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Diabetologia

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Open Access 01-05-2020 | Type 1 Diabetes | Article

Childhood type 1 diabetes: an environment-wide association study across England

Authors: Annalisa Sheehan, Anna Freni Sterrantino, Daniela Fecht, Paul Elliott, Susan Hodgson

Published in: Diabetologia | Issue 5/2020

Abstract

Aims/hypothesis

Type 1 diabetes is an autoimmune disease affecting ~400,000 people across the UK. It is likely that environmental factors trigger the disease process in genetically susceptible individuals. We assessed the associations between a wide range of environmental factors and childhood type 1 diabetes incidence in England, using an agnostic, ecological environment-wide association study (EnWAS) approach, to generate hypotheses about environmental triggers.

Methods

We undertook analyses at the local authority district (LAD) level using a national hospital episode statistics-based incident type 1 diabetes dataset comprising 13,948 individuals with diabetes aged 0–9 years over the period April 2000 to March 2011. We compiled LAD level estimates for a range of potential demographic and environmental risk factors including meteorological, land use and environmental pollution variables. The associations between type 1 diabetes incidence and risk factors were assessed via Poisson regression, disease mapping and ecological regression.

Results

Case counts by LAD varied from 1 to 236 (median 33, interquartile range 24–46). Overall type 1 diabetes incidence was 21.2 (95% CI 20.9, 21.6) per 100,000 individuals. The EnWAS and disease mapping indicated that 15 out of 53 demographic and environmental risk factors were significantly associated with diabetes incidence, after adjusting for multiple testing. These included air pollutants (particulate matter, nitrogen dioxide, nitrogen oxides, carbon monoxide; all inversely associated), as well as lead in soil, radon, outdoor light at night, overcrowding, population density and ethnicity. Disease mapping revealed spatial heterogeneity in type 1 diabetes risk. The ecological regression found an association between type 1 diabetes and the living environment domain of the Index of Multiple Deprivation (RR 0.995; 95% credible interval [CrI] 0.991, 0.998) and radon potential class (RR 1.044; 95% CrI 1.015, 1.074).

Conclusions/interpretation

Our analysis identifies a range of demographic and environmental factors associated with type 1 diabetes in children in England.

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Atkinson MA, Eisenbarth GS, Michels AW (2014) Type 1 diabetes. Lancet 383(9911):69–82. https://doi.org/10.1016/S0140-6736(13)60591-7 CrossRefPubMed

Hex N, Bartlett C, Wright D, Taylor M, Varley D (2012) Estimating the current and future costs of type 1 and type 2 diabetes in the UK, including direct health costs and indirect societal and productivity costs. Diabet Med 29(7):855–862. https://doi.org/10.1111/j.1464-5491.2012.03698.x CrossRefPubMed

MacFarlane AJ, Strom A, Scott FW (2009) Epigenetics: deciphering how environmental factors may modify autoimmune type 1 diabetes. Mamm Genome 20(9–10):624–632. https://doi.org/10.1007/s00335-009-9213-6 CrossRefPubMed

Craig ME, Kim KW, Isaacs SR et al (2019) Early-life factors contributing to type 1 diabetes. Diabetologia 62(10):1823–1834. https://doi.org/10.1007/s00125-019-4942-x CrossRefPubMed

Rewers M, Ludvigsson J (2016) Environmental risk factors for type 1 diabetes. Lancet 387(10035):2340–2348. https://doi.org/10.1016/S0140-6736(16)30507-4 CrossRefPubMedPubMedCentral

Diaz-Horta O, Baj A, Maccari G, Salvatoni A, Toniolo A (2012) Enteroviruses and causality of type 1 diabetes: how close are we? Pediatr Diabetes 13(1):92–99. https://doi.org/10.1111/j.1399-5448.2011.00790.x CrossRefPubMed

Sané F, Moumna I, Hober D (2011) Group B coxsackieviruses and autoimmunity: focus on type 1 diabetes. Expert Rev Clin Immunol 7(3):357–366CrossRef

Bodington MJ, Muzulu SI, Burden AC (1995) Spatial clustering in childhood diabetes: evidence of an environmental cause. Diabet Med 12(10):865–867. https://doi.org/10.1111/j.1464-5491.1995.tb00387.x CrossRefPubMed

Green A, Patterson CC, Eurodiab Tiger Study Group (2001) Trends in the incidence of childhood-onset diabetes in Europe 1989-1998. Diabetologia 44(Suppl. 3):B3–B8CrossRef

10.

Crow YJ, Alberti KG, Parkin JM (1991) Insulin dependent diabetes in childhood and material deprivation in northern England, 1977–86. BMJ 303(6795):158–160. https://doi.org/10.1136/bmj.303.6795.158 CrossRefPubMedPubMedCentral

11.

