Prenatal Microplastic Exposure Impairs Offspring Neurodevelopment through the Placenta-Brain Axis - Zhang, Qingluo (Exploring Science Academic Conference Series, 2026)
Microplastics have emerged as environmental pollutants capable of crossing the maternal-fetal barrier and exerting adverse effects on fetal neurodevelopment. This review aims to elucidate how prenatal microplastic exposure impairs fetal brain development via the placenta-brain axis and to delineate its underlying neurotoxic mechanisms. Following maternal exposure via inhalation, ingestion, or dermal contact, microplastics enter the placenta via the blood and lymphatic systems. They are more likely to cross the placental barrier via energy- dependent active transport mechanisms. Microplastics can infiltrate fetal brain tissue through placental blood exchange or amniotic fluid ingestion, causing direct neural damage. They disrupt placental homeostasis, indirectly inhibiting fetal nervous system development. Prenatal microplastic exposure can interfere with core developmental events, including neural tube formation, cell proliferation and differentiation, neuronal migration, synaptogenesis, and myelination, leading to offspring phenotypes such as reduced spatial memory, decreased learning ability, impaired motor coordination, and anxiety-like behaviors. Furthermore, we conducted a bioinformatic analysis of transcriptomic data from microplastic-exposed human placenta (GSE220756) and mouse prefrontal cortex (GSE160012) retrieved from the GEO database, identifying shared regulatory pathways centered on aberrant vascular development, immune-inflammatory imbalance, and apoptotic dysregulation, particularly angiogenic impairment, which may serve as a critical node linking placental dysfunction to fetal neurotoxicity. However, limitations persist: small bioinformatic sample sizes, confounded lactational sampling in animal studies, exposure doses exceeding environmental relevance, and immature nanoplastic detection. Future work should expand the clinical specimens, establish standardized prenatal exposure models, integrate multi-omics with machine learning to identify predictive biomarkers, and validate the roles of angiogenic signaling.