Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 2; peer review: 1 approved, 1 approved with reservations]

Background: Eating or skipping breakfast for weight interests scientific and lay communities. Our objective was to systematically review and meta-analyze causal effects of eating versus skipping breakfast on obesity-related anthropometric outcomes in humans. Methods: Six databases were searched for obesityand breakfastrelated terms (final search: 02 JAN 2020). Studies needed to isolate eating versus skipping breakfast in randomized controlled trials. Mean differences were synthesized using inverse variance random effects meta-analysis for each outcome. Positive estimates indicate higher outcomes in breakfast conditions (e.g., weight gain). Leave-one-out analysis for sensitivity and a secondary baseline habit-by-breakfast assignment analysis were performed. Risk of bias was assessed using the Cochrane risk of bias tool. Results: Ten articles (12 comparisons; 6d to 12wk) were included. Conditions included recommendations to eat versus skip breakfast, or provision of some or all meals. 95% confidence intervals of all main analyses included the null value of no difference for each outcome: body weight (0.17 kg [-0.40,0.73], k=12, n=487, I 2=74.5), BMI (0.07 kg/m 2 [-0.10,0.23, k=8, n=396, I 2=54.1), body fat percentage (-0.27% [1.01,0.47], k=6, n=179, I 2=52.4), fat mass (0.24 kg [-0.21,0.69], k=6, n=205, I 2=0.0), lean mass (0.18 kg [-0.08,0.44], k=6, n=205, I 2=6.7), waist circumference (0.18 cm [-1.77,2.13], k=4, n=102, I 2=78.7), waist:hip ratio (0.00 [-0.01,0.01], k=4, n=102, I 2=8.0), sagittal Open Peer Review Reviewer Status Invited Reviewers 1 2 version 3 (revision) 30 Jul 2021 version 2 (revision) 15 Oct 2020 report version 1 24 Feb 2020 report report Nerys Astbury , University of Oxford, Oxford, UK 1. Enhad A. Chowdhury , University of Bath, Bath, UK 2. Any reports and responses or comments on the article can be found at the end of the article. Page 1 of 32 F1000Research 2020, 9:140 Last updated: 16 AUG 2021

abdominal diameter (0.19 cm [-2.35,2.73], k=2, n=56, I 2 =0.0), and fat Introduction Whether one should eat or skip breakfast for weight control or loss is a topic of continued interest in both the scientific and lay communities. In 2013 1 , we documented how breakfast eating versus breakfast skipping served as an example of how beliefs about diet can go beyond the evidence within and beyond the scientific community. The evidence at the time was dominated by over 90 observational studies -most cross-sectional -leading us to conclude that eating versus skipping breakfast as a strategy for weight was a presumption: a belief "held to be true for which convincing evidence does not yet confirm or disprove their truth" 2,3 . The limited scientific evidence on the topic has been translated directly to the public. For instance, we noted in our prior paper that the website of the Dr. Oz Show included an article stating, "The fact is, when you're trying to lose body fat, you can't skip breakfast" 4 . More recently, Dr. Oz himself stated, "I think for 2020, the first thing I'm going to do is ban breakfast" 5 , and using the social media hashtag of #Team-NoBreakfast. Meanwhile, continued scientific interest in the topic is evidenced by many more cross-sectional observational and other studies having been published; more recent narrative review articles summarizing existing literature on the topic 6,7 ; a meta-analysis evaluating breakfast eating versus skipping on weight 8 that confirmed our prior registered preliminary analyses 9,10 ; and another group registering an analysis similar to ours after our registration (PROSPERO CRD42018110858; subsequently published 11 .
With mixed messaging over time about the importance of eating or skipping breakfast for the ongoing obesity epidemic, and with continued interest in the topic both scientifically and generally, it is important to synthesize the causal evidence on the effect of breakfast eating versus skipping on obesity and related outcomes, rather than relying on weaker study designs or popular opinion.
Since our earlier summaries, additional RCTs have been conducted and published (as reviewed herein). Herein, we extend our prior work to synthesize causal evidence from RCTs on eating versus skipping breakfast in humans on all reported obesity-related anthropometric outcomes we were able to extract from relevant literature.

