ArticleEnvironmental health : a global access science source2024
Quantitative association between lead exposure and amyotrophic lateral sclerosis: a Bayesian network-based predictive study.
Article in Environmental health : a global access science source, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Trace Elements Dyshomeostasis and Toxic Metals Neurotoxicity in Neurodegenerative Diseases.Biological trace element research · 2026Review
- Development and Validation of a Machine Learning Algorithm to Predict the Risk of Blood Transfusion after Total Hip Replacement in Patients with Femoral Neck Fractures: A Multicenter Retrospective Cohort Study.Orthopaedic surgery · 2024Article
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7 authors at 1 institution in 1 country.
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Abstract
backgroundEnvironmental lead (Pb) exposure have been suggested as a causative factor for amyotrophic lateral sclerosis (ALS). However, the role of Pb content of human body in ALS outcomes has not been quantified clearly. The purpose of this study was to apply Bayesian networks to forecast the risk of Pb exposure on the disease occurrence.
methodsWe retrospectively collected medical records of ALS inpatients who underwent blood Pb testing, while matched controlled inpatients on age, gender, hospital ward and admission time according to the radio of 1:9. Tree Augmented Naïve Bayes (TAN), a semi-naïve Bayes classifier, was established to predict probability of ALS or controls with risk factors.
resultsA total of 140 inpatients were included in this study. The whole blood Pb levels of ALS patients (57.00 μg/L) were more than twice as high as the controls (27.71 μg/L). Using the blood Pb concentrations to calculate probability of ALS, TAN produced the total coincidence rate of 90.00%. The specificity, sensitivity of Pb for ALS prediction was 0.79, or 0.74, respectively.
conclusionTherefore, these results provided quantitative evidence that Pb exposure may contribute to the development of ALS. Bayesian networks may be used to predict the ALS early onset with blood Pb levels.
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