The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
The association between maternal body mass index and child obesity: A systematic review and meta-analysis
Open accessAbstract
BACKGROUND: There is a global obesity crisis, particularly among women and disadvantaged populations. Early-life intervention to prevent childhood obesity is a priority for public health, global health, and clinical practice. Understanding the association between childhood obesity and maternal pre-pregnancy weight status would inform policy and practice by allowing one to estimate the potential for offspring health gain through channelling resources into intervention. This systematic review and meta-analysis aimed to examine the dose-response association between maternal body mass index (BMI) and childhood obesity in the offspring. METHODS AND FINDINGS: Searches in MEDLINE, Child Development & Adolescent Studies, CINAHL, Embase, and PsycInfo were carried out in August 2017 and updated in March 2019. Supplementary searches included hand-searching reference lists, performing citation searching, and contacting authors. Two researchers carried out independent screening, data extraction, and quality assessment. Observational studies published in English and reporting associations between continuous and/or categorical maternal and child BMI or z-score were included. Categorical outcomes were child obesity (≥95th percentile, primary outcome), overweight/obesity (≥85th percentile), and overweight (85th to 95th percentile). Linear and nonlinear dose-response meta-analyses were conducted using random effects models. Studies that could not be included in meta-analyses were summarised narratively. Seventy-nine of 41,301 studies identified met the inclusion criteria (n = 59 cohorts). Meta-analyses of child obesity included 20 studies (n = 88,872); child overweight/obesity, 22 studies (n = 181,800); and overweight, 10 studies (n = 53,238). Associations were nonlinear and there were significantly increased odds of child obesity with maternal obesity (odds ratio [OR] 3.64, 95% CI 2.68-4.95) and maternal overweight (OR 1.89, 95% CI 1.62-2.19). Significantly increased odds were observed for child overweight/obesity (OR 2.69, 95% CI 2.10-3.46) and for child overweight (OR 1.80, 95% CI 1.25, 2.59) with maternal obesity. A limitation of this research is that the included studies did not always report the data in a format that enabled inclusion in this complex meta-analysis. CONCLUSIONS: This research has identified a 264% increase in the odds of child obesity when mothers have obesity before conception. This study provides substantial evidence for the need to develop interventions that commence prior to conception, to support women of childbearing age with weight management in order to halt intergenerational obesity.
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Bias in meta-analysis detected by a simple, graphical test
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997) · BMJ · doi:10.1136/bmj.315.7109.629 · Cited by 56,722
Meta-analysis in clinical trials
DerSimonian, R., & Laird, N. (1986) · Controlled Clinical Trials · doi:10.1016/0197-2456(86)90046-2 · Cited by 39,552
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048
Dermyshi, E., Wang, Y., Yan, C., Hong, W., Qiu, G., Gong, X., & Zhang, T. (2017) · Neonatology · doi:10.1159/000454668 · Cited by 24,451
Meta-analysis of Observational Studies in Epidemiology<SUBTITLE>A Proposal for Reporting</SUBTITLE>
Stroup, D. F. (2000) · JAMA · doi:10.1001/jama.283.15.2008 · Cited by 20,623
Conducting Meta-Analyses in R with the metafor Package
Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464
Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., Mullany, E. C., Biryukov, S., Abbafati, C., Abera, S. F., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60460-8 · Cited by 11,952
Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.1. The Cochrane Collaboration
Higgins, J., Green, S. R., & Higgins, J. (2008) · Cited by 5,544
Trends in Obesity Among Adults in the United States, 2005 to 2014
Flegal, K. M., Kruszon-Moran, D., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2016) · JAMA · doi:10.1001/jama.2016.6458 · Cited by 2,803
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Thomson, K., Moffat, M., Arisa, O., Jesurasa, A., Richmond, C., Odeniyi, A., Bambra, C., Rankin, J., Brown, H., Bishop, J., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-042753 · Cited by 154
Self-citationPerspective: Obesity—an unexplained epidemic
Mozaffarian, D. (2022) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqac075 · Cited by 101
Louise, J., Poprzeczny, A. J., Deussen, A. R., Vinter, C., Tanvig, M., Jensen, D. M., Bogaerts, A., Devlieger, R., McAuliffe, F. M., Renault, K. M., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-01995-6 · Cited by 62
Norris, S. A., Draper, C. E., Prioreschi, A., Smuts, C., Ware, L. J., Dennis, C., Awadalla, P., Bassani, D., Bhutta, Z., Briollais, L., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-059914 · Cited by 56
McGowan, L., Lennon-Caughey, E., Chun, C., McKinley, M. C., & Woodside, J. V. (2020) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-020-2733-5 · Cited by 47
Metadata
- Authors
- Heslehurst, N., Vieira, R., Akhter, Z., Bailey, H., Slack, E., Ngongalah, L., Pemu, A., & Rankin, J.
- Venue
- PLOS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-06-11
- Volume
- 16
- Issue
- 6
- Pages
- e1002817
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- OpenAlex
- W2950421734
- Cited by
- 435
- References
- 108
- FWCI
- 9.41
- Type
- journal article
- Language
- en
- Open access
- gold
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