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This article goes back to previous discussions regarding gut microbiome and intestinal permeability in regard to inflammation.  Not only does the composition of the gut microbiome affect conditions from the previous module such as Crohn’s disease, it appears to play a role in inflammation present in persons with obesity.  This study looked at the gut bacteria in obese and non-obese persons and found a distinct difference in the amount of biodiversity with the obese group being much less diverse than the non-obese group.  As I discussed in a previous paper, diet plays a major role in the composition of an individual’s gut microbiome.  This study found that the subjects with higher BMI had a reduced percentage of anti-inflammatory bacteria and a higher percentage of pro-inflammatory bacteria compared to the other group.  This suggests that not only does obesity contribute to chronic inflammation from adipose tissue but also from the gut microbiome.

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Affiliation

¹ Department of General Surgery, NUTRIM, Maastricht University Medical Center, Maastricht, The Netherlands; Department of General Surgery, Atrium Medical Center Parkstad, Heerlen, The Netherlands.

Free article

Free article

Affiliation

¹ Department of General Surgery, NUTRIM, Maastricht University Medical Center, Maastricht, The Netherlands; Department of General Surgery, Atrium Medical Center Parkstad, Heerlen, The Netherlands.

Abstract

Objective: Intestinal microbiota have been suggested to contribute to the development of obesity, but the mechanism remains elusive. The relationship between microbiota composition, intestinal permeability, and inflammation in nonobese and obese subjects was investigated.

Design and methods: Fecal microbiota composition of 28 subjects (BMI 18.6-60.3 kg m(-2) ) was analyzed by a phylogenetic profiling microarray. Fecal calprotectin and plasma C-reactive protein levels were determined to evaluate intestinal and systemic inflammation. Furthermore, HbA1c , and plasma levels of transaminases and lipids were analyzed. Gastroduodenal, small intestinal, and colonic permeability were assessed by a multisaccharide test.

Results: Based on microbiota composition, the study population segregated into two clusters with predominantly obese (15/19) or exclusively nonobese (9/9) subjects. Whereas intestinal permeability did not differ between clusters, the obese cluster showed reduced bacterial diversity, a decreased Bacteroidetes/Firmicutes ratio, and an increased abundance of potential proinflammatory Proteobacteria. Interestingly, fecal calprotectin was only detectable in subjects within the obese microbiota cluster (n = 8/19, P = 0.02). Plasma C-reactive protein was also increased in these subjects (P = 0.0005), and correlated with the Bacteroidetes/Firmicutes ratio (rs = -0.41, P = 0.03).

Conclusions: Intestinal microbiota alterations in obese subjects are associated with local and systemic inflammation, suggesting that the obesity-related microbiota composition has a proinflammatory effect.

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leukocyte l1 antigen complex / metabolism

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national center for biotechnology information

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Human intestinal microbiota composition is associated with local and systemic inflammation in obesity - PubMed

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1 Department of General Surgery, NUTRIM, Maastricht University Medical Center, Maastricht, The Netherlands; Department of General Surgery, Atrium Medical Center Parkstad, Heerlen, The Netherlands. Free article Free article 1 Department of General Surgery, NUTRIM, Maastricht University Medical Center, Maastricht, The Netherlands; Department of General Surgery, Atrium Medical Center Parkstad, Heerlen, The Netherlands.

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Ultra-Processed Food and The Risk Of Overweight And Obesity: A Systematic Review And Meta-Analysis Of Observational Studies

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Ultra-Processed Food and The Risk Of Overweight And Obesity: A Systematic Review And Meta-Analysis Of Observational Studies

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This systematic review and meta-analysis was conducted to evaluate the relationship of consuming ultra-processed foods with obesity and overweight adults. One key finding of the meta analysis illustrated that there was a 26 percent increased odds of obesity with the consumption of ultra processed foods. Meta analysis are conducted to establish statistical significance. The authors were able to statistically support the connection between obesity and ultra processed foods. Ultra-processed foods are formulated to be easy to consume due to their manufacturing. They are quick meals and often instant and low in nutritional value. Ultra-processed foods have become widely accessible as the global food market continues to change.  This meta-analysis can support nutritional guidelines created for those with obesity because of the statistically significance identified with obesity and its prevalence with quality of the food consumed. Tracking food consumption and making conscious change to the quality of food consumed can serve as preventative treatment for those who are at a higher risk of obesity.

Askari, M., Heshmati, J., Shahinfar, H., Tripathi, N., & Daneshzad, E. (2020). Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies. International journal of obesity, 44(10), 2080-2091.

-Clarisa Williams

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Trends in Cigarette Smoking and Obesity in Appalachian Kentucky   -  Dierra Doaks

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Trends in Cigarette Smoking and Obesity in Appalachian Kentucky   -  Dierra Doaks

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The article I chose to write about this week is about the rates of obesity and cigarette smoking in Appalachian Kentucky. The purpose of the article was to determine the factors that are associated with smoking and obesity in health disparity regions like Appalachian Kentucky.  The method used for the study was examining data from phone surveys that ranged from 2001 to 2010.  The authors of this article explain that obesity rates in Appalachian adults are much higher than in non-Appalachian Kentucky or in the United States overall. All grades of obesity are associated with elevated rates or mortality which could help explain the fact that life expectancy in the Appalachian population is much lower than the general U.S population (about 4 and 3 years less for men and women). In 2015,  Kentucky was classified as one of the twelve states in the United States with an obesity prevalence rate greater than 30.9%. More than one-third of the Kentucky’s Appalachian population was considered obese in 2015. This article has a lot of interesting information needed to address the problems in these areas. I would be interested to see how these numbers have changed since this article was published.

Schoenberg, Nancy E et al. “Trends in Cigarette Smoking and Obesity in Appalachian Kentucky.” Southern medical journal (Birmingham, Ala.) 108.3 (2015): 170–177. Web.

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Obesity Stigma: Important Considerations for Public Health

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Obesity Stigma: Important Considerations for Public Health

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This study is important because as a society it's important to leave the stigma of obesity behind. The study addresses stigma and a way to motivate individuals who may be at an unhealthy weight or have bad habits in a proper way. A key finding I found very useful is how shame and prejudice influenced by stigma is not a healthy way to encourage weight loss. Instead, it does the complete opposite of being a type of social control to encourage an even worse behavior. In order for public health to improve, the stigma with weight loss must disappear. Stigmatizing obesity encourages a decline in mental and emotional health, which also eventually effects progressive weight loss. Physicians also knowing the research and trends regarding weight stigma and how often those individuals don't get regular cancer checks and or other healthcare screenings, will for sure help patient physician relationships. This article also has so much more great information worth seeking for further understanding.

Puhl, R. M., & Heuer, C. A. (2010). Obesity stigma: important considerations for public health. American journal of public health, 100(6), 1019–1028. https://doi.org/10.2105/AJPH.2009.159491

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Negative attitudes toward obese persons are pervasive in North American society. Numerous studies have documented harmful weight-based stereotypes that overweight and obese individuals are lazy, weak-willed, unsuccessful, unintelligent, lack self-discipline, have poor willpower, and are noncompliant with weight-loss treatment. These stereotypes give way to stigma, prejudice, and discrimination against obese persons in multiple domains of living, including the workplace, health care facilities, educational institutions, the mass media, and even in close interpersonal relationships. Perhaps because weight stigma remains a socially acceptable form of bias, negative attitudes and stereotypes toward obese persons have been frequently reported by employers, coworkers, teachers, physicians, nurses, medical students, dietitians, psychologists, peers, friends, family members, and even among children aged as young as 3 years.

Recent estimates suggest that the prevalence of weight discrimination has increased by 66% over the past decade, and is now comparable to prevalence rates of racial discrimination in America. Despite several decades of literature documenting weight stigma as a compelling social problem, this form of stigma is rarely challenged in North American society and its public health implications have been primarily ignored. Instead, prevailing societal attributions place blame on obese individuals for their excess weight, with common perceptions that weight stigmatization is justifiable (and perhaps necessary) because obese individuals are personally responsible for their weight, and that stigma might even serve as a useful tool to motivate obese persons to adopt healthier lifestyle behaviors.

We have examined existing evidence to address these assumptions about weight stigma and discuss their public health implications. Documentation of the stigma of obesity has been extensively reviewed elsewhere, thus, our aim was to highlight relevant evidence from this body of work to examine public health implications of weight stigma, an issue that has received little attention in the obesity field.

We obtained articles cited in this paper through comprehensive literature searches in computerized medical and social science databases, including PubMed, PsycINFO, and SCOPUS. Search terms were limited to various keyword combinations pertaining specifically to body weight and stigma descriptors to identify studies examining the relationship between weight stigma and public health, and emotional and physical health consequences of obesity stigma. (For examples of descriptor search terms, please refer to Puhl and Heuer.) We also conducted manual searches for specific authors and journals that have published relevant research on these topics. In addition, we retrieved references from a recent comprehensive systematic review of peer-reviewed research studies documenting bias and stigma toward obese individuals that we recently published. The vast majority (87%) of studies we included were published in the past decade, with the exception of several studies published earlier that are cited when relevant to the historical context of the discussion or to provide 7 examples of landmark studies.

On the basis of the current evidence, we conclude that weight stigma is not a beneficial public health tool for reducing obesity or improving health. Rather, stigmatization of obese individuals poses serious risks to their psychological and physical health, generates health disparities, and interferes with implementation of effective obesity prevention efforts. This evidence highlights the importance of addressing weight stigma as both a social justice issue and a priority in public health interventions to address obesity.

DISEASE STIGMA AND PUBLIC HEALTH

In the field of public health, stigma is a known enemy. Throughout history, stigma has imposed suffering on groups vulnerable to disease and impaired efforts to thwart the progression of those diseases. Disease stigma occurs when groups are blamed for their illnesses because they are viewed as immoral, unclean, or lazy. For example, in 19th century America, Irish immigrants were commonly believed to be responsible for epidemic diseases because they were "filthy and unmindful of public hygiene." As large numbers of Irish-born immigrants died of cholera and other diseases, many viewed their deaths as acts of retribution upon the "sinful and spiritually unworthy." When African Americans were dying from tuberculosis at the beginning of the 20th century, rather than investing in prevention or treatment of tuberculosis, many cities' authorities issued warnings to its White citizens against commingling with or hiring African Americans. Even the stigmatization of injection drug users and individuals with gonorrhea has been denounced as a barrier to testing and treatment. These examples have resulted in a broad understanding of the implications of stigma for public health. According to Herek et al.,

Historical examples abound of stigma interfering with collective responses to diseases ranging from cholera to syphilis. In all of these cases, the social construction of illness incorporated moral judgments about the circumstances in which it was contracted as well as preexisting hostility toward the groups perceived to be most affected by it.

