# Distinguishing Clinical and Genetic Risk of Suicidal Ideation from Attempts to Inform Prevention

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $543,736

## Abstract

One hundred and twenty-three Americans die by suicide every day, and 800,000 individuals die from
suicide globally every year. Five times as many people attempt suicide and 10-25 times as many
contemplate suicide every year. Rates of suicidal thoughts or ideation and suicidal behaviors are increasing.
Suicidal ideation alone causes mental and physical harm and is associated with worsened statuses of other
illnesses. Suicidal ideation is often documented by clinical providers in their notes but has been shown to
only be included in diagnostic or billing codes 3% of the time. Historical suicide attempts are also under-
captured by billing codes alone. Improving identification of those with suicidal ideation and attempts might
enhance prevention through earlier contact with those at risk.
 A growing literature shows that clinical predictive models with longitudinal electronic health records
(EHR) can predict suicide attempts with good performance. These models have also been used by groups
like ours to improve power of large-scale genetic analyses of suicide attempt risk. The investigators used
their validated models to identify the signal for suicide attempt, a “phenotype”, to run genetic analyses
showing suicide attempt risk is 4% heritable. This team also showed that suicide attempt risk was
significantly genetically correlated with depressive symptoms, neurotic symptoms, and schizophrenia.
 The investigators propose to validate a phenotype of suicidal ideation and to improve their existing
phenotypes of attempt risk to power large-scale genetic analyses across two major biobanks, Vanderbilt’s
BioVU and the UK Biobank. They will use natural language processing (NLP) and analytics on Vanderbilt’s
EHR to develop and test a phenotype of suicidal ideation. They will use NLP to improve capture of cases of
suicide attempt to refine existing algorithms. They will apply these phenotypes at scale to BioVU. Their
Stanford team members will use patient-reported suicidal ideation histories in another major biobank, UK
Biobank, to independently run genetic analyses of suicidal ideation risk in a different population. They will
further analyze clinical and genetic risk factors to better understand who will transition from suicidal ideation
to suicide attempt.
 The project combines expertise in clinical informatics, machine learning, and large-scale genomics,
as well as domain-specific expertise in suicide risk research. Spanning two major biobanks across two
countries, the algorithms and methods developed have maximal portability, facilitating next-step
investigations. Successful identification of suicidal ideation and attempt risk might inform clinical prevention.
Better understanding of risk factors that predict who will proceed from suicidal ideation to suicidal behaviors
would help allocate prevention resources to those who need them most.

## Key facts

- **NIH application ID:** 10292968
- **Project number:** 5R01MH121455-03
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Douglas Ruderfer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $543,736
- **Award type:** 5
- **Project period:** 2019-12-01 → 2024-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10292968

## Citation

> US National Institutes of Health, RePORTER application 10292968, Distinguishing Clinical and Genetic Risk of Suicidal Ideation from Attempts to Inform Prevention (5R01MH121455-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10292968. Licensed CC0.

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