The DOI matches its Crossref record; 541 of the 541 references it cites were found in a registry or an index.
Developmental origins of metabolic diseases
Open accessAbstract
Almost 2 billion adults in the world are overweight, and more than half of them are classified as obese, while nearly one-third of children globally experience poor growth and development. Given the vast amount of knowledge that has been gleaned from decades of research on growth and development, a number of questions remain as to why the world is now in the midst of a global epidemic of obesity accompanied by the “double burden of malnutrition,” where overweight coexists with underweight and micronutrient deficiencies. This challenge to the human condition can be attributed to nutritional and environmental exposures during pregnancy that may program a fetus to have a higher risk of chronic diseases in adulthood. To explore this concept, frequently called the developmental origins of health and disease (DOHaD), this review considers a host of factors and physiological mechanisms that drive a fetus or child toward a higher risk of obesity, fatty liver disease, hypertension, and/or type 2 diabetes (T2D). To that end, this review explores the epidemiology of DOHaD with discussions focused on adaptations to human energetics, placental development, dysmetabolism, and key environmental exposures that act to promote chronic diseases in adulthood. These areas are complementary and additive in understanding how providing the best conditions for optimal growth can create the best possible conditions for lifelong health. Moreover, understanding both physiological as well as epigenetic and molecular mechanisms for DOHaD is vital to most fully address the global issues of obesity and other chronic diseases.
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Mathai, S., Cutfield, W. S., Derraik, J. G. B., Dalziel, S. R., Harding, J. E., Robinson, E., Biggs, J. B., Jefferies, C., & Hofman, P. L. (2012) · Diabetes · doi:10.2337/db11-1672 · Cited by 61
Capobianco, E., Fornes, D. D., Linenberg, I. M., Powell, T. L., Jansson, T., & Jawerbaum, A. (2015) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2015.12.020 · Cited by 61
Self-citationEffects of paternal obesity on growth and adiposity of male rat offspring
Lecomte, V., Maloney, C., Wang, K. W., & Morris, M. J. (2016) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00262.2016 · Cited by 61
Sanchez-Garrido, M. A., Ruíz-Pino, F., Velasco, I., Barroso, A., Fernandois, D., Heras, V., Manfredi-Lozano, M., Vazquez, M. J., Castellano, J. M., Roa, J., et al. (2017) · Endocrinology · doi:10.1210/en.2017-00705 · Cited by 61
Effect of Birth Size and Catch-Up Growth on Adult Blood Pressure and Carotid Intima-Media Thickness
Leunissen, R. W. J., Kerkhof, G. F., Stijnen, T., & Hokken-Koelega, A. (2012) · Hormone Research in Paediatrics · doi:10.1159/000338791 · Cited by 60
Cited by
Huang, J., Li, Y., Chen, M., Cai, Z., Cai, Z., & Jiang, Z. (2024) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-024-01657-9 · Cited by 25
“…(such as hypertension, myocardial infarction, and stroke) [3, 4], metabolic diseases (including type 2 diabetes mellitus and fatty liver disease) [5], musculoskeletal ailments (like osteoarthritis) [6], and digestive system cancers (comprising esophagus, stomach, pancreas, liver, and colon)…”
BackgroundMaternal adiposity and perinatal and offspring outcomes: an umbrella review
Yang, Z., Feng, G., Gao, X., Yan, X., Li, Y., Wang, Y., Li, S., Jiang, Y., Zhao, S., Zhao, H., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01994-6 · Cited by 22
The association between prenatal exposure to bisphenol A and offspring obesity: A systematic review
Guo, J., Liu, K., Yang, J., & Su, Y. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.123391 · Cited by 20
Zhou, J., Teng, Y., Ouyang, J., Wu, P., Tong, J., Gao, G., Yan, S., Tao, F., & Huang, K. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.124.035754 · Cited by 7
Jia, Z., Qiu, F., He, Y., Chen, H., Yang, C., Liu, H., Zheng, T., Xu, S., Wang, S., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03629-z · Cited by 6
Metadata
- Authors
- Hoffman, D. J., Powell, T. L., Barrett, E. S., & Hardy, D. B.
- Venue
- Physiological Reviews
- Publisher
- American Physiological Society
- Published
- 2021-07-01
- Volume
- 101
- Issue
- 3
- Pages
- 739-795
- ISSN
- 0031-9333 (ISSN-L)
- 1522-1210
- OpenAlex
- W3108372566
- Cited by
- 466
- References
- 541
- FWCI
- 10.25
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.