A study on the modeling for obesity

This study primarily aims to develop an Agent Based Model (ABM) that can simulate the obesity rates based on statistical analysis and to find out how obesity is affected by risk factors in a Canadian environment. As obesity can have many causes, it is assumed that various risk factors, not just a decisive one, have an influence on obesity and they interact with one another. Therefore, unlike most previous studies, I approached the obesity problem as a Complex -Adaptive System (CAS). The data used for this study was provided by Statistics Canada, and the Canadian Community Health Survey (CCHS). This survey is a cross-sectional survey that collects self-reported information related to health status, health care utilization, and health determinants for the Canadian population. To build the Obesity ABM, it is necessary to find out which risk factors are closely associated with obesity and to what extent they interact with one another. Twelve categories of factors that are expected to influence the obesity rate were chosen on the basis of the related works. Through the statistical data analysis carried out, the main factors and variables for obesity were identified and their respective mathematical relationships obtained. From this, two categories that have several sub-factors for the obesity model were chosen. I implemented statistical data analysis on the CCHS dataset to see the interrelationship among the factors. Also, I implemented a year-to-year analysis that can show how people change their obesity status each year. Based on the data analysis result, I defined rules for how each risk factor changes each year. These rules are applied to the obesity model using NetLogo. The architecture of obesity model implementation consists of three main parts: The population module, the risk factor module, and the results module. Performance evaluation was conducted to examine whether the obesity model can simulate the obesity rate. For this evaluation, the data of CCHS from 2009 to 2014 and the result of the obesity model which is generated by simulation are compared. Model calibration was executed to fit the actual data to the model test result. The result of the model test shows that the percentage error is less than 5%. This means that the obesity model has high validity in predicting obesity for each risk factor. The obesity ABM is a useful tool to find out the risk factors related to obesity and their relationships in the Canadian population. Thus, this model can potentially assist to improve obesity management at various levels. At the individual level, everyone can find what kinds of strategies are best fit to improve her/his physical condition. Also, at a government or community level, it could help develop policies for people to continue to implement these strategies well. This will lead to reducing the associated social costs and help to promote national health.

The Effect of Socioeconomic and Environmental Factors on Obesity

This paper examined the effect of socio-economic and environmental factors on obesity in Cleveland (Ohio) using an OLS model and three spatial regression models: spatial error model, spatial lag model, and a spatial error model with a spatially lagged response (SEMSLR). Comparative assessment of the models showed that the SEMSLR and the spatial error models were the best models. The spatial effect from the various spatial regression models was statistically significant, indicating an essential spatial interaction among neighboring geographic units and the need to account for spatial dependency in obesity research. The authors also found a statistically significant positive association between the percentage of families below poverty, Black population, and SNAP recipient with obesity rate. The percentage of college-educated had a statistically significant negative association with the obesity rate. The study shows that health outcomes such as obesity are not randomly distributed but are more clustered in deprived and marginalized neighborhoods.

Organoleptic Test of Boba Flour Substitution of Pumpkin Seeds High in Fiber and Protein

In 2013, the obesity rate in Indonesia for people over the age of 18 was 14.8 percent; by 2018, the obesity rate had risen to 21.8% (Riskesdas, 2018a). The significant number of fat people in Indonesia is caused by high sugar consumption and low fiber consumption regularly. One hundred grams of pumpkin seeds contain 6 grams of fiber, 30.23 grams of protein, 7.2 mg/100 zinc, polyunsaturated fatty acids, and phytosterols (Nurhasim, Tamrin, and Wahab, 2017). The purpose of this study is to identify the panelists' level of preference for four formulations of pumpkin seed flour boba using assessment factors such as taste, color, texture, and aroma. The procedural model was used in this research and development. This development study utilizes an opinion-based process (Borg and Gall, 1984), which is then modified based on research needs. Organoleptic testing on boba products without added milk drinks on untrained panelists revealed a significant difference in color and texture parameters. There was no significant difference in panelist acceptability of aroma and taste characteristics. The findings of the untrained panelist's appraisal of boba added to a milk companion drink were then presented, revealing significant changes in the color and texture parameters. Furthermore, there were no statistically significant changes in the aroma and taste indices.

Hypothesizing Nutrigenomic-Based Precision Anti-Obesity Treatment and Prophylaxis: Should We Be Targeting Sarcopenia Induced Brain Dysfunction?

