Obesity Determinants, Discrimination and the need for upstream investment.

Obesity is a complex, interrelated, multifactorial disease. Although classified as a disease in 1948, much of society’s perception is that it is created by unhealthy food choices and excessive calories and, therefore, seen as a behavioural problem (James 2008). Fundamentally, obesity is an imbalance between calories consumed and calories expended, (BMR 60 to 70% of daily expenditure, plus energy for thermogenesis and muscle action), but what causes obesity is far more complex (Camacho & Ruppel, 2017). In 2008, the Obesity Society declared obesity as a chronic disease. Disease is defined by “physiological changes which negatively affect organs, cells and structures” (Jastreboff et al., 2019).

Since 1970, governments have refused to look at obesity as a growing health issue. Their focus was around malnutrition and disease in developing countries (James. 2008). Over the next 20 years, obesity and its associated cardiovascular diseases (CVD) were raised as a growing concern until 1995 when Taskforce came into existence and Standardised measurements in the form of BMI. This was so that comparisons could be made within age groups (Weir & Jan, 2019). BMI classifications were used globally to collect data and experts reassessed energy and protein needs, using estimated activity levels of that era and issued calorie intake and nutrition guidelines to reduce heart disease (Kritchevsky 1998). It is argued by many scientists, however, that the BMI measurements of overweight and obese populations is controversial and questioned over its accuracy, as it does not measure differences in body fat mass, it is thought that waist circumference to assess visceral fat as a measurement of risk for metabolic diseases would be more accurate (Stommel, & Schoenborn 2009; Nihiser et al., 2007).

Today, it is globally recognised that obesity is a pandemic along with its related diseases. (Prentice, 2006). A study measuring the cost of obesity and its associated diseases in 1995, not including weight loss support and prescriptions in America, included subjects who had an increasing BMI 27> - 30> alongside Diabetes Type 2, coronary heart disease (CHD), Hypertension, Breast, Endometrial and Colon Cancers, Gall bladder disease and Osteoarthritis (Wolf, & Colditz 1998). The total cost of obesity was $99.2 billion in 1995, it was also estimated that Americans were spending $33 billion per year on weight loss products (Wolf, & Colditz 1998). Predictions are that the US overweight and obese populations will double every decade if the trend continues, accounting for $956 billion (18% of total healthcare) per year in direct and indirect healthcare (Mitchell & Shaw, 2015). The cost to the NHS in 2006-2007 in dealing with obese and overweight patients was 5.1 billion (Scarborough, et al., 2011). Another forecast for the UK suggested treating obesity cost the country £9.4million and £470 million treating comorbidity diseases (Mitchell & Shaw, 2015).

The World Health Organization estimates 600 million adults globally fell into the obese category in 2014 (Nikooyeh et al.,2016). 2.8 million people die every year due to obesity. The WHO also put out figures in 2010 from Europe, Eastern Mediterranean and America stating 50% of women are overweight and the fastest rates of overweight individuals were children from upper-middle-income populations (World Health Organization 2011). Considerations of an ageing population and longer life expectancy needs to be considered, as well as how the global economy will cope due to the BMI correlation rising with age (Meeuwsen et al.,2010).

The rise of overweight or obese children (5-9 years) globally has risen exponentially from 1975 to 2016 (from 4% to 18%) (De Onis et al.,2010). This cross-sectional study used surveys from 144 countries and discovered that 43 million children (35 million in developing countries) were estimated to be overweight or obese. A further 92 million were at risk of becoming overweight. A 10-year systemic review covering a period of 2006-2016 in European children concluded that 17.9% of children aged 2–7 years were classified as overweight in 2010 (Garrido-Miguel et al.,2019). These trends are concerning due to the likelihood of obese children continuing to be overweight adults and the effects obesity has on mental well-being, education, and the risk of developing noncommunicable diseases.  

