This article examines the association of iron deficiency (ID) and iron deficiency anemia (IDA) with children's development and behavior, with the goal of providing recommendations to prevent the developmental loss associated with these conditions. Children's risk for ID and IDA is particularly high during the second 6 months of life when prenatal stores are depleted. Longitudinal studies from infancy through adolescence and early adulthood suggest that socioemotional development is uniquely vulnerable to ID and IDA, perhaps being associated with shared neural pathways, and the effects of early iron deficiencies may be irreversible. In addition to direct effects on brain function, ID and IDA may also affect child development indirectly through non-responsive mother-child interactions. Maternal ID is a global problem that may contribute to high rates of maternal depression and non-responsive caregiving. Intervention trials illustrate that children benefit from both nutritional intervention and early learning interventions that promote responsive mother-child interactions. Recommendations to reduce the developmental loss associated with ID and IDA are to reduce the incidence of these conditions by efforts to prevent premature birth, delay cord clamping, ensure adequate maternal iron status, provide iron-rich complementary foods, and ensure access to postnatal interventions that promote responsive mother-infant interaction patterns and early learning opportunities for infants.
Retinol (vitamin A) is thought to exert its effects through the actions of its metabolite, all-trans-retinoic acid (ATRA), on gene transcription mediated by retinoic acid receptors (RAR) and retinoic acid response elements (RARE). However, retinoic acid resistance limits the chemotherapeutic potential of ATRA. We examined the ability of retinol to inhibit the growth of ATRA-sensitive (HCT-15) and ATRA-resistant (HCT-116, SW620, and WiDR) human colon cancer cell lines. Retinol inhibited cell growth in a dose-responsive manner. Retinol was not metabolized to ATRA or any bioactive retinoid in two of the cell lines examined. HCT-116 and WiDR cells converted a small amount of retinol to ATRA; however, this amount of ATRA was unable to inhibit cell growth. To show that retinol was not inducing RARE-mediated transcription, each cell line was transfected with pRARE-chloramphenicol acetyltransferase (CAT) and treated with ATRA and retinol. Although treatment with ATRA increased CAT activity 5-fold in ATRAsensitive cells, retinol treatment did not increase CAT activity in any cell line examined. To show that growth inhibition due to retinol was ATRA, RAR, and RARE independent, a pan-RAR antagonist was used to block RAR signaling. Retinol-induced growth inhibition was not alleviated by the RAR antagonist in any cell line, but the antagonist alleviated ATRA-induced growth inhibition of HCT-15 cells. Retinol did not induce apoptosis, differentiation or necrosis, but affected cell cycle progression. Our data show that retinol acts through a novel, RAR-independent mechanism to inhibit colon cancer cell growth. (Cancer Res 2005; 65(21): 9923-33)
Objective Current methods for measuring regional body fat are expensive and inconvenient compared to the relative cost-effectiveness and ease-of-use of a stereovision body imaging (SBI) system. The primary goal of this research is to develop prediction models for android and gynoid fat by body measurements assessed via SBI and dual-energy x-ray absorptiometry (DXA). Subsequently, mathematical equations for prediction of total and regional (trunk, leg) body adiposity were established via parameters measured by SBI and DXA. Methods A total of 121 participants were randomly assigned into primary and cross-validation groups. Body measurements were obtained via traditional anthropometrics, SBI, and DXA. Multiple regression analysis was conducted to develop mathematical equations by demographics and SBI assessed body measurements as independent variables and body adiposity (fat mass and percent fat) as dependent variables. The validity of the prediction models was evaluated by a split sample method and Bland-Altman analysis. Results The R2 of the prediction equations for fat mass and percent body fat were 93.2% and 76.4% for android, and 91.4% and 66.5% for gynoid, respectively. The limits of agreement for the fat mass and percent fat were − 0.06 ± 0.87 kg and − 0.11 ± 1.97 % for android and − 0.04 ± 1.58 kg and − 0.19 ± 4.27 % for gynoid. Prediction values for fat mass and percent fat were 94.6% and 88.9% for total body, 93.9% and 71.0% for trunk, and 92.4% and 64.1% for leg, respectively. Conclusions The three-dimensional (3D) SBI produces reliable parameters that can predict android and gynoid, as well as total and regional (trunk, leg) fat mass.
Objective Abdominal visceral adiposity is related to risks for insulin resistance and metabolic perturbations. Magnetic resonance imaging (MRI) and computed tomography are advanced instruments that quantify abdominal adiposity; yet field use is constrained by their bulkiness and costliness. The purpose of this study is to develop prediction equations for total abdominal, subcutaneous, and visceral adiposity via anthropometrics, stereovision body imaging (SBI), and MRI. Design and Methods Participants (67 men and 55 women) were measured for anthropometrics, and abdominal adiposity volumes evaluated by MRI umbilicus scans. Body circumferences and central obesity were obtained via SBI. Prediction models were developed via multiple linear regression analysis, utilizing body measurements and demographics as independent predictors, and abdominal adiposity as a dependent variable. Cross-validation was performed by the data-splitting method. Results The final total abdominal adiposity prediction equation was –470.28+7.10waist circumference–91.01gender+5.74sagittal diameter (R²=89.9%); subcutaneous adiposity was –172.37+8.57waist circumference–62.65gender–450.16stereovision waist-to-hip ratio (R²=90.4%); and visceral adiposity was –96.76+11.48central obesity depth–5.09 central obesity width+204.74stereovision waist-to-hip ratio–18.59gender (R²=71.7%). R² significantly improved for predicting visceral fat when SBI variables were included, but not for total abdominal or subcutaneous adiposity. Conclusions SBI is effective for predicting visceral adiposity and the prediction equations derived from SBI measurements can assess obesity.
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