The dynamics of human body weight change

Evaluating the impact of sugar-sweetened beverages tax on overweight, obesity, and type 2 diabetes in an affluent Asian setting: A willingness-to-pay survey and simulation analysis

BACKGROUND

The potential health effects of taxing sugar-sweetened beverages (SSBs) has been insufficiently examined in Asian contexts. This study aimed to assess the impact of SSB taxation on the prevalence of obesity/overweight and type 2 diabetes mellitus (T2DM) in Hong Kong using a willingness-to-pay (WTP) survey and simulation analysis.

METHODS

A random telephone survey was conducted with 1000 adults from May to June 2020. We used a contingent valuation approach to assess individuals' WTP for SSBs under four tax payment scenarios (5%, 10%, 40%, and 50% of the current market price). Based on the WTP, a simulation analysis was conducted to project changes in SSB purchase and associated reductions in the prevalence of obesity/overweight and T2DM over a 10-year simulation period.

FINDINGS

When 5% and 10% taxation rates were introduced, approximately one-third of the population were unwilling to maintain their SSB purchase. Our simulation demonstrated a gradual decline in the prevalence of obesity/overweight and diabetes with a more pronounced decrease when higher taxation rates were introduced. 10% taxation resulted in a mean reduction of 1532.7 cases of overweight/obesity per 100 thousand population at the sixth year, while T2DM prevalence decreased by 267.1 (0.3%).

CONCLUSIONS

This study underscores the effects of an SSB tax on purchase behaviors and health outcomes in an affluent Asia setting, with a more pronounced influence on adult population. These findings are expected to inform policymakers in making decisions regarding an effective and equitable tax rate on SSBs.

Modelling remission from overweight type 2 diabetes reveals how altering advice may counter relapse

The development or remission of diet-induced overweight type 2 diabetes involves many biological changes which occur over very different timescales. Remission, defined by HbA1c<6.5%, or fasting plasma glucose concentration G<126 mg/dl, may be achieved rapidly by following weight loss guidelines. However, remission is often short-term, followed by relapse. Mathematical modelling provides a way of investigating a typical situation, in which patients are advised to lose weight and then maintain fat mass, a slow variable. Remission followed by relapse, in a modelling sense, is equivalent to changing from a remission trajectory with steady state G<126 mg/dl, to a relapse trajectory with steady state G≥126 mg/dl. Modelling predicts that a trajectory which maintains weight will be a relapse trajectory, if the fat mass chosen is too high, the threshold being dependent on the lipid to carbohydrate ratio of the diet. Modelling takes into account the effects of hepatic and pancreatic lipid on hepatic insulin sensitivity and β-cell function, respectively. This study leads to the suggestion that type 2 diabetes remission guidelines be given in terms of model parameters, not variables; that is, the patient should adhere to a given nutrition and exercise plan, rather than achieve a certain subset of variable values. The model predicts that calorie restriction, not weight loss, initiates remission from type 2 diabetes; and that advice of the form 'adhere to the diet and exercise plan' rather than 'achieve a certain weight loss' may help counter relapse.

Conjugating uncoupler compounds with hydrophobic hydrocarbon chains to achieve adipose tissue selective drug accumulation

One potential approach for treating obesity is to increase energy expenditure in brown and white adipose tissue. Here we aimed to achieve this outcome by targeting mitochondrial uncoupler compounds selectively to adipose tissue, thus avoiding side effects from uncoupling in other tissues. Selective drug accumulation in adipose tissue has been observed with many lipophilic compounds and dyes. Hence, we explored the feasibility of conjugating uncoupler compounds with a lipophilic C8-hydrocarbon chain via an ether bond. We found that substituting the trifluoromethoxy group in the uncoupler FCCP with a C8-hydrocarbon chain resulted in potent uncoupling activity. Nonetheless, the compound did not elicit therapeutic effects in mice, likely as a consequence of metabolic instability resulting from rapid ether bond cleavage. A lipophilic analog of the uncoupler compound 2,6-dinitrophenol, in which a C8-hydrocarbon chain was conjugated via an ether bond in the para-position (2,6-dinitro-4-(octyloxy)phenol), exhibited increased uncoupling activity compared to the parent compound. However, in vivo pharmacokinetics studies suggested that 2,6-dinitro-4-(octyloxy)phenol was also metabolically unstable. In conclusion, conjugation of a hydrophobic hydrocarbon chain to uncoupler compounds resulted in sustained or improved uncoupling activity. However, an ether bond linkage led to metabolic instability, indicating the need to conjugate lipophilic groups via other chemical bonds.

Baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss in individuals with obesity

Background

Weight loss effectively reduces cardiometabolic health risks among people with overweight and obesity, but inter-individual variability in weight loss maintenance is large. Here we studied whether baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss success.

Methods

Within the 8-month multicenter dietary intervention study DiOGenes, we classified a low weight-losers (low-WL) group and a high-WL group based on median weight loss percentage (9.9%) from 281 individuals. Using RNA sequencing, we identified the significantly differentially expressed genes between high-WL and low-WL at baseline and their enriched pathways. We used this information together with support vector machines with linear kernel to build classifier models that predict the weight loss classes.

Results

Prediction models based on a selection of genes that are associated with the discovered pathways 'lipid metabolism' (max AUC = 0.74, 95% CI [0.62-0.86]) and 'response to virus' (max AUC = 0.72, 95% CI [0.61-0.83]) predicted the weight-loss classes high-WL/low-WL significantly better than models based on randomly selected genes (P < 0.01). The performance of the models based on 'response to virus' genes is highly dependent on those genes that are also associated with lipid metabolism. Incorporation of baseline clinical factors into these models did not noticeably enhance the model performance in most of the runs. This study demonstrates that baseline adipose tissue gene expression data, together with supervised machine learning, facilitates the characterization of the determinants of successful weight loss.

Proposal of a Mathematical Model to Monitor Body Mass over Time in Subjects on a Diet

Nowadays, slimming diet methodology works within a reduction of body mass using a decrease of dietary energy intake. However, there is no suitable method for understanding the dynamic process of body mass metabolic transformation over time. In the present paper, we have developed a biomathematic model to explain the temporal trend of body mass and its variations of people who have undergone a change in their diet using the solving equation of the model. Data relating to sex, age, body mass, and BMI were collected, and the compartmental model used to interpret the body mass trends was constructed by assuming that the mass results from the sum of the metabolic processes: catabolic, anabolic, distributive. The validation of the model was carried out by variance analysis both on the total and individual data sets. The results confirm that the trend of body mass and its variations over time depends on metabolic rates. These are specific to each individual and characterize the distribution of nutritional molecules in the various body districts and the processes catabolic, anabolic, distributive. Body mass and its variations are justified by the metabolic transformations of the nutritional quantities. This would explain why energetically equal diets can correspond to people of different body mass and that energy-different diets can correspond to people of body mass at all similar.

