Omega-3 polyunsaturated fatty acids favourably modulate cardiometabolic biomarkers in type 2 diabetes: a meta-analysis and meta-regression of randomized controlled trials

BACKGROUND: Randomized controlled trials (RCTs) suggest that supplementation with omega-3 polyunsaturated fatty acids (n-3PUFAs) may favourably modify cardiometabolic biomarkers in type 2 diabetes (T2DM). Previous meta-analyses are limited by insufficient sample sizes and omission of meta-regression techniques, and a large number of RCTs have subsequently been published since the last comprehensive meta-analysis. Updated information regarding the impact of dosage, duration or an interaction between these two factors is therefore warranted. The objective was to comprehensively assess the effect of n-3PUFAs supplementation on cardiometabolic biomarkers including lipid profiles, inflammatory parameters, blood pressure, and indices of glycaemic control, in people with T2DM, and identify whether treatment dosage, duration or an interaction thereof modify these effects. METHODS: Databases including PubMed and MEDLINE were searched until 13th July 2017 for RCTs investigating the effect of n-3PUFAs supplementation on lipid profiles, inflammatory parameters, blood pressure, and indices of glycaemic control. Data were pooled using random-effects meta-analysis and presented as standardised mean difference (Hedges g) with 95% confidence intervals (95% CI). Meta-regression analysis was performed to investigate the effects of duration of supplementation and total dosage of n-3PUFAs as moderator variables where appropriate. RESULTS: A total of 45 RCTs were identified, involving 2674 people with T2DM. n-3PUFAs supplementation was associated with significant reductions in LDL [ES: - 0.10, (95% CI - 0.17, - 0.03); p = 0.007], VLDL (ES: - 0.26 (- 0.51, - 0.01); p = 0.044], triglycerides (ES: - 0.39 (- 0.55, - 0.24; p ≤ 0.001] and HbA1c (ES: - 0.27 (- 0.48, - 0.06); p = 0.010]. Moreover, n-3PUFAs supplementation was associated with reduction in plasma levels of TNF-α [ES: - 0.59 (- 1.17, - 0.01); p = 0.045] and IL-6 (ES: - 1.67 (- 3.14, - 0.20); p = 0.026]. All other lipid markers, indices of glycaemic control, inflammatory parameters, and blood pressure remained unchanged (p > 0.05). CONCLUSIONS: n-3PUFAs supplementation produces favourable hypolipidemic effects, a reduction in pro-inflammatory cytokine levels and improvement in glycaemia. Neither duration nor dosage appear to explain the observed heterogeneity in response to n-3PUFAs. Trial registration This trial was registered at http://www.crd.york.ac.uk as CRD42016050802

Heme oxygenase-1 regulates cell proliferation via carbon monoxide-mediated inhibition of T-type Ca2+ channels

Induction of the antioxidant enzyme heme oxygenase-1 (HO-1) affords cellular protection and suppresses proliferation of vascular smooth muscle cells (VSMCs) associated with a variety of pathological cardiovascular conditions including myocardial infarction and vascular injury. However, the underlying mechanisms are not fully understood. Over-expression of Cav3.2 T-type Ca2+ channels in HEK293 cells raised basal [Ca2+]i and increased proliferation as compared with non-transfected cells. Proliferation and [Ca2+]i levels were reduced to levels seen in non-transfected cells either by induction of HO-1 or exposure of cells to the HO-1 product, carbon monoxide (CO) (applied as the CO releasing molecule, CORM-3). In the aortic VSMC line A7r5, proliferation was also inhibited by induction of HO-1 or by exposure of cells to CO, and patch-clamp recordings indicated that CO inhibited T-type (as well as L-type) Ca2+ currents in these cells. Finally, in human saphenous vein smooth muscle cells, proliferation was reduced by T-type channel inhibition or by HO-1 induction or CO exposure. The effects of T-type channel blockade and HO-1 induction were non-additive. Collectively, these data indicate that HO-1 regulates proliferation via CO-mediated inhibition of T-type Ca2+ channels. This signalling pathway provides a novel means by which proliferation of VSMCs (and other cells) may be regulated therapeutically

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Allele-specific Expression Reveals Interactions Between Genetic Variation And Environment

Identifying interactions between genetics and the environment (GxE) remains challenging. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on associations between environmental variables and allele-specific expression. Combining whole-blood RNA-seq with extensive environmental annotations collected from 922 human individuals, we identified 35 GxE interactions, compared with only four using standard GxE interaction testing. EAGLE provides new opportunities for researchers to identify GxE interactions using functional genomic data