Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models.

Publisher: National Institute of Environmental Health Sciences Country of Publication: United States NLM ID: 0330411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-9924 (Electronic) Linking ISSN: 00916765 NLM ISO Abbreviation: Environ Health Perspect Subsets: MEDLINE

Original Publication: Research Triangle Park, N. C. National Institute of Environmental Health Sciences.

Body Mass Index* ; Exposome* ; Environmental Exposure*/statistics & numerical data; Humans ; Netherlands ; Residence Characteristics/statistics & numerical data ; Male ; Female ; Obesity/epidemiology ; Cohort Studies ; Random Forest

Background: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors.
Objectives: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies.
Methods: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies.
Results: Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5 -km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to € 300,000 . The directions of associations were less consistent for walkability and share of single residents.
Discussion: Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.

BMJ Open. 2018 Feb 3;8(2):e018504. (PMID: 29431129)
Environ Int. 2022 Jan;158:107015. (PMID: 34991269)
BMJ Open. 2021 Mar 4;11(3):e042078. (PMID: 33664071)
BMJ Open. 2023 Apr 17;13(4):e070180. (PMID: 37068904)
Environ Sci Pollut Res Int. 2022 Aug;29(37):55707-55727. (PMID: 35320480)
Obes Rev. 2019 Jan;20(1):41-54. (PMID: 30253075)
Environ Res. 2022 Jun;209:112785. (PMID: 35077718)
Int J Health Geogr. 2020 Nov 13;19(1):49. (PMID: 33187515)
Int J Health Geogr. 2021 Feb 1;20(1):7. (PMID: 33526041)
Prev Med. 2021 Dec;153:106790. (PMID: 34506813)
BMC Public Health. 2013 Apr 27;13:402. (PMID: 23621920)
Int J Epidemiol. 2014 Jun;43(3):721-30. (PMID: 23716619)
Tijdschr Gerontol Geriatr. 1998 Jun;29(3):120-9. (PMID: 9675779)
Sci Total Environ. 2020 Feb 1;702:135040. (PMID: 31726339)
Int J Epidemiol. 2018 Apr 1;47(2):396-396j. (PMID: 29244153)
Int J Epidemiol. 2012 Feb;41(1):24-32. (PMID: 22296988)
Int J Epidemiol. 2008 Dec;37(6):1236-41. (PMID: 18238821)
BMC Bioinformatics. 2009 Feb 19;10:62. (PMID: 19228411)
Bioinformatics. 2007 Jul 1;23(13):1607-15. (PMID: 17483500)
Curr Environ Health Rep. 2017 Dec;4(4):481-490. (PMID: 28988291)
Bioinformatics. 2009 Aug 1;25(15):1884-90. (PMID: 19460890)
Int J Audiol. 2009;48(10):684-91. (PMID: 19863354)
BMC Bioinformatics. 2008 Jul 11;9:307. (PMID: 18620558)
Am J Epidemiol. 2014 Mar 15;179(6):764-74. (PMID: 24589914)
Twin Res Hum Genet. 2019 Dec;22(6):623-636. (PMID: 31666148)
Bioinformatics. 2011 Jul 15;27(14):1986-94. (PMID: 21576180)
Eur J Epidemiol. 2014 Jun;29(6):439-51. (PMID: 24756374)
Stat Med. 2019 Feb 20;38(4):558-582. (PMID: 29869423)
Soc Psychiatry Psychiatr Epidemiol. 2012 Feb;47(2):203-13. (PMID: 21197531)
BMC Genet. 2018 Sep 17;19(Suppl 1):65. (PMID: 30255764)
Genomics. 2008 Dec;92(6):400-3. (PMID: 18565726)
Ethn Health. 2023 Apr;28(3):446-457. (PMID: 35289677)
Int J Epidemiol. 2011 Aug;40(4):868-76. (PMID: 21216744)
Soc Sci Med. 2020 Nov;265:113537. (PMID: 33250318)
Prev Med Rep. 2020 Apr 23;18:101098. (PMID: 32382494)
Obes Rev. 2021 Feb;22 Suppl 1:e12944. (PMID: 31507064)
Int J Hyg Environ Health. 2020 Jan;223(1):45-55. (PMID: 31679857)
Environ Health Perspect. 2015 Nov;123(11):1187-92. (PMID: 25840153)
Eur J Epidemiol. 2008;23(1):67-74. (PMID: 18075776)
Obes Rev. 2020 Nov;21(11):e13078. (PMID: 32677149)
Int J Behav Nutr Phys Act. 2022 May 2;19(1):50. (PMID: 35501815)
Vox Sang. 2019 Feb;114(2):117-128. (PMID: 30590867)
BMJ Open. 2019 Nov 14;9(11):e028238. (PMID: 31727643)
Brief Bioinform. 2019 Jan 18;20(1):178-189. (PMID: 28968705)
Int J Epidemiol. 2008 Dec;37(6):1227-35. (PMID: 18263649)
Curr Epidemiol Rep. 2019 Sep;6(3):291-299. (PMID: 31723546)
Environ Int. 2022 Dec;170:107592. (PMID: 36306550)

Date Created: 20240618 Date Completed: 20240618 Latest Revision: 20240704

20240704

PMC11218701

10.1289/EHP13393

38889167