Relevant bibliographies by topics / Exposomics

Academic literature on the topic 'Exposomics'

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Published: 25 May 2024

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Journal articles on the topic "Exposomics":

1

Neagu, Anca-Narcisa, Taniya Jayaweera, Lilian Corrice, Kaya Johnson, and Costel Darie. "Breast Cancer Exposomics." Life 14, no. 3 (March 18, 2024): 402. http://dx.doi.org/10.3390/life14030402.

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We are exposed to a mixture of environmental man-made and natural xenobiotics. We experience a wide spectrum of environmental exposure in our lifetime, including the effects of xenobiotics on gametogenesis and gametes that undergo fertilization as the starting point of individual development and, moreover, in utero exposure, which can itself cause the first somatic or germline mutation necessary for breast cancer (BC) initiation. Most xenobiotics are metabolized or/and bioaccumulate and biomagnify in our tissues and cells, including breast tissues, so the xenobiotic metabolism plays an important role in BC initiation and progression. Many considerations necessitate a more valuable explanation regarding the molecular mechanisms of action of xenobiotics which act as genotoxic and epigenetic carcinogens. Thus, exposomics and the exposome concept are based on the diversity and range of exposures to physical factors, synthetic chemicals, dietary components, and psychosocial stressors, as well as their associated biologic processes and molecular pathways. Existing evidence for BC risk (BCR) suggests that food-borne chemical carcinogens, air pollution, ionizing radiation, and socioeconomic status are closely related to breast carcinogenesis. The aim of this review was to depict the dynamics and kinetics of several xenobiotics involved in BC development, emphasizing the role of new omics fields related to BC exposomics, such as environmental toxicogenomics, epigenomics and interactomics, metagenomics, nutrigenomics, nutriproteomics, and nutrimiRomics. We are mainly focused on food and nutrition, as well as endocrine-disrupting chemicals (EDCs), involved in BC development. Overall, cell and tissue accumulation and xenobiotic metabolism or biotransformation can lead to modifications in breast tissue composition and breast cell morphology, DNA damage and genomic instability, epimutations, RNA-mediated and extracellular vesicle effects, aberrant blood methylation, stimulation of epithelial–mesenchymal transition (EMT), disruption of cell–cell junctions, reorganization of the actin cytoskeleton, metabolic reprogramming, and overexpression of mesenchymal genes. Moreover, the metabolism of xenobiotics into BC cells impacts almost all known carcinogenic pathways. Conversely, in our food, there are many bioactive compounds with anti-cancer potential, exerting pro-apoptotic roles, inhibiting cell cycle progression and proliferation, migration, invasion, DNA damage, and cell stress conditions. We can conclude that exposomics has a high potential to demonstrate how environmental exposure to xenobiotics acts as a double-edged sword, promoting or suppressing tumorigenesis in BC.
2

Casella, V., M. Franzini, M. T. Rocca, A. Pogliaghi, N. Fiscante, L. Raso, and F. Sapio. "CUSTOMIZED WEBGIS SOLUTIONS FOR EXPOSOMICS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1431–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1431-2020.

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Abstract. Exposomics is a science aiming at quantifying the effects on human health of all the factors influencing it, but genetic ones. They include environment, food, mobility habits and cultural factors. The percentage of the world’s population living in the urban areas is projected to increase in the next decades. Rising industrialization, urbanization and heterogeneity are leading to new challenges for public health and quality of life in the population. The prevalence of conditions such as asthma and cardiovascular diseases is increasing due to a change in lifestyle and air quality. This enlightens the necessity of targeted interventions to increase citizens’ quality of life and decrease their health risks. Within the EU H2020 PULSE project, a multi-technological system to assist the population in the prevention and treatment of asthma and type 2 diabetes has been developed. The system created in PULSE features several parts, such as a personal App for the citizens, a set of air quality sensors, a WebGIS and dashboards for the public health operators. Citizens are directly involved in an exchange paradigm in which they send their own data and receive feedbacks and suggestions about their health in return. The WebGIS is a very distinguishing element of the PULSE technology and the paper illustrates its main functionalities focusing on the distinguishing and innovative features developed.
3

Choi, Hyunok, Mark T. McAuley, and David A. Lawrence. "Prenatal exposures and exposomics of asthma." AIMS Environmental Science 2, no. 1 (2015): 87–109. http://dx.doi.org/10.3934/environsci.2015.1.87.

