Osteoarthritis and the risk of cardiovascular disease: a meta-analysis of observational studies

Previous observational studies have suggested a potential relationship between osteoarthritis (OA) and the risk of cardiovascular disease (CVD), with conflicting results. We aimed to provide a systematic and quantitative summary of the association between OA and the risk of CVD. We searched Medline and EMBASE to retrieve prospective and retrospective studies that reported risk estimates of the association between OA status and CVD risk. Pooled estimates were calculated by a random effects model. The search yielded 15 articles including a total of 358,944 participants, including 80,911 OA patients and 29,213 CVD patients. Overall, the risk of CVD was significantly increased by 24% (RR: 1.24, 95% CI: 1.12 to 1.37, P < 0.001) in patients with OA compared with the general population, with no significant publication bias. Furthermore, sensitivity analysis indicated that our results were robust and were not influenced by any one study. In conclusion, this meta-analysis provides strong evidence that OA is a significant risk factor for CVD.


Supplementary file
Contents:     Figure S4. Meta-regression analysis investigating potential effect of the mean age of the exposed group on CVD risk associated with OA. Figure S5. Meta-regression analysis investigating potential effect of the mean age of the non-exposed group on CVD risk associated with OA. NA Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

14, 15
Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.
14 Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.
14 Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

14, 15
Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

15
Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

15
Risk of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

16
Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2 ) for each meta-analysis.

RESULTS
Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

5
Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

5, 6
Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12).

6
Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

6, 7
Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency.

6-9
Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15).

DISCUSSION
Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

10-13
Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).

11, 12
Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research.

FUNDING
Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.   Quality level was defined as low (≤5), medium (6-8) or high (≥9) according to quality score. 2) Selection of the non exposed cohort a) drawn from the same community as the exposed cohort 2 2 2 2 2 2 2 2 2 2 2 b) drawn from a different source 1 c) no description of the derivation of the non   Circles represent individual results with the size of the circle being proportional to its weight in the random-effects meta-analysis.
Meta-regression line (solid line) was estimated using a random-effect linear meta-regression model with follow-up time as the covariate. The result indicated that there was no significant effect (p = 0.308) of follow-up time on CVD risk associated with OA. Figure S4. Meta-regression analysis investigating potential effect of the mean age of the exposed group on CVD risk associated with OA. Circles represent individual results with the size of the circle being proportional to its weight in the random-effects meta-analysis. Meta-regression line (solid line) was estimated using a random-effect linear meta-regression model with mean age of OA group (the exposed group) as the covariate. The result indicated that there was no significant effect (p = 0.196) of mean age of the exposed group on CVD risk associated with OA. Figure S5. Meta-regression analysis investigating potential effect of the mean age of the non-exposed group on CVD risk associated with OA.
Circles represent individual results with the size of the circle being proportional to its weight in the random-effects meta-analysis.
Meta-regression line (solid line) was estimated using a random-effect linear meta-regression model with mean age of control group (the non-exposed group) as the covariate. The result indicated that there was no significant effect (p = 0.575) of mean age of the non-exposed group on CVD risk associated with OA.   Figure S4. Meta-regression analysis investigating potential effect of the mean age of the exposed group on CVD risk associated with OA. Figure S5. Meta-regression analysis investigating potential effect of the mean age of the non-exposed group on CVD risk associated with OA.