Fructose toxicity: is the science ready for public health actions?

PURPOSE OF REVIEW: The assumption that fructose may be toxic and involved in the pathogenesis of noncommunicable diseases such as obesity, diabetes mellitus, dyslipidemia, and even cancer has resulted in the call for public health action, such as introducing taxes on sweetened beverages. This review evaluates the scientific basis for such action. RECENT FINDINGS: Although some studies hint towards some potential adverse effects of excessive fructose consumption especially when combined with excess energy intake, the results from clinical trials do not support a significant detrimental effect of fructose on metabolic health when consumed as part of a weight-maintaining diet in amounts consistent with the average-estimated fructose consumption in Western countries. However, definitive studies are missing. SUMMARY: Public health policies to eliminate or limit fructose in the diet should be considered premature. Instead, efforts should be made to promote a healthy lifestyle that includes physical activity and nutritious foods while avoiding intake of excess calories until solid evidence to support action against fructose is available. Public health is almost certainly to benefit more from policies that are aimed at promoting what is known to be good than from policies that are prohibiting what is not (yet) known to be bad

Strain and structure driven complex magnetic ordering of a CoO overlayer on Ir(100)

We have investigated the magnetic ordering in the ultrathin c(10$\times$2) CoO(111) film supported on Ir(100) on the basis of ab-initio calculations. We find a close relationship between the local structural properties of the oxide film and the induced magnetic order, leading to alternating ferromagnetically and anti-ferromagnetically ordered segments. While the local magnetic order is directly related to the geometric position of the Co atoms, the mismatch between the CoO film and the Ir substrate leads to a complex long-range order of the oxide.Comment: 5 pages, 4 figure

New Zealand blackcurrant extract enhances fat oxidation during prolonged cycling in endurance-trained females.

PURPOSE: New Zealand blackcurrant (NZBC) extract has previously been shown to increase fat oxidation during prolonged exercise, but this observation is limited to males. We examined whether NZBC intake also increases fat oxidation during prolonged exercise in females, and whether this was related to greater concentrations of circulating fatty acids. METHODS: In a randomised, crossover, double-blind design, 16 endurance-trained females (age: 28 ± 8 years, BMI: 21.3 ± 2.1 kg·m-2, VO2max: 43.7 ± 1.1 ml·kg-1·min-1) ingested 600 mg·day-1NZBC extract (CurraNZ™) or placebo (600 mg·day-1microcrystalline cellulose) for 7 days. On day 7, participants performed 120 min cycling at 65% VO2max, using online expired air sampling with blood samples collected at baseline and at 15 min intervals throughout exercise for analysis of glucose, NEFA and glycerol. RESULTS: NZBC extract increased mean fat oxidation by 27% during 120 min moderate-intensity cycling compared to placebo (P = 0.042), and mean carbohydrate oxidation tended to be lower (P = 0.063). Pre-exercise, plasma NEFA (P = 0.034) and glycerol (P = 0.051) concentrations were greater following NZBC intake, although there was no difference between conditions in the exercise-induced increase in plasma NEFA and glycerol concentrations (P > 0.05). Mean fat oxidation during exercise was moderately associated with pre-exercise plasma NEFA concentrations (r = 0.45, P = 0.016). CONCLUSIONS: Intake of NZBC extract for 7 days elevated resting concentrations of plasma NEFA and glycerol, indicative of higher lipolytic rates, and this may underpin the observed increase in fat oxidation during prolonged cycling in endurance-trained females

Toxicogenomic Analysis Suggests Chemical-Induced Sexual Dimorphism in the Expression of Metabolic Genes in Zebrafish Liver

10.1371/journal.pone.0051971PLoS ONE712

Association between maternal depression symptoms across the first eleven years of their child’s life and subsequent offspring suicidal ideation

Depression is common, especially in women of child-bearing age; prevalence estimates for this group range from 8% to 12%, and there is robust evidence that maternal depression is associated with mental health problems in offspring. Suicidal behaviour is a growing concern amongst young people and those exposed to maternal depression are likely to be especially at high risk. The aim of this study was to utilise a large, prospective population cohort to examine the relationship between depression symptom trajectories in mothers over the first eleven years of their child’s life and subsequent adolescent suicidal ideation. An additional aim was to test if associations were explained by maternal suicide attempt and offspring depressive disorder. Data were utilised from a population-based birth cohort: the Avon Longitudinal Study of Parents and Children. Maternal depression symptoms were assessed repeatedly from pregnancy to child age 11 years. Offspring suicidal ideation was assessed at age 16 years. Using multiple imputation, data for 10,559 families were analysed. Using latent class growth analysis, five distinct classes of maternal depression symptoms were identified (minimal, mild, increasing, sub-threshold, chronic-severe). The prevalence of past-year suicidal ideation at age 16 years was 15% (95% CI: 14-17%). Compared to offspring of mothers with minimal symptoms, the greatest risk of suicidal ideation was found for offspring of mothers with chronic-severe symptoms [OR 3.04 (95% CI 2.19,4.21)], with evidence for smaller increases in risk of suicidal ideation in offspring of mothers with sub-threshold, increasing and mild symptoms. These associations were not fully accounted for by maternal suicide attempt or offspring depression diagnosis. Twenty-six percent of non-depressed offspring of mothers with chronic-severe depression symptoms reported suicidal ideation. Risk for suicidal ideation should be considered in young people whose mothers have a history of sustained high levels of depression symptoms, even when the offspring themselves do not have a depression diagnosis

Random-phase approximation and its applications in computational chemistry and materials science

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of RPA, and its applications to realistic systems. With several illustrating applications, we discuss the implications of RPA for computational chemistry and materials science. The computational cost of RPA is also addressed which is critical for its widespread use in future applications. In addition, current correction schemes going beyond RPA and directions of further development will be discussed.Comment: 25 pages, 11 figures, published online in J. Mater. Sci. (2012

Estimation of metabolite networks with regard to a specific covariable: applications to plant and human data

In systems biology, where a main goal is acquiring knowledge of biological systems, one of the challenges is inferring biochemical interactions from different molecular entities such as metabolites. In this area, the metabolome possesses a unique place for reflecting “true exposure” by being sensitive to variation coming from genetics, time, and environmental stimuli. While influenced by many different reactions, often the research interest needs to be focused on variation coming from a certain source, i.e. a certain covariable Xm . Objective Here, we use network analysis methods to recover a set of metabolite relationships, by finding metabolites sharing a similar relation to Xm . Metabolite values are based on information coming from individuals’ Xm status which might interact with other covariables. Methods Alternative to using the original metabolite values, the total information is decomposed by utilizing a linear regression model and the part relevant to Xm is further used. For two datasets, two different network estimation methods are considered. The first is weighted gene co-expression network analysis based on correlation coefficients. The second method is graphical LASSO based on partial correlations. Results We observed that when using the parts related to the specific covariable of interest, resulting estimated networks display higher interconnectedness. Additionally, several groups of biologically associated metabolites (very large density lipoproteins, lipoproteins, etc.) were identified in the human data example. Conclusions This work demonstrates how information on the study design can be incorporated to estimate metabolite networks. As a result, sets of interconnected metabolites can be clustered together with respect to their relation to a covariable of interest