In the case of HIV/AIDS, the detrimental role of stigma has become so clear that national and international health agendas explicitly identify stigma and discrimination as major barriers to effectively addressing the epidemic. As early as the mid-1980s, just a few years after the disease was initially identified, discrimination against those at risk for HIV/AIDS was identified as counterproductive, and early public health policies included protections for patients' privacy and confidentiality. As it became more evident that stigma and discrimination were among the root causes of vulnerability to HIV/AIDS, the United Nations General Assembly Special Session on HIV/AIDS adopted the Declaration of Commitment in 2001, which pledged signatory states to "develop strategies to combat stigma and social exclusion connected with the epidemic." Subsequently, stigma and discrimination were chosen as the theme for the 2002-2003 World AIDS Campaign. In 2007, the Joint United Nations Programme on HIV/AIDS issued the report, Reducing HIV Stigma and Discrimination: A Critical Part of National AIDS Programmes, which provides strategies for centralizing the reduction of stigma and discrimination within national responses to the disease. Recommendations from the report include providing funding and programming activities for multifaceted national approaches to the reduction of HIV stigma and discrimination.

Thus, within current public health ideology there is clear recognition of the critical obstacles created by disease stigma. Bayer notes:

In the closing decades of the 20th century, a broadly shared view took hold that the stigmatization of those who were already vulnerable provided the context within which diseases spread, exacerbating morbidity and mortality... . In this view, it was the responsibility of public health officials to counteract stigma if they were to fulfill their mission to protect the communal health.

In sharp contrast, the stigma of obesity has not been addressed as a legitimate concern that requires the attention of those working to combat obesity, and is rarely discussed in the context of public health. In fact, weight stigma has been suggested by some as a method for obesity control. The lack of attention to weight stigma has persisted despite nearly 5 decades of scientific research documenting weight stigma and its consequences for obese individuals. Its absence was noted in the 1960s when stigma research was first emerging. In 1968, Cahnman published the article entitled "The Stigma of Obesity," in which he wrote:

Obesity is hardly ever mentioned in the writings of sociologists, and not at all in the literature on social deviance. This omission is amazing... . Clearly, in our kind of society, with its stress on affluence and upward mobility, being overweight is considered to be detrimental to health, a blemish to appearance, and a social disgrace.

This omission remains almost 50 years later. Even as obesity rates have risen dramatically, weight stigma is rarely, if ever, afforded the same recognition or intervention as other disease stigmas.

Although there is significant consensus that stigma undermines public health, this principle has not been applied to the obesity epidemic. Common societal assumptions about obesity, including the notion that obese individuals are to blame for their weight, contribute to the disregard of weight stigma and its impact on emotional and physical health. An examination of these assumptions in light of current scientific evidence reveals that obesity stigma creates significant barriers in efforts to address obesity and deserves recognition in the public health agenda.

COUNTERING ASSUMPTIONS THAT PERPETUATE WEIGHT STIGMA

Social constructions of body weight are ingrained in the way that our society perceives and reacts to obesity. According to Herek, the social meaning of disease involves assigning responsibility and blame, so that those afflicted with disease become regarded as either victims or perpetrators. Society regularly regards obese persons not as innocent victims, but as architects of their own ill health, personally responsible for their weight problems because of laziness and overeating. These common assumptions provide the foundation for weight stigma, a prejudice that is often dismissed as acceptable and necessary. Not only is weight stigma viewed as a beneficial incentive for weight loss, but it is also assumed that the condition of obesity is under personal control, implying that the social influence of weight stigma will be sufficient to produce change.

Although these assumptions about obesity and weight stigma are prevalent in our national mindset, considerable scientific evidence has emerged to challenge them. To optimize obesity prevention and intervention efforts, these assumptions must be addressed within the sphere of public health, with recognition of the harmful impact of weight stigma on quality of life and the need to eliminate stigma from current and future public health approaches to the obesity epidemic. We present scientific evidence relevant to these societal assumptions.

Weighty Misperceptions

Societal attributions about the causes of obesity contribute significantly to expressions of weight stigma. Experimental research in psychology consistently demonstrates that obese persons are stigmatized because their weight is perceived to be caused by factors within personal control (e.g., overeating and lack of exercise). More than 2 decades ago Weiner et al. first assessed the relationship between perceptions of personal responsibility and stigmatizing conditions, and found that conditions rated low on personal responsibility (such as Alzheimer's disease) were rated high on liking and elicited pity and intentions to help from others. However, individuals with stigmatizing conditions rated high on personal responsibility (e.g., obesity and drug addiction) were disliked, evoked little pity and high anger, and received low ratings of helping tendencies.

Research findings since that time have followed suit. In a study examining attitudes toward 66 different diseases and health conditions (including obesity), the attributed degree of personal responsibility for the disease predicted social distance and rejection by participants. Experimental research additionally shows that providing individuals with information emphasizing personal responsibility for obesity increases negative stereotypes toward obese persons, whereas information highlighting the complex etiology of obesity (such as biological and genetic contributors) improves attitudes and reduces stereotypes.

The view that obesity is a matter of personal responsibility is the prevailing message in the media. News coverage of the personal causes and solutions to obesity significantly outnumber other societal attributions of responsibility. Entertainment media also communicate anti-fat messages and reinforce perceptions that body weight is within personal control. The current societal message is that both the cause and the solution for obesity reside within the individual. Thus, the pervasiveness of the "personal responsibility" message plays a key role in stigmatization, and serves to justify stigma as an acceptable societal response.

However, this prevailing message does not accurately reflect the science. Many significant contributors to obesity are beyond the control of individuals. In addition to the important role of genetic and biological factors regulating body weight, multiple social and economic influences have significantly altered the environment to promote and reinforce obesity. As Seng Lee notes,

We have created a biology-environment mismatch, as the human weight regulation is unable to evolve fast enough to keep pace with the environmental change.

Advancements in workplace technology and reduction of manual labor have resulted in decreased energy expenditure. The built environment has decreased opportunities for healthy lifestyle behaviors through factors such as urban design, land use, public transportation availability,density and location of food stores and restaurants, and neighborhood barriers such as safety and walkability.

Significant changes have taken place in the food environment with increased accessibility of inexpensive foods. Prices of calorie-dense foods and beverages have decreased considerably in contrast to increasing prices of fresh fruits, vegetables, fish, and dairy items, contributing to increased consumption of unhealthy foods, especially as the portion sizes of these items have grown considerably larger. Significant marketing and advertising of unhealthy, energy-dense foods by the food industry contribute to excessive food consumption in important ways, especially for children, who are heavily targeted.

These complex societal and environmental conditions that have created obesity necessitate that we move beyond the narrow focus that targets the individual as both the culprit and the solution for obesity. Public health efforts must address the multiple forces contributing to the development and maintenance of obesity and recognize that individual behaviors are powerfully shaped by the obesogenic environment. As Cohen concludes, "a more accurate conceptualization of the obesity epidemic is that people are responding to the forces in their environment, rather than lacking in willpower and self-control." There is increasing consensus that environmental change is essential to the solution of obesity.

There is also considerable scientific consensus about the challenge of significant long-term weight loss. A systematic review of 80 randomized clinical trials of weight-loss interventions with at least 1 year of follow-up (including interventions of diet, diet and exercise, exercise, meal replacements, very-low-calorie diets, and weight-loss medications) found the mean weight loss across studies to be 5% to 9% at 6 months, with a subsequent plateau across most interventions. These findings parallel a recent meta-analysis of 46 randomized controlled trials that revealed a maximum net treatment effect of approximately 6% of body weight lost at 1-year follow-up. Many other recent scientific reviews of multiple weight-loss trials and programs produced, on average, no more than 10% weight loss at 1- or 2-year follow-up.

As a result of these and other consistent findings demonstrating modest results of most weight-loss interventions, there is recognition in the scientific community that existing dietary programs and medications can produce no more than an average of 10% weight loss. This evidence has prompted agreement among a number of expert panels and scientific groups (including the Institute of Medicine, World Health Organization, Preventive Task Force, Canadian Task Force of Preventive Health Care, and National Heart, Lung, and Blood Institute) that health care providers should counsel patients to set a goal of 10% reduction in total body weight rather than struggle to attain ideal body weight. For obese individuals who want to lose substantial body weight to improve their health (as opposed to individuals who want to obtain modest weight loss for aesthetic reasons), a 10% weight loss means that many obese persons will remain obese and continue to be vulnerable to weight stigma.

The high rate of weight regain following weight loss is equally concerning. Most weight losses are not maintained and individuals regain weight after completing treatment. Patients who have lost weight through lifestyle modification typically regain 30% to 35% of their lost weight during the year following treatment, and regain most (if not all) of their lost weight within 5 years. The consistent findings in this area indicate that preventing weight regain is extremely challenging. As a result, experts in the obesity field have concluded that weight regain occurs in practically all dietary and behavioral interventions, and other researchers have asserted that

Dieters who manage to sustain a weight loss are the rare exception, rather than the rule. Dieters who gain back more weight than they lost may very well be the norm, rather than an unlucky minority.

Despite weight regain, individuals can experience important improvements in health with modest weight loss of approximately 10%, including reductions in obesity-related health complications such as type 2 diabetes and hypertension and improvements in cardiovascular risk. However, even if modest weight loss improves some health indices, it is unlikely to significantly alter appearance or translate to a nonobese body mass index (BMI; weight in kilograms divided by height in meters squared) for most people, and is doubtful to be sufficient to reduce weight stigma and discrimination.

It is also important to note increasing research documenting a considerable percentage of overweight and obese persons who are metabolically healthy and nonoverweight individuals who exhibit metabolic and cardiovascular risk factors. Although excess weight can incur significant implications for disease risk, variation in health indices observed in people with similar body mass indicates caution in generalizations made about body size and health behaviors, which can further contribute to stigma and misleading stereotypes. For those individuals without metabolic risk factors, losing weight may not be important for improving health. The recognition that there are obese individuals who are metabolically healthy and nonoverweight individuals who are metabolically obese challenges weight-based stereotypes and reinforces the heterogeneous nature of obesity.

Because weight-based stereotypes and prejudice so often emerge from attributions that obesity is caused and maintained by personal characteristics such as laziness or lack of willpower, there is a clear need for increased public awareness and education about the complex etiology of obesity and the significant obstacles present in efforts to achieve sustainable weight loss. The prevailing societal and media messages that reinforce blame on obese persons need to be replaced with messages that obesity is a chronic disease with a complex etiology, and a lifelong condition for most obese persons.

