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Background: Network meta-analysis (NMA) has become a popular method to compare more than two treatments. This scoping review aimed to explore the characteristics and methodological quality of knowledge synthesis approaches underlying the NMA process. We also aimed to assess the statistical methods applied using the Analysis subdomain of the ISPOR checklist.Methods: Comprehensive literature searches were conducted in MEDLINE, PubMed, EMBASE, and Cochrane Database of Systematic Reviews from inception until April 14, 2015. References of relevant reviews were scanned. Eligible studies compared at least four different interventions from randomised controlled trials with an appropriate NMA approach. Two reviewers independently performed study selection and data abstraction of included articles. All discrepancies between reviewers were resolved by a third reviewer. Data analysis involved quantitative (frequencies) and qualitative (content analysis) methods. Quality was evaluated using the AMSTAR tool for the conduct of knowledge synthesis and the ISPOR tool for statistical analysis.Results: After screening 3538 citations and 877 full-text papers, 456 NMAs were included. These were published between 1997 and 2015, with 95% published after 2006. Most were conducted in Europe (51%) or North America (31%), and approximately one-third reported public sources of funding. Overall, 84% searched two or more electronic databases, 62% searched for grey literature, 58% performed duplicate study selection and data abstraction (independently), and 62% assessed risk of bias. Seventy-eight (17%) NMAs relied on previously conducted systematic reviews to obtain studies for inclusion in their NMA. Based on the AMSTAR tool, almost half of the NMAs incorporated quality appraisal results to formulate conclusions, 36% assessed publication bias, and 16% reported the source of funding. Based on the ISPOR tool, half of the NMAs did not report if an assessment for consistency was conducted or whether they accounted for inconsistency when present. Only 13% reported heterogeneity assumptions for the random-effects model.Conclusions: The knowledge synthesis methods and analytical process for NMAs are poorly reported and need improvement. [ABSTRACT FROM AUTHOR]

Leptin, an appetite-regulatory hormone, is also known to act as a proinflammatory adipokine. One of the effects of increased systemic leptin concentrations may be greater sensitivity to pain. We report the results of two studies examining the association between leptin and pain: a small pilot longitudinal study, followed by a large cross-sectional study. In Study 1, three women with physician-diagnosed fibromyalgia provided blood draws daily for 25 consecutive days, as well as daily self-reported musculoskeletal pain. Daily fluctuations in serum leptin were positively associated with pain across all three participants ( F (1,63) = 12.8, p < 0.001), with leptin predicting ∼49% of the pain variance. In Study 2, the relationship between leptin and body pain was examined in a retrospective cross-sectional analysis of 5676 generally healthy postmenopausal women from the Women's Health Initiative. Leptin levels obtained from single blood draws were tested for a relationship with self-reported body pain. Body mass index (BMI) was also included as a predictor of pain. Both leptin and BMI were found to be independently associated with self-reported pain ( p = 0.001 and p < 0.001, respectively), with higher leptin levels and greater BMI each being associated with greater pain. Leptin appears to be a predictor of body pain both within- and between-individuals and may be a driver of generalized pain states such as fibromyalgia. [ABSTRACT FROM AUTHOR]