Advanced maternal age and assisted reproductive technologies: outcomes, genomics, and real-world evidence

高龄产妇与辅助生殖技术:结局、基因组学和真实世界证据

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Abstract

INTRODUCTION: Advanced maternal age (AMA) and assisted reproductive technologies (ART) are becoming more common and are linked with higher obstetric and perinatal risks. Genomic tools and real-world data (RWD) are transforming risk prediction and care strategies, yet their application to the AMA + ART setting remains uneven. OBJECTIVE: To synthesize recent evidence on risks associated with AMA and ART, incorporate insights from reproductive genomics and RWD, and discuss their implications for clinical care and guideline development. METHODS: Narrative review of cohort studies, systematic reviews, and meta-analyses, supplemented by focused analyses of genomic approaches: expanded carrier screening, clinical exome sequencing, preimplantation genetic testing for monogenic disorders (PGT-M) and aneuploidy (PGT-A), emerging non-invasive PGT, and pharmacogenomics, as well as RWD infrastructures such as registries, EHR datasets, and trusted research environments. RESULTS: Maternal age (≥40 years) increases miscarriage, gestational diabetes, hypertensive disorders, placental abnormalities, cesarean delivery, preterm birth, stillbirth, and NICU admissions. ART independently raises risks of preeclampsia, placenta previa, preterm birth, low birthweight, cesarean delivery, and neonatal complications, including in singleton. Combined AMA + ART may produce additive or synergistic effects. Donor oocytes reduces miscarriage risk but may elevate preeclampsia risk via immunologic mechanisms. Genomic technologies enable the identification of infertility-related variants, prevention of genetic conditions, individualized ovarian stimulation, and AI-assisted embryo selection. RWD enhances evidence by capturing diverse populations, supporting comparative and long-term analyses. CONCLUSIONS: Pregnancies from AMA + ART should be managed as high risk. Integration of genomics technologies and RWD can support predictive, personalized care and inform urgently needed consensus guidelines.

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