Exploring the association between agricultural production systems and household diets in Viet Nam

The government of Viet Nam promotes an integrated and diversified production system that focuses on the symbiotic relationship of livestock, aquaculture, and fruits and vegetables (F&V), locally known as Vuon Ao Chuong (VAC). The expectation is that this system can prevent soil degradation, while improving dietary quality and income. This study examines the correlation between VAC production systems and diets using cross-sectional data from the 2016 round of the Viet Nam Household Living Standards Survey (VHLSS). Using ordinary least squares, we model four continuous outcome variables related to quantity consumed of fruits and vegetables, fiber, animal protein, and dietary energy; while using logistical regression, we model three indicator variables related to whether diets are balanced in terms of intake of dietary energy derived from carbohydrates, proteins, and fats. While individual components of VAC, such as aquaculture or F&V production, show a positive correlation with one or more dietary indicators, adoption of the full VAC system is found to be positively correlated only with dietary fiber consumption, making it challenging to establish a causal link between system adoption and improved dietary quality. However, we find that several socioeconomic variables, such as access to markets, household wealth, education of the household members, and household size are positively associated with one or more dietary indicators. Further research is needed to establish strong and causal relationships, or lack thereof, between VAC system and diets by exploiting the panel structure of VHLSS to examine the role of VAC in improving nutritional outcomes in Viet Nam.

Nutritional intakes of highly trained adolescent swimmers before, during, and after a national lockdown in the COVID-19 pandemic

Strict lockdown measures were introduced in response to the COVID-19 pandemic, which caused mass disruption to adolescent swimmers’ daily routines. To measure how lockdown impacted nutritional practices in this cohort, three-day photograph food diaries were analysed at three time points: before (January), during (April), and after (September) the first UK lockdown. Thirteen swimmers (aged 15 ± 1 years) from a high-performance swimming club submitted satisfactory food diaries at all time points. During lockdown, lower amounts of energy (45.3 ± 9.8 vs. 31.1 ± 7.7 kcal∙kg BM∙day^-1, p<0.001), carbohydrate (5.4 ± 1.2 vs. 3.5 ± 1.1 g∙kg BM∙day^-1, p<0.001), protein (2.3 ± 0.4 vs. 1.7 ± 0.4 g∙kg BM∙day^-1, p = 0.002), and fat (1.6 ± 0.4 vs. 1.1 ± 0.3 g∙kg BM∙day^-1, p = 0.011) were reported. After lockdown, no nutritional differences were found in comparison compared to before lockdown (energy: 44.0 ± 12.1 kcal∙kg BM∙day^-1; carbohydrate: 5.4 ± 1.4 g∙kg BM∙day^-1; protein: 2.1 ± 0.6 g∙kg BM∙day^-1; fat: 1.5 ± 0.6 g ∙kg BM∙day^-1, all p>0.05), despite fewer training hours being completed (15.0 ± 1.4 vs. 19.1 ± 2.2 h∙week^-1, p<0.001). These findings highlight the ability of adolescent swimmers to alter their nutrition based on their changing training circumstances when receiving sport nutrition support. However, some individuals displayed signs of suboptimal nutrition during lockdown that were not corrected once training resumed. This warrants future research to develop interactive education workshops that maintain focus and motivation towards optimal nutrition practices in isolated periods away from training.

Equitability of Individual and Population Interventions to Reduce Obesity: A Modeling Study in Mexico

INTRODUCTION

Modeling studies have estimated the potential impact and cost effectiveness of interventions to reduce obesity; few have focused on their equity across socioeconomic groups. This study aims to compare the equitability of individual- and population-level interventions to reduce obesity in Mexico.

METHODS

Mathematical models were implemented to estimate the expected effect of 2 sugar-sweetened beverage tax scenarios (10% and 20%) and bariatric surgery, pharmacotherapy, and dietary advice as individual interventions to reduce body weight. Individual interventions were modeled using meta-analytical weight change, inclusion and exclusion criteria, and the probability of access to healthcare services. For the tax, investigators obtained the baseline consumption of sugar-sweetened beverages from the National Health Survey 2012 and applied the reduction in sales observed in 2016 to estimate the caloric change and weight reduction. Implementation costs and cost per person, per kilogram, and equity were calculated for all interventions over a 1-year timeframe.

RESULTS

The 20% tax produced the largest estimated increase (4.50%) in normal BMI prevalence, was the most cost effective, and had the largest and most equitable decrease in obesity across socioeconomic categories. Pharmacotherapy and bariatric surgery produced sizable decreases in obesity prevalence (3.68% and 1.18%), particularly among the middle and high socioeconomic groups, whereas dietary advice had the lowest impact on normal and obese categories.

CONCLUSIONS

Individual interventions were effective in reducing obesity; yet, they were more expensive and less equitable than population interventions. Obesity in Mexico affects all socioeconomic groups; available interventions need to be carefully analyzed to tailor a national strategy that is both effective and equitable.

CKD subpopulations defined by risk-factors: A longitudinal analysis of electronic health records

Chronic kidney disease (CKD) is a progressive disease that evades early detection and is associated with various comorbidities. Although clinical comprehension and control of these comorbidities is crucial for CKD management, complex pathophysiological interactions and feedback loops make this a formidable task. We have developed a hybrid semimechanistic modeling methodology to investigate CKD progression. The model is represented as a system of ordinary differential equations with embedded neural networks and takes into account complex disease progression pathways, feedback loops, and effects of 53 medications to generate time trajectories of eight clinical biomarkers that capture CKD progression due to various risk factors. The model was applied to real world data of US patients with CKD to map the available longitudinal information onto a set of time‐invariant patient‐specific parameters with a clear biological interpretation. These parameters describing individual patients were used to segment the cohort using a clustering approach. Model‐based simulations were conducted to investigate cluster‐specific treatment strategies. The model was able to reliably reproduce the variability in biomarkers across the cohort. The clustering procedure segmented the cohort into five subpopulations – four with enhanced sensitivity to a specific risk factor (hypertension, hyperlipidemia, hyperglycemia, or impaired kidney) and one that is largely insensitive to any of the risk factors. Simulation studies were used to identify patient‐specific strategies to restrain or prevent CKD progression through management of specific risk factors. The semimechanistic model enables identification of disease progression phenotypes using longitudinal data that aid in prioritizing treatment strategies at individual patient level.

