North American Spine Society Journal (Dec 2024)

Association between modifiable and nonmodifiable risk factors with paralumbar muscle health in patients with lower back pain

  • John Fallon, BBA,
  • Jonathan Sgaglione, BS,
  • Matthew Rohde, BS,
  • Junho Song, MD,
  • Austen D. Katz, MD,
  • Alex Ngan, MD,
  • Sarah Trent, MD,
  • Bongseok Jung, BS,
  • Adam Strigenz, BA,
  • Mitchell Seitz, BS,
  • Joshua Zhang, BS,
  • Jeff Silber, MD,
  • David Essig, MD,
  • Sheeraz Qureshi, MD, MBA,
  • Sohrab Virk, MD, MBA

Journal volume & issue
Vol. 20
p. 100570

Abstract

Read online

ABSTRACT: Background: Prior studies have linked sarcopenia and fat infiltration in paraspinal muscles with lumbar pain, spinal pathology, and adverse postoperative outcomes in lumbar spine surgery. A recent magnetic resonance imaging (MRI)-based method for assessing muscle health, incorporating parameters such as Goutallier Classification (GC) and the Paralumbar Muscle Cross-Sectional Area to Body Mass Index ratio (PL-CSA/BMI), has shown that higher muscle grades correlate with significant improvements in patient-reported outcomes. Despite these advancements, there is limited research on the associations between paralumbar muscle health and factors such as age, BMI, walking tolerability, and spondylolisthesis. Our study aimed to evaluate such associations. Methods: This Institutional Review Board-approved retrospective cohort study included patients aged 18 or older presenting with back pain symptoms who underwent lumbar spine MRI within 12 months of presentation to a single orthopedic surgeon. Patients with incomplete imaging, cancer pathology, or trauma-induced injuries were excluded. MRI-based measurements of Lumbar Indentation Value (LIV), Goutallier Classification (GC), and PL-CSA/BMI were used as outcome measures. Paralumbar muscles on axial T2-weighted lumbar MRIs were outlined using ImageJ to determine the PL-CS and LIV through the L1–L5 disc spaces, with GC classified by the primary author. Quantile regression analysis was used for continuous variables, and negative binomial regression with an estimated ancillary parameter was applied for ordinal variables, with statistical significance set at p 1 block predicted LIV and GC at L2/L3 while predicting CSA/BMI at L4/L5. Increasing age was associated with decreased CSA at L1/L2, L2/L3 and L4/L5 while it was associated with decreased CSA/BMI and increased GC at all lumbar levels. Age was only associated with decreased LIV at L1/L2, L2/L3. Lastly, increasing BMI was associated with increased CSA, LIV, and decreased CSA/BMI at all lumbar levels and associated with increased GC at all lumbar levels except L4/L5. All statistically significant associations had p-values<.05. Conclusions: Our analysis determined that increasing age, increased BMI, spondylolisthesis, and walking intolerability are significantly associated with poor paralumbar muscle health. Alongside these findings we discovered that increased age, BMI, spondylolisthesis and walking intolerability were significantly associated with varying degrees of increased Goutallier classification and LIV. Future research is required to determine whether there can be individual alterations in paralumbar muscle health following changes in modifiable risk factors. Additionally future efforts should focus on elucidating the impact of the underlying mechanism behind certain nonmodifiable risk factors such as age on Goutallier classification and poorer paralumbar muscle health.

Keywords