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Jobst, Karl J., and Krystal Godri Pollitt. "Editorial overview: Exposomics, emerging exposures and analytical challenges." Current Opinion in Environmental Science & Health 15 (June 2020): A1—A3. http://dx.doi.org/10.1016/j.coesh.2020.08.001.

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Cooke, Marcus S., Chiung-Wen Hu, Yuan-Jhe Chang, and Mu-Rong Chao. "Urinary DNA adductomics – A novel approach for exposomics." Environment International 121 (December 2018): 1033–38. http://dx.doi.org/10.1016/j.envint.2018.10.041.

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Fan, Jung-wei, Jianrong Li, and Yves A. Lussier. "Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context." Journal of Healthcare Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3818302.

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Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics.
7

Vineis, P., M. Chadeau-Hyam, H. Gmuender, J. Gulliver, Z. Herceg, J. Kleinjans, M. Kogevinas, et al. "The exposome in practice: Design of the EXPOsOMICS project." International Journal of Hygiene and Environmental Health 220, no. 2 (March 2017): 142–51. http://dx.doi.org/10.1016/j.ijheh.2016.08.001.

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Schramm, Karl-Werner, Jingxian Wang, Yonghong Bi, Cedrique Temoka, Gerd Pfister, Bernhard Henkelmann, and Hagen Scherb. "Chemical- and effect-oriented exposomics: Three Gorges Reservoir (TGR)." Environmental Science and Pollution Research 20, no. 10 (November 22, 2012): 7057–62. http://dx.doi.org/10.1007/s11356-012-1319-9.

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Smith, Martyn T., Rosemarie de la Rosa, and Sarah I. Daniels. "Using exposomics to assess cumulative risks and promote health." Environmental and Molecular Mutagenesis 56, no. 9 (October 17, 2015): 715–23. http://dx.doi.org/10.1002/em.21985.

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McKeon, Thomas P., Vicky Tam, Wei-Ting Hwang, Paul Wileyto, Karen Glanz, and Trevor M. Penning. "Abstract PR06: Geocoding and integrating multiple environmental exposomics sources: Assessing population hazard to lung carcinogens in 421 zip codes of a cancer center catchment area." Cancer Epidemiology, Biomarkers & Prevention 29, no. 9_Supplement (September 1, 2020): PR06. http://dx.doi.org/10.1158/1538-7755.modpop19-pr06.

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Abstract To assess risk factors that contribute to lung cancer burden in the Abramson Cancer Center (ACC) catchment area, we integrated geospatial data of exposure to pollutants from publicly available EPA and NASA datasets. The study area covers the 421 zip codes that make up the 12 counties of the catchment area from which most of the ACC patients come. The counties include 5 that surround Philadelphia, 6 in New Jersey, and 1 in Delaware. Environmental exposure data, sourced from US-EPA Air Quality System (AQS) Data Mart, were focused on air pollutants since air pollution is recognized by the International Agency on Cancer (IARC) as a Group 1 human carcinogen. Exposomics data included: hourly, daily, and annual (1980 -2018) PM2.5, PM10, NO2; Hazardous Air Pollutants (HAPS); Volatile Organic Compounds (VOCs); (Air Quality Index) AQI; NONOxNOy monitoring; and annual Toxic Release Inventory (TRI) air emissions by chemical classifier and point source (1987 -2017). Annual NASA satellite-derived grids were incorporated for PM2.5 (1998-2016; 1 km resolution) and NOx (1997 - 2012; 10 km resolution). ESRI’s ArcGIS was used to develop programming scripts to automate the process of data integration, geocoding, and classifying chemical parameters by (1) status as a lung carcinogen with sufficient evidence of lung carcinogenesis; (2) status as one of the priority 16 EPA polycyclic aromatic hydrocarbons, as a surrogate marker of exposure to carcinogens; (3) status in the IARC rankings for Cancer Group; (4) status as a component of diesel exhaust; and (5) status as a VOC. 1-km search radius kernel density grids were generated for each air pollutant. We sliced the density estimates into ordinal rankings ranging from “10 = high” to “1 = low.” A hazard index may be generated by summing data layers of cumulative environmental exposomics in a process called map algebra. Spatial sorting and merging of exposome releases by facility, year, chemical and zip code concentration allow for addressing “low-hanging fruit” through summary statistics. Although the focus of this investigation is on lung cancer, the utility of the methodology may be applied to probe exposures related to other cancers. Incorporating more years or larger geographic areas of study may make exploring the risk of exposure possible for less prevalent cancers. In future studies, we are conducting statistical analysis to determine whether geocoded exposure data predict lung cancer risks in those vulnerable zip codes using electronic health record data of geolocations of lung cancer patients. This novel approach will help determine whether geocoded exposomics data are associated with cancer incidence. The hazard index was used to identify zip codes that are the most vulnerable to carcinogen exposure. Zip codes 19720, 19061, 08066, 08027, 19153, and 19145 scored highest on the hazard index based on cumulative exposure. (Supported by P30-CA-016520 and P30-ES013508.) This abstract is also being presented as Poster A08. Citation Format: Thomas P. McKeon, Vicky Tam, Wei-Ting Hwang, Paul Wileyto, Karen Glanz, Trevor M. Penning. Geocoding and integrating multiple environmental exposomics sources: Assessing population hazard to lung carcinogens in 421 zip codes of a cancer center catchment area [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr PR06.
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Journal articles

