An external exposome-wide association study of COVID-19 mortality in the United States

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

BACKGROUND

Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales.

OBJECTIVE

To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches.

METHODS

We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches.

RESULTS

We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models.

SIGNIFICANCE

The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes

Background

Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is crucial to implement effective social risk management strategies at the point of care.

Objective

To develop an electronic health records (EHR)-based machine learning (ML) analytical pipeline to address unmet social needs associated with hospitalization risk in patients with T2D.

Methods

We identified real-world patients with T2D from the EHR data from University of Florida (UF) Health Integrated Data Repository (IDR), incorporating both contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing instability). The 2015-2020 data were used for training and validation and 2021-2022 data for independent testing. We developed a machine learning analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) and fairness optimization.

Results

The study cohort included 10,192 real-world patients with T2D, with a mean age of 59 years and 58% female. Of the cohort, 50% were non-Hispanic White, 39% were non-Hispanic Black, 6% were Hispanic, and 5% were other races/ethnicities. Our iPsRS, including both contextual and individual-level SDoH as input factors, achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial and ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk because of SDoH, that is, the actual 1-year hospitalization rate in the top 5% of iPsRS was 28.1%, ~13 times as high as the bottom decile (2.2% for 1-year hospitalization rate).

Conclusion

Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in real word patients with T2D.

Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities

OBJECTIVE

To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions.

METHODS

In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs.

RESULTS

Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions.

CONCLUSIONS

To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.

Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States

Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM_2.5, O₃, SO₂, NO₂, and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM_2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO₂ and SO₂ were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner.

A spatial and contextual exposome-wide association study and polyexposomic score of COVID-19 hospitalization

Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals' spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18-24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.

An external exposome-wide association study of opioid use disorder diagnosed during pregnancy in Florida

The prevalence of opioid use disorder (OUD) during pregnancy has quadrupled in recent years and widely varies geographically in the US. However, few studies have examined which environmental factors are associated with OUD during pregnancy. We conducted an external exposome-wide association study (ExWAS) to investigate the associations between external environmental factors and OUD diagnosed during pregnancy. Data were obtained from a unique, statewide database in Florida comprising linked individual-level birth and electronic health records. A total of 255,228 pregnancies with conception dates between 2012 and 2016 were included. We examined 82 exposome measures characterizing seven aspects of the built and social environment and spatiotemporally linked them to each individual record. A two-phase procedure was utilized for the external ExWAS. In Phase 1, we randomly divided the data into a discovery set (50 %) and a replication set (50 %). Associations between exposome measures (normalized and standardized) and OUD initially diagnosed during pregnancy were examined using logistic regression. A total of 15 variables were significant in both the discovery and replication sets. In Phase 2, multivariable logistic regression was used to fit all variables selected from Phase 1. Measures of walkability (the national walkability index, OR: 1.23, 95 % CI: 1.17, 1.29), vacant land (the percent vacant land for 36 months or longer, OR: 1.06, 95 % CI: 1.00, 1.12) and food access (the percentage of low food access population that are seniors at 1/2 mile, OR: 1.47, 95 % CI: 1.38, 1.57) were each associated with diagnosis of OUD during pregnancy. This is the first external ExWAS of OUD during pregnancy, and the results suggest that low food access, high walkability, and high vacant land in under-resourced neighborhoods are associated with diagnosis of OUD during pregnancy. These findings could help develop complementary tools for universal screening for substance use and provide direction for future studies.

Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Methodological Challenges in Spatial and Contextual Exposome-Health Studies

The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual's life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals' external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs.

The impact of air pollution on COVID-19 incidence, severity, and mortality: A systematic review of studies in Europe and North America

BACKGROUND

Air pollution is speculated to increase the risks of COVID-19 spread, severity, and mortality.

OBJECTIVES

We systematically reviewed studies investigating the relationship between air pollution and COVID-19 cases, non-fatal severity, and mortality in North America and Europe.

METHODS

We searched PubMed, Web of Science, and Scopus for studies investigating the effects of harmful pollutants, including particulate matter with diameter ≤2.5 or 10 μm (PM_2.5 or PM₁₀), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and carbon monoxide (CO), on COVID-19 cases, severity, and deaths in Europe and North America through to June 19, 2021. Articles were included if they quantitatively measured the relationship between exposure to air pollution and COVID-19 health outcomes.

