Kathryn A. Kaiser

Will reducing sugar‐sweetened beverage consumption reduce obesity? Evidence supporting conjecture is strong, but evidence when testing effect is weak

We provide arguments to the debate question and update a previous meta‐analysis with recently published studies on effects of sugar‐sweetened beverages (SSBs) on body weight/composition indices (BWIs). We abstracted data from randomized controlled trials examining effects of consumption of SSBs on BWIs. Six new studies met these criteria: (i) human trials, (ii) ≥ 3 weeks duration, (iii) random assignment to conditions differing only in consumption of SSBs and (iv) including a BWI outcome. Updated meta‐analysis of a total of seven studies that added SSBs to persons’ diets showed dose‐dependent increases in weight. Updated meta‐analysis of eight studies attempting to reduce SSB consumption showed an equivocal effect on BWIs in all randomized subjects. When limited to subjects overweight at baseline, meta‐analysis showed a significant effect of roughly 0.25 standard deviations (more weight loss/less weight gain) relative to controls. Evidence to date is equivocal in showing that decreasing SSB consumption will reduce the prevalence of obesity. Although new evidence suggests that an effect may yet be demonstrable in some populations, the integrated effect size estimate remains very small and of equivocal statistical significance. Problems in this research area and suggestions for future research are highlighted.

Increased fruit and vegetable intake has no discernible effect on weight loss: a systematic review and meta-analysis

BACKGROUND A common dietary recommendation for weight loss, especially in lay public outlets, is to eat more fruit and vegetables (F/Vs). Without a compensatory reduction in total energy intake, significant weight loss would be unlikely. OBJECTIVE We aimed to synthesize the best available evidence on the effectiveness of the general recommendation to eat more F/Vs for weight loss or the prevention of weight gain. DESIGN We searched multiple databases for human randomized controlled trials that evaluated the effect of increased F/V intake on body weight. Inclusion criteria were as follows: ≥15 subjects/ treatment arm, ≥8-wk intervention, a stated primary or secondary outcome of body weight, the stated goal of the intervention was weight or fat loss or the prevention of weight or fat gain, and food intake provided or prescribed was of a variety of F/Vs that remained minimally processed. RESULTS Two studies met all criteria; 5 other studies met all criteria but one. The primary analysis indicated an effect size of weight change (outcome of interest) from baseline [standardized mean difference (SMD) for studies that met all criteria] of -0.16 (95% CI: -0.78, 0.46) (P = 0.60). The SMD for 7 studies that met all or most criteria was 0.04 (95% CI: -0.10, 0.17) (P = 0.62). CONCLUSIONS Studies to date do not support the proposition that recommendations to increase F/V intake or the home delivery or provision of F/Vs will cause weight loss. On the basis of the current evidence, recommending increased F/V consumption to treat or prevent obesity without explicitly combining this approach with efforts to reduce intake of other energy sources is unwarranted. This systematic review and meta-analysis was registered at http://www.crd.york.ac.uk/PROSPERO/ as CRD42013004688.

Reproducibility: A tragedy of errors

Just how error-prone and self-correcting is science? We have spent the past 18 months getting a sense of that. We are a group of researchers working on obesity, nutrition and energetics. In the summer of 2014, one of us (D.B.A.) read a research paper in a well-regarded journal estimating how a change in fast-food consumption would affect children’s weight, and he noted that the analysis applied a mathematical model that overestimated effects by more than tenfold. We and others submitted a letter1 to the editor explaining the problem. Months later, we were gratified to learn that the authors had elected to retract their paper. In the face of popular articles proclaiming that science is stumbling, this episode was an affirmation that science is self-correcting.

A systematic review and meta‐analysis of randomized controlled trials of the impact of sleep duration on adiposity and components of energy balance

Recent epidemiological and ecological trends in humans indicate a possible causal relationship between sleep duration and energy balance. We aimed to find experimental evidence that has tested this relationship between sleep duration and measures of body composition, food intake or biomarkers related to food intake. We conducted a systematic literature review using six databases throughout 7 August 2014. We sought reports of randomized controlled trials where sleep duration was manipulated and measured outcomes were body weight or other body composition metrics, food intake, and/or biomarkers related to eating. We found 18 unique studies meeting all criteria: eight studies with an outcome of body weight (4 – increased sleep, 4 – reduced sleep); four studies on food intake; four studies of sleep restriction on total energy expenditure and three of respiratory quotient; and four studies on leptin and/or ghrelin. Few controlled experimental studies have addressed the question of the effect of sleep on body weight/composition and eating. The available experimental literature suggests that sleep restriction increases food intake and total energy expenditure with inconsistent effects on integrated energy balance as operationalized by weight change. Future controlled trials that examine the impact of increased sleep on body weight/energy balance factors are warranted.

Influence of BMI and gender on postprandial hormone responses.

Objective: Influences of gender and body weight on the hormonal response to eating are not well understood. This study was conducted to determine a convenient time‐point to evaluate peak postprandial hormone responses and to test the hypothesis that gender and BMI interact to produce differences in postprandial secretion of selected humoral markers implicated in hunger and satiety.

