Tag: Sarcopenia

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The effect of paraspinal sarcopenia on postoperative sagittal balance: a multivariate analysis following multilevel lumbar fusion surgery

The Spine Journal 26 (2026) 709−719

This clinical study examines how paraspinal sarcopenia influences long-term sagittal alignment and functional outcomes after multilevel posterior lumbar interbody fusion. Using preoperative MRI/CT and serial radiographs, muscle cross-sectional area, fat infiltration, and spinopelvic parameters were measured to compare sarcopenic and nonsarcopenic patients over at least two years.

Results show multifidus atrophy and fatty infiltration, plus inadequate preoperative lumbar and segmental lordosis, independently predict postoperative sagittal imbalance and worse pain and disability. The authors recommend preoperative paraspinal muscle assessment, nutritional and rehabilitation optimization, and tissue-sparing techniques to improve long-term outcomes.

Study aim: Assessed how paraspinal sarcopenia affects long-term sagittal alignment and persistent pain/disability after multilevel posterior lumbar interbody fusion (PLIF), and identified risk factors for postoperative sagittal imbalance.

Design & cohort: Retrospective single-institution study of 213 multilevel PLIF patients with imaging follow-up through ≥2 years; sarcopenic (n=69) vs nonsarcopenic (n=143/144) groups were compared.

Sarcopenia definition: Grouping based on psoas muscle index (MI) at L3 with thresholds <6.36 cm²/m² (men) and <3.92 cm²/m² (women).

Key measurements: Quantified L3 psoas/erector spinae/multifidus muscle MI, fat infiltration (Goutallier grading), and muscle density (CT HU); tracked spinopelvic parameters including LL, SL, PT, PI-LL, SVA plus VAS and ODI outcomes.

Muscle differences by group: Sarcopenic patients had lower muscle indices and higher fat infiltration (especially erector spinae and multifidus), with no significant difference in muscle density reported.

Alignment & outcomes: Sarcopenic patients showed worse long-term sagittal alignment at final follow-up (differences in LL, SL, PT, PI-LL, SVA) and worse long-term VAS and ODI scores, despite similar preoperative clinical scores.

Independent risk factors: Multifidus atrophy (lower MMI) and multifidus fat infiltration plus insufficient preoperative LL and SL were independent predictors of long-term postoperative sagittal imbalance; psoas, erector spinae, and other balance parameters were not independently associated.

Practical implication: Better sagittal-balance maintenance was associated with larger paraspinal MI, reduced fat infiltration, and favorable preoperative LL/SL, supporting preoperative evaluation of muscle health, nutritional status, and alignment.

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Sarcopenia Predicts Early Adjacent Segment Disease Development After Anterior and Oblique Lumbar Interbody Fusion

Operative Neurosurgery 29:667–677, 2025

his clinical study evaluates whether sarcopenia, measured by psoas-to-vertebral body morphometrics on preoperative MRI, predicts early adjacent segment disease (ASD) within three years after anterior or oblique lumbar interbody fusion (ALIF/OLIF). Retrospective analysis of 104 patients found sarcopenia strongly associated with ASD, with sarcopenic patients showing markedly higher ASD rates and greater muscle fat infiltration.

The paper reports that psoas area, P:VBR ratios, and age remained significant predictors on multivariate analysis, while spinopelvic parameters lost significance after adjustment. The authors propose routine preoperative morphometric screening to guide surgical planning, risk counseling, and targeted perioperative optimization for high-risk patients.

• Sarcopenia: Preoperative sarcopenia, defined by reduced psoas muscle area relative to vertebral body area, is a strong independent predictor of early adjacent segment disease (ASD) within 3 years after anterior or oblique lumbar interbody fusion (ALIF/OLIF) ().

• ASD Incidence: 24% of patients developed ASD within 3 years post-ALIF/OLIF; 84.21% of sarcopenic patients developed ASD compared to 10.59% of nonsarcopenic patients.

• Morphometric Assessment: Psoas:vertebral body ratio (P:VBR) at L4 on preoperative MRI provides a simple, objective screening tool for sarcopenia and ASD risk stratification.

• Spinopelvic Parameters: Postoperative pelvic tilt (PT) and pelvic incidence-lumbar lordosis (PI-LL) mismatch were associated with ASD in univariate analysis, but not after multivariate adjustment; sarcopenia remained the strongest predictor.

• Other Risk Factors: Older age, higher preoperative disability (ODI), more instrumented/interbody levels, and prior lumbar surgery also increased ASD risk, while gender, BMI, and comorbidities did not.

• Instrumentation vs. Stand-alone Cages: Patients with stand-alone ALIF/OLIF (no posterior instrumentation) had lower rates of ASD, possibly due to less mechanical stress on adjacent segments.

• Clinical Implications: Identifying sarcopenia preoperatively can inform surgical planning, patient counseling, and may guide targeted pre- and postoperative interventions to improve outcome.

• Future Directions: Further research is needed to determine if interventions like resistance training or dietary modification can reduce ASD risk in sarcopenic patients, and to establish standardized diagnostic criteria for sarcopenia in spine surgery.

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Sarcopenia Predicts the Development of Early Adjacent Segment Disease After Transforaminal Lumbar Interbody Fusion

Neurosurgery 96:1044–1053, 2025

This study explores whether sarcopenia, measured by psoas morphometrics, predicts early adjacent segment disease (ASD) after transforaminal lumbar interbody fusion (TLIF). Results indicate that decreased psoas area and P:VBR are strong predictors of ASD within three years post-surgery, suggesting implications for surgical decision-making and patient counseling.

• Sarcopenia predicts early adjacent segment disease (ASD) after transforaminal lumbar interbody fusion (TLIF) surgery, as shown by decreased psoas area and P:VBR ratios.

• A retrospective study of 109 patients found that 22 (20.2%) developed ASD within 3 years post-surgery.

• Sarcopenic patients had significantly higher rates of ASD (83.33%) compared to nonsarcopenic patients (7.69%).

• Older age, diabetes, and preoperative ODI are significant predictors of ASD.

• Sarcopenia is a stronger predictor of ASD than spinopelvic parameters like PT, LL, and PI-LL mismatch.

• Identifying sarcopenic patients can guide surgical decisions and postoperative care to prevent ASD.

• The study suggests using psoas morphometrics as a simple tool to identify patients at risk for ASD.

• Further research is needed to validate findings and explore the role of sarcopenia in other surgical approaches.

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An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

European Spine Journal (2022) 31:2156–2164

Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset.

Methods A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers.

Results The median Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network, although occasional failures were noted. Cross-sectional area and fat fraction of the muscles were in agreement with published data.

Conclusions The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in an accurate and fully automated manner, although it is not without limitations. The model is therefore a suitable research tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

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