Dissertations / Theses on the topic "Exposomics":

1

Fan, Jung-wei, Jianrong Li, and Yves A. Lussier. "Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context." HINDAWI LTD, 2017. http://hdl.handle.net/10150/625835.

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Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics.
2

Matta, Komodo. "Study of the associations between exposure to endocrine disrupting chemicals and endometriosis : an integrative approach." Electronic Thesis or Diss., Nantes, Ecole nationale vétérinaire, 2021. http://www.theses.fr/2021ONIR155F.

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Les humains sont exposés quotidiennement à des mélanges complexes de polluants chimiques, dont certains sont susceptibles de perturber nos fonctions endocriniennes et de contribuer à des maladies reproductives comme l'endométriose. L'endométriose est une maladie peu connue qui touche environ 5 à 15 % des personnes ayant leurs règles. Elle se caractérise par la présence de tissus endométriaux en dehors de l'utérus, et peut présenter des symptômes graves et coûteux. Malgré les preuves épidémiologiques de plus en plus nombreuses qui confirment l'association entre certains produits chimiques perturbateurs endocriniens (PE) et l'endométriose, les mécanismes pathogéniques exacts par lesquels les expositions chimiques contribuent à l'apparition ou à l'aggravation de la maladie restent largement inconnus. Des preuves mécanistes permettant d'identifier les voies potentiellement perturbées soutiennent la plausibilité biologique de l'impact des facteurs environnementaux sur l'endométriose. En outre, des analyses à haute résolution des biomarqueurs d'exposition et d'effet sont nécessaires pour explorer le profil métabolomique perturbé des patientes atteintes d'endométriose. L'intégration des données d'exposition avec les biomarqueurs métaboliques peut être la clé pour élucider les voies moléculaires sous-jacentes par lesquelles les expositions chimiques peuvent contribuer à l'endométriose. Les outils statistiques actuellement utilisés en épidémiologie environnementale ne permettent pas de représenter de manière réaliste les effets de mélanges complexes d'expositions sur les effets sur la santé, ni de gérer des données de biomarqueurs colinéaires de haute dimension. Un cadre statistique robuste est nécessaire pour intégrer des données multipolluants et métaboliques de haute dimension dans une analyse multibloc. Dans ce contexte, cette thèse vise à caractériser le lien entre l'exposition aux polluants environnementaux et l'endométriose en découvrant les mécanismes d'action sous-jacents qui médient la relation par une synthèse des preuves mécanistiques et une analyse métabolomique à haute résolution, en développant un cadre statistique robuste pour répondre aux limites des modèles traditionnellement utilisés, et enfin en intégrant les biomarqueurs d'exposition et d'effet dans une analyse statistique multibloc appliquée d'une étude cas-témoins
Humans are exposed daily to complex mixtures of chemical pollutants, some of which have the potential to disrupt our bodies’ natural endocrine functions, and contribute to reproductive diseases like endometriosis. Endometriosis is a little understood disease which impacts an estimated 5-15% of individuals who menstruate. It is characterised by the presence of endometrial tissues outside of the uterus, and may have severe and costly symptoms. Despite growing epidemiological evidence thatsupports the association between some endocrine disrupting chemicals (EDCs) and endometriosis, the exact pathogenic mechanisms by which chemical exposures contribute to the onset or aggravation of the disease remain largely unknown. Mechanistic evidence to identify potential perturbed pathways to support the biological plausibility of the impact of environmental factors on endometriosis. Furthermore, high resolution analyses of biomarkers of exposure and effect are needed to explore the disrupted metabolomics profile of endometrosis patients. Integration of exposure data with metabolic biomarkersmay be the key to elucidating the underlying molecular pathways by which chemical exposures may contribute to endometriosis. The statistical tools currently used in environmental epidemiology fall short of being able to realistically represent the effects of complex exposure mixtures on health effects, and to manage high dimensional, collinear biomarkers data. A robust statistical framework is needed to integrate high dimensional multipollutant and metabolic data in a multiblock analysis. In this context, this thesis aims to characterise the link between exposure to environmental pollutants and endometriosis by uncovering the underlying mechanismsof action that mediate the relationship through a synthesis of mechanistic evidence and a high resolution metabolomics analysis, developing a robust statistical framework to address the limitations of traditionally used models, and finally integrating biomarkers of exposure and effect in an applied multiblock statistical analysis of acase-control study
3

