Determinants of selenium status in healthy adults

<p>Abstract</p> <p>Background</p> <p>Selenium (Se) status in non-deficient subjects is typically assessed by the Se contents of plasma/serum. That pool comprises two functional, specific selenoprotein components and at least one non-functional, non-specific components which respond differently to changes in Se intake. A more informative means of characterizing Se status in non-deficient individuals is needed.</p> <p>Methods</p> <p>Multiple biomarkers of Se status (plasma Se, serum selenoprotein P [SEPP1], plasma glutathione peroxidase activity [GPX3], buccal cell Se, urinary Se) were evaluated in relation to selenoprotein genotypes (GPX1, GPX3, SEPP1, SEP15), dietary Se intake, and parameters of single-carbon metabolism in a cohort of healthy, non-Se-deficient men (n = 106) and women (n = 155).</p> <p>Conclusions</p> <p>Plasma Se concentration was 142.0 ± 23.5 ng/ml, with GPX3 and serum-derived SEPP1 calculated to comprise 20% and 34%, respectively, of that total. The balance, comprised of non-specific components, accounted for virtually all of the interindividual variation in total plasma Se. Buccal cell Se was associated with age and plasma homocysteine (hCys), but not plasma Se. SEPP1 showed a quadratic relationship with body mass index, peaking at BMI 25-30. Urinary Se was greater in women than men, and was associated with metabolic body weight (kg^0.75), plasma folate, vitamin B₁₂and hCys (negatively). One <it>GPX1 </it>genotype (679T/T) was associated with significantly lower plasma Se levels than other allelic variants. Selenium intake, estimated from food frequency questionnaires, did not predict Se status as indicated by any biomarker. These results show that genotype, methyl-group status and BMI contribute to variation in Se biomarkers in Se-adequate individuals.</p

Penalised regression splines: theory and application to medical research

Generalised additive models (GAMs) allow for flexible functional dependence of a response variable on covariates. The aim of this article is to provide an accessible overview of GAMs based on the penalised likelihood approach with regression splines. In contrast to the classical backfitting, the penalised likelihood framework taken here provides researchers with an efficient computational method for automatic multiple smoothing parameter selection, which can determine the functional form of any relationship from the data. We illustrate through an example how the use of this methodology can help to gain insights into medical research

Meat Consumption is Associated with Less Stunting among Toddlers in Four Diverse Low-Income Settings

BACKGROUND: Early growth faltering is common but is difficult to reverse after the first 2 years of life. OBJECTIVE: To describe feeding practices and growth in infants and young children in diverse low-income settings prior to undertaking a complementary feeding trial. METHODS: This cross-sectional study was conducted through the Global Network for Women’s and Children’s Health Research in Guatemala, Democratic Republic of Congo, Zambia, and Pakistan. Feeding questionnaires were administered to convenience samples of mothers of 5- to 9-month old infants and 12- to 24-month-old toddlers. After standardized training, anthropometric measurements were obtained from the toddlers. Following the 2006 World Health Organization Growth Standards, stunting was defined as length-for-age < −2SD, and wasting as weight-for-length < −2SD. Logistic regression was applied to evaluate relationships between stunting and wasting and consumption of meat (including chicken and liver and not including fish). RESULTS: Data were obtained from 1,500 infants with a mean (± SD) age of 6.9 ± 1.4 months and 1,658 toddlers with a mean age of 17.2 ± 3.5 months. The majority of the subjects in both age groups were breastfed. Less than 25% of the infants received meat regularly, whereas 62% of toddlers consumed these foods regularly, although the rates varied widely among sites. Stunting rate ranged from 44% to 66% among sites; wasting prevalence was less than 10% at all sites. After controlling for covariates, consumption of meat was associated with a reduced likelihood of stunting (OR = 0.64; 95% CI, 0.46 to 0.90). CONCLUSIONS: The strikingly high stunting rates in these toddlers and the protective effect of meat consumption against stunting emphasize the need for interventions to improve complementary feeding practices, beginning in infancy