Michelle M Bohan Brown

Myths, Presumptions, and Facts about Obesity

BACKGROUND Many beliefs about obesity persist in the absence of supporting scientific evidence (presumptions); some persist despite contradicting evidence (myths). The promulgation of unsupported beliefs may yield poorly informed policy decisions, inaccurate clinical and public health recommendations, and an unproductive allocation of research resources and may divert attention away from useful, evidence-based information. METHODS Using Internet searches of popular media and scientific literature, we identified, reviewed, and classified obesity-related myths and presumptions. We also examined facts that are well supported by evidence, with an emphasis on those that have practical implications for public health, policy, or clinical recommendations. RESULTS We identified seven obesity-related myths concerning the effects of small sustained increases in energy intake or expenditure, establishment of realistic goals for weight loss, rapid weight loss, weight-loss readiness, physical-education classes, breast-feeding, and energy expended during sexual activity. We also identified six presumptions about the purported effects of regularly eating breakfast, early childhood experiences, eating fruits and vegetables, weight cycling, snacking, and the built (i.e., human-made) environment. Finally, we identified nine evidence-supported facts that are relevant for the formulation of sound public health, policy, or clinical recommendations. CONCLUSIONS False and scientifically unsupported beliefs about obesity are pervasive in both scientific literature and the popular press. (Funded by the National Institutes of Health.).

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.

Weighing the Evidence of Common Beliefs in Obesity Research

Obesity is a topic on which many views are strongly held in the absence of scientific evidence to support those views, and some views are strongly held despite evidence to contradict those views. We refer to the former as “presumptions” and the latter as “myths.” Here, we present nine myths and 10 presumptions surrounding the effects of rapid weight loss; setting realistic goals in weight loss therapy; stage of change or readiness to lose weight; physical education classes; breastfeeding; daily self-weighing; genetic contribution to obesity; the “Freshman 15”; food deserts; regularly eating (versus skipping) breakfast; eating close to bedtime; eating more fruits and vegetables; weight cycling (i.e., yo-yo dieting); snacking; built environment; reducing screen time in childhood obesity; portion size; participation in family mealtime; and drinking water as a means of weight loss. For each of these, we describe the belief and present evidence that the belief is widely held or stated, reasons to support the conjecture that the belief might be true, evidence to directly support or refute the belief, and findings from randomized controlled trials, if available. We conclude with a discussion of the implications of these determinations, conjecture on why so many myths and presumptions exist, and suggestions for limiting the spread of these and other unsubstantiated beliefs about the obesity domain.

Added sugars in the diet are positively associated with diastolic blood pressure and triglycerides in children

BACKGROUND Hypertension and dyslipidemia have traditionally been associated with dietary sodium and fat intakes, respectively; however, they have recently been associated with the consumption of added sugars in adults and older adolescents, but there is no clear indication of how early in the life span this association manifests. OBJECTIVE This study explored the cross-sectional association between added sugar (sugars not naturally occurring in foods) consumption in children, blood pressure (BP), and fasting blood lipids [triglycerides and total, low-density lipoprotein, and high-density lipoprotein (HDL) cholesterol]. DESIGN BP, blood lipids, and dietary intakes were obtained in a multiethnic pediatric sample aged 7-12 y of 122 European American (EA), 106 African American (AA), 84 Hispanic American (HA), and 8 mixed-race children participating in the Admixture Mapping of Ethnic and Racial Insulin Complex Outcomes (AMERICO) study-a cross-sectional study conducted in the Birmingham, AL, metro area investigating the effects of racial-ethnic differences on metabolic and health outcomes. Multiple regression analyses were performed to evaluate the relations of added sugars and sodium intakes with BP and of added sugars and dietary fat intakes with blood lipids. Models were controlled for sex, race-ethnicity, socioeconomic status, Tanner pubertal status, percentage body fat, physical activity, and total energy intake. RESULTS Added sugars were positively associated with diastolic BP (P = 0.0462, β = 0.0206) and serum triglycerides (P = 0.0206, β = 0.1090). Sodium was not significantly associated with either measure of BP nor was dietary fat with blood lipids. HA children had higher triglycerides but lower added sugar consumption than did either the AA or EA children. The AA participants had higher BP and HDL but lower triglycerides than did either the EA or HA children. CONCLUSIONS These data suggest that increased consumption of added sugars may be associated with adverse cardiovascular health factors in children, specifically elevated diastolic BP and triglycerides. Identification of dietary factors influencing cardiovascular health during childhood could serve as a tool to reduce cardiovascular disease risk. This trial was registered at clinicaltrials.gov as NCT00726778.