Genetics is thought to play a 40-70% part in a variety of ways. One factor is the body’s mechanisms needing to store fat for times of famine and low food supply. When an individual loses weight, the metabolic rate decreases to save energy expenditure until the individual regains the weight mass (Chiurazzi et al.,2020). Much like depression, obesity is thought to be a hypersensitive, neural circuitry issue struggling to respond to a toxic environment and, therefore, a ‘neurochemical disease’ (Bray 2004). A lack of leptin (a hormone signalling fullness) production is a known obesity factor. Leptin sensitivity damage is also common and thought to be due to hi Gi and trans-fat consumption causing dysfunction in feedback to the hypothalamus along with Ghrelin, Serotonin and Norepinephrine dysfunction (Lyell 2015). The theory that the body has a set point (ideal weight) to ensure survival suggests when individuals lose weight quickly (under two years) the biological behaviour mechanisms work hard to regain the set point (homeostasis). The energy gap between energy intake and energy expenditure resulting in excess weight gain is 50 to 150kcal/day (5% of daily calories). Epigenetic studies are key in learning more of the interplay of genetics and our obesogenic environment (easy availability of empty calories). Studies on animals showed switching back to a whole food diet restored hormones to optimal function and a new set point was achieved (Müller et al.,2010; MacLean, et al.,2011; Hall & Guo, 2017).

Obesity presents as excess storage of adipose tissue, a metabolically active endocrine organ. White adipose tissue (WAT) stored around organs in the upper quadrant’s correlates to a high predisposition to metabolic diseases due to this area housing metabolism, insulin production and activation as well as storing energy as triglycerides. The less risky subcutaneous fat (brown fat) thought to be protection against obesity is stored in the back, thigh, and buttock areas (Balistreri et al.,2010). Adipokines (over 600 hormones) within excessive WAT can cause increased chronic low-grade inflammation that predisposes individuals to comorbidity diseases (Mafra et al.,2008; Trayhurn, & Wood, 2004; Aguilar-Valle et al.,2015).

Obesity risks within offspring was thought to be a higher income country problem. However, recently low-income households have been dubbed dual burdened where one sibling is underweight and the other overweight, but both are malnourished (Bhurosy & Jeewon, 2014). Low-cost vegetable oils in cheap low nutrient foods supplied by the industrialised food market are thought to be the cause. Within epidemiology studies, mothers who are malnourished have babies whose metabolism are optimized for energy conservation to survive, leading to an increased risk of chronic obesity if exposed to a western diet (Bhurosy & Jeewon, 2014). Equally, Mothers who are obese are also thought to be at high risk of having offspring who later in life are more susceptible to obesity and comorbidity diseases due to foetal nutritional programming (Stirrat, & Reynolds, 2014). Placenta analysis showed increases of macrophages and inflammatory markers which are detrimental to the baby. Glucose, protein, and lipids are vital for foetal growth, an imbalance of nutrients caused by high glucose and pro-inflammatory foods cause more production of cortisol which could lead to high levels of insulin taken up by the placenta (Gohir et al.,2019). The Barker hypothesis suggests that environmental influence on foetal development may lead to impaired growth and long-term permanent effects on physiology structure and metabolism. A 2014 study revealed that it is thought to affect 30% of the UK antenatal group (Leddy et al.,2008; Stirrat, & Reynolds, 2014). Another cohort study from Sweden found similar results (Gustafsson et al.,2012). 

Five studies were conducted in America measuring state of the art interventions to reduce obesity for Hispanic and African American children living in dysfunctional, poverty-stricken environments with poor mental health. Control groups were provided with different aspects of intervention to assess the effectiveness. The children were given a high number of support hours, dance classes, reduced screen time, group behavioural classes to improve self-esteem and follow up support. The outcomes were sadly disheartening, none of the groups reduced their BMI’s, the studies showed that these interventions were not enough to make a change to obesity in young children and that bigger measures and government intervention is needed to make the area safe so that children can play outside, provide long term appropriate housing and access to healthy food to reduce obesity and associated mental health and metabolic diseases (Dietz 2019).

