Joint and interactive effects between health comorbidities and environmental exposures in predicting amyotrophic lateral sclerosis

Avocational exposure associations with ALS risk, survival, and phenotype: A Michigan-based case-control study

INTRODUCTION

Environmental exposures strongly influence ALS risk and identification is needed to reduce ALS burden. Participation in hobbies and exercise may alter ALS risk and phenotype, warranting an assessment to understand their contribution to the ALS exposome.

METHODS

Participants with ALS and healthy controls were recruited from University of Michigan and self-completed a survey to ascertain hobbies, exercise, and avocational exposures. Exposure variables were associated with ALS risk, survival, onset segment, and onset age.

RESULTS

ALS (n = 400) and control (n = 287) participants self-reported avocational activities. Cases were slightly older (median age 63.0 vs. 61.1 years, p = 0.019) and had a lower educational attainment (p < 0.001) compared to controls; otherwise, demographics were well balanced. Risks associating with ALS after multiple comparison correction included golfing (odds ratio (OR) 3.48, padjusted = 0.004), recreational dancing (OR 2.00, padjusted = 0.040), performing gardening or yard work (OR 1.71, padjusted = 0.040) five years prior to ALS and personal (OR 1.76, padjusted = 0.047) or family (OR 2.21, padjusted = 0.040) participation in woodworking, and personal participation in hunting and shooting (OR 1.89, padjusted = 0.040). No exposures associated with ALS survival and onset. Those reporting swimming (3.86 years, padjusted = 0.016) and weightlifting (3.83 years, padjusted = 0.020) exercise 5 years prior to ALS onset had an earlier onset age.

DISCUSSION

The identified exposures in this study may represent important modifiable ALS factors that influence ALS phenotype. Thus, exposures related to hobbies and exercise should be captured in studies examining the ALS exposome.

Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment

A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, investigating the effect of interactions among these ECs can be challenging. We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression and a machine-learning method termed Signed iterative Random Forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multiordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs no). WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical-chemical interactions associated with ASD.

Prenatal Lead Exposure Is Associated with Reduced Abundance of Beneficial Gut Microbial Cliques in Late Childhood: An Investigation Using Microbial Co-Occurrence Analysis (MiCA)

Many analytical methods used in gut microbiome research focus on either single bacterial taxa or the whole microbiome, ignoring multibacteria relationships (microbial cliques). We present a novel analytical approach to identify microbial cliques within the gut microbiome of children at 9-11 years associated with prenatal lead (Pb) exposure. Data came from a subset of participants (n = 123) in the Programming Research in Obesity, Growth, Environment and Social Stressors cohort. Pb concentrations were measured in maternal whole blood from the second and third trimesters of pregnancy. Stool samples collected at 9-11 years old underwent metagenomic sequencing to assess the gut microbiome. Using a novel analytical approach, Microbial Co-occurrence Analysis (MiCA), we paired a machine learning algorithm with randomization-based inference to first identify microbial cliques that were predictive of prenatal Pb exposure and then estimate the association between prenatal Pb exposure and microbial clique abundance. With second-trimester Pb exposure, we identified a two-taxa microbial clique that included Bifidobacterium adolescentis and Ruminococcus callidus and a three-taxa clique that also included Prevotella clara. Increasing second-trimester Pb exposure was associated with significantly increased odds of having the two-taxa microbial clique below the median relative abundance (odds ratio (OR) = 1.03, 95% confidence interval (CI) [1.01-1.05]). Using a novel combination of machine learning and causal inference, MiCA identified a significant association between second-trimester Pb exposure and the reduced abundance of a probiotic microbial clique within the gut microbiome in late childhood.

Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics

The multifaceted nature and swift progression of Amyotrophic Lateral Sclerosis (ALS) pose considerable challenges to our understanding of its evolution and interplay with comorbid conditions. This study seeks to elucidate the temporal dynamics of ALS progression and its interaction with associated diseases. We employed a principal tree-based model to decipher patterns within clinical data derived from a population-based database in Taiwan. The disease progression was portrayed as branched trajectories, each path representing a series of distinct stages. Each stage embodied the cumulative occurrence of co-existing diseases, depicted as nodes on the tree, with edges symbolizing potential transitions between these linked nodes. Our model identified eight distinct ALS patient trajectories, unveiling unique patterns of disease associations at various stages of progression. These patterns may suggest underlying disease mechanisms or risk factors. This research re-conceptualizes ALS progression as a migration through diverse stages, instead of the perspective of a sequence of isolated events. This new approach illuminates patterns of disease association across different progression phases. The insights obtained from this study hold the potential to inform doctors regarding the development of personalized treatment strategies, ultimately enhancing patient prognosis and quality of life.

