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
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".