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A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

Affiliations.

  • 1 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
  • 2 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia. Electronic address: [email protected].
  • 3 RIADI Laboratory, University of Manouba, Manouba, Tunisia; College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
  • 4 Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
  • PMID: 34426171
  • DOI: 10.1016/j.compbiomed.2021.104754

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Keywords: Diseases; Machine learning; Obesity; Overweight; Risk factors.

Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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Obesity research: Moving from bench to bedside to population

* E-mail: [email protected]

Affiliation Diabetes Research Program, Department of Medicine, New York University Grossman School of Medicine, New York, New York, United States of America

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  • Ann Marie Schmidt

PLOS

Published: December 4, 2023

  • https://doi.org/10.1371/journal.pbio.3002448
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Fig 1

Globally, obesity is on the rise. Research over the past 20 years has highlighted the far-reaching multisystem complications of obesity, but a better understanding of its complex pathogenesis is needed to identify safe and lasting solutions.

Citation: Schmidt AM (2023) Obesity research: Moving from bench to bedside to population. PLoS Biol 21(12): e3002448. https://doi.org/10.1371/journal.pbio.3002448

Copyright: © 2023 Ann Marie Schmidt. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: AMS received funding from U.S. Public Health Service (grants 2P01HL131481 and P01HL146367). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

Abbreviations: EDC, endocrine disruptor chemical; GIP, gastric inhibitory polypeptide; GLP1, glucagon-like peptide 1; HFCS, high-fructose corn syrup

This article is part of the PLOS Biology 20th anniversary collection.

Obesity is a multifaceted disorder, affecting individuals across their life span, with increased prevalence in persons from underrepresented groups. The complexity of obesity is underscored by the multiple hypotheses proposed to pinpoint its seminal mechanisms, such as the “energy balance” hypothesis and the “carbohydrate–insulin” model. It is generally accepted that host (including genetic factors)–environment interactions have critical roles in this disease. The recently framed “fructose survival hypothesis” proposes that high-fructose corn syrup (HFCS), through reduction in the cellular content of ATP, stimulates glycolysis and reduces mitochondrial oxidative phosphorylation, processes that stimulate hunger, foraging, weight gain, and fat accumulation [ 1 ]. The marked upswing in the use of HFCS in beverages and foods, beginning in the 1980s, has coincided with the rising prevalence of obesity.

The past few decades of scientific progress have dramatically transformed our understanding of pathogenic mechanisms of obesity ( Fig 1 ). Fundamental roles for inflammation were unveiled by the discovery that tumor necrosis factor-α contributed to insulin resistance and the risk for type 2 diabetes in obesity [ 2 ]. Recent work has ascribed contributory roles for multiple immune cell types, such as monocytes/macrophages, neutrophils, T cells, B cells, dendritic cells, and mast cells, in disturbances in glucose and insulin homeostasis in obesity. In the central nervous system, microglia and their interactions with hypothalamic neurons affect food intake, energy expenditure, and insulin sensitivity. In addition to cell-specific contributions of central and peripheral immune cells in obesity, roles for interorgan communication have been described. Extracellular vesicles emitted from immune cells and from adipocytes, as examples, are potent transmitters of obesogenic species that transfer diverse cargo, including microRNAs, proteins, metabolites, lipids, and organelles (such as mitochondria) to distant organs, affecting functions such as insulin sensitivity and, strikingly, cognition, through connections to the brain [ 3 ].

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Basic, clinical/translational, and epidemiological research has made great strides in the past few decades in uncovering novel components of cell-intrinsic, intercellular, and interorgan communications that contribute to the pathogenesis of obesity. Both endogenous and exogenous (environmental) stressors contribute to the myriad of metabolic perturbations that impact energy intake and expenditure; mediate innate disturbances in the multiple cell types affected in obesity in metabolic organelles and organs, including in immune cells; and impair beneficial interkingdom interactions of the mammalian host with the gut microbiome. The past few decades have also witnessed remarkable efforts to successfully treat obesity, such as the use of the incretin agonists and bariatric surgery. Yet, these and other strategies may be accompanied by resistance to weight loss, weight regain, adverse effects of interventions, and the challenges of lifelong implementation. Hence, through leveraging novel discoveries from the bench to the bedside to the population, additional strategies to prevent obesity and weight regain post-weight loss, such as the use of “wearables,” with potential for implementation of immediate and personalized behavior modifications, may hold great promise as complementary strategies to prevent and identify lasting treatments for obesity. Figure created with BioRender.

https://doi.org/10.1371/journal.pbio.3002448.g001

Beyond intercellular communication mediated by extracellular vesicles, the discovery of interactions between the host and the gut microbiome has suggested important roles for this interkingdom axis in obesity. Although disturbances in commensal gut microbiota species and their causal links to obesity are still debated, transplantation studies have demonstrated relationships between Firmicutes/Bacteroidetes ratios and obesity [ 4 ]. Evidence supports the concept that modulation of gut microbiota phyla modulates fundamental activities, such as thermogenesis and bile acid and lipid metabolism. Furthermore, compelling discoveries during the past few decades have illustrated specific mechanisms within adipocytes that exert profound effects on organismal homeostasis, such as adipose creatine metabolism, transforming growth factor/SMAD signaling, fibrosis [ 5 ], hypoxia and angiogenesis, mitochondrial dysfunction, cellular senescence, impairments in autophagy, and modulation of the circadian rhythm. Collectively, these recent discoveries set the stage for the identification of potential new therapeutic approaches in obesity.

Although the above discoveries focus largely on perturbations in energy metabolism (energy intake and expenditure) as drivers of obesity, a recently published study suggests that revisiting the timeline of obesogenic forces in 20th and 21st century society may be required. The authors tracked 320,962 Danish schoolchildren (born during 1930 to 1976) and 205,153 Danish male military conscripts (born during 1939 to 1959). Although the overall trend of the percentiles of the distributions of body mass index were linear across the years of birth, with percentiles below the 75th being nearly stable, those above the 75th percentile demonstrated a steadily steeper rise the more extreme the percentile; this was noted in the schoolchildren and the military conscripts [ 6 ]. The authors concluded that the emergence of the obesity epidemic might have preceded the appearance of the factors typically ascribed to mediating the obesogenic transformation of society by several decades. What are these underlying factors and their yet-to-be-discovered mechanisms?

First, in terms of endogenous factors relevant to individuals, stressors such as insufficient sleep and psychosocial stress may impact substrate metabolism, circulating appetite hormones, hunger, satiety, and weight gain [ 7 ]. Reduced access to healthy foods rich in vegetables and fruits but easy access to ultraprocessed ingredients in “food deserts” and “food swamps” caused excessive caloric intake and weight gain in clinical studies [ 8 ]. Second, exogenous environmental stresses have been associated with obesity. For example, air pollution has been directly linked to adipose tissue dysfunction [ 9 ], and ubiquitous endocrine disruptor chemicals (EDCs) such as bisphenols and phthalates (found in many items of daily life including plastics, food, clothing, cosmetics, and paper) are linked to metabolic dysfunction and the development of obesity [ 10 ]. Hence, factors specific to individuals and their environment may exacerbate their predisposition to obesity.

In addition to the effects of exposure to endogenous and exogenous stressors on the risk of obesity, transgenerational (passed through generations without direct exposure of stimulant) and intergenerational (direct exposure across generations) transmission of these stressors has also been demonstrated. A leading proposed mechanism is through epigenetic modulation of the genome, which then predisposes affected offspring to exacerbated responses to obesogenic conditions such as diet. A recent study suggested that transmission of disease risk might be mediated through transfer of maternal oocyte-derived dysfunctional mitochondria from mothers with obesity [ 11 ]. Additional mechanisms imparting obesogenic “memory” may be evoked through “trained immunity.”

Strikingly, the work of the past few decades has resulted in profound triumphs in the treatment of obesity. Multiple approved glucagon-like peptide 1 (GLP1) and gastric inhibitory polypeptide (GIP) agonists [ 12 ] (alone or in combinations) induce highly significant weight loss in persons with obesity [ 13 ]. However, adverse effects of these agents, such as pancreatitis and biliary disorders, have been reported [ 14 ]. Therefore, the long-term safety and tolerability of these drugs is yet to be determined. In addition to pharmacological agents, bariatric surgery has led to significant weight loss as well. However, efforts to induce weight loss through reduction in caloric intake and increased physical activity, pharmacological approaches, and bariatric surgery may not mediate long-term cures in obesity on account of resistance to weight loss, weight regain, adverse effects of interventions, and the challenges of lifelong implementation of these measures.

Where might efforts in combating obesity lie in the next decades? At the level of basic and translational science, the heterogeneity of metabolic organs could be uncovered through state-of-the-art spatial “omics” and single-cell RNA sequencing approaches. For example, analogous to the deepening understanding of the great diversity in immune cell subsets in homeostasis and disease, adipocyte heterogeneity has also been suggested, which may reflect nuances in pathogenesis and treatment approaches. Further, approaches to bolster brown fat and thermogenesis may offer promise to combat evolutionary forces to hoard and store fat. A better understanding of which interorgan communications may drive obesity will require intensive profiling of extracellular vesicles shed from multiple metabolic organs to identify their cargo and, critically, their destinations. In the three-dimensional space, the generation of organs-on-a-chip may facilitate the discovery of intermetabolic organ communications and their perturbations in the pathogenesis of obesity and the screening of new therapies.

Looking to prevention, recent epidemiological studies suggest that efforts to tackle obesity require intervention at multiple levels. The institution of public health policies to reduce air pollution and the vast employment of EDCs in common household products could impact the obesity epidemic. Where possible, the availability of fresh, healthy foods in lieu of highly processed foods may be of benefit. At the individual level, focused attention on day-to-day behaviors may yield long-term benefit in stemming the tide of obesity. “Wearable” devices that continuously monitor the quantity, timing, and patterns of food intake, physical activity, sleep duration and quality, and glycemic variability might stimulate on-the-spot and personalized behavior modulation to contribute to the prevention of obesity or of maintenance of the weight-reduced state.

Given the involvement of experts with wide-ranging expertise in the science of obesity, from basic science, through clinical/translational research to epidemiology and public health, it is reasonable to anticipate that the work of the next 2 decades will integrate burgeoning multidisciplinary discoveries to drive improved efforts to treat and prevent obesity.

Acknowledgments

The author is grateful to Ms. Latoya Woods of the Diabetes Research Program for assistance with the preparation of the manuscript and to Ms. Kristen Dancel-Manning for preparation of the Figure accompanying the manuscript.

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  • Published: 05 July 2024

A new framework for the diagnosis, staging and management of obesity in adults

  • Luca Busetto 1 ,
  • Dror Dicker 2 ,
  • Gema Frühbeck   ORCID: orcid.org/0000-0002-8305-7154 3 ,
  • Jason C. G. Halford 4 ,
  • Paolo Sbraccia 5 ,
  • Volkan Yumuk 6 &
  • Gijs H. Goossens   ORCID: orcid.org/0000-0002-2092-3019 7  

Nature Medicine ( 2024 ) Cite this article

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The European Association for the Study of Obesity presents a new framework for the diagnosis, staging and management of obesity in adults to better align with the concept of obesity as an adiposity-based chronic disease.

You have full access to this article via your institution.

Obesity is a multifactorial, chronic, relapsing, non-communicable disease marked by an abnormal and/or excessive accumulation of body fat that presents a risk to health. It is well established that obesity acts as a gateway to a range of other non-communicable and communicable diseases 1 , 2 , 3 .

Despite this wide recognition of obesity as a chronic disease, the clinical recommendations that guide the diagnosis of obesity and its management have not been aligned sufficiently with the clinical processes normally adopted for other chronic diseases. In many settings, the diagnosis of obesity is still based solely on body mass index (BMI) cut-off values, and does not reflect the role of adipose tissue distribution and function in the severity of the disease 1 . Moreover, the indications for using the different therapeutic approaches now available for obesity management remain mostly based on anthropometric measurements, rather than on a more complete clinical evaluation of the individual 4 . This is in sharp contrast with other chronic diseases, for which clear therapeutic indications are described, targets are set, and the choice of the type and intensity of treatment is based on the probability of reaching the treatment target, with adequate and prompt treatment intensification when the target is not reached.

To stimulate the development and implementation of clinical guidelines for obesity that are more aligned with those already in place for other chronic diseases, the European Association for the Study of Obesity (EASO) initiated and conducted a consensus process to propose a new framework for the diagnosis, staging and management of obesity in adults.

Consensus process

We performed a modified Delphi study 5 , 6 to identify a set of statements that can aid in the diagnosis, staging and management of obesity according to a framework that is more adherent to the concept of obesity as an adiposity-based chronic disease (ABCD) 1 . A steering committee identified by the EASO, consisting of the authors of this paper, discussed and prepared an initial set of statements used for a voting process by a group of experts. Voting was performed on a five-point scale, as follows: (1) strongly disagree; (2) disagree; (3) neither agree nor disagree; (4) agree; and (5) strongly agree. In each round of voting, experts were also asked to provide comments to explain their voting score, and responses were anonymized. The steering committee evaluated the voting and comments received at each round and generated a modified set of statements for the subsequent round of voting. Consensus was defined as ≥75% of expert agreement on a statement (score ≥4).

The steering committee retained responsibility for the selection of experts involved in the process. Selection was based on international reputation and known expertise in obesity science and management. In total, 29 experts were contacted, and all agreed to participate in the present study. Most of the experts belong to the endocrinology, nutrition or internal medicine fields (72%), but the group also included five bariatric surgeons, two primary care physicians and one expert on patient advocacy. A standard conflict of interest form was completed by each participant before the start of the Delphi process. This study was performed by the EASO without any external funding, and approval by the ethics committee was not required.

The study comprised three Delphi rounds. In the first round, 25 experts (86%) voted and commented on 30 original statements that were prepared by the steering committee. A total of 21 statements (70%) received consensus. The steering committee evaluated voting and comments and generated a second set of 28 statements submitted for a second Delphi round. In the second round, 24 experts (83%) voted and commented on the statements. A total of 24 statements (86%) received consensus. The steering committee discussed the comments received for the four non-consented statements, reconsidered the formulations of these statements, and submitted the four revised statements for the final Delphi round. In the third round, 24 experts (83%) voted and provided final comments related to the four revised statements. One of these four statements (statement 12) reached full consensus, whereas most experts approved the other three revised statements (statements 3–5), with only a few experts providing a score <3 (that is, strongly disagree or disagree). The steering committee performed a final revision and decided to approve a list of 28 statements, covering clinical diagnosis and staging of obesity, pillars of treatment, therapeutic targets, and initial level of intervention. The final list of statements and final percentages of approval by the experts is shown in Table 1 . A flowchart of the diagnostic and therapeutic pathways resulting from the statements is presented in Fig. 1 .

figure 1

This flowchart of the diagnostic and therapeutic pathways results from the statements in Table 1 . WtHR, waist-to-height ratio.

A chronic progressive disease

The recognition of obesity as a complex chronic non-communicable disease should inform the development of evidence-based guidelines for the diagnosis and management of obesity. We anticipate that, in conjunction with other ongoing initiatives 7 , this Delphi process will contribute to improving obesity management in adults living with obesity.

Based on current clinical evidence, the diagnosis of obesity should not be based solely on the presence of an abnormal and/or excessive fat accumulation (anthropometric component). The diagnosis of obesity should instead include a careful analysis of the present and potential effects that dysfunctional and/or excessive fat accumulation may have on health (clinical component) (statement 1). This statement aligns with what has been suggested by other recent guidelines on obesity management 8 , 9 . Moreover, this statement fully adheres to the concept that obesity should be considered a chronic progressive disease process that may transit from a relatively asymptomatic state to a phase in which abnormal and/or excessive fat accumulation is accompanied by health impairments, and finally to a life-threatening or disabling condition 10 .

Abdominal fat accumulation

An important novelty of our framework regards the anthropometric component of the diagnosis. The basis for this change is the recognition that BMI alone is insufficient as a diagnostic criterion, and that body fat distribution has a substantial effect on health. More specifically, the accumulation of abdominal fat is associated with an increased risk of developing cardiometabolic complications and is a stronger determinant of disease development than BMI, even in individuals with a BMI level below the standard cut-off values for obesity diagnosis 11 . This is reflected by two important statements. First, we make explicit that abdominal (visceral) fat accumulation is an important risk factor for health deterioration, also in people with low BMI and still free of overt clinical manifestations (statement 3). Second, the new framework includes people with lower BMI (≥25–30 kg/m 2 ) but increased abdominal fat accumulation and the presence of any medical, functional or psychological impairments of complications in the definition of obesity, hence reducing the risk of undertreatment in this particular group of patients in comparison to the current BMI-based definition of obesity (statement 4). The choice of introducing waist-to-height ratio, instead of waist circumference, in the diagnostic process is due to its superiority as a cardiometabolic disease risk marker 12 .

Diagnostics and staging

The clinical component of the diagnosis should include a systematic evaluation of medical, functional and psychological (such as mental health and eating behavior pathology) impairments in any person with obesity, as also suggested in other guidelines 8 , 9 . A detailed description of the clinical aspects and methodologies that need to be included in this systemic clinical evaluation was beyond the scope of this exercise. For the medical evaluation (statement 9), several documents are available to provide guidance 4 , 8 . For the functional and psychological evaluation, examination may be performed using an array of methods, ranging from easy-to-perform tests that are applicable in the primary care setting to more sophisticated tests, which may be reserved for specialized centers (statements 10 and 13). Considering the emerging problem of obesity in older individuals, statement 11 was included to emphasize the importance of performing a diagnostic assessment (muscle strength, performance and body composition) for sarcopenic obesity 13 . Finally, considering the strong association between obesity and several types of cancer, a statement calling for regular screening for obesity-related cancers in any person with obesity was included (statement 12).

Clinical staging processes are frequently used to evaluate and describe an individual’s health status and the progression of chronic diseases. Clinical staging usually expresses the severity of a disease in a simplified, condensed and standardized way. This has prognostic implications, and it may guide or mandate therapeutic interventions. In our Delphi process, the experts agreed on the importance of staging obesity as a chronic, relapsing disease, according to the severity of its clinical manifestations and complications (statement 14), as proposed by previous guidelines 9 .

Obesity management

Considering the pillars of treatment of people with obesity (statements 15–21), our recommendations substantially adhere to current available guidelines 4 , 8 , 9 . Behavioral modifications, including nutritional therapy, physical activity, stress reduction and sleep improvement, were agreed as main cornerstones of obesity management, with the possible addition of psychological therapy, obesity medications and metabolic or bariatric (surgical and endoscopic) procedures. For the latter two options, the steering committee discussed the fact that current guidelines are based on clinical evidence derived from clinical trials, in which inclusion criteria were mostly based on anthropometric cut-off values rather than on a complete clinical evaluation 4 , 8 , 9 , 14 . In current practice, the strict application of these evidence-based criteria precludes the use of obesity medications or metabolic/bariatric procedures in patients with a substantial burden of obesity disease, but low BMI values. Therefore, members of the steering committee proposed, and experts subsequently agreed (79%), that, in particular, the use of obesity medications should be considered in patients with BMI ≥ 25 kg/m 2 and a waist-to-height ratio > 0.5 and the presence of medical, functional or psychological impairments or complications, independently from current BMI cut-off values (statement 18). This statement may also be seen as a call to pharmacological companies and regulatory authorities to use inclusion criteria that are more adherent to the clinical staging of obesity and less to traditional BMI cut-offs when designing future clinical trials with obesity medications 15 .

Full agreement among the experts was reached for the statement that the management of obesity should move beyond weight loss alone, and should include the prevention, resolution or improvement of obesity-related complications, a better quality of life and mental wellbeing, and improvement of physical and social functioning and fitness (statement 22). This statement will move obesity management closer to the management of other non-communicable chronic diseases, in which the goal is not represented by short-term intermediate outcomes, but by long-term health benefits. Defining long-term personalized therapeutic goals should inform the discussion with the patients from the beginning of the treatment, considering the stage and severity of the disease, the available therapeutic options and possible concomitant side effects and risks, patient preferences, individual drivers of obesity and possible barriers to treatment (statements 23 and 24). Emphasis on the need for a long-term or life-long comprehensive treatment plan rather than short-term body weight reduction is warranted.

