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I feel you-monitoring environmental variables related to asthma in an integrated real-time frame

BMC Research Notes20158:431

https://doi.org/10.1186/s13104-015-1421-4

Received: 8 May 2014

Accepted: 7 September 2015

Published: 11 September 2015

Abstract

The study of asthma and other complex diseases has proven to be a “moving target” for researchers due to its complex aetiology, difficulty in definition, and immeasurable environmental effects. A large number of studies regarding the contribution of both genetic and environmental factors often result in contradictory results, in part due to the highly heterogeneous nature of asthma. Recent literature has focused on the epigenetic signatures of asthma caused by environmental factors, highlighting the importance of environment. However, unlike the genetic techniques, environmental assessment still lacks accuracy. A plausible solution for this problem would be an individual-based environmental exposure assessment, relying on new technologies such as personal real-time environmental sensors. This could prove to enable the assessment of the whole environmental exposure—or exposome—matching in terms of precision the genome that is emphasized in most studies so far. In addition, the measurement of the whole array of biological molecules, in response to the environment action, could help understand the context of the disease. The current perspective comprises a beyond-genetics integrated vision of omics technology coupled with real-time environmental measures targeting to enhance our comprehension of the disease genesis.

Keywords

AsthmaGeneticsEnvironmentEpigeneticsReal-time monitoringSensorsOmicsExposome

Discussion

Asthma is a complex disease influenced by the interaction of multiple environmental and genetic factors. It is defined as a chronic inflammation that narrows the airways, causing airflow obstruction, leading to wheezing, chest tightness and coughing [1]. The disease affects more than 235 million people worldwide [2] and it was responsible for 255,000 deaths in 2005, according to the World Health Organization [3].

Asthma is an ever-growing pathology especially in the westernized world, and a large number of studies assessing the contribution of both genetic and environmental factors to the disease aetiology have been reported [48].

However, the results are often contradictory. As an example, the association of the polymorphism C-589T in the promoter region of the interleukin 4 gene (IL4) (rs2243250) to asthma has been inconsistent across different populations, despite its biological plausibility as a candidate gene [9]. On the other hand, the GSDML-236 (rs7216389) SNP, whose biological role was unknown at the time of its discovery, has been successfully replicated as associated to different asthma phenotypes across a number of studies [1020].

But even among statistically robust GWA studies, non-replication and inconsistencies are still an issue [21]. In fact, GWA analyses may not capture all relevant variation, such as rare variants thought to have larger phenotypic effects than common variants. In addition, GWAs statistical approach may not be adequate to model the polygenic nature of asthma as well as its gene –environment interactions [22]. Similarly, on the environmental aspect, the effect of motor air pollution on asthma in children has also produced conflicting results [23]. Distinct study designs, dissimilar asthma definitions, different environmental setting and diverse genetic backgrounds are amongst the main explanations for the lack of consistency between studies [2426].

Moreover, asthma is a highly heterogeneous disease where various phenotypes can be defined, such as early-onset allergic asthma, late-onset eosinophilic, exercise-induced, obesity related and neutrophilic asthma [27]. Asthma endotypes have also been proposed as a way to break down the disease according to its pathophysiological mechanism. For instance, within a specific phenotype, as obesity-related asthma, a number of endotypes can emerge (airflow obstruction caused by obesity, severe steroid-dependent asthma, severe late-onset hypereosinophilic asthma, etc.) [28]. But, again, within an endotype, the disease severity can vary [28] adding to the complexity of the definition. Under these circumstances, new approaches for understanding the disease are needed.

We have recently proposed that rather than as a snapshot, asthma should be considered as a motion picture across evolution and in permanent interaction with its surrounding environment [29].

This becomes particularly relevant when one considers two key aspects: the significant increase of asthma in developed societies over the last two decades, and the epigenetic effects of certain environmental exposure over asthma.

