Potential contributors to variation in weight-loss response to liraglutide.

Obesity treatment requires a chronic state of negative energy balance. Obesity medications can help with this, increasing long-term dietary compliance by promoting satiety or reducing hunger. However, efficacy and safety of obesity medications vary for individuals. Early identification of non-responders to obesity medications may limit drug exposure while optimizing benefits for responders. This review summarizes factors that impact weight-loss response to liraglutide. Factors linked to greater weight loss on liraglutide include being female, not having diabetes, having relatively high baseline weight, and losing at least 4% of initial weight after 16 weeks of treatment. Other covariates that may predict treatment response but require further confirmation include central effects, nausea, gastric emptying of solids, and genotype. Baseline body mass index, race, and age seem less relevant for predicting weight-loss response to liraglutide. Lesser known and harder-to-measure factors such as cerebral blood flow, food cue reactivity, gut hormone levels, and dietary adherence possibly impact variability of response to liraglutide. This information should assist healthcare providers with establishing realistic weight-loss probability for individual patients. Future research should improve the ability to identify responders to liraglutide. Importantly, this review may provide a framework to identify responders to other obesity medications.

A short, attribution theory-based video intervention does not reduce weight bias in a nationally representative sample of registered dietitians: a randomized trial.

BACKGROUND/OBJECTIVES: Weight bias among registered dietitians (RDs) is a concern and effective interventions to reduce weight bias are sparse. Our objective was to determine if a short, attribution theory-based online video intervention would reduce weight bias in RDs. SUBJECTS/METHODS: Dietitians from a nationally representative sample were recruited for a randomized, parallel-arm study with online surveys at pre-, post-intervention and 1-month follow-up. One hundred and forty-seven RDs who watched one of three videos embedded in an online survey from June to August 2019 were considered for the analysis. RDs were randomized to watch either the intervention, positive control, or negative control video. The primary outcome was the change in the "blame" component of the Anti-Fat Attitude Test (AFAT) from pre-to immediate post-intervention. Differences in changes in AFAT and Implicit Association Test (IAT) scores across treatment groups were assessed via linear models; multiple imputation were performed for missing data. RESULTS: Baseline demographics, AFAT and IAT scores of the 147 participants who watched a video were not significantly different between the study groups (p > 0.05). The intervention group's AFAT-blame score reduced by an average of 0.05 between pre- and immediate post-intervention but was not statistically significant (p = 0.76, confidence intervals (CI) = -0.40, 0.30). Furthermore, there were no significant changes for AFAT-social, AFAT-physical subscores, and IAT within or between groups between pre- and immediate post-intervention (p > 0.05). Due to high attrition rates, the changes at 1-month follow-up are not reported. CONCLUSIONS: This study was the first to explore the effectiveness of an online video intervention to reduce weight bias in RDs. This study was unable to detect a significant impact of a short, attribution theory-based video intervention on weight bias in practicing RDs and future larger studies are needed to confirm our findings.

Biobehavioural approaches to prevention and treatment: A call for implementation science in obesity research.

Much progress has been made in the last 30 years in understanding the causes and mechanisms that contribute to obesity, yet widely available and successful strategies for prevention and treatment remain elusive at population levels. This paper discusses the biobehavioural framework and provides suggestions for applying it to enable greater progress in the science of obesity prevention and treatment, including an increased focus on implementation of science strategies. The objective is to promote a re-evaluation of current views about preventing and treating obesity within a unified biobehavioural framework. Further integration of research exploring how both behavioural and biological components interact is a critical step forward.

A randomized cross-over trial to determine the effect of a protein vs. carbohydrate preload on energy balance in ad libitum settings.

BACKGROUND: Although high protein diets have been tested in controlled environments for applications to weight management, it is not understood if adding high protein foods to the diet would impact ad libitum energy balance in the absence of other lifestyle changes. METHODS: This double-blinded randomized crossover trial compared the effects of a protein shake (PS) to a carbohydrate shake (CS), consumed prior to each major meal to equate to 20% of total energy needs over the course of the day, on energy balance over two 5-day treatment periods in healthy adults with BMI 20-30 kg/m2. Tri-axial accelerometers estimated physical activity energy expenditure. Ad libitum energy intake was measured in a laboratory kitchen. RESULTS: Energy balance was positive during both treatment periods but was not different between periods. There were no interactions between treatment and preload caloric dose or treatment and BMI status on energy balance. Satiety ratings did not differ for any pairwise comparisons between treatment and caloric dose. Controlling for gender and basal metabolic rate, thermic effect of food was greater for PS than CS. CONCLUSIONS: Preload periods significantly altered the macronutrient composition of the overall diet. This study found limited evidence that carbohydrate or protein preloads have differential effects on energy balance in short-term ad libitum settings. TRIAL REGISTRATION: This trial was pre-registered on clinicaltrials.gov as NCT02613065 on 11/30/2015.

