The obesity epidemic affects 1 in 3 people globally. Many factors contribute to this epidemic, but the increase in intake relative to energy expenditure remains the underlying mechanism for excess weight gain. Obesity results from a lack of physical activity, poor diet and can be hereditary.  Obesity is a public health issue because it increases the risk of cardiovascular events and death. Clinically, BMI is used as a screening tool. This Literature review discusses the importance of obesity as a public health issue, the risk factors and determinants of health associated with Obesity, and how they are associated. The history of the body mass index (BMI) was explored. I further discussed the arbitrariness of the BMI cut-points and how BMI as a screening tool has been ineffective at curbing the obesity epidemic. Finally, I proposed a simpler method for measuring adiposity and prognosticating health.

Keywords: Obesity, BMI, Anorexigenic, Physical Activity, Lifestyle modification, Criticism, Quetelet, WHO, CVD, 

The public health issue the policy is designed to address is Obesity. Obesity, which affects about 1 in 3 people globally, is considered an energy balance issue (Caballero, 2019). Clinically, Obesity is measured using body mass index (BMI) (Humphreys, 2010), with BMI ≥30 categorized as obese (Caballero, 2019). Obesity results from a positive energy balance due to energy expenditure through activities like exercise being less than energy conserving/accumulating activities like sedentary lifestyle and food consumption (Rogge, 2017). This has been the general understanding of Obesity extrapolated from the Laws of Thermodynamics (Trayhurn, 2005). obesity is a significant public health issue because the obesity rate is increasing (Chriqui, 2013), and obesity is associated with an increased risk of a cardiovascular event and all-cause mortality (Burkhauser et al., 2018; Dwivedi, 2020) due, mainly to the secretion of adipokines and pro-inflammatory cytokines from abdominal adipocytes (Carbone et al., 2019).

 

The by-product of the positive energy balance is fat accumulation (Levian et al., 2014). The fat accumulation is mainly stored in white adipose tissues as triacylglycerol (Trayhurn, 2005). In addition to white adipocytes, which function primarily to store lipid, other adipocytes are brown adipocytes and beige adipocytes (Rogge, 2017). Unlike white adipocytes, primarily located in the intraabdominal area, brown adipocytes and beige adipocytes are primarily involved in thermogenesis due to their higher concentration of mitochondria (Rogge, 2017). Physically, obesity results from the expansion of adipose tissue either through hypertrophy secondary to increased lipid accumulation in the adipocytes or from the increased number of adipocytes (hyperplasia) (Rogge, 2017).

 

Although the exact mechanism of the complex interplay between the many factors responsible for Obesity is poorly understood, experts seem to agree that the pathophysiology of Obesity is multifactorial (Rogge, 2017; Levian et al., 2014). It is now believed that environmental factors, genetic susceptibility, and neuroendocrine factors work together to create a tightly regulated homeostatic BMI set point for the individual (Rogge, 2017).

The urge to eat is controlled by peptide neurotransmitters, neuropeptide Y (NPY), and agouti-related peptide (AGRP) within the arcuate nucleus of the hypothalamus (Farias et al., 2011). Fasting, uncontrolled diabetes, reduced ATP levels, and leptin deficiency activate the arcuate nucleus to secrete NPY and AgRP (Rogge, 2017).  Other recently discovered orexigenic mediators are orexin A and orexin B (Rogge, 2017). Orexin A stimulates appetite secondary to activation by ghrelin and low plasma glucose level, while orexin B is thought to have a neutral effect on food intake (Rogge, 2017; Trayhurn, 2005).

 

The orexigenic pathway is counterbalanced by the anorexigenic pathway through the activation of the proopiomelanocortin (POMC) and cocaine-amphetamine regulator transcriptase (CART) peptide neurons in the hypothalamus by high plasma glucose level, insulin, and amylin (Rogge, 2017). As stated previously, an imbalance between the orexigenic and anorexigenic pathways in favor of a net increase in energy leads to Obesity (Rogge, 2017; Trayhurn, 2005; Caballero et al., 2019). Genetic polyphormisms in the beta-adrenergic receptors on adipocytes make adipocytes less responsive to the lipolytic effects of catecholamine, leading to Obesity in some individuals (Rogge 2017; Jocken et al., 2008). In addition, mutations in the POMC pathway disrupt the feedback mechanism needed to stop food consumption and increase energy expenditure activities (Rogge 2017). Physiologically, large adipocytes are less responsive to insulin, leading to conditions that favor adipocyte hyperplasia (Rogge 2017). Environmental factors such as safety, presence of sidewalks, streetlights, parks, high costs of gym memberships, and the presence of stray dogs in one's neighborhood are social determinants of health capable of tilting the energy balance in the direction of increased fat deposition in adipocytes (Wilcox et al., 2003).

