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Background: A lack of safety data on postpartum medication use presents a potential barrier to breastfeeding and may result in infant exposure to medications in breastmilk. The type and extent of medication use by lactating women requires investigation. Methods: Data were collected from the CHILD Cohort Study which enrolled pregnant women across Canada between 2008 and 2012. Participants completed questionnaires regarding medications and non-prescription medications used and breastfeeding status at 3, 6 and 12 months postpartum. Medications, along with self-reported reasons for medication use, were categorized by ontologies [hierarchical controlled vocabulary] as part of a large-scale curation effort to enable more robust investigations of reasons for medication use. Results: A total of 3542 mother-infant dyads were recruited to the CHILD study. Breastfeeding rates were 87.4%, 75.3%, 45.5% at 3, 6 and 12 months respectively. About 40% of women who were breastfeeding at 3 months used at least one prescription medication during the first three months postpartum; this proportion decreased over time to 29.5% % at 6 months and 32.8% at 12 months. The most commonly used prescription medication by breastfeeding women was domperidone at 3 months (9.0%, n = 229/2540) and 6 months (5.6%, n = 109/1948), and norethisterone at 12 months (4.1%, n = 48/1180). The vast majority of domperidone use by breastfeeding women (97.3%) was for lactation purposes which is off-label (signifying unapproved use of an approved medication). Non-prescription medications were more often used among breastfeeding than non-breastfeeding women (67.6% versus 48.9% at 3 months, p < 0.0001), The most commonly used non-prescription medications were multivitamins and Vitamin D at 3, 6 and 12 months postpartum. Conclusions: In Canada, medication use is common postpartum; 40% of breastfeeding women use prescription medications in the first 3 months postpartum. A diverse range of medications were used, with many women taking more than one prescription and non-prescription medicines. The most commonly used prescription medication by breastfeeding women were domperidone for off-label lactation support, signalling a need for more data on the efficacy of domperidone for this indication. This data should inform research priorities and communication strategies developed to optimize care during lactation. [ABSTRACT FROM AUTHOR]

Background: The gut microbiome undergoes primary ecological succession over the course of early life before achieving ecosystem stability around 3 years of age. These maturational patterns have been well-characterized for bacteria, but limited descriptions exist for other microbiota members, such as fungi. Further, our current understanding of the prevalence of different patterns of bacterial and fungal microbiome maturation and how inter-kingdom dynamics influence early-life microbiome establishment is limited. Results: We examined individual shifts in bacterial and fungal alpha diversity from 3 to 12 months of age in 100 infants from the CHILD Cohort Study. We identified divergent patterns of gut bacterial or fungal microbiome maturation in over 40% of infants, which were characterized by differences in community composition, inter-kingdom dynamics, and microbe-derived metabolites in urine, suggestive of alterations in the timing of ecosystem transitions. Known microbiome-modifying factors, such as formula feeding and delivery by C-section, were associated with atypical bacterial, but not fungal, microbiome maturation patterns. Instead, fungal microbiome maturation was influenced by prenatal exposure to artificially sweetened beverages and the bacterial microbiome, emphasizing the importance of inter-kingdom dynamics in early-life colonization patterns. Conclusions: These findings highlight the ecological and environmental factors underlying atypical patterns of microbiome maturation in infants, and the need to incorporate multi-kingdom and individual-level perspectives in microbiome research to improve our understandings of gut microbiome maturation patterns in early life and how they relate to host health. 5AtmDGbifCkZAT-p1B-BuF Video Abstract [ABSTRACT FROM AUTHOR]

Background: The infant gut is rapidly colonized by microorganisms soon after birth, and the composition of the microbiota is dynamic in the first year of life. Although a stable microbiome may not be established until 1 to 3 years after birth, the infant gut microbiota appears to be an important predictor of health outcomes in later life. Methods: We obtained stool at one year of age from 173 white Caucasian and 182 South Asian infants from two Canadian birth cohorts to gain insight into how maternal and early infancy exposures influence the development of the gut microbiota. We investigated whether the infant gut microbiota differed by ethnicity (referring to groups of people who have certain racial, cultural, religious, or other traits in common) and by breastfeeding status, while accounting for variations in maternal and infant exposures (such as maternal antibiotic use, gestational diabetes, vegetarianism, infant milk diet, time of introduction of solid food, infant birth weight, and weight gain in the first year). Results: We demonstrate that ethnicity and infant feeding practices independently influence the infant gut microbiome at 1 year, and that ethnic differences can be mapped to alpha diversity as well as a higher abundance of lactic acid bacteria in South Asians and a higher abundance of genera within the order Clostridiales in white Caucasians. Conclusions: The infant gut microbiome is influenced by ethnicity and breastfeeding in the first year of life. Ethnic differences in the gut microbiome may reflect maternal/infant dietary differences and whether these differences are associated with future cardiometabolic outcomes can only be determined after prospective follow-up. [ABSTRACT FROM AUTHOR]

Background: Asthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma., Methods: Clinically relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using BART., Results: BART, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at 3 years., Conclusions: BART demonstrated promising prediction performance when compared to other machine learning algorithms. Future research could validate the BART in an external cohort to evaluate its reliability and generalizability., (© 2024. The Author(s).)

Objective: Breastfeeding is associated with reduced postpartum depression, stronger parent-child relationships, and fewer behavioral disorders in early childhood. We tested the mediating roles of postpartum depression and parent-child relationship in the association between breastfeeding practices and child behavior., Study Design: We used standardized questionnaire data from a subset of the CHILD Cohort Study (n = 1,573) to measure postpartum depression at 6 months, 1 year and 2 years, parent-child relationship 1 year and 2 years, and child behavior at 5 years using the Child Behavior Checklist (range 0-100). Breastfeeding practices were measured at 3 months (none, partial, some expressed, all direct at the breast), 6 months (none, partial, exclusive), 12 months, and 24 months (no, yes). Confounders included birth factors, maternal characteristics, and socioeconomic status., Results: Breast milk feeding at 3 or 6 months was associated with - 1.13 (95% CI: -2.19-0.07) to -2.14 (95% CI: -3.46, -0.81) lower (better) child behavior scores. Reduced postpartum depression at 6 months mediated between 11.5% and 16.6% of the relationship between exclusive breast milk feeding at 3 months and better child behavior scores. Together, reduced postpartum depression at 1 year and reduced parent-child dysfunction at 2 years mediated between 21.9% and 32.1% of the relationship between breastfeeding at 12 months and better child behavior scores., Conclusion: Postpartum depression and parent-child relationship quality partially mediate the relationship between breastfeeding practices and child behavior. Breastfeeding, as well as efforts to support parental mental health and parent-child relationships, may help to improve child behavior., (© 2024. The Author(s).)