A mathematical model of food intake

The metabolic, hormonal and psychological determinants of the feeding behavior in humans are numerous and complex. A plausible model of the initiation, continuation and cessation of meals taking into account the most relevant such determinants would be very useful in simulating food intake over hours to days, thus providing input into existing models of nutrient absorption and metabolism. In the present work, a meal model is proposed, incorporating stomach distension, glycemic variations, ghrelin dynamics, cultural habits and influences on the initiation and continuation of meals, reflecting a combination of hedonic and appetite components. Given a set of parameter values (portraying a single subject), the timing and size of meals are stochastic. The model parameters are calibrated so as to reflect established medical knowledge on data of food intake from the National Health and Nutrition Examination Survey (NHANES) database during years 2015 and 2016.

Development and validation of prognostic models to estimate body weight loss in overweight and obese people

Introduction

Background: predicting weight loss outcomes from information collected from subjects before they start a weight management program is an objective strongly pursued by scientists who study energy balance. Objective: to develop and validate two prognostic models for the estimation of final body weight after a six-month intervention period. Material and methods: the present work was developed following the TRIPOD standard to report prognostic multivariable prediction models. A multivariable linear regression analysis was applied to 70 % of participants to identify the most relevant variables and develop the best prognostic model for body weight estimation. Then, 30 % of the remaining sample was used to validate the model. The study involved a 6-month intervention based on 25-30 % caloric restriction and exercise. A total of 239 volunteers who had participated in the PRONAF study, aged 18 to 50 years, with overweight or obesity (body mass index: 25-34.9 kg/m2), were enrolled. Body composition was estimated by dual-energy X-ray absorptiometry (DXA) and by hand-to-foot bioelectrical impedance (BIA) analysis. Results: prognostic models were developed and validated with a high correlation (0.954 and 0.951 for DXA and BIA, respectively), with the paired t-tests showing no significant differences between estimated and measured body weights. The mean difference, standard error, and 95 % confidence interval of the DXA model were 0.067 ± 0.547 (-1.036-1.170), and those of the BIA model were -0.105 ± 0.511 (-1.134-0.924). Conclusions: the models developed in this work make it possible to calculate the final BW of any participant engaged in an intervention like the one employed in this study based only on baseline body composition variables.

Data integration for prediction of weight loss in randomized controlled dietary trials

Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.

A Machine Learning Approach to Short-Term Body Weight Prediction in a Dietary Intervention Program

Weight and obesity management is one of the emerging challenges in current health management. Nutrient-gene interactions in human obesity (NUGENOB) seek to find various solutions to challenges posed by obesity and over-weight. This research was based on utilising a dietary intervention method as a means of addressing the problem of managing obesity and overweight. The dietary intervention program was done for a period of ten weeks. Traditional statistical techniques have been utilised in analyzing the potential gains in weight and diet intervention programs. This work investigates the applicability of machine learning to improve on the prediction of body weight in a dietary intervention program. Models that were utilised include Dynamic model, Machine Learning models (Linear regression, Support vector machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN)). The performance of these estimation models was compared based on evaluation metrics like RMSE, MAE and R2. The results indicate that the Machine learning models (ANN and RF) perform better than the other models in predicting body weight at the end of the dietary intervention program.

Low capacity to oxidize fat and body weight

For a given positive energy balance, a low capacity to oxidize fat could contribute to weight gain (low fat oxidation hypothesis). This hypothesis is based on the arguments that for a given stable diet and food quotient (FQ), the respiratory quotient (RQ) is higher in obesity prone (OP) than in obesity resistant individuals (OR) and that a high RQ predicts higher future weight gain. A review of 42 studies shows that there is no convincing experimental support to these arguments and thus for the low fat oxidation hypothesis. A power analysis also shows that this hypothesis might be impossible to experimentally confirm because very large numbers of subjects would be needed to reject the null hypotheses that the 24-h RQ is not different in OP and OR or that future weight gain is not different in individuals with a low and high 24-h RQ at baseline. A re-examination of the significance of the 24-hour and fasting RQ also shows that the assumption underlying the low fat oxidation hypothesis that a high RQ reflects a low capacity to oxidize fat is not valid: For a stable diet, the 24-h RQ entirely depends on FQ and energy balance, and the fasting RQ mainly depends on the FQ and energy balance and on the size of glycogen stores.

A Systematic Scoping Review of Surgically Manipulated Adipose Tissue and the Regulation of Energetics and Body Fat in Animals

OBJECTIVE

Surgical manipulations of adipose tissue by removal, or partial lipectomy, have demonstrated body fat compensation and recovered body weight, suggesting that the body is able to resist changes to body composition. However, the mechanisms underlying these observations are not well understood. The purpose of this scoping review is to provide an update on what is currently known about the regulation of energetics and body fat after surgical manipulations of adipose tissue in small mammals.

METHODS

PubMed and Scopus were searched to identify 64 eligible studies. Outcome measures included body fat, body weight, food intake, and circulating biomarkers.

RESULTS

Surgeries performed included lipectomy (72%) or transplantation (12%) in mice (35%), rats (35%), and other small mammals. Findings suggested that lipectomy did not have consistent long-term effects on reducing body weight and fat because regain occurred within 12 to 14 weeks post surgery. Hence, biological feedback mechanisms act to resist long-term changes of body weight or fat. Furthermore, whether this weight and fat regain occurred because of "passive" and "active" regulation under the "set point" or "settling point" theories cannot fully be discerned because of limitations in study designs and data collected.

CONCLUSIONS

The regulation of energetics and body fat are complex and dynamic processes that require further studies of the interplay of genetic, physiological, and behavioral factors.

Predictive Mathematical Models of Weight Loss

PURPOSE OF REVIEW

Validated thermodynamic energy balance models that predict weight change are ever more in use today. Delivery of model predictions using web-based applets and/or smart phones has transformed these models into viable clinical tools. Here, we provide the general framework for thermodynamic energy balance model derivation and highlight differences between thermodynamic energy balance models using four representatives.

RECENT FINDINGS

Energy balance models have been used to successfully improve dietary adherence, estimate the magnitude of food waste, and predict dropout from clinical weight loss trials. They are also being used to generate hypotheses in nutrition experiments. Applications of thermodynamic energy balance weight change prediction models range from clinical applications to modify behavior to deriving epidemiological conclusions. Novel future applications involve using these models to design experiments and provide support for treatment recommendations.