Ku, Mei-Sheng, and 古玫生. "Fetal Exposomes and Child Development: Using DNA Methylation Levels of Imprinted Genes as an Indicator." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/4jr2f5.

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碩士
國立臺灣大學
環境衛生研究所
103
Background In utero exposures have been suggested to be linked to adverse birth outcomes, neurodevelopment or child behavior, but the underlying mechanism remains elusive. DNA methylation, an essential epigenetic modification, of candidate imprinted genes might provide a quantitative screening marker for the effects of prenatal exposures on the development and neurobehavioral development of the infants, even the risk of developing certain disease in later life. Objective The objective of this study is to investigate the relationship between multiple fetal exposomes during pregnancy, epigenetic modifications and child birth and neurobehavioral outcomes at follow-up 2 and 7 years old, by quantifying DNA methylation levels of imprinted genes. Methods A total of 465 mother-infant pairs were included in this study from Taiwan Birth Panel Study (TBPS), collecting from 2004 to 2005. Fetal exposomes, including cotinine, 18 metals, 2 organophosphorous Pesticides, 4 perfluorinated compounds (PFCs) and 3 phenols, 4 phthalate metabolites were detected in umbilical cord blood and spot mother’s urine samples. Besides, DNA methylation levels of MEST and PEG3 imprinted gene were measured in leukocytes from umbilical cord blood. This study made use of data from structured questionnaires、fetal exposomes and DNA methylation levels to estimate the association between prenatal exposures, DNA mehtlyation levels of imprinted genes as well as child outcomes by partial least squares (PLS) regression and generalized linear mixed model. Results This study identified 11 relatively important factors among 32 fetal exposomes. Among these 11 exposomes, higher level group of Cu (pos1:-0.34, P=0.0357; pos3:-0.35, P=0.0277), Mo (pos1:β= -0.38, P=0.026; pos2:β= -0.35, P=0.0358), all level group of Ba (pos5: L-β=0.48, P=0.0359; M-β=0.66, P=0.0071; H-β=0.54, P=0.0322), and PFOS (L-β=-0.39, P=0.0148: H-β= -0.41, P=0.0128) were likely to alter methylation levels of MEST gene, whereas all level group of Cu (L-β=0.59, P=0.0042; M-β=0.57, P=0.0075; H-β=0.64, P=0.0022), low level group of Zn (β=0.45, P=0.0471), Ba (β=0.51, P=0.0151), Co (pos3:β=-0.46, P=0.0294; pos5: β=-0.49, P= 0.032) and low level group of cotinine (β=0.58, P=0.0267) might have differential methylation effects on PEG3 gene. Beside, hypo- or hypermethylation of MEST (CTCF binding region) might have increased risk of low birth size (Q3:OR=2.73, 95% CI=1.20-6.25).Further, hypermethylation of 2 CpGs on MEST ( promoter region) gene had increased risk of having behavior problem (pos3: OR=4.12, 95% CI=1.34-12.66; pos4: OR=3.79, 95% CI=1.21-11.89), compared with reference group, after adjusting for child sex, maternal education, maternal BMI and SGA. The same phenomenon was observed in hypomethylation of position 1 and 4 of MEST (CTCF binding region) (pos1: OR=5.02, 95% CI=1.49-16.98; pos4: OR=4.71, 95% CI=1.36-16.27). At some CpGs of PEG3, hyper- or hypermethylation might have protective effects on adverse child outcomes. Conclusion Our study indicates that there were 11 out of 32 fetal exposomes accounting for infant birth weight. Among them, three metals (Cu、Mo、Ba) and perfluorinated compounds(PFOS) might be associated with methylation levels of MEST imprinted gene, while four metals (Cu、Zn、Co、Ba) and cotinine (metabolite of nicotine) were correlated to methylation levels of PEG3. Moreover, either the increased or decreased methylation of imprinted genes as a result of different levels of fetal exposomes was likely to have increased risk of child birth outcomes and child neurodevelopment at 2 years old.
4

Chen, Chi-Hsin Sally, and 陳其欣. "Exposomic study on the association between multiple pollutants exposure and metabolome in residents living near No. 6 Naphtha Cracking Complex." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5sx28k.