The concept of obesity as a chronic disease and the discussion of therapeutic targets should also inform the choice of the initial level of intervention and eventual intensification of therapy (statements 26–28), avoiding the same repetitive and futile cycles of intervention that are not effective enough to achieve patient benefit, and preventing therapeutic inertia 16 .

This Delphi process represents the current vision of the EASO on the diagnosis, staging and management of obesity as a complex, relapsing, non-communicable chronic disease in adults. We anticipate that the recommendations outlined in this paper, in conjunction with other ongoing international initiatives 7 , will contribute to improved obesity management strategies that are more consistent with treatment algorithms already applied for other non-communicable chronic diseases. Moreover, this framework may aid scientific advancements and the development of new clinical practice guidelines.

Change history

11 july 2024.

In the version of the article initially published, the legend to Fig. 1 originally defined "WtHR" as "weight-to-height ratio". This has now been amended to "waist-to-height ratio" in the HTML and PDF versions of the article.

Frühbeck, G. et al. Obes. Facts 12 , 131–136 (2019).

Article   PubMed   PubMed Central   Google Scholar  

Dicker, D. et al. Obes. Facts 13 , 430–438 (2020).

Article   CAS   PubMed   Google Scholar  

Burki, T. Lancet Diabetes Endocrinol. 9 , 418 (2021).

Article   PubMed   Google Scholar  

Yumuk, V. et al. Obes. Facts 8 , 402–424 (2015).

Clayton, M. J. Educ. Psychol. 17 , 373–386 (1997).

Article   Google Scholar  

Hasson, F., Keeney, S. & McKenna, H. J. Adv. Nurs. 32 , 1008–1015 (2000).

Rubino, F. et al. Lancet Diabetes Endocrinol. 11 , 226–228 (2023).

Garvey, W. T. et al. Endocr. Pract. 22 , 1–203 (2016).

Wharton, S. et al. CMAJ 192 , E875–E891 (2020).

Bray, G. A., Kim, K. K., Wilding, J. P. H. & on behalf of World Obesity Federation. Obes. Rev. 18 , 715–723 (2017).

Goossens, G. H. Obes. Facts 10 , 207–215 (2017).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ashwell, M., Gunn, P. & Gibson, S. Obes. Rev. 13 , 275–286 (2012).

Donini, L. M. et al. Obes. Facts. 15 , 321–335 (2022).

Di Lorenzo, N. et al. Surg. Endosc. 34 , 2332–2358 (2020).

Agarwal, A. A., Narayan, A. & Stanford, F. C. JAMA Intern. Med. 184 , 341–342 (2024).

Busetto, L., Sbraccia, P. & Vettor, R. Eat Weight Disord. 27 , 761–768 (2022).

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List of experts

The Steering Commitee identifed by EASO for this project, consisting of the authors of this paper, takes full responsibility for the contents of this Comment. The below specialists participated in the voting process of the Delphi methodology, but not in interpretation of the results or writing of the Comment, and agreed to be mentioned here: M. Agarwal, R. Barazzoni, T. Comuzzie, M. De Luca, N. Di Lorenzo, D. Durrer-Schutz, T. Garvey, C. Hughes, L. Kaplan, C. LeRoux, J. Mechanick, N. Montano, Jean-M. Oppert, R. Peterli, K. Pietilainen, G. Prager, X. Ramos-Salas, D. Ryan, M. Rydén, A. Sharma, and E. van Rossum.

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Department of Medicine, University of Padova, Padova, Italy

Luca Busetto

Internal Medicine Department and Obesity Clinic, Hasharon Hospital-Rabin Medical Center, Petach-Tikva, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

Dror Dicker

Department of Endocrinology and Nutrition, Clínica Universidad de Navarra, CIBEROBN, IdiSNA, Pamplona, Spain

Gema Frühbeck

School of Psychology, University of Leeds, Leeds, UK

Jason C. G. Halford

Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy

Paolo Sbraccia

Division of Endocrinology, Metabolism and Diabetes, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey

Volkan Yumuk

Department of Human Biology, Institute of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University Medical Center⁺, Maastricht, The Netherlands

Gijs H. Goossens

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L.B. received personal funding from Novo Nordisk, Boehringer Ingelheim, Eli Lilly, Pfizer, Bruno Farmaceutici as a member of advisory boards, and from Rythms Pharmaceuticals and Pronokal as a speaker. D.D. received personal funding from Novo Nordisk, Boehringer Ingelheim, Eli Lilly as a member of advisory boards, and from Novo Nordisk, Boehringer Ingelheim, Eli Lilly as a speaker. G.F. received payment of honoraria from Lilly and Novo Nordisk as a member of advisory boards, and payment of honoraria for lectures as member of the OPEN Spain Initiative. The University of Leeds received funding from Novo Nordisk for J.C.G.H.’s participation in the ACTION-Teens study. P.S. received payment of honoraria and consulting fees from Novo Nordisk, Eli Lilly, Pfizer, Boehringer Ingelheim and Bruno Farmaceutici as a member of advisory boards. V.Y. received personal funding from Novo Nordisk and Eli Lilly as a member of advisory boards and from Novo Nordisk as a speaker. G.H.G. received research funding from the European Foundation for the Study of Diabetes, the Dutch Diabetes Research Foundation and the Dutch Research Council (NWO).

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obesity introduction research paper

Introduction to Obesity

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obesity introduction research paper

  • Imran Alam MBBS,BSc,FRCS(Glas),FRCSEd,MD 2 &
  • Sanjay Agrawal MS, FRCSEd, FRCSGlasg, FRCS 3 , 4  

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Obesity is defined as an abnormal or excessive accumulation of fat that may impair health. According to World Health Organization (WHO), any individual with a body mass index (BMI) greater than or equal to 30 kg/m 2 is obese and severe or class III obesity is defined as a BMI equal to or greater than 40 kg/m 2 ; this term is also used for individuals with a BMI between 30 and 39.9 kg/m 2 who have significant comorbidities. National Institute of Clinical Excellence (NICE) has recommended bariatric surgery for such individuals. The prevalence of severe obesity has increased significantly in the last two to three decades. Mexico and United States of America have highest prevalence in the world and United Kingdom is leading in Europe. BMI is used as a surrogate for adiposity. There are other methods like bioimpedance analysis, dual-energy x-ray absorptiometry (DEXA), hydrometry, computed tomography (CT), magnetic resonance imaging (MRI) and others but for all clinical and interventional purposes, BMI is used as a measure of obesity.

Fat is the main source of stored energy and it also secretes number of hormones and cytokines. Excess central fat deposition is associated with increased risk of morbidity and mortality. Overweight (BMI of 25 kg/m 2 to 29.9 kg/m 2 ) is associated with increased risk of comorbidities such as type 2 diabetes mellitus, cardiovascular diseases, respiratory disorders, infertility, certain forms of cancers, psychological and social problems; and the risk of these comorbidities increases significantly with further increase in BMI. The cost of treating obesity and associated comorbidity is causing significant burden on the health system. Conservative treatment has a high failure rate. Bariatric surgery performed primarily for weight reduction also causes resolution/remission of associated comorbidities.

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obesity introduction research paper

Strengths and Limitations of BMI in the Diagnosis of Obesity: What is the Path Forward?

Li Z, Bowerman S, Heber D. Health ramifications of the obesity epidemic. Surg Clin North Am. 2005;85(4):681–701. 4.

Article   PubMed   Google Scholar  

Falaschetti E, Malbut K, Primatesta P. Health Survey for England 2000: the general health of older people and their use of health services. London: The Stationery Office; 2002. Available from: http://discover.ukdataservice.ac.uk/Catalogue/?sn=4487&type=Data%20catalogue .

Google Scholar  

NICE clinical guidelines Obesity: Guidance on the prevention, identification, assessment and management of overweight and obesity in adults and children. Issued: December 2006 (last modified: January 2010). NICE 2002. http://www.evidence.nhs.uk/search?q=obesity%20surgery%202002%20NICE&ps=30 . Available from: http://www.nice.org.uk/guidance/cg43/resources/guidance-obesity-pdf .

MacLean LD, Rhode BM, Forse RA, et al. Late results of vertical banded gastroplasty for morbid and super obesity. Surgery. 1990;107(1):20–7.

CAS   PubMed   Google Scholar  

Bray GA. Definition, measurement, and classification of the syndromes of obesity. Int J Obes. 1978;2(2):99–112.

Organization for Economic Co-operation and Development. Obesity update. 2014. http://www.oecd.org/health/obesity-update.htm .

Marie N, Fleming T, Robinson M, Thomson B, Graetz N, Margono G, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980—2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–81.

Article   Google Scholar  

Health Survey England 2012. www.hscic.gov.uk/catalogue/PUB13218 .

Foresight. Tackling obesities: future choices [Internet]. London: Government Office for Science and Department of Health; 2007. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/287937/07–1184x-tackling-obesities-future-choices-report.pdf .

Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K. General and abdominal adiposity and risk of death in Europe. N Engl J Med. 2008;359:2105–20.

Article   CAS   PubMed   Google Scholar  

National Audit Office. Tackling obesity in England. Part 2; p. 16. Report by the comptroller and auditor general HC 220 Session 2000–2001: 15 February 2001. Available from: http://www.nao.org.uk/wp-content/uploads/2001/02/0001220.pdf .

Campbell IW, Haslam D. Chapter 1: What is obesity? In: Campbell IW, Haslam D, editors. Your questions answered-obesity. Edinburgh: Churchill Livingstone; 2005. p. 6. ISBN 0433074534.

Nightingale CM, Rudnicka AR, Owen CG, Cook DG, Whincup PH. Patterns of body size and adiposity among UK children of South Asian, black African-Caribbean and white European origin: Child Heart And health Study in England (CHASE Study). Int J Epidemiol. 2011;40(1):33–44.

Article   PubMed Central   PubMed   Google Scholar  

Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010;363(23):2211–9.

Botti TJ. Chapter 1: An irreconcilable conflict of interest 1607–1762. In: Botti T, editor. Envy of the world. An irreconcilable conflict of interest. New York: Algora Pub; 2006. p. 1607–762. ISBN 0875864317.

Tulsa World. MetLife expands beyond ‘slow growth’ U.S. market. [Internet]. 2010 [cited 14 March 2011]:1. Available from: http://www.tulsaworld.com/business/metlife-expands-beyond-slow-growth-u-s-market/image_5d394721-ff35–5512–9adf-6cbb55f497bc.html .

MetLife. MetLife and Fidelity introduce new retirement income solution: a variable annuity designed to provide lifetime income for those nearing or in retirement [Internet]. 2009. Available from: http://personal.fidelity.com/myfidelity/InsideFidelity/NewsCenter/mediadocs/metlife_income_solution.pdf .

Harrison GG. Height-weight tables. Ann Intern Med. 1985;103(6 (Pt 2)):989–94.

Metropolitan MLF. 1983 metropolitan height and weight table. Stat Bull Metrop Life Found. 1983;64(1):3–9.

Deitel M, Dixon J. Comorbidities of morbid obesity and determination of optimal weight. In: Deitel M, Dixon J, Gagner M, Madan A, Himpens J, editors. Handbook of obesity surgery. 1st ed. Toronto: FD-Communications; 2010. ISBN 978–0-9684426–5-4.

Hatoum IJ, Kaplan LM. Advantages of percent weight loss as a method of reporting weight loss after Roux-en-Y gastric bypass. Obesity (Silver Spring). 2013;21(8):1519–25.

Deitel M, Greenstein RJ. Recommendations for reporting weight loss. Obes Surg. 2003;13(2):159–60.

Health and social care information center. Statistics on obesity, physical activity and diet—England, 2014 [Internet]. London: Health and social care information center; 2014. Available from: http://www.hscic.gov.uk/catalogue/PUB13648/Obes-phys-acti-diet-eng-2014-rep.pdf .

Ali AT, Crowther NJ. Body fat distribution and insulin resistance. S Afr Med J. 2005;95(11):878–80.

Pouliet MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol. 1994;73(7):460–8.

Lean MEJ. Clinical handbook of weight management. London: Martin Dunitz, Ltd.; 1998.

Han TS, van Leer EM, Seidell JC, Lean ME. Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ. 1995;311(7017):1401–5.

Article   PubMed Central   CAS   PubMed   Google Scholar  

Marlowe F, Apicella C, Reed D. Men’s preferences for women’s profile waist-to-hip ratio in two societies. Evol Hum Behav. 2005;26:458–68.

Sun G, French CR, Martin GR, Younghusband B, Green RC, Xie YG, et al. Comparison of multifrequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am J Clin Nutr. 2005;81(1):74–8.

Goodsitt MM. Evaluation of a new set of calibration standards for the measurement of fat content via DPA and DXA. Med Phys. 1992;19:35–44.

Rothney MP, Brychta RJ, Schaefer EV, Chen KY, Skarulis MC. Body composition measured by dual-energy X-ray absorptiometry half-body scans in obese adults. Obesity (Silver Spring). Am J Clin Nutr. 1995;61(2):274–8.

Jensen MD, Sheedy PF. Measurement of abdominal and visceral fat with computed tomography and dual-energy x-ray absorptiometry. Am J Nutr. 1995;61(2):274–8.

CAS   Google Scholar  

Hu FB. Measurements of adiposity and body composition. In: Hu FB, editor. Obesity epidemiology. New York City: Oxford University Press; 2008. p. 53–83.

Fujioka S, Matsuzawa Y, Tokunaga K, Tarui S. Contribution of intra-abdominal fat accumulation to the impairment of glucose and lipid metabolism in human obesity. Metabolism. 1987;36(1):54–9.

Abate N, Garg A, Peshock RM, Stray Gundersen J, Adams-Huet B, Grundy SM. Relationship of generalized and regional adiposity to insulin sensitivity in men with NIDDM. Diabetes. 1996;45(12):1684–93.

Lemieux S, Prud’homme D, Bouchard C, Tremblay A, Després JP. A single threshold value of waist girth identifies normal-weight and overweight subjects with excess visceral adipose tissue. Am J Clin Nutr. 1996;64(5):685–93.

DHHS. Results of the Healthy Communities Survey. Tasmania, Tasmanian Public and Environment Health Service, Tasmania. Department of Health and Human Services. 1998. Available from: http://www.dhhs.tas.gov.au/__data/assets/pdf_file/0009/81747/Tasmanian_food_and_nutrition_policy_2004.pdf .

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Alam, I., Agrawal, S. (2016). Introduction to Obesity. In: Agrawal, S. (eds) Obesity, Bariatric and Metabolic Surgery. Springer, Cham. https://doi.org/10.1007/978-3-319-04343-2_1

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Obesity: An overview on its current perspectives and treatment options

  • Srinivas Nammi 1 , 3 ,
  • Saisudha Koka 1 ,
  • Krishna M Chinnala 2 &
  • Krishna M Boini 1 , 3  

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Obesity is a multi-factorial disorder, which is often associated with many other significant diseases such as diabetes, hypertension and other cardiovascular diseases, osteoarthritis and certain cancers. The management of obesity will therefore require a comprehensive range of strategies focussing on those with existing weight problems and also on those at high risk of developing obesity. Hence, prevention of obesity during childhood should be considered a priority, as there is a risk of persistence to adulthood. This article highlights various preventive aspects and treatment procedures of obesity with special emphasis on the latest research manifolds.

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Introduction

Obesity can be described as the "New World Syndrome". Its prevalence is on continuous rise in all age groups of many of the developed countries in the world. Statistical data reveals that the problem of obesity has increased from 12–20% in men and from 16–25% in women over the last ten years [ 1 ]. Recent studies suggest that nearly 15–20% of the middle aged European population are obese [ 2 ] and that in USA alone it is responsible for as many as 3,00,000 premature deaths each year [ 3 ]. Obese patients have been associated with increased risk of morbidity and mortality relative to those with ideal body weight [ 4 ]. Even modest weight reduction in the range of 5–10% of the initial body weight is associated with significant improvements in a wide range of co-morbid conditions [ 5 – 9 ]. Obesity, which was once viewed as the result of lack of will power, or a lifestyle "choice" – the choice to overeat and under exercise, is now being considered more appropriately by the modern world as a chronic disease, which requires effective strategies for its management.

Obesity, in simple terms, may be defined as a state of imbalance between calories ingested versus calories expended which would lead to excessive or abnormal fat accumulation. Body Mass Index (BMI) is a measure of weight corrected for height and which reflects the total body fat and has been the most accepted parameter for defining over weight [ 10 ].

Optimal BMI increases with age. WHO also classified over weight according to BMI [ 11 ]. There is a very good correlation between BMI and the percentage of body fat in large populations.

Percent Body fat = 1.2 (BMI) + 0.23 (age) - 10.8 (gender) - 5.4

Where gender = '1' for men and '0' for women.

It follows from this equation that for a given height and weight, the percentage of body fat is about 10% higher in women compared to men. The reason for this could be that in women, the excess body fat is usually distributed as subcutaneous fat and is mainly peripheral (thighs, buttocks, breasts) where as in men there is a relative excess of body fat stored in abdominal cavity as abdominal subcutaneous fat.

New classifications of over weight may be based on cut-off points for simple anthropometric measures such as waist hip ratio, total adiposity and intra-abdominal fatness. There exists a correlation between increased BMI, mortality due to allied risks which is depicted in Fig. 1

figure 1

Correlation between increased BMI and risk of mortality

Aetiology of obesity

Obesity is not a single disorder but a heterogeneous group of conditions with multiple causes each of which is ultimately expressed as obese phenotype. Obesity involves complex aetiological links between the genetic, metabolic and neural frameworks on one hand and behavior, food habits, physical activity and socio-cultural factors on the other (Table 1 ).

Genetic considerations

Although obesity had a genetic component, it is not a simple genetic disorder. There is an underlying genetic predisposition to obesity on to which environmental factors are layered. The discovery of 'ob' gene, which was mapped to chromosome 7, has led to a renewed interest in understanding the patho-biological basis of genetic predisposition in obesity. The 'ob' gene codes a hormone called leptin, a 167 amino acid protein and was supposed to be produced in white and brown adipose tissue and placenta [ 12 ]. The leptin receptors are concentrated in hypothalamus and belong to the same class of IL-2 and growth hormone receptors [ 13 ]. Any mutation of 'ob' gene leads to improper coding of leptin, which further results in obesity [ 14 ]. The effects of the 'ob' gene are mediated through effects on both energy intake and energy expenditure. Obesity can also be considered as a "complex trait" as many other genes coding proteins like apolipoprotein B, D, E, β 3 -adrenergic receptor [ 15 ], dopamine D 2 -receptor, tumor necrosis factor (TNF), glucocorticoid receptor etc. are associated with it. So far, 200 genes, gene markers and chromosomal regions have been associated with human obesity [ 16 ].

Neurobiology

Two neurotransmitters neuropeptide Y (NPY) and serotonin (5-HT) are found to play a major role in body weight regulation. NPY is a 36 amino acid peptide, which is concentrated mainly in the hypothalamus; a region crucial to regulation of appetite [ 17 ] has emerged as a possible key neurotransmitter candidate for the regulation of energy homeostasis. Increased NPY activity has been found in the hypothalamus of obese rodents [ 18 ]. NPY increases food in-take through its interaction with a unique Y5 subtype of NPY receptor and hence Y5 receptor antagonists could be effective in the treatment of obesity [ 19 ].

The inhibitory actions of 5-HT on food in-take have been localized to the hypothalamic para ventricular nucleus (PVN), the site at which NPY is most active in inducing feeding behavior [ 20 ]. 5-HT induced reduction in food in-take is mediated by post-synaptic 5-HT IB receptors. The hypophagic actions of 5-HT may be mediated at least partly through the NPY pathway. For example, 5-HT antagonist which stimulates feeding increases NPY concentrations in the arcuate and para ventricular nuclei of the hypothalamus [ 21 ]. Similarly, a 5-HT agonist, which reduces food intake significantly, reduces NPY concentrations in the hypothalamic para ventricular nucleus. Corticotrophin releasing factor (CRF) which also causes weight loss by reducing appetite and act in opposing to NPY on the regulation of energy balances. Cholecystokinin (CCK), a neurotransmitter present in the brain plays a physiological role as a meal termination (satiety) signal between the two receptors such as CCK A and CCK B , CCK acted at CCK A receptors [ 22 ]. Hence, CCK A agonist could also be useful in the treatment of obesity.

