The interplay between dietary fatty acids and gut microbiota influences host metabolism and hepatic steatosis.

Dietary lipids can affect metabolic health through gut microbiota-mediated mechanisms, but the influence of lipid-microbiota interaction on liver steatosis is largely unknown. We investigate the impact of dietary lipids on human gut microbiota composition and the effects of microbiota-lipid interactions on steatosis in male mice. In humans, low intake of saturated fatty acids (SFA) is associated with increased microbial diversity independent of fiber intake. In mice, poorly absorbed dietary long-chain SFA, particularly stearic acid, induce a shift in bile acid profile and improved metabolism and steatosis. These benefits are dependent on the gut microbiota, as they are transmitted by microbial transfer. Diets enriched in polyunsaturated fatty acids are protective against steatosis but have minor influence on the microbiota. In summary, we find that diets enriched in poorly absorbed long-chain SFA modulate gut microbiota profiles independent of fiber intake, and this interaction is relevant to improve metabolism and decrease liver steatosis.

The Longitudinal Changes in Subcutaneous Abdominal Tissue and Visceral Adipose Tissue Volumetries Are Associated with Iron Status.

Excess iron is known to trigger adipose tissue dysfunction and insulin resistance. Circulating markers of iron status have been associated with obesity and adipose tissue in cross-sectional studies. We aimed to evaluate whether iron status is linked to changes in abdominal adipose tissue longitudinally. Subcutaneous abdominal tissue (SAT) and visceral adipose tissue (VAT) and its quotient (pSAT) were assessed using magnetic resonance imaging (MRI), at baseline and after one year of follow-up, in 131 (79 in follow-up) apparently healthy subjects, with and without obesity. Insulin sensitivity (euglycemic- hyperinsulinemic clamp) and markers of iron status were also evaluated. Baseline serum hepcidin (p = 0.005 and p = 0.002) and ferritin (p = 0.02 and p = 0.01)) were associated with an increase in VAT and SAT over one year in all subjects, while serum transferrin (p = 0.01 and p = 0.03) and total iron-binding capacity (p = 0.02 and p = 0.04) were negatively associated. These associations were mainly observed in women and in subjects without obesity, and were independent of insulin sensitivity. After controlling for age and sex, serum hepcidin was significantly associated with changes in subcutaneous abdominal tissue index (iSAT) (ß = 0.406, p = 0.007) and visceral adipose tissue index (iVAT) (ß = 0.306, p = 0.04), while changes in insulin sensitivity (ß = 0.287, p = 0.03) and fasting triglycerides (ß = -0.285, p = 0.03) were associated with changes in pSAT. These data indicated that serum hepcidin are associated with longitudinal changes in SAT and VAT, independently of insulin sensitivity. This would be the first prospective study evaluating the redistribution of fat according to iron status and chronic inflammation.

Higher agreement in endovascular treatment decision-making than in parametric quantifications among automated CT perfusion software packages in acute ischemic stroke.

BACKGROUND AND OBJECTIVE: Estimates of parameters used to select patients for endovascular thrombectomy (EVT) for acute ischemic stroke differ among software packages for automated computed tomography (CT) perfusion analysis. To determine impact of these differences in decision making, we analyzed intra-observer and inter-observer agreement in recommendations about whether to perform EVT based on perfusion maps from 4 packages. METHODS: Perfusion CT datasets from 63 consecutive patients with suspected acute ischemic stroke were retrospectively postprocessed with 4 packages of Minerva, RAPID, Olea, and IntelliSpace Portal (ISP). We used Pearson correlation coefficients and Bland-Altman analysis to compare volumes of infarct core, penumbra, and mismatch calculated by Minerva and RAPID. We used kappa analysis to assess agreement among decisions of 3 radiologists about whether to recommend EVT based on maps generated by 4 packages. RESULTS: We found significant differences between using Minerva and RAPID to estimate penumbra (67.39±41.37mL vs. 78.35±45.38 mL, p < 0.001) and mismatch (48.41±32.03 vs. 61.27±32.73mL, p < 0.001), but not of infarct core (p = 0.230). Pearson correlation coefficients were 0.94 (95%CI:0.90-0.96) for infarct core, 0.87 (95%CI:0.79-0.91) for penumbra, and 0.72 (95%CI:0.57-0.83) for mismatch volumes (p < 0.001). Limits of agreements were (-21.22-25.02) for infarct core volumes, (-54.79-32.88) for penumbra volumes, and (-60.16-34.45) for mismatch volumes. Final agreement for EVT decision-making was substantial between Minerva vs. RAPID (k = 0.722), Minerva vs. Olea (k = 0.761), and RAPID vs. Olea (k = 0.782), but moderate for ISP vs. the other three. CONCLUSIONS: Despite quantitative differences in estimates of infarct core, penumbra, and mismatch using 4 software packages, their impact on radiologists' decisions about EVT is relatively small.