The financial barriers of eating a healthy diet for disadvantaged Socio-economic status (SES) individuals were measured in a systemic review and meta-analysis. A broad comparison of foods from a western diet were compared to the Mediterranean diet. The study made like for like comparisons in America which couldn’t reflect the complexity of factors that influence food choice but enabled a rough price match which highlighted significant increases in calorie and fat content within cheaper dairy products and lean meat, which were notably more expensive on a diet of 2000kcals per day the price difference was £1.50. (Rao et al.,2013). A Cross-sectional study from 12, 417 adults in the UK Fenland cohort Study (Tong et al,.2018).  assessed data to determine whether the Mediterranean diet was more expensive. The study also included assessing variables such as education levels, marital status, occupation and income. The Mediterranean diet did prove more expensive by 5.4 %   however costs were saved on a reduction in alcohol, red meat and sweet consumption. Measured against other countries studies, the evidence remains inconclusive. Adherence to a Mediterranean diet did highlight that the SES variables made a 20.7% difference to adherence (Tong et al.,2018). Other than the price, often low SES communities are not supplied with a variety of healthy foods and therefore have limited choice (Love et al.,2018).

SES as a correlation to obesity has been frequently highlighted in studies. The factors that play a role within this are: stigma, discrimination and social isolation, leading to internalisation of perceived labels such as lazy, weak-willed and unsuccessful which exacerbates mental health issues. These factors often lead to behaviours of self-medication via increased alcohol consumption, highly satisfying hi GI foods and sedentary lifestyles. A systematic review of America, UK and Canada found significant risks for individuals within low-income employment, however, after adjusting the bias the risk became much lower and indicated that many studies were unpublished due to negative findings (Kim & Knesebeck, 2018). A systematic review and meta-analysis (mostly 2003-2015) including a variety of countries, analysed the link between life course (SES) and obesity. The results showed that women of lower life course had a significantly higher odds of obesity, but not for men (Newton et al., 2017).

Historically humans were hunter gatherers, in order to survive life-threatening environments, physical endurance, strength and speed were required and theses are thought to lead to resistance against disease (Booth et al., 2017). Domestication has seen a decline of physical activity. Today humans can avoid all physical activity and still have their needs met (Sallis, & Owen, 1998; Trost et al,. 2002). Current trends have shown one in four adults and three in four adolescents do not meet the recommendations of physical activity. This is forecasted to increase further, leading to many more individuals at high risk of disease and early mortality (Laddu et al., 2021).   A lack of Infrastructure, access to facilities and availability of safe neighbourhoods with appropriate housing is well known to cause severe psychosocial stressors on families, especially expecting mothers, leading to obesity (Reece, 2021). The decline of physical activity and poor diet has shown changes in brain health and cognition as a result (Burkhalter & Hillman, 2011).  A cross-sectional micro-level study of 500 cities in America, analysed areas classed as high risk where access to healthy foods, limited open green spaces and access to support were poor. There were higher levels of pollution, as well as segregation and racial discrimination. The results revealed a strong connection between racial and ethnic groups living in poverty and obesity (Fitzpatrick et al., 2018; Vainik et al., 2018).

The UK government plans to reduce obesity within children by half by 2030. One of the factors to support this goal is to reduce aggressive and sophisticated marketing from large brands which are significant factors adding to the disease (Fagerberg et al.,2019) A proposal is being considered to ban advertising of ultra-processed foods between 5.30am-9.30pm on children’s targeted channels in response to studies concluding that children consume higher amounts of calories after viewing adverts for unhealthy products (Mytton et al,.2020).. A model using meta-analysis, based on 96% of children said to watch T.V within 5,100 homes, calculated how many times a child would be impacted by adverts. Each minute exposed to an advert equated to an extra 14.2kcals. SES were separated into groups to calculate the difference in recorded T.V exposure and a large cohort study was used for BMI figures. Predicted economic costs of workdays lost, social and health care needed for the current likelihood of long-term ill health, using the population data (5-17 years) to predict life expectancy based on BMI and the risk of noncommunicable diseases were used to simulate quality of life. The estimated results of implementing a ban on T.V advertising between 5.30am-9.30pm would be a decrease of 9.1kcal and a reduction of 4.6% reduction in obesity (40,000 fewer children) and 3.6% (120,000) overweight children based on the 2015 population (Mytton et al.,2020).

