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Sick fat: the good and the bad of old and new circulating markers of adipose tissue inflammation
Abstract
Adipose tissue (AT) is one of the largest endocrine organs contributing to metabolic homeostasis. The functional pleiotropism of AT depends on its ability to secrete a large number of hormones, cytokines, extracellular matrix proteins and growth factors, all influencing many local and systemic physiological and pathophysiological processes. In condition of chronic positive energy balance, adipocyte expansion, hypoxia, apoptosis and stress all lead to AT inflammation and dysfunction, and it has been demonstrated that this sick fat is a main risk factor for many metabolic disorders, such as type 2 diabetes mellitus, fatty liver, cardiovascular disease and cancer. AT dysfunction is tightly associated with aberrant secretion of bioactive peptides, the adipocytokines, and their blood concentrations often reflect the expression in the AT. Despite the existence of an association between AT dysfunction and systemic pro-inflammatory state, most of the circulating molecules detectable in obese and dysmetabolic individuals do not identify specifically the condition of sick fat. Based on this premise, this review provides a concise overview of "classic" and novel promising adipocytokines associated with AT inflammation and discusses possible critical approaches to their interpretation in clinical practice.
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“Beyond storage of lipids, adipose tissue acts as an endocrine organ that secretes adipokines and chemokines which modulate a wide array of metabolic function [32].”
From Antibody blockade of activin type II receptors preserves skeletal muscle mass and enhances fat loss during GLP-1 receptor agonism (2024) · cites this paper as [32]
In the citing paper
“These include several molecules that correlate directly with adipose tissue mass and can act as biological markers of inflammation and metabolic dysfunction [[32], [33], [34], [35]].”
From Antibody blockade of activin type II receptors preserves skeletal muscle mass and enhances fat loss during GLP-1 receptor agonism (2024) · cites this paper as [32]
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Metadata
- Authors
- Barchetta, I., Cimini, F. A., Ciccarelli, G., Baroni, M. G., & Cavallo, M. G.
- Affiliations
- Sapienza University of Rome (IT)
- Corresponding author
- Baroni, M. G.
- Cavallo, M. G.
- Venue
- Journal of Endocrinological Investigation
- Published
- 2019-05-09
- Volume
- 42
- Issue
- 11
- Pages
- 1257-1272
- ISSN
- 0391-4097 (ISSN-L)
- 1720-8386
- OpenAlex
- W2944626658
- Cited by
- 77
- References
- 239
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 5 · 2023: 18 · 2022: 9 · 2021: 13 · 2020: 17 · 2019: 3
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Endocrinological Investigation (publishedVersion)
- PubMed (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Archivio Istituzionale della Ricerca - Università degli Studi dell Aquila (submittedVersion)
- Funders
- Sapienza Università di Roma (RM1161550687D69, RM11715C3FCB9431)
- 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
- adipose tissue inflammation, adipokines, circulating biomarkers, adipose tissue dysfunction, obesity, type 2 diabetes, hepatic steatosis, cardiovascular disease, cancer, metabolic disorders, adipokine secretion
- MeSH terms
- Adipose Tissue, Animals, Humans, Inflammation, Biomarkers, Intra-Abdominal Fat
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.
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