Registration
Our study was registered with the PROSPERO international prospective register of systematic reviews (CRD42016033290) on 21 JAN 2016. The initial registration limited papers up to the registration date; however, because of the time between initial registration and this manuscript, the search was updated to 02 JAN 2020 (see Search and review strategy, below). Earlier versions of this work were published as abstracts for the American Society for Nutrition's Annual Meeting and Scientific Sessions 9,10 .
Inclusion and exclusion criteria Inclusion criteria were: • the study had at least one breakfast skipping condition and one breakfast eating condition regardless of modality (e.g., whether recommended or provisioned); • the study was a randomized, controlled trial (RCT); • study length (i.e., time on intervention) was greater than 72 hr; • participants were normal weight or greater, as defined by original study authors, who did not have diseases that influence weight; and • the study reported weight or other anthropometric outcomes.
Studies were excluded if: • participants had diseases or conditions that affected weight except for obesity, diabetes, and CVD; • breakfast eating versus breakfast skipping were confounded with other intervention components (e.g., study designs that altered intake to ensure individual weight maintenance; multicomponent behavioral interventions including breakfast without matching those components in the skipping group; or enforcing time restricted feeding apart from breakfast skipping).

Search and review strategy
Our first search was completed on 20 JAN 2016, the search refreshed on 26 JAN 2017, and the search finalized on 02 JAN 2020, with results from prior searches being deduplicated from subsequent searches.
In all search phases, searches were executed by using the application programming interfaces (APIs) of AltHealthWatch, CINAHL, Proquest Theses and Dissertations Global, PsycInfo, and Scopus using R (version 3.5.2). The following was used to search Scopus, with analogous search strategies adapted for the other databases: TITLE-ABS-KEY((Obesity OR obese OR adipose OR adiposity OR overweight* OR "over weight*" OR "weight gain*" OR "weight reduc*" OR "weight los*" OR "weight maint*" OR "weight decreas*" OR "weight control*" OR "weight restrict*" OR "BMI" OR

Amendments from Version 1
The new version of the article includes updated data for one of the included studies, and thus updated analyses, figures, and tables; subgroup analysis of assigned-by-baseline breakfast habits; and updated references and discussion. In addition, we updated the manuscript in accordance with reviewer comments, including conducting a formal subgroup analysis of comparing participant baseline breakfast habits with their assigned conditions. This additional analysis is briefly described in the manuscript, as well as more fully documented in the updated online materials posted in Zenodo.
Any further responses from the reviewers can be found at the end of the article REVISED "FMI" OR "BMIz" OR "zBMI" OR "weight percentile" OR "gestational weight" OR "weight for height" OR "waist circumference" OR "skinfold thickness" OR "body composition" OR "body size" OR "fat mass" OR "body fat" OR "body mass" OR "body weight" OR "bodyweight" OR "waist hip ratio") AND (breakfast OR "break fast" OR "morning fasting" OR "morning meal")) AND DOCTYPE(ar OR ip) AND SRCTYPE(j) Search results across databases were compared for duplication, including by title, abstract, and PubMed ID. Studies with titles and abstracts addressing animals that did not also include words related to human subjects were excluded programmatically. Titles and abstracts were then coded independently by at least two authors for inclusion/exclusion criteria. If both authors excluded a study for violation of any inclusion or exclusion criterion, it was excluded; if at least one did not exclude it, the paper was passed on for full text review.

