Conclusions Moderate certainty evidence shows that most macronutrient diets, over six months, result in modest weight loss and substantial improvements in cardiovascular risk factors, particularly blood pressure. At 12 months the effects on weight reduction and improvements in cardiovascular risk factors largely disappear.

Results 121 eligible trials with 21 942 patients were included and reported on 14 named diets and three control diets. Compared with usual diet, low carbohydrate and low fat diets had a similar effect at six months on weight loss (4.63 v 4.37 kg, both moderate certainty) and reduction in systolic blood pressure (5.14 mm Hg, moderate certainty v 5.05 mm Hg, low certainty) and diastolic blood pressure (3.21 v 2.85 mm Hg, both low certainty). Moderate macronutrient diets resulted in slightly less weight loss and blood pressure reductions. Low carbohydrate diets had less effect than low fat diets and moderate macronutrient diets on reduction in LDL cholesterol (1.01 mg/dL, low certainty v 7.08 mg/dL, moderate certainty v 5.22 mg/dL, moderate certainty, respectively) but an increase in HDL cholesterol (2.31 mg/dL, low certainty), whereas low fat (−1.88 mg/dL, moderate certainty) and moderate macronutrient (−0.89 mg/dL, moderate certainty) did not. Among popular named diets, those with the largest effect on weight reduction and blood pressure in comparison with usual diet were Atkins (weight 5.5 kg, systolic blood pressure 5.1 mm Hg, diastolic blood pressure 3.3 mm Hg), DASH (3.6 kg, 4.7 mm Hg, 2.9 mm Hg, respectively), and Zone (4.1 kg, 3.5 mm Hg, 2.3 mm Hg, respectively) at six months (all moderate certainty). No diets significantly improved levels of HDL cholesterol or C reactive protein at six months. Overall, weight loss diminished at 12 months among all macronutrient patterns and popular named diets, while the benefits for cardiovascular risk factors of all interventions, except the Mediterranean diet, essentially disappeared.

Review methods Two reviewers independently extracted data on study participants, interventions, and outcomes and assessed risk of bias, and the certainty of evidence using the GRADE (grading of recommendations, assessment, development, and evaluation) approach. A bayesian framework informed a series of random effects network meta-analyses to estimate the relative effectiveness of the diets.

We performed a systematic review and network meta-analysis of randomised controlled trials for improvements in weight loss and cardiovascular risk factors to determine the relative effectiveness and certainty of evidence among dietary macronutrient patterns and popular named dietary programmes for adults who are overweight or obese.

Proponents of Mediterranean-type and DASH-type (Dietary Approaches to Stop Hypertension) diets suggest that these diets can improve risk factors for cardiovascular disease through weight loss itself and owing to their limited sodium content and claimed anti-inflammatory properties. 9 Systematic reviews and meta-analyses have shown conflicting results for the dietary effect on markers of cardiovascular disease risk, including blood pressure, low density lipoprotein (LDL) and high density lipoprotein (HDL) cholesterol, and C reactive protein. 6 8 9 10 11 12 Few reviews have used rigorous meta-analytical techniques to obtain quantitative estimates of the relative effect of different diets. 4 13 14 Systematic reviews have relied on pairwise comparisons. These comparisons have failed to examine direct and indirect clinical trial data by conducting a network meta-analysis, and they have not dealt with the certainty (quality) of evidence using the widely accepted standard, the GRADE (grading of recommendations, assessment, development, and evaluation) approach. 15

Biological and physiological mechanisms have been proposed to explain why some dietary macronutrient patterns and popular dietary programmes should be better than others. A previous network meta-analysis, however, suggested that differences in weight loss between dietary patterns and individual popular named dietary programmes are small and unlikely to be important. 4 No systematic review and network meta-analysis has examined the comparative effectiveness of popular dietary programmes for reducing risk factors for cardiovascular disease, an area of continuing controversy. 5 6 7 8

The worldwide prevalence of obesity nearly tripled between 1975 and 2018. 1 In response, authorities have made dietary recommendations for weight management and cardiovascular risk reduction. 2 3 Diet programmes—some focusing on carbohydrate reduction and others on fat reduction—have been promoted widely by the media and have generated intense debates about their relative merit. Millions of people are trying to lose weight by changing their diet. Thus establishing the effect of dietary macronutrient patterns (carbohydrate reduction v fat reduction v moderate macronutrients) and popular named dietary programmes is important.