Environmental factors

These factors play a critical role in the development of obesity by unmasking genetic or metabolic susceptibilities. Environmental influences act via an increase in energy intake or a decrease in energy expenditure with little physical activity and hence there is increased likelihood of becoming obese. Sedentary behaviors, notably television watching, car ownership also contributes to the risk of obesity. The role of passive over consumption [ 23 ], eating disorders, and preference for high carbohydrate diet also play an important role in increasing the risk of obesity. Other food habits like smoking and alcohol consumption lowers body weight and results in higher BMI respectively.

Psycho-social impact

A number of individual characteristics may place individuals at increased risk of obesity. Restrained eating also plays a role in aetiology of obesity. Restrained eaters report more food carvings and binge eating [ 24 ]. One of the characteristic features of dietary restraints is the tendency towards disinhibited eating in particular circumstances. Restrained eaters may be more susceptible to the availability of highly palatable foods, which act as a stimulus for excess food consumption.

Obesity-associated diseases and risk factors

Cardiovascular diseases (cvd).

Hypertension

Coronary heart disease

Cerebrovascular disease

Varicose veins

Deep venous thrombosis

The increased risk of CVD is 2-fold in women of BMI 25–28.9 kg/m 2 and 3.6 fold for BMI in 29 kg/m 2 or more. In males a 10% increase in body weight increases risk of CVD by 38%, where as 20% weight risk corresponds with 86% increased risk. Blood pressure is increased by 6 mm systole and 4 mm diastole for a 10% gain in body fat. Hyper tension is prevalent in obese adults at a rate of 2.9 fold than non-obese population and weight reduction reduces risk of developing hyper tension [ 25 ].

Respiratory diseases

Sleep apnoea

Hypoventilation syndrome

There are a number of ways in which obesity affects lung function [ 26 ]. An increased amount of fat in the chest wall and abdomen limits respiratory excursion reducing lung volume. As the obesity worsens, so do the apnoeic episodes resulting in frequent awakening and the resultant sleep deprivation produces daytime somnolence.

Metabolic disorders

Hyperlipidemia

Diabetes mellitus

Insulin resistance

Menstrual irregularities

There is a consistent graded relationship between increased BMI and prevalence of NIDDM and insulin resistance [ 27 ]. Over 10 to 15 million Americans with type 2 diabetes are obese [ 28 ]. A mean weight loss of 7% weight reduces risk of developing type 2 diabetes by more than 55% [ 29 ]. BMI above 35 kg/m 2 increases the risk by 93 fold in women and by 42 fold in men. Obesity is associated with lipid disorders in which elevated levels of cholesterol, triglycerides, LDL-cholesterol and low levels of HDL-cholesterol are observed. For every 1 kg of weight loss, there is a corresponding reduction by about 1% in HDL and reduction by 3% of triglycerides. It has been observed that modest weight loss reduces lipid abnormalities [ 30 ] and diabetes mellitus [ 31 ].

Gastrointestinal disorders

Fatty liver and cirrhosis

Haemorrhoids

Colorectal cancer

Gall bladder disease is the most common gastrointestinal disorder in obese individuals. Obese women have a 2.7 fold increase in the prevalence of gall bladder disease. There is an increased risk of gallstones in individuals having BMI of 20 kg/m 2 or more. The mortality rates of cancer of the stomach and pancreas were higher in obese individuals.

Malignancies

Breast cancer

Endometrial Cancer

Prostrate Cancer

Cervical Cancer

Obese women have higher incidence of endometrial, ovarian, cervical and postmenopausal breast cancer, while obese men have incidents of prostrate cancer.

However, it remains to be confirmed whether these malignancies occur as a result of hormonal changes associated with obesity or due to specific dietary pattern.

Miscellaneous

Arthritis and bone mass

Stress is associated with the consumption of high fat foods and leads to weight gain. Obesity is also associated with osteoarthritis of hip and knee although in some cases, mechanical stress associated with obesity leads to osteoarthritis [ 32 ]. Obese women have a higher risk of obstetric complication and have increased risk of caesarean delivery due to variety of foetal size. Recently, an increased risk of neural tube defects especially spinabifida has been reported in women with BMI greater than 29 kg/m 2 .

Prevention of obesity

Obesity is a serious, chronic medical condition, which is associated with a wide range of debilitating and life threatening conditions. The fact that obesity prevalence continues to increase at an alarming rate in almost all regions of the world is of major concern. Hence, an effective control of obesity requires the development of coherent strategies that tackle the main issues related to preventing:

i) The development of over weight in normal weight individuals

ii) The progression of over weight to obesity in those who are already over weight

iii) Weight regain in those who have been over weight or obese in the past but who have since lost weight and

iv) Further worsening of a condition already established.

The prevention of obesity involves action at several levels i) Primary ii) Secondary iii) Tertiary [ 33 ]. Objective of primary prevention is to decrease the number of new cases, secondary prevention is to lower the rate of established cases in the community and tertiary prevention is to stabilize or reduce the amount of disability associated with the disorder. When the attention is focused on the multi-factorial condition such as coronary heart disease (CHD), primary prevention of this involves national programmes to control blood cholesterol levels and secondary prevention deals with reducing CHD risk in those with existing elevated blood cholesterol levels while tertiary action would be associated with preventing re-infarction in those who had a previous heart attack. However, this classification system for prevention of obesity results in a great deal of ambiguity and confusion. To avoid this, the US institute of medicine [ 34 ] has proposed alternative classification of system. The new system separates prevention efforts into 3 levels. Universal (or) public health measures (directed at every one in the population), selective (for a sub-group who may have an above average risk of developing obesity) and indicated (targeted at high risk individuals who may have a detectable amount of excess weight which fore-shadows obesity). However, preventive measures for any disorder may not be helpful in all cases hence, proper management strategies can be integrated along with prevention programmes.

Management of obesity

Management include both weight control or reducing excess body weight and maintaining that weight loss, as well as, initiating other measures to control associated risk factors. Periodic evaluation for obesity should be done by the measurement of BMI, measurement of waist circumference etc., to assess risk factors. Based on the evaluation, appropriate treatment can be suggested. Treatment may consist of modification of diet, increased physical activity, behavioral therapy, and in certain circumstances weight loss medication and surgery.

Dietary therapy

Restrictions of calories represent the first line therapy in all cases except in cases with pregnancy, lactation, terminal illness, anorexia nervosa, cholelithiasis and osteoporosis. Low calorie diets (LCD), which provide 100–1500 kcal/day, resulted in weight loss of 8% of baseline body weight over six months but on long run most of the lost weight is regained [ 35 ].

Very low calories diets (VLCD), which provide 300–800 kcal/day, can be useful in severely obese patients under strict medical supervision. They are found to produce 13% weight loss over six months, i.e. they produce greater initial weight loss than LCDs, however, the long-term (>1 year) weight loss by VLCD's is not found superior to that of the LCDs.

Meal replacement programmes and formula diets can be used as an effective tool in weight management [ 36 ]. Optifast, Medifast are available through physians or hospitals as part of packaged weight-reduction programmes. These products appear to be safe, but maintenance of weight loss over the long term is difficult.

Other over the counter (OTC) variations to formula diets includes Slimfast and Ultra slimfast. The consumer is instructed to drink the formulations and use it to replace one or two meals.

Fat substitutes like Olestra (Olean), which is a non-digestible, non-caloric fat, can be used in food preparations taken by obese patients.

It has been observed that calorie restriction alone has remarkable effects compared to exercise alone [ 37 – 39 ]. A loss of 5% initial weight achieved with diet and exercise is associated with significant improvement in glycylated haemoglobin A IC and that diet control can be useful to treat co morbidities of obesity such as diabetes [ 40 ].

Physical activity

All individuals can benefit from regular exercise [ 41 ]. Physical activity, which increases energy expenditure, has a positive role in reducing fat storage and adjusting energy balance in obese patients. Various exercises preceded and followed by short warm up and cool down sessions help to decrease abdominal fat, prevent loss of muscle mass. Studies revealed that patients who exercise regularly had increased cardio vascular fitness [ 42 , 43 ] along with betterment in their mental and emotional status. Hence a minimum of 30 minutes exercise is recommended for people of all ages [ 44 ] as part of comprehensive weight loss therapy.

Behaviour therapy

Behaviour therapy is a useful adjunct when incorporated into treatment for weight loss and weight maintenance. Patients need to be trained in gaining self-control of their eating habits. Behaviour modification programmes which seek to eliminate improper eating behaviours (eating while watching TV, eating too rapidly, eating when not hungry etc.,) include individual or group counseling of patients.

Self-help groups (weight watchers, Nutri-System) use a program of diet, education and self-monitoring like maintenance of logbook, keeping an account of food intake etc are beneficial.

Pharmacotherapy

Drug treatment is advised only for subjects with BMI > 27 and with associated risk factors or with a BMI > 30 [ 45 ] and thus at medical risk because of their obesity. It should not be used for "cosmetic" weight loss. Weight loss medications should be used only as an adjunct to dietary and exercise regimes coupled with a program of behavioural treatment and nutritional counseling.

Pharmacological approaches in obesity treatment

Most available weight loss medications are "appetite–suppressant" medications. The initial drugs used for appetite suppression were amphetamine [ 46 ], metamphetamine and phenmetrazine (Preludin) and are no longer used in treatment of obesity because of their high potential for abuse.

Inhibitors of 5-hyroxytryptamine (5-HT) reuptake, fenfluramine and dexfenfluramine were licensed for obesity but proved to cause pulmonary hyper tension and increased valvular heart disease [ 47 ] and have been withdrawn from the market. Drugs like phendimetrazine (Plegine), diethylpropion (Tenuate), phentermine (Lonamin) etc., are being marketed but have been classified as controlled substances and are recommended for short-term use only.

The newest agents available for weight loss are sibutramine (Meredia) and orlistat (Xenical). They are the only weight loss medications approved by the US Food and Drug Administration (FDA) for long-term use [ 48 ] in significantly obese patients, although their safety and effectiveness have not been established for use beyond one year.

Sibutramine is the serotonin and norepinephrine re-uptake inhibitor, which induces decreased food intake and increased thermogensis [ 49 – 52 ]. In clinical trials, sibutramine showed a statistical improvement in amount of weight lost versus placebo [ 53 ]. It limits decline of metabolic rate that typically accompanies weight loss [ 54 ]. However, this agent is contraindicated in-patient with known seizure disorders, high blood pressure, congestive heart failure (CHF) a history of myocardial infraction and arrhythmias.

Orlistat is a potent and irreversible inhibitor of gastric, pancreatic lipases. It blocks the digestion of approximately 30% of the ingested dietary triglycerides. Studies proved that it produces 5% more weight loss than in control groups [ 55 ]. It is now available on prescription as Xenical ® (Orlistat-120 mg). The most commonly reported side effects include oily stools, soft stool [ 56 ], and increased defecation and decreased absorption of fat-soluble vitamins (A, D, E and K). Hence, patient may be recommended intake of fat-soluble vitamins [ 57 ] along with it. When used in conjugation with diet it was found to improve glycemic control and cardiovascular disorders [ 58 , 59 ].

In general, monotherapy in obese patients produced sub-optimal weight loss [ 60 ] but the use of more than one weight loss medication at a time (combined drug therapy) is not approved [ 61 ] and hence such an off-label use of combinations of drugs for weight loss is not recommended except as part of a research study.

Drugs under development

There has been a wide search for effective drugs for the treatment of obesity. Some of the promising drug development research areas are mentioned below.

Amylin is a peptide secreted with insulin in response to food intake that shares many other properties with established adiposity signals like insulin and leptin. Its circulating levels can be correlated with body fat. Preclinical studies have shown that amylin complements the effects of insulin in mealtime glucose regulation via several effects, which include a suppression of post meal glucagon secretion, a decrease in gastric emptying, and a decrease in food intake [ 62 ]. The drug pramlintide, a synthetic analogue of amylin is currently in phase III trials.

11β-hydroxysteroid dehydrogenase type-1 (11β-HSD-1) is an enzyme that increases cortisol levels in adipocytes. Studies on mice lacking gene for 11β-HSD-1 suggest that they are resistant to diet induced obesity [ 63 ]. An 11β-HSD-1 inhibitor being developed by Biovitrum is currently in clinical testing.

Stimulation of β 3 adrenoreceptors (β 3 -ARs) by selective agonists improves insulin action and stimulates energy metabolism. In animals, chronic β 3 -AR agonist treatment causes body weight reduction, which is almost entirely due to decrease in body fat [ 64 ]. At least a dozen pharmaceutical companies are in the process of developing β 3 -AR drugs, some of which are already in human testing. AD9677 a β-adrenoceptor agonist is in phase II trails.

The botanical P57 is an extract of steroidal glycosides derived from South African Cactus . The potent appetite suppression may occur via the melanocortin-4 (MCR-4) saponins from the Platycodi radix and Salacia reticulata have been shown to inhibit pancreatic lipase, producing weight loss and reduction of fatty liver in laboratory animals [ 65 ]. Currently, P57 is in Phase II testing and Table 2 summarizes some other important drugs union are under clinical trials for the treatment of obesity.

Apart from drug treatment, surgery is also indicated when BMI is exceedingly high (>40 kg/m 2 or >30 kg/m 2 with obesity-related medical co-morbidities) and when other treatment modalities have failed [ 66 ]. The most popular surgical procedures used for treatment of severe obesities involve gastric portioning or gastroplasty and gastric by-pass. The gastroplasty procedures create a small gastric pouch, which is drained through a narrow calibrated stoma [ 67 , 68 ]. The intake of solids is therefore considerably limited. Gastric by-pass surgery creates a larger pouch emptied by an anastomosis directly into the jejunum, bypassing the duodenum. It is considered now as the most effective and safe surgery for morbid obesity [ 69 , 70 ]. This technique induces weight loss by combining restricted intake and a moderate degree of malabsorbtion [ 71 ]. Initial loss of weight is greater after this procedure than following gastroplasty [ 72 ].

Gastric and nutritional complications [ 73 ] may be serious implications of the surgery. Nutritional deficiencies and intractable vomiting are frequently associated with surgery. Surgical treatments for obesity resolve most co-morbidities of severe obesity such as hypertension [ 74 , 75 ], serum lipid levels [ 76 ] and diabetes mellitus [ 77 , 78 ].

Obesity is not a social condition but is a rampant disease. Obesity cannot be overviewed as just a matter of overeating and lack of will power but must be considered as a major genetic aetiology modified by environment and should be treated vigorously in the same manner that we now apply to other diseases. A better understanding of the aetiological determinants in individual subjects will provide a basis for more rational intervention to prevent this recalcitrant public health problem. With the increasing awareness and ongoing research in this area there is a considerable reason for optimism that the next coming years will bring better treatment for the obese.

Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL: Over weight and obesity in the United States: Prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord. 1998, 22: 39-47. 10.1038/sj.ijo.0800541.

Article   CAS   PubMed   Google Scholar  

Bjorntorp P: Obesity. Lancet. 1997, 350: 423-426. 10.1016/S0140-6736(97)04503-0.

US Department of Health and Human Services. Office of the Surgeon General: The surgeon General's call to action to prevent and decrease overweight and obesity. Rockville MD: United States Department of Health and Human Services. 2001

Google Scholar  

Manson JE, Willett WC, Stamfer MJ, Colditz GA, Hunter DJ, Hankinson SE, Hennekens CH, Speizer FE: Body weight and mortality among women. N Eng J Med. 1995, 333: 677-685. 10.1056/NEJM199509143331101.

Article   CAS   Google Scholar  

Blackburn GL: Effect of degree of weight loss on health benefits. Obes Res. 1995, 3: 211s-216s.

Article   PubMed   Google Scholar  

World Health Organization: Obesity: Preventing and managing the global epidemic. World Health Organisation Geneva. 2000

Goldstein DJ: Beneficial health effects of a modest weight loss. Int J Obes Relat Metab Disord. 1992, 16: 397-415.

CAS   PubMed   Google Scholar  

Bosello O, Armellini F, Zamboni M, Fitchet M: The benefits of modest weight loss in type-II diabetes. Int J Obes Relat Metab Disord. 1997, 21: S10-S13.

PubMed   Google Scholar  

Wing RR, Koeske R, Epstein LH, Nowalk MP, Gooding W, Becker D: Long term effects of modest weight loss in type-II diabetic patients. Arch Intern Med. 1987, 147: 1749-1753. 10.1001/archinte.147.10.1749.

Taylor RW, Keil D, Gold EJ, Williams SM, Goulding A: Body mass index, waist girth and waist-to-hip ratio as indexes of total and regional adiposity in women: evaluation using receiver operating characteristic curves. Am J Clin Nutr. 1998, 67: 44-49.

Jacob CS, Katherine MH: Assessing obesity classification and epidemology. Br Med Bull. 1997, 2: 239-

Zhang YY, Proencea R, Maffei M, Barone M, Leopold L, Friedman JM: Positional clone of the mouse obese gene and its human homologue. Nature. 1994, 372: 425-432. 10.1038/372425a0.

Auwerx J, Stales B: Leptin. Lancet. 1998, 351: 732-742. 10.1016/S0140-6736(97)06348-4.

Article   Google Scholar  

Andersson LB: Genes and obesity. Ann Med. 1996, 28: 5-7.

Arner P: The β 3 -adrenergic receptor – a cause & cure of obesity. N Engl J Med. 1995, 333: 382-383. 10.1056/NEJM199508103330612.

Chagnon YC, Perusse L, Weisnagel SJ, Rankinen T, Bouchard C: The human obesity gene map: the 1999 update. Obes Res. 2000, 8: 89-117.

Dryden S, Frankish H, Wang Q, Williams G: Neuropeptide Y and energy balance, one way ahead for the treatment of obesity?. Eur J Clin Invest. 1994, 24: 293-308.

Flier JS, Flier EM: Obesity and the hypothalamus: Novel peptides for new pathways. Cell. 1998, 92: 437-440. 10.1016/S0092-8674(00)80937-X.

Friedman JM: The alphabet of weight control. Nature. 1997, 385: 119-120. 10.1038/385119a0.

Shor Posnar G, Grinker JA, Marinescu C: Hypothalamic serotonin in the control of meal patterns and macronutrient selection. Brain Res Bull. 1986, 17: 663-671. 10.1016/0361-9230(86)90198-X.

Dryden S, Frankish H, Wang Q, Williams G: The serotonin antagonist methysergide increase NPY synthesis and secretion in the hypothalamus of rat. Brain Res. 1995, 699: 12-18. 10.1016/0006-8993(95)00841-D.

Boosalis MG, Gemayel N, Lee A, Bray GA, Laine L, Cohen H: Cholecystokinin and satiety: effect of hypothalamic obesity and gastric bubble insertion. Am J Physiol. 1992, 262: R241-244.

Spiegelman BM, Flier JS: Adipogenesis and obesity: round in out the big picture. Cell. 1996, 87: 377-389. 10.1016/S0092-8674(00)81359-8.

Wadley J: Dietary restraint and binge eating behaviour. Anal Modif. 1980, 4: 647-660.

Tuck ML, Sowers J, Dornfeld L, Kledzik G, Maxwell M: The effect of weight reduction on blood pressure, plasma rennin activity, and plasma aldosterone levels in obese patients. N Engl J Med. 1981, 304: 930-933.

Kolarzyk E, Kiec E, Wiater M: Effect of obesity on the ventilatory capacity of the respiratory system. I. Relation between basic spirometric indicators: vital capacity (VC) and forced expiratory volume (FEV1) and obesity. Med Pr. 1985, 36: 87-95.

Rahilly OS: Non insulin dependent diabetes mellitus: the gathering storm. Br Med J. 1997, 314: 955-960.