High Intensity Interval- vs Moderate Intensity- Training for Improving Cardiometabolic Health in Overweight or Obese Males: A Randomized Controlled Trial

Purpose To compare the effects of six weeks of high intensity interval training (HIIT) vs continuous moderate intensity training (MIT) for improving body composition, insulin sensitivity (SI), blood pressure, blood lipids, and cardiovascular fitness in a cohort of sedentary overweight or obese young men. We hypothesized that HIIT would result in similar improvements in body composition, cardiovascular fitness, blood lipids, and SI as compared to the MIT group, despite requiring only one hour of activity per week compared to five hours per week for the MIT group. Methods 28 sedentary overweight or obese men (age, 20 ± 1.5 years, body mass index 29.5 ± 3.3 kg/m2) participated in a six week exercise treatment. Participants were randomly assigned to HIIT or MIT and evaluated at baseline and post-training. DXA was used to assess body composition, graded treadmill exercise test to measure cardiovascular fitness, oral glucose tolerance to measure SI, nuclear magnetic resonance spectroscopy to assess lipoprotein particles, and automatic auscultation to measure blood pressure. Results A greater improvement in VO2peak was observed in MIT compared to HIIT (11.1% vs 2.83%, P = 0.0185) in the complete-case analysis. No differences were seen in the intention to treat analysis, and no other group differences were observed. Both exercise conditions were associated with temporal improvements in % body fat, total cholesterol, medium VLDL, medium HDL, triglycerides, SI, and VO2peak (P < 0.05). Conclusion Participation in HIIT or MIT exercise training displayed: 1) improved SI, 2) reduced blood lipids, 3) decreased % body fat, and 4) improved cardiovascular fitness. While both exercise groups led to similar improvements for most cardiometabolic risk factors assessed, MIT led to a greater improvement in overall cardiovascular fitness. Overall, these observations suggest that a relatively short duration of either HIIT or MIT training may improve cardiometabolic risk factors in previously sedentary overweight or obese young men, with no clear advantage between these two specific regimes (Clinical Trial Registry number NCT01935323). Trial Registration ClinicalTrials.gov NCT01935323

Short-term consumption of sucralose, a nonnutritive sweetener, is similar to water with regard to select markers of hunger signaling and short-term glucose homeostasis in women

Nonnutritive sweeteners have been used to lower the energy density of foods with the intention of affecting weight loss or weight maintenance. However, some epidemiological and animal evidence indicates an association between weight gain or insulin resistance and artificial sweetener consumption. In the present study, we hypothesized that the nonnutritive sweetener sucralose, a trichlorinated sucrose molecule, would elicit responses similar to water but different from sucrose and sucrose combined with sucralose on subjective and hormonal indications of hunger and short-term glucose homeostasis. Eight female volunteers (body mass index, 22.16 ± 1.71 kg/m(2); age, 21.75 ± 2.25 years) consumed sucrose and/or sucralose in water in a factorial design. Blood samples were taken at fasting and 30 and 60 minutes after treatment followed by a standardized breakfast across treatments, and blood samples were taken 30, 60, 90, and 120 minutes after breakfast. Plasma was analyzed for glucose, insulin, glucagon, triacylglycerols (TAG), and acylated ghrelin. Perceptions of hunger and other subjective measurements were assessed before each blood sample. No differences were detected in subjective responses, circulating triacylglycerol, or glucagon concentrations among treatments over time. Significant differences were observed in insulin, glucose, and acylated ghrelin concentrations over time only between sucrose-containing treatments and non-sucrose-containing treatments regardless of sucralose consumption. Therefore, sucralose may be a relatively inert nonnutritive sweetener with regard to hunger signaling and short-term glucose homeostasis.