Methods

We searched Medline, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), AMED (Allied and Complementary Medicine Database), and the Cochrane Central Register of Controlled Trials (CENTRAL) from database inception until September 2018. Search terms included extensive controlled vocabulary and keyword searches related to randomised controlled trials, diets, weight loss, and cardiovascular risk factors. Appendix text S1 presents the Medline search strategy. We reviewed reference lists from eligible trials and related reviews for additional eligible randomised controlled trials.

Eligible studies randomised adults (≥18 years) who were overweight (body mass index 25-29) or obese (≥30) to an eligible popular named diet or an alternative active or non-active control diet (eg, usual diet), and reported weight loss, changes in lipid profile, blood pressure, or C reactive protein levels at three months’ follow-up or longer.

We categorised dietary treatment groups in two ways: using dietary macronutrient patterns (low carbohydrate, low fat, and moderate macronutrient—similar to low fat, but slightly more fat and slightly less carbohydrate) and according to individual popular named dietary programmes.4 Dietary macronutrient patterns were established by macronutrient content (see table 1). Leading dietary programmes were identified through the explicit naming of the branded or popular diet, the referencing of popular or branded literature, or the naming of a brand as a funder of a randomised controlled trial reporting our target outcomes. The diet was labelled as brand-like when it met the definition of a branded diet but failed to name or reference the brand in the article. For example, dietary programmes that did not refer to Atkins but consisted of less than 40% of kilocalories from carbohydrates per day for the duration of study, or were funded by Atkins, were considered Atkins-like.1617 Appendix table S1 presents the characteristics of eligible dietary programmes.

Table 1 Nutritional patterns based on macronutrient composition View this table:

We included dietary programmes with structured advice for daily macronutrient, food, or caloric intake for a defined period (≥3 months). Eligible studies could or could not provide exercise (eg, walking, strength training) or behavioural support (eg, counselling, group support online or in person), and could include meal replacement products, but had to consist primarily of whole foods and could not include drugs.

We categorised eligible control diets as: usual diet (eg, wait list: participants were instructed to maintain their usual dietary habits), dietary advice (eg, received brochures, dietary materials including dietary guidelines, or consultation with a professional dietician by email or telephone), and low fat diet (≤30% fat with or without advice about lowering calories). We used the usual diet as our reference diet and presented results for the other diets against the reference diet.

Teams of two reviewers independently screened titles and abstracts for possible inclusion. If either reviewer considered a study potentially eligible, reviewers obtained and screened the full text. Reviewers resolved disagreements by discussion and, when necessary, through adjudication by a third reviewer.

Data abstraction and risk of bias assessment After pilot testing our data extraction forms, teams of two reviewers independently extracted demographic information, experimental and control interventions including exercise and behavioural support, and data on each outcome of interest. We focused on two sets of outcomes: weight loss and related markers of cardiovascular disease risk (systolic blood pressure, diastolic blood pressure, LDL cholesterol, HDL cholesterol, and C reactive protein) at six and 12 month follow-up (±3 months for both periods). Reviewers assessed the risk of bias for each individual randomised controlled trial independently and in duplicate using the Cochrane risk of bias tool.18 We assigned individual trials as high risk of bias if one of two key domains, allocation concealment or missing outcome data, was deemed high risk of bias; otherwise, we assigned individual trials as low risk of bias.