Ford ES, Giles WH, Dietz WH: Prevalence of the metabolic syndrome among US adults: findings from the Third National health and Nutrition Examination survey. J Amer Med Assoc. 2002, 287: 356-359. 10.1001/jama.287.3.356.

Franz MJ, Bantle JP, Beebe CA, Brunzell JD, Chiasson JL, Garg A, Holzmeister LA, Hoogwerf B, Mayer_Davis E, Mooradian AD, Purnell JQ, Wheeler M: Evidence based nutrition principles and recommendations for the treatment and prevention of diabetes and related complications. Diabetes care. 2002, 25: 148-198.

Dattilo AM, Kris-Etherton PM: Effects of weight reduction on blood lipids and lipoproteins: a meta-analysis. Am J Clin Nutr. 1992, 56: 320-328.

Anonymous: UK Prospective study on maturity onset diabetes. I. Effect of diet and sulphonylurea, insulin or biguainide therapy on fasting plasma glucose and body weight over one year. Diabetologia. 1983, 24: 404-411.

Davis MA, Neuhaus JM, Ettinger WH, Mueller WH: Body fat distributions and osteoarthritis. Am J Epidemiol. 1990, 132: 701-707.

Timothy PG: Key issues in the prevention of obesity. Br Med Bull. 1997, 53: 359-388.

US Institute of medicine: Reducing risks of mental disorders. Frontiers for preventive intervention research. 1994, Washington, National Academy Press

Wadden TA: Treatment of obesity by moderate and severe caloric restriction results of clinical research tracts. Ann Intern Med. 1993, 119: 688-693.

Ashley JM, St Jeor ST, Schrage JP, Perumean_Chaney SE, Gilbertson MC, McCall NL, Bovee V: Weight control in the physician's office. Arch Intern Med. 2001, 161: 1599-1604. 10.1001/archinte.161.13.1599.

Anderssen S, Holme I, Urdal P, Hjermann I: Diet and exercise intervention have favourable effects on blood pressure in hypertensives: The Oslo Diet and Exercise Study (ODES). Blood Press. 1995, 4: 343-349.

Bertram SR, Venter I, Stewart RI: Weight loss in obese women – exercise vs dietary education. S Afr Med J. 1990, 78: 15-18.

Wood PD, Stefanick ML, Dreon DM, Frey-Hewitt B, Garay SC, Williams PT, Superko HR, Fortman SP, Albers JJ, Vranizan KM, et al: Changes in plasma lipids and lipoproteins in overweight men during weight loss through dieting as compared with exercise. N Engl J Med. 1988, 319: 1173-1179.

Ditschuneit HH, Flechtner-Mors M, Johnson TD, Adler G: Metabolic and weight loss effects of a long term dietary intervention in obese patients. Am J Clin Nutr. 1999, 69: 198-204.

US Department of Health and Human services: Leading health indicators. Overweight and obesity. Healthy people 2010 (Conference ed. in two volumes). DC. US Department of Health and Human Services, Washington. 2000, 24-45.

Wyatt HR, Wing RR, Hill JO: The National weight control registry. In: Evaluation & Management of obesity. Edited by: Bessesen DH, Kushner RF. 2002, Philadelphia, Hanley & Belfus Inc, 199-224.

Schoeller DA, Shay K, Kushner RF: How much physical activity is needed to minimize weight gain in previously obese women?. Am J Clin Nutr. 1997, 66: 551-556.

Physical Activity and Health: A Report of the surgeon General PA. US Department of Health and Human services. 1996

National Institutes of Health (NHLBI): Clinical guidelines on the identification, evaluation and treatment of overweight and obesity in adults. The evidence report Washington D.C. National Institute of Health, Obese Res. 1998, 6: 51s-201s.

Lessof MH, Myerson A: Benzedrine sulfate as an aid to be the treatment of obesity. N Engl J Med. 1938, 218: 119-205.

Connolly HM, Crary JL, McGoon MD, Hensud DD, Edwards BS, Edwards WD: Valvular heart disease associated with fenfluramine – phentermine. N Engl J Med. 1997, 337: 783-10.1056/NEJM199708283370901.

James WP, Astrup A, Finer N, Hilsted J, Kopelman P, Rossner S, Saris WH, Van Gaal LF: Effect of sibutramine on weight maintenance after weight loss: a randomised trial. STORM Study Group. Sibutramine Trial of Obesity Reduction and Maintenance. Lancet. 2000, 356: 2119-2125. 10.1016/S0140-6736(00)03491-7.

Mun EC, Blackbur GL, Matthews JB: Current status of medical and surgical therapy for obesity. Gastroenterology. 2001, 120: 669-681.

Rolls BJ, Shide DJ, Thorwart ML, Ulbrecht JS: Sibutramine reduces food intake in non-dieting women with obesity. Obes Res. 1998, 6: 1-11.

Hansen DL, Toubro S, Stock MJ, Macdonald IA, Astrup A: Thermogenic effects of sibutramine in humans. Am J Clin Nutr. 1998, 68 : 1180-1186.

Seagle HM, Bessesen DH, Hill JO: Effects of sibutramine on resting metabolic rate and weight loss in over weight women. Obes Res. 1998, 6: 115-121.

Astrup A, Toubro S: When, for whom and how to use sibutramine?. Int J Obes Relat Metab Disord. 2001, 25 (Suppl 4): 52-57. Review

Luque CA: Sibutramine: a serotonin-norepinephrine reuptake inhibitor for the treatment of obesity. Ann Pharmacother. 1999, 33: 968-978. 10.1345/aph.18319.

Sjostrom L, Rissonen A, Andersen T, Boldrin M, Golay A, Koppeschaar HP, Krempf M: Randomised placebo-controlled trial of orlistat for weight loss and prevention of weight in obese patients. European Multicenter Orlistat study group. Lancet. 1998, 352: 167-172. 10.1016/S0140-6736(97)11509-4.

Hill JO, Hauptman J, Anderson JW, Fujioka K, O'Neil PM, Smith DK, Zavoral JH, Aronne LJ: Orlistat, a lipase inhibitor, for weight maintenance after conventional dieting: a 1-year study. Am J Clin Nutr. 1999, 69: 1108-1116.

Mc Duffie JR, Calis KA, Booth SL, Uwaifo GI, Yanovski JA: Effects of orlistat on fat soluble vitamins in obese adolescents. Pharmacotherapy. 2002, 22: 814-822.

Hollander PA, Elbein SC, Hirsch IB, Kelley D, McGill J, Taylor T, Weiss SR, Crockett SE, Kaplan RA, Comstock J, Lucas CP, Lodewick PA, Canovatchel W, Chung J, Hauptman J: Role of orlistat in the treatment of obese patients with type-2 diabetes. A 1-year randomized double blind study. Diabetes care. 1998, 21: 1288-1294.

Davidson MH, Hauptman J, DiGirolamo M, Foreyt JP, Halsted CH, Heber D, Heimburger DC, Lucas CP, Robbins DC, Chung J, Heymsfield SB: Weight control and risk factor reduction in obese subjects treated for 2 years with orlistat: a randomized controlled trial. J Amer Med Assoc. 1999, 281: 235-242. 10.1001/jama.281.3.235.

Wadden TA, Berkowitz RI, Sarwer DB, Prus-Wisniewski R, Steinberg C: Benefits of lifestyle modification in the pharmacologic treatment of obesity: a randomized trial. Arch Intern Med. 2001, 161: 218-227. 10.1001/archinte.161.2.218.

NHLBI: Prescription medication for treatment of obesity. [ http://www.niddk.nih.gov/health/nutrit/nutrit.htm ]

Rushing PA, Hagan MM, Seeley RJ, Lutz TA, Woods SC: Amylin: a novel action in the brain to reduce body weight. Endocrinology. 2000, 141: 850-853. 10.1210/en.141.2.850.

Stewart PM, Tomlinson JW: Cortisol, 11 beta-hydroxysteroid dehydrogenase type 1 and central obesity. Trends Endocrinol Metab. 2002, 13: 94-96. 10.1016/S1043-2760(02)00566-0.

Clapham JC, Arch JRS, Tadayyon M: Anti-obesity drugs: a critical review of current therapies and future opportunities. Pharmacol Ther. 2001, 89: 81-121. 10.1016/S0163-7258(00)00105-4.

Anonymous: P 57 and food intake. obesity Meds and Research News. 2000

Anonymous: NIH Conference. Gastrointestinal surgery for severe obesity. Consensus Development Conference Panel. Ann Intern Med. 1991, 115: 956-961.

Karl JG: Overview of surgical techniques for treating obesity. Am J Clin Nutr. 1992, 55: 552s-555s.

Ashley S, Bird DL, Sugden G, Royston CM: Vertical banded gastroplasty for the treatment of morbid obesity. Br J Surg. 1993, 80: 1421-1423.

Shikora SA, Benotti PN, Forre RA: Surgical Treatment of Obesity. In: Obesity, pathophysiology, psychology and treatment. Edited by: Blackburn GL, Kanders BS. 1994, New York Chapman & Hall, 264-282.

Sagar PM: Surgical treatment of morbid obesity. Br J Surg. 1995, 82: 732-739.

Lonroth H, Dalenback J, Haglind E, Josefsson K, Olbe L, Fagevik Olsen M, Lundell L: Vertical banded gastroplasty by laparoscopic technique in the treatment of morbid obesity. Surg Laparosc Endosc. 1996, 6: 102-107. 10.1097/00019509-199604000-00004.

Salmon PA, McArdle MO: The rationale and results of gastroplasty/gastric by-pass. Obes Surg. 1992, 2: 61-68. 10.1381/096089292765560565.

Seehra H, Macc Dermatt N, Lascelles RG, Taylor TV: Wernicke's encephalapathy after vertical banded gastroplasty for morbid obesity. Br Med J. 1996, 312: 434-

Foley EF, Benotti PN, Borlase BC, Hollingshead J, Blackburn GL: Impact of gastric restrictive surgery on hypertension in the morbidly obese. Am J Surg. 1992, 163: 294-297. 10.1016/0002-9610(92)90005-C.

Carson JL, Ruddy ME, Duff AE, Holmes NJ, Cody RP, Brolin RE: The effect of gastric bypass surgery on hypertension in the morbidity obese patients. Arch Intern Med. 1994, 154: 193-200. 10.1001/archinte.154.2.193.

Olsson SA, Petersson BG, Sorbris R, Nilsson-Ehle P: Effects of weight reduction after gastroplasty on glucose and lipid metabolism. Am J Clin Nutr. 1984, 40: 1273-1280.

Herbst CA, Hughes TA, Gwynne JT, Buckwalter JA: Gastric bariatric operation in insulin treated adults. Surgery. 1984, 95: 209-214.

Pories WJ, Swanson MS, MacDonald KG, Long SB, Morris PG, Brown BM, Barakat HA, deRamon RA, Israel G, Dolezal JM: Who would have thought it? An operation proves to be the most effective therapy for adult-onset diabetes mellitus. Ann Surg. 1995, 222: 339-350.

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Nammi, S., Koka, S., Chinnala, K.M. et al. Obesity: An overview on its current perspectives and treatment options. Nutr J 3 , 3 (2004). https://doi.org/10.1186/1475-2891-3-3

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Obesity has increasingly been identified as a critical global public health concern. This focus on obesity as a health priority raises complex bioethical issues. These include how obesity is defined and categorized, the implications of the centrality of personal responsibility in medical and public health approaches, how competing ethical frames impact social justice concerns, and the growing “moral panic” concerning obesity. A critical examination of how obesity is defined as a medical problem suggests that ethical approaches could be more productive if obesity were addressed as a social problem with medical consequences, rather than emphasizing it as a medical problem with social consequences.

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There has been a dramatic rise in the prevalence of obesity globally in the last three decades, and the World Health Organization (WHO) estimates around 11 % of the world’s total population is obese (WHO 2012). Obesity is seen as a major public health concern because it is widely recognized as a precipitating factor in the parallel emergence of chronic diseases as a primary cause of death in many countries. Obesity is often reported as a major drain on medical systems, and the growing obesity rates in developing countries are often cited as especially worrying in this regard. From a bioethics perspective, the focus on obesity as a health priority raises complex issues. This entry highlights inter-related and key bioethical dimensions of contemporary concerns around and approaches to obesity, including the means by which people are categorized as obese or not, the medicalization of obesity as a disease that needs to be treated, implications of the centrality of individual responsibility in medical and public health approaches, obesity as a social justice issue, and media and growing “moral panic.”

Obesity is most simply defined as an excess of adipose (fat) tissue, usually with negative health effects. However, this definition is problematic. Medically, as discussed below, the science of obesity is increasingly suggesting that many people can be both obese and healthy. However, “obese” and “obesity” are terms that have also entered everyday media and other public discourses in ways that are mostly negative and imply ill-health and disease. The growing assumption that obesity is defined as a negative characteristic is historically and culturally particular, in marked contrast to cross-cultural records that describe plump bodies as powerful, sexy, social, abundant, fertile, and certainly healthy (Brewis 2011a).

Ethical Dimensions

The Categorization of Obesity. A definition of obesity based upon the notion of excess body fat requires measurement against a standard of what constitutes “normal.” Given that human bodies are highly ecologically flexible and vary in averages across populations, the imposition of a single standard for classification as obese raises some complex bioethical issues. The most widely employed means to classify people as obese, and then assess variation in population levels of obesity, is through use of body mass index (BMI).

BMI does not directly measure body fat; rather, it is a proxy measure using the ratio of mass (weight) relative to height. Using statistical methods and prescriptive and risk models, four basic categories of weight (underweight, normal, overweight, obese) have been identified and are now widely applied, from the doctor’s office to large public health interventions. These standard categories are arbitrarily defined through cutoff points related to morbidity and mortality rates found in large-scale epidemiological studies, with obesity normally set at a BMI of 30 or higher.

While BMI as a measure of obesity is sometimes useful, particularly in clinical studies, because of both individual and population variation, this mapping of weight to health risk is not precise or even especially predictive. For example, there is growing evidence that many people clinically defined as obese prove to be metabolically healthy even as they are advised by doctors they need to lose weight, and that the level of obesity at which conditions like diabetes and heart disease become more prevalent differs across populations. Moreover, BMI does not discriminate between muscle mass, bone, connective tissue, and amount types of adipose tissue, obscuring accurate measurement of total body fat. As a result, people with highly-developed musculature are labeled obese by the measure, even when they have low levels of actual body fat. Further, some populations have greater bone density on average or shorter leg bone length resulting in falsely high BMI scores (Hruschka et al. 2013). For example, for decades there has been a public health concern focused on very high obesity risk in Pacific Island populations, but more recent studies have shown that the disease correlates of obesity emerge at higher levels of adiposity in comparison to other groups. Hence, the common standard for categorizing obesity probably misassigns a significant number of people and accordingly implies health risks where none may exist (and vice versa). Additionally, women have a higher percentage of body fat than men, and weight tends to increase in both genders as individuals age. Attempts to address the weaknesses in BMI classifications have resulted in alternative methods that more accurately measure the amount and distribution of body fat, but these use technologies or expertise that are difficult to implement in real-world settings.

Defining Obesity as a Disease. Defining obesity against a set standard of what is a normal or healthy level of body fat leads to an emphasis on prevention and cure, and underscores obesity as (1) a problem, with (2) an identifiable cause (diagnosis), and that (3) requires evaluation, intervention, management, and control. The central bioethical issue is this: regardless of how people are classified into an obese category, once so categorized it is generally assumed that labeling a person as unhealthy is warranted and medical or other intervention is necessary. Certainly, obesity has become increasingly identified as a major factor and index of ill-health over the last two decades. This culminated in the formal recognition of obesity as a disease by the American Medical Association in 2013, even in the absence of other risk factors or clinical symptoms. The growing medicalization of obesity as a condition explains why highly invasive and often risky medical treatments for obesity, such as bariatric surgery, are on the rise. The emphasis on excess weight as a health problem also negatively impacts how people view and relate to their own and others’ bodies and in ways that create emotional and social distress related to failing to meet social prescriptions for an ideal or acceptable body size.

Levels of Analyses and Ultimate Causation. Current scientific evidence on the causes of obesity can be analyzed at different levels, often working iteratively and in feedback with each other. At the genetic level, some individuals have a predisposition toward higher weights, weight gain, and difficulty in weight loss, related to genetic variants in appetite, metabolism, and activity. At the individual level, obesity is the result of excess calorie intake over calories expended through physical activity, but individual-level factors such as income, education level, ethnicity, age, and gender also predict differential risks of being obese, as does use of certain medications or comorbidities such as depression. Institutional factors such as health care access also matter.

At the community, neighborhood, or regional level, obesity risk accrues differently based solely on where people live. One factor in this pattern is the rapid urbanization of the world’s population: urbanization is associated with higher rates of obesity, and an increasing majority of humans live in cities. This correlation is due, in part, to the low cost of high density foods, changes in activity with the move to urban settings and structural and economic barriers to healthier lifestyles (Metzl and Hansen 2014). Further, within those cities, specific locales and their inhabitants’ lifestyles vary based upon social, spatial, and economic factors. The built environment of a particular locale is one example of how the physical expression of social, spatial, and economic factors relates to obesity prevalence: walkability, public transportation, access to fresh foods, safety, parks, light and shade, access to healthcare, and density all help shape obesity risk. For example, barriers in transportation and distance may make it difficult for residents to access healthy foods, while the perception by residents that the place they live in is unsafe or of poor quality may limit opportunities to be physically active. Social and economic factors also influence residential effects, including social exclusion, discrimination, and diminished economic infrastructure. Efforts to address residential effects often evoke stakeholder objections, as these efforts may inhibit personal choice, stigmatize neighborhood residents, or create changes that conflict with personal lifestyles and cultural values (ten Have et al. 2011).

Education and wealth, and most especially poverty, are also implicated in obesity risk. The relationship between income and obesity is complex and varies depending on the economic development of the resident country. Most nations, even the poorest, demonstrate some level of obesity, even in the presence of food shortages and undernutrition. The combination of under and over nutrition increases the likelihood of obesity and has significant implications in terms of health risks and negative health effects. As poorer nations become increasingly urbanized and industrialized, these problems are exacerbated, particularly as low income countries have fewer healthcare resources to meet the challenges posed by chronic conditions associated with obesity. This “dual burden” is also evident in middle-income countries: as economic changes at both the household and national level occur, families with a dual burden of having overweight and underweight individuals become increasingly prevalent.

Evidence suggests that income and obesity also rise together as inexpensive food becomes easily accessible. However, this trend reverses at the point where the apparent social costs of obesity outweigh the advantages. In middle to high-income countries, obesity tends to be inversely correlated with socioeconomic status, meaning that the highest obesity rates are found in those populations with the lowest incomes and with the lowest levels of educational achievement (Brewis 2011a). At a national level, BMI appears to rise in the early and accelerated phases of economic development due to a complex set of factors including urban migration, a shift from traditional occupations, and increased technology. At the individual level, poverty is contextual, demonstrating a complex residential pattern, with both rural and urban poverty linked to lower education and higher obesity.

While there have been some efforts to develop community-level interventions in line with increasing recognition of these upstream causes of obesity risk, medical and public health interventions continue to give the most attention to individual behavior change. The standard treatment model, often shared by clinicians and patients alike, is that the individual must lose excess weight by eating less and/or exercising more. This is despite decades of evidence that most such behavioral change strategies eventually fail to result in weight lost, and often serve to promote weight regain (Brewis 2011a).

Obesity and Social Justice Considerations. The role of proximate and ultimate factors discussed above means that obesity can be framed as a social justice issue, not solely a medical one. This suggests a very different course, emphasis, and pathway for public health interventions. Policies that seek to restrict behavior (passively or actively) can disproportionately affect the poor, the rural, and the malnourished. Of critical importance is who designs, implements, and evaluates these efforts. How do these interventions ethically impact personal physical health while promoting equality and maintaining individual autonomy? If population-level interventions are not necessarily individually beneficial and may in fact have psychosocial and cultural costs with their own negative health consequences, should public health entities intervene at all? These are some of the ethical issues that arise when the focus moves away from considering obesity fundamentally a medical problem to thinking about obesity at the aggregate level.