Meta-analysis
All data and code used to estimate effect sizes and metaanalyses are provided as Extended data at https://doi. org/10.5281/zenodo.4041392 12 . Additional details are included as comments within the code, including exact approaches to estimating each effect size within a study.
Effect sizes comparing breakfast eating versus skipping on each outcome were calculated for each study. Each effect size was calculated as a difference-in-difference in the native units of the outcome (e.g., kg for weight). Only outcomes for which there was more than one effect size were meta-analyzed: body weight, BMI, body fat percentage, fat mass, lean mass, fat free mass, adipose tissue mass, waist circumference, waist:hip ratio, fat mass index, sagittal abdominal diameter, and lean tissue mass. Lean mass, fat-free mass, and lean tissue mass were meta-analyzed together as 'lean mass'; fat mass and adipose tissue mass were meta-analyzed together as 'fat mass'. Total body water percentage and muscle mass are both reported only in Ogata et al. 13 ; although muscle mass as an outcome was excluded, Ogata et al. also reported lean mass, which is captured in the pooled lean mass analysis.
Farshchi et al. 14 reported pre and post means and standard deviations separately for each treatment period in a twoarm cross-over design. Although the unbiased estimate of the difference-in-difference was calculable from the pre and post means in each condition, the lack of information on the correlation of change within or between conditions precluded us from directly calculating the variance of the effect. We requested summaries from the authors, but the authors informed us they no longer had the raw data given that the paper was published in 2005. Thus, within-condition and between-condition correlations had to be estimated. Sievert et al. 8  Schlundt et al. 19 reported follow-up data at 6 months, but the methods descriptions were unclear as to whether the interventions to eat or skip breakfast were continued past the 12-week intervention. Authors were contacted about this detail and for additional outcomes data at 12 weeks that were either not directly reported or reported as no significant strata (i.e., habitual breakfast eaters or skippers) or treatment effects; the authors informed us they no longer had the raw data given the study was published in 1992. We therefore chose to only use the change in body weight data from 12-weeks. Independent effect sizes were estimated for habitual breakfast eaters and habitual breakfast skippers.
Dhurandhar et al. 20 reported body weight for the completersonly analysis in their paper. Because they registered their study as also measuring BMI, and because of the mention of an intention to treat analysis, we contacted the authors (one of whom, DBA, is a coauthor on the present meta-analysis), who provided us with summary data. Note that they also had a third group, in which participants received no specific breakfast eating or breakfast skipping recommendations; we limited our analysis to the intention to treat analyses of the breakfast eating and breakfast skipping groups. Independent effect sizes were estimated for habitual breakfast eaters and habitual breakfast skippers.
LeCheminant et al. 21 were contacted for estimates of change over time for data in their Table 3. The authors graciously provided estimates of change within each group for each outcome.
The data used herein, as shared by the authors, differs slightly from their publication because of increased precision and because of a reporting error in which percent body fat did, in fact, have a small but non-significant increase in the no breakfast group. This error does not change the results of their study, but the corrected values are used herein.
Ogata et al. 13  Leave-one-out analysis was used as a sensitivity analysis to investigate the influence of any single study for each outcome, in which each study was omitted from the dataset at a time, and then the meta-analysis was recalculated.
Effect estimates are displayed as mean differences with 95% confidence intervals in the native units of the outcome. I 2 (%) and p-values for tests of heterogeneity are also reported. No multiple-comparison corrections are applied within or among outcomes. There are few effect sizes (k=12), there is substantial design heterogeneity (e.g., study length, types of breakfast, different populations), and there is statistical heterogeneity in several outcomes; therefore, funnel plot asymmetry is not presented because visual estimation of asymmetry is unreliable for small k 25 , the test is underpowered for small k 26 , and any association between effect size and variance may plausibly be explained by study design or other factors rather than just publication bias 26 .
We calculated a secondary analysis comparing habitual breakfast eaters versus habitual breakfast skippers, as defined by the authors of the published papers. This subgroup analysis completed the preliminary analyses we initially published as abstracts 9,10 . If baseline habits were not reported, or if authors reported that the baseline habits of the participants were mixed, the study arms were excluded. The subgroup analysis was based on the same effect size estimates generated for the primary analysis. The same functions were used in R, except for modeling the outcome variable as a function of both assigned condition and baseline breakfast habits. Additional methods can be found in the subgroup analysis document and code located in https://doi.org/10.5281/zenodo.4041392.