Best (but oft-forgotten) practices: designing, analyzing, and reporting cluster randomized controlled trials

Cluster randomized controlled trials (cRCTs; also known as group randomized trials and community-randomized trials) are multilevel experiments in which units that are randomly assigned to experimental conditions are sets of grouped individuals, whereas outcomes are recorded at the individual level. In human cRCTs, clusters that are randomly assigned are typically families, classrooms, schools, worksites, or counties. With growing interest in community-based, public health, and policy interventions to reduce obesity or improve nutrition, the use of cRCTs has increased. Errors in the design, analysis, and interpretation of cRCTs are unfortunately all too common. This situation seems to stem in part from investigator confusion about how the unit of randomization affects causal inferences and the statistical procedures required for the valid estimation and testing of effects. In this article, we provide a brief introduction and overview of the importance of cRCTs and highlight and explain important considerations for the design, analysis, and reporting of cRCTs by using published examples.

Nutritional epidemiology in practice: learning from data or promulgating beliefs?

The world can be a scary place. Bad things sometimes happen to us and our loved ones, and few things are scarier than cancer. When we are facing frightening things, a sense of controllability can ease our fears (1). But experience and empirical evidence tell us that things are often more seemingly random and less predictable and controllable than we commonly think (2). Before Benjamin Franklin’s scientific investigation of lightning and invention of the lightning rod, lightning was a terrifying, seemingly random and uncontrollable, and often deadly occurrence. People sought explanations and control: they found those explanations in divine provenance and perceived control in the ringing of church bells. Unfortunately, ringing church bells does not actually dissuade lightning and often led to the deaths of bell ringers who ascended the bell towers in the midst of storms (3). One author described cancer as the result of a ‘‘lottery-like accumulation of stochastic mutations’’ (4). Faced with such a lottery that none of us voluntarily enter, we grasp desperately for signs of controllability. Would it not be wonderful if food itself, the daily sustenance that we all take and one of our greatest pleasures, offered such controllability? For centuries, we have sought causes and cures for cancer, and food has been a prime candidate in that search (5). Has this search and our collective conflict of interest in wanting to reduce our fear potentially affected our interpretation and reporting of research results, leading to bias in the scientific record? Are we like church-bell ringers in a storm? John Ioannidis has been a pioneer in creatively finding the skeletons in the epistemologic closet of the biomedical research community. In this issue of the Journal, he and Jonathan Schoenfeld do so again with the provocative and innovative flair we have come to expect (6). They raise an important question in nutritional epidemiology by asking, ‘‘Is everything we eat associated with cancer?’’ As they noted in the Discussion, analyzing all nutrientcancer interactions would be impossible. Yet, by selecting 50 ingredients from a cookbook, they ensured that the analysis would be relevant to common, familiar foods, including specific dairy products, meats, vegetables, and spices and even tea and rum. This method of selecting the subject of review was just as innovative as the question at hand. They found that almost threefourths of the articles they reviewed concluded that there was an increased or decreased risk of cancer attributed to various foods, with most evidence being at least nominally significant. It appears, then, that according to the published literature almost everything we eat is, in fact, associated with cancer. However, Schoenfeld and Ioannidis proceeded to show that biases exist in the nutrientcancer literature. The fidelity of research findings between nutrients and cancer may have been compromised in several ways. They identified an overstating of weak results (most associations were only weakly supported), a lack of consistent comparisons (inconsistent definitions of exposure and outcomes), and possible suppression of null findings (a bimodal distribution of outcomes, with a noticeable lack of null findings). Although Schoenfeld and Ioannidis (6) showed that biases exist in the nutrient-cancer literature, it is unclear what causes these breaches in scientific objectivity. Bias is not new to the field of science. Antoine-Laurent Lavoisier in the 1700s wrote about bias and its clouding of scientific findings, stating, ‘‘Imagination, on the contrary, which is ever wandering beyond the bounds of truth, joined to self-love and that self-confidence we are so apt to indulge, prompt us to draw conclusions which are not immediately derived from facts; so that we become in some measure interested in deceiving ourselves’’ (7). White hat bias, confirmation bias, and publication bias can lead to self-deception (8, 9). White hat bias, defined by Cope and Allison as ‘‘bias leading to distortion of research-based information in the service of what may be perceived as ‘righteous ends’,’’ may be a factor in the overstatement of research findings (8). In addition, overstatement of results can be influenced by confirmation bias, in which the overstated results match preconceived views and hypotheses, leading to acceptance of the results even if the results are weak or nonsignificant (9). When results are null, publishing can be difficult and can lead to publication bias in which significant findings are more likely to be published, further distorting our view of what is known (9). When results are presented in a biased manner, the distorted results are disseminated to the public through lay media (10).