The challenge is to consider both the ultimate (structural) as well as the proximate factors (nutrition, activity, and medical conditions) that shape obesity risk when developing obesity policy and interventions. Identifying the causes of obesity, when coupled with how it is defined, becomes important in the ethical frame used to intervene. To date, there have been multiple framings in approaches to combat the rise of obesity. These ethical frames are not mutually exclusive and often coexist within a particular approach. Understanding the ethical platform from which programs spring will enable better understanding of the consequences (intentional or unintentional), successes, and failures. Identifying obesity as a health problem is more than defining disease, biomedical risk, and treatment; assigning responsibility – individual or otherwise – becomes part of the equation. The increasing prevalence of obesity on a global scale is accompanied by concerns that society is harmed in some way. This sense of harm in turn is linked to the notion of blame. How responsibility and blame are assigned varies with different ethical frames.

Framing Obesity Solutions

Emphasis on Individual Responsibility. The notion of individual responsibility has dominated the discourse surrounding the obesity crisis and efforts to contain the problem. Individual responsibility is rooted in notions of individual autonomy based within a moralistic theory of personal determination. Morality frames emphasize the threat to social values and economic stability by focusing on personal choice and the impact these choices have on society (Boero 2012). A morality frame advances notions of normal, ideal, virtue, right, and wrong. In this frame, obesity is related to personal failings – a lack of self-discipline, restraint, rationality, and moral failings attributed to poor life choices (gluttony, sloth, and a lack of adherence to personal improvement). Obesity, therefore, is self-induced and harm is self-inflicted. Because the individual is responsible for their health and body, blame is personal and can take the form of value imperatives about who is obese or overweight and who is responsible. Interventions and public health campaigns using this frame focus on problem awareness, promote better individual health behaviors, and encourage personal responsibility. Interventions range from educational efforts to weight loss programs, “fat taxes” (on calorie or fat dense foods), and increased insurance rates for individuals with high BMIs. This type of framing, when used in conjunction with a medical definition of obesity, places the focus of the intervention on achieving a physical ideal body weight and ignores the psychosocial dimensions of health, even as it places responsibility upon the individual (as psychologically weak or morally lax). Stigmatization, discrimination, and negative self-image are the result, which have their own negative health consequences (Sagay 2013; Puhl and Heuer 2010).

Biomedical and Public Health Frames. The biomedical frame uses the language of risk to intervene and regulate the body in order to promote health or, more usually, decrease illness or disease. Obesity in this frame is seen as pathologic – a biological condition to be monitored, treated, and cured. The body is understood to be the recipient of treatment, a somewhat passive vessel that needs management by healthcare professionals (Sagay 2013). De-emphasizing personal responsibility can be helpful in decreasing stigma, but medicalization also promotes stigmatization by labeling obese bodies as sick. Framing obesity in terms of mortality and morbidity imparts urgency and authority to the issue. The locus for intervention is on proximate factors and responsibility remains with the individual-aspatient, though the medical system is a crucial partner in terms of defining the problem and determining and managing treatment. Generally individual and small-scale interventions focused on dietary choice, activity, and medical/surgical interventions are utilized in this context. However, the biomedical frame informs larger policy issues resulting in industry and governmental regulations generally rooted in economic analyses, such as differential insurance rates for individuals based upon weight, corporate programs to incentivize weight reduction or dietary choice, bans or taxes on sugar-sweetened beverages, and regulation of nutritional information on food products.

A public health frame assigns responsibility to the government (local, state, and federal). Public health entities are most often located within governments and are charged with setting standards, regulating and protecting public safety and promoting health, and minimizing or preventing public harm while at the same time ensuring individual liberty, privacy, and public access to needed resources. This equation differs internationally as notions of individual and public health are culturally constituted. In general, obesity is seen as a threat to public health and the approach taken is to reduce the threat, generally combining individual and systemic approaches to address the issue. Ethical approaches in this frame deal with the differential distribution of obesity across groups and subpopulations as prevalence and risk manifest variably within cultural groups, gender, socioeconomic status, etc. Financial triggers (incentives & disincentives), built environment changes that alter lifestyle options (slowing elevators, car-free zoning, food banning), and informational campaigns are often used or suggested within a public health intervention. Issues of justice and fairness can be particularly problematic in this framing as the dual focus of public health creates a tension between liberty and protection. Obesity at the individual level includes social and economic disparities as well as discrimination and psychological stress from weight bias. Addressing these issues within the systemic frames of government, business, and infrastructure (including larger social forces) can contribute to stigmatization, discrimination, and differential opportunities and access.

Thus, in practice, there is a smorgasbord of antiobesity efforts, structured within multiple framings – moralistic, biomedical, and public health – that tend to be disconnected from each other. Even assuming a universal definition of obesity and its determinants exists, the ethics of policy interventions still needs to be addressed. At the heart of the ethics, debate is concerned over individual choice, autonomy, and the exacerbation of stigma and discrimination. Rephrasing the two previous ethical questions might then ask: What are the individual’s essential rights and responsibilities concerning weight? Secondly, what is the responsibility of the government in providing healthy, safe environments for its citizens?

This tension between rights and responsibilities (individual, societal, and governmental) plays out differently globally. The body (and body size) is understood as a “domain of liberty and autonomy” (Tirosh 2014, p. 1801), but the expression of these values is differentially understood across societies. When seen as a lifestyle issue, obesity remains focused at the individual and local levels, to be dealt with through small-scale interventions in select populations to encourage individuals to control their weight and make healthier choices (moralistic frame). These types of interventions tend to ignore the complexity of factors (and responsibilities) underlying obesity and keep responsibility (and blame) with the individual. Growing public discourse has revolved around policy changes to combat the “rising epidemic” of obesity. Public health officials have supported this groundswell of opinion through campaigns to promote the adoption of a healthy lifestyle, emphasizing a diet high in fruits, vegetables, complex carbohydrates, and lean proteins and sufficient exercise – efforts that highlight personal choice and responsibility. Much of the work on prevention and intervention at this level has had mixed results. Even among public health practitioners who seek to address structural components underlying obesity, the political weight of the morality frame leads them to use “code language” such as “make the healthy choice, the easy choice.” Essentially structural changes are presented as changes enabling personal choice.

At a governmental level, rising healthcare costs in conjunction with rising obesity rates globally and concerns over the efficacy of individual-level interventions are frequently cited as an impetus for governmental strategies and policies to guide widespread interventions, primarily through legislation. Governmental interventions are influenced by the culture, political system, economics, and traditions of the nations involved, resulting in a spectrum of policies and programs globally. Efforts range from health education to restrictive taxes on unhealthy foods and beverages, with a goal of shaping behavior by restricting or coercing individual choice. In the European Union (EU), a concerted effort is being made to encourage voluntary action on the part of industry partners to alter nutrition and activity environments. Voluntary efforts to support decision-making through evidence-based information, self-regulation of product claims (labeling, advertisements) through the proposed establishment of an industry code of conduct, food redistribution (surplus fruits/vegetables) focused on children 4–12 years old, reformulation of foods to decrease sugar, fat, and salt, and sustainable urban transportation facilities to promote physical activity/ public infrastructure (Commission of the European Communities 2007) are examples of this type of intervention. In the USA, taxation of SSBs and calorie-dense foods has been implemented (or attempted), most notably in New York City and the Navajo Nation. China, Britain, and Mexico have all passed or attempted to enact legislation that aims to regulate behavior with an eye to reducing the economic burden of healthcare. Often, particular populations are targeted for interventions, as evidence indicates that obesity is more prevalent in these groups. Unfortunately, these efforts can take the form of value imperatives about who is obese or overweight and who is responsible, encouraging the spread of stigmatization and victimization (Puhl and Heuer 2010).

Some initiatives have sought to create structural or environmental changes to address the inequities, disparities, and deficits implicated in obesity (public health framing with social justice focus). Policies attempting to reduce the unequal distribution of resources, barriers to healthy foods and activities, and social and economic inequities can be found in new regulations requiring enhanced visibility and simplified nutritional labeling; limitations on commercial advertising of high density, low-nutrient foods to children; venue-specific banning of “unhealthy items” such as high-fat items in restaurants or SSBs in school vending machines; and limiting the proximity of fast-food restaurants to schools (Kass et al. 2014; ten Have et al. 2011). These types of initiatives still impact personal choice and liberty and have resulted in public debates regarding the role of government in regulating health. Impacting broader economic and social structures is more challenging from the local level, though increasingly tools like health impact assessments and health in all policies are being used to provide more equity in land use decisions, and have even been used to evaluate local minimum wage, affordable housing, and supplemental nutrition policies. Criticisms of obesity policies have ranged from concerns over the inhibition of individual autonomy, the expansion of the paternalistic “nanny” state (and subsequent economic burden), and the inequitable treatment and stigmatization of low-income populations.

Ethical discussions concerning interventions that limit choice or coerce behavior tend to be centered on arguments about legitimacy and utility. Legitimacy focuses on the value to society in instituting a particular policy or practice. Generally, the discussion revolves around the role of paternalism (soft or hard) in promoting the general welfare of the individual. Paternalism is best viewed as a sliding scale that ranges from promoting informed choice (information campaigns) through implementation of incentives (free or reduced costs, tax benefits, etc.) and ultimately various forms of coercion (bans, taxation). Utility looks at the cost-benefit ratio: is a policy or intervention likely to succeed and does it offer enough benefit to offset the reduction in choice, liberty, or privacy. Because there is little cohesion in how data is collected internationally, making evidence based comparisons of the effectiveness of different types of interventions is difficult. In general, arguments made for coercive policies are rooted in the premise that obesity is associated with higher morbidity and attendant higher costs of treatment. As previously noted, this is by no means a validated conclusion and therefore the utility of such efforts is suspect.

An example of this trade-off is the call for school districts to restrict soft drinks on school campuses. This type of intervention may have the unintended consequence of reducing the school’s revenue stream, resulting in less money available for student education or extra-curricular activities. Obesity prevalence is associated with poverty and disadvantage, disproportionately impacting precisely those communities whose schools need funding the most. Reduced funding may lead to a reduction in programming and healthy food options, elimination of physical education or play equipment, poor food quality to reduce costs, increased sedentism, and reduced educational opportunities (Crooks 2003). The result may be an environmental trade-off of biological costs for social benefits – poorer nutritional quality in order to provide education for all students and thus hopefully propel the students out of poverty.

Another example is the call to use social pressure tactics, similar to antismoking campaigns, to leverage public opinion toward acceptance of stringent governmental regulations. The trade-off here is to focus on increased legitimacy at the expense of utility. This type of intervention operates at the individual, acute, and proximate level and does not address any of the underlying structural conditions. Couched as “stigmatization lite” the argument is that overweight and obese individuals do not recognize their “problem” and need to be awakened to reality. Unfortunately increasing stigmatization of the individual has not been demonstrated to positively impact behavior change; rather, it produces the opposite impact. Discrimination is implicated in stress induced physiological responses associated with obesity that not only negatively impact health but also discourage potential participation in health-related activities. Beyond this, how is the level of stigma “titrated?” Increasing antiobesity thinking may contribute to the moral panic over the rise in obesity rates (Campos et al. 2006).

Stigmatization And Moral Panic

Obesity and Weight-related Stigma. Any discussion on bioethics needs to address the issue of stigmatization (and resulting victimization and discrimination) of obese individuals. Placing the responsibility for one’s weight on the individual has led to sanctioned discrimination in the form of diminished access to goods, services, and employment opportunities and higher healthcare costs for obese individuals. Obesity has even been used as evidence in child abuse cases and other legal interventions. Despite multiple framings of obesity as a medical and public health problem, the persistent focus on individual responsibility and autonomy continues to direct the understanding of obesity through the lens of morality – a platform for value imperatives and subsequent stigmatization.

Obesity stigma must be addressed within the social and structural conditions that produce it. That is, there must be recognition that even a focus on ultimate factors (zoning laws, bans, taxation, urban renewal) can have unintended consequences resulting in increased discrimination. In the past, public health concerns were often the result of an external agent (bacterial or viral agent, poor sanitation, cigarettes, etc.), allowing the focus of interventions to remain external to the body/self. However, weight (and excess weight) is rooted in the body itself – it is a domain of the self. Eating and movement are necessary components of life and are seen as highly personal, as one chooses what, when, and how to eat, move, and function bodily within personal environments. Because these activities are necessary (one cannot stop eating, for example), efforts have focused on changing personal decisions related to eating and activity. Attempts to alter these bodily functions with an external agent (medication, surgery) have had mixed results, but as long as eating and activity are categorized as personal choices, stigmatization will remain a factor.

Media and Corporate Roles. The “moral panic” that has resulted from the framing of obesity as an epidemic has produced a media onslaught. This begs the question of whether the media is reflecting this panic or creating it. Popular media promotes a thin ideal body size (particularly for women), while continuing to also promote the sale of obesogenic products. Fast food and junk food advertisements, product placement in movies, casting of thin ideal body types, and disparaging characterizations of obese characters are prevalent throughout multiple media formats. Visual representations of obese bodies that employ “de-evolution tropes” (which portray the human species as degenerating from more fit ancestors) are common. Media use (screen time) is certainly associated with increased snacking and requests for caloriedense foods and decreased activity and altered sleep patterns (American Academy of Pediatrics 2011).

The increasing documentation of these negative social and physical impacts of media treatment of obesity has led to a mishmash of corporate efforts and legislative calls to action. For example, the Disney Corporation has announced that it will no longer advertise “junk foods” on its television channel. However, Disney continues to promote thin body ideals in its movie and cartoon heroines. McDonald’s has been criticized for targeting children with “toy” gifts in their high fat and sugar Happy Meals. Several European Union countries have instituted restrictions on food advertising aimed at very young children. The impacts of the media on obesity risk and stigma bring to the fore the ongoing ethical conundrum concerning the extent to which governments should have control over media that promote unhealthy behaviors or stigmatization. Issues of free speech, government regulation, and equal access to opportunity and goods have all been cited as deterrents to government regulation of advertising and media. Combining this with a moralistic frame that castigates large bodies as personal failures and the bioethical landscape is messy indeed.

Obesity arises through individual behaviors shaped within varied epigenetic, cognitive, sociocultural, physical, material, political, and other institutional structures and environments. Bioethically, based on the discussion above, this entry suggests that obesity is perhaps more productively addressed as a social problem with medical consequences rather than a medical problem with social consequences. Competing frames of obesity, whether medically or otherwise problematized or not (moralistic, medical/ healthcare, public health, governmental), are rooted in concerns about the ethical behavior of members within the group, not about the larger social, economic, and political domains. Social justice models for obesity intervention rightly focus on the role of the built environment, but rarely tackle the ultimate determinants like poverty, education, and discrimination. Many complex bioethical questions remain: Is it possible to account for acute and chronic dimensions as well as proximate and ultimate factors and mitigate some of the unintended, negative consequences of interventions? How can health policies and interventions ethical approaches be constructed to take into account the very real social dimensions of weight and the body? If health is a public good, what are the ethical implications of not intervening?

Ultimately, being obese is both a private and public matter. While an individual’s weight is the result of multiple individual and biosocial components, the individual’s body is subject to public scrutiny and – increasingly – public regulation. The consequences of public efforts, both intended and unintended, need to be critically examined within the context of how obesity is defined as a problem, the frame used to address the problem as defined, and then how, with whom, and at what level various prevention and intervention efforts are implemented.

Bibliography :

  • American Academy of Pediatrics. (2011). Policy statement – children, adolescents, obesity, and the media. Pediatrics, 128(1), 201–208.
  • Boero, N. (2012). Killer fat: Media, medicine, and morals in the American “Obesity Epidemic.” New Brunswick: Rutgers University Press.
  • Brewis, A. (2011a). Obesity: Cultural and biocultural perspectives. New Brunswick: Rutgers University Press.
  • Campos, P., Saguy, A., Ernsberger, P., Oliver, E., & Gaesser, G. (2006). The epidemiology of overweight and obesity: Public health crisis or moral panic? International Journal of Epidemiology, 35, 55–60.
  • Commission of the European Communities. (2007). A strategy for Europe on nutrition, overweight and obesity related health issues. Brussels, SEC. 706–7.
  • Crooks, D. (2003). Nutrition and the sale of snack foods. Medical Anthropology Quarterly, 17(2), 182–199.
  • Hruschka, D. J., Rush, E., & Brewis, A. (2013). Population differences in the relationship between height, weight, and adiposity: An application of Burton’s model. American Journal of Physical Anthropology, 151, 68–76.
  • Kass, N., Hecht, K., Paul, A., & Birnbach, K. (2014). Ethics and obesity prevention: Ethical considerations in 3 approaches to reducing consumption of sugar-sweetened beverages. Public Health Ethics, 104(5), 787–795.
  • Metzl, J., & Hansen, H. (2014). Structural competency: Theorizing a new medical engagement with stigma and inequality. Social Science & Medicine, 103, 126–133.
  • Puhl, R., & Heuer, C. (2010). Obesity stigma: Important considerations for public health. Framing Health Matters, 100(6), 1019–1028.
  • Sagay, A. (2013). What’s wrong with fat? Oxford: Oxford University Press.
  • ten Have, M., de Beaufort, I. D., Teixera, P. J., Mackenbach, J. P., & van der Heide, A. (2011). Ethics and prevention of overweight and obesity: An inventory. Obesity Reviews, 12, 669–679.
  • Tirosh, Y. (2014). Three comments on paternalism in public health. Connecticut Law Review, 46(5), 1795–1816.
  • World Health Organization. (2012). Obesity and overweight: fact sheets. World Health Organization. Retrieved from https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight
  • Brewis, A. (2011b). Obesity: Cultural and biocultural perspectives. New Brunswick: Rutgers University Press.
  • Puhl, R. M., & Heuer, C. A. (2009). The stigma of obesity: A review and update. Obesity, 17(5), 941–964.
  • Saguy, A. (2012). What’s wrong with fat? Oxford: Oxford University Press.

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obesity introduction research paper

  • Introduction
  • Conclusions
  • Article Information

T2D indicates type 2 diabetes.

a The combined total of patients is not necessarily a sum of the individuals from each of the groups because individuals could be prescribed both glucagon-like peptide 1 receptor agonists (GLP-1RAs) and insulins or metformin during the study period.

b The combined total of patients is not necessarily a sum of the individuals from each of the groups because individuals could be prescribed both insulins and metformin during the study period.

Patients were followed up for as along as 15 years after the index event for both groups. Hazard ratios (HRs) rates were calculated using a Cox proportional hazards model with censoring applied. Overall risk equals the number of patients with outcomes during the follow-up time window divided by number of patients in the group at the beginning of the time window. For each outcome, the groups were separately propensity-score matched for covariates related to the outcome, and the outcome was compared between the matched groups. Each eligible individual was followed up from the index event until the occurrence of the outcomes, death, loss to follow-up, or 15 years after the index event, whichever occurred first.

Kaplan-Meier survival analysis was used. Each eligible individual was followed up from the index event until the occurrence of the outcomes, death, loss to follow-up, or 15 years after the index event, whichever occurred first.