Numerous examples of epigenetic effects over DNA, as a result of air pollution, have recently appeared in literature. For example, methylation levels in the promoter of NPSR1 gene, relate to asthma in children and adults as a result of smoking exposure [30]. High methylation levels in the 5′ UTR of ADRB2 gene also relate to severe childhood asthma, in association with indoor exposure to NO2 [31]. Particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) has been found to influence iNOS methylation pattern, affecting FeNO levels, a predictor for the future risk of asthma and wheeze [32]. Methylation patterns in a number of genes included in the asthma pathway were found associated to specific airborne particulate matter like black carbon (HLA-DOB, FCER1A, IL9, and PRG2), sulphate (HLA-DPA1, IL-10, RNASE3, CCL11) or both (FCER1G) [33]. Exposure to inhaled diesel particles and allergen have been found to induce hypomethylation within a CpG−408 site of the IL4 gene promoter, correlating to IgE production, central to the pathophysiology of asthma [34]. Finally, hypermethylation of the FOXP3 gene was observed in the regulatory T cells of asthmatic children exposed to high air pollution, as opposed to the ones subjected to low levels [35]. For most of these studies, the assessment of air pollution exposure was performed based either on questionnaires [30], seasonal collecting [31] or on nearby central monitoring sites in the study community [32, 33]. For the latter, in the case of absence of data, the gaps were filled by using data from nearby monitors [32]. Although these studies require further validation in larger samples, they provide an opportunity for reflexion on current practices pertaining environmental assessment.

Such environmental exposure assessments have certain drawbacks. The use of questionnaires, for example, may lead to the misclassification of exposure due to the participant’s difficulty in recalling exposure in a precise way, recall bias or even intentional false reporting of exposure [36]. Seasonal monitoring implies that exposure is only monitored in a discrete time frame, which means that a great deal of data is excluded. Finally, monitoring based on nearby sites also has downsides. Although monitoring can be estimated on a daily basis, gaps in the data monitoring may occur; in addition, this kind of monitoring assumes that the type and degree of exposure is exactly the same for distinct individuals living in the same spatio-temporal setting.

However, sensitive differences in individual exposure might occur that do not coincide with each other and/or with the ones reported for the surrounding environment. For example, a recent study found that the individual exposure to NO2 in a group of healthy adults living in Stockholm was significantly different than the one measured for the urban background level (13 versus 20 µg/m3). Time-activity patterns such as time spent at home and at the workplace explain part of the variation [37].

In this context, the development of an individual-based assessment of exposure to environmental factors could prove of crucial importance to understanding the dynamics of complex diseases such as asthma [38].

An increasing body of literature has proposed the use of new technologies, like mobile devices in personal health monitoring [3941] and, in particular, for assessing and self-managing asthma [42, 43]. However, most of these approaches are purely based either on phone calls [42] or text messaging [43] failing to provide a broader context for the disease study. An increasing number of apps for smartphones and tablets aimed to support asthma self-management exist nowadays, but they still lack reliability and accuracy and fail to provide the patient with the comprehensive information about the condition necessary for self-management [44].

Research-wise, mobile devices such as smartphones equipped with a GPS system can provide data on time and location of the patients. In parallel, they can use wireless sensor to record environmental variables (such as the level of humidity or smoke concentration, etc.), and also monitor certain physiological signals to help predict the causes of acute asthma episodes [38].

Examples of both environmental and health personal sensors exist. A research team has recently developed a real-time second and third-hand smoke sensor prototype that measures and records smoke through the adsorption of ambient nicotine vapor [45]. Electrochemical in vivo sensors, which offer near real-time measurement of analytes, have also been improved in the last decades and are now used to detect a range of in vivo targets such as glucose, glutamate, reactive nitrogen species and many neurochemicals [46].

Although there are still some limitations to the use of mobile devices for this particular kind of sensing, such as battery life, wireless coverage [47], and sensor synchronisation [38], technological advances in the area of Ubiquitous Computing are rapidly improving, leading to an endless sphere of applications, with great potential for public health research.

