Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 9, 1081 BT Amsterdam, The Netherlands; Division of Pain Medicine, Department of Anaesthesiology, Perioperative and Pain Medicine, Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA.
Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 9, 1081 BT Amsterdam, The Netherlands; SOMT University of Physiotherapy, Softwareweg 5, 3821 BN Amersfoort, the Netherlands.
Spine J. 2024 Jul;24(7):1253-1266. doi: 10.1016/j.spinee.2024.02.013. Epub 2024 Feb 28.
The role of lumbar paraspinal muscle health in back pain (BP) is not straightforward. Challenges in this field have included the lack of tools and large, heterogenous datasets to interrogate the association between muscle health and BP. Computer-vision models have been transformative in this space, enabling the automated quantification of muscle health and the processing of large datasets.
To investigate the associations between lumbar paraspinal muscle health and age, sex, BMI, physical activity, and BP in a large, heterogenous dataset using an automated computer-vision model.
Cross-sectional study.
Participants from the UK Biobank with abdominal Dixon fat-water MRI (N=9,564) were included (41.8% women, mean [SD] age: 63.5 [7.6] years, BMI: 26.4 [4.1] kg/m) of whom 6,953 reported no pain, 930 acute BP, and 1,681 chronic BP.
Intramuscular fat (IMF) and average cross-sectional area (aCSA) were automatically derived using a computer-vision model for the left and right lumbar multifidus (LM), erector spinae (ES), and psoas major (PM) from the L1 to L5 vertebral levels.
Two-tailed partial Pearson correlations were generated for each muscle to assess the relationships between the muscle measures (IMF and aCSA) and age (controlling for BMI, sex, and physical activity), BMI (controlling for age, sex, and physical activity), and physical activity (controlling for age, sex, and BMI). One-way ANCOVA was used to identify sex differences in IMF and aCSA for each muscle while controlling for age, BMI, and physical activity. Similarly, one-way ANCOVA was used to identify between-group differences (no pain, acute BP, and chronic BP) for each muscle and along the superior-inferior expanse of the lumbar spine while controlling for age, BMI, sex, and physical activity (α=0.05).
Females had higher IMF (LM mean difference [MD]=11.1%, ES MD=10.2%, PM MD=0.3%, p<.001) and lower aCSA (LM MD=47.6 mm, ES MD=350.0 mm, PM MD=321.5 mm, p<.001) for all muscles. Higher age was associated with higher IMF and lower aCSA for all muscles (r≥0.232, p<.001) except for LM and aCSA (r≤0.013, p≥.267). Higher BMI was associated with higher IMF and aCSA for all muscles (r≥0.174, p<.001). Higher physical activity was associated with lower IMF and higher aCSA for all muscles (r≥0.036, p≤.002) except for LM and aCSA (r≤0.010, p≥.405). People with chronic BP had higher IMF and lower aCSA than people with no pain (IMF MD≤1.6%, aCSA MD≤27.4 mm, p<.001) and higher IMF compared to acute BP (IMF MD≤1.1%, p≤.044). The differences between people with BP and people with no pain were not spatially localized to the inferior lumbar levels but broadly distributed across the lumbar spine.
Paraspinal muscle health is associated with age, BMI, sex, and physical activity with the exception of the association between LM aCSA and age and physical activity. People with BP (chronic>acute) have higher IMF and lower aCSA than people reporting no pain. The differences were not localized but broadly distributed across the lumbar spine. When interpreting measures of paraspinal muscle health in the research or clinical setting, the associations with age, BMI, sex, and physical activity should be considered.
腰椎旁肌健康与腰痛（BP）之间的关系并不简单。该领域的挑战包括缺乏工具和大型异质数据集来探究肌肉健康与 BP 之间的关联。计算机视觉模型在这一领域具有变革性，能够实现肌肉健康的自动量化和大型数据集的处理。
使用自动化计算机视觉模型研究大型异质数据集中心腰旁肌健康与年龄、性别、BMI、体力活动和 BP 之间的关联。
横断面研究。
纳入 UK Biobank 中具有腹部 Dixon 脂肪-水 MRI 的参与者（N=9564）（41.8%女性，平均[SD]年龄：63.5[7.6]岁，BMI：26.4[4.1]kg/m），其中 6953 人报告无疼痛，930 人急性 BP，1681 人慢性 BP。
使用计算机视觉模型自动从 L1 到 L5 椎体水平为左侧和右侧多裂肌（LM）、竖脊肌（ES）和腰大肌（PM）导出肌肉内脂肪（IMF）和平均横截面积（aCSA）。
对每块肌肉的肌肉测量值（IMF 和 aCSA）与年龄（控制 BMI、性别和体力活动）、BMI（控制年龄、性别和体力活动）和体力活动（控制年龄、性别和 BMI）之间进行双尾部分 Pearson 相关性分析。使用单向方差分析（ANCOVA）来识别每个肌肉的 IMF 和 aCSA 的性别差异，同时控制年龄、BMI 和体力活动。同样，使用单向方差分析（ANCOVA）来识别每个肌肉和腰椎上下沿的组间差异（无疼痛、急性 BP 和慢性 BP），同时控制年龄、BMI、性别和体力活动（α=0.05）。
女性的 IMF（LM 平均差异[MD]=11.1%，ES MD=10.2%，PM MD=0.3%，p<.001）和 aCSA（LM MD=47.6mm，ES MD=350.0mm，PM MD=321.5mm，p<.001）均高于所有肌肉。年龄与所有肌肉的 IMF 升高和 aCSA 降低相关（r≥0.232，p<.001），但 LM 和 aCSA 除外（r≤0.013，p≥.267）。BMI 与所有肌肉的 IMF 和 aCSA 升高相关（r≥0.174，p<.001）。体力活动与所有肌肉的 IMF 降低和 aCSA 升高相关（r≥0.036，p≤.002），但 LM 和 aCSA 除外（r≤0.010，p≥.405）。慢性 BP 患者的 IMF 高于无疼痛患者，aCSA 低于无疼痛患者（IMF MD≤1.6%，aCSA MD≤27.4mm，p<.001），且 IMF 高于急性 BP 患者（IMF MD≤1.1%，p≤.044）。BP 患者与无疼痛患者之间的差异不是局限于下腰椎水平，而是广泛分布于整个腰椎。
腰旁肌健康与年龄、BMI、性别和体力活动有关，除了 LM aCSA 与年龄和体力活动之间的关系。BP 患者（慢性>急性）的 IMF 高于无疼痛患者，aCSA 低于无疼痛患者。差异不是局限的，而是广泛分布于整个腰椎。在研究或临床环境中解释腰旁肌健康的测量值时，应考虑与年龄、BMI、性别和体力活动的关联。
