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Environmental exposures impact amyotrophic lateral sclerosis (ALS) risk and progression, a fatal and progressive neurodegenerative disease. Better characterization of these exposures is needed to decrease disease burden. To identify exposures in the residential setting that associate with ALS risk, survival, and onset segment. ALS and control participants recruited from University of Michigan completed a survey that ascertained exposure risks in the residential setting. ALS risk was assessed using logistic regression models followed by latent profile analysis to consider exposure profiles. A case-only analysis considered the contribution of the residential exposure variables via a Cox proportional hazards model for survival outcomes and multinomial logistic regression for onset segment, a polytomous outcome. This study included 367 ALS and 255 control participants. Twelve residential variables were associated with ALS risk after correcting for multiple comparison testing, with storage in an attached garage of chemical products including gasoline or kerosene (odds ratio (OR) = 1.14, padjusted < 0.001), gasoline-powered equipment (OR = 1.16, padjusted < 0.001), and lawn care products (OR = 1.15, padjusted < 0.001) representing the top three risk factors sorted by padjusted. Latent profile analysis indicated that storage of these chemical products in both attached and detached garages increased ALS risk. Although residential variables were not associated with poorer ALS survival following multiple testing corrections, storing pesticides, lawn care products, and woodworking supplies in the home were associated with shorter ALS survival using nominal p values. No exposures were associated with ALS onset segment. Residential exposures may be important modifiable components of the ALS susceptibility and prognosis exposome. [ABSTRACT FROM AUTHOR]

This article discusses a study published in the Journal of Neurology, Neurosurgery & Psychiatry that examines the association between persistent organic pollutants (POPs) and amyotrophic lateral sclerosis (ALS) risk and survival. The study found that higher plasma concentrations of POPs, specifically organochlorine pesticides (OCPs), were associated with greater ALS risk and shorter survival. The authors suggest that these findings support the idea that POPs may play a role in the development and progression of ALS, and that quantitative assessments of environmental pollutants could help identify individuals at risk for ALS and other neurodegenerative diseases. The article also provides information about the authors, ethical considerations, data availability, and references to other relevant studies. [Extracted from the article]

To identify associations between occupational settings and self-reported occupational exposures on amyotrophic lateral sclerosis (ALS) survival and phenotypes. All patients seen in the University of Michigan Pranger ALS Clinic were invited to complete an exposure assessment querying past occupations and exposures. Standard occupational classification (SOC) codes for each job and the severity of various exposure types were derived. Cox proportional hazards models associated all-cause mortality with occupational settings and the self-reported exposures after adjusting for sex, diagnosis age, revised El Escorial criteria, onset segment, revised ALS Functional Rating Scale (ALSFRS-R), and time from symptom onset to diagnosis. Multinomial logistic regression models with three categories, adjusted for age, assessed the association between occupational settings and exposures to onset segment. Among the 378 ALS participants (median age, 64.7 years; 54.4% male), poorer survival was associated with work in SOC code "Production Occupations" and marginally with work in "Military Occupation"; poor survival associated with self-reported occupational pesticide exposure in adjusted models. Among onset segments: cervical onset was associated with ALS participants having ever worked in "Buildings and Grounds Cleaning and Maintenance Occupations," "Construction and Extraction Occupations," and "Production Occupations"; bulbar onset with self-reported occupational exposure to radiation; and cervical onset with exposure to particulate matter, volatile organic compounds, metals, combustion and diesel exhaust, electromagnetic radiation, and radiation. Occupational settings and self-reported exposures influence ALS survival and onset segment. Further studies are needed to explore and understand these relationships, most advantageously using prospective cohorts and detailed ALS registries. [ABSTRACT FROM AUTHOR]

Introduction/Aims: Body mass index (BMI) is linked to amyotrophic lateral sclerosis (ALS) risk and prognosis, but additional research is needed. The aim of this study was to identify whether and when historical changes in BMI occurred in ALS participants, how these longer term trajectories associated with survival, and whether metabolomic profiles provided insight into potential mechanisms. Methods: ALS and control participants self‐reported body height and weight 10 (reference) and 5 years earlier, and at study entry (diagnosis for ALS participants). Generalized estimating equations evaluated differences in BMI trajectories between cases and controls. ALS survival was evaluated by BMI trajectory group using accelerated failure time models. BMI trajectories and survival associations were explored using published metabolomic profiling and correlation networks. Results: Ten‐year BMI trends differed between ALS and controls, with BMI loss in the 5 years before diagnosis despite BMI gains 10 to 5 years beforehand in both groups. An overall 10‐year drop in BMI associated with a 27.1% decrease in ALS survival (P =.010). Metabolomic networks in ALS participants showed dysregulation in sphingomyelin, bile acid, and plasmalogen subpathways. Discussion: ALS participants lost weight in the 5‐year period before enrollment. BMI trajectories had three distinct groups and the group with significant weight loss in the past 10 years had the worst survival. Participants with a high BMI and increase in weight in the 10 years before symptom onset also had shorter survival. Certain metabolomics profiles were associated with the BMI trajectories. Replicating these findings in prospective cohorts is warranted. See Editorial on pages 191‐192 in this issue [ABSTRACT FROM AUTHOR]

Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This article considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as "stacked" and "grouped" objective functions. Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package miselect. Simulations demonstrate that the "stacked" approaches are more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Biorepository aiming to identify the association between environmental pollutants and ALS risk. for this article are available online. [ABSTRACT FROM AUTHOR]

Since the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic, a focus of research has been to identify risk factors associated with COVID-19-related outcomes, such as testing and diagnosis, and use them to build prediction models. Existing studies have used data from digital surveys or electronic health records (EHRs), but very few have linked the two sources to build joint predictive models. In this study, we used survey data on 7,054 patients from the Michigan Genomics Initiative biorepository to evaluate how well self-reported data could be integrated with electronic records for the purpose of modeling COVID-19-related outcomes. We observed that among survey respondents, self-reported COVID-19 diagnosis captured a larger number of cases than the corresponding EHRs, suggesting that self-reported outcomes may be better than EHRs for distinguishing COVID-19 cases from controls. In the modeling context, we compared the utility of survey- and EHR-derived predictor variables in models of survey-reported COVID-19 testing and diagnosis. We found that survey-derived predictors produced uniformly stronger models than EHR-derived predictors—likely due to their specificity, temporal proximity, and breadth—and that combining predictors from both sources offered no consistent improvement compared to using survey-based predictors alone. Our results suggest that, even though general EHRs are useful in predictive models of COVID-19 outcomes, they may not be essential in those models when rich survey data are already available. The two data sources together may offer better prediction for COVID severity, but we did not have enough severe cases in the survey respondents to assess that hypothesis in in our study. [ABSTRACT FROM AUTHOR]

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose‐response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Existing penalized regression methods that account for exposure interactions either cannot accommodate nonlinear interactions while maintaining strong heredity or are computationally unstable in applications with limited sample size. In this article, we propose a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction effects among a set of exposures. We design the hierarchical integrative group least absolute shrinkage and selection operator (HiGLASSO) to (a) impose strong heredity constraints on two‐way interaction effects (hierarchical), (b) incorporate adaptive weights without necessitating initial coefficient estimates (integrative), and (c) induce sparsity for variable selection while respecting group structure (group LASSO). We prove sparsistency of the proposed method and apply HiGLASSO to an environmental toxicants dataset from the LIFECODES birth cohort, where the investigators are interested in understanding the joint effects of 21 urinary toxicant biomarkers on urinary 8‐isoprostane, a measure of oxidative stress. An implementation of HiGLASSO is available in the higlasso R package, accessible through the comprehensive R archive network. [ABSTRACT FROM AUTHOR]

Objective: To identify dysregulated metabolic pathways in amyotrophic lateral sclerosis (ALS) versus control participants through untargeted metabolomics.Methods: Untargeted metabolomics was performed on plasma from ALS participants (n=125) around 6.8 months after diagnosis and healthy controls (n=71). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon rank-sum tests, adjusted logistic regression and partial least squares-discriminant analysis (PLS-DA), while group lasso explored sub-pathway-level differences. Adjustment parameters included sex, age and body mass index (BMI). Metabolomics pathway enrichment analysis was performed on metabolites selected by the above methods. Finally, machine learning classification algorithms applied to group lasso-selected metabolites were evaluated for classifying case status.Results: There were no group differences in sex, age and BMI. Significant metabolites selected were 303 by Wilcoxon, 300 by logistic regression, 295 by PLS-DA and 259 by group lasso, corresponding to 11, 13, 12 and 22 enriched sub-pathways, respectively. 'Benzoate metabolism', 'ceramides', 'creatine metabolism', 'fatty acid metabolism (acyl carnitine, polyunsaturated)' and 'hexosylceramides' sub-pathways were enriched by all methods, and 'sphingomyelins' by all but Wilcoxon, indicating these pathways significantly associate with ALS. Finally, machine learning prediction of ALS cases using group lasso-selected metabolites achieved the best performance by regularised logistic regression with elastic net regularisation, with an area under the curve of 0.98 and specificity of 83%.Conclusion: In our analysis, ALS led to significant metabolic pathway alterations, which had correlations to known ALS pathomechanisms in the basic and clinical literature, and may represent important targets for future ALS therapeutics. [ABSTRACT FROM AUTHOR]

