Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 2,737

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

The epidemiology of obesity

Chooi, Yu Chung, Ding, Cherlyn, Magkos, Faidon·2019·Metabolism
DOI 10.1016/j.metabol.2018.09.005
Open this paper in CoChat — free account

Abstract

Obesity is a complex multifactorial disease. The worldwide prevalence of overweight and obesity has doubled since 1980 to an extent that nearly a third of the world's population is now classified as overweight or obese. Obesity rates have increased in all ages and both sexes irrespective of geographical locality, ethnicity or socioeconomic status, although the prevalence of obesity is generally greater in older persons and women. This trend was similar across regions and countries, although absolute prevalence rates of overweight and obesity varied widely. For some developed countries, the prevalence rates of obesity seem to have levelled off during the past few years. Body mass index (BMI) is typically used to define overweight and obesity in epidemiological studies. However, BMI has low sensitivity and there is a large inter-individual variability in the percent body fat for any given BMI value, partly attributed to age, sex, and ethnicity. For instance, Asians have greater percent body fat than Caucasians for the same BMI. Greater cardiometabolic risk has also been associated with the localization of excess fat in the visceral adipose tissue and ectopic depots (such as muscle and liver), as well as in cases of increased fat to lean mass ratio (e.g. metabolically-obese normal-weight). These data suggest that obesity may be far more common and requires more urgent attention than what large epidemiological studies suggest. Simply relying on BMI to assess its prevalence could hinder future interventions aimed at obesity prevention and control.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.metabol.2018.09.005

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 9 references

Research works printed but not linked: 17

  1. Czernichow, 2011, Body mass index, waist circumference and waist-hip ratio: which is the better discriminator of cardiovascular disease mortality risk?: evidence from an individual-participant meta-analysis of 82 864 participants from nine cohort studies, Obes Rev, 12, 680, 10.1111/j.1467-789X.2011.00879.x · DOI · Paper page
  2. Lauby-Secretan, 2016, Body fatness and cancer–viewpoint of the IARC working group, N Engl J Med, 375, 794, 10.1056/NEJMsr1606602 · DOI · Paper page
  3. Anandacoomarasamy, 2008, The impact of obesity on the musculoskeletal system, Int J Obes, 32, 211, 10.1038/sj.ijo.0803715 · DOI
  4. Kim, 2016, Estimating the medical care costs of obesity in the United States: systematic review, meta-analysis, and empirical analysis, Value Health, 19, 602, 10.1016/j.jval.2016.02.008 · DOI
  5. von Lengerke, 2011, Economic costs of adult obesity: a review of recent European studies with a focus on subgroup-specific costs, Maturitas, 69, 220, 10.1016/j.maturitas.2011.04.005 · DOI
  6. Ng, 2012, Time use and physical activity: a shift away from movement across the globe, Obes Rev, 13, 659, 10.1111/j.1467-789X.2011.00982.x · DOI
  7. Ladabaum, 2014, Obesity, abdominal obesity, physical activity, and caloric intake in US adults: 1988 to 2010, Am J Med, 127, 717, 10.1016/j.amjmed.2014.02.026 · DOI
  8. Afshin, 2017, Health effects of overweight and obesity in 195 countries over 25 years, N Engl J Med, 377, 13, 10.1056/NEJMoa1614362 · DOI · Paper page
  9. Stevens, 2012, National, regional, and global trends in adult overweight and obesity prevalences, Popul Health Metrics, 10, 22, 10.1186/1478-7954-10-22 · DOI
  10. He, 2017, Prevalence of overweight and obesity in 15.8 million men aged 15–49 years in rural China from 2010 to 2014, Sci Rep, 7, 5012, 10.1038/s41598-017-04135-4 · DOI
  11. Ng, 2014, Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013, Lancet, 384, 766, 10.1016/S0140-6736(14)60460-8 · DOI · Paper page
  12. Mi, 2015, Prevalence and secular trends in obesity among Chinese adults, 1991–2011, Am J Prev Med, 49, 661, 10.1016/j.amepre.2015.05.005 · DOI
  13. Wang, 2012, Epidemiology of general obesity, abdominal obesity and related risk factors in urban adults from 33 communities of Northeast China: the CHPSNE study, BMC Public Health, 12, 10.1186/1471-2458-12-967 · DOI
  14. Reynolds, 2007, Prevalence and risk factors of overweight and obesity in China, Obesity (Silver Spring), 15, 10, 10.1038/oby.2007.527 · DOI
  15. Zhou, 2002, Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults–study on optimal cut-off points of body mass index and waist circumference in Chinese adults, Biomed Environ Sci, 15, 83
  16. Gujral, 2017, Cardiometabolic abnormalities among normal-weight persons from five racial/ethnic groups in the United States: a cross-sectional analysis of two cohort studies, Ann Intern Med, 166, 628, 10.7326/M16-1895 · DOI
  17. Ding, 2016, Lean, but not healthy: the ‘metabolically obese, normal-weight’ phenotype, Curr Opin Clin Nutr Metab Care, 19, 408

Cited by

Metadata

Authors
Chooi, Y. C., Ding, C., & Magkos, F.
Venue
Metabolism
Publisher
Elsevier BV
Published
2019-03
Volume
92
Pages
6-10
ISSN
0026-0495 (ISSN-L)
DOI
10.1016/j.metabol.2018.09.005
OpenAlex
W2893673243
Cited by
2,737
References
9
FWCI
145.80
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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.