An Unlikely Motivator for Weight Loss

In his recent writings about the counterproductive nature of stigma on health, Burris asks "Where is the evidence that inculcating a sense of spoiled identity is a good way to get people to adopt healthier behaviors?" Indeed, despite the shame and prejudice induced by weight stigma, there is a perception that stigmatizing obese individuals may instill motivation to engage in healthier eating and exercise behaviors. The idea that stigma may be a useful tool of social control to discourage unhealthy behaviors and improve the health of stigmatized individuals has been debated, with some theorizing that individuals will act to change their behaviors to avoid being out of step with social norms and the resulting stigmatization.

Second, a number of studies have consistently demonstrated that experiencing weight stigma increases the likelihood of engaging in unhealthy eating behaviors and lower levels of physical activity, both of which exacerbate obesity and weight gain. Among youths, several studies have demonstrated that overweight children who experience weight-based teasing are more likely to engage in binge-eating and unhealthy weight control behaviors compared with overweight peers who are not teased, even after control for variables such as BMI and socioeconomic status. Prospective studies demonstrate that weight-based teasing in youths predicts binge eating and extreme weight-control practices 5 years later, after control for variables including age, race, and socioeconomic status. Other research has consistently documented a positive association between weight-based victimization and eating disorder symptoms and bulimia. Weight-based victimization among overweight youths has been linked to lower levels of physical activity, negative attitudes about sports, and lower participation in physical activity among overweight students.

Among overweight and obese adults, similar findings have emerged. In both clinical and nonclinical samples, adults who experience weight-based stigmatization engage in more frequent binge eating, are at increased risk for maladaptive eating patterns and eating disorder symptoms, and are more likely to have a diagnosis of binge eating disorder. Some research has found that psychological distress may mediate the association between stigma and binge eating, where experiences of stigma increase vulnerability to poor psychological functioning, which in turn increases risk of binge eating behaviors.

Rather than using stigma as an incentive to lose weight, it may be that supporting individuals with adaptive ways to cope with weight stigma can facilitate weight loss outcomes. A recent randomized treatment study found that, compared with wait-list controls, a brief 1-day intervention that taught patients acceptance-based strategies to cope with obesity-related stigma resulted in greater improvements in body mass, quality of life, perceived weight-related stigma, and psychological distress at 3-month follow-up. Thus, a model that can reduce the distress associated with stigma while simultaneously empowering weight control efforts provides a new treatment approach that seems worthwhile to pursue.

Although more work is needed to examine the impact of weight stigma on weight loss outcomes, the available evidence challenges the assumption that weight stigma is a useful tool for changing health behaviors. Instead, research shows that weight stigmatization reinforces unhealthy lifestyle behaviors that contribute to obesity, and is an unlikely method of inducing successful weight loss.

A Threat to Psychological and Physical Health

In addition to reinforcing unhealthy behaviors, weight stigma poses a significant threat to psychological and physical health. An accumulation of evidence demonstrates that weight stigma invokes psychological stress and emerging research suggests that this stress leads to poor physical health outcomes for obese individuals. Among both clinical and nonclinical samples of obese adults, weight stigmatization has been documented as a significant risk factor for depression, low self-esteem, and body dissatisfaction. These findings persist despite control for variables including age, gender, obesity onset, and BMI, indicating that, rather that being associated with excess body weight in itself, negative psychological outcomes are linked with experiences of weight-based stigmatization. In addition, a recent study examining a nationally representative sample of more than 9000 obese adults found that perceived weight discrimination was significantly associated with a current diagnosis of mood and anxiety disorders and mental health services use after control for sociodemographic characteristics and perceived stress.

Meunnig argued that the high degree of psychological stress experienced by obese persons as a result of weight stigma contributes to the pathophysiology associated with obesity, and that many of the adverse biochemical changes that are associated with adiposity can also be caused by the psychological stress that accompanies the experience of frequent weight-based discrimination. Social disadvantages may specifically affect obesity through chronic stress, anxiety, and negative mood, which are associated with abdominal obesity, and may increase risk for obesity by activating particular physiological mechanisms that can increase appetite and blunt the satiety system, increasing fat retention and food intake.

The belief that stigma-induced stress both exacerbates and triggers disease is frequently discussed in literature on the health effects of racial prejudice and discrimination. Research has demonstrated that African Americans who perceive racial discrimination or mistreatment have an increased risk of coronary events, breast cancer, coronary artery calcification, vascular reactivity, and elevated blood pressure, and higher substance use. These outcomes persist even when possible confounding variables-including sociodemographics, typical cardiovascular risk factors, and BMI-are controlled. Similar findings have emerged among Asian Americans and Chinese Americans, demonstrating that racial discrimination is associated with numerous chronic conditions such as heart disease, pain, and respiratory illness, even after related sociodemographic factors are controlled. These findings have led researchers to conclude that racism may act as a psychosocial stressor that elevates cardiovascular responses and in turn impairs health.

Some evidence points directly to links between perceived racial discrimination and obesity-related outcomes, including weight gain and metabolic abnormalities. Hunte and Williams found that perceived chronic discrimination was related to excess body fat accumulation. Irish, Jewish, Polish, and Italian Whites who perceived chronic discrimination were 2 to 6 times more likely to have a high-risk waist circumference than White individuals of the same ethnic descent who did not perceive chronic discrimination. Other research has found that individuals who internalize stigma may be especially vulnerable to abdominal obesity and glucose intolerance.

There are distinctions between prejudices based on race and weight (see Puhl and Latner for discussion), but these findings have important implications for the impact of weight stigma on health outcomes for obese individuals. Current evidence suggests that weight-based stigma and discrimination increase vulnerability to psychological distress that may contribute to poor physical health. Given the increased risk of adverse outcomes already present with obesity, the additional negative impact of weight stigmatization on health is concerning. As Link and Phelan concluded:

[T]he stress associated with stigma can be particularly difficult for those with disease-associated stigma. Not only are they at risk to develop other stress-related illnesses, but the clinical course of the stigmatized illness itself may be worsened and other outcomes affected, such as the ability to work or lead a normal social life.

Continued research in this area will help to clarify the relationship between weight stigma-induced stress and health.

A Threat to Quality Health Care

An accumulation of research has found that health care settings are a significant source of weight stigma, which undermines obese patients' opportunity to receive effective medical care. Both self-report and experimental research demonstrate negative stereotypes and attitudes toward obese patients by a range of health care providers and fitness professionals, including views that obese patients are lazy, lacking in self-discipline, dishonest, unintelligent, annoying, and noncompliant with treatment. There is also research indicating that providers spend less time in appointments and provide less health education with obese patients compared with thinner patients. In response, obese individuals frequently report experiences of weight bias in health care. Obese patients also indicate that they feel disrespected by providers, perceive that they will not be taken seriously because of their weight, report that their weight is blamed for all of their medical problems, and are reluctant to address their weight concerns with providers. All of these findings point to substandard health care experiences for obese individuals.

Health care utilization is also compromised by weight stigma. A number of studies demonstrate that obese persons are less likely to undergo age-appropriate preventive cancer screenings. Lower rates of preventive health care exist even after control for factors typically associated with reduced health care use, such as less education, lower income, lack of health insurance, and greater illness burden. Recent research indicates that weight stigma may be a specific contributor to these findings. Amy et al. surveyed 498 overweight and obese women (with health insurance and high access to health care) about their perceived barriers to routine gynecological cancer screenings. For women at the highest levels of obesity, 68% reported that they delayed seeking health care because of their weight, and 83% reported that their weight was a barrier to getting appropriate health care. When asked about specific reasons for delay of care, women reported disrespectful treatment and negative attitudes from providers, embarrassment about being weighed, receiving unsolicited advice to lose weight, and also reported that gowns, examination tables, and other medical equipment were too small to be functional for their body size. The percentage of women reporting these barriers increased with BMI.

As obese individuals are at a high risk for weight-related comorbidities, quality health care is essential. Acknowledging the detrimental effects of weight stigma in health care is essential for a better understanding of the cumulative impacts of weight stigma on public health.

Impaired Obesity Intervention Efforts

Discrimination manifests in illness and disease that society and governments do not adequately address due to the very discrimination causing the harm. On the contrary, society and the government tend to blame the victims and enact and interpret legislation based on the theory that the people are not taking appropriate responsibility for their own health. Obese individuals internally suffer from weight bias but also suffer because society blames them for their illness and thus relinquishes responsibility of addressing the underlying causes of their obesity.

The emphasis on nutrition education in the US approach to obesity reflects assumptions that rates of obesity have risen because Americans lack sufficient knowledge of the personal behaviors that lead to weight gain. For example, the US Department of Health and Human Services launched its "Small Steps" campaign in 2004, funded at $1.5 million annually, with the primary objective to increase awareness among "unhealthy" Americans. The campaign consists of a series of radio and television public service messages that "use humor to inspire overweight adults to incorporate 'Small Steps' into their hectic lives." The campaign's Web site also features a "Health Tip of the Day," such as Small Step #92: "Walk instead of sitting around." Critics have complained that the Small Steps campaign is ineffective and sidesteps the real causes of obesity.

It is also significant to note that federal and state legislative initiatives related to obesity have largely avoided addressing the societal and environmental causes of the disease. Governments have spent significant time considering legislation to protect the food and restaurant industry from potential civil injury claims compared with time spent developing obesity prevention policies. In arguing in favor of the "Personal Responsibility in Food Consumption Act," one Congressman stated, "This bill is about self-responsibility. If you eat too much, you get fat. It is your fault. Don't try to blame somebody else." In 2008, a Mississippi State House Bill was proposed to prohibit restaurants from serving food to any person who is obese. Such a deliberate attempt at overt discrimination underscores the prevalence of weight stigma, even among key decision makers.

Optimistically, recent efforts have attempted to address the environmental and structural contributors to obesity (e.g., through policies aimed at improving school food, enacting menu labeling legislation, taxing sodas, and improving healthy food access). However, larger-scale efforts are needed. To address obesity commensurate to its impact, a coordinated and well-funded response is critical.

CRITICAL IMPLICATIONS FOR PUBLIC HEALTH

With the current body of evidence, it is apparent that, as with other stigmas, weight stigma has negative implications for public health. Weight stigma threatens the psychological and physical health of obese individuals, impedes the implementation of effective efforts to prevent obesity, and exacerbates health disparities. summarizes individual and public health consequences of weight stigma that may ultimately worsen life outcomes for obese persons.

By limiting the national response for obesity to education about individual choices regarding nutrition and physical activity, important societal and environmental causes of obesity are overlooked, the economic and social disparities that contribute to obesity are ignored, and weight stigma and discrimination are equally disregarded. Indeed, public health policy can either protect those afflicted with a disease from discrimination, or can promote unfair treatment and disparities. Unfortunately, the US government has not addressed weight stigma and discrimination in formal legislation, leaving millions of obese people to suffer unfair treatment because of their weight. These social consequences may intensify the health disparities already faced by obese Americans. Because obesity is especially prevalent among poor or minority groups who live in disadvantaged areas, obese individuals often already belong to marginalized groups and experience multiple stigmatized statuses. Consequently, the stigmatization of obese individuals is inextricably linked to social inequalities.

