Tag: Suicide prediction

News

Grant for suicide research

We have received $1,494,898 from the American Foundation for Suicide Prevention (AFSP) for the Saving Lives project!

The project aims to improve precision in prediction and prevention of suicide by constructing a national cohort of genetic and environmental data. The ultimate goal is to decrease suicide rates.

Suicide is a tragic event for the individual, the relatives and society. We therefore make efforts to prevent suicide, and a typical way of doing this is to assess suicide risk. Still, studies in psychiatric populations show that current assessments available will classify one out of two suicidal individuals as low risk before an actual death by suicide, while 95% of those classified as high risk will eventually not die by suicide.

Why?

The causes of suicide are complex. Many different factors and life events contribute. To improve prediction, large studies and long follow-up are needed to reach meaningful sample sizes.

Using unique Swedish assets, of genetic and environmental data, and machine learning we have an opportunity a discover environmental and genetic risk factors associated with suicide in order to predict and prevent it.

We thank the AFSP for this grant.

Read more about the Saving lives project here.

News

PhD position in Precision Psychiatry available

We are looking for a new doctoral student to join our computational team!

Read more and apply here.

Do you want to contribute to top quality medical research?

In the research group we work to improve the lives of individuals with psychiatric conditions. A core theme of our research is to develop more accurate prediction models for both the risk of, and also consequences of, psychiatric conditions.

The main scientific objective of your PhD project is to advance knowledge in suicide prediction. Suicide is a catastrophic event for the individual, close relatives, colleagues, and society. Your project is focused on statistical modelling in two different studies. In both studies you will collaborate with colleagues at Oxford University (UK), University of North Carolina Chapel Hill (US) and Sweden.

🎯Research project 1 is a registry study of risk factors for suicide among psychiatric patients that have experienced compulsive mental care.

🎯Research project 2 you will be working with multimodal data (including genetic data) in a nationwide suicide cohort with the purpose of improving present prediction of suicide aiming to derive and validate better risk models for suicide in the total population.

The position is a part of the Rücklab computational team, led by John Wallert.

We look forward to receiving your application!

News

Two grants to research program about suicide prevention

In the end of 2021, project Saving lives received two grants: 4.9 million kronor from Forte (Swedish Research Council for Health, Working Life and Welfare) and 2.4 million kronor from The Swedish Research Council. The research program involves several members from the research group, and the principal investigator of the project is Christian Rück.

Congratulations! 🎯

Saving lives: Constructing a nationwide cohort with multimodal data to improve precision in prediction and prevention of suicide

Project summary:

Suicide is a major public health issue, causing severe impact on individuals and families, as well as relevant societal costs. In Sweden alone, ~70,000 years of potential life are lost each year due to suicide. Despite dedicated research efforts and prevention strategies, suicide-related outcomes are still difficult to predict and prevent. The goal of this research program is to utilize unique resources in Sweden to improve prediction of suicide by integrating environmental factors captured by national registers and genetic information using multi-modal modelling. 

In aim 1, we will create the world’s largest suicide biobank by collecting neonatal blood spots stored at the Swedish PKU biobank from 4,000 individuals that died by suicide, i.e. total coverage of all suicides in Sweden from 1975 and onwards. DNA will be extracted from each of these blood spots. Data from the national registers will be added, covering major risk factors across the lifetime (socioeconomic, demographic and medical). Further, blood spots and register data from 8000 matched controls with no suicidal outcomes will be available through collaboration. 

In aim 2, we will identify register-based risk factors and genetic variants associated with suicide by genotyping all DNA samples from cases and matched controls and performing a case-control genome-wide association study meta-analysis. 

In aim 3, we will combine hundreds of candidate suicide predictors from the national registers (covering demographics, socioeconomic status, In aim 3, we will combine hundreds of candidate suicide predictors from the national registers (covering demographics, socioeconomic status, electronic medical records, criminality) with genetic predictors (polygenic risk scores for suicide, depression, impulsivity, and substance misuse) using both established quantitative modelling and newer machine learning approaches.