RESULTS

From 2,482 articles screened, we included 116 studies reporting 355 separate pollutant-COVID-19 estimates. Approximately half of all evaluations on incidence were positive and significant associations (52.7%); for mortality the corresponding figure was similar (48.1%), while for non-fatal severity this figure was lower (41.2%). Longer-term exposure to pollutants appeared more likely to be positively associated with COVID-19 incidence (63.8%). PM_2.5, PM₁₀, O₃, NO₂, and CO were most strongly positively associated with COVID-19 incidence, while PM_2.5 and NO₂ with COVID-19 deaths. All studies were observational and most exhibited high risk of confounding and outcome measurement bias.

DISCUSSION

Air pollution may be associated with worse COVID-19 outcomes. Future research is needed to better test the air pollution-COVID-19 hypothesis, particularly using more robust study designs and COVID-19 measures that are less prone to measurement error and by considering co-pollutant interactions.

Identifying Contextual and Spatial Risk Factors for Post-Acute Sequelae of SARS-CoV-2 Infection: An EHR-based Cohort Study from the RECOVER Program

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the contextual and spatial risk factors for PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified contextual and spatial risk factors from nearly 200 environmental characteristics for 23 PASC symptoms and conditions of eight organ systems. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each contextual and spatial factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) contextual and spatial characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), criteria air pollutants (e.g., sulfur dioxide), particulate matter (PM _2.5 ) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, respiratory, blood, circulatory, endocrine, and other organ systems. Specific contextual and spatial risk factors for each PASC condition and symptom were different across New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular contextual and spatial characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Climate change, human health, and the exposome: Utilizing OMIC technologies to navigate an era of uncertainty

Climate change is an anthropogenic phenomenon that is alarming scientists and non-scientists alike. The emission of greenhouse gases is causing the temperature of the earth to rise and this increase is accompanied by a multitude of climate change-induced environmental exposures with potential health impacts. Tracking human exposure has been a major research interest of scientists worldwide. This has led to the development of exposome studies that examine internal and external individual exposures over their lifetime and correlate them to health. The monitoring of health has also benefited from significant technological advances in the field of "omics" technologies that analyze physiological changes on the nucleic acid, protein, and metabolism levels, among others. In this review, we discuss various climate change-induced environmental exposures and their potential health implications. We also highlight the potential integration of the technological advancements in the fields of exposome tracking, climate monitoring, and omics technologies shedding light on important questions that need to be answered.

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies

Purpose of review

Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health.

Recent findings

Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas.

Summary

In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google’s geo-spatial data.

Assuring Healthy Populations During the COVID-19 Pandemic: Recognizing Women's Contributions in Addressing Syndemic Interactions

A syndemic framework examines disease interactions and the contributions of structural, social, economic, and environmental factors that synergistically interact to contribute to adverse health outcomes. Populations residing in environments with structural susceptibilities experience health disparities and syndemics to a greater extent than their less vulnerable counterparts. The interactions among the social determinants of health (SDoH) and the COVID-19 pandemic have had different results for marginalized populations and have worsened health outcomes for many in this synergistic pandemic. Also, the exposome, the exposure measures for an individual over their lifetime and how those exposures relate to the individual's health, may help to explain why some populations experience more serious cases of COVID-19 compared to other groups. The purpose of this perspective is to: (1) examine the relationship between the syndemic model and the SDoH-exposome; (2) highlight, via specific examples, the contributions of female health professionals to SDoH and the COVID-19 syndemic in response to the Women in Science Research Topic, and (3) propose health policy to address syndemic-exposome interactions to help mitigate or prevent public health challenges. By investing in policies that assure health for all populations, the investments could pay dividends in the form of a less severe syndemic next time since we are starting from a place of health and not disease. Lastly, due to the magnification of underlying societal inequities laid bare during the COVID-19 syndemic, we support the expansion of the disease-focused syndemic model to include societal syndemics, such as systemic racism.

A focus on the neural exposome

Many neurological disorders have complex etiologies that include noninheritable factors, collectively called the neural exposome. The National Institute of Neurological Disorders and Stroke is developing a new office with goals to advance our understanding of the multiple causes of neurological illness and to enable the development of more effective interventions.

