Associations Between Serum Biomarkers and Pain and Pain‐Related Function in Older Adults with Low Back Pain: A Pilot Study
Bibliographic record
Abstract
OBJECTIVES: To examine the relationship between serum biomarkers and self-reported pain intensity and pain-related function, in addition to the contribution of magnetic resonance imaging (MRI) findings of lumbar spine degenerative changes, in older adults with chronic low back pain. DESIGN: Single-center cross-sectional cohort study. SETTING: Academic medical center. PARTICIPANTS: Individuals aged 60 and older with axial low back pain without radiculopathy or previously diagnosed osteoarthritis of the knee or hip or pain outside the low back that is more severe than the back pain (n = 43). MEASUREMENTS: To examine pain-related impairment, pain was measured on a pain thermometer and the McGill Pain Questionnaire Short Form was administered. To examine pain-related function or activity limitation, the Roland Morris Disability Questionnaire, Short Physical Performance Battery (SPPB), and repetitive trunk rotation were used. Single plasma samples were obtained before and after physical performance tests and analyzed for inflammatory markers (E-selectin and regulated on activation, normal T cell expressed and secreted (RANTES)), inhibitors of catabolic enzymes (tissue inhibitor of metalloproteinases-1 (TIMP-1)), markers of matrix turnover (C- telopeptide of type II collagen (CTX-II) and aggrecan chondroitin sulfate 846 (CS846)), and stress biomarkers (neuropeptide Y (NPY)). Conventional nongadolinium lumbar MRI was performed and analyzed quantitatively and clinically. RESULTS: Composite MRI measurements did not show significant correlation with pain or pain-related function. Basal levels and changes in serum biomarkers in response to activity, particularly NPY and RANTES, demonstrated associations with pain and pain-related function in addition to the explanatory power of MRI-based results. CONCLUSION: Serum biomarkers may be a metric for assessment of active disease in older adults, in whom imaging changes are ubiquitous. In addition, changing levels of biomarkers in response to activity suggests that they may be useful as metrics to measure treatment responses in future studies and may reflect potential targets for use in designing personalized treatment for older adults with low back pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".