Staines A, Bodansky HJ, McKinney PA et al (1997) Small area variation in the incidence of childhood insulin-dependent diabetes mellitus in Yorkshire, UK: links with overcrowding and population density. Int J Epidemiol 26(6):1307–1313. https://doi.org/10.1093/ije/26.6.1307 CrossRefPubMed

12.

Parslow RC, McKinney PA, Law GR, Staines A, Williams R, Bodansky HJ (1997) Incidence of childhood diabetes mellitus in Yorkshire, northern England, is associated with nitrate in drinking water: an ecological analysis. Diabetologia 40(5):550–556. https://doi.org/10.1007/s001250050714 CrossRefPubMed

13.

Dahlquist G, Mustonen L (1994) Childhood onset diabetes—time trends and climatological factors. Int J Epidemiol 23(6):1234–1241. https://doi.org/10.1093/ije/23.6.1234 CrossRefPubMed

14.

Hodgson S, Beale L, Parslow R, Feltbower RG, Jarup L (2012) Creating a national register of childhood type I diabetes using routinely collected hospital data. Pediatr Diabetes 13(3):235–243. https://doi.org/10.1111/j.1399-5448.2011.00815.x CrossRefPubMed

15.

UK Meteorological Office British Atmospheric Data Centre (2006) MIDAS land surface stations data (1853–current). Available from http://catalogue.ceda.ac.uk/uuid/220a65615218d5c9cc9e4785a3234bd0. Accessed 20 August 2019

16.

Cherrie MP, Wheeler BW, White PM, Sarran CE, Osborne NJ (2015) Coastal climate is associated with elevated solar irradiance and higher 25(OH)D level. Environ Int 77:76–84. https://doi.org/10.1016/j.envint.2015.01.005 CrossRefPubMed

17.

Fuller RM, Smith GM, Sanderson JM et al (2002) Land Cover Map 2000 (vector, GB). NERC Environmental Information Data Centre. Available from https://doi.org/10.5285/b79e887e-a2a7-4224-8fd7-e78066b950b3. Accessed 20 August 2019

18.

Ordnance Survey (2018) Greenspace data. Available from www.os.uk/opengreenspace Accessed 20 August 2019

19.

Natural England (2010) Nature nearby – accessible natural greenspace guidance (NE265). Available from https://webarchive.nationalarchives.gov.uk/20140605145320/http://publications.naturalengland.org.uk/publication/40004?category=47004. Accessed 20 August 2019

20.

Department for Environment, Food and Rural Affairs (Defra) (2013) Census 2001 rural urban classification for a range of geographies. Available from www.gov.uk/government/statistics/2001-rural-urban-definition-la-classification-and-other-geographies. Accessed 20 August 2019

21.

Public Health England (PHE)–British Geological Survey (BGS) (2007) Indicative atlas of radon in the United Kingdom. Available from www.bgs.ac.uk/radon/indicativeRadonDataset.html. Accessed 20 August 2019

22.

Department for Environment, Food and Rural Affairs (Defra) (2017) Modelled background pollution data. Available from https://uk-air.defra.gov.uk/data/pcm-data. Accessed 20 August 2019

23.

Vienneau D, de Hoogh K, Bechle MJ et al (2013) Western European land use regression incorporating satellite- and ground-based measurements of NO₂ and PM₁₀. Environ Sci Technol 47(23):13555–13564. https://doi.org/10.1021/es403089q CrossRefPubMed

24.

British Geological Survey G-BASE for the UK. Available from www.bgs.ac.uk/products/geochemistry/GbaseUK.html. Accessed 20 August 2019

25.

National Soil Inventory (NSI) Soils summary information. Available from www.bgs.ac.uk/gbase/sampleIndexMaps/SoilNSI.html. Accessed 20 August 2019

26.

Vienneau D (2010) Health impacts of agricultural land use change in Greece and Great Britain. Available from www.integrated-assessment.eu/eu/indexa8be.html?q=content/health_impacts_agricultural_land_use_change_greece_and_great_britain. Accessed 20 August 2019

27.

Briggs DJ, Gulliver J, Fecht D, Vienneau DM (2007) Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote Sens Environ 108(4):451–466CrossRef

28.

Office for National Statistics (2019) Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland. Available from www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland. Accessed 20 August 2019

29.

Office for National Statistics (2010) Overcrowded households. Available from https://data.gov.uk/dataset/440d9cd7-818d-4cca-b1c2-7bb54fd79c50/overcrowded-households. Accessed 20 August 2019

30.

Communities and Local Government (2007) Indices of deprivation 2004. Available from http://webarchive.nationalarchives.gov.uk/20100407164233/http://www.communities.gov.uk/archived/general-content/communities/indicesofdeprivation/216309/. Accessed 20 August 2019

31.

McLennan D, Barnes H, Noble M, Davies J, Garratt E and Dibben C (2011) The English Indices of Deprivation 2010. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/6320/1870718.pdf. Accessed 20 August 2019

32.

Besag J, York J, Mollié A (1991) Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math 43(1):1–20CrossRef

33.