For the public health community to address the widespread health and social disparities faced by obese people, we must move past the victim-blaming approach and instead advocate a comprehensive obesity prevention strategy that includes efforts to reduce weight-based stigma and discrimination. We offer 3 recommendations to achieve this goal.

First, it is essential for weight stigma to be addressed in obesity interventions, and for anti-stigma messages to be incorporated into obesity prevention campaigns. For example, interventions should focus on health as both the primary motivator and desired outcome for behavior change, rather than messages that emphasize achieving an ideal weight, which may perpetuate obesity stereotypes and chastise obese individuals. Unhealthy eating behaviors, such as fast food and soda consumption, can be discouraged for all people, regardless of their body size. It may be especially important to incorporate anti-stigma messages in interventions for youths, because of the vulnerability of obese children to the negative emotional and physical health consequences of weight-based victimization. A 2005 report issued by the Institute of Medicine acknowledged the importance of considering weight-based stigmatization in obesity interventions for youths and recommended shifting the focus of prevention efforts to emphasize behaviors that promote health rather than appearance. Similarly, the Society for Nutrition Education also recommends that school-based obesity prevention programs include promotion of weight tolerance and school policies prohibit weight-based teasing and victimization. MacLean et al. asserted a range of stigma-specific recommendations for public health interventions for obesity, including evaluation of the social impact of existing interventions on stigma, providing stigma-reduction training for health care professionals, screening public health communication messages for stigmatizing content, seeking perspectives from obese persons in efforts to identify solutions to stigmatizing programs, and ensuring consistent implementation of nonstigmatizing messages.

Second, obesity prevention efforts need to expand beyond educational campaigns focused on individual behaviors toward larger-scale, coordinated policies that initiate social changes to help reverse the societal and environmental conditions that create obesity in the first place. Adler and Stewart proposed a "behavioral justice" approach to address obesity, which highlights the need to provide sufficient resources in the environment that enable individuals to engage in health-promoting behaviors. They posited that individuals should only be held responsible for engaging in healthy behaviors if they have full access to conditions that enable those behaviors. That is, unless people have adequate resources (e.g., access to affordable, healthy foods) to resist the obesogenic environment, it is too difficult (and unjust) to expect individual actions to be successful. Thus, although this approach does not ignore personal responsibility, the emphasis is shifted from personal blame to an issue of social justice. Efforts to create environmental changes that support responsible behaviors will serve to improve health and reduce health disparities for all Americans, regardless of their weight. Acknowledging the complex etiology of obesity will also help reduce weight stigma and its negative consequences. As more local and national resources are allocated toward obesity prevention, it is important to ensure that resources are used productively and that weight stigma does not undermine new efforts.

Third, efforts to improve the health of obese individuals will be facilitated by legislation to prohibit weight-based discrimination. Currently, no federal legislation exists to protect obese individuals from discrimination on the basis of weight. Michigan is the only state that prohibits employment discrimination on the basis of weight, and the District of Columbia and the California cities of San Francisco and Santa Cruz are the only other areas to include body size in human rights ordinances. Thus, as weight discrimination increases, overweight and obese individuals have no means to seek legal redress for wrongful discrimination. This lack of protection for obese individuals allows discrimination in employment, education, and health care to persist, contributing to health disparities, morbidity, and mortality. Meaningful legal remedies are clearly needed to protect against weight discrimination, both at a state and federal level. The accumulation of science documenting the negative consequences of weight stigma and discrimination provides ample justification for legal action. Public health professionals can play an important role in supporting necessary protections for obese individuals.

The stigmatization of obesity is pervasive, damaging, and threatens core public health values. Rates of overweight and obesity are as high as 76% for some groups in the United States. By ignoring weight stigma, the public health community ignores substantial suffering of many Americans. To effectively address the obesity epidemic and improve public health, it is essential to challenge common societal assumptions that perpetuate weight stigma, and prioritize discussions of weight stigma in the national discourse on obesity.

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Obesity Stigma: Important Considerations for Public Health

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Negative attitudes toward obese persons are pervasive in North American society. Numerous studies have documented harmful weight-based stereotypes that overweight and obese individuals are lazy, weak-willed, unsuccessful, unintelligent, lack self-discipline, have poor willpower, and are noncompliant with weight-loss treatment.

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Joe Devine: Obesity and insulin resistance

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Joe Devine: Obesity and insulin resistance

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The article I chose to write about his week is about insulin resistance and its affect on obesity. There is a complicated relationship between insulin resistance and obesity and the study suggests that insulin has a regulatory role on the biology of adipocyte tissue. While the insulin signaling cascade has many steps within these tissues an important step is their abliity to stimulate glucose transport and lipogenesis. However, the study points out that excessive adipose tissue and low adipose tissue can cause insulin resistance. This is a confounding example that highlights the ability of adipocytes to act as endocrine cells because of their ability to affect cells through secretions. There is a lot of moving parts to to insulin resistance and its correlation to obesity which prompts future research that will prove to be difficult.

Kahn, B. B., & Flier, J. S. (2000). Obesity and insulin resistance. The Journal of clinical investigation, 106(4), 473–481. https://doi.org/10.1172/JCI10842

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Fat as a Risk Factor for OverconsumptionSatiation, Satiety, and Patterns of Eating

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Fat as a Risk Factor for OverconsumptionSatiation, Satiety, and Patterns of Eating

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This study was important because it assessed the risk of consuming foods with high fat content. The term passive overconsumption is used to explain how foods with a high fat content a prominent impact on appetite don’t have, making it easy for someone to overeat without realizing. This study found that patients with a higher fat intake in their diet had a higher rate of obesity than patients with a lower fat intake in their diet. This is important in nutritional sciences because people need to be aware of how much fat they are consuming in their diet. This is important in a clinical setting because it can be used preventatively, as well as in response to obesity. All patients should focus on trying to eat low-fat foods to avoid passive overconsumption.

Blundell JE, MacDiarmid JI. Fat as a risk factor for overconsumption: satiation, satiety, and patterns of eating. J Am Diet Assoc. 1997 Jul;97(7 Suppl):S63-9. doi: 10.1016/s0002-8223(97)00733-5. PMID: 9216571.

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Kate Mashburn: LA sprouts randomized controlled nutrition, cooking and gardening programme reduces obesity and metabolic risk in Hispanic/Latino youth

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Kate Mashburn: LA sprouts randomized controlled nutrition, cooking and gardening programme reduces obesity and metabolic risk in Hispanic/Latino youth

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This article really interested me because it ties what we are learning in class with a part of my research project involving government programs influencing healthcare. In the study, they showed how an interactive gardening and cooking program promotes better eating habits in children which, in turn, lowers the chances of obesity. This is important because rather that lecturing children on making better food choices, the study showed kids HOW to make those choices. They made it a learning experience. The study mainly focused on a lower-income latino group in Los Angeles. The study concluded that this program was a success. Participants lowered their overall body mass and waist circumference. However, it called for a longer study that broadened its participants and location so that more people can learn how to make better nutritional choices. This is definitely warranted because as more of these studies show successful results, it would be more likely to obtain government funding for a permanent program being established in these communities.

Gatto NM, Martinez LC, Spruijt-Metz D, Davis JN. LA sprouts randomized controlled nutrition, cooking and gardening programme reduces obesity and metabolic risk in Hispanic/Latino youth. Pediatr Obes. 2017 Feb;12(1):28-37. doi: 10.1111/ijpo.12102. Epub 2016 Feb 22. PMID: 26909882; PMCID: PMC5362120.

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I chose this paper because it bridges obesity and cancer, using inflammation. A BMI of 30 or greater is how obesity is defined and over 35% of American adults find themselves in that category. Obesity has been a growing area of concern for America for some time now and it is estimated that 20% of adult deaths in the US between 1986 and 2006 were due to obesity. When someone is eating nutrients in excess, they are localized to metabolic tissues and triggers inflammation. These inflammatory sites trigger the innate immune system to bring in macrophages and neutrophils. In an inflammatory environment, immune cells are polar and can be triggered to change their function or secretions. This changes to a tumor promoting environment and this study directly links obesity to increased incidence of breast cancer.

This study shows why nutritional sciences is a very important course and more people should take it. Knowing how to better manage your lifestyle and food choices can save your life. Clinical implications could mean doing more anti-inflammatory immunotherapy in obese populations on a large scale to help prevent the increased risk of cancer. Immunotherapy and control of the symptoms are the target for those people who may not be able to exercise or have the luxury of a balanced diet due to budget or food availability.

Kolb, R., Sutterwala, F. S., & Zhang, W. (2016). Obesity and cancer: inflammation bridges the two. Current opinion in pharmacology, 29, 77–89. https://doi.org/10.1016/j.coph.2016.07.005

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Obesity and cancer: inflammation bridges the two

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Obesity is a growing public health problem and affects 35% US adults. Obesity increases the risk of many cancer types and is associated with poor outcomes. Clinical management of cancer patients has been essentially the same between normal weight and ...

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Obesity and Nutrition in Acute Respiratory Distress Syndrome

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Obesity and Nutrition in Acute Respiratory Distress Syndrome

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For this module I decided to look into an article that covered obesity, nutrition and how those two related to respiratory distress syndrome. More specifically so, this relationship was studied in critically ill patients. I found this study to be important because it really shed some light on some important points such as greater body mass index (BMI) is associated with increased length of mechanical ventilation, intensive care and hospital stay. Also the fact that enteral nutrition (tube feeding) should be used in the vast majority of acute respiratory distress syndrome patients, and the role for parenteral nutrition is extremely limited. One key finding to this study that I found to be very interesting was the fact that critically ill patients who are obese are found to be at greater risk to develop acute respiratory distress syndrome but somehow are also found to have greater odds of survival while in the hospital. I believe the clinical implications of this study are how to treat a patient that is obese versus a patient who is not that is suffering from acute respiratory distress syndrome. This article touched base on the amount of protein that should be delivered, enteric feeding versus parenteral feeding, full calorie feedings and the differences in treatment between patients and their BMI.

Stapleton, Renee D., and Benjamin T. Suratt. “Obesity and Nutrition in Acute Respiratory Distress Syndrome.” Clinics in chest medicine 35.4 (2014): 655–671. Web.

