Association of Beta‐Blocker Use With Less Prevalent Joint Pain and Lower Opioid Requirement in People With Osteoarthritis
Bibliographic record
Abstract
Objective Recent findings suggest that β‐adrenergic blockers have antinociceptive properties. The aim of this study was to compare levels of large‐joint pain between those taking adrenergic blockers and those taking other antihypertensive medications. Methods Data from the Genetics of Osteoarthritis and Lifestyle (GOAL) study, a secondary‐care cohort of osteoarthritis (OA) patients, were used. Joint pain was assessed using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scores in 873 individuals with symptomatic hip and/or knee OA and hypertension, who were taking ≥1 prescription antihypertensive medications. The association between adrenergic blocker prescription and at least moderate joint pain (WOMAC score <75) and use of prescription analgesics was examined using binary logistic regression. Analyses were adjusted for age, sex, body mass index, knee or hip OA, history of joint replacement (at other joints), anxiety, and depression. Results The use of β‐adrenergic blockers was associated with lower WOMAC pain scores and with a lower prevalence of joint pain after adjustment for demographic variables and comorbidity (adjusted odds ratio [ORadj] for pain 0.68 [95% confidence interval (95% CI) 0.51, 0.92]; P < 0.011). No associations with pain were observed with use of alpha‐blockers (ORadj for pain 0.94 [95% CI 0.55, 1.58]) or with any other class of antihypertensive medications. Prescription of beta‐blockers was also associated negatively with opioid use (ORadj for opioids 0.73 [95% CI 0.54, 0.98]; P < 0.037) and with the use of prescription analgesics in general (ORadj for analgesics 0.74 [95% CI 0.56, 0.94]; P < 0.032). Conclusion The use of beta‐blockers is associated with less joint pain and a lower use of opioids and other analgesics in individuals with symptomatic large‐joint OA. This observation needs to be confirmed by other studies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".