In conclusion, there are complex factors that lead health downstream to noncommunicative disease have only recently been studied. There is difficulty in finding determinants for upstream solutions to obesity and its associated health conditions. Studies which isolate correlates and implement solutions often produce little or no improvements, which does not support the single determinates as a factor, whereas in reality, multilayer solutions are needed Kim, & Lim, 2019). A study suggested the complexity of influences on obesity are first and foremost environmental and social, taking personal choice, control and responsibility away from affected groups. Food choices and lifestyle are a result of the conditions an individual or community finds themselves in, rather than the cause (Lakerveld, & Mackenbach, 2017).

It appears that obesity is a side effect of social, economic policies and life circumstances. It is a problem that requires many solutions, financial investment and a change in Government policies. Addressing stigma, education for medical staff, investment of exercise and accessible healthy foods as well as a reduction of advertisements, and the rise in easily accessible cheap ultra-processed foods by big chain companies who have filtrated every town and city (World Health Organization, 2015; Kim, & Lim, 2019).

Governments reliance on the free market (oil and food chain giants) to solve medical and social problems is a conflict of interest. Financially, dopamine addiction in the form of foods and computer games sales provide a consistent and easy income for sophisticated companies with endless finances to dominate every country (Domingo-Rodriguez et al.,2020; Gearhardt, & Schulte, 2021). It is, however, unclear why some people are more resilient to food addiction than others and further epidemiology studies would be helpful (Domingo-Rodriguez et al. 2020). Today it is far easier to be unhealthy (MacLean et al.,2017). To be healthy an individual needs education, support, economic and social security to live a life avoiding unhealthy foods and time spent sedentary (Rao et al.,2018). Studies have shown the physical inactivity pandemic is strongly correlated to built-up environments lacking transport infrastructure, promotion and investment of safe cycle routes to school and high-quality parks, as well as a lack of supervised free sports and play areas especially for lower SES. Studies have shown an 84% increased participation when sports and playtimes were supervised and could support the current gap in economic status where only 34.1% of children in families who earn less than $25,000 participated in sports (Rahman et al.,2011).

Appropriate Government actions such as a sugar tax, fruit and vegetable subsidies, safe green spaces and a reduction in exposure to hazardous toxins and carcinogens are all recommendations to Governments (Rawal et al.,2018). However, these recommendations will not be enough to tackle the need for appropriate, safe neighbourhoods of the most disadvantaged in society. Targeted projects to support communities, would require radical intervention, multi-layered support and infrastructure (Nelson et al.,2018; Baidal et al.,2017).

References

Aguilar-Valles, A., Inoue, W., Rummel, C., & Luheshi, G. N. (2015). Obesity, adipokines and neuroinflammation. Neuropharmacology, 96, 124-134.

Balistreri, C. R., Caruso, C., & Candore, G. (2010). The role of adipose tissue and adipokines in obesity-related inflammatory diseases. Mediators of inflammation, 2010.

Bhurosy, T., & Jeewon, R. (2014). Overweight and obesity epidemic in developing countries: a problem with diet, physical activity, or socioeconomic status? The Scientific World Journal, 2014..

Bray, G. A. (2004). Obesity is a chronic, relapsing neurochemical disease. International journal of obesity, 28(1), 34-38.