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Mitchell Watts: Scientific evidence of diets for weight loss: Different macronutrient composition, intermittent fasting, and popular diets

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Mitchell Watts: Scientific evidence of diets for weight loss: Different macronutrient composition, intermittent fasting, and popular diets

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This review compared various types of popular diets on their effects on weight loss. The basics for a diet to be effective in weight loss include reducing excessive energy intake, enhancing diet quality, and increasing energy expenditure. This review compared diets based on the manipulation of macronutrient content (low fat, high protein, low carb, ketogenic, etc), diets based on the restriction of specific foods or food groups (gluten free, paleo, vegetarian/vegan, Mediterranean), and diets based on the manipulation of timing (fasting).

What was found is that in the short term, diets based on high protein/low-carb and fasting diets were most effective at weight loss, implying that this could be beneficial to jump start weight loss. However, there are potential adverse metabolic effects of these diets, such as with long term adherence to a ketogenic diet. However, in the long term, there were no major differences in the amount of weight loss between diets; therefore, the long-term focus should be on diet quality. The Mediterranean diet for instance focuses on intake of high-quality nutritious foods, and it is easier to adhere to than the more restrictive diets.

Overall, the clinical implication of this review is that any one diet could work for someone aiming to lose weight, with potential options for short term weight loss and long-term maintenance. The one important thing to note is that a higher adherence, regardless of diet type, is the determining factor in predicting successful weight loss. Therefore, the focus should be on finding strategies to improve adherence to diets to improve success with weight loss dietary therapies.

Freire R. (2020). Scientific evidence of diets for weight loss: Different macronutrient composition, intermittent fasting, and popular diets. Nutrition (Burbank, Los Angeles County, Calif.), 69, 110549. https://doi.org/10.1016/j.nut.2019.07.001

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doi: 10.1016/j.nut.2019.07.001.

Affiliation

¹ Mucosal Immunology and Biology Research Center and Center for Celiac Research and Treatment, Department of Pediatrics, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA. Electronic address: rachelhorta.freire@gmail.com.

doi: 10.1016/j.nut.2019.07.001.

Affiliation

¹ Mucosal Immunology and Biology Research Center and Center for Celiac Research and Treatment, Department of Pediatrics, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA. Electronic address: rachelhorta.freire@gmail.com.

Abstract

New dietary strategies have been created to treat overweight and obesity and have become popular and widely adopted. Nonetheless, they are mainly based on personal impressions and reports published in books and magazines, rather than on scientific evidence. Animal models and human clinical trials have been employed to study changes in body composition and metabolic outcomes to determine the most effective diet. However, the studies present many limitations and should be carefully analyzed. The aim of this review was to discuss the scientific evidence of three categories of diets for weight loss. There is no one most effective diet to promote weight loss. In the short term, high-protein, low-carbohydrate diets and intermittent fasting are suggested to promote greater weight loss and could be adopted as a jumpstart. However, owing to adverse effects, caution is required. In the long term, current evidence indicates that different diets promoted similar weight loss and adherence to diets will predict their success. Finally, it is fundamental to adopt a diet that creates a negative energy balance and focuses on good food quality to promote health.

Keywords: Fasting; Macronutrient; Obesity; Popular diets; Weight-loss.

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Jane L, Atkinson G, Jaime V, Hamilton S, Waller G, Harrison S. Jane L, et al. JBI Database System Rev Implement Rep. 2015 Oct;13(10):60-8. doi: 10.11124/jbisrir-2015-2363. JBI Database System Rev Implement Rep. 2015. PMID: 26571283

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Koehler K, Drenowatz C. Koehler K, et al. Front Nutr. 2022 Jan 13;8:835535. doi: 10.3389/fnut.2021.835535. eCollection 2021. Front Nutr. 2022. PMID: 35096953 Free PMC article. No abstract available.

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national center for biotechnology information

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Scientific evidence of diets for weight loss: Different macronutrient composition, intermittent fasting, and popular diets - PubMed

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New dietary strategies have been created to treat overweight and obesity and have become popular and widely adopted. Nonetheless, they are mainly based on personal impressions and reports published in books and magazines, rather than on scientific evidence. Animal models and human clinical trials ha ...

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Perceived Weight Discrimination and C-reactive Protein

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Perceived Weight Discrimination and C-reactive Protein

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Tamunoerenomie Inimgba-Olunwa

The purpose of the article was to figure out if there was an association between weight discrimination and any physiological markers of health. The authors focused on the interaction between perceived weight discrimination and C-reactive protein (CRP). The CRP is a systemic inflammation marker, which plays a major role as a predictor of cardiovascular disease and diabetes. CRP has also been shown in previous studies to be associated with depression and other forms of perceived discrimination. A key finding in the study was that weight discrimination is associated with an increase in circulating CRP, and this association is moderated by BMI. This relates to nutritional sciences because the health consequences that come along with obesity may never be overcome if better behavioral coping mechanisms associated with perceived weight discrimination are not discussed, as this would only worsen physical health outcomes. Previous articles also discussed that perceiving weight discrimination is definitely associated with risk of becoming and remaining obese over four years. This is because the behavioral ways of coping with weight discrimination has been shown to be associated with higher inflammation. The clinical implications of the study was to identify the link between weight discrimination and physiological mechanisms, and eventually the impact that has on physical health.

Sutin, Stephan, Y., Luchetti, M., & Terracciano, A. (2014). Perceived weight discrimination and C‐reactive protein. Obesity (Silver Spring, Md.), 22(9), 1959–1961. https://doi.org/10.1002/oby.20789

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Low-Carbohydrate Diet versus Low-Fat Diet for Obesity and Hyperlipidemia

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Low-Carbohydrate Diet versus Low-Fat Diet for Obesity and Hyperlipidemia

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Ellen Reusch

For this week's wall, I found this article discussing how a low-carbohydrate diet was retained better by patients and resulted in greater weight loss when compared with a low-fat diet. This study included generally healthy participants that were not using any prescription medication or weight loss / diet pills. Participants were randomized to either a low-carbohydrate or a low-fat diet. They were given guidelines for how to prepare their meals and were encouraged to exercise, but not required. Over 24 weeks, the patients who followed a low-carbohydrate diet experienced more weight loss than those in the low-fat diet group. Important to note - the patients who followed a low-carbohydrate diet reported more adverse events related to the diet than the patients that followed a low-fat diet. However, only a small percentage of patients dropped out of the study due to intolerable adverse events. I think this study is important because a high percentage of our population is trying to lose weight at any given time. There are so many "fad diets" that gain popularity and may or may not actually be good for patients to follow in the long term or even in the short term. I think a lot of patients who want to lose weight immediately think they need to cut out all sources of fat and all carbohydrates altogether, which is not a healthy situation at all and could actually do more harm than good. This study highlights how important it is to understand what is happening to your body when you cut out certain food groups. It's important for patients to understand that losing weight is not always reliant on simply decreasing your caloric intake, but sometimes on balancing your caloric intake between all the food groups. This article couple be helpful for some patients who may be struggling to lose weight by following a low-fat diet - perhaps a low-carbohydrate diet would suit them better or help them reach their goals quicker.

Yancy, et al. (2004) Low-Carbohydrate Diet versus Low-Fat Diet for Obesity and Hyperlipidemia. ACPJournals. https://www.acpjournals.org/doi/pdf/10.7326/0003-4819-140-10-200405180-00006

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If the address matches an existing account you will receive an email with instructions to reset your password.

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Sidney Harm: Comparative Effectiveness of Childhood Obesity Interventions in Pediatric Primary Care: A Cluster-Randomized Clinical Trial

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Sidney Harm: Comparative Effectiveness of Childhood Obesity Interventions in Pediatric Primary Care: A Cluster-Randomized Clinical Trial

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This weeks topic is obesity, it is well known that the prevalence of obesity has steeply increased throughout the last decade. This randomized cluster study looks at the pediatric population and the effectiveness of different intervention types on pediatric obesity. The population included 549 individuals which were in the age range from 6-12 years old with their body mass index (BMI) within the 95% percentile or above. In the intervention groups they used a computerized clinical decision support which adds to the electronic health records and provides; printable educational handouts, referrals to weight management programs, and more helpful resources for obese individuals. There were two intervention groups and one control, one intervention group consists of clinical decision support as well as the individual/family receiving an intervention of self-guided behavior changes. The second intervention group also had clinical decision support but had individualized family coaching. Not surprisingly, it was found both intervention groups had improvements within the participants BMI compared to the control group. A interesting finding within this study was that the group who received family coaching had less improvement for BMI compared to the other intervention group who got the self-guided behavior changes intervention.1 This connects well with this module since this study focused on interventions that were more lifestyle based instead of ‘fat shaming’ that we witnessed in one of our readings this week about weight stigma.

References:

1.     Taveras, Marshall, R., Kleinman, K. P., Gillman, M. W., Hacker, K., Horan, C. M., Smith, R. L., Price, S., Sharifi, M., Rifas-Shiman, S. L., & Simon, S. R. (2015). Comparative Effectiveness of Childhood Obesity Interventions in Pediatric Primary Care: A Cluster-Randomized Clinical Trial. JAMA Pediatrics, 169(6), 535–542. https://doi.org/10.1001/jamapediatrics.2015.0182

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Andrea Ballinger: Weight Bias and Quality of Care of Patients with Obesity 

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Andrea Ballinger: Weight Bias and Quality of Care of Patients with Obesity 

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The modules this week highlighted that typically, alterations in diet and exercise are recommended for obese patients, when in reality, they are two small factors that may impact patients' health. The paper that I chose for this week's topic is a literature review that focuses on the negative impact physician weight bias can have on the clinical care received by obese patients. Perceived weight discrimination is shown to have a great number of consequences on health outcomes. Consequences discussed in the paper include a decreased compliance to provider recommendations, less weight loss, worse mental health outcomes, impaired patient-centered communication, and fewer clinic visits. What I appreciated about this paper is that it did not place blame on the patient and simply offer nutritional strategies, instead, it provided tangible solutions on the account of the provider. One suggestion included reducing focus on body weight when performing health screenings for possible diseases in which obesity is a risk factor. This paper has great clinical implications in that providers could become aware of any biases they may have, and learn how to provide patients with safer spaces to receive clinical care.

Phelan, S M et al. “Impact of weight bias and stigma on quality of care and outcomes for patients with obesity.” Obesity reviews : an official journal of the International Association for the Study of Obesity vol. 16,4 (2015): 319-26. doi:10.1111/obr.12266

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Impact of weight bias and stigma on quality of care and outcomes for patients with obesity

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The objective of this study was to critically review the empirical evidence from all relevant disciplines regarding obesity stigma in order to (i) determine the implications of obesity stigma for hea...

