A Prospective Pilot Study Investigating the Musculoskeletal Pain in Postmenopausal Breast Cancer Patients Receiving Aromatase Inhibitor Therapy
Bibliographic record
Abstract
BACKGROUND: Although arthralgia is a known adverse effect of aromatase inhibitor (ai) treatment in postmenopausal breast cancer patients, few studies have carried out a comprehensive evaluation of the nature, onset, and incidence of musculoskeletal (msk) pain in these patients. We therefore used a pilot study to identify conditions or markers predictive of pain. METHODS: For 24 weeks, we monitored 30 eligible postmenopausal women starting ai therapy. Pre-existing and incident msk conditions and pain were assessed clinically and with ultrasonography of the hands and wrists. In addition, patient questionnaires were used to assess pain before and during ai therapy. Biochemical markers were measured at baseline and at regular intervals after anastrozole therapy began. Gene profiling studies were carried out before and 48 hours after the initial ai administration. RESULTS: Over the 24-week study period, 20 participants (67%) showed no pain symptoms; 5 (17%) experienced low or moderate pain at baseline, which did not increase with ai treatment; and during therapy, 5 (17%) showed exacerbation of pain attributable to osteoarthritis of the hand and to finger flexor tenosynovitis. Although all 30 participants had some degree of msk conditions before anastrozole therapy started, the pre-existing conditions did not necessarily predispose the women to increased pain during anastrozole treatment. Higher levels of urinary N-telopeptides of type i collagen were associated with the groups presenting pain, suggesting a higher extent of pre-existing bone resorption, without significant evolution over the 24-week treatment period. Slightly higher levels of 1,25(OH)(2) vitamin D(3) were observed at baseline in patients with pain increase, but did not significantly change during treatment; however, average levels of 25(OH) vitamin D(3) increased, likely because of supplementation. Although biochemical markers did not discriminate efficiently between pain groups, a signature of 166 genes in peripheral blood mononuclear cells was identified that could stratify patients into the various groups observed in this pilot study. The gene signature was enriched in components of inflammatory signalling and chemokine expression, of antitumoural immunity pathways, and of metabolic response to hormones and xenobiotics, although no clinically significant association could be made in the present study, considering the small number of patients. Nevertheless, the observed trend suggests the feasibility of developing surrogate predictive markers of msk pain. Patient compliance was high in this study and was not affected by pain exacerbation. CONCLUSIONS: Baseline msk assessment showed pre-existing causes for pain in most of the study patients before initiation of the ai. Exacerbation of existing osteoarthritis pain and tenosynovial symptoms was the primary cause of pain increase. Musculoskeletal pain assessment at baseline and prompt treatment of pain symptoms may help to optimize adherence to ai therapy. The value of routinely assessing inflammatory markers such as C-reactive protein and erythrocyte sedimentation rate was not supported by our pilot study. Gene expression profiles in peripheral blood mononuclear cells may be further explored in larger-scale studies as stratification markers to identify patients at risk of developing arthralgia.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".