Is an Energy Surplus Required to Maximize Skeletal Muscle Hypertrophy Associated With Resistance Training

Resistance training is commonly prescribed to enhance strength/power qualities and is achieved via improved neuromuscular recruitment, fiber type transition, and/ or skeletal muscle hypertrophy. The rate and amount of muscle hypertrophy associated with resistance training is influenced by a wide array of variables including the training program, plus training experience, gender, genetic predisposition, and nutritional status of the individual. Various dietary interventions have been proposed to influence muscle hypertrophy, including manipulation of protein intake, specific supplement prescription, and creation of an energy surplus. While recent research has provided significant insight into optimization of dietary protein intake and application of evidence based supplements, the specific energy surplus required to facilitate muscle hypertrophy is unknown. However, there is clear evidence of an anabolic stimulus possible from an energy surplus, even independent of resistance training. Common textbook recommendations are often based solely on the assumed energy stored within the tissue being assimilated. Unfortunately, such guidance likely fails to account for other energetically expensive processes associated with muscle hypertrophy, the acute metabolic adjustments that occur in response to an energy surplus, or individual nuances like training experience and energy status of the individual. Given the ambiguous nature of these calculations, it is not surprising to see broad ranging guidance on energy needs. These estimates have never been validated in a resistance training population to confirm the "sweet spot" for an energy surplus that facilitates optimal rates of muscle gain relative to fat mass. This review not only addresses the influence of an energy surplus on resistance training outcomes, but also explores other pertinent issues, including "how much should energy intake be increased," "where should this extra energy come from," and "when should this extra energy be consumed." Several gaps in the literature are identified, with the hope this will stimulate further research interest in this area. Having a broader appreciation of these issues will assist practitioners in the establishment of dietary strategies that facilitate resistance training adaptations while also addressing other important nutrition related issues such as optimization of fuelling and recovery goals. Practical issues like the management of satiety when attempting to increase energy intake are also addressed.

Urinary markers of hydration during 3-day water restriction and graded rehydration

PURPOSE

This investigation had three purposes: (a) to evaluate changes in hydration biomarkers in response to a graded rehydration intervention (GRHI) following 3 days of water restriction (WR), (b) assess within-day variation in urine concentrations, and (c) quantify the volume of fluid needed to return to euhydration as demonstrated by change in U_col.

METHODS

115 adult males and females were observed during 1 week of habitual fluid intake, 3 days of fluid restriction (1000 mL day^-1), and a fourth day in which the sample was randomized into five different GRHI groups: no additional water, CON; additional 500 mL, G_+0.50; additional 1000 mL, G_+1.00; additional 1500 mL, G_+1.50; additional 2250 mL, G_+2.25. All urine was collected on 1 day of the baseline week, during the final 2 days of the WR, and during the day of GRHI, and evaluated for urine osmolality, color, and specific gravity.

RESULTS

Following the GRHI, only G_+1.50 and G_+2.25 resulted in all urinary values being significantly different from CON. The mean volume of water increase was significantly greater for those whose U_col changed from > 4 to < 4 (+ 1435 ± 812 mL) than those whose U_col remained ≥ 4 (+ 667 ± 722 mL, p < 0.001).

CONCLUSIONS

An additional 500 mL of water is not sufficient, while approximately 1500 mL of additional water (for a total intake between 2990 and 3515 mL day^-1) is required to return to a urine color associated with adequate water intake, following 3 days of WR.

Microsimulation model of child and adolescent overweight: making use of what we already know

BACKGROUND

New Zealand has high rates of child overweight and obesity when compared with other countries. Despite an abundance of research documenting the problem, it is unclear what the most effective policy changes or interventions are, and how policy changes might unfold over time within complex systems.

METHODS

We use estimates derived from meta-analyses to create a dynamic microsimulation model of child overweight (including obesity). Using census records we created a synthetic birth cohort of 10,000 children. Information on parental education, ethnicity and father's socio-economic position at birth were taken from census records. We used the New Zealand Health Survey to estimate population base rates for the prevalence of overweight and obesity. Information on other modifiers (such as maternal smoking, breastfeeding, preterm birth, regular breakfast consumption and so forth) were taken from three birth cohorts: Christchurch Health and Development Study, The Dunedin Multidisciplinary Health and Development Study and the Pacific Islands Families Study. Published intervention studies were used to derive plausible estimates for changes to modifiers.

RESULTS

Reducing the proportion of mothers classified as overweight and obesity (-3.31(95% CI -3.55; -3.07) percentage points), reducing the proportion of children watching two or more hours of TV (-3.78(95% CI -4.01; -3.54)), increasing the proportion of children eating breakfast regularly (-1.71(95% CI -1.96; -1.46)), and reducing the proportion of children born with high birth weights (-1.36(95% CI -1.61; -1.11)), lead to sizable decreases in the estimated prevalence of child overweight (including obesity). Reducing the proportion of mothers giving birth by caesarean (-0.23(95% CI -0.49; -0.23)) and increasing parental education (-0.07(95% CI -0.31; 0.18)) did not impact upon child overweight rates.

CONCLUSIONS

We created a working simulation model of New Zealand children that can be accessed by policy makers and researchers to determine known relationships between predictors and child overweight, as well as potential gains from targeting specific pathways.

Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake

The number of overweight people reached 1.9 billion in 2016. Lifespan decrease and many diseases have been linked to obesity. Efficient ways to monitor and control body weight are needed. The objective of this work is to explore the use of a model predictive control approach to manage bodyweight in response to energy intake. The analysis is performed based on data obtained during the Minnesota starvation experiment, with weekly measurements on body weight and energy intake for 32 male participants over the course of 27 weeks. A first order dynamic auto-regression with exogenous variables model exhibits the best prediction, with an average mean relative prediction error value of 1.01 ± 0.02% for 1 week-ahead predictions. Then, the performance of a model predictive control algorithm, following a predefined bodyweight trajectory, is tested. Root mean square errors of 0.30 ± 0.06 kg and 9 ± 3 kcal day−1 are found between the desired target and simulated bodyweights, and between the measured energy intake and advised by the controller energy intake, respectively. The model predictive control approach for bodyweight allows calculating the needed energy intake in order to follow a predefined target bodyweight reference trajectory. This study shows a first possible step towards real-time active control of human bodyweight.

Expected changes in obesity after reformulation to reduce added sugars in beverages: A modeling study

BACKGROUND

Several strategies have been proposed to reduce the intake of added sugars in the population. In Mexico, a 10% sugar-sweetened beverages (SSBs) tax was implemented in 2014, and the implementation of other nutritional policies, such as product reformulation to reduce added sugars, is under discussion. WHO recommends that all individuals consume less than 10% of their total energy intake (TEI) from added sugars. We propose gradually reducing added sugars in SSBs to achieve an average 10% consumption of added sugars in the Mexican population over 10 years and to estimate the expected impact of reformulation in adult body weight and obesity.

METHODS AND FINDINGS

Baseline consumption for added sugars and SSBs, sex, age, socioeconomic status (SES), height, and weight for Mexican adults were obtained from the 2012 Mexico National Health and Nutrition Survey (ENSANUT). On average, 12.6% of the TEI was contributed by added sugars; we defined a 50% reduction in added sugars in SSBs over 10 years as a reformulation target. Using a dynamic weight change model, sugar reductions were translated into individual expected changes in body weight assuming a 43% caloric compensation and a 2-year lag for the full effect of reformulation to occur. Results were stratified by sex, age, and SES. Twelve years after reformulation, the TEI from added sugars is expected to decrease to 10%, assuming no compensation from added sugars; 44% of the population would still be above WHO recommendations, requiring further sugar reductions to food. Body weight could be reduced by 1.3 kg (95% CI -1.4 to -1.2) in the adult population, and obesity could decrease 3.9 percentage points (pp; -12.5% relative to baseline). Our sensitivity analyses suggest that the impact of the intervention could vary from 0.12 kg after 6 months to 1.52 kg in the long term.

