Understanding, predicting, and treating depression in pregnancy to improve mothers and offspring mental health outcomes

Informations projet

HappyMums

N° de convention de subvention: 101057390

DOI

10.3030/101057390

Date de signature de la CE 10 Novembre 2022

Date de début 1 Novembre 2022

Date de fin 31 Octobre 2026

Financé au titre de

• Health

Coût total

€ 8 925 244,75

Contribution de l’UE

€ 8 925 241,00

Coordonné par

UNIVERSITA DEGLI STUDI DI MILANO
Italy

Periodic Reporting for period 1 - HappyMums (Understanding, predicting, and treating depression in pregnancy to improvemothers and offspring mental health outcomes)

Période du rapport: 2022-11-01 au 2024-04-30

Résumé du contexte et des objectifs généraux du projet

Perinatal mental disorders contribute enormously to societal and health burdens. According to the World Health Organization (WHO), depression is the most common psychiatric morbidity in pregnancy, affecting nearly 30% of pregnant women around the world, with an increased rate observed after the COVID-19 pandemic.
Women suffering from depression during pregnancy often fail to receive timely treatments because they may be unaware of their condition or because they don’t disclose their symptoms to healthcare professionals, because they cannot afford treatment or because they are afraid of perceived stigma. Importantly, perinatal mental disorders also increase the risk for negative behavioural outcomes in the offspring, through a combination of biological effects in utero and psychosocial factors during the early years.
In this context, the goal of HappyMums is to understand, predict, and treat depression in pregnancy, to improve mental health outcomes of both mothers and their offspring.
Specifically, HappyMums aims to: i) understand the main risk factors involved in the development of maternal depression in pregnancy and the underlying biological mechanisms and shed light on the efficacy of interventions; ii) understand the mechanisms that affect the foetal environmental biology, shaping offspring’s risk for developing negative mental outcomes later in life; iii) identify the prenatal and postnatal factors that exacerbate, or buffer, the risk shaped in utero; iv) develop a digital platform where a mobile phone App will collect different biomarkers (AI tools-based data integrated with biological, clinical, medical, environmental and lifestyle data) and will support the adoption of protective lifestyle attitudes among women. The App will be also connected to a dashboard to be used by clinicians, for early detection and monitoring of depressive symptoms in pregnancy.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

HappyMums combines data from large cohorts of pregnant women and offspring around the world as well as animal models, to identify risk factors for maternal depression and the chain of molecular and biological events that are modulated by the onset of depressive symptoms in pregnancy and their consequences on the offspring. The work so far has been devoted to laying the groundwork for comprehensive analyses across different longitudinal data from large population-based birth studies and case-control studies of perinatal samples focusing on data generation, cleaning, quality control and harmonization of multiple measures of genetic, biological, environmental, lifestyle and demographic factors in mothers and the offspring.
At the same time, we successfully established and characterized 3 different mouse models of depressive-like phenotype during gestation. Moreover, we carried out a pilot experiment to establish and validate the protocol for the prenatal stress paradigm on an innovative live-bearing fish model that will be used to dissect the role of placenta.
Finally, thanks to the results obtained from co-creation meetings and other dedicated activities where multi-stakeholder requirements have been collected, the first version of the HappyMums mobile application and of the dashboard to be used by women and the clinicians respectively, have been developed and are now available. They will be tested in the HappyMums clinical study, which will recruit and monitor a sample of pregnant women at high-risk of depression, across 7 clinical recruitment centres in Europe. The set-up phase of the study, which included the preparation of the master protocol and all the related documents (recruitment materials, interviews and questionnaires, participant information sheets and consent forms) and the procedures to get ethical approval from the various ethics committees is almost complete, with 2 centres that already obtained approval and all the others in the process of getting it.
Since the beginning, great effort has been devoted to promoting the HappyMums project and raising awareness of the importance of perinatal mental health. To target a wide range of audiences, various channels have already been implemented and activities carried out. In particular, the project website (www.happymums.eu) and several social media accounts have been set up. The latter have been used to share project updates and different content regarding pregnancy and motherhood, to help women adopt healthier lifestyles and behaviours. Also, the HappyMums project has been presented in scientific symposia and generated data included in several papers; moreover, strong links have been established with other relevant projects, especially those funded within the same call.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

HappyMums will generate novel knowledge on clinical, biological, lifestyle, and environmental factors that contribute to depressive symptoms during the perinatal period and how these factors can influence the foetal programming of the exposed offspring, with potential negative effects on their development. HappyMums will also improve our understanding of the long-term impact of perinatal depression on offspring neurodevelopmental outcomes from birth to adolescence, and of the pre- and postnatal factors that could further influence the offspring’s prenatally primed vulnerability to develop negative outcomes.
By employing this integrative approach, different factors and underlying biological mechanisms can be identified and measured. Rather than being considered separately, these biomarkers or factors will be integrated into unified statistical machine-learning models.
This will enable the identification of biomarkers for a reliable and accurate diagnosis and prognosis, allowing personalized risk prediction and interventions. These biomarkers could then be made available to women, even beyond pregnancy, to help assess their mental health.
All this knowledge will also allow the identification of novel targets (e.g. biological mechanisms or pathways) that could be used for the development of novel drugs, the repurposing of already existing medications or the development of non-pharmacological interventions to prevent or treat depressive symptoms.
The development of a digital platform specifically tailored for pregnant women, aimed at early screening and symptom monitoring, will contribute to improving our diagnostic system by using this bottom-up approach, driven by the integration of heterogeneous data, in contrast with the current top-down model based on a limited classification system.

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