Rising rates of gestational diabetes mellitus (GDM) and related complications have prompted calls to identify potentially modifiable risk factors that are associated with gestational diabetes mellitus (GDM). We systematically reviewed the scientific literature for observational studies examining specific dietary and/or physical activity (PA) factors and risk of GDM. Our search included PubMed, Medline, CINAHL/EBSCO, Science Direct and EMBASE, and identified 1167 articles, of which 40 met our inclusion criteria (e.g., singleton pregnancy, reported diet or PA data during pre-pregnancy/early pregnancy and GDM as an outcome measure). Studies were assessed for quality using a modified Quality Criteria Checklist from American Dietetic Association. Of the final 40 studies, 72% obtained a positive quality rating and 28% were rated neutral. The final analysis incorporated data on 30,871 pregnant women. Dietary studies were categorised into either caffeine, carbohydrate, fat, protein, calcium, fast food and recognized dietary patterns. Diets such as Mediterranean Diet (MedDiet), Dietary Approaches to Stop Hypertension (DASH) diet and Alternate Healthy Eating Index diet (AHEI) were associated with 15–38% reduced relative risk of GDM. In contrast, frequent consumption of potato, meat/processed meats, and protein (% energy) derived from animal sources was associated with an increased risk of GDM. Compared to no PA, any pre-pregnancy or early pregnancy PA was associated with 30% and 21% reduced odds of GDM, respectively. Engaging in >90 min/week of leisure time PA before pregnancy was associated with 46% decreased odds of GDM. We conclude that diets resembling MedDiet/DASH diet as well as higher PA levels before or in early pregnancy were associated with lower risks or odds of GDM respectively. The systematic review was registered at PROSPERO (www.crd.york.ac.uk/PROSPERO) as CRD42016027795.

Although meta‐analysis has become an essential tool in ecology and evolution, reporting of meta‐analytic results can still be much improved. To aid this, we have introduced the orchard plot, which presents not only overall estimates and their confidence intervals, but also shows corresponding heterogeneity (as prediction intervals) and individual effect sizes. Here, we have added significant enhancements by integrating many new functionalities intoorchaRd 2.0. This updated version allows the visualisation of heteroscedasticity (different variances across levels of a categorical moderator), marginal estimates (e.g. marginalising out effects other than the one visualised), conditional estimates (i.e. estimates of different groups conditioned upon specific values of a continuous variable) and visualisations of all types of interactions between two categorical/continuous moderators. orchaRd 2.0has additional functions which calculate key statistics from multilevel meta‐analytic models such asI²andR². Importantly,orchaRd 2.0contributes to better reporting by complying with PRISMA‐EcoEvo (preferred reporting items for systematic reviews and meta‐analyses in ecology and evolution). Taken together,orchaRd 2.0can improve the presentation of meta‐analytic results and facilitate the exploration of previously neglected patterns. In addition, as a part of a literature survey, we found that graphical packages are rarely cited (~3%). We plea that researchers credit developers and maintainers of graphical packages, for example, by citations in a figure legend, acknowledging the use of relevant packages.

The log response ratio, lnRR, is the most frequently used effect size statistic for meta‐analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study‐specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta‐analyses of lnRR, regardless of ‘missingness’.