Assessment of Vitamin D status and association with inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project.

BACKGROUND: It is unclear whether 25(OH)D concentrations in children and female adults may be influenced by inflammation and thus require adjustment when estimating the population prevalence of vitamin D deficiency. OBJECTIVES: We examined correlations between inflammation biomarkers, CRP or alpha-1-acid glycoprotein (AGP), and serum 25(OH)D concentrations among preschool children (PSC; 6-59 mo) and nonpregnant females of reproductive age (FRA; 15-49 y). METHODS: We analyzed cross-sectional data from 6 nationally representative nutrition surveys (Afghanistan, Cambodia, Pakistan, UK, USA, and Vietnam) conducted among PSC (n = 9880) and FRA (n = 14,749) from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia project. Rank correlations between CRP or AGP and 25(OH)D concentrations were examined while taking into account complex survey design effects. RESULTS: Among both PSC and FRA, correlations between inflammation and vitamin D biomarkers were weak and inconsistent across surveys. For PSC, correlation coefficients between CRP and 25(OH)D concentrations ranged from -0.04 to 0.08, and correlations between AGP and 25(OH)D ranged from 0.01 to 0.05. Correlation coefficients between CRP and 25(OH)D for FRA ranged from -0.11 to 0.14, and correlations between AGP and 25(OH)D concentrations ranged from -0.05 to 0.01. CONCLUSIONS: Based on the weak and inconsistent correlations between CRP or AGP and 25(OH)D, there is no rationale to adjust for these inflammation biomarkers when estimating population prevalence of vitamin D deficiency in PSC or FRA.

AI-PLAX: AI-based placental assessment and examination using photos.

Post-delivery analysis of the placenta is useful for evaluating health risks of both the mother and baby. In the U.S., however, only about 20% of placentas are assessed by pathology exams, and placental data is often missed in pregnancy research because of the additional time, cost, and expertise needed. A computer-based tool that can be used in any delivery setting at the time of birth to provide an immediate and comprehensive placental assessment would have the potential to not only to improve health care, but also to radically improve medical knowledge. In this paper, we tackle the problem of automatic placental assessment and examination using photos. More concretely, we first address morphological characterization, which includes the tasks of placental image segmentation, umbilical cord insertion point localization, and maternal/fetal side classification. We also tackle clinically meaningful feature analysis of placentas, which comprises detection of retained placenta (i.e., incomplete placenta), umbilical cord knot, meconium, abruption, chorioamnionitis, and hypercoiled cord, and categorization of umbilical cord insertion type. We curated a dataset consisting of approximately 1300 placenta images taken at Northwestern Memorial Hospital, with hand-labeled pixel-level segmentation map, cord insertion point and other information extracted from the associated pathology reports. We developed the AI-based Placental Assessment and Examination system (AI-PLAX), which is a novel two-stage photograph-based pipeline for fully automated analysis. In the first stage, we use three encoder-decoder convolutional neural networks with a shared encoder to address morphological characterization tasks by employing a transfer-learning training strategy. In the second stage, we employ distinct sub-models to solve different feature analysis tasks by using both the photograph and the output of the first stage. We evaluated the effectiveness of our pipeline by using the curated dataset as well as the pathology reports in the medical record. Through extensive experiments, we demonstrate our system is able to produce accurate morphological characterization and very promising performance on aforementioned feature analysis tasks, all of which may possess clinical impact and contribute to future pregnancy research. This work is the first for comprehensive, automated, computer-based placental analysis and will serve as a launchpad for potentially multiple future innovations.

Setting research priorities on multiple micronutrient supplementation in pregnancy.

Prenatal micronutrient deficiencies are associated with negative maternal and birth outcomes. Multiple micronutrient supplementation (MMS) during pregnancy is a cost-effective intervention to reduce these adverse outcomes. However, important knowledge gaps remain in the implementation of MMS interventions. The Child Health and Nutrition Research Initiative (CHNRI) methodology was applied to inform the direction of research and investments needed to support the implementation of MMS interventions for pregnant women in low- and middle-income countries (LMIC). Following CHNRI methodology guidelines, a group of international experts in nutrition and maternal health provided and ranked the research questions that most urgently need to be resolved for prenatal MMS interventions to be successfully implemented. Seventy-three research questions were received, analyzed, and reorganized, resulting in 35 consolidated research questions. These were scored against four criteria, yielding a priority ranking where the top 10 research options focused on strategies to increase antenatal care attendance and MMS adherence, methods needed to identify populations more likely to benefit from MMS interventions and some discovery issues (e.g., potential benefit of extending MMS through lactation). This exercise prioritized 35 discrete research questions that merit serious consideration for the potential of MMS during pregnancy to be optimized in LMIC.

Nutrition knowledge predicts eating behavior of all food groups except fruits and vegetables among adults in the Paso del Norte region: Qué Sabrosa Vida.

OBJECTIVE: To examine the association between nutrition knowledge and eating behavior in a predominantly Mexican American population on the Texas-Mexico border. DESIGN: Cross-sectional using data from the baseline survey of the Qué Sabrosa Vida community nutrition initiative. SETTING: El Paso and surrounding counties in Texas. PARTICIPANTS: Data gathered in 2000 using random-digit dialing telephone survey. Response rate was 42.6% and final sample size was 963. MAIN OUTCOME MEASURES: Knowledge of recommended servings of food items was the independent variable and number of servings of food items consumed was the dependent variable. DATA ANALYSIS: Multiple logistic regression was used to examine the association between nutrition knowledge and eating behavior. RESULTS: 74% of the population was Mexican American. Nutrition knowledge was a significant predictor of eating behavior for grains (odds ratio [OR] = 6.42; 95% confidence interval [CI]: 2.4, 17.1), dairy (OR = 2.25; 95% CI: 1.5, 3.4), meats (OR = 2.02; 95% CI: 1.5, 2.8), beans (OR = 8.18; 95% CI: 5.1, 13.0), water (OR = 2.49; 95% CI: 1.7, 3.6), but not for fruits and (nonstarchy) vegetables (OR = 1.69; 95% CI: 0.89, 3.2). CONCLUSIONS AND IMPLICATIONS: Nutrition knowledge predicts eating behavior for all food groups except fruits and vegetables. The role of cultural factors in eating behavior should be investigated to elucidate this finding. Results have implications for developing nutrition education programs for Mexican Americans.