A blueprint for a new commercial driving epidemiology: An emerging paradigm grounded in integrative exposome and network epistemologies.

Excess health and safety risks of commercial drivers are largely determined by, embedded in, or operate as complex, dynamic, and randomly determined systems with interacting parts. Yet, prevailing epidemiology is entrenched in narrow, deterministic, and static exposure-response frameworks along with ensuing inadequate data and limiting methods, thereby perpetuating an incomplete understanding of commercial drivers' health and safety risks. This paper is grounded in our ongoing research that conceptualizes health and safety challenges of working people as multilayered "wholes" of interacting work and nonwork factors, exemplified by complex-systems epistemologies. Building upon and expanding these assumptions, herein we: (a) discuss how insights from integrative exposome and network-science-based frameworks can enhance our understanding of commercial drivers' chronic disease and injury burden; (b) introduce the "working life exposome of commercial driving" (WLE-CD)-an array of multifactorial and interdependent work and nonwork exposures and associated biological responses that concurrently or sequentially impact commercial drivers' health and safety during and beyond their work tenure; (c) conceptualize commercial drivers' health and safety risks as multilayered networks centered on the WLE-CD and network relational patterns and topological properties-that is, arrangement, connections, and relationships among network components-that largely govern risk dynamics; and (d) elucidate how integrative exposome and network-science-based innovations can contribute to a more comprehensive understanding of commercial drivers' chronic disease and injury risk dynamics. Development, validation, and proliferation of this emerging discourse can move commercial driving epidemiology to the frontier of science with implications for policy, action, other working populations, and population health at large.

Association between trajectories of the neighborhood social exposome and mental health in late adolescence: A FinnTwin12 cohort study.

BACKGROUND: Adolescent mental health problems impose a significant burden. Exploring evolving social environments could enhance comprehension of their impact on mental health. We aimed to depict the trajectories of the neighborhood social exposome from middle to late adolescence and assess the intricate relationship between them and late adolescent mental health. METHODS: Participants (n = 3965) from the FinnTwin12 cohort with completed questionnaires at age 17 were used. Nine mental health measures were assessed. The social exposome comprised 28 neighborhood social indicators. Trajectories of these indicators from ages 12 to 17 were summarized via latent growth curve modeling into growth factors, including baseline intercept. Mixture effects of all growth factors were assessed through quantile-based g-computation. Repeated generalized linear regressions identified significant growth factors. Sex stratification was performed. RESULTS: The linear-quadratic model was the most optimal trajectory model. No mixture effect was detected. Regression models showed some growth factors saliently linked to the p-factor, internalizing problems, anxiety, hyperactivity, and aggression. The majority of them were baseline intercepts. Quadratic growth factors about mother tongues correlated with anxiety among sex-combined participants and males. The linear growth factor in the proportion of households of couples without children was associated with internalizing problems in females. LIMITATIONS: We were limited to including only neighborhood-level social exposures, and the multilevel contextual exposome situation interfered with our assessment. CONCLUSIONS: Trajectories of the social neighborhood exposome modestly influenced late adolescent mental health. Tackling root causes of social inequalities through targeted programs for living conditions could improve adolescent mental health.

Insights into the early-life chemical exposome of Nigerian infants and potential correlations with the developing gut microbiome.

Early-life exposure to natural and synthetic chemicals can impact acute and chronic health conditions. Here, a suspect screening workflow anchored on high-resolution mass spectrometry was applied to elucidate xenobiotics in breast milk and matching stool samples collected from Nigerian mother-infant pairs (n = 11) at three time points. Potential correlations between xenobiotic exposure and the developing gut microbiome, as determined by 16S rRNA gene amplicon sequencing, were subsequently explored. Overall, 12,192 and 16,461 features were acquired in the breast milk and stool samples, respectively. Following quality control and suspect screening, 562 and 864 features remained, respectively, with 149 of these features present in both matrices. Taking advantage of 242 authentic reference standards measured for confirmatory purposes of food bio-actives and toxicants, 34 features in breast milk and 68 features in stool were identified and semi-quantified. Moreover, 51 and 78 features were annotated with spectral library matching, as well as 416 and 652 by in silico fragmentation tools in breast milk and stool, respectively. The analytical workflow proved its versatility to simultaneously determine a diverse panel of chemical classes including mycotoxins, endocrine-disrupting chemicals (EDCs), antibiotics, plasticizers, perfluorinated alkylated substances (PFAS), and pesticides, although it was originally optimized for polyphenols. Spearman rank correlation of the identified features revealed significant correlations between chemicals of the same classification such as polyphenols. One-way ANOVA and differential abundance analysis of the data obtained from stool samples revealed that molecules of plant-based origin elevated as complementary foods were introduced to the infants' diets. Annotated compounds in the stool, such as tricetin, positively correlated with the genus Blautia. Moreover, vulgaxanthin negatively correlated with Escherichia-Shigella. Despite the limited sample size, this exploratory study provides high-quality exposure data of matched biospecimens obtained from mother-infant pairs in sub-Saharan Africa and shows potential correlations between the chemical exposome and the gut microbiome.