Burkhalter, T. M., & Hillman, C. H. (2011). A narrative review of physical activity, nutrition, and obesity to cognition and scholastic performance across the human lifespan. Advances in Nutrition, 2(2), 201S-206S.

Chen, Y., Peng, Q., Yang, Y., Zheng, S., Wang, Y., & Lu, W. (2019). The prevalence and increasing trends of overweight, general obesity, and abdominal obesity among Chinese adults: a repeated cross-sectional study. BMC Public Health, 19(1), 1-18.

Chiurazzi, M., Cozzolino, M., Orsini, R. C., Di Maro, M., Di Minno, M. N. D., & Colantuoni, A. (2020). Impact of genetic variations and epigenetic mechanisms on the risk of obesity. International Journal of Molecular Sciences, 21(23), 9035.

De Onis, M., Blössner, M., & Borghi, E. (2010). Global prevalence and trends of overweight and obesity among preschool children. The American journal of clinical nutrition, 92(5), 1257-1264.

Dietz, W. H. (2019). We need a new approach to prevent obesity in low-income minority populations. Pediatrics, 143(6).

Domingo-Rodriguez, L., de Azua, I. R., Dominguez, E., Senabre, E., Serra, I., Kummer, S., ... & Maldonado, R. (2020). A specific prelimbic-nucleus accumbens pathway controls resilience versus vulnerability to food addiction. Nature communications, 11(1), 1-16.

Fagerberg, P., Langlet, B., Oravsky, A., Sandborg, J., Löf, M., & Ioakimidis, I. (2019). Ultra-processed food advertisements dominate the food advertising landscape in two Stockholm areas with low vs high socioeconomic status. Is it time for regulatory action?. BMC Public Health, 19(1), 1-10.

Fitzpatrick, K. M., Shi, X., Willis, D., & Niemeier, J. (2018). Obesity and place: chronic disease in the 500 largest US cities. Obesity research & clinical practice, 12(5), 421-425.

Garrido-Miguel, M., Oliveira, A., Cavero-Redondo, I., Álvarez-Bueno, C., Pozuelo-Carrascosa, D. P., Soriano-Cano, A., & Martínez-Vizcaíno, V. (2019). Prevalence of overweight and obesity among European preschool children: a systematic review and meta-regression by food group consumption. Nutrients, 11(7), 1698.

Gearhardt, A. N., & Schulte, E. M. (2021). Is Food Addictive? A Review of the Science. Annual Review of Nutrition, 41.

Gohir, W., Kennedy, K. M., Wallace, J. G., Saoi, M., Bellissimo, C. J., Britz‐McKibbin, P., ... & Sloboda, D. M. (2019). High‐fat diet intake modulates maternal intestinal adaptations to pregnancy and results in placental hypoxia, as well as altered fetal gut barrier proteins and immune markers. The Journal of physiology, 597(12), 3029-3051.

Gustafsson, P. E., Persson, M., & Hammarström, A. (2012). Socio-economic disadvantage and body mass over the life course in women and men: results from the Northern Swedish Cohort. The European Journal of Public Health, 22(3), 322-327.

Hall, K. D., & Guo, J. (2017). Obesity energetics: body weight regulation and the effects of diet composition. Gastroenterology, 152(7), 1718-1727.

James, W. P. T. (2008). WHO recognition of the global obesity epidemic. International journal of obesity, 32(7), S120-S126.

Jastreboff, A. M., Kotz, C. M., Kahan, S., Kelly, A. S., & Heymsfield, S. B. (2019). Obesity as a disease: the obesity society 2018 position statement. Obesity, 27(1), 7-9.

Kelly, T., Yang, W., Chen, C. S., Reynolds, K., & He, J. (2008). Global burden of obesity in 2005 and projections to 2030. International journal of obesity, 32(9), 1431-1437.

Kim, J., & Lim, H. (2019). Nutritional management in childhood obesity. Journal of obesity & metabolic syndrome, 28(4), 225.