Predicting adult weight change in the real world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure

Background:Public health and clinical interventions for obesity in free-living adults may be diminished by individual compensation for the intervention. Approaches to predict weight outcomes do not account for all mechanisms of compensation, so they are not well suited to predict outcomes in free-living adults. Our objective was to quantify the range of compensation in energy intake or expenditure observed in human randomized controlled trials (RCTs).Methods:We searched multiple databases (PubMed, CINAHL, SCOPUS, Cochrane, ProQuest, PsycInfo) up to 1 August 2012 for RCTs evaluating the effect of dietary and/or physical activity interventions on body weight/composition. Inclusion criteria: subjects per treatment arm ⩾5; ⩾1 week intervention; a reported outcome of body weight/body composition; the intervention was either a prescribed amount of over- or underfeeding and/or supervised or monitored physical activity was prescribed; ⩾80% compliance; and an objective method was used to verify compliance with the intervention (for example, observation and electronic monitoring). Data were independently extracted and analyzed by multiple reviewers with consensus reached by discussion. We compared observed weight change with predicted weight change using two models that predict weight change accounting only for metabolic compensation.Findings:Twenty-eight studies met inclusion criteria. Overfeeding studies indicate 96% less weight gain than expected if no compensation occurred. Dietary restriction and exercise studies may result in up to 12–44% and 55–64% less weight loss than expected, respectively, under an assumption of no behavioral compensation.Interpretation:Compensation is substantial even in high-compliance conditions, resulting in far less weight change than would be expected. The simple algorithm we report allows for more realistic predictions of intervention effects in free-living populations by accounting for the significant compensation that occurs.

Can a weight loss of one pound a week be achieved with a 3500-kcal deficit? Commentary on a commonly accepted rule.

Despite theoretical evidence that the model commonly referred to as the 3500-kcal rule grossly overestimates actual weight loss, widespread application of the 3500-kcal formula continues to appear in textbooks, on respected government- and health-related websites, and scientific research publications. Here we demonstrate the risk of applying the 3500-kcal rule even as a convenient estimate by comparing predicted against actual weight loss in seven weight loss experiments conducted in confinement under total supervision or objectively measured energy intake. We offer three newly developed, downloadable applications housed in Microsoft Excel and Java, which simulates a rigorously validated, dynamic model of weight change. The first two tools available at http://www.pbrc.edu/sswcp, provide a convenient alternative method for providing patients with projected weight loss/gain estimates in response to changes in dietary intake. The second tool, which can be downloaded from the URL http://www.pbrc.edu/mswcp, projects estimated weight loss simultaneously for multiple subjects. This tool was developed to inform weight change experimental design and analysis. While complex dynamic models may not be directly tractable, the newly developed tools offer the opportunity to deliver dynamic model predictions as a convenient and significantly more accurate alternative to the 3500-kcal rule.

Positive relationship between support group attendance and one-year postoperative weight loss in gastric banding patients

BACKGROUND Few empirical reports of studies examining the association between bariatric after care support group attendance and weight loss outcomes have been published. The present study investigated the association between the number of support group meetings attended and percentage of excess weight loss at 12 months after gastric banding surgery. The setting was a private practice at which no-cost, professionally led support group meetings were held weekly. METHODS The medical records and support group attendance logs were examined for the dates of attendance, frequency of attendance (or no attendance) in relation to the percentage of excess weight loss (n = 102; 88.2% women; mean age 45.6 ± 11.3 years; mean baseline body mass index 46.4 ± 8.8 kg/m(2)). Linear regression models were used to assess the relationship between the number of group meetings attended and the percentage of excess weight of loss with age and baseline body mass index used as optional independent variables. RESULTS A significant linear relationship was found between support group meeting attendance and the percentage of excess weight loss with simple regression analysis (adjusted R(2) = .061, P = .007), with age (adjusted R(2) = .100, P = .002) and the baseline body mass index added to the model (adjusted R(2) = .072, P = .011). CONCLUSION The results of the present study add to the growing evidence of the positive relationship between the frequency of support group attendance and the percentage of excess weight loss. Future studies should examine patient motivational characteristics in relation to support group participation and other aspects of compliance with aftercare recommendations to investigate unique effects of each part of the treatment program on weight loss outcomes.

Common scientific and statistical errors in obesity research.

This review identifies 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) excessive and undisclosed multiple testing and “P‐value hacking,” 4) mishandling of clustering in cluster randomized trials, 5) misconceptions about nonparametric tests, 6) mishandling of missing data, 7) miscalculation of effect sizes, 8) ignoring regression to the mean, 9) ignoring confirmation bias, and 10) insufficient statistical reporting. It is hoped that discussion of these errors can improve the quality of obesity research by helping researchers to implement proper statistical practice and to know when to seek the help of a statistician.

Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry

Objectives To summarise logistical aspects of recently completed systematic reviews that were registered in the International Prospective Register of Systematic Reviews (PROSPERO) registry to quantify the time and resources required to complete such projects. Design Meta-analysis. Data sources and study selection All of the 195 registered and completed reviews (status from the PROSPERO registry) with associated publications at the time of our search (1 July 2014). Data extraction All authors extracted data using registry entries and publication information related to the data sources used, the number of initially retrieved citations, the final number of included studies, the time between registration date to publication date and number of authors involved for completion of each publication. Information related to funding and geographical location was also recorded when reported. Results The mean estimated time to complete the project and publish the review was 67.3 weeks (IQR=42). The number of studies found in the literature searches ranged from 27 to 92 020; the mean yield rate of included studies was 2.94% (IQR=2.5); and the mean number of authors per review was 5, SD=3. Funded reviews took significantly longer to complete and publish (mean=42 vs 26 weeks) and involved more authors and team members (mean=6.8 vs 4.8 people) than those that did not report funding (both p<0.001). Conclusions Systematic reviews presently take much time and require large amounts of human resources. In the light of the ever-increasing volume of published studies, application of existing computing and informatics technology should be applied to decrease this time and resource burden. We discuss recently published guidelines that provide a framework to make finding and accessing relevant literature less burdensome.