# Identifying genetic predictors of outcomes for Veterans with chronic low back pain and lumbosacral spinal disorders

> **NIH VA I01** · VA PUGET SOUND HEALTHCARE SYSTEM · 2024 · —

## Abstract

Low back pain (LBP) is the #1 contributor to disability globally and the 4th most prevalent reason for new VA
disability compensation. The societal burden of LBP is largely attributed to 2 distinct subgroups of patients: (1)
those who use healthcare resources for chronic (persistent or recurrent) LBP; and (2) those undergoing
surgical treatments for specific spine-related conditions associated with LBP and/or neuropathic
symptoms/signs, such as lumbosacral radicular syndrome (LSRS) and symptomatic lumbar spinal stenosis
(SLSS). Personalized approaches to improve the efficiency of care and treatment outcomes for these
subgroups of Veterans have the potential to reduce the burden of LBP for the Veteran population. Stratified
care for LBP based on prognosis showed early promise when linked to clinical decisions regarding physical
therapy. More robust effects from stratified care may come through improving the feasibility and prognostic
ability of risk stratification or linking risk stratification to clinical decisions regarding treatments with large
magnitude effects in subgroups of patients with LBP (e.g., decompression surgery for LSRS). The proposed
research will apply these two approaches to improving stratified care for LBP, which will develop and
validate powerful prediction models using clinical electronic health record (EHR) and genomic data.
This research will two parts to achieve each of the two study aims. Part I will involve genome-wide association
study (GWAS) meta-analyses to predict outcomes for LBP-associated conditions, including participants from
the Million Veteran Program (MVP), the Electronic Medical Records and Genomics Network phase 3
(eMERGE3) network, and the UK Biobank, as well as summary data from other genomic biobanks. Part II will
involve the development and validation of multivariable prognostic models for LBP-related outcomes. First,
multivariable prognostic models will be developed using a cross-validation approach in 80% of the MVP
sample, using only clinical data (visits, diagnoses, pharmacy, vital signs, etc.) from the VA EHR; only
genomic data (genome-wide PRSs); and both clinical and genomic data. Next, the best-performing
multivariable models developed in each aim will be validated in an independent 20% sample of MVP
participants, the eMERGE network phase 3, and UK Biobank. Aim 1. Develop and validate prognostic
models for the risk of chronic LBP with healthcare use (CLBP-HU) in Veterans. These models will identify
Veterans with LBP of substantial impact sufficient to warrant healthcare use, who should be prioritized for
rehabilitative pain treatments. GWAS of CLBP-HU will be conducted. Validated variants will be characterized
and their potential biological roles examined. Multivariable models for predicting CLBP-HU will then be
developed and compared with each other. These models will be informed by (a) EHR-defined clinical data, (b)
genomic data (genome-wide PRSs), and (c) both clinical and genomic data. Hypo...

## Key facts

- **NIH application ID:** 10756965
- **Project number:** 5I01RX004291-02
- **Recipient organization:** VA PUGET SOUND HEALTHCARE SYSTEM
- **Principal Investigator:** Pradeep Suri
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2023-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10756965

## Citation

> US National Institutes of Health, RePORTER application 10756965, Identifying genetic predictors of outcomes for Veterans with chronic low back pain and lumbosacral spinal disorders (5I01RX004291-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10756965. Licensed CC0.

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