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博士
國立臺灣大學
職業醫學與工業衛生研究所
107
Background: Exposomics is an important methodology in environmental health research. Recently, a branching paradigm, the Public Health Exposome Approach, focuses on the impact of exposures on the overall health of a population within a particular region. This dissertation focuses on the exposomics study of residents living near No. 6 Naphtha Cracking Complex, the largest petrochemical complex in Taiwan, and aim to clarify the association between exposure levels, metabolome, and early health effect biomarkers. Material and Methods: We classified 273 study subjects as high exposure group (children aged 9-15 N=43; elderly aged > 55 N=77) and low exposure group (children N=75; elderly N=78) by the distance from their homes to the complex, and urinary levels of exposure biomarkers vanadium (V) and polycyclic aromatic hydrocarbon (PAHs) metabolite 1-hydroxypyrene (1-OHP). We analyzed (1) external exposures: distance from their homes to main emission points of the complex, road area surrounding homes, and ambient levels of V and PAHs at homes using previously established models; (2) internal exposures: urinary levels of exposure biomarkers, arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), mercury (Hg), lead (Pb), vanadium (V), manganese (Mn), copper (Cu), strontium (Sr), thallium (Tl), and 1-OHP; (3) metabolome: urine metabolomics was analyzed using two dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS), and serum metabolomics and lipidomics were analyzed using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-qTOFMS); (4) early health effects: urinary levels of oxidative stress biomarkers, and serum acylcarnitines. We applied “meet-in-the-middle” approach to identify potential intermediate biomarkers connecting exposures with early health effects, and pathway analysis to find biological mechanisms affected by exposure to multiple pollutants. Results: In both children and elderly subjects, high exposure group lived closer to main emission points of the complex, had elevated ambient levels of V and PAHs at home locations, and increased urinary exposure biomarkers and oxidative stress biomarkers compared to low exposure group. Urine metabolomics identified age-dependent biological pathways that associated multiple pollutants exposure with increased oxidative stress, including tryptophan metabolism in children, and serine, glycine, and threonine metabolism in elderly subjects. In addition, potential exposure biomarkers decane, dodecane, and tridecane were identified in both children and elderly subjects. Serum metabolomics found 10 potential metabolites possibly linking increased exposure to IARC group 1 carcinogens (As, Cd, Cr, Ni) and group 2 carcinogens (V, Hg, PAHs) with elevated oxidative stress and deregulated serum acylcarnitines. Purine metabolism was identified as the possible mechanism affected by children’s exposure to carcinogens. Serum lipidomics results in children also showed significant difference between high and low exposure groups. We found 21 lipids associated with multiple industrial pollutants exposure, including lysophosphatidylcholines, phosphatidylcholines, sphingomyelins, and phosphatidylinositols. All four types of lipids were associated with urinary oxidative stress biomarkers and/or serum acylcarnitines. Conclusion: Public health exposome approach could be used in a large petrochemical industry influenced region to identify vulnerable populations, and understand how multiple industrial pollutants exposure are affecting critical biological mechanisms, leading to early health effects that may be precursors to chronic and acute diseases. Urine metabolomics analyzed via GC-based method could be used to identify children and elderly as vulnerable populations in regions influenced by a large petrochemical industry, and found age-dependent pathways linking multiple exposures to increased oxidative stress. Serum metabolomics analyzed via LC-based method could be used to find biological pathways affected by multiple industrial carcinogenic pollutants exposure in children and adolescents, that could be linked to cancer-related early health effects. Serum lipidomics analyzed via LC-based method could be used to identify in children and adolescents exposed to multiple industrial pollutants, lipid profile changes that have been implicated in liver dysfunctions. Based on our findings, we suggest significant reduction of petrochemical industrial emissions from the complex to decrease multiple pollutants exposure and metabolic abnormalities, and continued follow up on of residents’ health. This dissertation also attests the application of exposomics as a public health research tool, in the investigation of current and potential health impacts of industrial pollution on nearby residents, providing information for future identification of novel personalized health indicators and exposure biomarkers, and establishment of individual risk index.

