The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
The role of infiltrating immune cells in dysfunctional adipose tissue
Open accessAbstract
Adipose tissue (AT) dysfunction, characterized by loss of its homeostatic functions, is a hallmark of non-communicable diseases. It is characterized by chronic low-grade inflammation and is observed in obesity, metabolic disorders such as insulin resistance and diabetes. While classically it has been identified by increased cytokine or chemokine expression, such as increased MCP-1, RANTES, IL-6, interferon (IFN) gamma or TNFα, mechanistically, immune cell infiltration is a prominent feature of the dysfunctional AT. These immune cells include M1 and M2 macrophages, effector and memory T cells, IL-10 producing FoxP3+ T regulatory cells, natural killer and NKT cells and granulocytes. Immune composition varies, depending on the stage and the type of pathology. Infiltrating immune cells not only produce cytokines but also metalloproteinases, reactive oxygen species, and chemokines that participate in tissue remodelling, cell signalling, and regulation of immunity. The presence of inflammatory cells in AT affects adjacent tissues and organs. In blood vessels, perivascular AT inflammation leads to vascular remodelling, superoxide production, endothelial dysfunction with loss of nitric oxide (NO) bioavailability, contributing to vascular disease, atherosclerosis, and plaque instability. Dysfunctional AT also releases adipokines such as leptin, resistin, and visfatin that promote metabolic dysfunction, alter systemic homeostasis, sympathetic outflow, glucose handling, and insulin sensitivity. Anti-inflammatory and protective adiponectin is reduced. AT may also serve as an important reservoir and possible site of activation in autoimmune-mediated and inflammatory diseases. Thus, reciprocal regulation between immune cell infiltration and AT dysfunction is a promising future therapeutic target.
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
Obesity is associated with macrophage accumulation in adipose tissue
Weisberg, S. P., McCann, D., Desai, M., Rosenbaum, M., Leibel, R. L., & Ferrante, A. W. (2003) · Journal of Clinical Investigation · doi:10.1172/jci200319246 · Cited by 10,041
Bettelli, E., Carrier, Y., Gao, W., Korn, T., Strom, T. B., Oukka, M., Weiner, H. L., & Kuchroo, V. K. (2006) · Nature · doi:10.1038/nature04753 · Cited by 6,992
Xu, H., Barnes, G. T., Yang, Q., Tan, G., Yang, D., Chou, C. J., Sole, J., Nichols, A., Ross, J. S., Tartaglia, L. A., et al. (2003) · Journal of Clinical Investigation · doi:10.1172/jci200319451 · Cited by 6,760
Obesity induces a phenotypic switch in adipose tissue macrophage polarization
Lumeng, C. N., Bodzin, J. L., & Saltiel, A. R. (2007) · Journal of Clinical Investigation · doi:10.1172/jci29881 · Cited by 4,664
Adipokines in inflammation and metabolic disease
Ouchi, N., Parker, J. L., Lugus, J. J., & Walsh, K. (2011) · Nature Reviews Immunology · doi:10.1038/nri2921 · Cited by 4,625
TLR4 links innate immunity and fatty acid-induced insulin resistance
Shi, H., Kokoeva, M. V., Inouye, K., Tzameli, I., Yin, H., & Flier, J. S. (2006) · Journal of Clinical Investigation · doi:10.1172/jci28898 · Cited by 3,690
Functional Brown Adipose Tissue in Healthy Adults
Virtanen, K. A., Lidell, M. E., Orava, J., Heglind, M., Westergren, R., Niemi, T., Taittonen, M., Laine, J., Savisto, N.-J., Enerbäck, S., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808949 · Cited by 3,058
Feuerer, M., Herrero, L., Cipolletta, D., Naaz, A., Wong, J., Nayer, A., Lee, J., Goldfine, A. B., Benoist, C., Shoelson, S., et al. (2009) · Nature Medicine · doi:10.1038/nm.2002 · Cited by 2,145
Macrophage-specific PPARγ controls alternative activation and improves insulin resistance
Odegaard, J. I., Ricardo-Gonzalez, R. R., Goforth, M. H., Morel, C. R., Subramanian, V., Mukundan, L., Eagle, A. R., Vats, D., Brombacher, F., Ferrante, A. W., et al. (2007) · Nature · doi:10.1038/nature05894 · Cited by 2,107 · cited 1×
“type by regulating fatty acid storage and, in doing so, reduces obesity and improves insulin resistance (Figure 2).(119) While the metabolic state plays a role in macrophage recruitment and polarization, ATMs in turn have important effects on AT metabolism (Figure 2).”