Kim, T. J., & von dem Knesebeck, O. (2018). Income and obesity: what is the direction of the relationship? A systematic review and meta-analysis. BMJ open, 8(1), e019862.

Kritchevsky, D. (1998). History of recommendations to the public about dietary fat. The Journal of nutrition, 128(2), 449S-452S.

Laddu, D., Paluch, A. E., & LaMonte, M. J. (2021). The role of the built environment in promoting movement and physical activity across the lifespan: Implications for public health. Progress in Cardiovascular Diseases.

Lakerveld, J., & Mackenbach, J. (2017). The upstream determinants of adult obesity. Obesity facts, 10(3), 216-222.

Leddy, M. A., Power, M. L., & Schulkin, J. (2008). The impact of maternal obesity on maternal and fetal health. Reviews in obstetrics and gynecology, 1(4), 170.

Love, P., Whelan, J., Bell, C., Grainger, F., Russell, C., Lewis, M., & Lee, A. (2018). Healthy diets in rural Victoria—cheaper than unhealthy alternatives, yet unaffordable. International journal of environmental research and public health, 15(11), 2469.

Lyell, I. (2015). Lifestyle and environmental risk factors associated with overweight/obesity in white women from the Lejweleputswa District, South Africa (Doctoral dissertation, University of the Free State).

MacLean, P. S., Bergouignan, A., Cornier, M. A., & Jackman, M. R. (2011). Biology's response to dieting: the impetus for weight regain. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 301(3), R581-R600.

MacLean, P. S., Blundell, J. E., Mennella, J. A., & Batterham, R. L. (2017). Biological control of appetite: a daunting complexity. Obesity, 25, S8-S16.

Mafra, D., Guebre-Egziabher, F., & Fouque, D. (2008). Body mass index, muscle and fat in chronic kidney disease: questions about survival. Nephrology dialysis transplantation, 23(8), 2461-2466.

Meeuwsen, S., Horgan, G. W., & Elia, M. (2010). The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clinical nutrition, 29(5), 560-566.

Mitchell, S., & Shaw, D. (2015). The worldwide epidemic of female obesity. Best practice & research Clinical obstetrics & gynaecology, 29(3), 289-299.

Müller, M. J., Bosy-Westphal, A., & Heymsfield, S. B. (2010). Is there evidence for a set point that regulates human body weight?. F1000 medicine reports, 2.

Mytton, O. T., Boyland, E., Adams, J., Collins, B., O’Connell, M., Russell, S. J., ... & Cobiac, L. J. (2020). The potential health impact of restricting less-healthy food and beverage advertising on UK television between 05.30 and 21.00 hours: A modelling study. PLoS medicine, 17(10), e1003212.

Nelson, C. C., Colchamiro, R., Perkins, M., Taveras, E. M., Leung-Strle, P., Kwass, J., & Woo Baidal, J. A. (2018). Racial/ethnic differences in the effectiveness of a multisector childhood obesity prevention intervention. American journal of public health, 108(9), 1200-1206.

Newton, S., Braithwaite, D., & Akinyemiju, T. F. (2017). Socio-economic status over the life course and obesity: Systematic review and meta-analysis. PloS one, 12(5), e0177151.

Nihiser, A. J., Lee, S. M., Wechsler, H., McKenna, M., Odom, E., Reinold, C., ... & Grummer‐Strawn, L. (2007). Body mass index measurement in schools. Journal of School Health, 77(10), 651-671.

Nikooyeh, B., Abdollahi, Z., Salehi, F., Nourisaeidlou, S., Hajifaraji, M., Zahedirad, M., ... & Neyestani, T. R. (2016). Prevalence of obesity and overweight and its associated factors in urban adults from West Azerbaijan, Iran: the National Food and Nutritional Surveillance Program (NFNSP). Nutrition and Food Sciences Research, 3(2), 21-26.