A prospective exploration of the urban exposome in relation to headache in the Dutch population-based Occupational and environmental health cohort study (AMIGO).

OBJECTIVE: Headache is one of the most prevalent and disabling health conditions globally. We prospectively explored the urban exposome in relation to weekly occurrence of headache episodes using data from the Dutch population-based Occupational and Environmental Health Cohort Study (AMIGO). MATERIAL AND METHODS: Participants (N = 7,339) completed baseline and follow-up questionnaires in 2011 and 2015, reporting headache frequency. Information on the urban exposome covered 80 exposures across 10 domains, such as air pollution, electromagnetic fields, and lifestyle and socio-demographic characteristics. We first identified all relevant exposures using the Boruta algorithm and then, for each exposure separately, we estimated the average treatment effect (ATE) and related standard error (SE) by training causal forests adjusted for age, depression diagnosis, painkiller use, general health indicator, sleep disturbance index and weekly occurrence of headache episodes at baseline. RESULTS: Occurrence of weekly headache was 12.5 % at baseline and 11.1 % at follow-up. Boruta selected five air pollutants (NO2, NOX, PM10, silicon in PM10, iron in PM2.5) and one urban temperature measure (heat island effect) as factors contributing to the occurrence of weekly headache episodes at follow-up. The estimated causal effect of each exposure on weekly headache indicated positive associations. NO2 showed the largest effect (ATE = 0.007 per interquartile range (IQR) increase; SE = 0.004), followed by PM10 (ATE = 0.006 per IQR increase; SE = 0.004), heat island effect (ATE = 0.006 per one-degree Celsius increase; SE = 0.007), NOx (ATE = 0.004 per IQR increase; SE = 0.004), iron in PM2.5 (ATE = 0.003 per IQR increase; SE = 0.004), and silicon in PM10 (ATE = 0.003 per IQR increase; SE = 0.004). CONCLUSION: Our results suggested that exposure to air pollution and heat island effects contributed to the reporting of weekly headache episodes in the study population.

New Directions in Geroscience: Integrating Social and Behavioral Drivers of Biological Aging.

ABSTRACT: The "geroscience hypothesis" posits that slowing the physiological processes of aging would lead to delayed disease onset and longer healthspan and lifespan. This shift from a focus on solely treating existing disease to slowing the aging process is a shift toward prevention, including a focus on risk factors found in the social environment. Although geroscience traditionally has focused on the molecular and cellular drivers of biological aging, more fundamental causes of aging may be found in the social exposome-the complex array of human social environmental exposures that shape health and disease. The social exposome may interact with physiological processes to accelerate aging biology. In this commentary, we review the potential of these insights to shape the emerging field of translational geroscience. The articles in this special issue highlight how social stress and social determinants of health are associated with biomarkers of aging such as inflammation, epigenetic clocks, and telomeres, and spotlight promising interventions to mitigate stress-related inflammation. For geroscience to incorporate the social exposome into its translational agenda, studies are needed that elucidate and quantify the effects of social exposures on aging and that consider social exposures as intervention targets. The life course perspective allows us to measure both exposures and aging biology over time including sensitive periods of development and major social transitions. In addition, given rapid changes in the measurement of aging biology, which include machine learning techniques, multisystem phenotypes of aging are being developed to better reflect whole body aging, replacing reliance on single system biomarkers. In this expanded and more integrated field of translational geroscience, strategies targeting factors in the social exposome hold promise for achieving aging health equity and extending healthy longevity.

Socio-demographic inequalities influence differences in the chemical exposome among Swedish adolescents.

