A screening tool to detect clinical manganese neurotoxicity

Authors

Brad A. Racette, Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA; School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, 27 St. Andrews Road, Parktown 2193, South Africa. Electronic address: racetteb@wustl.edu.
Anat Gross, Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA.
Susan R. Criswell, Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA.
Harvey Checkoway, University of California, San Diego, Department of Family Medicine and Public Health, Department of Neurosciences, La Jolla, CA, USA.
Susan Searles Nielsen, Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA.

Document Type

Article

Abstract

Manganese (Mn) over-exposure in occupational settings is associated with basal ganglia toxicity and a movement disorder characterized by parkinsonism (i.e., the signs and symptoms of Parkinson disease). A simple test to help non-neurologists identify workers with clinical Mn neurotoxicity represents an unmet need. In a cohort of Mn-exposed workers from welding worksites, with extensive clinical data, we developed a linear regression model to predict the Unified Parkinson Disease Rating Scale motor subsection part 3 (UPDRS3) score. We primarily considered factors easily obtained in a primary care or occupational medicine clinic, specifically easily assessed signs of parkinsonism and factors likely to be associated with UPDRS3 such as age, timed motor task results, and selected symptoms/conditions. Secondarily we considered other demographic variables and welding exposure. We based the model on 596 examined workers age≤65years and with timed motor task data. We selected the model based on simplicity for clinical application, biologic plausibility, and statistical significance and magnitude of regression coefficients. The model contained age, timed motor task scores for each hand, and indicators of action tremor, speech difficulty, anxiety, depression, loneliness, pain and current cigarette smoking. When we examined how well the model identified workers with clinically significant parkinsonism (UPDRS3≥15) the receiver operating characteristic area under the curve (AUC) was 0.72 (95% confidence interval [CI] 0.67, 0.77). With a cut point that provided 80% sensitivity, specificity was 52%, the positive predictive value in our cohort was 29%, and the negative predictive value was 92%. Using the same cut point for predicted UPDRS3, the AUC was nearly identical for UPDRS3≥10, and was 0.83 (95% CI 0.76, 0.90) for UPDRS3≥20. Since welding exposure data was not required after including its putative effects, this model may help identify workers with clinically significant Mn neurotoxicity in a variety of settings, as a first step in a tiered occupational screening program.

Medical Subject Headings

Adult; Female; Humans; Male; Manganese Poisoning (diagnosis); Middle Aged; Occupational Diseases (chemically induced, diagnosis); Occupational Exposure; Parkinsonian Disorders (chemically induced, diagnosis); ROC Curve

Publication Date

1-1-2018

Publication Title

Neurotoxicology

E-ISSN

1872-9711

Volume

64

First Page

12

Last Page

18

PubMed ID

28274800

Digital Object Identifier (DOI)

10.1016/j.neuro.2017.02.009

Recommended Citation

Racette, Brad A.; Gross, Anat; Criswell, Susan R.; Checkoway, Harvey; and Searles Nielsen, Susan, "A screening tool to detect clinical manganese neurotoxicity" (2018). Neurology. 1100.
https://scholar.barrowneuro.org/neurology/1100