Risk of bias
Risk of bias was assessed independently by two investigators (MMBB/JEM for all but Ogata 2019 and MMBB/AWB for Ogata 2019) using Cochrane's Risk of Bias Tool 27 . Given that the interventions are obvious to participants (eating versus skipping breakfast), we only coded blinding of personnel, and readers should be aware of the risk of non-blinded interventions. We do not use the approach of assigning a binary risk of bias to an entire study (e.g., if one criterion is high risk in a study, the entire study is considered high risk); however, we provide the individual ratings for each article and readers can apply such an approach if they wish.

PRISMA diagram
The search results are shown in the PRISMA diagram in Figure 1. The results of each of the three phases of the search are shown.

Inclusion table
Ten papers were included with 12 effect sizes (see Table 1 for descriptions). Briefly, of the 10 studies included: six were conducted in the United States, three in the United Kingdom, and one in Japan; two were cross-over RCTs and eight were parallel arm RCTs; length ranged from 6 days to 16 weeks; five provisioned some or all foods and five were recommendations for dietary consumption; two stratified on baseline eating or skipping habits, two included only habitual breakfast eaters, three included only habitual breakfast skippers, two reported mixed baseline habits, and one did not specify baseline habits; four reported race/ethnicity of participants; four included females only, one included males only, and five included both females and males. For breakfast definitions, dietary compositions, and timing, see Table 1 and Figure 2. Breakfast definitions and timing of consumption varied amongst the studies included and ranged from highly controlled and prescribed to broad recommendations ( Figure 2). Figure 3 shows a composite forest plot that includes all metaanalyzable, obesity-related, anthropometric outcomes. In all cases, the 95% confidence intervals included the null of no differences between skipping and eating breakfast (frequently interpreted as "not statistically significant"). Table 2 shows the numerical estimates of the values displayed in the forest plots. Therefore, no discernible effects of breakfast eating or breakfast skipping on body weight (kg), BMI (kg/m 2 ), body fat percentage (%), fat mass (kg), lean mass (kg), waist (cm), Figure 1. PRISMA diagram. Three searches were undertaken. For searches 2 and 3, the numbers in parentheses represent unique results to that search. *Several 'papers from other sources' were identified in prior searches, but those papers were captured by the third search.

Meta-analyses of anthropometric outcomes
waist:hip ratio, sagittal abdominal diameter (cm) and fat mass index (kg/m 2 ) were found in these primary analyses.
In the secondary analysis comparing habitual breakfast eaters versus habitual breakfast skippers (as defined by the authors of the published papers), forest plots and a summary table can be found in the subgroup between group forest plots document and subgroup between group summary table located in https://doi.org/10.5281/zenodo.4041392. Briefly, there was no discernible effect of stratification of baseline habits on four of the outcomes: body weight (4 effect sizes for habitual breakfast eaters; 4 effect sizes for habitual breakfast skippers), body fat percentage (2 and 2 effect sizes, respectively), fat mass (1 and 2 effect sizes, respectively), or lean mass (1 and 2 effect sizes, respectively). Insufficient study arms existed to test differences for waist circumference, waist:hip ratio, sagittal abdominal diameter, and fat mass index. Only BMI was statistically significant (2 and 4 effect sizes, respectively). The negative estimate of -0.36[-0.65,-0.07] BMI units indicates that individuals assigned to conditions consistent with their baseline habits had lower values than those assigned opposite of their habits. That is, habitual breakfast skippers had lower BMI when skipping breakfast and habitual breakfast eaters had lower BMI when eating breakfast, compared to skippers eating and eaters skipping. coded as high risk of bias for the criterion of blinding participants and personnel because the authors clearly indicated that personnel were not blinded. Given that the interventions are obvious to participants (eating versus skipping breakfast), we only focus on blinding of personnel, and readers should be aware of the risk of non-blinded interventions. On the other hand, many of the categories in the risk of bias in each study were unclear, and it is therefore uncertain whether the risk was high or low.