Advances in food processing technology over the past decades have made food, especially calorie-dense meals, cheaper and easily accessible; leading to Obesity through large portion size consumption, high glycemic load, increased concentration of saturated fat, and low content of fiber, phytochemicals, and nutrients, (Ludwig, 2011).   In addition, advances in automobile technology have contributed to increased physical inactivity secondary to increased access to faster cars, and advances in other technologies like computer games and television have complementarily caused more people to be inactive. (Lear et al., 2014). The production of calorie-dense food due to increased demand contributes to greenhouse emissions leading to increasing global temperatures (Minos et al., 2016).

 

Unfortunately, the categorization of Obesity based on BMI is a flawed assessment of adiposity as adiposity is only weakly correlated with Obesity (Burkhauser et al., 2008). Moreover, most obesity-related complications result from abdominal fats deep to the Scarpa's fascia (Goossens, 2017). Other studies have shown that BMI is not an accurate measurement of abdominal visceral adiposity (Gonzalez et al., 2017); hence a poor predictor of cardiovascular risk factors and all-cause mortality in individuals (Goossens, 2017). In addition, BMI does not distinguish between fat, muscle, and bone (McCarthy et al., 2006; Carbone et al., 2019). Furthermore, BMI measurements mask muscle loss in critically ill patients, leading to poorer outcomes due to a lack of timely intervention (Gonzalez et al., 2017). 

 

Despite the flaw in BMI measurements and limited usefulness in predicting cardiovascular complications (Carbone et al., 2019), organizations like the National Academy of Medicine continue to recommend school-based BMI screening in children (Thompson et al., 2017). In addition, the U.S. Preventive Services Task Force (USPSTF) recommends that clinicians should screen all adults of obesity and refer patients with a body mass index (BMI) of 30 kg/m2 or higher to an intensive treatment regimen (Moyer, 2012). Other organizations that have recommended obesity screenings based on BMI are the National Institutes of Health (NIH), the Canadian Task Force on Preventive Health Care, The American Congress of Obstetricians and Gynecologists (ACOG), and the American Academy of Family Physicians (Moyer, 2012).

 

Using BMI as a surrogate for fat adiposity has proven to be ineffective, especially when predicting CVD risk factors in racial minorities (Rush et al., 2007); for instance, BMI under-predicts coronary artery disease and diabetes in South Asians due to their higher proportion of fat relative to BMI when compared with other ethnicities (Goossens, 2017; Rush et al., 2007). Also, results from Pennsylvania, Arkansas, California, and Mexico show that the prevalence of Obesity in children stayed the same or got worse over time despite the rigorous implementation of school-based BMI screening, and there is anecdotal evidence of psychological damage to children labeled overweight or obese (Thompson et al., 2017).

 

In summary, the increased rate of Obesity resulting in excess morbidity and mortality from chronic illnesses is an epidemic (Komaroff, 2016) that has been confounding physicians since the days of Hippocrates (Christopoulou-Aletra et al., 2004). Attempts at quantifying Obesity objectively started in 1832 by Adolphe Quetelet, a statistician, who started recording the weight and height of Belgians in order to determine the characteristics of an average man and the distribution of various human characteristics around the average man (Nuttall, 2015)." The Quetelet index, as Adolphe Quetelet's result was termed, raises issues of generalizability since the study was primarily conducted in Anglo-Saxon populations (Eknoyan, 2007).  Based on the studies done by Ancel Keys, the Quetelet index was "validated," and the term body mass index (weight/height2) index was introduced into our consciousness as a measure of total body fatness (Keys et al., 1972). The BMI cutoff points (Underweight, Less than the 5th percentile, Less than 18.5 kg/m2; Healthy weight, 5th percentile to less than the 85th percentile, 18.5 to 24.9 kg/m2; Overweight, 85th percentile to less than the 95th percentile, 25.0 to 29.9 kg/m2; Obese, Equal to or greater than the 95th percentile, 30.0 kg/m2 or greater) were first introduced into the literature following its publication by the World Health Organization (WHO) in 1995 "based on the "visual inspection of the relationship between BMI and mortality (Komarinoff, 2016)." BMI's use in clinical practice to prognosticate health is due to its simplicity (Gutin, 2018). And in this context, it is inappropriate because of the arbitrariness of BMI cut-points for identifying health risks (Gutin, 2018; Komarinoff 2016), its lack of generalizability (Eknoyan, 2007; Gutin, 2018), unreliability at predicting cardiovascular complications (Goossens, 2017; Rush et al., 2007) and general distrust of BMI as a health measuring tool (Gutin, 2018).

