%0 Journal Article
%T Fatty Infiltration in Paraspinal Muscles: Predicting the Outcome of Lumbar Surgery and Postoperative Complications.
%A Wang Z
%A Zhao Z
%A Li Z
%A Gao J
%A Li Y
%J World Neurosurg
%V 190
%N 0
%D 2024 Jul 15
%M 39019431
%F 2.21
%R 10.1016/j.wneu.2024.07.074
%X Lumbar spine disorders often cause lower back pain, lower limb radiating pain, restricted movement, and neurological dysfunction, which seriously affect the quality of life of middle-aged and older people. It has been found that pathological changes in the spine often cause changes in the morphology and function of the paraspinal muscles (PSMs). Fatty infiltration (FI) in PSMs is closely associated with disc degeneration and Modic changes. And FI causes inflammatory responses that exacerbate the progression of lumbar spine disease and disrupt postoperative recovery. Magnetic resonance imaging can better distinguish between fat and muscle tissue with the threshold technique. Three-dimensional magnetic resonance imaging multi-echo imaging techniques such as water-fat separation and proton density are currently popular for studying FI. Muscle fat content obtained based on these imaging sequences has greater accuracy, visualization, acquisition speed, and utility. The proton density fat fraction calculated from these techniques has been shown to evaluate more subtle changes in PSMs. Magnetic resonance spectroscopy can accurately reflect the relationship between FI and the degeneration of PSMs by measuring intracellular and extracellular lipid values to quantify muscle fat. We have pooled and analyzed published studies and found that patients with spinal disorders often exhibit FI in PSMs. Some studies suggest an association between FI and adverse surgical outcomes, although conflicting results exist. These suggest that clinicians should consider FI when assessing surgical risks and outcomes. Future studies should focus on understanding the biological mechanisms underlying FI and its predictive value in spinal surgery, providing valuable insights for clinical decision-making.