Genetic Admixture and Obesity: Recent Perspectives and Future Applications

The process of the colonization of the New World that occurred centuries ago served as a natural experiment, creating unique combinations of genetic material in newly formed admixed populations. Through a genetic admixture approach, the identification and genotyping of ancestry informative markers have allowed for the estimation of proportions of ancestral parental populations among individuals in a sample. These admixture estimates have been used in different ways to understand the genetic contributions to individual variation in obesity and body composition parameters, particularly among diverse admixed groups known to differ in obesity prevalence within the United States. Although progress has been made through the use of genetic admixture approaches, further investigations are needed in order to explore the interaction of environmental factors with the degree of genetic ancestry in individuals. A challenge to confront at this time would be to further stratify and define environments in progressively more granular terms, including nutrients, muscle biology, stress responses at the cellular level, and the social and built environments.

Concerning Sichieri R, Cunha DB: Obes Facts 2014;7:221–232. The Assertion that Controlling for Baseline (Pre-Randomization) Covariates in Randomized Controlled Trials Leads to Bias is False.

We read with interest the article ‘Unbalanced Baseline in School-Based Interventions to Prevent Obesity: Adjustment Can Lead to Bias – a Systematic Review’ [1] , hereafter ‘ the article ’. We agree with the authors that more rigor is needed in research on obesity treatment and prevention, and in the design, analysis, and reporting of cluster randomized controlled trials (cRCTs) [2] , also called group randomized trials. Unfortunately, rather than offering clarifying information, the article is based on incorrect statistical reasoning and inaccurate statements about what past publications have shown. The fundamental conclusion as stated in its title and elsewhere in the article is incorrect. For example, the statement ‘Although adjusting for the baseline values of parameters (sic, variables – Li et al.) that are highly influenced by baseline values is a standard procedure, this approach can bias the results …’ is simply untrue. Such erroneous conclusions could lead researchers to avoid legitimate power-enhancing analytic methods, and should be retracted. Adjusting for pre-randomization covariates in randomized trials does not introduce bias nor invalidate significance tests. This is known from statistical principles and requires neither simulation nor meta-analyses. By definition and design, in randomized experiments prerandomization covariates are independent of treatment assignment, with the exception of chance deviations which are accommodated in the calculation of frequentist significance tests and their associated p values. If the outcome variable (Y) is measured pre-randomization (Y 0 ) and at the end of the study (Y 1 ), then using either Y 1 or Y 0 – Y 1 as outcomes and either