A randomized trial of selenium supplementation and risk of type-2 diabetes, as assessed by plasma adiponectin

Background: Evidence that selenium affects the risk of type-2 diabetes is conflicting, with observational studies and a few randomized trials showing both lower and higher risk linked to the level of selenium intake and status. We investigated the effect of selenium supplementation on the risk of type-2 diabetes in a population of relatively low selenium status as part of the UK PRECISE (PREvention of Cancer by Intervention with SElenium) pilot study. Plasma adiponectin concentration, a recognised independent predictor of type-2 diabetes risk and known to be correlated with circulating selenoprotein P, was the biomarker chosen. Methods: In a randomized, double-blind, placebo-controlled trial, five hundred and one elderly volunteers were randomly assigned to a six-month intervention with 100, 200 or 300 μg selenium/d as high-selenium or placebo yeast. Adiponectin concentration was measured by ELISA at baseline and after six months of treatment in 473 participants with one or both plasma samples available. Results: Mean (SD) plasma selenium concentration was 88.5 ng/g (19.1) at baseline and increased significantly in the selenium-treatment groups. In baseline cross-sectional analyses, the fully adjusted geometric mean of plasma adiponectin was 14% lower (95% CI, 0-27%) in the highest than in the lowest quartile of plasma selenium (P for linear trend = 0.04). In analyses across randomized groups, however, selenium supplementation had no effect on adiponectin levels after six months of treatment (P = 0.96). Conclusions: These findings are reassuring as they did not show a diabetogenic effect of a six-month supplementation with selenium in this sample of elderly individuals of relatively low selenium status

High selenium intake and increased diabetes risk: experimental evidence for interplay between selenium and carbohydrate metabolism

The essential trace element selenium has long been considered to exhibit anti-diabetic and insulin-mimetic properties, but recent epidemiological studies indicated supranutritional selenium intake and high plasma selenium levels as possible risk factors for development of type 2 diabetes, pointing to adverse effects of selenium on carbohydrate metabolism in humans. However, increased plasma selenium levels might be both a consequence and a cause of diabetes. We summarize current evidence for an interference of selenium compounds with insulin-regulated molecular pathways, most notably the phosphoinositide-3-kinase/protein kinase B signaling cascade, which may underlie some of the pro- and anti-diabetic actions of selenium. Furthermore, we discuss reports of hyperinsulinemia, hyperglycemia and insulin resistance in mice overexpressing the selenoenzyme glutathione peroxidase 1. The peroxisomal proliferator-activated receptor gamma coactivator 1α represents a key regulator for biosynthesis of the physiological selenium transporter, selenoprotein P, as well as for hepatic gluconeogenesis. As proliferator-activated receptor gamma coactivator 1α has been shown to be up-regulated in livers of diabetic animals and to promote insulin resistance, we hypothesize that dysregulated pathways in carbohydrate metabolism and a disturbance of selenium homeostasis are linked via proliferator-activated receptor gamma coactivator 1α

Boosting and lassoing new prostate cancer SNP risk factors and their connection to selenium

We begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors using lasso or boosting variable selection. We then illustrate the use of the protocol with a set of prostate cancer data and show that it recovers known risk factors. Finally, we use the protocol to identify new and important SNP based risk factors for prostate cancer and further seek evidence for or against the hypothesis of an anticancer function for Selenium in prostate cancer. We find that the anticancer effect may depend on the SNP-SNP interaction and, in particular, which alleles are present

Association between maternal vitamin D status in pregnancy and neurodevelopmental outcomes in childhood:results from the Avon Longitudinal Study of Parents and Children (ALSPAC)

Seafood intake in pregnancy has been positively associated with childhood cognitive outcomes which could potentially relate to the high vitamin-D content of oily fish. However, whether higher maternal vitamin D status [serum 25-hydroxy-vitamin D, 25(OH)D] in pregnancy is associated with a reduced risk of offspring suboptimal neurodevelopmental outcomes is unclear. A total of 7065 mother-child pairs were studied from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort who had data for both serum total 25(OH)D concentration in pregnancy and at least one measure of offspring neurodevelopment (pre-school development at 6–42 months; “Strengths and Difficulties Questionnaire” scores at 7 years; IQ at 8 years; reading ability at 9 years). After adjustment for confounders, children of vitamin-D deficient mothers (< 50.0 nmol/L) were more likely to have scores in the lowest quartile for gross motor development at 30 months (OR 1.20 95% CI 1.03, 1.40), fine motor development at 30 months (OR 1.23 95% CI 1.05, 1.44), and social development at 42 months (OR 1.20 95% CI 1.01, 1.41) than vitamin-D sufficient mothers (≥ 50.0 nmol/L). No associations were found with neurodevelopmental outcomes, including IQ, measured at older ages. However, our results suggest that deficient maternal vitamin D status in pregnancy may have adverse effects on some measures of motor and social development in children under 4 years. Prevention of vitamin D deficiency may be important for preventing suboptimal development in the first 4 years of life

Boosting and lassoing new prostate cancer SNP risk factors and their connection to selenium

We begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors using lasso or boosting variable selection. We then illustrate the use of the protocol with a set of prostate cancer data and show that it recovers known risk factors. Finally, we use the protocol to identify new and important SNP based risk factors for prostate cancer and further seek evidence for or against the hypothesis of an anticancer function for Selenium in prostate cancer. We find that the anticancer effect may depend on the SNP-SNP interaction and, in particular, which alleles are present