Background: The United States Centers for Disease Control and Prevention (CDC) estimates a total obesity rate of 30% for 12 states and a 20% obesity rate nationwide. The obesity epidemic continues to increase in spite of preventative measures undertaken worldwide. Pharmacological treatments promise to reduce total fat mass. However, medications may have significant side effects and can be potentially fatal. Data Retrieval: This brief review, based on a PUBMED search of the key terms “Obesity” and” Sarcopenia,” will present evidence to corroborate the existence of Reward Deficiency Syndrome (RDS) in obesity and the involvement of catecholaminergic pathways in substance seeking behavior, particularly as it relates to carbohydrates cravings. Expert Opinion: The genetic basis and future genetic testing of children for risk of aberrant generalized craving behavior are considered a prevention method. Here we present evidence supporting the use of precursor amino acid therapy and modulation of enkephalinase, MOA, and COMT inhibition in key brain regions. Such treatments manifest in improved levels of dopamine/norepinephrine, GABA, serotonin, and enkephalins. We also present evidence substantiating insulin sensitivity enhancement via Chromium salts, which affect dopamine neuronal synthesis regulation. We believe our unique combination of natural ingredients will influence many pathways leading to the promotion of well-being and normal healthy metabolic functioning. Sarcopenia has been shown to reduce angiogenesis and possible cerebral blood flow. Exercise seems to provide a significant benefit to overcome this obesity-promoting loss of muscle density. Conclusion: Utilization of proposed nutrigenomic formulae based on coupling genetic obesity risk testing promotes generalized anti-craving of carbohydrates and can inhibit carbohydrate bingeing, inducing significant healthy fat loss and relapse prevention.

Obesity, Lung Cancer, and The Paradox of its Association: A Narrative Review

Background: The obesity rate in Indonesia always increases every year. RISKESDAS (Indonesian National Health Research Data) 2007, 2013 and 2018 showed that obesity rate is always increasing in Indonesia. Lung cancer is the most common cancer in Indonesia which causes death. Objective: This review aims to explain various diseases which are associated with obesity, risk factors of lung cancer and the association between them. Methods: Literature search was conducted in pubmed and textbooks regarding obesity and lung cancer risk factors. The literature search on association between obesity and lung cancer was done in pubmed with the keyword "(Lung Cancer [Title]) AND (Obesity [Title])". Results: Based on the research conducted, it was found that obesity was associated with various diseases including type 2 diabetes, dyslipidemia, cancer risk, mood disorders, heart disease, hypertension, liver disease, and reproductive disorders. Furthermore, there are various risk factors for lung cancer, including gender, genetics, tobacco use, and exposure to toxic agents. The association between obesity and lung cancer is a paradoxical phenomenon that occurs, in which obese patients have a lower risk of developing lung cancer based on the meta-analysis research (RR: 0.79; 95% CI 0.73-0.85). Conclusion: Obesity can increase the risk of various diseases, and lung cancer which is one of the most common cancers in Indonesia also has various risk factors. However, current clinical research studies have shown that obesity actually reduces the risk of lung cancer. Thus we advise researchers around the world to further enhance experimental research either in clinical or laboratory about the body mechanisms that can explain this phenomenon. Regardless of this paradox association, we also suggest that the public should keep on controlling body weight because of the risk of various diseases associated with obesity.

The Relation Among Obesity and Sugar Consumption: A Machine Learning Approach

We have collected two data sets. First data set consisted of 45 thousand data and second one 43. One data set consisted of food information , like calorie count, sugar in per 100 gram, fat in per 100 gram and so on. Second data set consisted of Obesity rate among USA people from age 0 to 80. We wanted to show a relation with sugar intake and obesity rate. Last of all our experiment found that ther's a significance evidence that there's a link between obesity and sugar intake . We used the machine learning approach for our experimental analysis.

The Relation Among Obesity and Sugar Consumption: A Machine Learning Approach

We have collected two data sets. First data set consisted of 45 thousand data and second one 43. One data set consisted of food information , like calorie count, sugar in per 100 gram, fat in per 100 gram and so on. Second data set consisted of Obesity rate among USA people from age 0 to 80. We wanted to show a relation with sugar intake and obesity rate. Last of all our experiment found that ther's a significance evidence that there's a link between obesity and sugar intake . We used the machine learning approach for our experimental analysis.

