Poster 177 Serum Biomarkers Relate to Pain and Pain Related Disability in Older Adults With Low Back Pain
Bibliographic record
Abstract
G. A. Sowa, Research grants: NIH, NIDRR. Intervertebral disc degeneration, as assessed by imaging studies, is ubiquitous among aging patients, and has poor correlation with patient symptoms. Our objective was to examine in older adults with chronic low back pain the relationship between plasma biomarkers and pain severity, pain-related activity limitation, and magnetic resonance imaging (MRI) degenerative changes. Cohort study. Academic medical center. We recruited a cohort of 44 adults 65 or older with axial low back pain for > 3 months. Exclusion criteria included serious illness, acute flare, contraindication to MRI, lumbar surgery, radiation of pain into the lower extremities, dementia, uncontrolled psychiatric illness, knee or hip osteoarthritis, or other pain more severe than the back pain. N/A. Clinical outcome measures included pain score, McGill Pain Questionnaire Short Form, and Roland-Morris Questionnaire. Performance based measures included the short physical performance battery and Repetitive Trunk Rotation. Single plasma samples were analyzed for E-selectin, RANTES (inflammatory markers), TIMP-1 (inhibitor of catabolic enzymes), CTX-II, CS846 (markers of matrix turnover), and NPY (stress biomarker). Conventional non-gadolinium lumbar MRI was obtained and analyzed quantitatively and clinically. Serum biomarkers demonstrated greater correlation with pain and pain related disability (RANTES R2 = 0.2; NPY R2 = 0.22) than MRI (R2 = 0-0.07). Statistically significant (P<.05) differences for gain in predictive value of clinical metrics over MRI were noted for TIMP-1, RANTES, NPY, CS846, and CTX-II. The strongest correlation in this cohort was between NPY levels and depression (R2 = 0.29). Combining biomarkers demonstrated even greater correlation, with moderate correlations between RANTES/NPY and depression (R2 =0.36) and CS846 /NPY and McGill affective score (R2 = 0.3). These results demonstrate correlations in novel biomarkers with clinical status, above and beyond the contribution of MRI. These novel serum based biomarkers reflect potential targets for use in designing individualized 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".