Maternal Nutrition and Placental Biology in Early-Life Programming
A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition in Women".
Deadline for manuscript submissions: 25 June 2026 | Viewed by 9
Special Issue Editors
Interests: placenta; pregnancy exposure; fetal development; neonatal health; early programming; environmental impact
Special Issue Information
Dear Colleagues,
This Special Issue, “Maternal Nutrition and Placental Biology in Early-Life Programming”, highlights the placenta as the central integrator of maternal diet, metabolic status, and co-exposures that shape fetal development and long-term health. We invite original research, reviews, and methodological papers that elucidate mechanistic links between prenatal nutrition—dietary patterns, macro-/micronutrients, one-carbon metabolism, molecular signalings, and gestational weight dynamics—and placental structure and function. Of particular interest are multi-omics approaches, placental pathways (nutrient transport, immune signaling, vascularization, circadian regulation), and studies connecting these signatures to infant birth and developmental phenotypes. We welcome work that addresses health equity, including populations in resource-limited or agricultural settings, and studies that evaluate interactions between nutrition and the broader prenatal exposome (e.g., endocrine disruptors, pesticides), as well as interventions and policy-relevant insights. Methodological contributions on mixture modeling, causal inference, and integrative analytics are encouraged. Our aim is to assemble a rigorous, translational evidence base that advances biomarker discovery, clarifies sensitive windows, and informs precision nutrition strategies to improve maternal and child health across diverse communities.
Dr. Yewei Wang
Dr. Elizabeth M. Kennedy
Guest Editors
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Keywords
- maternal nutrition
- placental biology
- early-life programming
- multi-omics
- immune signaling
- gestational weight gain
- prenatal exposome
- endocrine disruptors
- mixture modeling
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