were included in this study and divided into a training set and a test set. All participants underwent a structural magnetic resonance imaging (MRI) scan and neuropsychological evaluation. Individual cortical thickness, subcortical structure, and white matter volume were extracted, and their association with HD severity was analyzed. After feature selection, a machine-learning model was established using a support vector machine in the training set. The severity of HD was then predicted in the test set. RESULTS:Atrophy of the right precentral cortex and the right fusiform gyrus was significantly associated with HD. No association was found between HD and volume of white matter or subcortical structures. Favorable and optimal performance of machine learning on HD severity prediction was achieved using feature selection, giving a correlation coefficient (r) of .7516 and a coefficient of determination (R2 ) of .5649 (P < .001). CONCLUSION:The brain morphological changes were associated with HD. Excellent prediction of the severity of HD was achieved using machine learning based on neuroimaging.
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Project description:BackgroundEfforts to identify fluid biomarkers of Parkinson's disease (PD) have intensified in the last decade. As the role of inflammation in PD pathophysiology becomes increasingly recognized, investigators aim to define inflammatory signatures to help elucidate underlying mechanisms of disease pathogenesis and aid in identification of patients with inflammatory endophenotypes that could benefit from immunomodulatory interventions. However, discordant results in the literature and a lack of information regarding the stability of inflammatory factors over a 24-h period have hampered progress.MethodsHere, we measured inflammatory proteins in serum and CSF of a small cohort of PD (n?=?12) and age-matched healthy control (HC) subjects (n?=?6) at 11 time points across 24 h to (1) identify potential diurnal variation, (2) reveal differences in PD vs HC, and (3) to correlate with CSF levels of amyloid ? (A?) and ?-synuclein in an effort to generate data-driven hypotheses regarding candidate biomarkers of PD.ResultsDespite significant variability in other factors, a repeated measures two-way analysis of variance by time and disease state for each analyte revealed that serum IFN?, TNF, and neutrophil gelatinase-associated lipocalin (NGAL) were stable across 24 h and different between HC and PD. Regression analysis revealed that C-reactive protein (CRP) was the only factor with a strong linear relationship between CSF and serum. PD and HC subjects showed significantly different relationships between CSF A? proteins and ?-synuclein and specific inflammatory factors, and CSF IFN? and serum IL-8 positively correlated with clinical measures of PD. Finally, linear discriminant analysis revealed that serum TNF and CSF ?-synuclein discriminated between PD and HC with a minimum of 82% sensitivity and 83% specificity.ConclusionsOur findings identify a panel of inflammatory factors in serum and CSF that can be reliably measured, distinguish between PD and HC, and monitor inflammation as disease progresses or in response to interventional therapies. This panel may aid in generating hypotheses and feasible experimental designs towards identifying biomarkers of neurodegenerative disease by focusing on analytes that remain stable regardless of time of sample collection.
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Project description:Identifying sickle cell disease patients at high risk of complications could lead to personalized treatment and better prognosis but despite many advances prediction of the clinical course of these patients remains elusive. We propose a system-type approach to discover profiles of multiple, common biomarkers that correlate with morbidity and mortality in sickle cell disease. We used cluster analysis to discover 17 signatures of 17 common circulating biomarkers in 2320 participants of the Cooperative Study of Sickle Cell Disease, and evaluated the association of these signatures with risk for stroke, pain, leg ulceration, acute chest syndrome, avascular necrosis, seizure, death, and trend of fetal hemoglobin and hemolysis using longitudinally collected data. The analysis shows that some of the signatures are associated with reduced risk for complications, while others are associated with increased risk for complications. We also show that these signatures repeat in two more contemporary studies of sickle cell disease and correlate with recently discovered biomarkers of pulmonary vascular disease. With replication and further study, these biomarker signatures could become an important and affordable precision medicine tool to aid treatment and management of the disease.
| S-EPMC6087499 | biostudies-literature