Modest doses of beta-glucan do not reduce concentrations of potentially atherogenic lipoproteins

BACKGROUND: In 1997, the US Food and Drug Administration passed a unique ruling that allowed oat bran to be registered as the first cholesterol-reducing food at a dosage of 3 g beta-glucan/d. OBJECTIVE: The effects of a low dose of oat bran in the background diet only were investigated in volunteers with mild-to-moderate hyperlipidemia. DESIGN: The study was a double-blind, placebo-controlled, randomized, parallel study. Sixty-two healthy men (n = 31) and women (n = 31) were randomly allocated to consume either 20 g oat bran concentrate (OBC; containing 3 g beta-glucan) or 20 g wheat bran (control) daily for 8 wk. Fasting blood samples were collected at weeks -1, 0, 4, 8, and 12. A subgroup (n = 17) was studied postprandially after consumption of 2 meals (containing no OBC or wheat bran) at baseline and after supplementation. Fasting plasma samples were analyzed for total cholesterol, HDL cholesterol, triacylglycerol, glucose, and insulin. LDL cholesterol was measured by using the Friedewald formula. The postprandial samples were anlayzed for triacylglycerol, glucose, and insulin. RESULTS: No significant difference was observed in fasting plasma cholesterol, LDL cholesterol, glucose, or insulin between the OBC and wheat-bran groups. HDL-cholesterol concentrations fell significantly from weeks 0 to 8 in the OBC group (P = 0.05). There was a significant increase in fasting glucose concentrations after both OBC (P = 0.03) and wheat-bran (P = 0.02) consumption. No significant difference was found between the OBC and wheat-bran groups in any of the postprandial variables measured. CONCLUSIONS: A low dosage of beta-glucan (3 g/d) did not significantly reduce total cholesterol or LDL cholesterol in volunteers with plasma cholesterol concentrations representative of a middle-aged UK population.

Variation in the FFAR1 gene modifies BMI, body composition and plasma lipids but not plasma insulin or Beta-cell function in overweight subjects

Background FFAR1 receptor is a long chain fatty acid G-protein coupled receptor which is expressed widely, but found in high density in the pancreas and central nervous system. It has been suggested that FFAR1 may play a role in insulin sensitivity, lipotoxicity and is associated with type 2 diabetes. Here we investigate the effect of three common SNPs of FFAR1 (rs2301151; rs16970264; rs1573611) on pancreatic function, BMI, body composition and plasma lipids. Methodology/Principal Findings For this enquiry we used the baseline RISCK data, which provides a cohort of overweight subjects at increased cardiometabolic risk with detailed phenotyping. The key findings were SNPs of the FFAR1 gene region were associated with differences in body composition and lipids, and the effects of the 3 SNPs combined were cumulative on BMI, body composition and total cholesterol. The effects on BMI and body fat were predominantly mediated by rs1573611 (1.06 kg/m2 higher (P = 0.009) BMI and 1.53% higher (P = 0.002) body fat per C allele). Differences in plasma lipids were also associated with the BMI-increasing allele of rs2301151 including higher total cholesterol (0.2 mmol/L per G allele, P = 0.01) and with the variant A allele of rs16970264 associated with lower total (0.3 mmol/L, P = 0.02) and LDL (0.2 mmol/L, P<0.05) cholesterol, but also with lower HDL-cholesterol (0.09 mmol/L, P<0.05) although the difference was not apparent when controlling for multiple testing. There were no statistically significant effects of the three SNPs on insulin sensitivity or beta cell function. However accumulated risk allele showed a lower beta cell function on increasing plasma fatty acids with a carbon chain greater than six. Conclusions/Significance Differences in body composition and lipids associated with common SNPs in the FFAR1 gene were apparently not mediated by changes in insulin sensitivity or beta-cell function.

Discounted cash flow: accounting for uncertainty

Valuation is the process of estimating price. The methods used to determine value attempt to model the thought processes of the market and thus estimate price by reference to observed historic data. This can be done using either an explicit model, that models the worth calculation of the most likely bidder, or an implicit model, that that uses historic data suitably adjusted as a short cut to determine value by reference to previous similar sales. The former is generally referred to as the Discounted Cash Flow (DCF) model and the latter as the capitalisation (or All Risk Yield) model. However, regardless of the technique used, the valuation will be affected by uncertainties. Uncertainty in the comparable data available; uncertainty in the current and future market conditions and uncertainty in the specific inputs for the subject property. These input uncertainties will translate into an uncertainty with the output figure, the estimate of price. In a previous paper, we have considered the way in which uncertainty is allowed for in the capitalisation model in the UK. In this paper, we extend the analysis to look at the way in which uncertainty can be incorporated into the explicit DCF model. This is done by recognising that the input variables are uncertain and will have a probability distribution pertaining to each of them. Thus buy utilising a probability-based valuation model (using Crystal Ball) it is possible to incorporate uncertainty into the analysis and address the shortcomings of the current model. Although the capitalisation model is discussed, the paper concentrates upon the application of Crystal Ball to the Discounted Cash Flow approach.