Sensitivity analysis: Leave-one-out analysis
The leave-one-out analysis is shown in Figure 5. Little difference is noted among the analyses, with substantial overlap of confidence intervals in all cases. When considering statistical significance (i.e., confidence intervals that do not include 0), leaving Farshchi et al. 14 out of the analysis results in significantly greater BMI in the breakfast conditions than the skipping conditions. When Leidy et al. 17 is excluded, fat mass is greater in the breakfast than the skipping conditions. Waist:hip ratio is centered on zero with no estimable confidence interval when Chowdhury et al. 23 is left out because the other three estimates are all 0.00. We reiterate that none of these summaries took multiple comparisons into account.

Notable exclusions
Notable exclusions are located in Table 3. Broad areas to note are the lack of a skipping group for comparison to breakfast groups, intervention periods that were less than 72 hr in duration, studies that had the comparison of interest but did not measure body weight, and studies whose primary purpose did not isolate breakfast eating versus breakfast skipping, such as time  3 Definitions paraphrased from each study paper. 4 ATM, adipose tissue mass; BF%, body fat percentage; BW, body weight; FFM, fat-free mass; FM, fat mass; FMI, fat mass index; LM, lean mass; LTM, lean tissue mass; MM, muscle mass; SAD, sagittal abdominal diameters; TBWP, total body water percentage; WC, waist circumference; WHR, waist:hip ratio. Some additional outcomes might have been mentioned in the paper, but quantitative results may not have been reported after the intervention.  Table 3; and the bottom is a legend for the figure. 'Inferred eating window' represents the times we inferred that participants were permitted or recommended to consume food as reported in the papers; 'specified eating window', 'breakfast eating window', and 'assigned eating times' were reported by the authors in either absolute or relative times (e.g., number of hours since waking). For more details for the included studies, see Table 1.  Table 2 for the numerical values of these seven analyses, plus the sagittal abdominal diameter and fat mass index. Studies without point estimates and confidence intervals within an outcome indicates that the study did not report that outcome. 95% confidence intervals for individual studies and for the width of the diamond representing the summary estimate are presented. Horizontal dotted lines for the summary of the meta-analyses represent the 95% prediction interval. For the column 'Habit': e, habitual eaters; s, habitual skippers; u, unspecified or mixed. Values to the left of 0 indicate that the breakfast condition had a greater decrease in the outcome relative to the skipping condition. For all outcomes except lean mass, values to the left would therefore traditionally be considered 'favoring' the breakfast condition (e.g., lower body weight). In this meta-analysis, our included studies were all conducted in adults/adolescents, but, as noted in Table 3, there have been several related studies conducted in children; however, none of the studies in children had a true skipping group. For instance, Rosado et al. 30 had a control group with no intervention, which is not equivalent to assigning children to skip breakfast. Similarly, Powell et al. 31 did have a group that was assigned to consume a slice of orange as an attention placebo control, but again the children were not assigned to otherwise skip breakfast.