Relatively little is known about the relationship between socio-demographic factors and the chemical exposome in adolescent populations. This knowledge gap hampers global efforts to meet certain UN sustainability goals. The present work addresses this problem in Swedish adolescents by discerning patterns within the chemical exposome and identify demographic groups susceptible to heightened exposures. Enlisting the Riksmaten Adolescents 2016-17 (RMA) study population (N = 1082) in human-biomonitoring, and using proportional odds ordinal logistic regression models, we examined the associations between concentrations of a diverse array of substances (N = 63) with the determinants: gender, age, participant/maternal birth country income per capita level, parental education levels, and geographic place of living (longitude/latitude). Participant/maternal birth country exhibited a significant association with the concentrations of 46 substances, followed by gender (N = 41), and longitude (N = 37). Notably, individuals born in high-income countries by high-income country mothers demonstrated substantially higher estimated adjusted means (EAM) concentrations of polychlorinated biphenyls (PCBs), brominated flame retardants (BFRs) and per- and polyfluoroalkyl substances (PFASs) compared to those born in low-income countries by low-income country mothers. A reverse trend was observed for cobalt (Co), cadmium (Cd), lead (Pb), aluminium (Al), chlorinated pesticides, and phthalate metabolites. Males exhibited higher EAM concentrations of chromium (Cr), mercury (Hg), Pb, PCBs, chlorinated pesticides, BFRs and PFASs than females. In contrast, females displayed higher EAM concentrations of Mn, Co, Cd and metabolites of phthalates and phosphorous flame retardants, and phenolic substances. Geographical disparities, indicative of north-to-south or west-to-east substance concentrations gradients, were identified in Sweden. Only a limited number of lifestyle, physiological and dietary factors were identified as possible drivers of demographic inequalities for specific substances. This research underscores birth country, gender, and geographical disparities as contributors to exposure differences among Swedish adolescents. Identifying underlying drivers is crucial to addressing societal inequalities associated with chemical exposure and aligning with UN sustainability goals.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era.

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.

Environmental Exposome and Atrial Fibrillation: Emerging Evidence and Future Directions.

There has been increased awareness of the linkage between environmental exposures and cardiovascular health and disease. Atrial fibrillation is the most common sustained cardiac arrhythmia, affecting millions of people worldwide and contributing to substantial morbidity and mortality. Although numerous studies have explored the role of genetic and lifestyle factors in the development and progression of atrial fibrillation, the potential impact of environmental determinants on this prevalent condition has received comparatively less attention. This review aims to provide a comprehensive overview of the current evidence on environmental determinants of atrial fibrillation, encompassing factors such as air pollution, temperature, humidity, and other meteorologic conditions, noise pollution, greenspace, and the social environment. We discuss the existing evidence from epidemiological and mechanistic studies, critically evaluating the strengths and limitations of these investigations and the potential underlying biological mechanisms through which environmental exposures may affect atrial fibrillation risk. Furthermore, we address the potential implications of these findings for public health and clinical practice and identify knowledge gaps and future research directions in this emerging field.

Comparison of maternal venous blood metabolomics collected as dried blood spots, dried blood microsamplers, and plasma for integrative environmental health research.

Use of capillary blood devices for exposome research can deepen our understanding of the intricate relationship between environment and health, and open up new avenues for preventive and personalized medicine, particularly for vulnerable populations. While the potential of these whole blood devices to accurately measure chemicals and metabolites has been demonstrated, how untargeted metabolomics data from these samplers can be integrated with previous and ongoing environmental health studies that have used conventional blood collection approaches is not yet clear. Therefore, we performed a comprehensive comparison between relative-quantitative metabolite profiles measured in venous blood collected with dried whole blood microsamplers (DBM), dried whole blood spots (DBS), and plasma from 54 mothers in an ethnically diverse population. We determined that a majority of the 309 chemicals and metabolites showed similar median intensity rank, moderate correlation, and moderate agreement between participant-quantiled intraclass correlation coefficients (ICCs) for pair-wise comparisons among the three biomatrices. In particular, whole blood sample types, DBM and DBS, were in highest agreement across metabolite comparison metrics, followed by metabolites measured in DBM and plasma, and then metabolites measured in DBS and plasma. We provide descriptive characteristics and measurement summaries as a reference database. This includes unique metabolites that were particularly concordant or discordant in pairwise comparisons. Our results demonstrate that the range of metabolites from untargeted metabolomics data collected with DBM, DBS, and plasma provides biologically relevant information for use in independent exposome investigations. However, before meta-analysis with combined datasets are performed, robust statistical approaches that integrate untargeted metabolomics data collected on different blood matrices need to be developed.

