The impact of oligofructose on stimulation of gut hormones, appetite regulation and adiposity

Objective: To investigate the effect of nutrient stimulation of gut hormones by oligofructose supplementation on appetite, energy intake (EI), body weight (BW) and adiposity in overweight and obese volunteers. Methods: In a parallel, single-blind and placebo-controlled study, 22 healthy overweight and obese volunteers were randomly allocated to receive 30 g day−1 oligofructose or cellulose for 6 weeks following a 2-week run-in. Subjective appetite and side effect scores, breath hydrogen, serum short chain fatty acids (SCFAs), plasma gut hormones, glucose and insulin concentrations, EI, BW and adiposity were quantified at baseline and post-supplementation. Results: Oligofructose increased breath hydrogen (P < 0.0001), late acetate concentrations (P = 0.024), tended to increase total area under the curve (tAUC)420mins peptide YY (PYY) (P = 0.056) and reduced tAUC450mins hunger (P = 0.034) and motivation to eat (P = 0.013) when compared with cellulose. However, there was no significant difference between the groups in other parameters although within group analyses showed an increase in glucagon-like peptide 1 (GLP-1) (P = 0.006) in the cellulose group and a decrease in EI during ad libitum meal in both groups. Conclusions: Oligofructose increased plasma PYY concentrations and suppressed appetite, while cellulose increased GLP-1 concentrations. EI decreased in both groups. However, these positive effects did not translate into changes in BW or adiposity.

Post awakening salivary cortisol secretion and trait well-being: The importance of sample timing accuracy

Indices of post awakening cortisol secretion (PACS), include the rise in cortisol(cortisol awakening response: CAR) and overall cortisol concentrations (e.g. area under the curve with reference to ground: AUCg) in the first 30—45 min. Both are commonly investigated in relation to psychosocial variables. Although sampling within the domestic setting is ecologically valid, participant non-adherence to the required timing protocol results in erroneous measurement of PACS and this may explain discrepancies in the literature linking these measures to trait well-being (TWB). We have previously shown that delays of little over 5 min(between awakening and the start of sampling) to result in erroneous CAR estimates. In this study, we report for the first time on the negative impact of sample timing inaccuracy (verified by electronic-monitoring) on the efficacy to detect significant relationships between PACS and TWB when measured in the domestic setting.Healthy females (N = 49, 20.5 ± 2.8 years) selected for differences in TWB collected saliva samples (S1—4) on 4 days at 0, 15, 30, 45 min post awakening, to determine PACS. Adherence to the sampling protocol was objectively monitored using a combination of electronic estimates of awakening (actigraphy) and sampling times (track caps).Relationships between PACS and TWB were found to depend on sample timing accuracy. Lower TWB was associated with higher post awakening cortisol AUCg in proportion to the mean sample timing accuracy (p < .005). There was no association between TWB and the CAR even taking into account sample timing accuracy. These results highlight the importance of careful electronic monitoring of participant adherence for measurement of PACS in the domestic setting. Mean sample timing inaccuracy, mainly associated with delays of >5 min between awakening and collection of sample 1 (median = 8 min delay), negatively impacts on the sensitivity of analysis to detect associations between PACS and TWB.

Housing at the neighbourhood level: a review of the initial approaches to neighbourhood development plans under the Localism Act 2011 in England

The Localism Act 2011 created an opportunity for local communities to form neighbourhood forums and to prepare their own neighbourhood development plans in urban and rural areas in England. Initial reactions suggested that, rather than leading to the development of more housing, these initiatives would confirm all the stereotypes of local residents blocking unwanted development in their defined neighbourhoods. However, neighbourhood plans need to be in general conformity with the core strategies of higher-tier plans and often make provision for more new homes than planned before 2011. This article discusses the role and purpose of neighbourhood plans, the evidence base on which they are founded and some of the legal challenges which have helped clarify procedures. It then identifies two types of plan based on the ways housing strategies and evidence of need are reflected in a sample of 10 plans which have been made to date. It concludes that the voluntary nature of localism to date tends to favour more rural and affluent areas and ends with an assessment of the impact of neighbourhood plans on the planning process. It suggests that the implications for spatial planning may be far-reaching.

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.