CONCLUSIONS

Reformulation to reduce added sugars in SSBs could produce large reductions in sugar consumption and obesity in the Mexican adult population. This study is limited by the use of a single dietary recall and by data collected in all seasons except summer; still, these limitations should lead to conservative estimates of the reformulation effect. Reformulation success could depend on government enforcement and industry and consumer response, for which further research and evidence are needed.

Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction

Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight from these large, relatively unstructured datasets. We review both physiological models and machine learning results across a diverse range of applications in energy homeostasis, and highlight how modelling and machine learning can work together to improve predictive ability. We collect quantitative details in a comprehensive mathematical supplement. We also discuss the prospects of forecasting homeostatic behaviour and stress the importance of characterizing stochasticity within and between individuals in order to provide practical, tailored forecasts and guidance to combat the spread of obesity.

A mathematical model of the effects of resistance exercise-induced muscle hypertrophy on body composition

PURPOSE

Current diet and exercise methods used to maintain or improve body composition often have poor long-term outcomes. We hypothesize that resistance exercise (RE) should aid in the maintenance of a healthy body composition by preserving lean mass (LM) and metabolic rate.

METHOD

We extended a previously developed energy balance model of human metabolism to include muscle hypertrophy in response to RE. We first fit model parameters to a hypothetical individual to simulate an RE program and then compared the effects of a hypocaloric diet only to the diet with either cardiovascular exercise (CE) or RE. We then simulated a cohort of individuals with different responses to RE by varying the parameters controlling it using Latin Hypercube Sampling (LHS). Finally, we fit the model to mean data from an elderly population on an RE program.

CONCLUSION

The model is able to reproduce the time course of change in LM in response to RE and can be used to generate a simulated cohort for in silico clinical studies. Simulations suggest that the additional LM generated by RE may shift the body composition to a healthier state.

Neighborhood Physical Environment and Changes in Body Mass Index: Results From the Multi-Ethnic Study of Atherosclerosis

Longitudinal associations between neighborhood characteristics and body mass index (BMI; weight (kg)/height (m)2) were assessed from 2000 to 2011 among 5,919 participants in the Multi-Ethnic Study of Atherosclerosis. The perceived availability of healthy food and walking environment were assessed via surveys, and 1-mile (1.6-km) densities of supermarkets, fruit-and-vegetable stores, and recreational facilities were obtained through a commercial database. Econometric fixed-effects models were used to estimate the association between within-person changes in neighborhood characteristics and within-person change in BMI. In fully adjusted models, a 1-standard-deviation increase in the healthy food environment index was associated with a 0.16-kg/m2 decrease in BMI (95% confidence interval (CI): -0.27, -0.06) among participants with obesity at baseline. A 1-standard-deviation increase in the physical activity environment index was associated with 0.13-kg/m2 (95% CI: -0.24, -0.02) and 0.14-kg/m2 (95% CI: -0.27, -0.01) decreases in BMI for participants who were overweight and obese at baseline, respectively. Paradoxically, increases in the physical activity index were associated with BMI increases in persons who were normal-weight at baseline. This study provides preliminary longitudinal evidence that favorable changes in neighborhood physical environments are related to BMI reductions in obese persons, who comprise a substantial proportion of the US population.

Modeling energy intake and body weight effects of a long-acting amylin analogue

The inhibitory effect of anti-obesity drugs on energy intake (EI) is counter-acted by feedback regulation of the appetite control circuit leading to drug tolerance. This complicates the design and interpretation of EI studies in rodents that are used for anti-obesity drug development. Here, we investigated a synthetic long-acting analogue of the appetite-suppressing peptide hormone amylin (LAMY) in lean and diet-induced obese (DIO) rats. EI and body weight (BW) were measured daily and LAMY concentrations in plasma were assessed using defined time points following subcutaneous administration of the LAMY at different dosing regimens. Overall, 6 pharmacodynamic (PD) studies including a total of 173 rats were considered in this evaluation. Treatment caused a dose-dependent reduction in EI and BW, although multiple dosing indicated the development of tolerance over time. This behavior could be adequately described by a population model including homeostatic feedback of EI and a turnover model describing the relationship between EI and BW. The model was evaluated by testing its ability to predict BW loss in a toxicology study and was utilized to improve the understanding of dosing regimens for obesity therapy. As such, the model proved to be a valuable tool for the design and interpretation of rodent studies used in anti-obesity drug development.

Lower critical temperature and cold-induced thermogenesis of lean and overweight humans are inversely related to body mass and basal metabolic rate

It is colloquially stated that body size plays a role in the human response to cold, but the magnitude and details of this interaction are unclear. To explore the inherent influence of body size on cold-exposed metabolism, we investigated the relation between body composition and resting metabolic rate in humans at thermoneutrality and during cooling within the nonshivering thermogenesis range. Body composition and resting energy expenditure were measured in 20 lean and 20 overweight men at thermoneutrality and during individualized cold exposure. Metabolic rates as a function of ambient temperature were investigated considering the variability in body mass and composition. We observed an inverse relationship between body size and the lower critical temperature (LCT), i.e. the threshold where thermoneutrality ends and cold activates thermogenesis. LCT was higher in lean than overweight subjects (22.1 ± 0.6 vs 19.5 ± 0.5°C, p < 0.001). Below LCT, minimum conductance was identical between lean and overweight (100 ± 4 vs 97 ± 3kcal/°C/day respectively, p = 0.45). Overweight individuals had higher basal metabolic rate (BMR) explained mostly by the higher lean mass, and lower cold-induced thermogenesis (CIT) per degree of cold exposure. Below thermoneutrality, energy expenditure did not scale to lean body mass. Overweight subjects had lower heat loss per body surface area (44.7 ± 1.3 vs 54.7 ± 2.3kcal/°C/m²/day, p < 0.001). We conclude that larger body sizes possessed reduced LCT as explained by higher BMR related to more lean mass rather than a change in whole-body conductance. Thus, larger individuals with higher lean mass need to be exposed to colder temperatures to activate CIT, not because of increased insulation, but because of a higher basal heat generation. Our study suggests that the distinct effects of body size and composition on energy expenditure should be taken in account when exploring the metabolism of humans exposed to cold.