Summary
The causal effect of eating versus skipping breakfast on obesity-related anthropometric outcomes was non-significantly different from zero across body weight, BMI, body fat percentage, fat mass, lean mass, waist circumference, waist:hip ratio, sagittal abdominal diameter, and fat mass index. Our results largely match our prior analyses 9,10 , as well as the analysis of body weight conducted by Sievert et al. 8 .
The choices of inclusion/exclusion criteria, adjustments, and assumptions to use when meta-analyzing data can influence the interpretation of results, so we highlight some of our choices here. Our choice of including studies greater than 72 hours in duration (with the shortest actually included being 6 days) should avoid including experiments measuring only transient differences from hydration, glycogen, or other very acute physiological responses. Furthermore, this approach allowed us to collate all relevant experiments and enables readers to reanalyze our results with their preferred study duration cutoff should the reader so choose. We chose to define breakfast eating versus breakfast skipping according to the intentions of the original study authors where possible. This was primarily done because a common recommendation is to eat breakfast to lose weight without any other qualifications.
In doing so, we included studies that 1) may or may not have been on a weight loss background, 2) included a range of weight categories, and 3) included a variety of breakfast compositions, timings, and definitions. Another limitation that was well state by one of our reviewers: "Many of the studies included in this and other SR [systematic reviews] are small, and are often conducted in participants with a mixed usual breakfast habit who are not overweight or attempting to limit their energy intake or reduce their body weight." While we cannot rule out that there may be some statistically significant combination of studies, subgroups, splitting-versus-pooling of different breakfasts, or different imputation strategies, we note that the results are fairly consistently centered near zero. However, Sievert et al. 8 and  The subgroup analysis of baseline breakfast habits included few effect sizes to estimate interaction effects between baseline breakfast habits and breakfast assignment. We could test for differences in 5 of the outcomes. Of these, BMI showed a significant interaction between baseline habits and assigned condition. The statistical significance of these secondary estimates should be considered within the following contexts. In the leaveone-out analyses, the 95% confidence intervals did not differ substantially from other leave-one-out analyses. In the subgroup analyses, few effect sizes were available for comparison. There were 4 treatment effect estimates each for habitual Figure 5. Leave-one-out analysis. Within each column, the diamond represents the meta-analytic summary estimate when leaving out the study in a particular row. Row and column combinations without diamonds represent outcomes that are not reported for that particular study. *The waist:hip ratio had no estimable confidence interval because the three remaining estimates were all 0.00. Sagittal abdominal diameter and fat mass index were only included in the two papers from the Bath Breakfast Project (Betts et al. and Chowdhury et al.), and therefore a leave-one-out analysis would include only a single study; outcomes of muscle mass and total body water percent were only included in Ogata et al., and so a leave-one-out analysis is not possible. Not about breakfast Adults; did not include weight outcomes; compared 1 vs 3 meals per day with weight being deliberately maintained (see Figure 2) Hirsch 1975 29 Not about breakfast Adults; dinner only versus breakfast only (see Figure 2)

Reason for exclusion* Notes
Keim 1997 44 Not about breakfast Adult Women; distribution of calories as 70% morning versus 70% evening Tinsley 2019 45 Not about breakfast Adult women; time-restricted feeding versus not (see Figure 2) Wehrens 2017 28 Not about breakfast Adult men; non-randomized order; all meals (not just breakfast) shifted 5 hours (see Figure 2) breakfast eaters and habitual breakfast skippers for body weight, while there were only 2 and 4, respectively, for BMI. Some outcomes had only 1 and 2 effect estimates for eaters and skippers, respectively. In all cases, we did not adjust for multiple comparisons because of their exploratory nature. Given the small departure of the confidence intervals from 0, it is likely these would no longer be statistically significant if multiple comparisons were considered. Even if effects turned out to be non-zero, the 95% confidence and prediction intervals of the outcomes include effect sizes of low clinical significance, and thus further work would be needed to determine if non-zero effects are actually clinically meaningful.
Despite this relative consistency in summary effect sizes, we note that there was substantial design heterogeneity. The length of studies, for instance, varied substantially. To be confident in effects of obesity-related interventions, longer term studies are desired. However, the need for longer-term studies is often to guard against concluding that early effects (weeks to months) will result in sustained weight loss over months to years. Given the overall null findings herein, suggesting a need for longer studies would serve to test whether these relatively acute null findings reflect long-term adaptations to establishing breakfast habits. In addition, some have argued that it is not merely eating versus skipping breakfast that is important, but rather that the type of breakfast matters (c.f., Leidy et al. 2016 7 ). Such an argument does not invalidate the question asked or the findings of this meta-analysis, however. If, for instance, a breakfast of a particular characteristic is what influences weight -be it fiber content, protein, energetic load, timing from waking, or others -then the question would not be whether eating versus skipping breakfast matters; rather, research would need to test the effects of that particular breakfast versus comparator groups, whether those comparator groups be different breakfasts or no breakfast at all.
We clarify that our results are limited to obesity-related anthropometric outcomes. As stated previously, "Just because breakfast consumption may not have a statistically significant effect on weight does not make breakfast a bad recommendation" 56 , nor does it necessarily make it a good recommendation. Our results do not inform whether eating versus skipping breakfast is of value for blood glucose control (c.f., 57 ), cardiometabolic risk factors (c.f., 11,15 ), school performance (c.f., 58 ), or other outcomes; nor do our results inform the effects of eating versus skipping breakfast as part of a multicomponent behavioral/intensive lifestyle intervention or time restriction paradigm (e.g., early vs late time-restricted feeding). The inference of the results of this study are limited to the broad and simplistic recommendation to eat or skip breakfast to affect anthropometric outcomes.