Rue H, Martino S, Chopin N (2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Ser B Stat Methodol 71(2):319–392CrossRef

34.

Freni-Sterrantino A, Ventrucci M, Rue H (2018) A note on intrinsic conditional autoregressive models for disconnected graphs. Spat Spatiotemporal Epidemiol 26:25–34CrossRef

35.

Riebler A, Sorbye SH, Simpson D, Rue H (2016) An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Stat Methods Med Res 25(4):1145–1165. https://doi.org/10.1177/0962280216660421 CrossRefPubMed

36.

Harron KL, McKinney PA, Feltbower RG et al (2011) Incidence rate trends in childhood type 1 diabetes in Yorkshire, UK 1978-2007: effects of deprivation and age at diagnosis in the South Asian and non-South Asian populations. Diabet Med 28(12):1508–1513. https://doi.org/10.1111/j.1464-5491.2011.03413.x CrossRefPubMed

37.

Noble M, Wright G, Dibben C et al (2004) The English indices of deprivation 2004 (revised). Report to the Office of the Deputy Prime Minister. Office of the Deputy Prime Minister, Neighbourhood Renewal Unit, London

38.

Patterson CC, Carson DJ, Hadden DR (1996) Epidemiology of childhood IDDM in Northern Ireland 1989-1994: low incidence in areas with highest population density and most household crowding. Northern Ireland Diabetes Study Group. Diabetologia 39(9):1063–1069. https://doi.org/10.1007/bf00400655 CrossRefPubMed

39.

Hathout EH, Beeson WL, Nahab F, Rabadi A, Thomas W, Mace JW (2002) Role of exposure to air pollutants in the development of type 1 diabetes before and after 5 yr of age. Pediatr Diabetes 3(4):184–188. https://doi.org/10.1034/j.1399-5448.2002.30403.x CrossRefPubMed

40.

Hathout EH, Beeson WL, Ischander M, Rao R, Mace JW (2006) Air pollution and type 1 diabetes in children. Pediatr Diabetes 7(2):81–87CrossRef

41.

Beyerlein A, Krasmann M, Thiering E et al (2015) Ambient air pollution and early manifestation of type 1 diabetes. Epidemiology 26(3):e31–e32. https://doi.org/10.1097/EDE.0000000000000254 CrossRefPubMed

42.

Valera P, Zavattari P, Sanna A et al (2015) Zinc and other metals deficiencies and risk of type 1 diabetes: an ecological study in the high risk Sardinia island. PLoS One 10(11):e0141262CrossRef

43.

Sutton P, Roberts D, Elvidge C, Baugh K (2001) Census from heaven: an estimate of the global human population using night-time satellite imagery. Int J Remote Sens 22(16):3061–3076CrossRef

44.

Cherubini V, Carle F, Gesuita R et al (1999) Large incidence variation of type I diabetes in central–southern Italy 1990–1995: lower risk in rural areas. Diabetologia 42(7):789–792. https://doi.org/10.1007/s001250051228 CrossRefPubMed

45.

Haynes A, Bulsara MK, Bower C, Codde JP, Jones TW, Davis EA (2006) Independent effects of socioeconomic status and place of residence on the incidence of childhood type 1 diabetes in Western Australia. Pediatr Diabetes 7(2):94–100. https://doi.org/10.1111/j.1399-543X.2006.00153.x CrossRefPubMed

46.

Hansell AL, Beale LA, Ghosh RE et al (2014) The environment and health atlas for England and Wales. Oxford University Press, Oxford

47.

Mohr SB, Garland CF, Gorham ED, Garland FC (2008) The association between ultraviolet B irradiance, vitamin D status and incidence rates of type 1 diabetes in 51 regions worldwide. Diabetologia 51(8):1391–1398. https://doi.org/10.1007/s00125-008-1061-5 CrossRefPubMed

48.

Rignell-Hydbom A, Elfving M, Ivarsson SA et al (2010) A nested case-control study of intrauterine exposure to persistent organochlorine pollutants in relation to risk of type 1 diabetes. PLoS One 5:e11281CrossRef

49.

Tuomi T, Santoro N, Caprio S, Cai M, Weng J, Groop L (2014) The many faces of diabetes: a disease with increasing heterogeneity. Lancet 383(9922):1084–1094CrossRef

50.

Norman PD The changing geography of deprivation in Britain: exploiting small area census data 1971 to 2011. In GISRUK 2015 Proceedings 2015 Jul 22: 465–474

Title: Childhood type 1 diabetes: an environment-wide association study across England
Authors: Annalisa Sheehan
Anna Freni Sterrantino
Daniela Fecht
Paul Elliott
Susan Hodgson
Publication date: 01-05-2020
Publisher: Springer Berlin Heidelberg
Keyword: Type 1 Diabetes
Published in: Diabetologia / Issue 5/2020
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI: https://doi.org/10.1007/s00125-020-05087-7

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Aims/hypothesis

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