Patients were followed up for as long as 15 years after the index event for both groups. Hazard ratios (HRs) were calculated using a Cox proportional hazards model with censoring applied. Overall risk equals the number of patients with outcomes during the follow-up time window divided by the number of patients in the group at the beginning of the time window. For each outcome, the groups were separately propensity-score matched for covariates related to the outcome, and the outcome was compared between the matched groups. Each eligible individual was followed up from the index event until the occurrence of the outcomes, death, loss to follow-up, or 15 years after the index event, whichever occurred first.

eAppendix. Database

eTable 1. Clinical diagnosis, and other codes used in the platform that are used to determine the status of variables for study population definitions, exposures, outcomes, and those for propensity-score matching for groups

eTable 2. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for baseline covariates related to esophageal cancer for the study populations of patients with T2D and no history of any OAC

eTable 3. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to breast cancer for the study populations of women (age 55 and older) with T2D and no history of any OAC

eTable 4. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to endometrial cancer for the study populations of women with T2D and no history of any OAC

eTable 5. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to gallbladder cancer for the study populations of patients with T2D and no history of any OAC

eTable 6. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to stomach cancer for the study populations of patients with T2D and no history of any OAC

eTable 7. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to kidney cancer for the study populations of patients with T2D and no history of any OAC

eTable 8. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to liver cancer for the study populations of patients with T2D and no history of any OAC

eTable 9. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to ovarian cancer for the study populations of women with T2D and no history of any OAC

eTable 10. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to pancreatic cancer for the study populations of patients with T2D and no history of any OAC

eTable 11. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to thyroid cancer for the study populations of patients with T2D and no history of any OAC

eTable 12. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to meningioma for the study populations of patients with T2D and no history of any OAC

eTable 13. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to multiple myeloma for the study populations of patients with T2D and no history of any OAC

eTable 14. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to esophageal cancer for the study populations of patients with T2D and no history of any OAC

eTable 15. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to breast cancer for the study populations of women (age 55 and older) with T2D and no history of any OAC

eTable 16. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to colorectal cancer for the study populations of patients with T2D and no history of any OAC

eTable 17. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to endometrial cancer for the study populations of women with T2D and no history of any OAC

eTable 18. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to gallbladder cancer for the study populations of patients with T2D and no history of any OAC

eTable 19. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to stomach cancer for the study populations of patients with T2D and no history of any OAC

eTable 20. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to kidney cancer for the study populations of patients with T2D and no history of any OAC

eTable 21. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to liver cancer for the study populations of patients with T2D and no history of any OAC

eTable 22. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to ovarian cancer for the study populations of women with T2D and no history of any OAC

eTable 23. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to pancreatic cancer for the study populations of patients with T2D and no history of any OAC

eTable 24. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to thyroid cancer for the study populations of patients with T2D and no history of any OAC

eTable 25. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to meningioma for the study populations of patients with T2D and no history of any OAC

eTable 26. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to multiple myeloma for the study populations of patients with T2D and no history of any OAC

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Wang L , Xu R , Kaelber DC , Berger NA. Glucagon-Like Peptide 1 Receptor Agonists and 13 Obesity-Associated Cancers in Patients With Type 2 Diabetes. JAMA Netw Open. 2024;7(7):e2421305. doi:10.1001/jamanetworkopen.2024.21305

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Glucagon-Like Peptide 1 Receptor Agonists and 13 Obesity-Associated Cancers in Patients With Type 2 Diabetes

  • 1 Center for Science, Health, and Society, Case Western Reserve University School of Medicine, Cleveland, Ohio
  • 2 Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, Ohio
  • 3 Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
  • 4 Departments of Internal Medicine, Pediatrics, and Population and Quantitative Health Sciences and the Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, Ohio

Question   Is there clinical evidence supporting the potential benefits of glucagon-like peptide receptor agonists (GLP-1RAs) for the prevention of 13 obesity-associated cancers (OACs)?

Findings   This cohort study of more than 1.6 million patients with type 2 diabetes (T2D) who had no prior diagnosis of 13 OACs found that patients with T2D treated with GLP-1RAs vs insulin had a significant risk reduction in 10 of 13 OACs, including esophageal, colorectal, endometrial, gallbladder, kidney, liver, ovarian, and pancreatic cancer as well as meningioma and multiple myeloma. No decrease in cancer risk was associated with GLP-1RAs compared with metformin.

Meaning   This study provides clinical data suggesting that GLP-1RAs may reduce the risk of specific OACs compared with insulins.

Importance   Thirteen human malignant neoplasms have been identified as obesity-associated cancers (OACs), ie, the presence of excess body fat is associated with increased risk of developing cancer and worse prognosis in patients with these specific tumors. The glucagon-like peptide receptor agonist (GLP-1RA) class of pharmaceuticals are effective agents for the treatment of type 2 diabetes (T2D) and for achieving weight loss, but the association of GLP-1RAs with the incident risk of 13 OACs is unclear.

Objective   To compare the incident risk of each of the 13 OACs in patients with T2D who were prescribed GLP-1RAs vs insulins or metformin.

Design, Setting, and Participants   This retrospective cohort study was based on a nationwide multicenter database of electronic health records (EHRs) of 113 million US patients. The study population included 1 651 452 patients with T2D who had no prior diagnosis of OACs and were prescribed GLP-1RAs, insulins, or metformin during March 2005 to November 2018. Data analysis was conducted on April 26, 2024.

Exposures   Prescription of GLP-1RAs, insulins, or metformin.

Main Outcomes and Measures   Incident (first-time) diagnosis of each of the 13 OACs occurring during a 15-year follow-up after the exposure was examined using Cox proportional hazard and Kaplan-Meier survival analyses with censoring applied. Hazard ratios (HRs), cumulative incidences, and 95% CIs were calculated. All models were adjusted for confounders at baseline by propensity-score matching baseline covariates.

Results   In the study population of 1 651 452 patients with T2D (mean [SD] age, 59.8 [15.1] years; 827 873 [50.1%] male and 775 687 [47.0%] female participants; 5780 [0.4%] American Indian or Alaska Native, 65 893 [4.0%] Asian, 281 242 [17.0%] Black, 13 707 [0.8%] Native Hawaiian or Other Pacific Islander, and 1 000 780 [60.6%] White participants), GLP-1RAs compared with insulin were associated with a significant risk reduction in 10 of 13 OACs, including in gallbladder cancer (HR, 0.35; 95% CI, 0.15-0.83), meningioma (HR, 0.37; 95% CI, 0.18-0.74), pancreatic cancer (HR, 0.41; 95% CI, 0.33-0.50), hepatocellular carcinoma (HR, 0.47; 95% CI, 0.36-0.61), ovarian cancer (HR, 0.52; 95% CI, 0.03-0.74), colorectal cancer (HR, 0.54; 95% CI, 0.46-0.64), multiple myeloma (HR, 0.59; 95% CI, 0.44-0.77), esophageal cancer (HR, 0.60; 95% CI, 0.42-0.86), endometrial cancer (HR, 0.74; 95% CI, 0.60-0.91), and kidney cancer (HR, 0.76; 95% CI, 0.64-0.91). Although not statistically significant, the HR for stomach cancer was less than 1 among patients who took GLP-1RAs compared with those who took insulin (HR, 0.73; 95% CI, 0.51-1.03). GLP-1RAs were not associated with a reduced risk of postmenopausal breast cancer or thyroid cancer. Of those cancers that showed a decreased risk among patients taking GLP-1RAs compared with those taking insulin, HRs for patients taking GLP-1RAs vs those taking metformin for colorectal and gallbladder cancer were less than 1, but the risk reduction was not statistically significant. Compared with metformin, GLP-1RAs were not associated with a decreased risk of any cancers, but were associated with an increased risk of kidney cancer (HR, 1.54; 95% CI, 1.27-1.87).

Conclusions and Relevance   In this study, GLP-1RAs were associated with lower risks of specific types of OACs compared with insulins or metformin in patients with T2D. These findings provide preliminary evidence of the potential benefit of GLP-1RAs for cancer prevention in high-risk populations and support further preclinical and clinical studies for the prevention of certain OACs.

Thirteen human malignant neoplasms have been identified as obesity-associated cancers (OAC), ie, the presence of excess body fat is associated with increased risk of developing cancer and worse prognosis in patients with these specific tumors. 1 Obesity also contributes to insulin resistance and type 2 diabetes (T2D), which may further increase the risk and worsen the prognosis of the OACs. 2 , 3

The glucagon-like peptide 1 receptor agonist (GLP-1RA) class of pharmaceuticals are highly effective agents for the treatment of T2D and for achieving weight loss. 4 - 9 GLP-1RAs have further been shown to reduce the risk of adverse cardiovascular outcomes in patients with obesity 10 and to contribute to the resolution of nonalcoholic steatohepatitis. 11 Because of their efficacy in controlling T2D, obesity, and related comorbidities, we hypothesized that these agents might reduce the risk of the OACs. We recently reported that GLP-1RAs were associated with lower risks for colorectal cancer, 12 an OAC. Otherwise, clinical evidence of the potential clinical benefits of GLP-1RA in preventing OAC has not been systematically assessed. Here we conducted a nationwide multicenter retrospective cohort study in patients with T2D who were prescribed GLP-1RAs vs insulins or metformin to determine whether GLP-1RAs were associated with changes in the risk of each of 13 OACs, including esophageal, breast, colorectal, endometrial, gallbladder, stomach, kidney, ovarian, pancreatic, and thyroid cancer as well as hepatocellular carcinoma, meningioma, and multiple myeloma. 1

We used the TriNetX platform to access deidentified electronic health records (EHRs) of 113 million patients from 64 health care organizations across 50 states, covering diverse age, racial and ethnic, income, and insurance groups and clinical settings. 13 , 14 The platform’s built-in analytic functions allow patient-level analyses, while only reporting population-level data. The platform has been used for retrospective cohort studies. 15 - 26 Similar to this study, we have examined the association of GLP-1RAs with colorectal cancer incidence in patients with T2D 12 and the associations of GLP-1RA (semaglutide) with suicidal ideations 27 and with cannabis use in patients with obesity and those with T2D. 28 The MetroHealth System institutional review board determined that the research as described in this study was not human participant research and institutional review board approval and informed consent were not required. This cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

Available data elements of EHRs include extensive information on demographics, diagnoses ( International Statistical Classification of Diseases and Related Health Problems, Tenth Revision ), medications (Anatomical Therapeutic Chemical and medical prescription normalized medical prescription or RxNorm), procedures ( Current Procedural Terminology ), laboratory tests (Logical Observation Identifiers Names and Codes), genomics, visits, and socioeconomic and lifestyle information. The data on the analytic platform have been expanded to include oncology-specific data from cancer registry data from North American Association of Central Cancer Registries (NAACCR) records and other data resources. 14

Self-reported sex, race, and ethnicity data from contributing health care systems are mapped by according to Office of Management and Budget standards into (1) race, American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, and unknown race; and (2) ethnicity, Hispanic or Latinx, not Hispanic or Latinx, or unknown ethnicity. All covariates are either binary, categorical, or continuous but essentially guaranteed to exist. Age is guaranteed to exist. Missing sex values are represented using “unknown sex.” The missing data for race and ethnicity are presented as “unknown race” or “unknown ethnicity.” For other variables, including medical conditions, procedures, laboratory tests, and socioeconomic determinants of health, the value is either present or absent so missing is not pertinent.

The study population comprised 1 651 452 patients with a diagnosis of T2D who had medical encounters with health care organizations and were prescribed GLP-1RAs vs insulin or metformin between March 2005 and November 2018 and had no history of any of the 13 OACs. The study population was divided into exposure and comparison groups. For comparing GLP-1RAs with insulins, the study population was divided into a GLP-1RA/no insulin group (48 983 patients prescribed a GLP-1RA but not insulins) and a insulin/no GLP-1RA group (1 044 745 patients prescribed insulins but not GLP-1RAs). For comparing GLP-1RAs with metformin, the study population was divided into a GLP-1RA/no metformin group (32 365 patients prescribed a GLP-1RA but not metformin) and a metformin/no GLP-1RA group (856 160 patients prescribed metformin but not GLP-1RAs) ( Figure 1 ).

The 13 OACs are esophageal, breast, colorectal, endometrial, gallbladder, stomach, kidney, ovarian, pancreatic, and thyroid cancer as well as hepatocellular carcinoma, meningioma, and multiple myeloma. 1 Each of the 13 OACs was examined as a separate outcome in groups that were propensity-score matched for covariates related to the specific OAC. For each OAC outcome, the exposure and comparison groups (ie, GLP-1RA/no insulin vs insulin/no GLP-1RA groups and GLP-1RA/no metformin vs metformin/no GLP-1RA groups) were propensity-score matched (1:1 using nearest neighbor greedy matching) for baseline covariates related to the specific OAC, including demographic characteristics (age, sex, race, and ethnicity); adverse socioeconomic determinants of health; family and personal history of cancer; genetic susceptibility to cancer; preexisting medical conditions, including obesity and overweight; and medical procedures, including cancer screening, bariatric surgery, and prior prescription of antidiabetes medications. Each eligible individual was followed up from the index event (the first prescription of GLP-1RAs, insulins, or metformin during March 2005 to November 2018) until the occurrence of the outcomes, death, loss to follow-up, or 15 years after the index event, whichever occurred first. Cox proportional hazard analyses were used to compare rates of time to events on a daily basis during the follow-up time after the index event. Hazard ratios (HRs) and 95% CIs were calculated. Cumulative incidences were estimated using the Kaplan-Meier survival analysis. All models are adjusted for confounders at baseline by propensity-score matching baseline covariates.

The data were collected and analyzed on April 26, 2024, within the analytics platform. All statistical analyses in this study, including propensity-score matching, Kaplan-Meier survival analysis, and Cox proportional hazard analysis were done using built-in functions within the platform that are implemented using Survival version 3.2-3 in R version 4.0.2 (R Project for Statistical Computing) and libraries and utilities for data science and statistics in Python version 3.7 (Python Software Foundation) and Java version 11.0.16 (Oracle). Details of clinical codes for eligibility criteria, treatment strategies, outcomes, and baseline covariates are in eTable 1 in Supplement 1 .

The study population included 1 651 452 patients with T2D (mean [SD] age, 59.8 [15.1] years; 827 873 [50.1%] male and 775 687 [47.0%] female participants; 5780 [0.4%] American Indian or Alaska Native, 65 893 [4.0%] Asian, 281 242 [17.0%] Black, 13 707 [0.8%] Native Hawaiian or Other Pacific Islander, and 1 000 780 [60.6%] White participants). For comparing GLP-1RAs with insulins in patients with T2D, the study population included 1 093 728 patients with T2D who had no prior diagnosis of any OAC and were prescribed GLP-1RAs or insulins but not both between March 2005 and November 2018. The GLP-1RA/no insulin group (n = 48 983) compared with the insulin/no GLP-1RA group (n = 1 044 475) was younger; included more women and White participants; had a higher prevalence of family history of cancer, obesity or overweight, medical encounters for cancer screening, and prior prescriptions of other antidiabetic agents, including insulins, metformin, dipeptidyl peptidase 4 (DPP-4) inhibitors, sodium-glucose cotransporter 2 (SGLT2) inhibitors, sulfonylureas, thiazolidinediones, and α-glucosidase inhibitors. For each OAC outcome, the GLP-1RA/no insulin and the insulin/no GLP-1RA groups were separately matched for covariates associated with the OAC. The Table shows the characteristics of the GLP-1RA/no insulin and insulin/no GLP-1RA groups before and after propensity-score matching for covariates related to colorectal cancer. The characteristics of the exposure and comparison groups before and after matching for each of the other 12 OACs are in eTables 2 to 13 in Supplement 1 .

Compared with insulins, GLP-1RAs were associated with a significantly lower risk of 10 of the 13 OACs, including gallbladder cancer (HR, 0.35; 95% CI, 0.15-0.83), meningioma (HR, 0.37; 95% CI, 0.18-0.74), pancreatic cancer (HR, 0.41; 95% CI, 0.33-0.50), hepatocellular carcinoma (HR, 0.47; 95% CI, 0.36-0.61), ovarian cancer (HR, 0.52; 95% CI, 0.03-0.74), colorectal cancer (HR, 0.54; 95% CI, 0.46-0.64), multiple myeloma (HR, 0.59; 95% CI, 0.44-0.77), esophageal cancer (HR, 0.60; 95% CI, 0.42-0.86), endometrial cancer (HR, 0.74; 95% CI, 0.60-0.91), and kidney cancer (HR, 0.76; 95% CI, 0.64-0.91). The HR for stomach cancer among patients taking GLP-1RAs vs those taking insulin was less than 1, but it was not statistically significant (HR, 0.73; 95% CI, 0.51-1.03). GLP-1RAs were not associated with risk of postmenopausal breast cancer or thyroid cancer ( Figure 2 ). Figure 3 shows the cumulative incidences of colorectal cancer and liver cancer comparing GLP-1RAs with insulins. The mean (SD) follow-up time for the outcome of colorectal cancer was 2074.7 (435.3) days for the GLP-1RA/no insulin group and 1981.8 (471.1) days for the insulin/no GLP-1RA group. The mean (SD) follow-up time for the outcome of liver cancer was 2023.1 (1112.6) days for the GLP-1RA/no insulin group and 2037.9 (766.4) days for the insulin/no GLP-1RA group.

For comparing GLP-1RAs with metformin in patients with T2D, the study population included 888 525 patients with T2D who had no prior diagnosis of any OAC and were prescribed GLP-1RAs or metformin but not both between March 2005 and November 2018. For each OAC outcome, the GLP-1RA/no metformin group (n = 32 365) and the metformin/no GLP-1RA group (n = 856 160) were separately matched for covariates related to the OAC (eTables 14-26 in Supplement 1 ). Compared with metformin, GLP-1RAs were not associated with a lower risk of colorectal cancer, gallbladder cancer, and meningioma but were associated with an increased risk of kidney cancer ( Figure 4 ). Figure 3 shows the cumulative incidences of colorectal cancer and liver cancer by comparing GLP-1RAs with metformin. The mean (SD) follow-up time for the outcome of colorectal cancer was 1967.2 (592.2) days for the GLP-1RA/no metformin group and 2101.6 (576.0) days for metformin/no GLP-1RA group. The mean (SD) follow-up time for the outcome of liver cancer was 1970.9 (426.0) days for the GLP-1RA/no metformin group and 2129.8 (514.7) days for metformin/no GLP-1RA group.

Using a data platform 29 to analyze more than 15 years of longitudinal EHRs of a US population-based cohort of more than 100 million individuals, we found that in patients with T2D who had no history of any OAC, GLP-1RAs compared with insulins were associated with a significant risk reduction in 10 of 13 OACs, including esophageal, colorectal, kidney, pancreatic, gallbladder, ovarian, endometrial, and liver cancers as well as meningioma and multiple myeloma. Decreased risk reduction that did not reach statistical significance was also noted for stomach cancer. Of those cancers that showed decreased risk of GLP-1RAs compared with insulin, risk reduction was also noted for GLP-1RAs relative to metformin for colorectal cancer, gallbladder, and meningiomas, although these findings were not statistically significant.

Our observations on the reduction in the incidence of OACs in patients with T2D treated with GLP-1RAs compare favorably with the OAC-reducing effects of intensive lifestyle intervention (ILI) observed in the Look AHEAD trial (Action for Health in Diabetes) 30 and with the results of metabolic-bariatric surgery as recently reported in the SPLENDID (Surgical Procedure and Long-term Effectiveness In Neoplastic Disease Incidence and Death) trial. 31 The Look AHEAD study, a randomized clinical trial in which 4859 patients with T2D and overweight or obesity (age, 45-76 years; median follow-up, 11 years) were randomized to an ILI or diabetes support and education group, found a 16% reduction in risk for OAC (HR, 0.84; 95% CI, 0.68-1.04). 30 The SPLENDID trial, a matched cohort study, compared 5053 patients with obesity with 25 265 nonsurgical matched controls, with a median age of 46 years and median follow-up of 6.1 years, showed an OAC risk reduction of 32%, (HR, 0.68; 95% CI, 0.53-0.87). 31

A recent 9-year follow-up population-based historical cohort study 32 conducted in Israel reported a decrease (although not statistically significant) in incidence of pancreatic cancer (HR, 0.50; 95% CI, 0.15-1.71) in patients with T2D treated with GLP-1RAs compared with insulin. 32 Our US population-based study, with 15 years of follow-up and a larger sample size, now extends these observations, suggesting that treatment of patients with T2D with GLP-1RAs vs insulin is associated with a significantly decreased incidence of pancreatic cancer (HR, 0.41; 95% CI, 0.33-0.50).

