Predicting Parkinson's disease and its pathology via simple clinical variables.

Details

• Personal Author:
  Akbilgic O ; Alexandrov AV ; Butler L ; Chinthala LK ; Davis RL ; Goldman SM ; Gunturkun F ; Kamaleswaran R ; Karabayir I ; Masaki K ; Petrovitch H ; Ross GW ; Tanner CM ; Tsivgoulis G
• Description:
  Background: Parkinson's disease (PD) is a chronic, disabling neurodegenerative disorder. Objective: To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests. Methods: Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995-2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density. Results: The study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76-0.89) or 5 years (AUC 0.77, 95%CI 0.71-0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (r = -0.57, p < 0.01). Conclusion: Machine learning can identify persons likely to develop PD during the prodromal period using questionnaires and simple non-invasive tests. Correlation with neuropathology suggests that true model accuracy may be considerably higher than estimates based solely on clinical diagnosis. [Description provided by NIOSH]
• Subjects:
  Aging Autopsy Environmental Pollution Genetics Men Nervous System Occupational Health Risk Assessment Risk Factors Safety Surveys And Questionnaires Women

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• Keywords:
  Author Keywords: Lewy Body Pathology; Parkinson's Disease; Machine Learning; Neuron Density Parkinsons Disease; Aging; Questionnaires; Autopsies; Models; Neuropathology; Environmental Contamination; Genetics; Risk Factors; Brain Disorders; Diseases; Humans; Men; Women; Statistical Analysis;
• ISSN:
  1877-7171
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Georgia ; Hawaii ; Illinois ; North Carolina ; OSHA Region 4 ; OSHA Region 5 ; OSHA Region 9 ; Tennessee
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  341-351
• Volume:
  12
• Issue:
  1
• NIOSHTIC Number:
  nn:20065095
• Citation:
  J Parkinsons Dis 2022 Jan; 12(1):341-351
• Contact Point Address:
  Oguz Akbilgic, PhD, Wake Forest School of Medicine, 1 Medical Center Boulevard, Winston-Salem, NC 27157
• Email:
  oakbilgi@wakehealth.edu
• Federal Fiscal Year:
  2022
• Performing Organization:
  University of California, Berkeley
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Journal of Parkinson's Disease
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:c5f11cee1860ea63d6a00401087ed8ee332913451d44e509f6405c658e382ed53e1d1dbce290c0f511b568ca21f0a768b410c37fa796f45a0c4c6de093dde0ba
• Download URL:
  https://stacks.cdc.gov/view/cdc/249330/cdc_249330_DS1.pdf
• File Type:
  [PDF - 395.63 KB ]