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Adipocytokines in obesity and metabolic disease
Open accessAbstract
The current global obesity pandemic is the leading cause for the soaring rates of metabolic diseases, especially diabetes, cardiovascular disease, hypertension, and non-alcoholic hepatosteatosis. Efforts devoted to find cures for obesity and associated disorders in the past two decades have prompted intensive interest in adipocyte biology, and have led to major advances in the mechanistic understanding of adipose tissue as an essential endocrine organ. Adipose tissue secretes an array of hormones (adipokines) that signal key organs to maintain metabolic homeostasis, and their dysfunction has been causally linked to a wide range of metabolic diseases. In addition, obesity induces production of inflammatory cytokines (often referred to together with adipokines as adipocytokines) and infiltration of immune cells into adipose tissue, which creates a state of chronic low-grade inflammation. Metabolic inflammation has been increasingly recognized as a unifying mechanism linking obesity to a broad spectrum of pathological conditions. This review focuses on classic examples of adipocytokines that have helped to form the basis of the endocrine and inflammatory roles of adipose tissue, and it also details a few newly characterized adipocytokines that provide fresh insights into adipose biology. Studies of adipocytokines in clinical settings and their therapeutic potential are also discussed.
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“By acting on white adipocyte mass, mitochondrial uncouplers could protect against diabetes, as limiting hyperaccumulation of triacyglycerol (TAG) in these cells might limit organelle stress [91,273,274,275] and modifications in the expression pattern of genes encoding adipokines, which are known to communicate the level of adiposity to other organs [221,276].”
From Mitochondrial Uncoupling: A Key Controller of Biological Processes in Physiology and Diseases (2019) · cites this paper as [221]
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Особливості ураження нирок у хворих на цукровий діабет 2-го типу з ожирінням
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Metadata
- Authors
- Cao, H.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Cao, Haiming
- ORCID
- Cao, Haiming
- Venue
- Journal of Endocrinology
- Publisher
- Bioscientifica
- Published
- 2014-02
- Volume
- 220
- Issue
- 2
- Pages
- T47-T59
- ISSN
- 0022-0795 (ISSN-L)
- 1479-6805
- PMC
- PMC3887367
- OpenAlex
- W2159748100
- Cited by
- 705
- References
- 154
- Citations by year
- 2026: 19 · 2025: 28 · 2024: 42 · 2023: 57 · 2022: 66 · 2021: 80 · 2020: 83 · 2019: 58 · 2018: 67 · 2017: 53 · 2016: 63 · 2015: 61 · 2014: 27
- FWCI
- 9.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Endocrinology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (HL006103-02, ZIAHL006103, ZIAHL006159)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
- Health Sciences › Medicine › Physiology (medical) › Adipose Tissue and Metabolism
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Disease and Adiposity
- Keywords
- adipokines, obesity, metabolic disorders, adipose tissue endocrine function, metabolic inflammation, low-grade inflammation, adipocyte biology, inflammatory cytokines, immune cell infiltration, diabetes mellitus, cardiovascular disease, hypertension, non-alcoholic fatty liver disease, adipose tissue hormones, therapeutic target
- MeSH terms
- Adipose Tissue, Animals, Endocrine System, Humans, Inflammation, Metabolic Diseases, Obesity, Adipokines
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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.
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