The YOUTH-GEMs project studies the genetic & environmental factors influencing mental health of young people between 12-24 years. Through self-awareness, early detection and -if needed- interventions we hope to prevent or alleviate mental distress.
From people who experience mental health issues, more than 75% experience this before they turn 24 years. This might not be surprising because it is during young adulthood that the brain develops rapidly and is still highly “plastic”. Unfortunately, we don’t yet know how exactly the brain changes, which brain cells are affected, and which factors can prevent mental illness or even drive illness.
However, we know that both our genetic makeup (“nature”) and the environment (“nurture”) we grow up in, impact our mental well-being. Research also shows that the impact of environmental factors is different for people with different genetic makeups. Meaning, that there is an interaction between the genes and the environment, so-called “gene-environment interaction.” For instance, experiencing trauma during childhood or using drugs could harm mental well-being for everyone. Yet, this impact will be different for someone with a specific genetic susceptibility.
Therefore, this project investigates the “gene-environment interaction” during young adolescence. We do this by including young experts into all steps of the research, be it during clinical studies, research into epigenetics (fine-tuning of the genes), artificial intelligence and electronic (e)-health .
The project is a collaboration of 18 partners, from Spain, Ireland, the UK, the Netherlands, Estonia, Croatia, Serbia, Germany, Italy, and Australia.
Like all EU-funded large research projects the project is divided into different subtasks called work packages, led by a designated project partner.
Coordination & Project Management
This work package manages project risks and ensures compliance with legal and ethical standards, while also promoting effective communication, financial management, and innovation. Tasks include setting up management structures, monitoring progress, and managing contracts and intellectual property.
Engagement, Ethics & Networking
We focus on “Engagement, Ethics & Networking” to help young people, public health officers, and healthcare workers become more aware of mental health concerns. Furthermore, we want them to be engaged and empowered to help shape the research in this area and work on solutions and approaches together. We also want to solve ethical, privacy, and security issues related to personal mental health information and the participants’ data which we will collect.
To do this, we’re using a type of research called “participatory health research” where we work directly with young experts aged 16 to 24. We believe in sharing knowledge and involving young experts as much as possible, so we’re setting up a learning community where we can share ideas and stories. Principles of participation that we use, are: shared learning and maximizing the participation of young experts. That is why we will set up a learning community that fosters interactions and relationships based on mutual respect and trust in which ideas and stories are shared that support the broader aim of the project. In this way, different types of knowledge are combined and new knowledge is co-created.
Within the participatory research we have a few activities planned. We get together at informal meetings where we discuss the project’s science, so-called science cafe sessions. We also create a plan for our communication – inside and outside of the project. Further building our network, working on digital storytelling to share personal stories, and developing e-health solutions for self-help purposes represent other important aspects. In all activities young experts will be involved- from the planning to the roll-out. For some of the activities we also ask university students to participate, as well as carers, teachers and/or scientists, doctors or others with the necessary expertise.
Partnering with Euro Youth Mental Health, young experts are involved in all steps of the project. Euro Youth Mental health is a non-profit organization run by young volunteers who aim to improve mental health support for young people across Europe.
In the Youth-GEMs project, we want to learn about what affects mental health. We know that our environment and life events play a big role, but some mental health issues can also be passed down from our family and are thus genetic. We’re calling genetic factors that affect our mental health “predictors” because they can sometimes help us predict how our mental health will develop.
To find “predictors” we need to collect a lot of information from as different people as possible. This information will therefore come from many different sources and might even be in different languages. So, we need to make sure that all the information follows the same rules, is collected and described the same way and is translated into the same language. This is called data harmonization.
Due to the nature of genetic data being highly sensitive and personal it needs to be protected and we are careful about how it is used and who it is shared with. Not only does it inform us about the participant’s genetic make-up, but also their family’s. Therefore, specific protocols have been put in place describing how we handle and analyse this data (data processing).
Finally, when we analyse large amounts of data (big data) we can search for patterns in the data and come up with a way (a “model”) to predict which factors influence our mental health most. Since we will need such large quantities of data to do this, we can’t possibly store all this data in one place. So, we leave the data where it is (locally) and send our analysis ‘model’ to the data, to run the analysis on it. We call this Federated learning, because when visiting the local data, the model will “learn” from this data, and then comes back to the data scientist with the results.
