The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Association Between Serum Uric Acid and Development of Type 2 Diabetes
Open accessAbstract
OBJECTIVE: To systematically evaluate the association between serum uric acid (SUA) level and subsequent development of type 2 diabetes. RESEARCH DESIGN AND METHODS: We searched Medline (31 March from 1966 to 2009) and Embase (31 March from 1980 to 2009) for observational cohort studies examining the association between SUA and the risk of type 2 diabetes by manual literature search. Relative risks (RRs) for each 1 mg/dl increase in SUA were pooled by using a random-effects model. The studies included were stratified into subgroups representing different study characteristics, and meta-regression analyses were performed to investigate the effect of these characteristics on the association between SUA level and type 2 diabetes risk. RESULTS: The search yielded 11 cohort studies (42,834 participants) that reported 3,305 incident cases of type 2 diabetes during follow-up periods ranging from 2.0 to 13.5 years. The pooled RR of a 1 mg/dl increase in SUA was 1.17 (95% CI 1.09–1.25). Study results were consistently significant (i.e., >1) across characteristics of participants and study design. Publication bias was both visually and statistically suggested (P = 0.03 for Egger's test, 0.06). Adjustment for publication bias attenuated the pooled RR per mg/dl increase in SUA (RR 1.11 [95% CI 1.03–1.20]), but the association remained statistically significant (P = 0.009). CONCLUSIONS: The current meta-analysis suggests that SUA level is positively associated with the development of type 2 diabetes regardless of various study characteristics. Further research should attempt to determine whether it is effective to utilize SUA level as a predictor of type 2 diabetes for its primary prevention.
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Bias in meta-analysis detected by a simple, graphical test
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997) · BMJ · doi:10.1136/bmj.315.7109.629 · Cited by 56,722
Meta-analysis in clinical trials
DerSimonian, R., & Laird, N. (1986) · Controlled Clinical Trials · doi:10.1016/0197-2456(86)90046-2 · Cited by 39,552
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763
Meta-analysis of Observational Studies in Epidemiology<SUBTITLE>A Proposal for Reporting</SUBTITLE>
Stroup, D. F. (2000) · JAMA · doi:10.1001/jama.283.15.2008 · Cited by 20,623
Operating characteristics of a rank correlation test for publication bias
Begg, C. B., & Mazumdar, M. (1994) · Biometrics · doi:10.2307/2533446 · Cited by 16,955
Duval, S., & Tweedie, R. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00455.x · Cited by 14,099
Zhang, J., & Yu, K. F. (1998) · JAMA · doi:10.1001/jama.280.19.1690 · Cited by 4,138
Publication and related bias in meta-analysis
Sterne, J. A. C., Gavaghan, D., & Egger, M. (2000) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(00)00242-0 · Cited by 2,282
Danesh, J., Collins, R., Appleby, P., & Peto, R. (1998) · JAMA · doi:10.1001/jama.279.18.1477 · Cited by 2,026
Empirical assessment of effect of publication bias on meta-analyses
Sutton, A. J. (2000) · BMJ · doi:10.1136/bmj.320.7249.1574 · Cited by 1,398
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Metadata
- Authors
- Kodama, S., Saito, K., Yachi, Y., Asumi, M., Sugawara, A., Totsuka, K., Saito, A., & Sone, H.
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2009-06-23
- Volume
- 32
- Issue
- 9
- Pages
- 1737-1742
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W2098458611
- Cited by
- 549
- References
- 34
- FWCI
- 10.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
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