Objective The aim of this study was to test associations of prepregnancy BMI, gestational weight gain, oral glucose challenge test results, and postpartum weight loss as predictors of breast milk leptin, insulin, and adiponectin concentrations and whether these relationships vary over time. Methods Milk was collected at 1 and 3 months from 135 exclusively breastfeeding women from the longitudinal Mothers and Infants Linked for Healthy Growth (MILk) study. Hormones were assayed in skimmed samples using ELISA. Mixed‐effects linear regression models were employed to assess main effects and effect‐by‐time interactions on hormone concentrations. Results In adjusted models, BMI was positively associated with milk leptin (P < 0.001) and insulin (P = 0.03) and negatively associated with milk adiponectin (P = 0.02); however, the association was stronger with insulin and weaker with adiponectin at 3 months than at 1 month (time interaction P = 0.017 for insulin and P = 0.045 for adiponectin). Gestational weight gain was positively associated and postpartum weight loss was negatively associated with milk leptin (both P < 0.001), independent of BMI. Oral glucose challenge test results were not associated with these milk hormone concentrations. Conclusions Maternal weight status before, during, and after pregnancy contributes to interindividual variation in human milk composition. Continuing work will assess the role of these and other milk bioactive factors in altering infant metabolic outcomes.

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Recent national reports call for increasing the quantitative acumen of biology students. The BioSQuaRE represents an assessment tool based on such reports. The iterative development of the instrument by science and mathematics faculty in collaboration with educational psychologists is described, and the tool’s psychometric properties are summarized.

SERJ has provided a high quality professional publication venue for researchers in statistics education for close to a decade. This paper presents a review of the articles published to explore what they suggest about the field of statistics education, the researchers, the questions addressed, and the growing knowledge base on teaching and learning statistics. We present a detailed analysis of these articles in order to address the following questions: What is being published and why, who is publishing research in SERJ, how is the research being carried out, and what do the results suggest about future research? Implications for future directions in statistics education research are suggested. First published November 2011 at Statistics Education Research Journal: Archives