A Prospective Pilot Study Investigating the Musculoskeletal Pain in Postmenopausal Breast Cancer Patients Receiving Aromatase Inhibitor Therapy
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».