 

Consequently, despite the wide adoption of BMI screening in the medical community, obesity rates continue to go up (Gutin 2018; Thompson et al., 2017). Recommendations of lifestyle changes by health care providers is a source of frustration to patients with BMI greater than 30 kg/m2 because, in the best circumstances, lifestyle changes have not been effective as a weight-loss strategy due to the tendency to regain the lost weight in the long run, since lifestyle changes do not impact the tightly regulated BMI set point of the individual (Rogge 2017). "A BMI less than 30 kg/m2 does not exclude the metabolic risks associated with an excess of adiposity. Even with normal weight, an unhealthy metabolic profile may be present at any BMI, and BMI alone is not enough for its identification. Other techniques of body composition assessment should be implemented in our clinical practice to better identify them (Gonzalez et al., 2017).” Instead of BMI measurement, my recommendation is that waist circumference should be adopted as the screening tool to prognosticate health as it is a better predictor of adiposity (Gutin, 2018; Nuttall, 2015)

 

References

1.      Caballero B. (2019). Humans against Obesity: Who Will Win?. Advances in nutrition (Bethesda, Md.), 10(suppl_1), S4–S9. https://doi.org/10.1093/advances/nmy055

2.      Rogge, M.M. (2017). Gautam B. Biology of Obesity and weight regain: Implications for clinical practice. J Am Assoc Nurse Pract., 29(S1):S15-S29. doi: 10.1002/2327-6924.12504. PMID: 29024550.

3.      Levian, C., Ruiz, E., & Yang, X. (2014). The pathogenesis of Obesity from a genomic and systems biology perspective. The Yale journal of biology and medicine, 87(2), 113–126.

4.      Trayhurn, P. (2005). The biology of Obesity. Proceedings of the Nutrition Society, 64(01), 31–38. doi:10.1079/pns2004406

5.      Chriqui J. F. (2013). Obesity Prevention Policies in U.S. States and Localities: Lessons from the Field. Current obesity reports, 2(3), 200–210. https://doi.org/10.1007/s13679-013-0063-x

6.      Dwivedi, A.K., Dubey, P., Cistola, D.P., Reddy, S.Y. (2020). Association Between Obesity and Cardiovascular Outcomes: Updated Evidence from Meta-analysis Studies. Curr Cardiol Rep., 22(4):25. doi: 10.1007/s11886-020-1273-y. PMID: 32166448.

7.      Burkhauser, R. V., & Cawley, J. (2008). Beyond BMI: The value of more accurate measures of fatness and obesity in social science research. Journal of Health Economics, 27(2), 519–529. doi:10.1016/j.jhealeco.2007.05.00

8.      Carbone, S., Canada, J. M., Billingsley, H. E., Siddiqui, M. S., Elagizi, A., & Lavie, C. J. (2019). Obesity paradox in cardiovascular disease: where do we stand?. Vascular health and risk management, 15, 89–100. https://doi.org/10.2147/VHRM.S168946

9.      Humphreys S. (2010). The unethical use of BMI in contemporary general practice. The British journal of general practice: the journal of the Royal College of General Practitioners, 60(578), 696–697. https://doi.org/10.3399/bjgp10X515548

10.   Farias, M. M., Cuevas, A. M., & Rodriguez, F. (2011). Set-Point Theory and Obesity. Metabolic Syndrome and Related Disorders, 9(2), 85–89. doi:10.1089/met.2010.0090

11.   Jocken, J. W. E., & Blaak, E. E. (2008). Catecholamine-induced lipolysis in adipose tissue and skeletal muscle in Obesity. Physiology & Behavior, 94(2), 219–230. doi:10.1016/j.physbeh.2008.01.002

12.   Wilcox, S., Bopp, M., Oberrecht, L., Kammermann, S., & McElmurray, C. (2003). Psychosocial and perceived environmental correlates of physical activity in rural and older African American and white women. JOURNALS OF GERONTOLOGY SERIES B-PSYCHOLOGICAL SCIENCES AND SOCIAL SCIENCES, 58(6), P329-P337. doi:10.1093/geronb/58.6.P329