Exploring Relevant Time Windows in the Association Between PM2.5 Exposure and Amyotrophic Lateral Sclerosis: A Case-Control Study in Denmark

Studies suggest a link between particulate matter less than or equal to 2.5 μm in diameter (PM2.5) and amyotrophic lateral sclerosis (ALS), but to our knowledge critical exposure windows have not been examined. We performed a case-control study in the Danish population spanning the years 1989-2013. Cases were selected from the Danish National Patient Registry based on International Classification of Diseases codes. Five controls were randomly selected from the Danish Civil Registry and matched to a case on vital status, age, and sex. PM2.5 concentration at residential addresses was assigned using monthly predictions from a dispersion model. We used conditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for confounding. We evaluated exposure to averaged PM2.5 concentrations 12-24 months, 2-6 years, and 2-11 years pre-ALS diagnosis; annual lagged exposures up to 11 years prediagnosis; and cumulative associations for exposure in lags 1-5 years and 1-10 years prediagnosis, allowing for varying association estimates by year. We identified 3,983 cases and 19,915 controls. Cumulative exposure to PM2.5 in the period 2-6 years prediagnosis was associated with ALS (OR = 1.06, 95% CI: 0.99, 1.13). Exposures in the second, third, and fourth years prediagnosis were individually associated with higher odds of ALS (e.g., for lag 1, OR = 1.04, 95% CI: 1.00, 1.08). Exposure to PM2.5 within 6 years before diagnosis may represent a critical exposure window for ALS.

Urbanization, air pollution, and water pollution: Identification of potential environmental risk factors associated with amyotrophic lateral sclerosis using systematic reviews

Introduction

Despite decades of research, causes of ALS remain unclear. To evaluate recent hypotheses of plausible environmental factors, the aim of this study was to synthesize and appraise literature on the potential associations between the surrounding environment, including urbanization, air pollution and water pollution, and ALS.

Methods

We conducted a series (n = 3) of systematic reviews in PubMed and Scopus to identify epidemiological studies assessing relationships between urbanization, air pollution and water pollution with the development of ALS.

Results

The combined search strategy led to the inclusion of 44 articles pertaining to at least one exposure of interest. Of the 25 included urbanization studies, four of nine studies on living in rural areas and three of seven studies on living in more highly urbanized/dense areas found positive associations to ALS. There were also three of five studies for exposure to electromagnetic fields and/or proximity to powerlines that found positive associations to ALS. Three case-control studies for each of diesel exhaust and nitrogen dioxide found positive associations with the development of ALS, with the latter showing a dose-response in one study. Three studies for each of high selenium content in drinking water and proximity to lakes prone to cyanobacterial blooms also found positive associations to ALS.

Conclusion

Whereas markers of air and water pollution appear as potential risk factors for ALS, results are mixed for the role of urbanization.

The Role of Epigenetics in Neuroinflammatory-Driven Diseases

Neurodegenerative disorders are characterized by the progressive loss of central and/or peripheral nervous system neurons. Within this context, neuroinflammation comes up as one of the main factors linked to neurodegeneration progression. In fact, neuroinflammation has been recognized as an outstanding factor for Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and multiple sclerosis (MS). Interestingly, neuroinflammatory diseases are characterized by dramatic changes in the epigenetic profile, which might provide novel prognostic and therapeutic factors towards neuroinflammatory treatment. Deep changes in DNA and histone methylation, along with histone acetylation and altered non-coding RNA expression, have been reported at the onset of inflammatory diseases. The aim of this work is to review the current knowledge on this field.

Epigenome-wide DNA methylation study of whole blood in patients with sporadic amyotrophic lateral sclerosis

Background:

Epigenetics, and especially DNA methylation, contributes to the pathogenesis of sporadic amyotrophic lateral sclerosis (SALS). This study aimed to investigate the role of DNA methylation in SALS using whole blood of SALS patients.

Methods:

In total, 32 SALS patients and 32 healthy controls were enrolled in this study. DNA was isolated from whole blood collected from the participants. DNA methylation profiles were generated using Infinium MethylationEPIC BeadChip.

