Interrelationships among metabolic syndrome, bone-derived cytokines, and the most common metabolic syndrome-related diseases negatively affecting bone quality

Abstract

Metabolic syndrome (MetS), as a set of medical conditions including hyperglycemia, hypertension, abdominal obesity, and dyslipidemia, represents a highly prevalent disease cluster worldwide. The individual components of MetS together increase the risk of MetS-related disorders. Recent research has demonstrated that bone, as an endocrine organ, releases several systemic cytokines (osteokines), including fibroblast growth factor 23 (FGF23), lipocalin 2 (LCN2), and sclerostin (SCL). This review not only summarizes current knowledge about MetS, osteokines and the most common MetS-related diseases with a detrimental impact on bone quality (type 2 diabetes mellitus: T2DM; cardiovascular diseases: CVDs; osteoporosis: OP), but also provides new interpretations of the relationships between osteokines and individual components of MetS, as well as between osteokines and MetS-related diseases mentioned above. In this context, particular emphasis was given on available clinical studies. According to the latest knowledge, FGF23 may become a useful biomarker for obesity, T2DM, and CVDs, as FGF23 levels were increased in patients suffering from these diseases. LCN2 could serve as an indicator of obesity, dyslipidemia, T2DM, and CVDs. The levels of LCN2 positively correlated with obesity indicators, triglycerides, and negatively correlated with high-density lipoprotein (HDL) cholesterol. Furthermore, subjects with T2DM and CVDs had higher LCN2 levels. SCL may act as a potential biomarker predicting the incidence of MetS including all its components, T2DM, CVDs, and OP. Elevated SCL levels were noted in individuals with T2DM, CVDs and reduced in patients with OP. The aforementioned bone-derived cytokines have the potential to serve as promising predictors and prospective treatment targets for MetS and MetS-related diseases negatively affecting bone quality.

Keywords: Bone health; Cardiovascular diseases; Fibroblast growth factor 23; Lipocalin 2; Metabolic syndrome; Osteoporosis; Sclerostin; Type 2 diabetes mellitus.

Fig. 1

Individual components of MetS including visceral obesity, low serum high-density lipoprotein (HDL) cholesterol, hyperglycemia, high serum triglycerides (TGs), and hypertension affect bone health through different mechanisms. Visceral fat releases adipokines and pro-inflammatory cytokines, including tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), interleukin 1β (IL-1β), which stimulate osteoclast differentiation through activation of receptor activator of NF-κB ligand (RANKL)/receptor activator of NF-κB (RANK)/osteoprotegerin (OPG) pathway. Obesity also depletes mesenchymal stem cells (MSC) for adipocyte formation at the expense of osteoblasts. Obese individuals exert elevated leptin and decreased adiponectin levels, and higher oxidative stress, which ultimately suppress osteoblastogenesis and stimulate osteoclastogenesis. Low HDL is correlated with increased levels of oxidative stress and oxidized lipids that stimulate adipocyte differentiation while suppressing osteoblast differentiation through upregulation of peroxisome proliferator-activated receptor γ (PPARγ). In addition, low HDL stimulates osteogenic activity in vascular cells. Elevated TGs, usually present with low HDL cholesterol, can induce endothelial cell dysfunction and potentiate atherogenic changes. Hyperglycemia causes enhanced inflammatory response, accumulation of advanced glycation end products (AGEs), and disturbances in calcium (Ca) metabolism, favoring bone resorption over bone formation. Arterial hypertension affects bone mainly through increased excretion of Ca in the urine, resulting in activation of parathyroid hormone (PTH), thereby raising bone resorption. Finally, multiple mechanisms involving visceral fat accumulation, alterations in lipid profile and blood pressure are correlated with lower serum osteocalcin levels, suggesting reduced bone formation (Created with BioRender.com)

Fig. 2

Human FGF23 structure prediction according to AlphaFold Protein Structure Database (RRID: SCR_023662; [264, 265]). 3D visualization of FGF23 structure prediction with colored per-residue confidence metric (pLDDT) is shown. The structures of the signal sequence and N-terminal peptide demonstrate the highest confidence score. Positions of glycosylation, phosphorylation, and disulfide bond on FGF23 molecule are illustrated according to the database UniProt (RRID: SCR_002380). Binding regions for α-Klotho are labeled according to Suzuki et al. [266]

Fig. 3

Human LCN2 structure prediction according to AlphaFold Protein Structure Database (RRID: SCR_023662; [264, 265]). 3D visualization of LCN2 structure prediction with colored per-residue confidence metric (pLDDT) is shown. Positions of glycosylation, modified residue, and disulfide bond on LCN2 molecule are illustrated according to the database UniProt (RRID: SCR_002380)

Fig. 4

Human SCL structure prediction according to AlphaFold Protein Structure Database (RRID: SCR_023662; [264, 265]). 3D visualization of SCL structure prediction with colored per-residue confidence metric (pLDDT) is shown. Positions of glycosylation and disulfide bonds on the SCL molecule are illustrated according to the database UniProt (RRID: SCR_002380)