13.   Ludwig, D. S. (2011). Technology, Diet, and the Burden of Chronic Disease. JAMA, 305(13), 1352. doi:10.1001/jama.2011.380 

14.   Minos, D., Butzlaff, I., Demmler, K. M., & Rischke, R. (2016). Economic Growth, Climate Change, and Obesity. Current Obesity Reports, 5(4), 441–448. doi:10.1007/s13679-016-0234-7

15.   Lear, S. A., Teo, K., Gasevic, D., Zhang, X., Poirier, P. P., Rangarajan, S., Seron, P., Kelishadi, R., Tamil, A. M., Kruger, A., Iqbal, R., Swidan, H., Gómez-Arbeláez, D., Yusuf, R., Chifamba, J., Kutty, V. R., Karsıdag, K., Kumar, R., Li, W., Szuba, A., … Prospective Urban Rural Epidemiology (PURE) study (2014). The association between ownership of common household devices and Obesity and diabetes in high, middle and low-income countries. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 186(4), 258–266. https://doi.org/10.1503/cmaj.131090

16.   Thompson, H. R., & Madsen, K. A. (2017). The Report Card on BMI Report Cards. Current obesity reports, 6(2), 163–167. https://doi.org/10.1007/s13679-017-0259-6

17.   Moyer, V. A. (2012). Screening for and Management of Obesity in Adults: U.S. Preventive Services Task Force Recommendation Statement. Annals of Internal Medicine. doi:10.7326/0003-4819-157-5-201209040-00475

18.   Rush, E. C., Goedecke, J. H., Jennings, C., Micklesfield, L., Dugas, L., Lambert, E. V., & Plank, L. D. (2007). BMI, fat and muscle differences in urban women of five ethnicities from two countries. International Journal of Obesity, 31(8), 1232–1239. doi:10.1038/sj.ijo.0803576

19.   McCarthy, H. D., Cole, T. J., Fry, T., Jebb, S. A., & Prentice, A. M. (2006). Body fat reference curves for children. International Journal of Obesity, 30(4), 598–602. doi:10.1038/sj.ijo.0803232

20.   Goossens G. H. (2017). The Metabolic Phenotype in Obesity: Fat Mass, Body Fat Distribution, and Adipose Tissue Function. Obesity facts, 10(3), 207–215. https://doi.org/10.1159/000471488

21.   Gonzalez, M. C., Correia, M. I. T. D., & Heymsfield, S. B. (2017). A requiem for BMI in the clinical setting. Current Opinion in Clinical Nutrition and Metabolic Care, 20(5), 314–321. doi:10.1097/mco.0000000000000395

22.   Doble, B., Wordsworth, S., Rogers, C. A., Welbourn, R., Byrne, J., Blazeby, J. M., & By-Band-Sleeve Trial Management Group (2017). What Are the Real Procedural Costs of Bariatric Surgery? A Systematic Literature Review of Published Cost Analyses. Obesity surgery, 27(8), 2179–2192. https://doi.org/10.1007/s11695-017-2749-8

23.   Komaroff, Marina. "For Researchers on Obesity: Historical Review of Extra Body Weight Definitions." Journal of obesity vol. 2016 (2016): 2460285. doi:10.1155/2016/2460285

24.   Christopoulou-Aletra, H., & Papavramidou, N. (2004). Methods Used by the Hippocratic Physicians for Weight Reduction. World Journal of Surgery, 28(5), 513–517. doi:10.1007/s00268-004-7373-9

25.   Nuttall F. Q. (2015). Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutrition today, 50(3), 117–128. https://doi.org/10.1097/NT.0000000000000092

26.   Eknoyan, G. (2007). Adolphe Quetelet (1796 1874) the average man and indices of Obesity. Nephrology Dialysis Transplantation, 23(1), 47–51. doi:10.1093/ndt/gfm517

27.   Keys, A., Fidanza, F., Karvonen, M. J., Kimura, N., & Taylor, H. L. (1972). Indices of relative weight and Obesity. Journal of Chronic Diseases, 25(6-7), 329–343. doi:10.1016/0021-9681(72)90027-6

28.   Gutin I. (2018). In BMI We Trust: Reframing the Body Mass Index as a Measure of Health. Social theory & health : STH, 16(3), 256–271. https://doi.org/10.1057/s41285-017-0055-0