Thus, one of the current goals is the real-time monitoring of the environmental factors that affect disease. From a general perspective, in most gene-environment studies, genotyping has been emphasized in comparison to environmental assessment because genotyping techniques are accurate and systematic, while environmental exposure measures still lack precision [48]. Focusing on this asymmetry, the term “exposome” was coined back in 2005, referring to the concept of assessing one’s life-course environmental exposures (including lifestyle factors) from the prenatal period onwards [49].

Currently, a number of large-scale projects are focused on addressing and developing tools to evaluate the whole environmental exposure and its impact on human health [5053]. The Human Exposome Project intends to evaluate a number of environmental exposures, from diet to lifestyle and behaviour, combined with genetics and medicine in order to prevent and treat a number of diseases [50]. Similarly, the HELIX project, aims to measure prenatal and postnatal exposure to a variety of chemical and physical variables by using smartphone linked sensors, as well as omics techniques to determine molecular profiles related to exposure [51]. The EXPOsOMICS project, targets to assess the exposome through a personal exposure monitoring system, including sensors, smartphones, geo-referencing and satellites, and will also look into biological samples for internal makers of external exposure, through omics technologies [52]. Finally, the HEALS project focuses on assessing individual exposure to conventional and emerging environmental stressors, using a range of novel technologies, such as mobile phone apps, coupled with DNA sequencing, epigenetic DNA modifications, and gene expression aimed to assess disease phenotypes [53].

The word “-omics” refers to the thorough study of sets of biological molecules with high-throughput techniques providing a more comprehensive analysis of biological systems [54]. Some omics technologies in environmental health include transcriptomics, epigenomics, proteomics and metabolomics [54].

The current focus on the real-time assessment of the exposome, and the inclusion of omics analysis clearly aims for an integrative vision of disease as the result of individual susceptibility to exposure. Rather than the traditional nature versus nurture paradigm, the current model integrates both concepts, leading to a nature et nurture coordinated approach, that can significantly advance our understanding of complex non-communicable diseases, like asthma.

Declarations

Acknowledgements

We thank Professor Vassilis Kostakos, University of Oulu, for carefully reading and comments on the paper.

Compliance with ethical guidelines

Competing interests The author declares no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Human Genetics Laboratory, University of Madeira
(2)
Department of Computer Science and Engineering, University of Oulu