Expected population weight and diabetes impact of the 1-peso-per-litre tax to sugar sweetened beverages in Mexico

STUDY QUESTION

What effect on body mass index, obesity and diabetes can we expect from the 1-peso-per-litre tax to sugar sweetened beverages in Mexico?

METHODS

Using recently published estimates of the reductions in beverage purchases due to the tax, we modelled its expected long-term impacts on body mass index (BMI), obesity and diabetes. Microsimulations based on a nationally representative dataset were used to estimate the impact of the tax on BMI and obesity. A Markov population model, built upon an age-period-cohort model of diabetes incidence, was used to estimate the impact on diagnosed diabetes in Mexico. To analyse the potential of tax increases we also modelled a 2-peso-per-litre tax scenario.

STUDY ANSWER AND LIMITATIONS

Ten years after the implementation of the tax, we expect an average reduction of 0.15 kg/m2 per person, which translates into a 2.54% reduction in obesity prevalence. People in the lowest level of socioeconomic status and those between 20 and 35 years of age showed the largest reductions in BMI and overweight and obesity prevalence. Simulations show that by 2030, under the current implementation of 1-peso-per-litre, the tax would prevent 86 to 134 thousand cases of diabetes. Overall, the 2-peso-per-litre scenario is expected to produce twice as much of a reduction. These estimates assume the tax effect on consumption remains stable over time. Sensitivity analyses were conducted to assess the robustness of findings; similar results were obtained with various parameter assumptions and alternative modelling approaches.

WHAT THIS STUDY ADDS

The sugar-sweetened beverages tax in Mexico is expected to produce sizable and sustained reductions in obesity and diabetes. Increasing the tax could produce larger benefits. While encouraging, estimates will need to be updated once data on direct changes in consumption becomes available.

Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations

Most adults are overweight or obese in many western countries. Several population-level interventions on the physical, economical, political, or sociocultural environment have thus attempted to achieve a healthier weight. These interventions have involved different weight-related behaviours, such as food behaviours. Agent-based models (ABMs) have the potential to help policymakers evaluate food behaviour interventions from a systems perspective. However, fully realizing this potential involves a complex procedure starting with obtaining and analyzing data to populate the model and eventually identifying more efficient cross-sectoral policies. Current procedures for ABMs of food behaviours are mostly rooted in one technique, often ignore the food environment beyond home and work, and underutilize rich datasets. In this paper, we address some of these limitations to better support policymakers through two contributions. First, via a scoping review, we highlight readily available datasets and techniques to deal with these limitations independently. Second, we propose a three steps' process to tackle all limitations together and discuss its use to develop future models for food behaviours. We acknowledge that this integrated process is a leap forward in ABMs. However, this long-term objective is well-worth addressing as it can generate robust findings to effectively inform the design of food behaviour interventions.

Changes in Energy Expenditure with Weight Gain and Weight Loss in Humans

Metabolic adaptation to weight changes relates to body weight control, obesity and malnutrition. Adaptive thermogenesis (AT) refers to changes in resting and non-resting energy expenditure (REE and nREE) which are independent from changes in fat-free mass (FFM) and FFM composition. AT differs in response to changes in energy balance. With negative energy balance, AT is directed towards energy sparing. It relates to a reset of biological defence of body weight and mainly refers to REE. After weight loss, AT of nREE adds to weight maintenance. During overfeeding, energy dissipation is explained by AT of the nREE component only. As to body weight regulation during weight loss, AT relates to two different set points with a settling between them. During early weight loss, the first set is related to depleted glycogen stores associated with the fall in insulin secretion where AT adds to meet brain's energy needs. During maintenance of reduced weight, the second set is related to low leptin levels keeping energy expenditure low to prevent triglyceride stores getting too low which is a risk for some basic biological functions (e.g., reproduction). Innovative topics of AT in humans are on its definition and assessment, its dynamics related to weight loss and its constitutional and neuro-endocrine determinants.

PF-05231023, a long-acting FGF21 analogue, decreases body weight by reduction of food intake in non-human primates

PF-05231023, a long-acting FGF21 analogue, is a promising potential pharmacotherapy for the treatment of obesity and associated comorbidities. Previous studies have shown the potential of FGF21 and FGF21-like compounds to decrease body weight in mice, non-human primates, and humans; the precise mechanisms of action remain unclear. In particular, there have been conflicting reports on the degree to which FGF21-induced weight loss in non-human primates is attributable to a decrease in food intake versus an increase in energy expenditure. Here, we present a semi-mechanistic mathematical model of energy balance and body composition developed from similar work in mice. This model links PF-05231023 administration and washout to changes in food intake, which in turn drives changes in body weight. The model is calibrated to and compared with recently published data from cynomolgus macaques treated with PF-05231023, demonstrating its accuracy in describing pharmacotherapy-induced weight loss in these animals. The results are consistent with the hypothesis that PF-05231023 decreases body weight in cynomolgus macaques solely by a reduction in food intake, with no direct effect on energy expenditure.

Evaluation of a Mathematical Model of Rat Body Weight Regulation in Application to Caloric Restriction and Drug Treatment Studies

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology.

Weight-HbA1c-insulin-glucose model for describing disease progression of type 2 diabetes

A previous semi‐mechanistic model described changes in fasting serum insulin (FSI), fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c) in patients with type 2 diabetic mellitus (T2DM) by modeling insulin sensitivity and β‐cell function. It was later suggested that change in body weight could affect insulin sensitivity, which this study evaluated in a population model to describe the disease progression of T2DM. Nonlinear mixed effects modeling was performed on data from 181 obese patients with newly diagnosed T2DM managed with diet and exercise for 67 weeks. Baseline β‐cell function and insulin sensitivity were 61% and 25% of normal, respectively. Management with diet and exercise (mean change in body weight = −4.1 kg) was associated with an increase of insulin sensitivity (30.1%) at the end of the study. Changes in insulin sensitivity were associated with a decrease of FPG (range, 7.8–7.3 mmol/L) and HbA1c (6.7–6.4%). Weight change as an effector on insulin sensitivity was successfully evaluated in a semi‐mechanistic population model.

