Diabetes mellitus and hyperlipidaemia as risk factors for frequent pain in the back, neck and/or shoulders/arms among adults in Stockholm 2006 to 2010 – Results from the Stockholm Public Health Cohort
Notice bibliographique
Résumé
BACKGROUND AND AIMS: Frequent back, neck and/or shoulder pain (BNSP) are common conditions which pose high burden for the society. Results from previous studies suggest that diabetes and hyperlipidaemia may be associated with a higher risk of getting such conditions, but there is in general, few studies based on longitudinal designs. The aim of this study was therefore to compare the risk of developing frequent BNSP in men and women with and without diabetes and/or hyperlipidaemia. METHODS: A longitudinal study based on the Stockholm Public Health Cohort was conducted based on subjects aged 45-84, who were free from pain at the mentioned sites in 2006 and followed up until 2010. The data in the current study is based on questionnaires, except socioeconomic status which was derived from Statistics Sweden. The exposure diabetes and hyperlipidaemia was self-reported and, a categorical variable was created; without any of the conditions, with hyperlipidaemia only, with diabetes only and with both conditions. The outcome frequent BNSP was defined using the following questions in the questionnaire in 2010: "During the past 6months, have you had pain in the neck or upper part of the back?", "During the past 6months, have you had pain in the lower back?", and "During the past 6months, have you had pain in the shoulders/arms?". All questions had three possible response options: no; yes, a couple of days per month or less often and; yes, a couple of days per week or more often. Those who reported weekly pain to at least one of these questions were considered to having frequent BNSP. Binomial regressions were run to calculate the crude and adjusted risk ratio (RR) in men and women separately. Additional analysis was performed in order to control for potential bias derived from individuals lost to follow-up. RESULTS: A total of 10,044 subjects fulfilled the criteria to be included in the study. The mean age of the sample was 60years and evenly distributed by sex. After adjusting for age, body mass index, physical activity, high blood pressure and socioeconomic status, the RR for frequent BNSP among men with diabetes was 1.64 (95% CI: 1.23-2.18) and 1.19 (95% CI: 0.98-1.44) for hyperlipidaemia compared to men with neither diabetes nor hyperlipidaemia. Among women the corresponding RRs were 0.92 (95% CI: 0.60-1.14) and 1.23 (95% CI: 1.03-1.46). Having both diabetes and hyperlipidaemia at baseline was not associated with increased risk of frequent BNSP. Diabetes and hyperlipidaemia seems to be associated with an increased risk for frequent BNSP and the risk may differ between men and women. Behaviours and/or biological underlying mechanisms may explain the results. CONCLUSIONS: This study suggests that metabolic diseases such as diabetes and hyperlipidaemia may have an impact on the pathophysiology of frequent BNSP and thus, contributes to the knowledge in musculoskeletal health. Furthermore, it confirms that men and women may differ in terms of risk factors for BNSP. IMPLICATIONS: Health professionals should contemplate the results from this study when planning primary prevention strategies.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».