The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Blueberry polyphenols extract as a potential prebiotic with anti-obesity effects on C57BL/6 J mice by modulating the gut microbiota
Abstract
Polyphenols are known for their various health benefits. Blueberries are dietary sources of polyphenols with reported health benefits. However, the role of blueberry polyphenols in alleviating obesity is not completely understood. This study investigated the potential positive effect of blueberry polyphenol extract (PPE) on high-fat diet (HFD)-induced obesity in C57BL/6 J mice by modulation of the gut microbiota. Four-week-old C57BL/6 J mice were fed a normal-fat diet or HFD with or without PPE or Orlistat for 12 weeks. Mice fed HFD exhibited increased body weight and adipose tissue weight and disordered lipid metabolism. In contrast, PPE inhibited body weight gain and returned lipid metabolism to normal. Furthermore, 16S rRNA gene sequencing of the fecal microbiota suggested that PPE changed the composition of the gut microbiota in C57BL/6 J mice and modulated specific bacteria such as Proteobacteria, Deferribacteres, Actinobacteria, Bifidobacterium, Desulfovibrio, Adlercreutzia, Helicobacter, Flexispira, and Prevotella. Orlistat also improved obesity and metabolic alterations of HFD mice and modulated the composition of the gut microbiota. Our findings suggest that PPE, as a potential prebiotic agent, influences the gut microbiota to positively affect HFD-induced obesity in C57BL/6 J mice.
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
A SIMPLE METHOD FOR THE ISOLATION AND PURIFICATION OF TOTAL LIPIDES FROM ANIMAL TISSUES
Folch, J., Lees, M., & Stanley, G. H. S. (1957) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(18)64849-5 · Cited by 65,189
Metagenomic biomarker discovery and explanation
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011) · Genome Biology · doi:10.1186/gb-2011-12-6-r60 · Cited by 16,192
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
Self-citationDietary Modulation of the Human Colonic Microbiota: Introducing the Concept of Prebiotics
Gibson, G. R., & Roberfroid, M. B. (1995) · The Journal of Nutrition · doi:10.1093/jn/125.6.1401 · Cited by 7,667
Dose translation from animal to human studies revisited.
Reagan‐Shaw, S., Nihal, M., & Ahmad, N. (2008) · The FASEB Journal · doi:10.1096/fj.07-9574lsf · Cited by 6,524
Linking long-term dietary patterns with gut microbial enterotypes
Wu, G. D., Chen, J., Hoffmann, C., Bittinger, K., Chen, Y.-Y., Keilbaugh, S. A., Bewtra, M., Knights, D., Walters, W. A., Knight, R., et al. (2011) · Science · doi:10.1126/science.1208344 · Cited by 6,513
Gibson, G. R., Hutkins, R., Sanders, M. E., Prescott, S. L., Reimer, R. A., Salminen, S. J., Scott, K., Stanton, C., Swanson, K. S., Cani, P. D., et al. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.75 · Cited by 5,634
Host-gut microbiota metabolic interactions
Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., & Pettersson, S. (2012) · Science · doi:10.1126/science.1223813 · Cited by 4,760
Richness of human gut microbiome correlates with metabolic markers
Le Chatelier, E., Nielsen, T., Qin, J., Prifti, E., Hildebrand, F., Falony, G., Almeida, M., Arumugam, M., Batto, J.-M., Kennedy, S., et al. (2013) · Nature · doi:10.1038/nature12506 · Cited by 4,662
Proteobacteria: Microbial signature of dysbiosis in gut microbiota
Shin, N.-R., Whon, T. W., & Bae, J.-W. (2015) · Trends in Biotechnology · doi:10.1016/j.tibtech.2015.06.011 · Cited by 3,954
Cited by
Effects of Synbiotic Supplement on Human Gut Microbiota, Body Composition and Weight Loss in Obesity
Sergeev, I. N., Aljutaily, T., Walton, G., & Huarte, E. (2020) · Nutrients · doi:10.3390/nu12010222 · Cited by 331
Hong, Y., Sheng, L., Zhong, J., Tao, X., Zhu, W., Ma, J., Yan, J., Zhao, A., Zheng, X., Wu, G., et al. (2021) · Gut Microbes · doi:10.1080/19490976.2021.1930874 · Cited by 280
Self-citationTreatment strategies against diabetes: Success so far and challenges ahead.
Khursheed, R., Singh, S. K., Wadhwa, S., Kapoor, B., Gulati, M., Kumar, R., Ramanunny, A. K., Awasthi, A., & Dua, K. (2019) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2019.172625 · Cited by 239
Sun, Y., Geng, W., Pan, Y., Wang, J., Xiao, P., & Wang, Y. (2019) · Food & Function · doi:10.1039/c8fo02096e · Cited by 89
Self-citationGut microbiome approaches to treat obesity in humans
Bianchi, F., Duque, A. L. R. F., Saad, S. M. I., & Sivieri, K. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-018-9570-8 · Cited by 63
Metadata
- Authors
- Jiao, X., Wang, Y., Lin, Y., Lang, Y., Li, E., Zhang, X., Zhang, Q., Feng, Y., Meng, X., & Li, B.
- Venue
- The Journal of Nutritional Biochemistry
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 64
- Pages
- 88-100
- ISSN
- 0955-2863 (ISSN-L)
- OpenAlex
- W2885374912
- Cited by
- 312
- References
- 70
- FWCI
- 10.83
- Type
- journal article
- Language
- en
- Open access
- closed
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: Blueberry polyphenols extract as a potential prebiotic with anti-obesity effects on C57BL/6 J mice by modulating the gut microbiota. https://app.cochat.ai/papers/10.1016/j.jnutbio.2018.07.008
- The paper
- Jiao, X., Wang, Y., Lin, Y., Lang, Y., Li, E., Zhang, X., Zhang, Q., Feng, Y., Meng, X., & Li, B. (2019). Blueberry polyphenols extract as a potential prebiotic with anti-obesity effects on C57BL/6 J mice by modulating the gut microbiota. The Journal of Nutritional Biochemistry. https://doi.org/10.1016/j.jnutbio.2018.07.008
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.