A toxicological perspective on climate change and the exposome.

Climate change is accompanied by changes in the exposome, including increased heat, ground-level ozone, and other air pollutants, infectious agents, pollens, and psychosocial stress. These exposures alter the internal component of the exposome and account for some of the health effects of climate change. The adverse outcome pathways describe biological events leading to an unfavorable health outcome. In this perspective study, I propose to use this toxicological framework to better describe the biological steps linking a stressor associated with climate change to an adverse outcome. Such a framework also allows for better identification of possible interactions between stressors related to climate change and others, such as chemical pollution. More generally, I call for the incorporation of climate change as part of the exposome and for improved identification of the biological pathways involved in its health effects.

Assessing the contribution of the chemical exposome to neurodegenerative disease.

Over the past few decades, numerous environmental chemicals from solvents to pesticides have been suggested to be involved in the development and progression of neurodegenerative diseases. Most of the evidence has accumulated from occupational or cohort studies in humans or laboratory research in animal models, with a range of chemicals being implicated. What has been missing is a systematic approach analogous to genome-wide association studies, which have identified dozens of genes involved in Alzheimer's disease, Parkinson's disease and other neurodegenerative diseases. Fortunately, it is now possible to study hundreds to thousands of chemical features under the exposome framework. This Perspective explores how advances in mass spectrometry make it possible to generate exposomic data to complement genomic data and thereby better understand neurodegenerative diseases.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding.

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Understanding the Cardiovascular and Metabolic Health Effects of Air Pollution in the Context of Cumulative Exposomic Impacts.

Poor air quality accounts for more than 9 million deaths a year globally according to recent estimates. A large portion of these deaths are attributable to cardiovascular causes, with evidence indicating that air pollution may also play an important role in the genesis of key cardiometabolic risk factors. Air pollution is not experienced in isolation but is part of a complex system, influenced by a host of other external environmental exposures, and interacting with intrinsic biologic factors and susceptibility to ultimately determine cardiovascular and metabolic outcomes. Given that the same fossil fuel emission sources that cause climate change also result in air pollution, there is a need for robust approaches that can not only limit climate change but also eliminate air pollution health effects, with an emphasis of protecting the most susceptible but also targeting interventions at the most vulnerable populations. In this review, we summarize the current state of epidemiologic and mechanistic evidence underpinning the association of air pollution with cardiometabolic disease and how complex interactions with other exposures and individual characteristics may modify these associations. We identify gaps in the current literature and suggest emerging approaches for policy makers to holistically approach cardiometabolic health risk and impact assessment.

DNA modifications: Biomarkers for the exposome?

The concept of the exposome is the encompassing of all the environmental exposures, both exogenous and endogenous, across the life course. Many, if not all, of these exposures can result in the generation of reactive species, and/or the modulation of cellular processes, that can lead to a breadth of modifications of DNA, the nature of which may be used to infer their origin. Because of their role in cell function, such modifications have been associated with various major human diseases, including cancer, and so their assessment is crucial. Historically, most methods have been able to only measure one or a few DNA modifications at a time, limiting the information available. With the development of DNA adductomics, which aims to determine the totality of DNA modifications, a far more comprehensive picture of the DNA adduct burden can be gained. Importantly, DNA adductomics can facilitate a "top-down" investigative approach whereby patterns of adducts may be used to trace and identify the originating exposure source. This, together with other 'omic approaches, represents a major tool for unraveling the complexities of the exposome and hence allow a better a understanding of the environmental origins of disease.

Early-life external exposome in children 2-5 years old in Colombia.

