Metabolic changes in response to varying whole-grain wheat and rye intake.

Epidemiological studies have shown associations between whole-grain intake and lowered disease risk. A sufficient level of whole-grain intake to reach the health benefits has not been established, and there is limited knowledge about the impact of whole-grain intake on metabolite levels. In this clinical intervention study, we aimed to identify plasma and urine metabolites associated with two different intake levels of whole-grain wheat and rye and to correlate them with clinical plasma biomarkers. Healthy volunteers (N = 68) were divided into two groups receiving either whole-grain wheat or whole-grain rye in two four-week interventions with 48 and 96 g/d of whole grains consumed. The metabolomics of the plasma samples was performed with UPLC-QTOF-MS. Plasma alkylresorcinols were quantified with GC-MS and plasma and urinary mammalian lignans with HPLC-ECD. The high-dose intervention impacted the metabolite profile, including microbial metabolites, more in the rye-enriched diet compared with wheat. Among the increased metabolites were alkylresorcinol glucuronides, sinapyl alcohol, and pipecolic acid betaine, while the decreased metabolites included acylcarnitines and ether lipids. Plasma alkylresorcinols, urinary enterolactone, and total mammalian lignans reflected the study diets in a dose-dependent manner. Several key metabolites linked with whole-grain consumption and gut microbial metabolism increased in a linear manner between the two interventions. The results reveal that an increase in whole-grain intake, particularly rye, is strongly reflected in the metabolite profile, is correlated with clinical variables, and suggests that a diet rich in whole grains promotes the growth and/or metabolism of microbes producing potentially beneficial microbial metabolites.

End-of-life care and advance care planning for outpatients with inoperable aortic aneurysms.

OBJECTIVE: A significant proportion of patients with abdominal and thoracic aortic aneurysms (AA) do not proceed to intervention after reaching treatment threshold diameter due to a combination of poor cardiovascular reserve, frailty, and aortic morphology. This patient cohort has a high mortality; however, until this study, there exist no studies on the end-of-life care conservatively managed patients receive. METHODS: This is a retrospective multicenter cohort study of 220 conservatively managed patients with AA referred to Leeds Vascular Institute (UK) and Maastricht University Medical Centre (the Netherlands) for intervention between 2017 and 2021. Demographic details, mortality, cause of death, advance care planning and palliative care outcomes were analysed to examine predictors of palliative care referral and efficacy of palliative care consultation. RESULTS: A total of 1506 patients with AA were seen over this time period, giving a nonintervention rate of 15%. There was a 3-year mortality rate of 55%, a median survival of 364 days, and rupture was the reported cause of death in 18% of the decedents. Median follow-up was 34 months. Only 8% of all patients and 16% of decedents received a palliative care consultation, which took place a median of 3.5 days before death. Patients >81 years of age were more likely to have advance care planning. Only 5% and 23% of conservatively managed patients had documentation of preferred place of death and care priorities respectively. Patients with a palliative care consultation were more likely to have these services in place. CONCLUSIONS: Only a small proportion of conservatively treated patients had advance care planning and this was far below international guidelines on end-of-life care for adults, which recommends it for each of these patients. Pathways and guidance should be implemented to ensure patients not offered AA intervention receive end-of-life care and advance care planning.

A novel Bayesian hierarchical model for road safety hotspot prediction.

In this paper, we propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of our model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. Our model specification enables the classical empirical Bayes formulation - commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period - to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify our uncertainty in these predictions. The main contributions of our model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our model. We conclude that our model accurately predicts future accident counts, with point estimates from the predictive distribution matching observed counts extremely well.

Linking road casualty and clinical data to assess the effectiveness of mobile safety enforcement cameras: a before and after study.

OBJECTIVES: To use police STATS19 road casualty data and accident and emergency and in-patient information to estimate the impact of mobile safety cameras on the cost of treating individuals injured in road traffic collisions. DESIGN: A data-matching and costing exercise to link casualty and clinical information in a 'before' and 'after' study of 56 mobile safety cameras. SETTING: The Northumbria Police Force area of the UK covering six local authority districts. PARTICIPANTS: Slight, serious and fatal casualties involved in road traffic collisions at mobile camera sites in the case-study area between April 2001-March 2003 and April 2004-March 2006. PRIMARY AND SECONDARY OUTCOME MEASURES: Changes in the number and severity of casualties at the mobile camera sites between the 'before' and 'after' period that can be attributed to mobile safety camera activity, and any impacts these changes had on the 'cost of treatment saved' by the secondary healthcare service in the case-study area. RESULTS: Using tariff values for accident and emergency and In-patient Health Resource Groups, the impacts of the cameras in terms of the 'cost of treatment saved' are in the range £12 500-£15 000 per annum. However, inconsistencies between databases resulted in approximately one-third of the casualties not being matched successfully in the clinical databases. The number of closed fractures requiring investigations, treatment and follow-up care reduced considerably, although this was offset by an increase in head injury contusions and open fractures that require high-cost investigations and extensive in-patient care. CONCLUSIONS: Road safety cameras could have a significant impact in terms of 'cost of treatment saved'. However, it is argued that investigating the impacts of road safety measures in the future should be based on Fully Bayesian techniques as they can produce more reliable estimates of the effects of regression to mean and general trends in casualty statistics.