References

  1. National Asthma Education and Prevention Program, Third Expert Panel on the Diagnosis and Management of Asthma: Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma. Bethesda (MD); 2007.Google Scholar
  2. Asthma Fact sheet No 307. http://www.who.int/mediacentre/factsheets/fs307/en/. Accessed 10 April 2014.
  3. Makino S, Sagara H. Evolution of asthma concept and effect of current asthma management guidelines. Allergy Asthma Immunol Res. 2010;2(Suppl 3):172–6.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Asher M, Stewart A, Mallol J, Montefort S, Lai C, Aït-Khaled N, et al. Which population level environmental factors are associated with asthma, rhinoconjunctivitis and eczema? Review of the ecological analyses of ISAAC Phase One. Respir Res. 2010;11:8.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Ober C, Yao T. The genetics of asthma and allergic disease: a 21st century perspective. Immunol Rev. 2011;242(Suppl 1):10–30.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Zhang Y, Moffatt M, Cookson WO. Genetic and genomic approaches to asthma: new insights for the origins. Curr Opin Pulm Med. 2012;18(Suppl 1):6–13.View ArticlePubMedGoogle Scholar
  7. March M, Sleiman P, Hakonarson H. Genetic polymorphisms and associated susceptibility to asthma. Int J Gen Med. 2013;6:253–65.PubMed CentralPubMedGoogle Scholar
  8. Dick S, Doust E, Cowle H, Ayres JG, Turner S. Associations between environmental exposures and asthma control and exacerbations in young children: a systematic review. BMJ Open. 2014;4:e003827.PubMed CentralView ArticlePubMedGoogle Scholar
  9. Li Y, Guo B, Zhang L, Han J, Wu B, Xiong H. Association between C-589T polymorphisms of interleukin-4 gene promoter and asthma: a meta-analysis. Respir Med. 2008;102(Suppl 7):984–92.View ArticlePubMedGoogle Scholar
  10. Moffatt MF, Kabesch M, Liang L, Dixon AL, Strachan D, Heath S, et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature. 2007;448(Suppl 7152):470–3.View ArticlePubMedGoogle Scholar
  11. Madore AM, Tremblay K, Hudson TJ, Laprise C. Replication of an association between 17q21 SNPs and asthma in a French-Canadian familial collection. Hum Genet. 2008;123(Suppl 1):93–5.View ArticlePubMedGoogle Scholar
  12. Tavendale R, Macgregor DF, Mukhopadhyay S, Palmer CN. A polymorphism controlling ORMDL3 expression is associated with asthma that is poorly controlled by current medications. J Allergy Clin Immunol. 2008;121(Suppl 4):860–3.View ArticlePubMedGoogle Scholar
  13. Galanter J, Choudhry S, Eng C, Nazario S, Rodríguez-Santana JR, Casal J, et al. ORMDL3 gene is associated with asthma in three ethnically diverse populations. Am J Respir Crit Care Med. 2008;177(Suppl 11):1194–200.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Wu H, Romieu I, Sienra-Monge JJ, Li H, del Rio-Navarro BE, London SJ. Genetic variation in ORM1Llike 3 (ORMDL3) and gasdermin-like (GSDML) and childhood asthma. Allergy. 2009;64(Suppl 4):629–35.PubMed CentralView ArticlePubMedGoogle Scholar
  15. Bisgaard H, Bønnelykke K, Sleiman PM, Brasholt M, Chawes B, Kreiner-Møller E, et al. Chromosome 17q21 gene variants are associated with asthma and exacerbations but not atopy in early childhood. Am J Respir Crit Care Med. 2009;179(Suppl 3):179–85.View ArticlePubMedGoogle Scholar
  16. Halapi E, Gudbjartsson DF, Jonsdottir GM, Bjornsdottir US, Thorleifsson G, Helgadottir H, et al. A sequence variant on 17q21 is associated with age at onset and severity of asthma. Eur J Hum Genet. 2010;18(Suppl 8):902–8.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Binia A, Khorasani N, Bhavsar PK, Adcock I, Brightling CE, Chung KF, et al. Chromosome 17q21 SNP and severe asthma. J Hum Genet. 2011;56(Suppl 1):97–8.PubMed CentralView ArticlePubMedGoogle Scholar
