Deborah Louise O'COnnor

The Canada Prenatal Nutrition Program (CPNP) is a federal initiative that addresses the critical impact of early nutrition and perinatal care on health outcomes by providing community‐based perinatal health and nutrition services to socially and economically vulnerable women and their infants. Using a flexible, locally adaptable community programming model, the CPNP aims to improve the health of women and their babies by promoting healthy birth weights and supporting breastfeeding; fostering community partnerships; and providing culturally sensitive support. To identify priorities for future research and outcome measurement within the program's monitoring and evaluation framework, we engaged CPNP personnel from 26 sites across the province of Ontario as expert knowledge holders in a Modified Delphi study. Among participating sites, 57% were hosted within a community‐based agency, 23% within an Indigenous‐led agency, and 20% within a public health unit. Participants ranked priorities based on what they believed best reflected CPNP clients' needs, contributed to evidence‐based program development, and aligned with the realities of community‐based service delivery. All priorities were viewed as important for programming. From the relative rankings, two research topics were highest‐ranked: assessing the impacts of the CPNP and conducting a needs assessment to identify community‐defined perinatal health needs. Maternal mental health was the highest‐ranked outcome measure, because of its perceived central role in supporting parent–infant health, well‐being, and social connectedness. Collectively, these applied findings offer strategic direction to advance the CPNP's adaptive program delivery model, and demonstrate the methodological value of engaging community knowledge holders to inform perinatal program delivery.

Understanding how human milk composition, particularly the milk microbiota, shapes infant gut microbial colonization and health is a rapidly evolving area of investigation. Normalized absolute abundance data is increasingly used in gut microbiome research to accurately reflect community dynamics and avoid compositional artifacts associated with relative abundance data. With relative abundance data, a change in one microbial taxa can significantly affect the apparent abundance of others, resulting in erroneous interpretation of findings. Here, we validated a method for quantifying normalized abundances from human milk 16S rRNA gene sequencing data using a spike-in control, a more efficient and cost-effective alternative to separately performing quantitative polymerase chain reaction. We compared both methods on 30 milk samples from 27 mothers and found no significant differences in community profiles in terms of diversity metrics and total microbial loads. These findings advance normalization strategies to more accurately reflect biological changes in the human milk microbiota.

Introduction: Mother’s milk is the gold standard for feeding newborns. Despite lactation support while in hospital, supplementation rates remain high in Canadian well-baby units at 35–50%. When supplementation is needed, the choice between formula milk and pasteurised human donor milk (donor milk) remains uncertain with a lack of clinical trials to inform this practice. This study aims to compare the effect of supplementing mother’s milk with donor milk versus formula in infants at higher risk for supplementation (infants of diabetic mothers, infants born small for gestational age or with a birth weight less than 2.5 kg and late preterm infants born between 35 ^0/7 and 36 ^6/7 weeks gestation). Methods and analysis: This is an ongoing, open-label, single-centre, randomised controlled trial conducted at Mount Sinai Hospital, Toronto, Canada. A total of 112 infants (56 per group) will be randomised to receive donor milk or infant formula as a supplement to mother’s milk during their initial hospital stay, when supplementation is deemed necessary by the family and/or healthcare team. The primary outcome is exclusive human milk feeding at 4 months of age. Secondary outcomes include any or exclusive human milk feeding at 1, 2 and 3 months; infant growth and health indicators and breastfeeding self-efficacy. Exploratory outcomes encompass infant temperament; parental mental health (assessed using the State-Trait Anxiety Inventory and Edinburgh Postnatal Depression Scale); milk cortisol concentrations; and informal milk sharing comparing donor milk and formula supplementation. Follow-up includes monthly telephone assessments and a virtual or in-person visit at 4 months post partum. Data will be analysed using intention-to-treat principles. Ethics and dissemination: The CanDo trial has received ethics approval from the Mount Sinai Hospital Research Ethics Board and the University of Toronto. Results will be disseminated through peer-reviewed journals, conference presentations and stakeholder engagement with hospital and public health decision-makers. Findings will address a critical evidence gap regarding the use of donor milk supplementation in well-baby units and may inform future clinical practice and policy in newborn feeding. Trial registration number: NCT06315127 .

