Dataset Information

Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis.

ABSTRACT:

Background

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Limited evidence suggests ALS diagnosis may be associated with air pollution exposure and specifically traffic-related pollutants.

Methods

In this population-based case-control study, we used 3,937 ALS cases from the Danish National Patient Register diagnosed during 1989-2013 and matched on age, sex, year of birth, and vital status to 19,333 population-based controls free of ALS at index date. We used validated predictions of elemental carbon (EC), nitrogen oxides (NO x ), carbon monoxide (CO), and fine particles (PM 2.5 ) to assign 1-, 5-, and 10-year average exposures pre-ALS diagnosis at study participants' present and historical residential addresses. We used an adjusted Bayesian hierarchical conditional logistic model to estimate individual pollutant associations and joint and average associations for traffic-related pollutants (EC, NO x , CO).

Results

For a standard deviation (SD) increase in 5-year average concentrations, EC (SD = 0.42 µg/m 3 ) had a high probability of individual association with increased odds of ALS (11.5%; 95% credible interval [CrI] = -1.0%, 25.6%; 96.3% posterior probability of positive association), with negative associations for NO x (SD = 20 µg/m 3 ) (-4.6%; 95% CrI = 18.1%, 8.9%; 27.8% posterior probability of positive association), CO (SD = 106 µg/m 3 ) (-3.2%; 95% CrI = 14.4%, 10.0%; 26.7% posterior probability of positive association), and a null association for nonelemental carbon fine particles (non-EC PM 2.5 ) (SD = 2.37 µg/m 3 ) (0.7%; 95% CrI = 9.2%, 12.4%). We found no association between ALS and joint or average traffic pollution concentrations.

Conclusions

This study found high probability of a positive association between ALS diagnosis and EC concentration. Further work is needed to understand the role of traffic-related air pollution in ALS pathogenesis.

SUBMITTER: Parks RM

PROVIDER: S-EPMC9560992 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis.

Parks Robbie M RM Nunez Yanelli Y Balalian Arin A AA Gibson Elizabeth A EA Hansen Johnni J Raaschou-Nielsen Ole O Ketzel Matthias M Khan Jibran J Brandt Jørgen J Vermeulen Roel R Peters Susan S Goldsmith Jeff J Re Diane B DB Weisskopf Marc G MG Kioumourtzoglou Marianthi-Anna MA

Epidemiology (Cambridge, Mass.) 20220729 6

<h4>Background</h4>Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Limited evidence suggests ALS diagnosis may be associated with air pollution exposure and specifically traffic-related pollutants.<h4>Methods</h4>In this population-based case-control study, we used 3,937 ALS cases from the Danish National Patient Register diagnosed during 1989-2013 and matched on age, sex, year of birth, and vital status to 19,333 population-based controls free of ALS at index date. We ...[more]

PMID: 35944145

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Project description:BACKGROUND:Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease, is a rare disease with extreme between-subject variability, especially with respect to rate of disease progression. This makes modelling a subject's disease progression, which is measured by the ALS Functional Rating Scale (ALSFRS), very difficult. Consider the problem of predicting a subject's ALSFRS score at 9 or 12 months after a given time-point. METHODS:We obtained ALS subject data from the Pooled Resource Open-Access ALS Clinical Trials Database, a collection of data from various ALS clinical trials. Due to the typical linearity of the ALSFRS, we consider several Bayesian hierarchical linear models. These include a mixture model (to account for the two potential classes of "fast" and "slow" ALS progressors) as well as an onset-anchored model, in which an additional artificial data-point, using time of disease onset, is utilized to improve predictive performance. RESULTS:The onset-anchored model had a drastically reduced posterior predictive mean-square-error distributions, when compared to the Bayesian hierarchical linear model or the mixture model under a cross-validation approach. No covariates, other than time of disease onset, consistently improved predictive performance in either the Bayesian hierarchical linear model or the onset-anchored model. CONCLUSIONS:Augmenting patient data with an additional artificial data-point, or onset anchor, can drastically improve predictive modelling in ALS by reducing the variability of estimated parameters at the cost of a slight increase in bias. This onset-anchored model is extremely useful if predictions are desired directly after a single baseline measure (such as at the first day of a clinical trial), a feat that would be very difficult without the onset-anchor. This approach could be useful in modelling other diseases that have bounded progression scales (e.g. Parkinson's disease, Huntington's disease, or inclusion-body myositis). It is our hope that this model can be used by clinicians and statisticians to improve the efficacy of clinical trials and aid in finding treatments for ALS.

Project description:IntroductionAmyotrophic lateral sclerosis (ALS) is a rare, devastating neurodegenerative disease that affects upper and lower motor neurons. To date, no effective treatment or reliable biomarker for ALS has been developed. In recent years, many factors have been proposed as possible biomarkers of ALS; however, no consensus has been reached. Therefore, a reliable biomarker is urgently needed. Eosinophils may play a crucial role in healthy humans and diseases, and serve as a biomarker for many chronic diseases.MethodsRoutine blood test results were collected from 66 healthy controls and 59 patients with ALS. The percentages and total numbers of each cell population were analyzed, and the correlation between these indicators and patient ALS functional rating scale-revised (ALSFRS-R) score or disease progression rate (ΔFS score) was analyzed.ResultsCompared to healthy controls, the number of blood leukocytes, neutrophils, monocytes, and basophils was significantly decreased in patients with ALS (p = 0.002, p = 0.001, p = 0.049, and p < 0.0001, respectively). There was an increase in the number of eosinophils (p < 0.0001), but no difference in the number of lymphocytes between patients with ALS and healthy controls was found (p = 0.563). Compared to healthy controls, the percentage of neutrophils was decreased and the percentage of lymphocytes and eosinophils was increased in patients with ALS (p = 0.01, p = 0.012, and p = 0.001, respectively). There was no difference between patients with ALS and healthy controls in the percentage of monocytes and basophils (p = 0.622 and p = 0.09, respectively). However, only the percentage and number of eosinophils had a correlation with the ΔFS score. Further multivariate analysis revealed a significant correlation between the disease duration, eosinophil count and percentage, and the disease progression rate (p < 0.0001, p = 0.048, and p = 0.023, respectively). The neutrophil-to-eosinophil ratio (NER), lymphocyte-to-eosinophil ratio (LER), and monocyte-to-eosinophil ratio (MER) were significantly lower in patients with ALS than in healthy controls. However, only the LER was significantly correlated with the ΔFS score.ConclusionThese observations implicate neutrophils, lymphocytes, and eosinophils as important factors, and increasing eosinophil counts were negatively correlated with the ΔFS score in patients with ALS.

Dataset Information

Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis.

Background

Methods

Results

Conclusions

Publications

Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis.

Similar Datasets

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