Social and economic disadvantage are hypothesized to increase the risk of disease and death via accelerated biological aging. Given that US blacks are socially and economically disadvantaged relative to whites, health disparities scholars expected that blacks would have shorter telomere length–a biomarker of cell aging–than whites. Yet the majority of studies have found that blacks have longer telomere length than whites. Using data from the National Health and Nutrition Examination Survey (n = 3,761; 28.3% non-Hispanic black, 71.7% non-Hispanic white), we found that leukocyte telomere length was 4.00% (95% CI: 1.12%, 6.87%) longer among blacks compared to whites in the full sample, but differences were greatest among those with lower SES (5.66%; 95% CI: 0.10%, 10.32%), intermediate among those with middle SES (4.14%; 95% CI: 0.05%, 8.24%), and smallest among those with higher SES (2.33%; 95% CI: −3.02%, 7.67%). These results challenge purely genetic explanations for race differences in telomere length and point to a potential social-environmental cause of longer telomere length in US blacks. [ABSTRACT FROM AUTHOR]

Objective: To determine whether persistent organic pollutants (POP) affect amyotrophic lateral sclerosis (ALS) survival.Methods: ALS participants seen at the University of Michigan (Ann Arbor, MI, USA) provided plasma samples for measurement of POPs. ALS disease and clinical features were collected prospectively from the medical records. Survival models used a composite summary measure of exposure due to multiple POPs (environmental risk score or ERS).Results: 167 participants (40.7% female, n=68) with ALS were recruited, of which 119 died during the study period. Median diagnostic age was 60.9 years (IQR 52.7-68.2), median time from symptom onset to diagnosis was 1.01 years (IQR 0.67-1.67), bulbar onset 28.7%, cervical onset 33.5% and lumbar onset 37.7%. Participants in the highest quartile of ERS (representing highest composite exposure), adjusting for age at diagnosis, sex and other covariates had a 2.07 times greater hazards rate of mortality (p=0.018, 95% CI 1.13 to 3.80) compared with those in the lowest quartile. Pollutants with the largest contribution to the ERS were polybrominated diphenyl ethers 154 (HR 1.53, 95% CI 0.90 to 2.61), polychlorinated biphenyls (PCB) 118 (HR 1.50, 95% CI 0.95 to 2.39), PCB 138 (HR 1.69, 95% CI 0.99 to 2.90), PCB 151 (HR 1.46, 95% CI 1.01 to 2.10), PCB 175 (HR 1.53, 95% CI 0.98 to 2.40) and p,p'-DDE (HR 1.39, 95% CI 1.07 to 1.81).Conclusions: Higher concentrations of POPs in plasma are associated with reduced ALS survival, independent of age, gender, segment of onset and other covariates. This study helps characterise and quantify the combined effects of POPs on ALS and supports the concept that environmental exposures play a role in disease pathogenesis. [ABSTRACT FROM AUTHOR]

Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies. [ABSTRACT FROM AUTHOR]