Low glycemic-index foods may reduce postprandial oxidative stress by reducing postprandial glucose excursions, but the evidence for this is limited by dietary confounders. To determine whether reducing postprandial glucose per se reduces postprandial oxidative stress, overnight-fasted participants (BMI 25.0–39.9 kg/m², n = 18) consumed four test meals in random order: 75 g dextrose solution (Dex) within 5 min (bolus/noC), Dex slowly over 3.25 h (sipping/noC), bolus with 1 g vitamin C (bolus/C) and sipping with 1 g vitamin C (sipping/C). Venous blood was taken at intervals over 6 h; a standard lunch was consumed at 4 h. Sipping flattened postprandial glucose and insulin and reduced free fatty acid rebound compared to bolus (p < 0.05). Vitamin C raised serum vitamin C from ~20 to ~55 μmol/L. The total peroxyl radical trapping antioxidant potential (TRAP) increments differed after lunch, with a main effect of vitamin C at 5 h (mean ± SEM; C 70 ± 23 vs. noC −29 ± 27; p = 0.016) and main effects of rate (sipping 57 ± 25 vs. bolus −71 ± 28; p = 0.0002) and vitamin C (C 58 ± 25 vs. noC −73 ± 28; p = 0.0003) at 6 h. By multiple regression analysis, the TRAP area under the curve (AUC) was positively associated with the insulin AUC (p < 0.001) and negatively with the glucose and vitamin C AUCs (p < 0.05). The oxidized LDL increments were higher 6 h after sipping than bolus (7 ± 7 vs. −20 ± 7, p = 0.005). The oxidized LDL AUC was negatively associated with the TRAP AUC (p < 0.001). These results support the hypothesis that reducing postprandial glucose reduces postprandial oxidative stress.

Importance: Breastfeeding supports lifelong health, but socioeconomic and racial disparities persist. Biases in hospital formula supplementation practices may be an underlying contributor. Objective: To examine whether nonmedically indicated hospital formula supplementation of term-born breastfed newborns is associated with neighborhood socioeconomic status and/or maternal race. Design, Setting, and Participants: Provincial registry data were used to build a cohort of all live births of term-born singleton infants who initiated breastfeeding in Ontario, Canada, hospitals from April 1, 2015 through March 31, 2021, and for whom prenatal screening data were available. Of 570 936 eligible births, 148 888 were excluded, primarily due to missing outcome data, preterm birth, or not initiating breastfeeding. These data were analyzed from December 2023 through October 2025. Exposures: The 2 exposures were socioeconomic status, derived by linking maternal postal codes with 2021 Ontario Marginalization Index neighborhood-level quintiles for material resources, and maternal race (Asian, Black, White, or other [Indigenous, multiracial, or unknown race]), determined from prenatal screening data. Main Outcome and Measure: The primary outcome was nonmedically indicated formula supplementation, determined from hospital feeding records. Results: This cohort included 422 048 maternal-infant dyads, 28% of whom were in the Asian racial group, 7% in the Black racial group, 59% in the White racial group, and 5% in the other racial group. Overall, 27% of infants received nonmedically indicated formula supplementation, with an increase from 23% to 32% over the study period. Participants in the Asian, Black, and other racial groups were more likely than those in the White group to be in the most marginalized socioeconomic quintile (20%, 43%, and 23% vs 16%). Risk of nonmedically indicated formula supplementation increased in a gradient across quintiles of increasing socioeconomic marginalization (quintile 5 vs quintile 1: adjusted relative risk [aRR], 1.68; 95% CI, 1.64-1.72) and was significantly elevated for the Asian (aRR, 2.69; 95% CI, 2.64-2.74), Black (aRR, 2.07; 95% CI, 2.01-2.13), and other (aRR, 1.43; 95% CI, 1.39-1.48) racial groups compared with the White group. Conclusions and Relevance: In this population-level analysis, nonmedically indicated formula supplementation prevalence was high and increased over time, with elevated risk associated with socioeconomic marginalization and maternal racialization. Increased hospital adherence to breastfeeding support guidelines is needed to improve health equity.

Problem definition: Human donor milk provides critical nutrition for millions of infants who are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. For these milk banks, an approach is needed to optimize pooling decisions. Methodology/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time. Managerial implications: This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support. History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0455 .

Introduction To meet the elevated nutritional requirements of very low birthweight (Methods and analysis This is a three-arm, pragmatic, multicentre, double-blind, randomised clinical trial of 615 human milk–fed infants born either (1) ≤1250 g or (2) Ethics and dissemination Ethical approval was obtained from Clinical Trials Ontario (CTO) and local research ethics boards that are not CTO members. Study findings will be disseminated to clinicians at seminars and conferences and in peer-reviewed publications. Trial registration number NCT05308134.