Risk factors for rotator cuff disease: A systematic review and meta-analysis of diabetes, hypertension, and hyperlipidemia

Abstract

Background: Rotator cuff disease is a common cause of shoulder pain. Comorbidities such as diabetes, hypertension, and hyperlipidemia may be associated with rotator cuff disease, likely because of mechanisms related to vascular insufficiency.

Objectives: We performed a systematic review of the association of diabetes, hypertension, and hyperlipidemia with the diagnosis of rotator cuff disease.

Methods: Following systematic queries of PubMed, Embase, Cochrane, CINAHL, and Science Direct, articles meeting eligibility criteria and reporting on the association of one or more risk factors (diabetes, hypertension, and hyperlipidemia) and rotator cuff disease were considered. Meta-analysis was performed to quantitatively summarize the associations between each risk factor and rotator cuff disease. We assessed study quality with the Newcastle-Ottawa Scale (NOS) and performed a qualitative assessment of risk of bias.

Results: After a full-text review of 212 articles, 12 articles assessing diabetes, 5 assessing hypertension and 8 assessing hyperlipidemia were eligible. The odds of having rotator cuff disease was increased with diabetes (odds ratio [OR] 1.49, 95% confidence interval [CI] 1.43-1.55), hypertension (OR 1.40, 95% CI 1.19-1.65) and hyperlipidemia/dyslipidemia (OR 1.48, 95% CI 1.42-1.55). Diabetes was also specifically associated with rotator cuff tears (OR 1.28, 95% CI 1.07-1.52). Synthesizing assessment for risk of bias suggested that current epidemiologic evidence for an association was plausible for diabetes and hyperlipidemia but not hypertension.

Conclusions: Diabetes, hypertension, and hyperlipidemia were associated with rotator cuff disease in our meta-analysis. However, the possibility of bias exists for all 3 co-morbidities evaluated and is likely highest for hypertension. High-quality studies with the ability to incorporate time since first diagnosis of co-morbidity are scarce and much needed.

Keywords: diabetes; hyperlipidemia; hypertension; meta-analysis; observational studies; rotator cuff disease; rotator cuff tears; systemtic review.

Figure 1.

PRISMA flow chart of the selection of articles.

Figure 2.. Forest plot and funnel plot from inverse variance fixed-effects meta-analysis showing associations between diabetes status and rotator cuff disease.

a) Shows effect estimates (odds ratios) and 95% confidence intervals and the assumed weights of contributing studies evaluating the association between diabetes and rotator cuff disease. The meta-analysis odds ratio was computed using the individual effect estimates weighted by the inverse of the variance for each study. b) Funnel plot of the estimates included in the meta-analysis depicted in 2a. The funnel plot shows the distribution of effect estimates as a function of the standard error of each reporting study. Each point represents a contributing effect estimate, and symmetrical distribution of these points around the central vertical line (the meta-analysis log[odds]) suggests a lack of evidence of small-study bias, whereas a lack of symmetry is suggestive of small-study bias. ES, effect estimate

Figure 3.. Forest plot from inverse variance fixed-effects meta-analysis showing associations between hypertension status and rotator cuff disease.

Shows effect estimates (odds ratios) and 95% confidence intervals, and the assumed weights of contributing studies evaluating the association between hypertension and rotator cuff disease. The meta-analysis odds ratio was computed using the individual effect estimates weighted by the inverse of the variance for each study. ES, effect estimate

Figure 4.. Forest plot from inverse variance fixed-effects meta-analysis showing associations between hyperlipidemia/dyslipidemia status and rotator cuff disease.

Shows effect estimates (odds ratios) and 95% confidence intervals, and the assumed weights of contributing studies evaluating the association between hyperlipidemia/dyslipidemia and rotator cuff disease. The meta-analysis odds ratio was computed using the individual effect estimates weighted by the inverse of the variance for each study. ES, effect estimate