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Obesity, BMI, and Health: a Critical Review

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Obesity, BMI, and Health: a Critical Review

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For this weeks paper, I wanted to look further into Body Mass Index (BMI) and its uses within the medical community. From our course work this week, we have seen that even though BMI is the most commonly used practice to determine body fatness, it is not always the most accurate/inclusive option. This review further states this by using a multitude of studies to show how BMI may not always be the best option when determining obesity in individuals. One problem with BMI is that it does not differentiate between lean mass and fat on the body. This means that people who are higher in lean muscle might fall into a higher BMI category, even though they are not overweight. Another problem is that it doesn't asses body fat location, but it has been shown that the placement of body fat can help determine the individual's health outcomes (morbidity, disease, etc.). One interesting finding was from the EPIC observational study, which found that the risk of death (when combining all factors to look at morbidity) was actually lower in the men with a BMI of 26.5-26.8, even though it is in the pre-obese category of the BMI chart.

This relates to nutritional sciences because the BMI scale has been used habitually since the seventies (it was initially created in  the 1800's and was called the Quetelet Index, named after its creator) in determining an individual's risk for obesity and insurance reasons. BMI is calculated by weight (kg)/ height ^2 (m^2). There have been numerous other ways found that help complete the full picture when identifying an individual's body mass and risk for additional diseases. Some examples of these are skin fold thickness, waist-circumference, underwater-weighing, and DEXA (dual-energy x-ray absorptiometry).

A clinical implication from this study is that we shouldn't rely so heavily on BMI to determine whether or not someone is at risk of obesity (and the conditions underlying it). This is because BMI fails to account for so many other  factors of obesity, by simply focusing on a weight versus height ratio. This could be fixed by taking some influence off of BMI, and including other tests beyond the BMI to fully the determine the individual's condition.

Nuttall, F. Q. (2015). Body mass index. Nutrition Today, 50(3), 117–128. https://doi.org/10.1097/nt.0000000000000092

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Body fatness has been an important psychosocial issue among humans for millennia. It is clearly manifested by paleolithic statuettes of exceedingly plump women. This suggests being "full figured" was highly desirable at least for women. In contrast, images of obese people, males or females, are never exhibited in ancient Egyptian funerary wall paintings, stellae, or statues suggesting that fatness was not considered to be a desirable trait there. This also is the case in artifacts from other cultures in the Middle East in that era. Why the degree of fatness has varied in different cultures is not clear. However, it may have depended on the availability of a reliable food supply and the effort required in obtaining it.

More recently, the degree of rotundity considered ideal also has varied considerably in the general population, but particularly for young women. Before the 1920s, "full figured" women were considered to be desirable as long as the distribution was hourglass in type. However, the 1920s Flapper era introduced abbreviated and revealing dresses. The result was that thinness was not only desirable but also required. This concept has moderated but still influences women's views of beauty and eating habits at present.

Fatness as a Personal or Society Issue

Traditionally, a person's fatness has been defined at a personal level as well as at a societal level. However, this is difficult to quantify. That is, each individual has his/her own perception of how fat he/she should be. As indicated above, this often depends on a general concept of societal norms or is due to peer pressure. For example, currently in Western societies, young women are often concerned about their body image, and most consider themselves to be too fat, even though they are well within population-based references. This is not only due to societal concepts of an ideal degree of fatness, but also due to thinness being a goal promulgated by the fashion industry and reinforced by commercial advertising.

At a societal level, although poorly described or quantified, there also is a degree of fatness beyond which a person generally is considered to be unacceptably fat; that is, there is an ill-defined threshold at which a person is labeled as being "fat" or "obese." However, it is based on the "I can't define it but I know it when I see it" concept. In addition, implicit in this context is that the location of the excess fat plays a role, as does a person's age. It is much more acceptable to be "overweight" when one is old than when one is young. Also particularly in women, the accumulation of fat in certain areas of the body is considered to be much more acceptable than in other areas. For example, truncal (belly fat) accumulation would be considered to be less acceptable than the accumulation of fat in the peripelvic and thigh areas as well as in the breast area 1; that is, one may be statistically "fat" but with an appropriate figure be merely referred to "as amply endowed" or "pleasingly plump."

The social consequences of being "too fat" are severe. Discrimination begins in childhood and results in serious emotional scars. Societal discrimination limits career choices, and indeed many career paths are closed to those considered to be too fat. Also, societal stigmatization often impairs a person's ability to express his/her intellectual and other talents; that is, they become underachievers. In addition, the potential pool of mates is limited because of their perceived unattractiveness. Thus, obese people tend to marry other obese people and, parenthetically, to produce obese children. 2- 4

Fatness as a Medical Issue

Not only the societal but also the functional and indirectly the medical consequences of an excessive accumulation of fat also have been recognized for millennia. Nevertheless, the concept that "body build" (fatness) is a major population-based medical issue gained popularity in this country only shortly before 1900. Life insurance data accumulated at that time 5 and subsequently 6 indicated that body weight, adjusted for height (Wt/Ht), was an independent determinant of life expectancy, and in 1910, the effects of being overweight were noted to be greater for younger people than for the elderly. 6

Subsequently, the Metropolitan Life Insurance Company in 1959 published tables of average body weights for heights (Wt/Ht) by gender and at different ages. 7 This was based on data from 1935 to 1953 from more than 4 million adults, mostly men, insured by 26 different insurance companies. The risk for development of certain diseases as well as mortality data related to Wt/Ht differences also were analyzed and reported in the 1960 Statistical Bulletin of the Metropolitan Life Insurance Co. 8, 9

The Wt/Ht tables were used for many years as a reference for population-based studies. If a person's Wt/Ht was 20% above or below the mean for that height category, he/she was considered to be overweight or underweight, respectively. The insurance data also indicated the ratios of weights for heights (the term used was "body build") at which mortality was lowest in adults. The latter was referred to as the "ideal" or later the "desirable" weight. All of these data were periodically updated. 10 Interestingly, from 1959 to 1983, the desirable weight, that is, the weight/height representing the lowest mortality had increased. 10- 12 However, a "desirable body" weight for height was invariably lower than the average weight for height in the insured population. 7, 10, 13

Problems With the Wt/Ht (Body Build) Index

Early on it was recognized that tall people had a lower death rate than did short people 7, 8, 13 with the same Wt/Ht ratio. It also was recognized that a person's height in general and leg length in particular could affect the calculated body mass adjusted for height. A person's bony frame, that is, bone mass, also could affect the interpretation of this ratio. In general, it reflected whether one was narrowly or broadly built. Thus, efforts were made to eliminate lower limb length and frame size as variables. 7, 10 The strategy was to develop representations of body build, that is, charts of weight/height that were independent of these variables. The overall goal was to have the same distribution of Wt/Ht at each level of height.

Mathematical Adjustment of Body Build

Mathematically, the issue of adjusting body build for differences in height was approached with the concept that the body, particularly the trunk, could be considered as being a 3-dimensional volume or mass. Thus if a tall person were simply a scaled-up version of a short person, weight would increase approximately with the cube of height. 16 Indeed, several equations were developed and tested based on this concept; that is, the cube root of weight divided by height ( ³√Wt/Ht) or weight/height, 3 and so on, but none were ideal. 17 This is because tall people are not just scaled-up versions of short people. As indicated previously, they tend to be more narrowly built resulting in a greater lean/fat proportion of body mass.

Later, it was shown that the body mass for height actually scaled best with weight for height when the height was raised to the 1.6 to 1.7 exponent (Wt/Ht, 1.6 etc). 18 Thus, with an increase in Ht, the effect of Ht on the ratio is exponential, whereas the change in Wt is linear. This has the effect of Ht on the ratio to be magnified as Ht increases. Overall, it results in a lower ratio in tall people than will be obtained with just a Wt/Ht ratio. Thus, it potentially compensates for a narrower build in tall compared with short people.

This exponent is not convenient for use in population-based studies, and it was determined that Wt/Ht ² generally was satisfactory. 16, 18 The latter represents the Quetelet Index. It was developed by Dr Quetelet in the 1800s.

Lambert Adolph Jacque QueteletAdoption of the BMI as an Index of Obesity

In 1972, Keys et al 16 severely criticized the validity of Metropolitan Life Insurance published data per se, and the then-published tables of desirable weight for height, as well as the tables used to define people who were underweight or overweight. 7 (The pejorative term "obese" was rarely used in that era.) Instead, Keys et al, using better documented weight for height data, popularized the Quetelet Index in population-based studies. They referred to it as the body mass index (BMI). Thus, Quetelet Index = body weight (kilograms) divided by height squared (meters) = BMI.

As indicated above, by squaring the height, it reduces the contribution of leg length in the equation and tends to normalize the body mass distribution at each level of height; that is, it reduces the effect of a variance in height in the relationship of weight to height. This was considered to be important because most of body fat is in the trunk. Nevertheless, as also pointed out by Keys et al, 16 even the BMI rather poorly represents a person's percent of body fat.

BMI Distribution in a Normal Population

Although a BMI determination reduces the effect of lower-extremity length on the Wt/Ht ratio, whether one uses the BMI or merely the ratio of weight to height, the population distribution is still not Gaussian. That is, it is not symmetrical but is always skewed to the right, that is, toward a higher ratio of weight (body mass) to height. For example, the distribution of BMIs in adult American men and women was determined in 1923 in 1026 individuals (Figure). 27 The median BMI was 24, but the mean BMI was 25. The distribution curve clearly indicated a skewing toward an increase in BMI, and this trend has continued. 26

WHO and the Categorization of BMIs Into Quartiles

In 1993, the WHO assembled an Expert Consultation Group with a charge of developing uniform categories of the BMI. The results were published as a technical report in 1995. 25 Four categories were established: underweight, normal, overweight, and obese. An individual would be considered to be underweight if his/her BMI was in the range of 15 to 19.9, normal weight if the BMI was 20 to 24.9, overweight if the BMI was 25 to 29.9, and obese if it was 30 to 35 or greater. Using linear regression, a BMI of 16.9 in men and 13.7 in women represents a complete absence of body fat stores. 31

The above 4 categories are similar to those suggested by John S. Garrow in 1981, 31, 32 but the terminology was changed. The terminology he used was "desirable" for a BMI up to 25, "grade I obesity" between 25 and 29.9, "grade II obesity," between 30 and 40, and "grade III obesity" for BMI greater than 40.

The latter classification was based on Rosenbaum and colleagues' 33 own data obtained in a survey of an adult population, aged 16 to 64 years, in Great Britain and published in 1985.

The population-based data indicated the majority of people were in the "desirable" range of the BMI distribution as indicated in Table . Unfortunately, this distribution is not and has not been similar to those found in other surveys. The BMIs have been higher.

