• Karim Khattab, Lucas K Dziesinski, Rebecca Crawford, Alex Ballatori, Priya Nyayapati, Roland Krug, Aaron Fields, Conor W O'Neill, Jeffrey C Lotz, and Jeannie F Bailey.
• Department of Orthopaedic Surgery, University of California, San Francisco 95 Kirkham St, San Francisco, CA, 94122, USA.
• Eur Spine J. 2022 Nov 1; 31 (11): 287528832875-2883.

PurposeFat infiltration (FI) of the paraspinal muscles (PSMs) measured using MRI is an aspect of muscle quality and is considered to be worse in chronic low back pain (cLBP) patients. However, there is not a clear association between paraspinal muscle FI and cLBP, leaving the clinical importance of paraspinal muscle composition unestablished. The spatial distribution of FI in the PSMs may inform mechanistic understanding of non-specific cLBP as it relates to degenerative intervertebral disc (IVD) pathology. We hypothesized that paraspinal muscle fat-mapping would reveal distinct FI distribution patterns in relation to cLBP symptoms and proximity to symptomatic IVD degeneration.MethodsFrom advanced-sequence water-fat MRI of 40 axial cLBP patients and 21 controls, we examined the spatial distribution of paraspinal muscle FI in relation to the center of rotation at the L4L5 disc. Using statistical parametric mapping, we compared FI patterns for multifidus (MF), erector spinae (ES), and psoas between patients and controls, and to the presence and severity of adjacent degenerative IVD pathology.ResultsThe spatial distribution of PSMs FI differs between PSMs and according to symptoms and the adjacent degenerative IVD pathology. Furthermore, the region of MF closest to the disc center of rotation appears most susceptible to FI in the presence of symptomatic IVD degeneration.ConclusionOur study identified spatial distribution patterns of FI in the PSMs as a potential diagnostic biomarker that may also provide granular mechanistic insights into spine biomechanics related to cLBP, as well as advancing the use of prior summary measures limited to overall muscle FI.© 2022. The Author(s).

16 keywords...

hide…