Functional Genomics of the Human Brain
Mental health is influenced by a combination of our genes and the environment we grow up in. Changes in how our genes work can affect how our brain functions and ultimately this effects our mental well-being. To help prevent and treat mental illness, it’s important to understand how our genes work and how they’re fine-tuned in specific cell types of the brain during development when we go through important stages of childhood and adolescence. This fine-tuning is controlled by certain parts of our genes, so-called gene regulatory elements.
In this part of the project, we want to learn more about these gene regulatory elements and how they’re related to specific types of brain cells and different developmental stages. Because we can’t analyse this in depth in living people, we look at brain cells from people who have passed away and also culture human brain cells in the lab.
Some mental illnesses already start at a young age and we know about several genetic risk factors that are shared amongst different mental illnesses. But we don’t know why some people who have these risk factors fall ill and others don’t. So, we want to find out if fine-tuning the gene regulatory elements might play a role in this. If we understand which brain cells at which developmental stage are affected and how, we know what to look for and, in the future, can find options to prevent or treat the illness. Once we have this information, doctors could for instance predict who is at risk for different mental illnesses and together they could decide what to do to prevent the illness from happening.
We want to find out how genes, experiences, and their interaction impact mental health during young adulthood. By studying the specific psychological, developmental, biological, and genetic mechanisms we can hopefully learn why some people are more susceptible and others more resilient to mental illness. Data inference refers to the conclusion that’s drawn from evidence by looking at different types of data.
To do this we will look closely at data which has already been collected from big groups of young people from around the world including the Netherlands, the United Kingdom, the United States, and Australia. In these studies, scientist have collected a lot of information and have already analysed the data with statistical analysis. Now, we want to combine knowledge from work package 4, focusing on the gene fine-tuning, or gene regulatory elements, with machine learning, a type of artificial intelligence learning.
Once we have identified which gene regulatory elements play a role in the development of mental illness (WP4) we will then test if these genes can also explain mental illness development in these large world-wide studies.
Machine learning can help us with finding patterns we could not find otherwise. It helps us to look at large information at the same time, investigating all possible interactions. We can then study the dynamic, interactive, and time-dependent influence of genetic, but also “non-genetic” environmental factors, such as childhood adversities, stressful life events, peer-bullying, and drug use, and investigate how this all is connected to each other. Hopefully we can then find out how it influences mental health outcomes.
Data integration & Prediction
Machine learning, a sub-field of Artificial Intelligence (AI), is an advanced technology that allows computers to learn like humans do. For some areas, like reasoning and predictions machine learning often performs far better than people.
Machine Learning is already used in healthcare where it can perform key clinical tasks, such as risk prediction, early and precise diagnosis, or personalized treatment planning.
In the Youth-GEMs project, we want to create a machine-learning tool which will be built into a mobile app. With this app, you could then monitor your mental health and it would let you know early on before you may start experiencing problems. It does this by analysing your clinical data, data about your lifestyle, the environment you live in and your behaviour.
Our hope is that this app could then give you the opportunity to start early intervention to prevent mental health issues. To make sure the app is not only accurate in its predictions but also that you trust it with all of your data, the project will bring together experts from different fields. AI scientists, people with digital technology expertise, clinical researchers, mental health experts, people experiencing mental illnesses, biomedical ethicists, social scientists and legal experts will work together to make sure the app meets the needs and expectations of everyone involved.
We will also focus on making the app fair, non-discriminatory, transparent, and easy to understand. We want to create a tool that is safe and ethical for all adolescents to use, and that follows all relevant laws and regulations.
Our goal is to make sure the research from the other work-packages can actually help young people and the professionals working with them in real life. We’ll run a clinical study with about 1,000 European and 1,000 Australian young people who are seeking help for the first time. In this study we will test if the work done by our project partners is useful for young people experiencing mental illness and their doctors. This study will run for 1 year and will help us identify factors influencing how mental illness evolves over time in young people and potential ways of helping them. Together with young experts we will define which factors are important to be measured and these will be included in the clinical study.
The objective of this work package is to make sure that the project meets all the ethical requirements set out for it. This work package defines the ethical requirements that the project must follow.