TABLE 1

At the time that the WHO classification was published, the National Institutes of Health (NIH) in the United States classified people with a BMI of 27.8 (men) and 27.3 (women) or greater as being overweight. If they were below this BMI, they were considered to be "normal." This was based on an 85% cutoff point of people examined in the National Health and Nutrition Examination Study (NHANES) II. 12, 22, 34 Subsequently, in 1998, the cutoff point between normal and overweight was reduced to a BMI of 25 to bring it into line with the 4 categories in the WHO guidelines. 25, 35 Parenthetically, this instantaneously converted millions of Americans from being "normal weight" to being "overweight."

The new terminology appears to be a bit presumptuous and careless because the BMI is not a direct measure of percent of fat mass, and the dynamic concept that those in the former "overweight" category are now in the "preobesity" category invariably going on to "obesity" is not the case. Also those with a lower BMI initially, but with a dynamic weight gain over time, would have to transition through this category in order to become classified as "obese" regardless of the terminology. By analogy, should those classified as "underweight" now be referred to as being "prenormal"?

Distribution of BMI in the General Population

Regardless of the terminology and population reference issues, at present the BMI is the currency by which we define the obesity issue throughout the Western world. It was developed for the convenience of the epidemiologists, and indeed it did provide a uniform codification of body weight for height reporting. The BMI categories are shown in (Table ).

Table 2

BMI as a Determinant of Body Fat Mass

From an anatomical and metabolic perspective, the term obesity should refer to an excessive accumulation of body fat (triacylglycerols), and upon these grounds, the accuracy of the BMI as a determinant of body fat mass has been repeatedly questioned, 16, 39- 41, 46- 48 because it clearly has limitations in this regard. Gender, age, ethnic group, and leg length are important variables. 45, 49- 55 It should be noted that in population-based studies women generally have a BMI that is lower than that in men, even though their fat mass relative to their body build or BMI is considerably greater (~20% to 45%+).

The relatively poor correlation between percent of body fat mass and BMI also clearly has been shown more recently in the NHANES III database in which bioelectrical impedance was used to estimate the fat component of body composition. 51 In subjects with a BMI of 25 kg/m ², the percent of body fat in men varied between 14% and 35%, and in women it varied between 26% and 43%. Thus, using the NIH-suggested criterion based on percent of body fat to define obesity, subjects with a BMI of 25, a group that would be considered to be essentially normal, were associated with a body fat mass that varied again between low normal to obese. Also it is of interest that in the entire NHANES cohort, the BMI correlated better with lean body mass than with fat mass in men. 51 More recent NHANES data also indicate a poor correlation of BMI with percent of body fat, particularly in men. 58

In addition, in a recent study in individuals with or without diabetes in which the loss of lean body mass with aging was reported, the mean BMI in those without diabetes was 26.8. In those with diabetes, the BMI was 29.1; that is, it was higher as generally expected. However, the percent of lean body mass was the same; that is, the increased BMI in those with diabetes was not due only to an excessive accumulation of fat. 59

Trends in Body Weight and Height

Over the past several decades, there has been an increase in BMI in the general population. This has resulted in predictions of a public health disaster. It should be recognized that in the United States during the period from 1960 to 2002 not only has the mean weight increased by 24 lb among men aged 20 to 74 years, but also the height has increased by about 1 in. We can then calculate that the weight increase per year has only been 0.57 lb, and as indicated above, this could be due to an increase in lean mass rather than fat mass, or it may be a combination of the two. In women, there was a similar increase in weight and height. 40

Overall, the history of changes in height and weight in Western European men and probably women has been that of an increase in both weight and height. In the 17th century, the average height of men in Northern Europe was ~5 ft 3 in. It now approaches 6 ft. 61 These data suggest that the BMI categories should be adjusted upward periodically to accommodate population-based changes. Improvements in mortality rates also suggest an adjustment would be useful.

Body Fat Location

More specifically, both visceral fat accumulation 68, 69 and an expanded girth have been associated with development of insulin resistance, diabetes, and risk for coronary heart disease and hypertension. 63, 64, 70- 74 Accumulation of fat in the abdominal area appears to correlate best with triacylglycerols accumulating in the liver and skeletal muscle. These may actually represent the pathogeneticially important metabolic consequences that result in insulin resistance and more directly correlate with development of the above adverse medical conditions. 68, 75, 76 Incidentally, the relatively small accumulation of fat in these organs would not be detectible by BMI determinations, and they do not correlate simply with total body fat mass. 75

The Life Cycle and Location of Accumulated Fat

It is of some interest that accumulation of fat in the lower body at puberty in females is unique to humans, is not present in any of the great apes, and most likely is estrogen mediated. 1

In a teleological sense, why this occurs, if due to estrogen, is uncertain. It could represent a means of maintaining body fat during pregnancy without an undue expansion in abdominal girth. It also may act as a counterbalance when women carry a child either during pregnancy or afterward. It also may be a space-filling fat site due to the relatively larger pelvis in postpubertal females. 81 Overall, it may represent an adaptation to the human upright bipedal posture. In any event, it results in a lower center of gravity among women compared with men. Indeed, the lower body segment in females becomes ~40% greater than in males (quoted in Singh, 1993), 1 and it has strong sex-related overtones.

Not only is thigh fat greater in women than in men, but also women generally have a preponderance of slow-twitch fibers, whereas men have a preponderance of fast-twitch fibers in their quadriceps muscles, as do upper-body-obese women, 82 suggesting either genetic or earlier developmental differentiation events. Could this be an adaptation for load-bearing versus speed as a group survival adaptation?

As indicated above, the accumulation of fat with aging in both sexes tends to occur in the truncal area and is associated with an increase in visceral fat. In women, this could be explained by a decrease in circulating estradiol, that is, a decreased estrogen/testosterone ratio associated with the menopause. (Again of some interest, it is only humans who have a defined menopause).

In men, with aging, there is a decrease in testosterone and a relative increase in estrogen, resulting in a decrease in the testosterone/estrogen ratio. 83 Thus, in men, a change in sex hormone concentrations could possibly explain the increased accumulation of fat in general. However, why there is a preferential accumulation in the truncal location, that is, why they too develop an increase in visceral fat, is unclear. Clearly, location of fat in this area would help to maintain mobility. The latter could be of great importance in hunter-gatherer societies and in defense of the tribe. Perhaps the distribution is programmed by gender earlier in life.

Methods of Estimating Body Fat Mass and Location of the Fat

At present, simple, accurate methods for measuring percent of body fat and, in particular, body fat in different fat depots are not available. The indirect methods currently in use for estimating total percent of body fat include underwater weighing, an air displacement and density determination using a Bod Pod, a bioelectrical impedance analyzer, and a determination of the isotopically labeled water mass. In the past, determination of the total body radioactive potassium and thus metabolizing tissue mass have been used to estimate lean body mass, and by difference, the fat mass. 86

Anthropometric determination of fat mass directly has been done using skin-fold thickness measured at various sites. 87 A dual-energy x-ray absorptiometry (DEXA) scan, which provides a 3-dimensional picture of body organ densities, can be used for estimating total body fat. Its location also can be determined. Single computed tomography (CT) slices of the abdomen and thigh can be used to obtain 2 dimensions of those fat depots from which a 3-dimensional fat area can be reconstructed. This also can be done using magnetic resonance imaging, but magnetic resonance imaging is very expensive. One cannot do serial sections of the body using CT to determine fat mass because of the excess radiation associated with this procedure.

All of the previously mentioned methods use certain assumptions in the calculation of body fat mass, and all are subject to potential error. Nevertheless, there are more specific methods of determining body fat mass than is the BMI. Important information regarding the location of the stored fat also can be determined with some methods.

BMI and Morbidity and Mortality

The BMI classification system currently is being widely used in population-based studies to assess the risk for mortality in the different categories of BMI. It also is being used in regard to specific etiologies for mortality risk. However, as with its use to estimate percent of body fat, it is a rather crude approach. Even when some comorbidities are considered, the correlation of mortality rates with BMI often does not take into consideration such factors as family history of diabetes, hypertension, coronary heart disease, metabolic syndrome, dyslipidemias, familial longevity or the family prevalence of carcinomas, and so on. Recently it has been reported that more than 50% of susceptibility to coronary artery disease is accounted for by genetic variants. 92

Frequently, when correlations are made they also do not take into consideration a past as well as a current history of smoking, alcohol abuse, serious mental disorders or the duration of obesity, when in the life cycle it appeared, and whether the body weight is relatively stable or rapidly progressive, that is, type 1 or type 2 obesity. 93, 94 In most population-based studies, only the initial weight and/or BMI are given, even though weight as well as fat stores are known to increase and height to decrease with aging. In addition, the rate of weight gain varies among individuals, 7, 94, 95 as does the loss of muscle mass. 95 Muscle mass has been correlated negatively with insulin resistance and prediabetes. 96 Lastly, population-based studies do not take into consideration the present and past history of a person's occupation, medication-induced obesity, and how comorbidities are being treated. All of the above are significant issues.

More Explicit Problems in Relating the BMI to Medical IssuesThe "Obesity Epidemic"

Recently, there has been concern that an epidemic of obesity is occurring, not only in the United States, but also worldwide based on BMI data. In the NHANES data, there has been a change in the mean but to a greater extent in the distribution of BMI for adults aged 20 to 74 years in the United States. 26 That is, the mean BMI has increased, but there has been a greater increase in skewing toward the right and very large BMI. This results in more individuals being categorized as "obese." The reason for the recent increase in mean BMI, but particularly in those in the obese category, is unknown, although there are many speculations. The dramatic decrease in smoking is likely to have been a contributor. 91, 103- 106 Smoking contributes to population-based BMI by at least 2 mechanisms. Smoking impairs appetite per se. It also is pathogenetically important in the development of chronic obstructive pulmonary disease, which itself results in a lower body mass. Of some interest, NHANES data also indicate that the trend of an increase in BMI has not continued since 1999 in women and only modestly in men. 58 Smoking rates also have stabilized at a low level.

Is Being "Overweight" by BMI Criteria a Medical Issue?

Regardless of an observed increased skewing in the BMI distribution, it is important to note that several recent studies indicate that for most of us being a bit overweight (preobese?) as determined by BMI may not be so bad. 107- 111

The EPIC observational study is a population-based study that includes 359 387 individuals aged 25 to 70 years living in Europe. 109 The mean age of this group at the initiation of the study was 51.5 years, and the mean follow-up has been 9.7 ± 2 years. In this study, both the crude and adjusted relative risk of death among men was actually the lowest in those with a BMI of 26.5 to 28, that is, those in the overweight (preobese) category. Also, a significant increase in risk of death was present only among those with a BMI of less than 21 or greater than 30. That is, there is a wide range of BMIs in the central part of this population in which there was relatively little impact of BMI on risk of death over a 9.7-year period.