Dissecting Long-Term Glucose Metabolism Identifies New Susceptibility Period for Metabolic Dysfunction in Aged Mice

Metabolic disorders, like diabetes and obesity, are pathogenic outcomes of imbalance in glucose metabolism. Nutrient excess and mitochondrial imbalance are implicated in dysfunctional glucose metabolism with age. We used conplastic mouse strains with defined mitochondrial DNA (mtDNA) mutations on a common nuclear genomic background, and administered a high-fat diet up to 18 months of age. The conplastic mouse strain B6-mt^FVB, with a mutation in the mt-Atp8 gene, conferred β-cell dysfunction and impaired glucose tolerance after high-fat diet. To our surprise, despite of this functional deficit, blood glucose levels adapted to perturbations with age. Blood glucose levels were particularly sensitive to perturbations at the early age of 3 to 6 months. Overall the dynamics consisted of a peak between 3–6 months followed by adaptation by 12 months of age. With the help of mathematical modeling we delineate how body weight, insulin and leptin regulate this non-linear blood glucose dynamics. The model predicted a second rise in glucose between 15 and 21 months, which could be experimentally confirmed as a secondary peak. We therefore hypothesize that these two peaks correspond to two sensitive periods of life, where perturbations to the basal metabolism can mark the system for vulnerability to pathologies at later age. Further mathematical modeling may perspectively allow the design of targeted periods for therapeutic interventions and could predict effects on weight loss and insulin levels under conditions of pre-diabetic obesity.

Modeling the dynamics of stable isotope tissue-diet enrichment

Reconstructions of dietary composition and trophic level from stable isotope measurements of animal tissue rely on predictable offsets of stable isotope ratios from diet to tissue. Physiological processes associated with metabolism shape tissue stable isotope ratios, and as such the spacing between stable isotope ratios of diet and tissue may be influenced by processes such as growth, nutritional stress, and disease. Here, we develop a model of incorporation stable isotopes in diet to tissues by coupling stable isotope dynamics to a model of macronutrient energy metabolism. We use the model to explore the effect of changes in dietary intake, both composition and amount, and in energy expenditure, on body mass and carbon and nitrogen stable isotope ratios of tissue.

Modeling body mass variation: incorporating social influence into calculations of caloric intake and energy expenditure

Variations in individual body mass and composition have long been a key focus in the health sciences, particularly now that overweight and obesity are considered as public health problems. We study a mathematical model that describes body mass variations which are determined by the energy balance between caloric intake and total energy expenditure. To calculate the change in caloric intake and energy expenditure over time, we proposed a relationship for each of these quantities, and we used measured values that are reported in the literature for the initial conditions. To account for small variations in the daily energy balance of an individual, we include social interactions as the multiplication of two terms: social proximity and social influence. We observe that social interactions have a considerable effect when the body mass of an individual is quite constant and social interactions take random values. However, when an individual's mass value changes (either increases or decreases), social interactions do not have a notable effect. In our simulation, we tested two different models that describe the body mass composition, and it resulted that one fits better the data.

Computer modeling of obesity links theoretical energetic measures with experimental measures of fuel use for lean and obese men

The goal of this research was to use a computational model of human metabolism to predict energy metabolism for lean and obese men. The model is composed of 6 state variables representing amino acids, muscle protein, visceral protein, glucose, triglycerides, and fatty acids (FAs). Differential equations represent carbohydrate, amino acid, and FA uptake and output by tissues based on ATP creation and use for both lean and obese men. Model parameterization is based on data from previous studies. Results from sensitivity analyses indicate that model predictions of resting energy expenditure (REE) and respiratory quotient (RQ) are dependent on FA and glucose oxidation rates with the highest sensitivity coefficients (0.6, 0.8 and 0.43, 0.15, respectively, for lean and obese models). Metabolizable energy (ME) is influenced by ingested energy intake with a sensitivity coefficient of 0.98, and a phosphate-to-oxygen ratio by FA oxidation rate and amino acid oxidation rate (0.32, 0.24 and 0.55, 0.65 for lean and obese models, respectively). Simulations of previously published studies showed that the model is able to predict ME ranging from 6.6 to 9.3 with 0% differences between published and model values, and RQ ranging from 0.79 to 0.86 with 1% differences between published and model values. REEs >7 MJ/d are predicted with 6% differences between published and model values. Glucose oxidation increases by ∼0.59 mol/d, RQ increases by 0.03, REE increases by 2 MJ/d, and heat production increases by 1.8 MJ/d in the obese model compared with lean model simulations. Increased FA oxidation results in higher changes in RQ and lower relative changes in REE. These results suggest that because fat mass is directly related to REE and rate of FA oxidation, body fat content could be used as a predictor of RQ.

A metabolic-epidemiological microsimulation model to estimate the changes in energy intake and physical activity necessary to meet the Healthy People 2020 obesity objective

OBJECTIVES

We combined a metabolic and an epidemiological model of obesity to estimate changes in calorie intake and physical activity necessary to achieve the Healthy People 2020 objective of reducing adult obesity prevalence from 33.9% to 30.5%.

METHODS

We used the National Health and Nutrition Examination Survey (1999-2010) to construct and validate a microsimulation model of the US population aged 10 years and older, for 2010 to 2020.

RESULTS

Obesity prevalence is expected to shift toward older adults, and disparities are expected to widen between White, higher-income groups and minority, lower-income groups if recent calorie consumption and expenditure trends continue into the future. Although a less than 10% reduction in daily calorie intake or increase in physical activity would in theory achieve the Healthy People 2020 objective, no single population-level intervention is likely to achieve the target alone, and individual weight-loss attempts are even more unlikely to achieve the target.

CONCLUSIONS

Changes in calorie intake and physical activity portend rising inequalities in obesity prevalence. These changes require multiple simultaneous population interventions.

A predictive model of the dynamics of body weight and food intake in rats submitted to caloric restrictions

Dynamics of body weight and food intake can be studied by temporally perturbing food availability. This perturbation can be obtained by modifying the amount of available food over time while keeping the overall food quantity constant. To describe food intake dynamics, we developed a mathematical model that describes body weight, fat mass, fat-free mass, energy expenditure and food intake dynamics in rats. In addition, the model considers regulation of food intake by leptin, ghrelin and glucose. We tested our model on rats experiencing temporally variable food availability. Our model is able to predict body weight and food intake variations by taking into account energy expenditure dynamics based on a memory of the previous food intake. This model allowed us to estimate this memory lag to approximately 8 days. It also explains how important variations in food availability during periods longer than these 8 days can induce body weight gains.

A novel system dynamics model of female obesity and fertility

OBJECTIVES

Our objective was to create a system dynamics model specific to weight gain and obesity in women of reproductive age that could inform future health policies and have the potential for use in preconception interventions targeting obese women.

METHODS

We used our system dynamics model of obesity in women to test various strategies for family building, including ovulation induction versus weight loss to improve ovulation. Outcomes included relative fecundability, postpartum body mass index, and mortality.

RESULTS

Our system dynamics model demonstrated that obese women who become pregnant exhibit increasing obesity levels over time with elevated morbidity and mortality. Alternatively, obese women who lose weight prior to pregnancy have improved reproductive outcomes but may risk an age-related decline in fertility, which can affect overall family size.

CONCLUSIONS

Our model highlights important public health issues regarding obesity in women of reproductive age. The model may be useful in preconception counseling of obese women who are attempting to balance the competing risks associated with age-related declines in fertility and clinically meaningful weight loss.

