A network analysis of nutritional markers and maternal perinatal mental health in the French EDEN cohort.

BACKGROUND: Perinatal maternal depression and anxiety are associated with adverse maternal outcomes, and nutrition may play an important role in their emergence. Previous research shows that certain micro and macronutrients found in different dietary patterns may associate with perinatal mood disorders. This study aims to explore relationships between nutrition during pregnancy and perinatal maternal depression and anxiety symptoms using network analyses. METHODS: Using data from the French EDEN mother-child cohort, the sample consisted of 1438 women with available mental health outcomes (CES-D, STAI and EPDS) and nutritional markers collected from food frequency questionnaires. Four networks were constructed to explore the relationships between prenatal nutrient status, dietary patterns, and perinatal mental health, while accounting for important confounders. RESULTS: The Healthy dietary pattern was associated with the presence of vital micronutrients, while the Western dietary pattern was consistently associated with poorer intake of specific micronutrients and contained an excess of certain macronutrients. Western dietary pattern and symptoms of postnatal depression were connected by a positive edge in both the macronutrient and micronutrient networks. Lower education levels were associated with higher Western dietary pattern scores, from which a positive edge linked to postnatal depression symptoms in both models. CONCLUSIONS: A Western dietary pattern was associated with increased symptoms of postnatal depression in our adjusted network models; The Healthy dietary pattern was associated with essential micronutrients but not with symptoms of depression or anxiety. Perinatal mental health might be impacted by specific dietary patterns in the context of psychosocial and physical stress associated with pregnancy.

A Case for Synthetic Data in Regulatory Decision-Making in Europe.

Regulators are faced with many challenges surrounding health data usage, including privacy, fragmentation, validity, and generalizability, especially in the European Union, for which synthetic data may provide innovative solutions. Synthetic data, defined as data artificially generated rather than captured in the real world, are increasingly being used for healthcare research purposes as a proxy to real-world data (RWD). Currently, there are barriers particularly challenging in Europe, where sharing patient's data is strictly regulated, costly, and time-consuming, causing delays in evidence generation and regulatory approvals. Recent initiatives are encouraging the use of synthetic data in regulatory decision making and health technology assessment to overcome these challenges, but synthetic data have still to overcome realistic obstacles before their adoption by researchers and regulators in Europe. Thus, the emerging use of RWD and synthetic data by pharmaceutical and medical device industries calls regulatory bodies to provide a framework for proper evidence generation and informed regulatory decision making. As the provision of data becomes more ubiquitous in scientific research, so will innovations in artificial intelligence, machine learning, and generation of synthetic data, making the exploration and intricacies of this topic all the more important and timely. In this review, we discuss the potential merits and challenges of synthetic data in the context of decision making in the European regulatory environment. We explore the current uses of synthetic data and ongoing initiatives, the value of synthetic data for regulatory purposes, and realistic barriers to the adoption of synthetic data in healthcare.