BackgroundHuman epicardial adipose tissue is a source of inflammatory mediators
Mazurek, T., Zhang, L., Zalewski, A., Mannion, J. D., Diehl, J. T., Arafat, H., Sarov-Blat, L., O’Brien, S., Keiper, E. A., Johnson, A. G., et al. (2003) · Circulation · doi:10.1161/01.cir.0000099542.57313.c5 · Cited by 1,966
Cited by
Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms
Petrie, J. R., Guzik, T. J., & Touyz, R. M. (2018) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2017.12.005 · Cited by 1,787
Self-citationThe compelling link between physical activity and the body's defense system
Nieman, D. C., & Wentz, L. M. (2019) · Journal of Sport and Health Science · doi:10.1016/j.jshs.2018.09.009 · Cited by 1,340
Periodontitis is associated with hypertension: a systematic review and meta-analysis
Muñoz Aguilera, E., Suvan, J., Buti, J., Czesnikiewicz-Guzik, M., Barbosa Ribeiro, A., Orlandi, M., Guzik, T. J., Hingorani, A. D., Nart, J., & D’Aiuto, F. (2020) · Cardiovascular Research · doi:10.1093/cvr/cvz201 · Cited by 394
Self-citationEndotoxin-initiated inflammation reduces testosterone production in men of reproductive age
Tremellen, K., McPhee, N., Pearce, K., Benson, S., Schedlowski, M., & Engler, H. (2018) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00279.2017 · Cited by 98
Kahn, D., Macias, E., Zarini, S., Garfield, A., Zemski Berry, K., MacLean, P., Gerszten, R. E., Libby, A., Solt, C., Schoen, J., et al. (2022) · Endocrinology · doi:10.1210/endocr/bqac140 · Cited by 97
Noonan, J., Asiala, S. M., Grassia, G., MacRitchie, N., Gracie, K., Carson, J., Moores, M., Girolami, M., Bradshaw, A. C., Guzik, T. J., et al. (2018) · Theranostics · doi:10.7150/thno.28665 · Cited by 69
Self-citationCzesnikiewicz‐Guzik, M., Nosalski, R., Mikolajczyk, T. P., Vidler, F., Dohnal, T., Dembowska, E., Graham, D., Harrison, D. G., & Guzik, T. J. (2019) · British Journal of Pharmacology · doi:10.1111/bph.14536 · Cited by 61
Self-citationHow does adipose tissue contribute to inflammageing?
Zamboni, M., Nori, N., Brunelli, A., & Zoico, E. (2021) · Experimental Gerontology · doi:10.1016/j.exger.2020.111162 · Cited by 55
Sharma, S., Sridhar, S., McIntosh, A., Messow, C.-M., Aguilera, E. M., Del Pinto, R., Pietropaoli, D., Gorska, R., Siedlinski, M., Maffia, P., et al. (2021) · Pharmacological Research · doi:10.1016/j.phrs.2021.105511 · Cited by 53
Self-citationGuo, Y., Zhang, Q., Yang, D., Chen, P., & Xiao, W. (2024) · Biomolecules · doi:10.3390/biom14030246 · Cited by 19
Pilling, D., Cox, N., Thomson, M. A., Karhadkar, T. R., & Gomer, R. H. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.08.005 · Cited by 17
Metadata
- Authors
- Guzik, T. J., Skiba, D. S., Touyz, R. M., & Harrison, D. G.
- Venue
- Cardiovascular Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-07-01
- Volume
- 113
- Issue
- 9
- Pages
- 1009-1023
- ISSN
- 0008-6363 (ISSN-L)
- 1755-3245
- OpenAlex
- W2735267600
- Cited by
- 445
- References
- 32
- FWCI
- 28.01
- 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
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.
