Investigating the associations between lumbar paraspinal muscle health and age, BMI, sex, physical activity, and back pain using an automated computer-vision model: a UK Biobank study.

BACKGROUND CONTEXT: 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. PURPOSE: 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. DESIGN: Cross-sectional study. PATIENT SAMPLE: 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/m2) of whom 6,953 reported no pain, 930 acute BP, and 1,681 chronic BP. OUTCOME MEASURES: 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. METHODS: 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). RESULTS: 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 mm2, ES MD=350.0 mm2, PM MD=321.5 mm2, 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 mm2, 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. CONCLUSIONS: 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.

The Problem of Pain in the United States: A Population-Based Characterization of Biopsychosocial Correlates of High Impact Chronic Pain Using the National Health Interview Survey.

Over 20 million adults in the United States live with high impact chronic pain (HICP), or chronic pain that limits life or work activities for ≥3 months. It is critically important to differentiate people with HICP from those who sustain normal activities although experiencing chronic pain. Therefore, we aim to help clinicians and researchers identify those with HICP by: 1) developing models that identify factors associated with HICP using the 2016 national health interview survey (NHIS) and 2) evaluating the performances of those models overall and by sociodemographic subgroups (sex, age, and race/ethnicity). Our analysis included 32,980 respondents. We fitted logistic regression models with LASSO (a parametric model) and random forest (a nonparametric model) for predicting HICP using the whole sample. Both models performed well. The most important factors associated with HICP were those related to underlying ill-health (arthritis and rheumatism, hospitalizations, and emergency department visits) and poor psychological well-being. These factors can be used for identifying higher-risk sub-groups for screening for HICP. We will externally validate these findings in future work. We need future studies that longitudinally predict the initiation and maintenance of HICP, then use this information to prevent HICP and direct patients to optimal treatments. PERSPECTIVE: Our study developed models to identify factors associated with high-impact chronic pain (HICP) using the 2016 National Health Interview Survey. There was homogeneity in the factors associated with HICP by gender, age, and race/ethnicity. Understanding these risk factors is crucial to support the identification of populations and individuals at highest risk for developing HICP and improve access to interventions that target these high-risk subgroups.

Spinal manipulation and modulation of pain sensitivity in persistent low back pain: a secondary cluster analysis of a randomized trial.

BACKGROUND: Pain hypersensitivity can be assessed using Quantitative Sensory Testing (QST) and is associated with persistent low back pain. Spinal manipulation appears to modify pain hypersensitivity, and this could function as one mechanism leading to clinical improvements. In the current study, we applied a comprehensive QST battery to assess pain sensitivity in a cohort of low back pain patients before and after spinal manipulation to improve our understanding of the association between QST and clinical improvements. This study addresses two questions: Are clinical improvements following spinal manipulation in low back pain patients contingent on pain hypersensitivity, and does pain sensitivity change following spinal manipulation? METHODS: We performed a secondary analysis of data from a randomized clinical trial. One hundred and thirty-two participants with persistent LBP were treated with spinal manipulation four times over two weeks. Patient-reported outcomes and QST were assessed at baseline, after the fourth spinal manipulation session, and 14-days later. The clinical outcomes were changes in low back pain intensity and disability. Using latent profile analysis, we categorized the participants into clusters depending on their baseline QST scores. We used linear mixed models to examine the association between clusters and changes in patient-reported outcomes and QST. RESULTS: Two clusters emerged: a Sensitized and a Not sensitized. The former had significantly lower regional pressure and thermal pain thresholds, remote pressure pain tolerance, and lower inhibitory conditioned pain modulation than the Not sensitized group. However, we only found between-cluster differences for regional pressure pain threshold following spinal manipulation. Thus, the clusters were not associated with patient-reported pain and disability changes or the remaining QST outcomes. CONCLUSIONS: We report that the baseline QST profile was not associated with clinical improvements following spinal manipulation. We did observe a substantial change for regional pressure pain threshold, which suggests that any effect of spinal manipulation on pain sensitivity is most likely to be observed as changes in regional, mechanical pain threshold. However, the mechanism that invokes clinical improvement and pain sensitivity changes appear distinct. Due to methodological caveats, we advise caution when interpreting the results. TRIAL REGISTRATION: Clinical.Trial.gov identifier: NCT04086667 , registered 11 September 2019 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04086667.