The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Non-alcoholic fatty liver disease: the hepatic consequence of obesity and the metabolic syndrome
Open accessAbstract
Non-alcoholic fatty liver disease (NAFLD) is now the most common liver disease in both adults and children worldwide. As a disease spectrum, NAFLD may progress from simple steatosis to steatohepatitis, advanced fibrosis and cirrhosis. An estimated 20–35% of the general population has steatosis, 10% of whom will develop the more progressive non-alcoholic steatohepatitis associated with markedly increased risk of cardiovascular- and liver-related mortality. Development of NAFLD is strongly linked to components of the metabolic syndrome including obesity, insulin resistance, dyslipidaemia and type 2 diabetes. The recognition that NAFLD is an independent risk factor for CVD is a major public health concern. There is a great need for a sensitive non-invasive test for the early detection and assessment of the stage of NAFLD that could also be used to monitor response to treatment. The cellular and molecular aetiology of NAFLD is multi-factorial; genetic polymorphisms influencing NAFLD have been identified and nutrition is a modifiable environmental factor influencing NAFLD progression. Weight loss through diet and exercise is the primary recommendation in the clinical management of NAFLD. The application of systems biology to the identification of NAFLD biomarkers and factors involved in NAFLD progression is an area of promising research.
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.
Integrity signals
Registry
Cites
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.
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
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
Steatohepatitis: A tale of two “hits”?
Day, C. P., & James, O. F. W. (1998) · Gastroenterology · doi:10.1016/s0016-5085(98)70599-2 · Cited by 4,285
Donnelly, K. L., Smith, C. I., Schwarzenberg, S. J., Jessurun, J., Boldt, M. D., & Parks, E. J. (2005) · Journal of Clinical Investigation · doi:10.1172/jci23621 · Cited by 3,604
Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease
Romeo, S., Kozlitina, J., Xing, C., Pertsemlidis, A., Cox, D., Pennacchio, L. A., Boerwinkle, E., Cohen, J. C., & Hobbs, H. H. (2008) · Nature Genetics · doi:10.1038/ng.257 · Cited by 3,416
Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity.
MATTEONI, C., YOUNOSSI, Z., GRAMLICH, T., BOPARAI, N., LIU, Y., & MCCULLOUGH, A. (1999) · Gastroenterology · doi:10.1016/s0016-5085(99)70506-8 · Cited by 3,345
The Natural History of Nonalcoholic Fatty Liver Disease: A Population-Based Cohort Study
Adams, L. A., Lymp, J. F., St. Sauver, J., Sanderson, S. O., Lindor, K. D., Feldstein, A., & Angulo, P. (2005) · Gastroenterology · doi:10.1053/j.gastro.2005.04.014 · Cited by 2,890
Molecular mediators of hepatic steatosis and liver injury
Browning, J. D., & Horton, J. D. (2004) · Journal of Clinical Investigation · doi:10.1172/jci200422422 · Cited by 1,772
Systems Biology and New Technologies Enable Predictive and Preventative Medicine
Hood, L., Heath, J. R., Phelps, M. E., & Lin, B. (2004) · Science · doi:10.1126/science.1104635 · Cited by 1,108 · cited 1×
“context of human health it is believed that systems biology is essential to future drug discovery and 10 indeed, to a future of personalized, predictive and preventative medicine.((119)) In this vision, drug 11 targets would be rational and identified through the analysis of normal and disease perturbed 12 networks, w…”
BackgroundFabbrini, E., Mohammed, B. S., Magkos, F., Korenblat, K. M., Patterson, B. W., & Klein, S. (2008) · Gastroenterology · doi:10.1053/j.gastro.2007.11.038 · Cited by 631
Leite, N. C., Salles, G. F., Araujo, A. L. E., Villela‐Nogueira, C. A., & Cardoso, C. R. L. (2009) · Liver International · doi:10.1111/j.1478-3231.2008.01718.x · Cited by 531
Cited by
Garvey, W. T., Mechanick, J. I., Brett, E. M., Garber, A. J., Hurley, D. L., Jastreboff, A. M., Nadolsky, K., Pessah-Pollack, R., & Plodkowski, R. (2016) · Endocrine Practice · doi:10.4158/ep161365.gl · Cited by 1,914
Metabolic Syndrome and Insulin Resistance: Underlying Causes and Modification by Exercise Training
Roberts, C. K., Hevener, A. L., & Barnard, R. J. (2013) · Comprehensive Physiology · doi:10.1002/cphy.c110062 · Cited by 831
Obesity, adiposity, and dyslipidemia: a consensus statement from the National Lipid Association
Bays, H. E., Toth, P. P., Kris-Etherton, P. M., Abate, N., Aronne, L. J., Brown, W. V., Gonzalez-Campoy, J. M., Jones, S. R., Kumar, R., La Forge, R., et al. (2013) · Journal of Clinical Lipidology · doi:10.1016/j.jacl.2013.04.001 · Cited by 521
Adipokines: A treasure trove for the discovery of biomarkers for metabolic disorders
Lehr, S., Hartwig, S., & Sell, H. (2012) · PROTEOMICS – Clinical Applications · doi:10.1002/prca.201100052 · Cited by 352
Kinlen, D., Cody, D., & O’Shea, D. (2018) · QJM: An International Journal of Medicine · doi:10.1093/qjmed/hcx152 · Cited by 308
Metadata
- Authors
- Moore, J. B.
- Venue
- Proceedings of the Nutrition Society
- Publisher
- Cambridge University Press (CUP)
- Published
- 2010-02-17
- Volume
- 69
- Issue
- 2
- Pages
- 211-220
- ISSN
- 0029-6651 (ISSN-L)
- 1475-2719
- OpenAlex
- W2142052994
- Cited by
- 217
- References
- 11
- FWCI
- 10.90
- Type
- journal article
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Non-alcoholic fatty liver disease: the hepatic consequence of obesity and the metabolic syndrome. https://app.cochat.ai/papers/10.1017/s0029665110000030
- The paper
- Moore, J. B. (2010). Non-alcoholic fatty liver disease: the hepatic consequence of obesity and the metabolic syndrome. Proceedings of the Nutrition Society. https://doi.org/10.1017/s0029665110000030
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.