Data synthesis and statistical methods When reported, we used mean change and standard deviations. When authors reported data as measures before and after intervention, we used methods outlined in the Cochrane Handbook to calculate mean change and standard deviations for change.18 When standard deviations were missing, we estimated them from standard errors, P values, confidence intervals, or graphs. If none of these methods was possible, we derived standard deviations from other studies included in our network meta-analysis using a validated imputation technique.19 Appendix text S2 presents details of the missing standard deviations imputed for each outcome. We performed statistical analyses for dietary macronutrient patterns based on five nodes (moderate macronutrients, low carbohydrate, low fat, dietary advice, and usual diet) and for popular named diets based on 17 nodes (14 popular named dietary programmes and three control diets). We used bayesian random effects models to obtain the pooled direct estimates and corresponding forest plots of the available direct comparisons.20 We assessed heterogeneity between randomised controlled trials for each direct comparison with visual inspection of the forest plots and the I2 statistic. We then performed a series of random effects network meta-analyses within a bayesian framework using Markov chain Monte-Carlo simulation methods.2122 For each analysis, we used three chains with 100 000 iterations after an initial burn-in of 10 000. We assessed convergence based on trace plots and time series plots. We measured the goodness of model fit by the posterior mean of the overall residual deviance; in a well fitting model the residual deviance should be close to the number of data points included in the analysis.19 We used vague priors and dealt with the extent of heterogeneity in each network analysis using a common heterogeneity variance (τ); we categorised results as low (from 0.1 to 0.5), moderate (>0.5 to 1.0), and high (>1.0).2324 To estimate the precision of the effects, we used 95% credible intervals, by means of the 2.5 and 97.5 centiles obtained from the simulations.25 We used the node splitting method to generate the effect size and credible intervals for the indirect comparison and for the statistical test of incoherence (also known as inconsistency) between direct and indirect estimates.26 We calculated the ranking probabilities of being the best, second best, and so on for all treatment options and used the surface under the cumulative ranking curve to rank the intervention hierarchy in the network meta-analysis.27 We considered two effect modifiers that were modelled as present or absent if they were included in an overall dietary programme: exercise and behavioural support. Exercise was defined as having explicit instructions for weekly physical activities and categorised as exercise or no exercise. Diets with at least two group or individual sessions a month for the first three months were considered to provide behavioural support.28 We performed a network meta-regression assuming a common coefficient across comparisons to explore the effect of exercise and behavioural support for each outcome.29 Three sensitivity analyses were conducted by restricting studies to trials with individuals who were overweight or obese, but who were otherwise healthy; those with a low risk of bias; and investigator initiated randomised trials, thus removing trials that were funded partly or wholly by diet companies. We used the networkplot command of Stata version 15.1 (StataCorp, College Station, TX) to draw the network plots,30 and WinBUGS version 1.4.3 (MRC Biostatistics Unit, Cambridge, UK) and R version 3.4.3 (R Core Team, Vienna, Austria) with gemtc package for statistical analyses.

Assessing certainty of evidence We rated the certainty of evidence for each network estimate using the GRADE framework, which classifies evidence as high, moderate, low, or very low certainty. The starting point for certainty in direct estimates for randomised controlled trials is high, but could be rated down based on limitations in risk of bias, imprecision, inconsistency (heterogeneity), indirectness, and publication bias.15 We rated the certainty of evidence for each direct comparison according to standard GRADE guidance for pairwise meta-analysis.3132 Indirect effect estimates were calculated from available “loops” of evidence, which included first order loops (based on a single common comparator treatment—that is the difference between treatment A and B is based on comparisons of A and C as well as B and C) or higher order loops (more than one intervening treatment connecting the two interventions). We assessed the evidence for indirect and network estimates focusing on the dominant first order loop,31 rating certainty of indirect evidence as the lowest certainty of the direct comparisons informing that dominant loop. In the absence of a first order loop, we used a higher order loop to rate certainty of evidence and used the lowest of the ratings of certainty for the direct estimates contributing to the loop. We considered further rating down each indirect comparison for intransitivity if the distribution of effect modifiers differed in the contributing direct comparisons.31 For the network estimate, we started with the certainty of evidence from the direct or indirect evidence that dominated the comparison and, subsequently, considered rating down our certainty in the network estimate for incoherence between the indirect and direct estimates for imprecision (wide credible intervals) around the treatment effect estimates. When serious incoherence was present, we used, as the best estimate, that with the higher certainty of the direct and indirect evidence.32 Appendix text S3 presents additional details of the GRADE assessment.

Summary of more and less preferred treatments To optimise the presentation of results for the 17 diet (14 popular, three control) network meta-analysis, we applied a new approach to summarise the results, establishing different groups of interventions (from the most to the least effective) based on the effect estimates obtained from the meta-analysis and their certainty of evidence.33 For each outcome, we created three groups of interventions. Firstly, the reference diet (usual diet) and diets that did not differ from the reference (that is, confidence interval crossed mean difference=0), which we refer to as “among the least effective”. Secondly, diets superior to the reference, but not superior to any other diet superior to the reference (which we call category 1 and describe as “inferior to the most effective, but superior to the least effective”). Lastly, diets that proved superior to at least one category 1 diet (which we call “among the most effective”). We then divided all three categories into two groups: those with moderate or high certainty evidence relative to the usual diet, and those with low or very low certainty evidence relative to the usual diet.