The Association Between Polyphenols Intake of Pregnant Mothers and Neurodevelopment Indices in Their Children: The Prospective Epidemiological Research Studies in Iran Birth Cohort-Isfahan

Abstract

Background: Polyphenol intake is important for neurological health; however, limited evidence has examined the association between maternal dietary polyphenol intake during pregnancy and children's neurodevelopment. This study aimed to investigate the relationship between maternal polyphenol consumption across pregnancy trimesters and neurodevelopmental outcomes in children. Methods: This prospective cohort study included 945 mother-child pairs from the Isfahan Birth Cohort. Maternal dietary intake was assessed using a validated 90-item food frequency questionnaire. Polyphenol intake was estimated using the Phenol-Explorer database. Children's neurodevelopment and behavioral outcomes were evaluated using the Ages and Stages Questionnaire and the Child Behavior Checklist. Results: Higher maternal polyphenol intake during the first trimester was associated with lower odds of adverse behavioral outcomes at 2 years of age. In adjusted models, the second (odds ratio OR: 0.370, 95% confidence interval CI: 0.197-0.693) and third tertiles (OR: 0.523, 95% CI: 0.297-0.920) of polyphenol intake were associated with reduced odds compared to the lowest tertile. Similar associations were observed for the second tertile of polyphenol intake during the first trimester (OR: 0.317, 95% CI: 0.123-0.816) and third trimester (OR: 0.380, 95% CI: 0.152-0.954). No consistent associations were observed at later follow-up ages. Conclusions: Higher maternal polyphenol intake during pregnancy, particularly in early gestation, may be associated with more favorable early neurodevelopmental and behavioral outcomes. However, these associations were not consistent across all ages, and causality cannot be inferred. Further studies using objective measures of polyphenol exposure and long-term follow-up are warranted. (c) 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.