| Literature DB >> 28684692 |
Xiao-Fei Guo1, Xin Li2, Meiqi Shi3, Duo Li4,5.
Abstract
The associations between n-3 polyunsaturated fatty acids (PUFAs) and metabolic syndrome (MetS) risk have demonstrated inconsistent results. The present study aimed to investigate whether higher circulating n-3 PUFAs and dietary n-3 PUFAs intake have a protective effect on MetS risk. A systematic literature search in the PubMed, Scopus, and Chinese National Knowledge Infrastructure (CNKI) databases was conducted up to March 2017. Odd ratios (ORs) from case-control and cross-sectional studies were combined using a random-effects model for the highest versus lowest category. The differences of n-3 PUFAs between healthy subjects and patients with MetS were calculated as weighted mean difference (WMD) by using a random-effects model. Seven case-control and 20 cross-sectional studies were included. A higher plasma/serum n-3 PUFAs was associated with a lower MetS risk (Pooled OR = 0.63, 95% CI: 0.49, 0.81). The plasma/serum n-3 PUFAs in controls was significantly higher than cases (WMD: 0.24; 95% CI: 0.04, 0.43), especially docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA). However, no significant association was found between dietary intake of n-3 PUFAs or fish and MetS risk. The present study provides substantial evidence of a higher circulating n-3 PUFAs associated with a lower MetS risk. The circulating n-3 PUFAs can be regarded as biomarkers indicating MetS risk, especially DPA and DHA.Entities:
Keywords: docosahexaenoic acid; docosapentaenoic acid; meta-analysis; metabolic syndrome; n-3 polyunsaturated fatty acids
Mesh:
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Year: 2017 PMID: 28684692 PMCID: PMC5537818 DOI: 10.3390/nu9070703
Source DB: PubMed Journal: Nutrients ISSN: 2072-6643 Impact factor: 5.717
Figure 1Flow chart of the study selection process.
Figure 2Forest plot to quantify the association of circulating n-3 PUFAs or EPA with metabolic syndrome risk. The pooled ORs were calculated by using a random-effects model for the highest versus lowest category. The diamonds denote summary risk estimate, and horizontal lines represent 95% CI. Abbreviations: EPA, eicosapentaenoic acid; PUFA, polyunsaturated fatty acid; OR, odd ratio.
Figure 3Forest plot to quantify the association of circulating DPA or DHA with metabolic syndrome risk. The pooled ORs were calculated by using a random-effects model for the highest versus lowest category. The diamonds denote summary risk estimate, and horizontal lines represent 95% CI. Abbreviations: DHA, docosahexaenoic acid; DPA, docosapentaenoic acid; OR, odd ratio.
Figure 4Differences of circulating n-3 PUFAs or EPA composition between cases and controls. The pooled effect was calculated by using a random-effects model. The diamonds denote summary risk estimate, and horizontal lines represent 95% CI. Abbreviations: EPA, eicosapentaenoic acid; PUFA, polyunsaturated fatty acid.
Figure 5Differences of circulating DPA or DHA composition between cases and controls. The pooled effect was calculated by using a random-effects model. The diamonds denote summary risk estimate, and horizontal lines represent 95% CI. Abbreviations: DHA, docosahexaenoic acid; DPA, docosapentaenoic acid.
Figure 6The underlying mechanisms of n-3 PUFAs protecting metabolic syndrome. Dysfunctions of lipid metabolism and inflammation contribute to metabolic syndrome. The high lipolytic rate in visceral adipose provides the liver with large amounts of FFAs. Impaired fat oxidation stimulates fatty acid esterification into TG, together with an augmented synthesis of Apo B, cholesterol and the secretion of VLDL. Moreover. FFAs may result in the activation of TLR4 pathways. Fet-A functions as an adaptor between FFAs and TLR4 signaling in lipid-induced inflammation. FFAs stimulate TLR-4 signaling by binding Fet-A, which then binds TLR-4. JNK, IKK, and PKR play important roles in upregulating the transcription factors (including AP-1, NF-κB, and IRF), resulting in the production of inflammatory cytokines. Moreover, these kinases can inhibit insulin signaling via serine phosphorylation of IRS-1. n-3 PUFAs modulate lipid and lipoprotein metabolism. Reduced VLDL production in the liver largely results from decreased availability of FFAs released from adipose stores, together with suppression of lipogenic genes and induction of genes involved in fatty acid oxidation. Inhibition of FFAs released from visceral adipose tissue due to a higher circulating n-3 PUFAs concentration, the TLR-4/MyD88 signaling pathway would be suppressed. Accordingly, the expression levels of PKR, IKK, JNK are inhibited. Finally, the release of inflammatory cytokines from adipocytes will be decreased. PPAR are transcription factors and regulate gene expression. PPAR are activated by non-covalent binding of ligands, such as n-3 PUFAs and eicosanoid mediators. Through activation of PPAR, n-3 PUFAs are able to regulate metabolism and other cell and tissue responses, including adipocyte differentiation and inflammation. Activation of GPR120 by n-3 FUFAs through binding β-arrestin 2 and TAB1 could inhibit pro-inflammatory pathways. Abbreviations: PUFA, polyunsaturated fatty acid; FFAs, free fatty acids; Apo B, apolipoprotein B; IDL, intermediate-density lipoprotein; LPL, lipoprotein lipase; TAG, triacylglycerol; FA, fatty acid; Fet-A, Fetuin-A; TLR-4, toll-like receptor 4; AP-1, activator protein-1; IKK, inhibitor of NF-κB kinase; IRF, interferon regulatory factor; NF-κB, nuclear factor-κB; IRS-1, insulin receptor substrate 1; JNK, c-jun N-terminal kinase; PKR, protein kinase R; TRIF, TIR domain-containing adapter-inducing interferon-β; MyD88, myeloid differentiation factor 88. TAK1, transforming growth factor-activated kinase 1; TAB1, transforming growth factor-β activated kinase 1 binding protein 1; PPAR, peroxisome proliferator-activated receptor; GPR120, G-protein-coupled receptor 120.