Maternal Plastic Exposure, Placental Microplastics, and Neonatal Anthropometry Outcomes: Evidence From a Human Placenta Study Publisher Pubmed



Alizadehfard F ; Chamani A ; Sobhanardakani S ; Hamzeh F
Source: Environmental Pollution Published:2026

Abstract

Microplastic (MP) pollution is a pervasive environmental concern, with growing evidence that MPs can cross biological barriers and accumulate in human tissues, including the placenta. This raises significant concerns for fetal development, yet maternal exposure pathways and their quantitative associations with neonatal health remain poorly characterized. This study aimed to quantify the concentration and characteristics of MPs in human placenta, identify behavioral and environmental predictors of maternal exposure, and evaluate the relationship between placental MP burden and neonatal outcomes. Placental samples (maternal decidua) from 20 mothers in Isfahan, Iran, were analyzed for MPs (>10 μm) using mass-normalized concentrations (MP g−1), with stringent contamination controls. Maternal exposure was assessed via a 19-item inventory, and neonatal anthropometric measures were recorded. A total of 76 MPs were detected after correction for background contamination detected in procedural blanks, with concentrations varied across individuals, ranging from 0.000 to 0.103 MP g−1. Detected MPs were predominantly fragment-shaped (80.3%) and predominantly around 10 μm in size, with black and brown particles constituting nearly 70% of the total. Spearman correlation analyses showed that placental MP concentration was negatively associated with neonatal length (r = −0.809, p < 0.001). Simple linear regression models further indicated inverse associations between MP concentration and neonatal length (β = −52.747, p < 0.001). These findings suggest that MP concentration in the human placenta is quantifiable and variable, and may show potential associations with maternal lifestyle factors and indicators of neonatal growth. © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.