In contrast to the risk reduction shown for most of the OACs, thyroid cancer showed no statistically different risk in patients treated with GLP-1RAs compared with insulins. Studies in rodents indicate that GLP-1RAs promote thyroid C-cell hyperplasia and medullary thyroid carcinoma (MTC) by a GLP-1R mediated increase in calcitonin synthesis. 33 High levels of fasting serum insulin and insulin resistance are associated with an increased risk of thyroid cancer. 34 Although clinical evidence for an association of thyroid cancer with the use of GLP-1RAs has been reported as inconclusive, 35 the findings from our study together with previous reports of insulins promoting cancer growth suggest that GLP-1RAs might be associated with increased risk of thyroid cancer. Our results are further supported by a recent report 36 by the French National Health Cancer Data System showing that the use of GLP-1RAs for 1 to 3 years was associated with increased risk of all thyroid cancers (adjusted HR, 1.78; 95% CI 1.04-3.05). 36 These studies support the package warnings included with GLP-1RAs that these agents are contraindicated in patients with multiple endocrine neoplasia syndrome type 2 and that patients should be counseled regarding the potential risk of MTC and symptoms of thyroid tumors.

Kidney cancers showed an increased risk with GLP-1RA treatment relative to that with metformin (HR, 1.54; 95% CI 1.27-1.87) but a decrease relative to insulin (HR, 0.76; 95% CI 0.64-0.91). GLP-1RAs have direct effects on kidney function mediated by GLP-1Rs in renal vasculature; however, these are not associated with increased mitogenesis, 37 and to our knowledge, there have been no previous reports of kidney cancers with the use of GLP1-RAs. These divergent risks require further clinical and mechanistic studies for full evaluation. Nonetheless, they suggest the need for continued monitoring in patients being treated with GLP-1RAs.

Our study, with follow-up over 15 years, found no signs of increase or decrease in risk for breast cancer in postmenopausal women with T2D being treated with GLP-1RAs compared with those being treated with insulin or metformin. GLP-1RAs have been shown to reduce the growth of murine and human breast cancer cell lines in vitro and in vivo murine models. 38 However, a meta-analysis of more than 50 randomized clinical trials, evaluating GLP-1RAs in women aged between 45 to 70 years and followed up from 24 weeks to 7.5 years, showed no differences in benign, premalignant, or malignant breast neoplasms in patients treated with GLP-1RAs compared with other antidiabetic agents or placebos. 39 A more recent population-based cohort study of 44 984 women 40 years and older treated with GLP-1RAs or other antidiabetic agents for a mean of 3.5 years showed no overall significant difference in the risk for breast cancer occurrence. However, an increased risk (HR, 2.66; 95% CI, 1.32-5.38) was noted for those treated between 2 to 3 years with a return to null after more than 3 years’ treatment. 40 Interestingly, the SPLENDID trial of bariatric surgery for weight reduction, which found an overall 32% risk reduction for OACs, showed no significant difference among women for incidence of overall or postmenopausal breast cancer. 31 This lack of effect on breast cancer risk needs to be further investigated to determine the impact of longer duration of therapy as well as to more fully understand the relation between GLP-1RAs and estrogen metabolism. The lack of breast cancer risk reduction by GLP-1RAs and the similar lack of protection by bariatric surgery may also suggest the possibility that factors determining the incidence of breast cancer in patients with overweight or obesity may have been initiated long before intervention with GLP-1RAs and/or bariatric surgery and therefore require earlier intervention to affect risk reduction. The concept that early intervention might reduce breast cancer incidence is supported by the observation that both pregnancy and breastfeeding reduce the incidence of breast cancer. 41 , 42

Our study has several limitations. First, this is a retrospective observational study of patient EHRs, which has inherent limitations including overdiagnosis, underdiagnosis, and misdiagnosis; unmeasured or uncontrolled confounders; and biases. Although we controlled for an extensive list of variables, these limitations and biases could not be fully eliminated; therefore, no causal inferences can be drawn. Second, patients in our study represented those who had medical encounters with health care systems contributing to the data platform. Although both the exposure and comparison groups were drawn from the same EHR database and from the same time period, which should not significantly affect the HR calculations, results from the platform need to be validated in other EHR databases and analytics platforms. Third, the status of incident cancer was based on the presence of first-ever diagnosis codes of OACs documented in patient EHRs, which also included oncology-specific data from cancer registry data, such as NAACCR records. However, it is unknown how well cancer diagnoses are captured in patient EHRs. For this study, the main interest was the relative risk (or HR) of cancer diagnosis. Since all patients in the study population were drawn from the same health care organizations in the data platform, cancer underdiagnosis, misdiagnosis, or overdiagnosis should not have a substantial impact on the relative risk analysis. Fourth, the built-in functions did not allow us to control for variables (eg, weight loss) that occurred after the index event and to identify individual patient data, which precludes our ability to correlate risk reduction with a degree of weight loss, which was demonstrated to be particularly important in the SPLENDID bariatric study. 25 In addition, we could not explicitly control for health care utilization and insurance type although the study population included patients who had medical encounters with health care organizations and were withdrawn from the same 64 health care organizations in the network. Finally, due to the lack of patients’ medication adherence information in EHRs, we used intention-to-treat (medication prescriptions) as a causal contrast of interest regardless of whether the individuals adhered to their medications and the duration of the medication use.

In this study of patients with T2D who were cancer free at baseline, taking GLP-1RAs compared with insulin was associated with a lower risk of 10 of 13 OACs. The potential cancer-preventative effects of OACs by GLP-1RAs warrant further long-term studies as well as studies of individual newer and possibly more effective antidiabetic and weight loss agents as well as those with multihormone agonist activities. Studies are also warranted to evaluate the preventive effects of these agents on non-OACs. In addition, the associations of the GLP-1RA targeted pharmacologic agents with cancer risk should be compared with the use of ILI and metabolic-bariatric surgery for the control of obesity and diabetes. As noted previously, it will be important to correlate these associations with the control of T2D and obesity. Moreover, given that T2D and overweight or obesity have negative impacts on patients during cancer therapy, GLP-1RAs should be evaluated for control of these comorbid conditions during cancer therapy as well as for secondary prevention to delay cancer recurrence.

Accepted for Publication: May 9, 2024.

Published: July 5, 2024. doi:10.1001/jamanetworkopen.2024.21305

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Wang L et al. JAMA Network Open .

Corresponding Authors: Nathan A. Berger, MD, Center for Science, Health, and Society, Case Comprehensive Cancer Center ( [email protected] ), and Rong Xu, PhD, Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH 44106 ( [email protected] ).

Author Contributions: Dr Xu and Ms Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Xu, Berger.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Xu, Berger.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Wang, Xu.

Obtained funding: Xu, Berger.

Administrative, technical, or material support: Xu, Kaelber, Berger.

Supervision: Xu, Kaelber, Berger.

Conflict of Interest Disclosures: Drs Kaelber and Berger reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study. No other disclosures were reported.

Funding/Support: We acknowledge support from National Cancer Institute Case Comprehensive Cancer Center (grant Nos. CA221718 and CA043703), American Cancer Society (grant No. RSG-16-049-01–MPC), The Landon Foundation–American Association for Cancer Research (award No. 15-20-27-XU), NIH Director’s New Innovator Award Program (award No. DP2HD084068), National Institute on Aging (grant Nos. AG057557, AG061388, AG062272, and AG07664), and the National Institute on Alcohol Abuse and Alcoholism (grant No. AA029831).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Childhood and Adolescent Obesity in the United States: A Public Health Concern

Adekunle sanyaolu.

1 Federal Ministry of Health, Abuja, Nigeria

Chuku Okorie

2 Essex County College, Newark, NJ, USA

3 Saint James School of Medicine, Anguilla, British West Indies

Jennifer Locke

Saif rehman.

Childhood and adolescent obesity have reached epidemic levels in the United States. Currently, about 17% of US children are presenting with obesity. Obesity can affect all aspects of the children including their psychological as well as cardiovascular health; also, their overall physical health is affected. The association between obesity and other conditions makes it a public health concern for children and adolescents. Due to the increase in the prevalence of obesity among children, a variety of research studies have been conducted to discover what associations and risk factors increase the probability that a child will present with obesity. While a complete picture of all the risk factors associated with obesity remains elusive, the combination of diet, exercise, physiological factors, and psychological factors is important in the control and prevention of childhood obesity; thus, all researchers agree that prevention is the key strategy for controlling the current problem. Primary prevention methods are aimed at educating the child and family, as well as encouraging appropriate diet and exercise from a young age through adulthood, while secondary prevention is targeted at lessening the effect of childhood obesity to prevent the child from continuing the unhealthy habits and obesity into adulthood. A combination of both primary and secondary prevention is necessary to achieve the best results. This review article highlights the health implications including physiological and psychological factors comorbidities, as well as the epidemiology, risk factors, prevention, and control of childhood and adolescent obesity in the United States.

Introduction

Childhood and adolescent obesity have reached epidemic levels in the United States, affecting the lives of millions of people. In the past 3 decades, the prevalence of childhood obesity has more than doubled in children and tripled in adolescents. 1 The latest data from the National Health and Nutrition Examination Survey show that the prevalence of obesity among US children and adolescents was 18.5% in 2015-2016. Overall, the prevalence of obesity among adolescents (12-19 years; 20.6%) and school-aged children (6-11 years; 18.4%) was higher than among preschool-aged children (2-5 years; 13.9%). School-aged boys (20.4%) had a higher prevalence of obesity than preschool-aged boys (14.3%). Adolescent girls (20.9%) had a higher prevalence of obesity than preschool-aged girls (13.5%; Figure 1 ). 1 Moreover, the rates of obesity have been steadily rising from 1999-2000 through 2015-2016 ( Figure 2 ). 1 According to Ahmad et al, 80% of adolescents aged 10 to 14 years, 25% of children younger than the age of 5 years, and 50% of children aged 6 to 9 years with obesity are at risk of remaining adults with obesity. 2

An external file that holds a picture, illustration, etc.
Object name is 10.1177_2333794X19891305-fig1.jpg

Prevalence of obesity among children and adolescents aged 2 to 19 years, by sex and age: the United States, 2015-2016.

An external file that holds a picture, illustration, etc.
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Trends in obesity prevalence among children and adolescents aged 2 to 19 years: the United States, 1999-2000 through 2015-2016.

Obesity can affect all aspects of children and adolescents including but not limited to their psychological health and cardiovascular health and also their overall physical health. 3 The association between obesity and morbid outcomes makes it a public health concern for children and adolescents. 4 Obesity has an enormous impact on both physical and psychological health. Consequently, it is associated with several comorbidity conditions such as hypertension, hyperlipidemia, diabetes, sleep apnea, poor self-esteem, and even serious forms of depression. 5 In addition, children with obesity who were followed-up to adulthood were much more likely to suffer from cardiovascular and digestive diseases. 3 The increase in body fat also exposes the children to increase in the risk of numerous forms of cancers, such as breast, colon, esophageal, kidney, and pancreatic cancers. 6

Due to its public health significance, the increasing trend in childhood obesity needs to be closely monitored. 7 However, these trends have proved to be challenging to quantify and compare. While there are many factors and areas to consider when discussing obesity in children and adolescents, there are a few trends that are evident in recent studies. For example, the prevalence of obesity varies among ethnic groups, age, sex, education levels, and socioeconomic status. A report published by the National Center for Health Statistics using data from the National Health and Nutrition Examination Survey provides the most recent national estimates from 2015 to 2016 on obesity prevalence by sex, age, race, and overall estimates from 1999-2000 through 2015-2016. 1 Prevalence of obesity among non-Hispanic black (22.0%) and Hispanic (25.8%) children and adolescents aged 2 to 19 years was higher than among both non-Hispanic white (14.1%) and non-Hispanic Asian (11.0%) children and adolescents. There were no significant differences in the prevalence of obesity between non-Hispanic white and non-Hispanic Asian children and adolescents or between non-Hispanic black and Hispanic children and adolescents. The pattern among girls was similar to the pattern in all children and adolescents. The prevalence of obesity was 25.1% in non-Hispanic black, 23.6% in Hispanic, 13.5% in non-Hispanic white, and 10.1% in non-Hispanic Asian girls. The pattern among boys was similar to the pattern in all children and adolescents except that Hispanic boys (28.0%) had a higher prevalence of obesity than non-Hispanic black boys (19.0%; Figure 3 ). 1 This review article is aimed at studying the health implications including physical and psychological factors and comorbidities, as well as the epidemiology, risk factors, prevention, and control of childhood and adolescent obesity in the United States.

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Prevalence of obesity among children and adolescents aged 2 to 19 years, by sex and race and Hispanic origin: the United States, 2015-2016.

Methodology

We performed a literature search using online electronic databases (PubMed, MedlinePlus, Mendeley, Google Scholar, Research Gate, Global Health, and Scopus) using the keywords “childhood,” “adolescents,” “obesity,” “BMI,” and “overweight.” Articles were retrieved and selected based on relevance to the research question.

Ethical Approval and Informed Consent

Ethics approval and informed consent were not required for this narrative review.

Definition of Childhood Obesity

Defining obesity requires a suitable measurement of body fat and an appropriate cutoff range. 8 Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared, rounded to 1 decimal place. Obesity in children and adolescents was defined as a BMI of greater than or equal to the age- and sex-specific 95th percentile and overweight with a BMI between the 85th and 95th percentiles of the 2000 Centers for Disease Control and Prevention (CDC) growth charts. 9

However, the use of the BMI percentile according to the age/sex of the CDC growth charts for very high BMIs can result in estimates that differ substantially from those that are observed, 10 , 11 and this constrains the maximum BMI that is attainable at given sex and age. 12 , 13 These limitations have resulted in the classification of severe obesity as a BMI ≥120% of the 95th percentile rather than a percentile greater than the 95th percentile. 11 , 14 A BMI of 120% of the 95th percentile corresponds to a BMI of ~35 among 16 to 18 year olds.

Physiology of Energy Regulation and Obesity

Obesity is a chronic multifactorial disease, characterized by an excessive accumulation of adipose tissue, commonly as a result of excessive food intake and/or low energy expenditure. Obesity can be triggered by genetic, psychological, lifestyle, nutritional, environmental, and hormonal factors. 15

Obesity is found in individuals that are susceptible genetically and involves the biological defense of an elevated body fat mass, the mechanism of which could be explained in part by interactions between brain reward and homeostatic circuits, inflammatory signaling, accumulation of lipid metabolites, or other mechanisms that impair hypothalamic neurons. 16

Normal energy regulation physiology is under tight neurohormonal control. The neurohormonal control is performed in the central nervous system through neuroendocrine connections, in which circulating peripheral hormones, such as leptin and insulin, provide signals to specialized neurons of the hypothalamus reflecting body fat stores and induces appropriate responses to maintain the stability of these stores. The hypothalamic region is where the center of the regulation of hunger and satiety is located. Some of them target the activity of endogenous peptides, such as ghrelin, pancreatic polypeptide, 17 peptide YY, and neuropeptide Y, 18 as well as their receptors.

The physiology of energy regulation may result in obesity in susceptible people when it goes awry from genetic and environmental modulators. There is strong evidence of the majority of obesity cases that are associated with central resistance to both leptin and insulin actions. 19 , 20 The environmental modulators equally play critical roles in obesity. Changes in the circadian clock are associated with temporal alterations in feeding behavior and increased weight gain. 21 Stress interferes with cognitive processes such as executive function and self-regulation. Second, stress can affect behavior by inducing overeating and consumption of foods that are high in calories, fat, or sugar; by decreasing physical activity; and by shortening sleep. Third, stress triggers physiological changes in the hypothalamic-pituitary-adrenal axis, reward processing in the brain, and possibly the gut microbiome. Finally, stress can stimulate the production of biochemical hormones and peptides such as leptin, ghrelin, and neuropeptide Y. 17

The lateral hypothalamus (LH) plays a fundamental role in regulating feeding and reward-related behaviors; however, the contributions of neuronal subpopulations in the LH are yet to be identified thoroughly. 22 The LH has also been associated with other aspects of body weight regulation, such as physical activity and thermogenesis. 23 The LH contains a heterogeneous assembly of neuronal cell populations, in which γ-aminobutyric acid (GABA) neurons predominate. 23 LH GABA neurons are known to mediate multiple behaviors important for body weight regulation, thus altering energy expenditure. 23

Etiology and Risk Factors

Excess body fat is a major health concern in childhood and adolescent populations. The dramatic increase in childhood obesity foreshadows the serious health consequences of their adult life. As obesity begins from childhood and spans through adult life, it becomes increasingly more difficult to treat successfully. Being able to identify the risk factors and potential causes of childhood obesity is one of the best strategies for preventing the epidemic. 24

According to the Morbidity and Mortality Weekly Report released in 2011, there is an acceptance that there is no single cause of childhood obesity and that energy imbalance is just a part of the numerous factors. 25 Many children have a discrepancy between what is taken in and what is expended. 26 For example, children with obesity consume approximately 1000 calories more than what is necessary for their body to function healthily and to be able to participate in regular physical activities. Over 10 years, there will be an excess of 57 pounds of unnecessary weight. With excessive caloric intake, as well as sedentary lifestyles, childhood obesity will continue to rise if no changes are implemented. Adding daily physical activity, better sleep patterns, as well as dietary changes can help decrease the number of excess calories and help with obesity-related problems in the future.

Also, during childhood, excess fat accumulates when the increase in caloric intake exceeds the total energy expenditure. 26 Furthermore, children living in the United States today compared with children living in the 1900s are participating in more than 6 hours per day activities on social media. This includes but is not limited to traditional television, video gaming, and blogging/Facebook activities. An additional economic rationalization for the increase in childhood obesity is technology. In other words, Americans can now eat more in less time.

In a study, Cutler et al found that an increase in consumption of food tends to be related to technology innovation in food production and transportation. Technology has thus made it increasingly possible for firms to mass prepare food and ship to consumers for ready consumption, thereby taking advantage of scale economies in food preparation. The result of this change has been a significant reduction in the time costs for food production. These lower time costs have led to increased food consumption and, ultimately, increased weights. 27 Eliminating the time cost of food preparation disproportionately increases consumption for hyperbolic discounters because time delay is a particularly important mechanism for discouraging those individuals from consuming. 27 Society today prefers immediate satisfaction with regard to food and convenience over the long-term goals of living a long, healthy life. The availability of high-caloric, less-expensive food coupled with the extensive advertisement and easy accessibility of these foods has contributed immensely to the rising trend of obesity. 28 For example, there have been reductions in the price of McDonalds and Coca-Cola (5.44% and 34.89%, respectively) between 1990 and 2007, while there was about a 17% increase in the price of fruits and vegetables between 1997 and 2003. 29

Likewise, only 16% of children walk or bike to school today as compared with 42% in the late 1960s. However, the distance, convenience, weather, scanty sidewalks, and anxiety about crimes against children could all contribute to this difference. Furthermore, with elementary, middle, and high school combined, only 13.8% of these schools provide adequate daily physical education classes for at least 4 hours a week. 30

Some other potential risk factors have been reported through research studies that involve issues that affect the child in utero and childhood. Table 1 represents potential risk factors and confounders of childhood obesity. 31

Potential Risk Factors of Childhood Obesity.

Family characteristicsParent’s BMI during pregnancy
Number of siblings of the child at 18 months
The ethnicity of the child
Age of the mother at delivery
Childhood lifestyleTime spent watching TV
Time in the car per day (weekdays/weekend)
Duration of night sleep
Dietary pattern
Infant feedingBreast feeding/formula feeding
Age of introduction to solid foods
Intrauterine and perinatal factorsBirthweight
Sex
Maternal parity
Maternal smoking during pregnancy (28-32 weeks)
Season of birth (winter, summer, fall, spring)
Number of fetuses
OtherMaternal social class (SES)
Maternal education
Energy intake of the child

Abbreviations: BMI, body mass index; SES, socioeconomic status.

