Network meta-analysis reaches nutrition research

Network meta-analysis (NMA) is increasingly recognized as a promising evidence synthesis method commonly allowing stronger conclusions on the comparative effectiveness of healthcare interventions than conventional pairwise metaanalysis [1]. Its strength arises from the fact that it allows to synthesize both direct and indirect evidence from randomized trials. It is hence timely that Hui et al. recently published an NMA in the European Journal of Nutrition [2], comparing the effects of different whole grains (oat, brown rice, barley, and wheat) and brans (oat bran and wheat bran) on blood lipids (total cholesterol, LDL-C, HDL-C, and triacylglycerols), using data from 55 trials. NMA allows inference on every possible pairwise comparison of interventions within a connected network. For example, in the paper by Hui et al. [2], oat bran and barley have not been directly compared in a randomized trial, but each has been compared with wheat (Fig. 1). As such, an indirect comparison between oat bran and barley can be obtained. Sometimes, the relative effects estimated by the network may rely to a notable extent on indirect comparisons (i.e., for which no trials were ever conducted); the influence of direct and indirect evidence on the results can be seen using the contribution matrix [3, 4]. In fact, in the NMA by Hui et al., the contribution of direct evidence to the relative effects estimated by the network was very low ranging from 0.3% (oat vs. wheat) to 15.9% (wheat vs. control). Nutrition research can substantially profit from the potential of NMA. However, it is crucial that authors meticulously plan, conduct, and report NMA [5, 6]; in particular, authors should follow a study protocol published a priori so as to improve transparency and perform a rigorous risk of bias assessment within and across studies as well as an evaluation of the quality of evidence. As Hui et al. are among the pioneers of applying NMA to the field of nutrition research, we draw on their article to highlight some methodological challenges that require specific attention when performing NMA. Summary effects from NMA are usually presented in a league table including all comparisons: Hui et al. [2] identified oat bran as the most effective intervention strategy, revealing clinically relevant mean differences (MD) in comparison with the control diet [improvements in total cholesterol (TC) (MD: − 0.35 mmol/L, 95% CI − 0.47, − 0.23 mmol/L) and LDL-C (MD: − 0.32 mmol/L, 95% CI − 0.44, − 0.19 mmol/L)]. Another unique feature of NMA is its ability to rank interventions in relation with the studied outcomes, using the distribution of the ranking probabilities and the surface under the cumulative ranking curve (SUCRA) [7]. SUCRA ranges from 0%, i.e., the treatment always ranks last without uncertainty, to 100%, i.e., the treatment always ranks first without uncertainty. In the NMA by Hui et al. [2] oat bran ranked as the best treatment for TC (SUCRA: 97%), LDL-C (SUCRA: 97%), and triacylglycerols (TG) (SUCRA: 78%), followed by oat (SUCRA: 79% for TC, 64% for LDL-C, 76% for TG). The extent to which NMA allows valid indirect inference depends on the extent to which the fundamental assumption of NMA usually called the ‘transitivity’, assumption is likely to be plausible. Transitivity requires that the trials comparing different sets of interventions are appreciably comparable in characteristics (other than the interventions being compared) which may affect the outcome [8, 9]. Transitivity should be evaluated prior to conducting NMA [8, 9], e.g., by examining whether the distributions of potential * Lukas Schwingshackl lukas.schwingshackl@dife.de