  18. Yu J, Kang MJ, Kim BJ, Kwon JW, Song YH, Choi WA, et al. Polymorphisms in GSDMA and GSDMB are associated with asthma susceptibility, atopy and BHR. Pediatr Pulmonol. 2011;46(Suppl 7):701–8.View ArticlePubMedGoogle Scholar
  19. Zhao Y-F, Luo Y-M, Xiong W, Wu X-L. Genetic variation in ORMDL3 gene may contribute to the risk of asthma: a meta-analysis. Hum Immunol. 2014;75(Suppl 9):960–7.View ArticlePubMedGoogle Scholar
  20. Shi H, Cheng D, Yi L, Huo X, Zhang K, Zhen G. Association between ORMDL3 polymorphism and susceptibility to asthma: a meta-analysis. Int J Clin Exp Med. 2015;8(Suppl 3):3173–83.PubMed CentralPubMedGoogle Scholar
  21. Guicheng Z, Goldblatt J, Lesouef P. The era of genome-wide association studies: opportunities and challenges for asthma genetics. J Hum Genet. 2009;54(11):624–8.View ArticleGoogle Scholar
  22. Igartua C, Myers RA, Mathias RA, Pino-Yanes M, Eng C, Graves PE, et al. Ethnic-specific associations of rare and low-frequency DNA sequence variants with asthma. Nat Commun. 2015;6:5965. doi:10.1038/ncomms6965.PubMed CentralView ArticlePubMedGoogle Scholar
  23. Gasana J, Dillikar D, Mendy A, Forno E, Ramos Vieira E. Motor vehicle air pollution and asthma in children: a meta-analysis. Environ Res. 2012;117:36–45.View ArticlePubMedGoogle Scholar
  24. Burchard E, Ziv E, Coyle N, Gomez S, Tang H, Karter A, et al. The importance of race and ethnic background in biomedical research and clinical practice. N Engl J Med. 2003;348(Suppl 12):1170–5.View ArticlePubMedGoogle Scholar
  25. Schedel M, Depner M, Schoen C, Weiland S, Vogelberg C, Niggemann B, et al. The role of polymorphisms in ADAM33, a disintegrin and metalloprotease 33, in childhood asthma and lung function in two German populations. Respir Res. 2006;7:91.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Hizawa N. Beta-2 adrenergic receptor genetic polymorphisms and asthma. J Clin Pharm Ther. 2009;34(Suppl 6):631–43.View ArticlePubMedGoogle Scholar
  27. Wenzel S. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat Med. 2012;18(Suppl 5):716–25.View ArticlePubMedGoogle Scholar
  28. Lötvall J, Akdis C, Bacharier L, Bjermer L, Casale T, Custovic A, et al. Asthma endotypes: a new approach to classification of disease entities within the asthma syndrome. J Allergy Clin Immunol. 2011;127(Suppl 2):355–60.View ArticlePubMedGoogle Scholar
  29. Berenguer A, Rosa A, Brehm A. Asthma-snapshot or motion picture? Front Genet. 2013;4:73.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Reinius L, Gref A, Sääf A, Acevedo N, Joerink M, Kupczyk M, et al. DNA methylation in the Neuropeptide S Receptor 1 (NPSR1) promoter in relation to asthma and environmental factors. PLoS One. 2013;8(Suppl 1):e53877.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Fu A, Leaderer B, Gent J, Leaderer D, Zhu Y. An environmental epigenetic study of ADRB2 5′-UTR methylation and childhood asthma severity. Clin Exp Allergy. 2012;42(Suppl 11):1575–81.PubMed CentralView ArticlePubMedGoogle Scholar
  32. Salam M, Byun H, Lurmann F, Breton C, Wang X, Eckel S, et al. Genetic and epigenetic variations in inducible nitric oxide synthase promoter, particulate pollution, and exhaled nitric oxide levels in children. J Allergy Clin Immunol. 2012;129(Suppl 1):232–9.PubMed CentralView ArticlePubMedGoogle Scholar
  33. Sofer T, Baccarelli A, Cantone L, Coull B, Maity A, Lin X, et al. Exposure to airborne particulate matter is associated with methylation pattern in the asthma pathway. Epigenomics. 2013;5(Suppl 2):147–54.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Liu J, Ballaney M, Al-alem U, Quan C, Jin X, Perera F, et al. Combined inhaled diesel exhaust particles and allergen exposure alter methylation of T helper genes and IgE production in vivo. Toxicol Sci. 2008;102(Suppl 1):76–81.PubMed CentralPubMedGoogle Scholar