Environmental effects on acute exacerbations of respiratory diseases: A real-world big data study

BACKGROUND

The effects of weather periods, race/ethnicity, and sex on environmental triggers for respiratory exacerbations are not well understood. This study linked the OneFlorida network (~15 million patients) with an external exposome database to analyze environmental triggers for asthma, bronchitis, and COPD exacerbations while accounting for seasonality, sex, and race/ethnicity.

METHODS

This is a case-crossover study of OneFlorida database from 2012 to 2017 examining associations of asthma, bronchitis, and COPD exacerbations with exposures to heat index, PM 2.5 and O 3. We spatiotemporally linked exposures using patients' residential addresses to generate average exposures during hazard and control periods, with each case serving as its own control. We considered age, sex, race/ethnicity, and neighborhood deprivation index as potential effect modifiers in conditional logistic regression models.

RESULTS

A total of 1,148,506 exacerbations among 533,446 patients were included. Across all three conditions, hotter heat indices conferred increasing exacerbation odds, except during November to March, where the opposite was seen. There were significant differences when stratified by race/ethnicity (e.g., for asthma in April, May, and October, heat index quartile 4, odds were 1.49 (95% confidence interval (CI) 1.42-1.57) for Non-Hispanic Blacks and 2.04 (95% CI 1.92-2.17) for Hispanics compared to 1.27 (95% CI 1.19-1.36) for Non-Hispanic Whites). Pediatric patients' odds of asthma and bronchitis exacerbations were significantly lower than adults in certain circumstances (e.g., for asthma during June - September, pediatric odds 0.71 (95% CI 0.68-0.74) and adult odds 0.82 (95% CI 0.79-0.85) for the highest quartile of PM 2.5).

CONCLUSION

This study of acute exacerbations of asthma, bronchitis, and COPD found exacerbation risk after exposure to heat index, PM 2.5 and O 3 varies by weather period, age, and race/ethnicity. Future work can build upon these results to alert vulnerable populations to exacerbation triggers.

Contributing factors common to COVID‑19 and gastrointestinal cancer

The devastating complications of coronavirus disease 2019 (COVID-19) result from the dysfunctional immune response of an individual following the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Multiple toxic stressors and behaviors contribute to underlying immune system dysfunction. SARS-CoV-2 exploits the dysfunctional immune system to trigger a chain of events, ultimately leading to COVID-19. The authors have previously identified a number of contributing factors (CFs) common to myriad chronic diseases. Based on these observations, it was hypothesized that there may be a significant overlap between CFs associated with COVID-19 and gastrointestinal cancer (GIC). Thus, in the present study, a streamlined dot-product approach was used initially to identify potential CFs that affect COVID-19 and GIC directly (i.e., the simultaneous occurrence of CFs and disease in the same article). The nascent character of the COVID-19 core literature (~1-year-old) did not allow sufficient time for the direct effects of numerous CFs on COVID-19 to emerge from laboratory experiments and epidemiological studies. Therefore, a literature-related discovery approach was used to augment the COVID-19 core literature-based ‘direct impact’ CFs with discovery-based ‘indirect impact’ CFs [CFs were identified in the non-COVID-19 biomedical literature that had the same biomarker impact pattern (e.g., hyperinflammation, hypercoagulation, hypoxia, etc.) as was shown in the COVID-19 literature]. Approximately 2,250 candidate direct impact CFs in common between GIC and COVID-19 were identified, albeit some being variants of the same concept. As commonality proof of concept, 75 potential CFs that appeared promising were selected, and 63 overlapping COVID-19/GIC potential/candidate CFs were validated with biological plausibility. In total, 42 of the 63 were overlapping direct impact COVID-19/GIC CFs, and the remaining 21 were candidate GIC CFs that overlapped with indirect impact COVID-19 CFs. On the whole, the present study demonstrates that COVID-19 and GIC share a number of common risk/CFs, including behaviors and toxic exposures, that impair immune function. A key component of immune system health is the removal of those factors that contribute to immune system dysfunction in the first place. This requires a paradigm shift from traditional Western medicine, which often focuses on treatment, rather than prevention.

