Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models.
Trynke R de Jong, Nicolette R den Braver, Alfred Wagtendonk, Mark van de Wiel, Mariëlle A Beenackers, Brenda W J H Penninx, Karien Stronks, Dorret I Boomsma, Albertine J Oldehinkel, Joline W J Beulens, Irina Motoc, Gonneke Willemsen, Coen D A Stehouwer, Eric Moll van Charante, Margreet Ten Have, Lützen Portengen, Jeroen Lakerveld, Annemarie Koster, Roel Vermeulen, Anke Wesselius, Katja van den Hurk, Natasja M van Schoor, Anke Huss, Gerard Hoek, Marieke F van Wier, Monique Verschuren, Martin van Boxtel, Haykanush Ohanyan
Journal: Environmental health perspectives
2024;132(6):67007
PMID: 38889167
Abstract
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 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 . 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.
Address:
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Health Behaviours and Chronic Diseases, Amsterdam Public Health, Amsterdam, the Netherlands.; Upstream Team, Amsterdam UMC, VU University Amsterdam, Amsterdam, the Netherlands.; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Health Behaviours and Chronic Diseases, Amsterdam Public Health, Amsterdam, the Netherlands.; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Health Behaviours and Chronic Diseases, Amsterdam Public Health, Amsterdam, the Netherlands.; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Health Behaviours and Chronic Diseases, Amsterdam Public Health, Amsterdam, the Netherlands.; Upstream Team, Amsterdam UMC, VU University Amsterdam, Amsterdam, the Netherlands.; Lifelines Cohort & Biobank, Roden, the Netherlands.; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.; National Institute for Public Health and the Environment, Bilthoven, the Netherlands.; Donor Medicine Research - Donor Studies, Sanquin Research, Amsterdam, the Netherlands.; Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.; Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Aging & Later Life, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.; School for Cardiovascular Diseases (CARIM), Maastricht University, Maastricht, the Netherlands.; Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands.; School for Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, the Netherlands.; Department of Epidemiology, Maastricht University, Maastricht, the Netherlands.; Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands.; Department of Social Medicine, Maastricht University, Maastricht, the Netherlands.; Trimbos-Instituut, Netherlands Institute of Mental Health and Addiction, Utrecht, the Netherlands.; Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Mood, Anxiety, Psychosis, Sleep & Stress Program, Mental Health Program and Amsterdam Neuroscience, Amsterdam Public Health, Amsterdam, the Netherlands.; Department of Otolaryngology-Head and Neck Surgery, section Ear and Hearing, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Quality of Care, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Amsterdam Reproduction & Development Research Institute, Amsterdam, the Netherlands.; Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.; Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.; Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Health Behaviours and Chronic Diseases, Amsterdam Public Health, Amsterdam, the Netherlands.; Upstream Team, Amsterdam UMC, VU University Amsterdam, Amsterdam, the Netherlands.; Lifelines Cohort & Biobank, Roden, the Netherlands.; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.; Lifelines Cohort & Biobank, Roden, the Netherlands.
MeSH Terms:
Humans,
Body Mass Index,
Exposome,
Netherlands,
Environmental Exposure,
Residence Characteristics,
Male,
Female,
Obesity,
Cohort Studies,
Random Forest