Effect of reducing ultraprocessed food consumption on obesity among US children and adolescents aged 7–18 years: evidence from a simulation model

BackgroundChildren and adolescents in the USA consume large amounts of daily calories from ultraprocessed foods (UPFs). Recent evidence links UPF consumption to increased body fat in youth. We aimed to estimate the potential impact of reducing UPF consumption on childhood obesity rate in the USA.MethodsWe developed a microsimulation model to project the effect of reducing UPF consumption in children’s diet on reducing the prevalence of overweight or obesity among US youth. The model incorporated nationally representative data on body mass index (BMI) percentile and dietary intake of 5804 children and adolescents aged 7–18 years from the National Health and Nutrition Examination Survey 2011–2016, and the effect of reducing UPF consumption on calorie intake from a recent randomised controlled trial. Uncertainties of model inputs were incorporated using probabilistic sensitivity analysis with 1000 simulations.ResultsReducing UPFs in children’s diet was estimated to result in a median of −2.09 kg/m2 (95% uncertainty interval −3.21 to –0.80) reduction in BMI among children and adolescents aged 7–18 years. The median prevalence of overweight (BMI percentile ≥85th) and obesity (BMI percentile ≥95th percentile) was reduced from 37.0% (35.9%, 38.1%) to 20.9% (15.1%, 29.9%) and from 20.1% (19.2%, 21.0%) to 11.0% (7.86%, 15.8%), respectively. Larger BMI and weight reductions were seen among boys than girls, adolescents than children, non-Hispanic black and Hispanic youth than non-Hispanic white youth, and those with lower levels of parental education and family income.ConclusionsReducing UPF consumption in children’s diet has the potential to substantially reduce childhood obesity rate among children and adolescents in the USA.

Obesity rates in renal stone formers from various countries

Objective: To collect evidence on the rate of obesity in renal stone formers (RSFs) living in different climatic areas and consuming different diets. Materials and methods: Data of adult renal stone formers were retrospectively collected by members of U-merge from 13 participant centers in Argentina, Brazil, Bulgaria (2), China, India, Iraq (2), Italy (2), Nigeria, Pakistan and Poland. The following data were collected: age, gender, weight, height, stone analysis and procedure of stone removal. Results: In total, 1689 renal stone formers (1032 males, 657 females) from 10 countries were considered. Average age was 48 (±14) years, male to female ratio was 1.57 (M/F 1032/657), the average body mass index (BMI) was 26.5 (±4.8) kg/m2. The obesity rates of RSFs in different countries were significantly different from each other. The highest rates were observed in Pakistan (50%), Iraq (32%), and Brazil (32%), while the lowest rates were observed in China (2%), Nigeria (3%) and Italy (10%). Intermediate rates were observed in Argentina (17%), Bulgaria (17%), India (15%) and Poland (22%). The age-adjusted obesity rate of RSFs was higher than the age-adjusted obesity rate in the general population in Brazil, India, and Pakistan, whereas it was lower in Argentina, Bulgaria, China, Italy, and Nigeria, and similar in Iraq and Poland. Conclusions: The age-adjusted obesity rate of RSFs was not higher than the age-adjusted obesity rate of the general population in most countries. The relationship between obesity and the risk of kidney stone formation should be reconsidered by further studies carried out in different populations.

The majority of the variation in COVID-19 rates between nations is explained by median age, obesity rate, and island status

Since the World Health Organization declared SARS-CoV-2 to be a global pandemic on March 11, 2020, nearly every nation on earth has reported infections. Incidence and prevalence of COVID-19 case rates have demonstrated extreme geospatial and temporal variability across the globe. The outbreaks in some countries are extreme and devastating, while other countries face outbreaks that are relatively minor. The causes of these differences between nations remain poorly understood, and identifying the factors that underlie this variation is critical to understand the dynamics of this disease in order to better respond to this and future pandemics. Here, we examine four factors that we anticipated would explain much of the variation in COVID-19 rates between nations: median age, obesity rate, island status, and strength of border closure measures. Clinical evidence suggests that age and obesity increase both the likelihood of infection and transmission in individual patients, which make them plausible demographic factors. The third factor, whether or not each country is an island nation, was selected because the geographical isolation of islands is expected to influence COVID-19 transmission. The fourth factor of border closure was selected because of its anticipated interaction with island nation status. Together, these four variables are able to explain a majority of the international variance in COVID-19 case rates. Using a dataset of 190 countries, simple modeling based on these four factors and their interactions explains more than 70% of the total variance between countries. With additional covariates, more complex modeling and higher-order interactions explains more than 80% of the variance. These novel findings offer a solution to explain the unusual global variation of COVID-19 that has remained largely elusive throughout the pandemic.