Genetic risk of chronic pain conditions associated with risk of suicide death through an integrative analysis of EHR and genomics data

Abstract Chronic pain represents heritable conditions linked to suicide death. It has been suggested that a shared genetic predisposition may contribute to this relationship, but there has not yet been a comprehensive assessment of genetic and clinical overlaps of different types of chronic pain with suicide death. Here, we integrated whole-genome sequencing and electronic health records from 986 unrelated individuals of European ancestry who died by suicide in the Utah Suicide Mortality Research Study and 415 ancestrally-matched population controls selected for absence of disease. Polygenic scores (PGSs) for seven distinct types of chronic pain were calculated and tested in the suicide cohort. We observed significant positive associations of PGSs for multisite chronic pain (PGS MCP ) and chronic widespread pain (PGS CWP ) with suicide mortality. Sex-stratified analyses showed elevations in both males and females. Pain diagnosis-stratified analyses revealed associations with suicide death regardless of chronic pain diagnoses. Follow-up tests of PGSs for more specific pain conditions showed additional associations with suicide death for: 1) monoarticular arthritis, 2) back pain, and 3) chronic inflammatory demyelinating polyneuropathy across all suicide death individuals, and 4) irritable bowel syndrome within males only. In a multiple logistic regression test of all chronic pain PGSs associating suicide death status, four types of pain remained uniquely associated with suicide death, highlighting distinct subgroups within suicide death: some attributed to MCP and CWP, and others associated with monoarticular arthritis or chronic inflammatory demyelinating polyneuropathy. This cohort study reports associations between suicide death and PGSs from various pain conditions, regardless of sex or chronic pain diagnosis, suggesting that combining genetic and clinical risk factors may better identify genetic overlap, causal directions, and/or specific gene pathways.