Prentice, A. M. (2006). The emerging epidemic of obesity in developing countries. International journal of epidemiology, 35(1), 93-99.

Rahman, T., Cushing, R. A., & Jackson, R. J. (2011). Contributions of built environment to childhood obesity. Mount Sinai Journal of Medicine: A Journal of Translational and Personalized Medicine, 78(1), 49-57.

Rao, M., Afshin, A., Singh, G., & Mozaffarian, D. (2013). Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ open, 3(12), e004277.

Rao, P., Rodriguez, R. L., & Shoemaker, S. P. (2018). Addressing the sugar, salt, and fat issue the science of food way.

Rawal, L. B., Kanda, K., Mahumud, R. A., Joshi, D., Mehata, S., Shrestha, N., ... & Renzaho, A. (2018). Prevalence of underweight, overweight and obesity and their associated risk factors in Nepalese adults: data from a Nationwide Survey, 2016. PloS one, 13(11), e0205912.

Reece, J. (2021). More Than Shelter: Housing for Urban Maternal and Infant Health. International Journal of Environmental Research and Public Health, 18(7), 3331.

Ren, D., Li, M., Duan, C., & Rui, L. (2005). Identification of SH2-B as a key regulator of leptin sensitivity, energy balance, and body weight in mice. Cell metabolism, 2(2), 95-104.

Sallis, J. F., & Owen, N. (1998). Physical activity and behavioral medicine. SAGE publications.

Scarborough, P., Bhatnagar, P., Wickramasinghe, K. K., Allender, S., Foster, C., & Rayner, M. (2011). The economic burden of ill health due to diet, physical inactivity, smoking, alcohol and obesity in the UK: an update to 2006–07 NHS costs. Journal of public health, 33(4), 527-535.

Siddharth, S., & Sharma, D. (2018). Racial disparity and triple-negative breast cancer in African-American women: a multifaceted affair between obesity, biology, and socioeconomic determinants. Cancers, 10(12), 514.

Stirrat, L. I., & Reynolds, R. M. (2014). Effects of maternal obesity on early and long-term outcomes for offspring. Research and Reports in Neonatology, 4, 43.

Stommel, M., & Schoenborn, C. A. (2009). Accuracy and usefulness of BMI measures based on self-reported weight and height: findings from the NHANES & NHIS 2001-2006. BMC public health, 9(1), 1-10.

Tong, T. Y., Imamura, F., Monsivais, P., Brage, S., Griffin, S. J., Wareham, N. J., & Forouhi, N. G. (2018). Dietary cost associated with adherence to the Mediterranean diet, and its variation by socio-economic factors in the UK Fenland Study. British Journal of Nutrition, 119(6), 685-694.

Trayhurn, P., & Wood, I. S. (2004). Adipokines: inflammation and the pleiotropic role of white adipose tissue. British journal of nutrition, 92(3), 347-355.

Trost, S. G., Owen, N., Bauman, A. E., Sallis, J. F., & Brown, W. (2002). Correlates of adults’ participation in physical activity: review and update. Medicine & science in sports & exercise, 34(12), 1996-2001.

Vainik, U., Baker, T. E., Dadar, M., Zeighami, Y., Michaud, A., Zhang, Y., ... & Dagher, A. (2018). Neurobehavioral correlates of obesity are largely heritable. Proceedings of the National Academy of Sciences, 115(37), 9312-9317

Disease Statement (worldobesityday.org)

Weir, C. B., & Jan, A. (2019). BMI classification percentile and cut off points.

Wolf, A. M., & Colditz, G. A. (1998). Current estimates of the economic cost of obesity in the United States. Obesity research, 6(2), 97-106.

World Health Organization. (2011). Global status report on noncommunicable diseases 2010.

World Health Organization. (2011). Global status report on noncommunicable diseases 2010.

World Health Organization. (2015). Interim report of the commission on ending childhood obesity (No. WHO/NMH/PND/ECHO/15.1). World Health Organization.