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The Epidemiology of Obesity: A Big Picture
Open accessAbstract
The epidemic of overweight and obesity presents a major challenge to chronic disease prevention and health across the life course around the world. Fueled by economic growth, industrialization, mechanized transport, urbanization, an increasingly sedentary lifestyle, and a nutritional transition to processed foods and high-calorie diets over the last 30 years, many countries have witnessed the prevalence of obesity in its citizens double and even quadruple. A rising prevalence of childhood obesity, in particular, forebodes a staggering burden of disease in individuals and healthcare systems in the decades to come. A complex, multifactorial disease, with genetic, behavioral, socioeconomic, and environmental origins, obesity raises the risk of debilitating morbidity and mortality. Relying primarily on epidemiologic evidence published within the last decade, this non-exhaustive review discusses the extent of the obesity epidemic, its risk factors—known and novel—, sequelae, and economic impact across the globe.
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Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
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A review of current guidelines for the treatment of obesity
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Obesity I: Overview and molecular and biochemical mechanisms
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Sarcopenic obesity in ageing: cardiovascular outcomes and mortality
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Obesity and the risk of primary liver cancer: A systematic review and meta-analysis
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Mitochondrial uncoupler BAM15 reverses diet-induced obesity and insulin resistance in mice
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Patient-Driven Diabetes Care of the Future in the Technology Era
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Bracko, O., Vinarcsik, L. K., Cruz Hernández, J. C., Ruiz-Uribe, N. E., Haft-Javaherian, M., Falkenhain, K., Ramanauskaite, E. M., Ali, M., Mohapatra, A., Swallow, M. A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-65908-y · Cited by 85
The prevalence of low muscle mass associated with obesity in the USA
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Dysregulated resting state functional connectivity and obesity: A systematic review
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“At the same time, worldwide rates of overweight and obesity continue to increase for reasons that may include economic growth and industrialization, sedentary lifestyles, limited access to physical activity, and transitions to processed foods and high-calorie diets [3].”
Sarcopenic obesity and health outcomes: An umbrella review of systematic reviews with meta‐analysis
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Precision medicine in adult and pediatric obesity: a clinical perspective
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Insulin Regulation of Hepatic Lipid Homeostasis
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Is Glucagon Receptor Activation the Thermogenic Solution for Treating Obesity?
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Primary Cilia as a Signaling Platform for Control of Energy Metabolism
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Digital Health Solutions for Weight Loss and Obesity: A Narrative Review
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“The most commonly used measure for weight by health organisations is the body mass index (BMI), which is a basic tool to classify groups of populations [3,4].”
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“The underlying cause of obesity is a chronic imbalance between energy intake and energy expenditure in favour of the former, leading to an accumulation of body weight and in particular body fat mass (Hruby & Hu, 2015).”
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PPARG expression patterns and correlations in obesity
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“Basically, obesity and overweight are the outcome of an energy imbalance between calories consumed and calories expended [2], and over the last decades, their prevalence has doubled and even quadrupled [3].”
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“Obesity is a complex, multifactorial, and highly prevalent disease that should be treated accordingly [1].”
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Metadata
- Authors
- Hruby, A., & Hu, F. B.
- Venue
- PharmacoEconomics
- Published
- 2014-12-04
- Volume
- 33
- Issue
- 7
- Pages
- 673-689
- ISSN
- 1170-7690 (ISSN-L)
- 1179-2027
- OpenAlex
- W2066805219
- Cited by
- 2,916
- References
- 34
- FWCI
- 28.49
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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.
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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.