Exposome studies are advancing in high-income countries to understand how multiple environmental exposures impact health. However, there is a significant research gap in low- and middle-income and tropical countries. We aimed to describe the spatiotemporal variation of the external exposome, its correlation structure between and within exposure groups, and its dimensionality. A one-year follow-up cohort study of 506 children under 5 in two cities in Colombia was conducted to evaluate asthma, acute respiratory infections, and DNA damage. We examined 48 environmental exposures during pregnancy and 168 during childhood in eight exposure groups, including atmospheric pollutants, natural spaces, meteorology, built environment, traffic, indoor exposure, and socioeconomic capital. The exposome was estimated using geographic information systems, remote sensing, spatiotemporal modeling, and questionnaires. The median age of children at study entry was 3.7 years (interquartile range: 2.9-4.3). Air pollution and natural spaces exposure decreased from pregnancy to childhood, while socioeconomic capital increased. The highest median correlations within exposure groups were observed in meteorology (r = 0.85), traffic (r = 0.83), and atmospheric pollutants (r = 0.64). Important correlations between variables from different exposure groups were found, such as atmospheric pollutants and meteorology (r = 0.76), natural spaces (r = -0.34), and the built environment (r = 0.53). Twenty principal components explained 70%, and 57 explained 95% of the total variance in the childhood exposome. Our findings show that there is an important spatiotemporal variation in the exposome of children under 5. This is the first characterization of the external exposome in urban areas of Latin America and highlights its complexity, but also the need to better characterize and understand the exposome in order to optimize its analysis and applications in local interventions aimed at improving the health conditions and well-being of the child population and contributing to environmental health decision-making.

Association of the external environmental exposome and obesity: A comprehensive nationwide study in 2019 among Chinese children and adolescents.

OBJECTIVE: Children and adolescents are particularly vulnerable to the effects of various environmental factors, which could disrupt growth processes and potentially lead to obesity. Currently, comprehensive and systematic assessments of these environmental exposures during developmental periods are lacking. Therefore, this study aims to evaluate the association between external environmental exposures and the incidence of obesity in children and adolescents. METHODS: Data was collected from the 2019 Chinese National Survey on Students' Constitution and Health, including 214,659 Han children aged 7 to 19. Body Mass Index (BMI) and BMI-for-age z-score (zBMI) were the metrics used to assess overweight and obesity prevalence. The study assessed 18 environmental factors, including air pollutants, natural space, land cover, meteorological conditions, built environment, road conditions, and artificial light at night. Exposome-wide association study (ExWAS) to analyze individual exposures' associations with health outcomes, and Weighted Quantile Sum (WQS) to assess cumulative exposure effects. RESULTS: Among the children and adolescents, there were 24.2 % participants classified as overweight or obesity. Notably, 17 out of 18 environmental factors exhibited significant associations with zBMI and overweight/obesity. Seven air pollutants, road conditions, and built density were positively correlated with higher zBMI and obesity risk, while NDVI, forests, and meteorological factors showed negative correlations. Co-exposure analysis highlighted that SO2, ALAN, PM10, and trunk road density significantly increased zBMI, whereas rainfall, grassland, and forest exposure reduced it. Theoretically reduction in the number and prevalence of cases was calculated, indicating potential reductions in prevalence of up to 4.51 % for positive exposures and 5.09 % for negative exposures. Notably, substantial reductions were observed in regions with high pollution levels. CONCLUSION: This large-scale investigation, encompassing various environmental exposures in schools, highlights the significant impact of air pollution, road characteristics, rainfall, and forest coverage on childhood obesity.

Hair-Derived Exposome Exploration of Cardiometabolic Health: Piloting a Bayesian Multitrait Variable Selection Approach.

Cardiometabolic health is complex and characterized by an ensemble of correlated and/or co-occurring conditions including obesity, dyslipidemia, hypertension, and diabetes mellitus. It is affected by social, lifestyle, and environmental factors, which in-turn exhibit complex correlation patterns. To account for the complexity of (i) exposure profiles and (ii) health outcomes, we propose to use a multitrait Bayesian variable selection approach and identify a sparse set of exposures jointly explanatory of the complex cardiometabolic health status. Using data from a subset (N = 941 participants) of the nutrition, environment, and cardiovascular health (NESCAV) study, we evaluated the link between measurements of the cumulative exposure to (N = 33) pollutants derived from hair and cardiometabolic health as proxied by up to nine measured traits. Our multitrait analysis showed increased statistical power, compared to single-trait analyses, to detect subtle contributions of exposures to a set of clinical phenotypes, while providing parsimonious results with improved interpretability. We identified six exposures that were jointly explanatory of cardiometabolic health as modeled by six complementary traits, of which, we identified strong associations between hexachlorobenzene and trifluralin exposure and adverse cardiometabolic health, including traits of obesity, dyslipidemia, and hypertension. This supports the use of this type of approach for the joint modeling, in an exposome context, of correlated exposures in relation to complex and multifaceted outcomes.