The effects of a teaching intervention on weight bias among kinesiology undergraduate students.

OBJECTIVES: Weight bias is present among kinesiology professionals and this may cause a significant negative impact on their clients with obesity. Thus, our objective was to test if learning about uncontrollable cause of obesity and about weight bias would reduce explicit and implicit weight bias among kinesiology undergraduate students compared to the traditional curriculum which is more focused on controllable causes of weight gain. METHODS: We recruited undergraduates from two classes of the same kinesiology major course taught by the same instructor. In-class teaching activities consisted of 80 min lecture on day 1, video watching session and a group activity on day 3 for both groups. Intervention group (n = 33) learned about uncontrollable causes of obesity and about weight bias and had activities to evoke empathy. Control group (n = 34) learned the traditional curriculum where they learned the role of exercise and diet in weight management. We measured explicit and implicit weight bias using Anti-Fat Attitude Test (AFAT) and Implicit Association Test (IAT), respectively pre-intervention, immediate post intervention and 1 month later. RESULTS: In mixed model analysis, AFAT Blame scores had significant group by time interaction (p < 0.001). Blame scores significantly reduced with mean differences (standard error (SE)) of -0.35 (0.08) post intervention (p < 0.001) and persisted to be reduced with mean differences (SE) of -0.39 (0.08) even after 4-week follow-up (p < 0.001) only in the intervention group. Odds of having less implicit weight bias was significantly lower at 4-week follow-up than pre-intervention (odds ratio = 0.4; 95% CI: 0.22-0.73) in the control group but no changes were seen in the intervention group. CONCLUSIONS: "Blame" component of explicit weight bias significantly decreased when students learned about controllable causes of obesity and weight bias, but implicit bias did not reduce. However, implicit weight bias appears to increase when education on obesity is limited to diet and exercise interventions as taught in the traditional curriculum.

Subjective social status is associated with compensation for large meals - A prospective pilot study.

OBJECTIVES: Subjective social status (SSS) is known to be inversely associated with obesity. Our objective was to determine if SSS is associated with eating behaviors that would predispose to weight gain, specifically, with inadequate compensation for excess energy consumed during a single large meal. Therefore, we conducted a pilot study to determine the association of SSS with 24-h energy balance, 24-h and post-lunch energy intake, changes in body composition and changes in adjusted resting energy expenditure on days when a high-energy lunch was consumed in free-living human subjects. METHOD: Female participants (7 normal weight and 10 overweight) consumed 60% of' estimated 24-h energy requirements as a lunchtime meal in the laboratory for 14 days. Subjective social status was measured at baseline using the MacArthur Scale. Remote Food Photography Method was used to record food intake outside of the lab on days 1-2, 7-8, and 12-13. Associations of 24-h energy balance, 24-h and post-lunch energy intake, changes in adjusted resting energy expenditure and changes in percent body fat (measured by dual x-ray absorptiometry) with SSS were studied. RESULTS: Mean (standard deviation) age and BMI were 36.29 (8.25) years and 26.43 (2.32) kg/m2, respectively. Lower SSS was significantly associated with positive energy balance (p for trend 0.002), and higher post-lunch energy intake (p = 0.02) when controlled for age and initial body mass index. CONCLUSIONS: Our pilot data show that lower SSS is associated with higher post-lunch energy intake, which is indicative of poor energy compensation following a large meal. Over a longer time period, this could result in fat mass gain. Studies that are of longer duration and well-powered are warranted to confirm our findings.

Body Mass Index and Subjective Social Status: The Coronary Artery Risk Development in Young Adults Study.

OBJECTIVE: Subjective social status (SSS), or perceived social status, may explain, in part, the relationship between socioeconomic status (SES) and obesity. The objective of this study was to test whether SSS mediates the relationship between two indicators of SES (income and education) and body mass index (BMI). METHODS: A cross-sectional, structural equation path analysis was applied to the Coronary Artery Risk Development in Young Adults (CARDIA) study (n = 2,624). The analysis tested whether SSS (MacArthur scale), education, and income were associated with BMI at the year 20 examination (adjusting for sex, age, and race), and it was hypothesized that the associations of education and income with BMI would be at least partly mediated by SSS. RESULTS: SSS had a significant direct effect on BMI (-0.21, P = 0.018). Education had a significant direct relationship with SSS (0.11, P < 0.001) and a small but significant indirect relationship with BMI through SSS (-0.02, P = 0.022). Although income did not have a significant direct relationship with BMI, it did have a significant indirect relationship through SSS (b = -0.05, P = 0.019). CONCLUSIONS: Results are consistent with the hypothesized model in which SSS partially mediates the relationship between SES indicators and BMI.

True, true, unrelated? A review of recent evidence for a causal influence of breakfast on obesity.