Conclusion
There was no discernible effect of eating or skipping breakfast on obesity-related anthropometric measures when pooling studies with substantial design heterogeneity and sometimes statistical heterogeneity in our primary analyses.

Data availability
Underlying data All data underlying the results are available as part of the article and no additional source data are required.
This project contains the following extended data: • calculations.R (calculates individual effect sizes for each study) • metaanalysis with subgroup.R (reproduces the composite forest plot, leave-one-out plot, the summary We appreciate Dr. Astbury's thoughtful comments. The comments reinforce that there are many details about recommendations to eat or skip breakfast that need to be considered beyond simplistic advice, including duration of intervention and to whom the advice is being supplied. Our prior revision addressed one such concern: whether eating versus skipping differs depending on baseline habits. Here, we supply an additional analysis accounting for duration of intervention and include more details about the participants in the original studies. Study (and, by extension, intervention) duration is important for the chronic management and prevention of obesity. Yet, all study duration thresholds for original studies and for meta-analysis inclusion are arbitrary. Our choice of 72 hours is arbitrarily short, but extends beyond just differences in eating (e.g., differences in weight from the actual digestive contents of eating versus skipping). Another meta-analysis arbitrarily chose 4 weeks as the cutoff, and we interpret Dr. Astbury's remarks as suggesting 12+ weeks would be more reasonable. To address differences in study duration threshold for study inclusion, we now present a cumulative meta-analysis. This cumulative meta-analysis adds studies from the longest to the shortest duration. Studies (and strata within studies) are added by duration. Within a study duration (that is, if more than one study is of the same duration), studies and strata are added in an arbitrary order as organized in the data set. The cumulative metaanalysis including all studies of a particular duration or longer are represented by the last addition from top to bottom in the supplementary cumulative meta-analysis figure. Body weight and BMI had enough effect sizes to meaningfully synthesize results cumulatively across study durations. Using Dr. Astbury's reasonable suggestion of 12 weeks, there is no significant differences between skipping and eating breakfast on body weight and BMI, as shown in the supplementary cumulative meta-analysis figure (see results in the row for Schlundt 1992s for body weight and Leidy 2015 for BMI). Using the 4 week cutoff used in another review, there is a significant estimate in favor of skipping, as shown in the supplementary cumulative meta-analysis figure (see the row for LeCheminant 2017). We emphasize that because this cumulative meta-analysis results in multiple testing, interpretation of any statistically significant results should be made with caution.
Regarding the baseline obesity status, weight stability, and context of studies, we now include two new columns in Table 1. One column presents the baseline participant BMIs, and the other column presents information about weight stability, weight-related inclusion criteria, and context of the study. We discuss the importance of these considerations in the discussion section. Unfortunately, stratification by these factors is in some cases not easy to classify for a subgroup meta-analysis (e.g., baseline BMI categories are not harmonized across studies). Even in cases where classification seems possible, there are too few effect reference is added.

Methods
I generally found the description of the methods to be thorough and clear.
"One breakfast skipping condition" and one "breakfast eating" condition. While the authors state regardless of modality -can I clarify whether this is based upon the original authors original definitions of these conditions (I assume this to be the case -but think it is useful to state here if so, in the same way that you have below in this section for weight status)?
I found the description of included and excluded studies to be very thorough.
As a non-statistician I do not consider myself well placed to examine the meta-analytical methods used thoroughly, this would be more appropriate for a statistician to evaluate. At face value, there does not seem to be anything particularly unusual about the approach employed.   Table 2. I also appreciate this may be for practical reasons, but the figures with only one missing value on the axis look a little strange -I presume this might be because the labels do not fit, but if easily adjustable I would suggest it might be beneficial to add this single missing label.