Short and long-term energy intake patterns and their implications for human body weight regulation

Adults consume millions of kilocalories over the course of a few years, but the typical weight gain amounts to only a few thousand kilocalories of stored energy. Furthermore, food intake is highly variable from day to day and yet body weight is remarkably stable. These facts have been used as evidence to support the hypothesis that human body weight is regulated by active control of food intake operating on both short and long time scales. Here, we demonstrate that active control of human food intake on short time scales is not required for body weight stability and that the current evidence for long term control of food intake is equivocal. To provide more data on this issue, we emphasize the urgent need for developing new methods for accurately measuring energy intake changes over long time scales. We propose that repeated body weight measurements can be used along with mathematical modeling to calculate long-term changes in energy intake and thereby quantify adherence to a diet intervention and provide dynamic feedback to individuals that seek to control their body weight.

Modeling social transmission dynamics of unhealthy behaviors for evaluating prevention and treatment interventions on childhood obesity

Research evidence indicates that obesity has spread through social networks, but lever points for interventions based on overlapping networks are not well studied. The objective of our research was to construct and parameterize a system dynamics model of the social transmission of behaviors through adult and youth influence in order to explore hypotheses and identify plausible lever points for future childhood obesity intervention research. Our objectives were: (1) to assess the sensitivity of childhood overweight and obesity prevalence to peer and adult social transmission rates, and (2) to test the effect of combinations of prevention and treatment interventions on the prevalence of childhood overweight and obesity. To address the first objective, we conducted two-way sensitivity analyses of adult-to-child and child-to-child social transmission in relation to childhood overweight and obesity prevalence. For the second objective, alternative combinations of prevention and treatment interventions were tested by varying model parameters of social transmission and weight loss behavior rates. Our results indicated child overweight and obesity prevalence might be slightly more sensitive to the same relative change in the adult-to-child compared to the child-to-child social transmission rate. In our simulations, alternatives with treatment alone, compared to prevention alone, reduced the prevalence of childhood overweight and obesity more after 10 years (1.2–1.8% and 0.2–1.0% greater reduction when targeted at children and adults respectively). Also, as the impact of adult interventions on children was increased, the rank of six alternatives that included adults became better (i.e., resulting in lower 10 year childhood overweight and obesity prevalence) than alternatives that only involved children. The findings imply that social transmission dynamics should be considered when designing both prevention and treatment intervention approaches. Finally, targeting adults may be more efficient, and research should strengthen and expand adult-focused interventions that have a high residual impact on children.

A modeling approach for compounds affecting body composition

Body composition and body mass are pivotal clinical endpoints in studies of welfare diseases. We present a combined effort of established and new mathematical models based on rigorous monitoring of energy intake (EI) and body mass in mice. Specifically, we parameterize a mechanistic turnover model based on the law of energy conservation coupled to a drug mechanism model. Key model variables are fat-free mass (FFM) and fat mass (FM), governed by EI and energy expenditure (EE). An empirical Forbes curve relating FFM to FM was derived experimentally for female C57BL/6 mice. The Forbes curve differs from a previously reported curve for male C57BL/6 mice, and we thoroughly analyse how the choice of Forbes curve impacts model predictions. The drug mechanism function acts on EI or EE, or both. Drug mechanism parameters (two to three parameters) and system parameters (up to six free parameters) could be estimated with good precision (coefficients of variation typically <20 % and not greater than 40 % in our analyses). Model simulations were done to predict the EE and FM change at different drug provocations in mice. In addition, we simulated body mass and FM changes at different drug provocations using a similar model for man. Surprisingly, model simulations indicate that an increase in EI (e.g. 10 %) was more efficient than an equal lowering of EI. Also, the relative change in body mass and FM is greater in man than in mouse at the same relative change in either EI or EE. We acknowledge that this assumes the same drug mechanism impact across the two species. A set of recommendations regarding the Forbes curve, vehicle control groups, dual action on EI and loss, and translational aspects are discussed. This quantitative approach significantly improves data interpretation, disease system understanding, safety assessment and translation across species.

Dynamics of childhood growth and obesity: development and validation of a quantitative mathematical model

BACKGROUND

Clinicians and policy makers need the ability to predict quantitatively how childhood bodyweight will respond to obesity interventions.

METHODS

We developed and validated a mathematical model of childhood energy balance that accounts for healthy growth and development of obesity, and that makes quantitative predictions about weight-management interventions. The model was calibrated to reference body composition data in healthy children and validated by comparing model predictions with data other than those used to build the model.

FINDINGS

The model accurately simulated the changes in body composition and energy expenditure reported in reference data during healthy growth, and predicted increases in energy intake from ages 5-18 years of roughly 1200 kcal per day in boys and 900 kcal per day in girls. Development of childhood obesity necessitated a substantially greater excess energy intake than for development of adult obesity. Furthermore, excess energy intake in overweight and obese children calculated by the model greatly exceeded the typical energy balance calculated on the basis of growth charts. At the population level, the excess weight of US children in 2003-06 was associated with a mean increase in energy intake of roughly 200 kcal per day per child compared with similar children in 1971-74 [corrected]. The model also suggests that therapeutic windows when children can outgrow obesity without losing weight might exist, especially during periods of high growth potential in boys who are not severely obese.

INTERPRETATION

This model quantifies the energy excess underlying obesity and calculates the necessary intervention magnitude to achieve bodyweight change in children. Policy makers and clinicians now have a quantitative technique for understanding the childhood obesity epidemic and planning interventions to control it.

FUNDING

Intramural Research Program of the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Connecting micro dynamics and population distributions in system dynamics models

Researchers use system dynamics models to capture the mean behavior of groups of indistinguishable population elements (e.g., people) aggregated in stock variables. Yet, many modeling problems require capturing the heterogeneity across elements with respect to some attribute(s) (e.g., body weight). This paper presents a new method to connect the micro-level dynamics associated with elements in a population with the macro-level population distribution along an attribute of interest without the need to explicitly model every element. We apply the proposed method to model the distribution of Body Mass Index and its changes over time in a sample population of American women obtained from the U.S. National Health and Nutrition Examination Survey. Comparing the results with those obtained from an individual-based model that captures the same phenomena shows that our proposed method delivers accurate results with less computation than the individual-based model.

Reconstruction of the drive underlying food intake and its control by leptin and dieting

The intake of food and the expenditure of calories is modelled by a system of differential equations. The state variables are the amount of calories stored in adipose tissue and the level of plasma leptin. The model has as input a drive that controls the intake of food. This drive consists of a collective of physiological and psychological incentives to eat or to stop eating. An individual based approach is presented by which the parameters of the system can be set using data of a subject. The method of analysis is fully worked out using weight data of two persons. The model is prone to extensions by transferring incentives being part of the input to the collection of state variables.