  35. Ji H, Khurana H. Genetic and epigenetic influence on the response to environmental particulate matter. J Allergy Clin Immunol. 2012;129(Suppl 1):33–41.PubMed CentralView ArticlePubMedGoogle Scholar
  36. Jaakkola M, Jaakkola J. Assessment of exposure to environmental tobacco smoke. Eur Respir J. 1997;10(Suppl 10):2384–97.View ArticlePubMedGoogle Scholar
  37. Bellander T, Wichmann J, Lind T. Individual exposure to NO2 in relation to spatial and temporal exposure indices in Stockholm, Sweden: the INDEX study. PLoS One. 2012;7(Suppl 6):e39536.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Bae W, Alkobaisi S, Narayanappa S, Liu C. A Real-time health monitoring system for evaluating environmental exposures. JSW. 2013;8(Suppl 4):791–801.Google Scholar
  39. Wesolowski A, Eagle N, Noor A, Snow R, Buckee C. Heterogeneous mobile phone ownership and usage patterns in Kenya. PLoS One. 2012;7(Suppl 4):e35319.PubMed CentralView ArticlePubMedGoogle Scholar
  40. Postolache O, Girão P, Postolache G. Pervasive sensing and m-health: vital signs and daily activity monitoring. In: Mukhopadhyay SC, Postolache OA, editors. Pervasive and mobile sensing and computing for healthcare, vol. 2. Berlin: Springer-Verlag; 2013. p. 1–41.View ArticleGoogle Scholar
  41. Vazquez-Prokopec G, Bisanzio D, Stoddard S, Paz-Soldan V, Morrison A, Elder JP, et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS One. 2013;8(Suppl 4):e58802.PubMed CentralView ArticlePubMedGoogle Scholar
  42. Mulvaney S, Ho Y-X, Cala C, Chen Q, Nian H, Patterson B, et al. Assessing adolescent asthma symptoms and adherence using mobile phones. J Med Internet Res. 2013;15(Suppl 7):e141.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Rhee H, Allen J, Mammen J, Swift M. Mobile phone-based asthma self-management aid for adolescents (mASMAA): a feasibility study. Patient Prefer Adherence. 2014;8:63–72.PubMed CentralView ArticlePubMedGoogle Scholar
  44. Huckvale K, Mate C, Morrison C, Car J. Apps for asthma self-management: a systematic assessment of content and tools. BMC Med. 2012;10:144.PubMed CentralView ArticlePubMedGoogle Scholar
  45. Liu Y, Antwi-Boampong S, BelBruno J, Crane M, Tanski S. Detection of secondhand cigarette smoke via nicotine using conductive polymer films. Nicotine Tob Res. 2013;15(Suppl 9):1511–8.PubMed CentralView ArticlePubMedGoogle Scholar
  46. Eckert M, Vu P, Zhang K, Kang D, Ali M, Xu C, et al. Novel molecular and nanosensors for in vivo sensing. Theranostics. 2013;3(Suppl 8):583–94.PubMed CentralView ArticlePubMedGoogle Scholar
  47. Baig M, Gholamhosseini H, Connolly M. A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Med Biol Eng Comput. 2013;51(Suppl 5):485–95.View ArticlePubMedGoogle Scholar
  48. Vineis P. A self-fulfilling prophecy: are we underestimating the role of the environment in gene-environment interaction research? Int J Epidemiol. 2004;33(Suppl 5):945–6.View ArticlePubMedGoogle Scholar
  49. Wild C. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev. 2005;14(Suppl 8):1847–50.View ArticlePubMedGoogle Scholar
  50. The Human Exposome Project. http://humanexposomeproject.com. Accessed 10 April 2014.
  51. Helix: Building the early life exposome. http://www.projecthelix.eu. Accessed 10 April 2014.
  52. Exposomics. http://www.exposomicsproject.eu/mission. Accessed 10 April 2014.
  53. HEALS. http://www.heals-eu.eu. Accessed 10 April 2014.
  54. Vineis P, Khan A, Vlaanderen J, Vermeulen R. The impact of new research technologies on our understanding of environmental causes of disease: the concept of clinical vulnerability. Environ Health. 2009;8:54.PubMed CentralView ArticlePubMedGoogle Scholar

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