PURPOSE OF REVIEW: It has long been known that breakfast skipping is associated with obesity; however, well controlled clinical trials have only recently been conducted to determine a causative relationship. RECENT FINDINGS: Skipping breakfast is likely to be detrimental for insulin sensitivity; however, randomized trials suggest that it does not play a large role in weight gain or weight loss. Specific breakfast strategies such as certain types of breakfast, and consuming more calories in the morning relative to the evening, however, may produce greater weight loss. SUMMARY: The act of eating breakfast, compared to skipping it, does not appear to have a large impact on body weight. More specific strategies regarding the size and type of breakfast may be required for a substantial effect on weight loss, and improving glycemic regulation may be an important reason to consume breakfast rather than skipping it.

Regarding Obesity as a Disease: Evolving Policies and Their Implications.

In 2013, the American Medical Association recognized obesity as a complex, chronic disease requiring medical attention. Defining obesity as a disease is a very public process, largely driven by expectation of costs and benefits. Although the public has been slow to embrace this definition, evidence is emerging for broader awareness of influencing factors beyond personal choice. This decision seems to be working with other factors to bring more access to care, less blame for people with the condition, and more favorable conditions for research to identify effective strategies for prevention and clinical care to reduce the impact.

The food-insecurity obesity paradox: A resource scarcity hypothesis.

Food insecurity is paradoxically associated with obesity in the United States. Current hypotheses to explain this phenomenon are descriptive regarding the low food security population's dietary and physical activity habits, but are not mechanistic. Herein it is proposed that a resource scarcity hypothesis may explain this paradox, such that fattening is a physiologically regulated response to threatened food supply that occurs specifically in low social status individuals. Evidence that this may be occurring, the implications for addressing the food insecurity-obesity paradox, and future areas of research, are reviewed and discussed.

Exercise following Mental Work Prevented Overeating.

UNLABELLED: Mental work may promote caloric intake, whereas exercise may offset positive energy balance by decreasing energy intake and increasing energy expenditure. PURPOSE: This study aimed to replicate previous findings that mental work increases caloric intake compared with a rest condition and assess whether exercise after mental work can offset this effect. METHODS: Thirty-eight male and female university students were randomly assigned to mental work + rest (MW + R) or mental work + exercise (MW + E). Participants also completed a baseline rest (BR) visit consisting of no mental work or exercise. Visit order was counterbalanced. During the MW + R or MW + E visit, participants completed a 20-min mental task and either a 15-min rest (MW + R) or a 15-min interval exercise (MW + E). Each visit ended with an ad libitum pizza lunch. A two-way repeated-measures ANOVA was used to compare eating behavior between groups. RESULTS: Participants in the MW + R condition consumed an average of 100 more kilocalories compared with BR (633.3 ± 72.9 and 533.9 ± 67.7, respectively, P = 0.02), and participants in MW + E consumed an average of 25 kcal less compared with BR (432.3 ± 69.2 and 456.5 ± 64.2, respectively, P > 0.05). When including the estimated energy expenditure of exercise in the MW + E conditions, participants were in negative energy balance by an average of 98.5 ± 41.5 kcal, resulting in a significant difference in energy balance between the two groups (P = 0.001). CONCLUSION: An acute bout of interval exercise after mental work resulted in significantly decreased food consumption compared with a nonexercise condition. These results suggest that an acute bout of exercise may be used to offset positive energy balance induced by mental tasks.

Integrated genomic and BMI analysis for type 2 diabetes risk assessment.

Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by Genome Wide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, evaluated with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5245 subjects (4306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 SNPs associated to T2D. We fitted Logistic Regressions and Bayesian Regularized Neural Networks and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy.

Assessment of whole-genome regression for type II diabetes.

Lifestyle and genetic factors play a large role in the development of Type 2 Diabetes (T2D). Despite the important role of genetic factors, genetic information is not incorporated into the clinical assessment of T2D risk. We assessed and compared Whole Genome Regression methods to predict the T2D status of 5,245 subjects from the Framingham Heart Study. For evaluating each method we constructed the following set of regression models: A clinical baseline model (CBM) which included non-genetic covariates only. CBM was extended by adding the first two marker-derived principal components and 65 SNPs identified by a recent GWAS consortium for T2D (M-65SNPs). Subsequently, it was further extended by adding 249,798 genome-wide SNPs from a high-density array. The Bayesian models used to incorporate genome-wide marker information as predictors were: Bayes A, Bayes Cπ, Bayesian LASSO (BL), and the Genomic Best Linear Unbiased Prediction (G-BLUP). Results included estimates of the genetic variance and heritability, genetic scores for T2D, and predictive ability evaluated in a 10-fold cross-validation. The predictive AUC estimates for CBM and M-65SNPs were: 0.668 and 0.684, respectively. We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066). The highest predictive AUC among the genome-wide marker Bayesian models was 0.681 for the Bayesian LASSO. Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information. Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D. Our results suggest that the Bayes Cπ and the G-BLUP models with a large set of genome-wide markers could be used for predicting risk to T2D, as an alternative to using high-density arrays when selected markers from large consortiums for a given complex trait or disease are unavailable.