Book chapters on the topic "Exposomics":

1

Dagnino, Sonia, and Jessica Laine. "Exposomics and Environmental Monitoring." In Toxicology for the Health and Pharmaceutical Sciences, 343–57. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780203730584-20.

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Maitre, Léa, and Martine Vrijheid. "Exposomics: The Exposome in Early Life." In Health Impacts of Developmental Exposure to Environmental Chemicals, 463–84. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0520-1_18.

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Demetriou, Christiana A., Davide Degli Esposti, Kristi Pullen Fedinick, and Paolo Vineis. "EXPOsOMICs: Meet-in-the-Middle and Network Perturbation." In Unraveling the Exposome, 349–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89321-1_14.

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Smith, Martyn T., Cliona M. McHale, and Rosemarie de la Rosa. "Using Exposomics to Assess Cumulative Risks from Multiple Environmental Stressors." In Unraveling the Exposome, 3–22. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89321-1_1.

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Guchet, Xavier. "Exposomics in the Era of Personalized Medicine: A Critical Analysis." In Personalized Medicine in the Making, 207–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-74804-3_11.

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Jackson, Chandra L., and Gary W. Miller. "Using technology and exposomics to understand and address sleep health disparities." In Reference Module in Neuroscience and Biobehavioral Psychology. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-12-822963-7.00358-3.

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González, Juan R., and Alejandro Cáceres. "Exposomic studies." In Omic Association Studies with R and Bioconductor, 263–90. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429440557-9.

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Hawthorne, Christopher, Luis Marco Ruiz, and Guillermo Lopez Campos. "Mapping Exposome Derived Phenotypes into SNOMED Codes." In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230351.

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Human phenotypes define the healthy or diseased status of an individual and they arise from the complex interactions between environmental and genetic factors. The whole set of human exposures constitute the human exposome. These exposures have multiple sources including physical and socioeconomic factors. In this manuscript we have used text mining techniques to retrieve 1295 and 1903 Human Phenotype Ontology terms associated with these exposome factors and we have subsequently mapped 83% and 90% of the HPO terms respectively) into SNOMED as a clinically actionable code. We have developed a proof-of-concept approach to facilitate the integration of exposomic and clinical data
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Zhang, Lei, Xiang He, Jiliu Liu, Yi Zhang, Xiaohui Zuo, and Guoping Li. "Exploration of Multi-Aspect Development of Chronic Obstructive Pulmonary Disease Pathogenesis, Diagnosis, and Treatment Management." In Chronic Obstructive Pulmonary Disease - A Compendium of Medicine and the Humanities [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.106643.

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Abstract:
Chronic obstructive pulmonary disease (COPD) is a common, preventable, and treatable chronic respiratory disease, which is characterized by persistent airflow limitation and respiratory symptoms. Pathological changes are mainly airway and/or alveolar structural abnormalities. Numerous factors, such as exposure to harmful particles or gases, genetic susceptibility, abnormal inflammatory responses, and abnormal lung development, are involved in the pathogenesis of COPD, those which determine the heterogeneity of COPD. Individuals show different pathophysiological changes, different disease evolution rules, and different clinical manifestations due to different etiologies, different susceptibility genes, and different chronic processes of “injury-inflammation-repair.” Therefore, disease managers need to conduct a multifaceted assessment of the whole body and the local area from the individual characteristics of COPD. With the sustained advancement of new technologies, from multiple perspectives, including genomics, exposomes, transcriptomics, mechanisms related to inflammation and immune regulation, microbiota, metabolomics, imaging features and radiomics, and the interaction of lungs and systemic organs to further explore the law of the occurrence and development of COPD, and finally, form an optimized prevention and treatment strategy. On the basis of thorough exploration, a COPD evaluation system that can meet clinical needs will be finally formed, so as to formulate scientific and effective individualized management strategies.

Conference papers on the topic "Exposomics":

1

Gouripeddi, Ramkiran, Le-Thuy Tran, Randy Madsen, Tanvi Gangadhar, Peter Mo, Nicole Burnett, Ryan Butcher, Katherine Sward, and Julio Facelli. "An Architecture for Metadata-driven Integration of Heterogeneous Sensor and Health Data for Translational Exposomic Research." In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2019. http://dx.doi.org/10.1109/bhi.2019.8834657.

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