Nature-versus-nurture considered harmful: Actionability as an alternative tool for understanding the exposome from an ethical perspective.

Exposome research is put forward as a major tool for solving the nature-versus-nurture debate because the exposome is said to represent "the nature of nurture." Against this influential idea, we argue that the adoption of the nature-versus-nurture debate into the exposome research program is a mistake that needs to be undone to allow for a proper bioethical assessment of exposome research. We first argue that this adoption is originally based on an equivocation between the traditional nature-versus-nurture debate and a debate about disease prediction/etiology. Second, due to this mistake, exposome research is pushed to adopt a limited conception of agential control that is harmful to one's thinking about the good that exposome research can do for human health and wellbeing. To fully excise the nature-versus-nurture debate from exposome research, we argue that exposome researchers and bioethicists need to think about the exposome afresh from the perspective of actionability. We define the concept of actionability and related concepts and show how these can be used to analyze the ethical aspects of the exposome. In particular, we focus on refuting the popular "gun analogy" in exposome research, returning results to study participants and risk-taking in the context of a well-lived life.

Modeling the impact of urban and hospital eco-exposomes on antibiotic-resistance dynamics in wastewaters.

The emergence and selection of antibiotic resistance is a major public health problem worldwide. The presence of antibiotic-resistant bacteria (ARBs) in natural and anthropogenic environments threatens the sustainability of efforts to reduce resistance in human and animal populations. Here, we use mathematical modeling of the selective effect of antibiotics and contaminants on the dynamics of bacterial resistance in water to analyze longitudinal spatio-temporal data collected in hospital and urban wastewater between 2012 and 2015. Samples were collected monthly during the study period at four different sites in Haute-Savoie, France: hospital and urban wastewater, before and after water treatment plants. Three different categories of exposure variables were collected simultaneously: 1) heavy metals, 2) antibiotics and 3) surfactants for a total of 13 drugs/molecules; in parallel to the normalized abundance of 88 individual genes and mobile genetic elements, mostly conferring resistance to antibiotics. A simple hypothesis-driven model describing weekly antibiotic resistance gene (ARG) dynamics was proposed to fit the available data, assuming that normalized gene abundance is proportional to antibiotic resistant bacteria (ARB) populations in water. The detected compounds were found to influence the dynamics of 17 genes found at multiple sites. While mercury and vancomycin were associated with increased ARG and affected the dynamics of 10 and 12 identified genes respectively, surfactants antagonistically affected the dynamics of three genes. The models proposed here make it possible to analyze the relationship between the persistence of resistance genes in the aquatic environment and specific compounds associated with human activities from longitudinal data. Our analysis of French data over 2012-2015 identified mercury and vancomycin as co-selectors for some ARGs.

Medication use and risk of amyotrophic lateral sclerosis: using machine learning for an exposome-wide screen of a large clinical database.

BACKGROUND: Accumulating evidence suggests that non-genetic factors have important etiologic roles in amyotrophic lateral sclerosis (ALS), yet identification of specific culprit factors has been challenging. Many medications target biological pathways implicated in ALS pathogenesis, and screening large pharmacologic datasets for signals could greatly accelerate the identification of risk-modulating pharmacologic factors for ALS. METHOD: We conducted a high-dimensional screening of patients' history of medication use and ALS risk using an advanced machine learning approach based on gradient-boosted decision trees coupled with Bayesian model optimization and repeated data sampling. Clinical and medication dispensing data were obtained from a large Israeli health fund for 501 ALS cases and 4,998 matched controls using a lag period of 3 or 5 years prior to ALS diagnosis for ascertaining medication exposure. RESULTS: Of over 1,000 different medication classes, we identified 8 classes that were consistently associated with increased ALS risk across independently trained models, where most are indicated for control of symptoms implicated in ALS. Some suggestive protective effects were also observed, notably for vitamin E. DISCUSSION: Our results indicate that use of certain medications well before the typically recognized prodromal period was associated with ALS risk. This could result because these medications increase ALS risk or could indicate that ALS symptoms can manifest well before suggested prodromal periods. The results also provide further evidence that vitamin E may be a protective factor for ALS. Targeted studies should be performed to elucidate the possible pathophysiological mechanisms while providing insights for therapeutics design.