Catalano et al argues that maternal BMI before conception, independent of maternal glucose status or birth weight, is a strong predictor of childhood obesity. 32 Infants at the highest quarter for weight at 8 and 18 months are more likely to become children with obesity at age 7, than children in the lower quarters. Certain behaviors have been linked to childhood obesity and overweight; these are a lack of physical activity and unhealthy eating patterns (eating more food away from home, drinking more sugar-sweetened drinks, and snacking more frequently), resulting in excess energy intake. 22 , 31 In addition, when one parent presents with obesity, there is an increased potential for the child to become obese over the years. Naturally, the risk is higher for the children when both parents present with obesity. Furthermore, a study that followed children over time observed that children who got less sleep <10.5 hours at age 3 were 45% more likely to be children with obesity at the age of 7, than children who got greater than 12 hours of sleep during their first 3 years of life. 33 , 34

While all the above-mentioned factors are informative, there is still the need for further research concerning childhood and adolescent obesity and obesity in general. Risk factors for obesity in childhood are still somewhat uncertain, and evidence-based research for preventative strategies is lacking. Moreover, effective action to prevent the childhood obesity epidemic requires evidence-based on early life risk factors, and this evidence, unfortunately, is still incomplete. Furthermore, a research study has attempted to capture the complete picture of childhood obesity early life course risk factors. In the study, they identified that parental BMI and gestational weight gain among other factors should be considered in prevention programs. 35

Health Effects of Childhood Obesity

Childhood obesity is known to have a significant impact on both physical and psychological health. Sahoo et al stated that “childhood obesity can profoundly affect children’s physical health, social and emotional well-being, as well as self-esteem.” They associated poor academic performance and a lower quality of life experienced by the child with childhood obesity. They also stated that “metabolic, cardiovascular, orthopedic, neurological, hepatic, pulmonary, and menstrual disorders among others are consequences of childhood obesity.” 36 There are many health consequences of childhood obesity, and three of the more common ones are sleep apnea, diabetes, and cardiovascular diseases. 36

Psychological Consequences of Obesity

Several studies related to childhood and adolescent obesity have focused primarily on physiological consequences. Other studies have been conducted regarding the association between psychiatric disorders and obesity; these have resulted in conflict due to obesity being found to be an insignificant factor for psychopathology. However, a comparative study by Britz et al found that high rates of mood, anxiety, somatoform, and eating disorders were detected among children with obesity. The study also observed that most psychiatric disorders began after the onset of obesity. In this large population-based study, it was found that a staggering 60% of females and 35% of males reported that they have engaged in binge eating and expressed a lack of control over their diet. 37

Goldfield et al conducted a study among 1400 adolescents with obesity, overweight, and normal weight in grades 7 to 12. Their BMIs, as determined by the International Obesity Task Force, were the criteria used to define each group. Each participant completed a questionnaire on body images, eating behaviors, and moods. Adolescents with obesity reported significantly higher body dissatisfaction, social isolation, depression symptoms, anhedonia, and negative self-esteem than those of normal weight. 38 There is widespread stigmatization of people with obesity that causes harm rather than the intention to motivate people to lose weight. Stigma contributes to behaviors such as binge eating, social isolation, avoidance of health care services, decreased physical activity, and increased weight gain, which worsens obesity and creates additional barriers to healthy behavior change. 39 Weight-based bullying in youth is considered a common, serious problem in many countries. 40 In a study conducted by O’Brien et al, to test whether the association between weight stigma experiences and disordered eating behaviors, that is, emotional eating, uncontrolled eating, and loss-of-control eating, are mediated by weight bias internalization and psychological distress among 634 undergraduate university students, and results of statistical analyses showed that weight stigma was significantly associated with all measures of disordered eating, and with weight bias internalization and psychological distress. 41

Asthma and Obesity

There is mounting evidence that childhood obesity is a risk factor for the development of asthma. 42 A research study was conducted by Belamarich et al to investigate 1322 children aged 4 to 9 years with asthma. Obesity, as defined by the CDC, is the BMI, with weight and height being greater than the 95th percentile. This was the criteria used to identify the 249 children with obesity, while the BMI between the 5th and 95th percentile identified the children who were not obese. After a baseline assessment was done, the 9-month study found that the children with obesity had a higher number of days of wheezing over 2 weeks (4.0 vs 3.4) and as well had more unscheduled emergency hospital visits (39% vs 31%). 42

Obesity directly correlates with the severity of asthma, as well as poor response to corticosteroids. 43 In fact, children with obesity who also have a history of asthma are more challenging to control and linked to worse quality of life. 44 A prospective trial found that weight loss in patients with obesity and a history of asthma can significantly aid them to control the asthma attacks. 43

Chronic Inflammation and Childhood Obesity

Lumeng and Saltiel reported that obesity in children affects multiple organ systems and predisposes them to diseases. The effect of obesity on the tissue can manifest in the development of insulin-resistant type 2 diabetes, the risk of cancer, and pulmonary diseases. 45

The inflammatory response to obesity triggers pathogens, systematic increases in circulatory inflammatory cytokines, and acute-phase reactants (eg, C-reactive proteins), which inflames the tissues. This is often caused by the activation of tissue leukocytes. Chronic inflammation in children with obesity can induce meta-inflammation that is unique when compared with other inflammatory paradigms (eg, infection, autoimmune diseases). 45 Researchers have reported that children with obesity are at risk of lifelong meta-inflammation. In these children, the inflammatory markers are elevated as early as in the third year of life. 45 , 46 This has been linked to heart disease later in life. 19 The long-term consequences of such findings can cause cumulative vascular damage that correlates with the increased weight status. 47

The short-term and long-term effects of obesity on the health of children is a significant concern because of the negative psychological and health consequences. 46 The potential negative psychological outcomes are depressive symptoms, poor body image, low self-esteem, a risk for eating disorders, and behavior and learning problems. Additional negative health consequences include insulin resistance, type 2 diabetes, asthma, hypertension, high total, and low-density lipoprotein cholesterol and triglyceride levels in the blood, low high-density lipoprotein cholesterol levels in the blood, sleep apnea, early puberty, orthopedic problems, and nonalcoholic steatohepatitis 46 , 47 ( Figure 4 ). Children with obesity are more likely to become adults with obesity, thus increasing their risk for several diseases before they even reach their teen years. 48

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Comorbidities and potential health consequences of childhood obesity. 47

Prevention and Control

There are two primary components to the prevention and control of childhood obesity.

The first is to educate parents on proper nutritional requirements for their children and the second is to implement the learned information. Educating parents on proper nutrition and dietary caloric intake requirements for their children is at the forefront for the prevention of obesity; however, the way the information is disseminated may affect the usefulness of the information. For example, one of the main limitations to the education of parents about childhood obesity is that typically written information is used as the conduit to health information and disease prevention. 49 The Growing Right Onto Wellness (GROW) trial used a systematic assessment of patient education material that was used for the prevention of childhood obesity in the low health literate population. 49 Results suggest that the average readability is of grade 6 level (SMOG [Simple Measure of Gobbledygook] Index 5.63 ± 0.76 and Fry graph 6.0 ± 0.85) and that adjustment of education material must be done for low health literate populations to adequately comprehend educational material and maintain motivation on the prevention of childhood obesity. 49 A similar study was conducted to further support this improvement when using color-coordinated diagrams to help parents visualize instead of trying to comprehend with numbers and words. It proved to be successful as parents were able to see where they were going wrong and make the necessary changes in their children’s diet. 49

Similarly, the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development conducted a study on 744 adolescents and parents, and analyzed data to determine if parental (maternal and paternal, individually) reactions to children’s behavior was related to childhood obesity. 50 The study concluded that informing parents that their attitude toward their children’s behaviors will play a prominent role in preventing childhood obesity. 50 Parental education on nutrition, health, and the involvement of politicians, physicians, and school personnel are key for the prevention of childhood obesity. However, community and educational institutions have begun legislating and incorporating programs such as providing healthy foods at schools and also health information sessions directed toward young individuals, aimed at preventing childhood obesity in the United States and Canada. 51

Another effective prevention measure against childhood obesity is the awareness of parents on the meal and snack portion sizes. In a systematic review conducted on the effects of portion size manipulation with children and portion education/training interventions on dietary intake with parents, it was determined that the ability of adults to accurately estimate portion size improved following education/training. 52 Education of parents and children on diet requirements has its limitations in that the information must be easy to understand and be easily accessible in order to be practical. Making the available education materials easier to understand from just tables and numbers to more relatable aspects such as colors or figures, parents were able to visualize the changes they need to make whether it is with regard to portion sizes or even seeing how much childhood obesity is present in their family. Although much of the literature provided to parents is targeted to help those with lower numeracy skills, many parents benefited from the information being comparative from right/wrong and good/bad with regard to dieting. 49

The study recommended that proper educational materials, including useful and understandable literature, be used to control meal portion sizes and to help parents identify when children are at risk of obesity. Similarly, healthy eating practices should be taught by schools as a mandatory and essential method in the prevention of childhood obesity. 52

The implementation of healthy eating practices and adequate exercise regimes are essential in the prevention and control of childhood obesity. For example, information from systematic reviews, randomized controlled trials, and well-designed observational studies indicate that evidence-based prevention and control of childhood obesity can be accomplished with the collaboration of community/school, primary health care, and home-based/family-based interventions that involve both physical activity and dietary component. 53 In particular, the control of children with obesity is of significant value, as is the prevention of obesity. Two randomized control trials of 182 families were conducted from November 2005 to September 2007, and they studied the efficacy of US pediatric obesity treatment guidelines in children aged 4 to 9 years with a standardized BMI (ZBMI) greater than the 85 percentile. 54 Briefly, Trial 1 studied the impact on ZBMI by reducing snack foods and sugar-sweetened beverages and increasing fruits, vegetables, and low-fat dairy. 54 Trial 2 studied the impact on ZBMI by decreasing sugar-sweetened beverages and increasing physical activity and increasing low-fat milk consumption and reducing television watching. In Trial 1, the resulting ZBMI reduced within 6 months, and this was maintained through to the 12th month (ΔZBMI 0-12 months = −0.12 ± 0.22). 53 In Trial 2, the resulting ZBMI reduced within 6 months and continued to improve till the 12 months (ΔZBMI 0-12 months = −0.16 ± 0.31). 50

A similar cluster-randomized trial in England studied the effects of the reduction of carbonated beverages on the number of children with obesity in 29 classes (644 children). 51 Results indicate that a decrease of 0.6 glasses of carbonated drinks (250 mL) over three days per week decreased the number of children with obesity by 0.2%, while the control group increased by 7.5% (mean difference = 7.7%, 2.2% to 13.1%) at 12 months. However, diet control is only one component of the control and prevention of childhood obesity, while adequate exercise is another. 55

A systematic review and meta-analyses of the impact of diet and exercise programs (single or combined) was done on their effects on metabolic risk reduction in the pediatric population. 56 Analyses indicated that the addition of exercise to dietary intervention led to greater improvements in the levels of high-density lipoprotein cholesterol (3.86 mg/dL; 95% confidence interval [CI] = 2.70 to 4.63), fasting glucose (−2.16 mg/dL; 95% CI = −3.78 to −0.72), and fasting insulin (−2.75 µIU/mL; 95% CI = −4.50 to −1.00) over 6 months. 56 Diet and exercise are both important factors in the control and prevention of childhood obesity. It is our recommendation that parents and community (teachers and doctors) should be involved in identifying children at risk based on their BMI and participate in implementing practices such as good diet control through the reduction of sugary drinks, fatty foods, and also encouraging safe exercise programs to prevent and control childhood obesity in the society. 56

While all of the previous data express the more obvious prevention methods with regard to childhood obesity, it is imperative to note that ensuring that the whole family is involved in the intervention will yield the greatest results. 2 All current studies indicate that families must be included in childhood treatment of obesity. However, for the success of the child’s weight loss program, it is vital that the parents understand that the causes of obesity are often a mixture of four factors: genetic causes, parental habits, overeating, and poor exercise habits. Thus, instilling some responsibility on the parents and informing them that controlled food preparation, diet control, and family participation in physical activities will all assist in the treatment and control of obesity in their children. 2

Childhood obesity has increased significantly in recent decades and has quickly become a public health crisis in the United States and all over the world. Its increase in prevalence has provoked widespread research efforts to identify the factors that contributed to these changes. 57 Obesity starts with an imbalance between caloric intake and caloric expenditure. 58 Children with obesity are at greater risk of adult obesity; therefore, if we can educate and improve the health habits of families even before they start having children, this can help reduce the increasing rate of childhood obesity in the United States. Parents and caregivers with proper education on the causes and consequences of childhood obesity can help prevent childhood obesity by providing healthy meals and snacks, daily physical activity, and nutrition education to their family members. 59 Families need to take the approach of not adapting to their family being on a diet but more of a healthy lifestyle. A family’s home environment can influence children at a young age; therefore, making changes starting in the household early can educate and influence them to grow up healthy. Although prevention programs may be more expensive in the short term, the long-term benefits acquired through prevention are much more likely to save an even greater amount of health care costs. Not only will the children have a better childhood and self-esteem, but prevention programs can also decrease the incidence of cardiovascular diseases, diabetes, stroke, and possibly cancers in adulthood. 60 The overall need to decrease the obesity rate will help children and their families in the generations to come by building a healthy lifestyle and environment. In order to tackle the climbing obesity rate, overall health and lifestyle needs to be a priority as they balance one with the other. 49 While effective interventions to thwart childhood obesity still remain elusive, the sustainability of the interventions already in place will enable children and their families to adopt these important health behaviors as lifelong practices and improve their health. 58

Treatment of Obesity and the Physiology of Energy Regulation

As discussed previously, a variety of mechanisms participate in weight regulation and the development of obesity in children, including genetics, developmental influences (“metabolic programming” or epigenetics), individual and family health behaviors, and environmental factors. Among these potential mechanisms, only environmental factors are potentially modifiable during childhood and adolescence.

Unfortunately, despite intensive lifestyle modifications and support for healthy practices within the children’s environment, some children will continue to struggle with extreme excess weight and associated comorbidities. 61 , 62 Therefore, a combination of pharmacotherapy and lifestyle modification can be considered. 61 Overweight children should not be treated with medications unless significant, severe comorbidities persist despite lifestyle modification. The use of pharmacotherapy should also be considered in overweight children with a strong family history of type 2 diabetes or cardiovascular risk factors. Constant bidirectional communication between the brain and the gastrointestinal tract, as well as the brain and other relevant tissues (ie, adipose tissue, pancreas, and liver), ensures that the brain constantly perceives and responds accordingly to the energy status/needs of the body. This elegant biological system is subject to disruption by a toxic obesogenic environment, leading to syndromes such as leptin and insulin resistance, and ultimately further exposing individuals who are obese to further weight gain and type 2 diabetes mellitus. Currently, the only Food and Drug Administration–approved prescription drug indicated for the treatment of pediatric obesity is orlistat (Xenical; Genentech USA, Inc, South San Francisco, CA). 63 Orlistat works by inhibiting gastric and pancreatic lipases, the enzymes that break down triglycerides in the intestine. Moreover, imaging studies in humans are beginning to examine the influence that higher- order/hedonic brain regions have on homeostatic areas, as well as their responsiveness to homeostatic peripheral signals. With a greater understanding of these mechanisms, the field moves closer to understanding and eventually treating the casualties of obesity.

The number of children with obesity in the United States has increased substantially over the years; due to its public health significance, the increasing trends need to be closely monitored. While a complete picture of all the risk factors associated with obesity remains elusive, many of the studies agreed that prevention is the key strategy for controlling the current problem. Since the combination of diet, exercise, and physiological and psychological factors are all important factors in the control and prevention of childhood obesity, primary prevention methods should be aimed at educating the child and family and encouraging appropriate diet and exercise from a young age through adulthood while secondary prevention should be targeted at lessening the effect of childhood obesity by preventing the child from continuing unhealthy habits and obesity into adulthood. A combination of primary and secondary prevention is necessary to achieve the best results. Thus, a combined implementation of both types of preventions can significantly help lower the current prevalence of childhood and adolescent obesity in the United States. Failure to take appropriate actions could lead to serious public health consequences.

Author Contributions: AS: Contributed to conception and design; drafted manuscript; gave final approval; agrees to be accountable for all aspects of work ensuring integrity and accuracy.

XQ: Contributed to the acquisition, analysis, and interpretation.

JL: Contributed to the acquisition, analysis, and interpretation.

SR: Contributed to the acquisition, analysis, and interpretation.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

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    1. Introduction. Obesity is a complex, multifactorial, and largely preventable disease (), affecting, along with overweight, over a third of the world's population today (2,3).If secular trends continue, by 2030 an estimated 38% of the world's adult population will be overweight and another 20% will be obese ().In the USA, the most dire projections based on earlier secular trends point to ...

  16. Definition and introduction to epidemiology of obesity

    Overview and definition of obesity. Obesity is a global, complex, multifactorial, and generally preventable disease [1]. The global prevalence of obesity has doubled in the past 40 years regardless of sex, age, ethnicity, or socioeconomic status. Today, more than one-third of the world's population is classified as obese or overweight [2].

  17. (PDF) The Obesity Epidemic: Understanding the Causes ...

    This paper provid es a comprehensive overview of the obesity epidemic, examining its causes, consequences, and potential solutions. It explores the complex interplay of genetic, environmental, and ...

  18. PDF Obesity as a Disease: The Obesity Society 2018 Position Statement

    The Obesity Society (TOS) first published a position statement on obe-sity as a disease in 2008 (1). This statement reflected the thoughtful deliberations and consensus of Society members that was published in the same year (2). In 2016, an updated in-house position paper affirmed the 2008 declaration, stating, "TOS recommits to its position ...

  19. Obesity: An overview on its current perspectives and treatment options

    Obesity is a multi-factorial disorder, which is often associated with many other significant diseases such as diabetes, hypertension and other cardiovascular diseases, osteoarthritis and certain cancers. The management of obesity will therefore require a comprehensive range of strategies focussing on those with existing weight problems and also on those at high risk of developing obesity ...

  20. PDF Obesity: An Introduction and Evaluation

    States obesity is estimated to cause an excess 111,909 to 365,000 death per year, while 1 million (7.7%) of deaths in the European Union are attributed to excess weight . [9,10] Classification: Obesity is a medical condition in which excess body fat has accumulated to the extent that it may have an adverse effect on health. [11]

  21. Epidemiology of Obesity in Adults: Latest Trends

    Introduction. Obesity is linked with elevated risk of non-communicable diseases (NCDs) [].An increasing trend in obesity prevalence since the early 1980s has posed a significant population health burden across the globe [] while obesity prevalence varies by region and country [1, 3].Country-specific trends in obesity are generally tracked using longitudinal panel or repeated cross-sectional ...

  22. Obesity Research Paper

    View sample obesity research paper. ... Introduction. There has been a dramatic rise in the prevalence of obesity globally in the last three decades, and the World Health Organization (WHO) estimates around 11 % of the world's total population is obese (WHO 2012). Obesity is seen as a major public health concern because it is widely ...

  23. Glucagon-Like Peptide 1 Receptor Agonists and 13 Obesity-Associated

    T2D indicates type 2 diabetes. a The combined total of patients is not necessarily a sum of the individuals from each of the groups because individuals could be prescribed both glucagon-like peptide 1 receptor agonists (GLP-1RAs) and insulins or metformin during the study period.. b The combined total of patients is not necessarily a sum of the individuals from each of the groups because ...

  24. Adult obesity complications: challenges and clinical impact

    Introduction. Adult obesity [body mass index (BMI) >30 kg/m 2] was estimated to affect 10.8% of men (266 million) and 14.9% of women (375 million) worldwide in 2014. This has more than doubled when compared with worldwide figures in 1975 where 3.2% of men and 6.4% of women were obese. If this trend persists, by 2025, 18% of men and 21% of women ...

  25. Proportion and number of cancer cases and deaths attributable to

    INTRODUCTION. We previously estimated that about 660,000 (42% of all) incident cancer cases and 265,000 (45% of all) cancer deaths in the United States in 2014 were attributable to potentially modifiable risk factors. 1 However, information on risk factors associated with specific cancer types and the magnitude of associations may evolve over time and since the publication of our previous ...

  26. Childhood and Adolescent Obesity in the United States: A Public Health

    Introduction. Childhood and adolescent obesity have reached epidemic levels in the United States, affecting the lives of millions of people. In the past 3 decades, the prevalence of childhood obesity has more than doubled in children and tripled in adolescents. 1 The latest data from the National Health and Nutrition Examination Survey show that the prevalence of obesity among US children and ...