Results:

We identified 34 significant differentially methylated positions (DMPs) in whole blood from SALS patients, compared with the healthy controls. Of these DMPs, five were hypermethylated and 29 were hypomethylated; they corresponded to 13 genes. For the DMPs, ATAD3B and BLK were hypermethylated, whereas DDO, IQCE, ABCB1, DNAH9, FIGN, NRP1, TMEM87B, CCSAP, ST6GALNAC5, MYOM2, and RUSC1-AS1 were hypomethylated. We also identified 12 differentially methylated regions (DMRs), related to 12 genes (NWD1, LDHD, CIS, IQCE, TNF, PDE1C, LGALS1, CSNK1E, LRRC23, ENO2, ELOVL2, and ELOVL2-AS1). According to data from the Kyoto Encyclopedia of Genes and Genomes database, DNAH9 and TNF are involved in the amyotrophic lateral sclerosis (ALS) pathway. Correlation analysis between clinical features and DNA methylation profiling indicated that the methylation level of ELOVL2 and ARID1B was positively associated with the age of onset (r = 0.86, adjust P = 0.001) and disease duration (r = 0.83, adjust P = 0.01), respectively.

Conclusions:

We found aberrant methylation in DMP- and DMR-related genes, implying that many epigenetic alterations, such as the hypomethylation of DNAH9 and TNF, play important roles in ALS etiology. These findings can be helpful for developing new therapeutic interventions.

A Multipollutant Approach to Estimating Causal Effects of Air Pollution Mixtures on Overall Mortality in a Large, Prospective Cohort

BACKGROUND

Several studies have confirmed associations between air pollution and overall mortality, but it is unclear to what extent these associations reflect causal relationships. Moreover, few studies to our knowledge have accounted for complex mixtures of air pollution. In this study, we evaluate the causal effects of a mixture of air pollutants on overall mortality in a large, prospective cohort of Dutch individuals.

METHODS

We evaluated 86,882 individuals from the LIFEWORK study, assessing overall mortality between 2013 and 2017 through national registry linkage. We predicted outdoor concentration of five air pollutants (PM2.5, PM10, NO2, PM2.5 absorbance, and oxidative potential) with land-use regression. We used logistic regression and mixture modeling (weighted quantile sum and boosted regression tree models) to identify potential confounders, assess pollutants' relevance in the mixture-outcome association, and investigate interactions and nonlinearities. Based on these results, we built a multivariate generalized propensity score model to estimate the causal effects of pollutant mixtures.

RESULTS

Regression model results were influenced by multicollinearity. Weighted quantile sum and boosted regression tree models indicated that all components contributed to a positive linear association with the outcome, with PM2.5 being the most relevant contributor. In the multivariate propensity score model, PM2.5 (OR=1.18, 95% CI: 1.08-1.29) and PM10 (OR=1.02, 95% CI: 0.91-1.14) were associated with increased odds of mortality per interquartile range increase.

CONCLUSION

Using novel methods for causal inference and mixture modeling in a large prospective cohort, this study strengthened the causal interpretation of air pollution effects on overall mortality, emphasizing the primary role of PM2.5 within the pollutant mixture.

Diabetes Mellitus and Amyotrophic Lateral Sclerosis: A Systematic Review

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a degenerative disorder which affects the motor neurons. Growing evidence suggests that ALS may impact the metabolic system, including the glucose metabolism. Several studies investigated the role of Diabetes Mellitus (DM) as risk and/or prognostic factor. However, a clear correlation between DM and ALS has not been defined. In this review, we focus on the role of DM in ALS, examining the different hypotheses on how perturbations of glucose metabolism may interact with the pathophysiology and the course of ALS.

METHODS

We undertook an independent PubMed literature search, using the following search terms: ((ALS) OR (Amyotrophic Lateral Sclerosis) OR (Motor Neuron Disease)) AND ((Diabetes) OR (Glucose Intolerance) OR (Hyperglycemia)). Review and original articles were considered.

RESULTS

DM appears not to affect ALS severity, progression, and survival. Contrasting data suggested a protective role of DM on the occurrence of ALS in elderly and an opposite effect in younger subjects.

CONCLUSIONS

The actual clinical and pathophysiological correlation between DM and ALS is unclear. Large longitudinal prospective studies are needed. Achieving large sample sizes comparable to those of common complex diseases like DM is a challenge for a rare disease like ALS. Collaborative efforts could overcome this specific issue.