Semantic standards of external exposome data

An individual's health and conditions are associated with a complex interplay between the individual's genetics and his or her exposures to both internal and external environments. Much attention has been placed on characterizing of the genome in the past; nevertheless, genetics only account for about 10% of an individual's health conditions, while the remaining appears to be determined by environmental factors and gene-environment interactions. To comprehensively understand the causes of diseases and prevent them, environmental exposures, especially the external exposome, need to be systematically explored. However, the heterogeneity of the external exposome data sources (e.g., same exposure variables using different nomenclature in different data sources, or vice versa, two variables have the same or similar name but measure different exposures in reality) increases the difficulty of analyzing and understanding the associations between environmental exposures and health outcomes. To solve the issue, the development of semantic standards using an ontology-driven approach is inevitable because ontologies can (1) provide a unambiguous and consistent understanding of the variables in heterogeneous data sources, and (2) explicitly express and model the context of the variables and relationships between those variables. We conducted a review of existing ontology for the external exposome and found only four relevant ontologies. Further, the four existing ontologies are limited: they (1) often ignored the spatiotemporal characteristics of external exposome data, and (2) were developed in isolation from other conceptual frameworks (e.g., the socioecological model and the social determinants of health). Moving forward, the combination of multi-domain and multi-scale data (i.e., genome, phenome and exposome at different granularity) and different conceptual frameworks is the basis of health outcomes research in the future.

Relationship between Autism Spectrum Disorder and Pesticides: A Systematic Review of Human and Preclinical Models

Autism spectrum disorder (ASD) is a complex set of neurodevelopmental pathologies characterized by impoverished social and communicative abilities and stereotyped behaviors. Although its genetic basis is unquestionable, the involvement of environmental factors such as exposure to pesticides has also been proposed. Despite the systematic analyses of this relationship in humans, there are no specific reviews including both human and preclinical models. The present systematic review summarizes, analyzes, and discusses recent advances in preclinical and epidemiological studies. We included 45 human and 16 preclinical studies. These studies focused on Organophosphates (OP), Organochlorine (OC), Pyrethroid (PT), Neonicotinoid (NN), Carbamate (CM), and mixed exposures. Preclinical studies, where the OP Chlorpyrifos (CPF) compound is the one most studied, pointed to an association between gestational exposure and increased ASD-like behaviors, although the data are inconclusive with regard to other ages or pesticides. Studies in humans focused on prenatal exposure to OP and OC agents, and report cognitive and behavioral alterations related to ASD symptomatology. The results of both suggest that gestational exposure to certain OP agents could be linked to the clinical signs of ASD. Future experimental studies should focus on extending the analysis of ASD-like behaviors in preclinical models and include exposure patterns similar to those observed in human studies.

Neurotropic Viruses, Astrocytes, and COVID-19

At the end of 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was discovered in China, causing a new coronavirus disease, termed COVID-19 by the WHO on February 11, 2020. At the time of this paper (January 31, 2021), more than 100 million cases have been recorded, which have claimed over 2 million lives worldwide. The most important clinical presentation of COVID-19 is severe pneumonia; however, many patients present various neurological symptoms, ranging from loss of olfaction, nausea, dizziness, and headache to encephalopathy and stroke, with a high prevalence of inflammatory central nervous system (CNS) syndromes. SARS-CoV-2 may also target the respiratory center in the brainstem and cause silent hypoxemia. However, the neurotropic mechanism(s) by which SARS-CoV-2 affects the CNS remain(s) unclear. In this paper, we first address the involvement of astrocytes in COVID-19 and then elucidate the present knowledge on SARS-CoV-2 as a neurotropic virus as well as several other neurotropic flaviviruses (with a particular emphasis on the West Nile virus, tick-borne encephalitis virus, and Zika virus) to highlight the neurotropic mechanisms that target astroglial cells in the CNS. These key homeostasis-providing cells in the CNS exhibit many functions that act as a favorable milieu for virus replication and possibly a favorable environment for SARS-CoV-2 as well. The role of astrocytes in COVID-19 pathology, related to aging and neurodegenerative disorders, and environmental factors, is discussed. Understanding these mechanisms is key to better understanding the pathophysiology of COVID-19 and for developing new strategies to mitigate the neurotropic manifestations of COVID-19.