Discussion
I generally found the discussion clear and highlighted appropriately the limitations based on the scope of the work.
As outlined for the introduction -the work of Bonnet is now published, and is potentially of interest, as they suggest based on their analysis a small but significantly reduced body weight (-0.54kg (CI: -1.05 to -0.03), which is also broadly in line with Sievert, suggesting a very small reduction in body mass with breakfast skipping. If possible, I would suggest this study is cited.
I understand that clearly any effects in favour of breakfast skipping for body mass are small, but I think as the two other meta-analyses conducted conclude this, this is worth incorporating into your second paragraph about the potential for non-zero effects as a point of context. I don't think this impacts upon your general point that any potential for effects are likely of low clinical significance.
I think that with such a small set of studies with a very large variation between them, narrative reviews can often be valuable in this context for examining and considering the nuance of the results from different designs. I think it is good that you have clearly highlighted the limits of the meta-analytical approach used.
I appreciate your point that longer term studies are often to determine the longer-term trajectory of interventions that may show short term impacts on body composition. Given that two other meta-analyses conclude that there may be a significant reduction overall (albeit small) with breakfast skipping is there also added value in determining if breakfast skipping over long durations causes more substantial weight loss?
I am unclear as to the relevance of the "[j]ust because"? If this is a typo -it should be amended.
While I think it is helpful that you have outlined the limitations of your analysis for informing the overall benefit of breakfast, I have two suggestions: I think it is relevant to acknowledge that breakfast consumption/omission has previously been shown to impact upon specific components of energy balance that may have independent effects upon health.
○ I would suggest providing some references for studies/reviews examining the different factors that you highlight as not being examined in the present work (i.e. blood glucose control, cardiometabolic risk etc), should the reader be interested in those aspects. ○ © 2020 Astbury N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Nerys Astbury
Nuffield Department of Primary Care Health Sciences, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK This is a systematic review of randomised controlled trials comparing the consumption of breakfast with skipping breakfast and follows other similar SR on breakfast eating/skipping.
There has been a long held debate regarding the potential effect of breakfast on energy intake, body weight and anthropometric measures, as the authors note this was driven by observational studies, but in recent years more RCT have emerged investigating effects.
The methods are meretriciously reported and follow standard or gold standard procedures.
Justification for some inclusion criteria should be included -to allow the reader to make a judgement whether the methods of this paper match its intended objectives. Since outcome is bodyweight/composition what is the purpose of including studies of 72hrs duration where likelihood of effect is minimal?
I feel that the discussion is weak and could be strengthened considerably. Limitations of this manuscript are not limited to the statistical methods and assumptions for dealing with missing data.
Limitations of the current review should be discussed and critically analysed. Many of the studies included in this and other SR are small, and are often conducted in participants with a mixed usual breakfast habit who are not overweight or attempting to limit their energy intake or reduce their body weight. Further discussion on the clinical/research implications of this SR (and similar ones recently published) is warranted.
The absence of is a substantial clinical trial of breakfast eating/skipping within the context of weight loss attempt is notable, could this be related to issues surrounding potential conflicts of interest and industry involvement?, -would be worth discussion. Some mention of contextual setting is important-it would be difficult to determine if eating/skipping breakfast aids/impairs weight loss if the participants in are not attempting to limit daily energy intake or weight loss. However breakfast may affect appetite regulation, thus aiding/impairing adherence to energy restricted diets (or it may not)? Could usual skippers made to eat have different response than usual consumers made to skip? Could sub-group differences be affecting ability to detect effects of breakfast on body weight and composition?
Is the work clearly and accurately presented and does it cite the current literature? Partly

Is the study design appropriate and is the work technically sound? Yes
Are sufficient details of methods and analysis provided to allow replication by others?