Meta-analyses summarize evidence about an association across multiple sources of information, increasing statistical power and exploring sources of heterogeneity. Yet, meta-analyses often neglect the causal structure behind an association, failing to distinguish between total and direct effects. When summarizing the effect of an exposure on an outcome in the presence of a mediator, it is essential that the total effect is provided. However, when the total effect is unavailable, some meta-analyses include the direct effect in place of the total effect, biasing the summary of the association. We develop methods to estimate point and interval estimates of the mediation proportion and total effect in this setting, filling an important methodological gap in existing evaluation approaches. In addition to reducing bias, by leveraging a summary mediation proportion, our method is able to include a wider range of studies in the meta-analysis, thus providing more efficient estimates. The methodology is illustrated by a meta-analysis of sugar-sweetened beverage (SSB) consumption in relation to the incidence of type II diabetes, where the estimated summary total effect increased by about 1/3.