Even modest prediction accuracy of genomic models can have large clinical utility.

Whole Genome Prediction (WGP) jointly fits thousands of SNPs into a regression model to yield estimates for the contribution of markers to the overall variance of a particular trait, and for their associations with that trait. To date, WGP has offered only modest prediction accuracy, but in some cases even modest prediction accuracy may be useful. We provide an illustration of this using a theoretical simulation that used WGP to predict weight loss after bariatric surgery with moderate accuracy (R (2) = 0.07) to assess the clinical utility of WGP despite these limitations. Prevention of Type 2 Diabetes (T2DM) post-surgery was considered the major outcome. Treating only patients above predefined threshold of predicted weight loss in our simulation, in the realistic context of finite resources for the surgery, significantly reduced lifetime risk of T2DM in the treatable population by selecting those most likely to succeed. Thus, our example illustrates how WGP may be clinically useful in some situations, and even with moderate accuracy, may provide a clear path for turning personalized medicine from theory to reality.

The aetiology of obesity beyond eating more and exercising less.

Although recent increases in availability of energy dense, processed foods and reductions in institutionally driven physical activity have created an environment that is permissible for obesity to occur, several other factors may contribute to the development of obesity in this context. We review evidence for eleven such factors: endocrine disruptors, intrauterine effects, epigenetics, maternal age, differential fecundity and assortative mating by body mass index, microorganisms, reduction in variability of ambient temperatures, smoking cessation, sleep debt, and pharmaceutical iatrogenesis. Evidence for the role of endocrine disruptors, microorganisms, ambient temperatures, sleep and reproductive factors is accumulating, but additional research is needed to confirm the causative role of these factors in human obesity. However, the role of certain pharmaceuticals and smoking cessation in development of human obesity is clear. Practice points for consideration and future research needed are highlighted for each factor.

The effectiveness of breakfast recommendations on weight loss: a randomized controlled trial.

BACKGROUND: Breakfast is associated with lower body weight in observational studies. Public health authorities commonly recommend breakfast consumption to reduce obesity, but the effectiveness of adopting these recommendations for reducing body weight is unknown. OBJECTIVE: We tested the relative effectiveness of a recommendation to eat or skip breakfast on weight loss in adults trying to lose weight in a free-living setting. DESIGN: We conducted a multisite, 16-wk, 3-parallel-arm randomized controlled trial in otherwise healthy overweight and obese adults [body mass index (in kg/m²) between 25 and 40] aged 20-65 y. Our primary outcome was weight change. We compared weight change in a control group with weight loss in experimental groups told to eat breakfast or to skip breakfast [no breakfast (NB)]. Randomization was stratified by prerandomization breakfast eating habits. A total of 309 participants were randomly assigned. RESULTS: A total of 283 of the 309 participants who were randomly assigned completed the intervention. Treatment assignment did not have a significant effect on weight loss, and there was no interaction between initial breakfast eating status and treatment. Among skippers, mean (±SD) baseline weight-, age-, sex-, site-, and race-adjusted weight changes were -0.71 ± 1.16, -0.76 ± 1.26, and -0.61 ± 1.18 kg for the control, breakfast, and NB groups, respectively. Among breakfast consumers, mean (±SD) baseline weight-, age-, sex-, site-, and race-adjusted weight changes were -0.53 ± 1.16, -0.59 ± 1.06, and -0.71 ± 1.17 kg for the control, breakfast, and NB groups, respectively. Self-reported compliance with the recommendation was 93.6% for the breakfast group and 92.4% for the NB group. CONCLUSIONS: A recommendation to eat or skip breakfast for weight loss was effective at changing self-reported breakfast eating habits, but contrary to widely espoused views this had no discernable effect on weight loss in free-living adults who were attempting to lose weight.

Methodological framework: discovering foods or nutrients with a causative influence on body weight in free-living settings.

A significant increase in food availability has been implicated in the worldwide obesity epidemic, and there is significant interest in understanding if specific foods may contribute to weight gain or aid in weight loss. Many foods have been studied in controlled laboratory studies or short-term studies, and these findings are often the basis for food-based public health recommendations to reduce or prevent obesity. Unfortunately, often these findings are not applicable to free-living settings where many interacting factors influence energy balance and body weight. Therefore, a proposed set of evidence criteria for making a food-based public health recommendation to reduce or prevent obesity is outlined and discussed herein, to serve as a basis for future committee or panel discussion and formalization. Using these criteria as a basis for making strong evidence-based recommendations will improve the state of science and policy in this area.