• First study to assess maternal metals in relation to birth outcomes in Puerto Rico. • Highlights the importance of assessing the effects of mixtures on health outcomes. • Pb, even at low-levels, was the most strongly associated with risk of preterm birth. • Elevated Mn and Zn exposure may adversely affect birth outcomes. In previous studies, exposures to heavy metals such as Pb and Cd have been associated with adverse birth outcomes; however, knowledge on effects at low levels of exposure and of other elements remain limited. We examined individual and mixture effects of metals and metalloids on birth outcomes among 812 pregnant women in the Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) cohort. We measured 16 essential and non-essential metal(loid)s in maternal blood collected at 16–20 and 24–28 weeks gestation. We used linear and logistic regression to independently examine associations between geometric mean (GM) concentrations of each metal across visits and gestational age, birthweight z-scores, preterm birth, small for gestational age (SGA), and large for gestational age (LGA). We evaluated effect modification with infant sex*metal interaction terms. To identify critical windows of susceptibility, birth outcomes were regressed on visit-specific metal concentrations. Furthermore, average metal concentrations were divided into tertiles to examine the potential for non-linear relationships. We used elastic net (ENET) regularization to construct Environmental Risk Score (ERS) as a metal risk score and Bayesian Kernel Machine Regression (BKMR) to identify individual metals most critical to each outcome, accounting for correlated exposures. In adjusted models, an interquartile range (IQR) increase in GM lead (Pb) was associated with 1.63 higher odds of preterm birth (95%CI = 1.17, 2.28) and 2 days shorter gestational age (95% CI = −3.1, −0.5). Manganese (Mn) and zinc (Zn) were also associated with higher odds of preterm birth and shorter gestational age; the associations were strongest among the highest tertile for Mn and among females for Zn. Mercury (Hg) was associated with higher risk of preterm birth at the later window of pregnancy. Ni measured later in pregnancy was associated with lower odds of SGA. ENET and BKMR models selected similar metals as "important" predictors of birth outcomes. The association between ERS and preterm birth was assessed and the third tertile of ERS was significantly associated with an elevated odds ratio of 2.13 (95% CI = 1.12, 5.49) for preterm birth compared to the first tertile. As the PROTECT cohort has lower Pb concentrations (GM = 0.33 μg/dL) compared to the mainland US, our findings suggest that low-level prenatal lead exposure, as well as elevated Mn and Zn exposure, may adversely affect birth outcomes. Improved understanding on environmental factors contributing to preterm birth, together with sustainable technologies to remove contamination, will have a direct impact in Puerto Rico and elsewhere. [ABSTRACT FROM AUTHOR]

Given the potential adverse health effects related to toxic trace metal exposure and insufficient or excessive levels of essential trace metals in pregnant women and their fetuses, the present study characterizes biomarkers of metal and metalloid exposure at repeated time points during pregnancy among women in Puerto Rico. We recruited 1040 pregnant women from prenatal clinics and collected urine, blood, and questionnaire data on demographics, product use, food consumption, and water usage at up to three visits. All samples were analyzed for 16 metal(loid)s: arsenic (As), barium (Ba), beryllium (Be), cadmium (Cd), cobalt (Co), chromium (Cr), cesium (Cs), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), titanium (Ti), uranium (U), vanadium (V), and zinc (Zn). Urine samples were additionally analyzed for molybdenum (Mo), platinum (Pt), antimony (Sb), tin (Sn), and tungsten (W). Mean concentrations of most metal(loid)s were higher among participants compared to the general US female population. We found weak to moderate correlations for inter-matrix comparisons, and moderate to strong correlations between several metal(loid)s measured within each biological matrix. Blood concentrations of Cu, Zn, Mn, Hg, and Pb were shown to reflect reliable biomarkers of exposure. For other metals, repeated samples are recommended for exposure assessment in epidemiology studies. Predictors of metal(loid) biomarkers included fish and rice consumption (urinary As), fish and canned food (blood Hg), drinking public water (blood Pb), smoking (blood Cd), and iron/folic acid supplement use (urinary Cs, Mo, and Sb). Characterization of metal(loid) biomarker variation over time and between matrices, and identification of important exposure sources, may inform future epidemiology studies and exposure reduction strategies. • First study to assess exposure to multiple metal(loid)s among pregnant women in Puerto Rico. • Concentration of most metals among pregnant women were higher than women of same age in NHANES. • Reliable biomarkers of exposures identified by examining biomarkers over time and between matrices. • Exposure predictors: fish, rice-As, fish-Hg, public water-Pb, smoking-Cd, supplement-Cs, Mo, and Sb.. [ABSTRACT FROM AUTHOR]

Social and economic disadvantage are hypothesized to increase the risk of disease and death via accelerated biological aging. Given that US blacks are socially and economically disadvantaged relative to whites, health disparities scholars expected that blacks would have shorter telomere length-a biomarker of cell aging-than whites. Yet the majority of studies have found that blacks have longer telomere length than whites. Using data from the National Health and Nutrition Examination Survey (n = 3,761; 28.3% non-Hispanic black, 71.7% non-Hispanic white), we found that leukocyte telomere length was 4.00% (95% CI: 1.12%, 6.87%) longer among blacks compared to whites in the full sample, but differences were greatest among those with lower SES (5.66%; 95% CI: 0.10%, 10.32%), intermediate among those with middle SES (4.14%; 95% CI: 0.05%, 8.24%), and smallest among those with higher SES (2.33%; 95% CI: -3.02%, 7.67%). These results challenge purely genetic explanations for race differences in telomere length and point to a potential social-environmental cause of longer telomere length in US blacks.