Similar data were obtained in the NIH-American Association of Retired Persons study of 527 265 men and women between the ages of 50 and 71 years in the United States and followed for up to 10 years. 110 The lowest death rate in the entire cohort was among those in the "overweight" category, and this was particularly true among the men. There also was a broad range of BMIs over which there was little difference in mortality (BMI of 23.5 to 30).

The NHANES data going back to 1971 and up to 1994 also indicate that the relative mortality risk is lowest in men with a BMI of 25 to 30 in all age groups, that is, from the age of 25 years up to the age of 70 years. 107 In addition, the risk of mortality was little affected by a BMI from 18.5 up to a BMI of 30 in all age categories. Indeed, in those older than 70 years, there was little impact on the death rate even if they were in the obese category. Similar results have been reported for women in the NHANES reports. 112 The lowest mortality occurred with a BMI of 27.

In a Canadian study, the age-adjusted mortality rate over 13 years in men was essentially unchanged in those with a BMI of 18.5 up to 35, that is, from the Normal Weight category through the obesity class I category. In women, there was only a modest increase over the same range. 113

In summary, there is a large range of BMIs over which there is little association with the death rate. Generally, the range is from a BMI of 21 up to and often including 30. It is centered in the 24-to-28 BMI range. This information is not entirely new. Andres 114 in 1980 summarized 16 different population-based studies in which anthropometrically determined obesity was not associated with increased mortality rate. A detailed analysis in 1960 of the Metropolitan Life Insurance data also suggested little increase in mortality rates in people with a degree of overweight less than 20% or more above the average for a given height and age (quoted in Keys et al 97).

Interestingly, in the EPIC observational Study, 109 when the waist circumference-to-BMI ratio was calculated, that is, adjusting the waist circumference for BMI, it tended to linearize the association of BMI with risk for death, and the ratio was greatest for those with a low BMI. Thus, even if an individual had a low BMI but a relatively increased waist circumference, the risk was increased. Indeed, for any given BMI, a 5-cm increase in circumference increased the risk of death by a factor of 1.17 among men and 1.13 for women. Also in this study, the overall greatest mortality risk was in those individuals with the lowest BMI and not those with the highest BMI. Nevertheless, even in the category with the lowest BMI, adjusting for waist circumference affected the mortality rate negatively. This again indicates the importance of the location of body fat in addition to the total amount of fat accumulated.

A recent analysis of 50 prospective observational studies indicated the lowest mortality at a BMI of 23 to 25. However, these data were obtained in the 1970s and 1980s in an aggregate population with a mean BMI of 24.8, that is, lower than at present. The increased mortality at higher BMI's was modest up to a BMI of 27.5, and the authors could account for the excess mortality largely on the risk factors known to be associated with obesity. The latter are currently being much better treated than in that era. 115

Issues to be Resolved When Relating BMI With Health Determinants

Overall, a major unresolved issue is which factor of the following is more important in the prediction of comorbidities such as cardiovascular disease, diabetes, hypertension, malignancies, or overall death rates. Is it BMI, total body fat mass, or the distribution of body fat, that is, visceral versus subcutaneous, or upper body fat accumulation (as determined by abdominal circumference, or a waist/hip ratio, or some combination of these, and so on)? The EPIC 109 data suggest that where fat is accumulated is much more important than merely the BMI, with the exception of those with an exceeding large total fat mass.

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Body Mass Index: Obesity, BMI, and Health: A Critical Review

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Body fatness has been an important psychosocial issue among humans for millennia. It is clearly manifested by paleolithic statuettes of exceedingly plump women. This suggests being "full figured" was highly desirable at least for women.

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Rhoda Inimgba: Genetic susceptibility to obesity and diet intakes: associationand interaction analyses in the Malmo¨ Diet and Cancer Study.

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Rhoda Inimgba: Genetic susceptibility to obesity and diet intakes: associationand interaction analyses in the Malmo¨ Diet and Cancer Study.

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Obesity is one of the major health problems that keep increasing globally. This health problem could be as a result of environmental factors, genetic factors or even both. This study is important because it focuses on the gene-environment factors that could potentially cause obesity. The study talks about if genetic susceptibility to obesity has any association with the diet the person takes in and if also the food taken in by the individual changes the genetic susceptibility. The key finding of the study is that the risk alleles associated to obesity and the individual obesity loci may play a role in the regulation of the amount of food intake and energy and some individual loci may also have an interaction with the diet. This finding relates to nutritional sciences as it shows that the amount of food a person takes in that may lead to obesity may have something to do with their genes and they do not have to utterly change their diet but could just try to have a healthy lifestyle. The clinical implications of this study is that it helps physicians encourage their patients to live a healthy lifestyle and prevent their patients from doing some devastating diets when their weight gain could have nothing to do with the food they intake.

Rukh, G., Sonestedt, E., Melander, O., Hedblad, B., Wirfält, E., Ericson, U., & Orho-Melander, M. (2013). Genetic susceptibility to obesity and Diet intakes: Association and interaction analyses in the Malmö Diet and Cancer Study. Genes & Nutrition, 8(6), 535–547. https://doi.org/10.1007/s12263-013-0352-8.

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Ivana Djuricic--Gut microbiome and obesity

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Ivana Djuricic--Gut microbiome and obesity

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Delzenne, & Cani, P. D. (2011). Interaction between obesity and the gut microbiota: relevance in nutrition. Annual Review of Nutrition, 31(1), 15–31. https://doi.org/10.1146/annurev-nutr-072610-145146

This paper points out the importance of the gut microbiome when it comes to obesity and how a high fat diet can change your microbiome to make you more susceptible to metabolic diseases. Short chain fatty acids, indirect nutrients produced by gut microbiota, have roles in regulating immunity, energy metabolism and adipose tissue expansion. Changes in gut microbiota are correlated with obesity and with type 2 diabetes.

One interesting finding in the paper is that a study found that taking prebiotics, thereby changing the gut microbiota composition, was associated with greater satiety and reduced hunger as well as food consumption in healthy people. There is a potential for prebiotics to control gut-related inflammation for obese people and would improve the gut barrier function so that prebiotics could help blunt the effect obesity has on inflammation and lipid homeostasis. So, nutrition-wise, adding prebiotics to a dietary plan for people with obesity could potentially have positive effects on their inflammation status and reduce hunger thereby help the patients lose weight.

Clinically, for patients with diabetes and liver diseases as well as obesity, prebiotics could prove to lessen the symptoms/effects that obesity and inflammation help to foster.

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Seonwook Kim: High fat diet induces brain insulin resistance and cognitive impairment in mice

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Seonwook Kim: High fat diet induces brain insulin resistance and cognitive impairment in mice

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The article that I selected for this week is how high-fat diet induces brain insulin resistance and cognitive impairment. It is known that high-fat diet-induced obesity is associated with insulin resistance and other chronic diet related illness, including dementia such as Alzheimer's disease. This study investigated that diet-induced changes in peripheral insulin sensitivity affect brain insulin signaling and cognitive functions. The key finding of this study is that insulin sensitivity might contribute to cognitive impairment. The nutritional design for this study is that one mice group fed high-fat diet that contain 40% energy from fat with liquid sugar (HFS) and the other group fed normal chow diet that contain 12% energy from fat. HFS group significant increase in obesity and insulin resistance. In addition, HFS mice brain exhibited biochemical changes related to increased amyloid beta deposition and neurofibrillary tangle formation and decreased synaptic plasticity. Those are all important for cognitive function. As a clinical implication, avoiding high-fat diet is beneficial to not only obese patients but also cognitive dysfunction patients.

Kothari V, Luo Y, Tornabene T, O'Neill AM, Greene MW, Geetha T, Babu JR. High fat diet induces brain insulin resistance and cognitive impairment in mice. Biochim Biophys Acta Mol Basis Dis. 2017 Feb;1863(2):499-508. doi: 10.1016/j.bbadis.2016.10.006. Epub 2016 Oct 19. PMID: 27771511.

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Affiliations

¹ Department of Nutrition, Dietetics, and Hospitality Management, Auburn University, Auburn, AL 36849, United States.

² Department of Biology, Auburn University at Montgomery, AL 36124, United States.

³ Department of Chemistry, Auburn University at Montgomery, AL 36124, United States.

⁴ Department of Nutrition, Dietetics, and Hospitality Management, Auburn University, Auburn, AL 36849, United States. Electronic address: jeganrb@auburn.edu.

Free article

Free article

Affiliations

¹ Department of Nutrition, Dietetics, and Hospitality Management, Auburn University, Auburn, AL 36849, United States.

² Department of Biology, Auburn University at Montgomery, AL 36124, United States.

³ Department of Chemistry, Auburn University at Montgomery, AL 36124, United States.

⁴ Department of Nutrition, Dietetics, and Hospitality Management, Auburn University, Auburn, AL 36849, United States. Electronic address: jeganrb@auburn.edu.

Abstract

High fat diet-induced obesity is associated with insulin resistance (IR) and other chronic, diet related illnesses, including dementia. Alzheimer disease is the most common form of dementia, and is characterized by the presence of amyloid plaques and neurofibrillary tangles in brain. This study was designed to determine whether diet-induced changes in peripheral insulin sensitivity could contribute to alterations in brain insulin signaling and cognitive functions. Six week old, male C57BL/6NHsd mice were randomly assigned a high fat diet (40% energy from fat) with 42g/L liquid sugar (HFS) added to the drinking water or a normal chow diet (12% energy from fat) for 14weeks. Metabolic phenotypes were characterized for energy expenditure, physical activity, and food intake, and glucose and insulin tolerance tests. In addition, we examined the changes in protein expression related to brain insulin signaling and cognitive function. Mice fed HFS exhibited a statistically significant increase in obesity, and lower glucose and insulin tolerance as compared to animals fed the normal chow diet. In brain, HFS elicited IR as evidenced by a significant decrease in tyrosine phosphorylation of insulin receptor and an increase serine phosphorylation of IRS-1. These changes were accompanied by inflammatory (NFκB, JNK) and stress responses (p38 MAPK, CHOP) in whole brain lysate. In addition, HFS mouse brain exhibited biochemical changes related to increased amyloid beta deposition and neurofibrillary tangle formation, and decreased synaptic plasticity. These results suggested changes in insulin sensitivity might contribute to cognitive impairment associated with the HFS diet in mice.

Keywords: Alzheimer disease; Insulin resistance; Obesity; Western diet.

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national center for biotechnology information

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High fat diet induces brain insulin resistance and cognitive impairment in mice - PubMed

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High fat diet-induced obesity is associated with insulin resistance (IR) and other chronic, diet related illnesses, including dementia. Alzheimer disease is the most common form of dementia, and is characterized by the presence of amyloid plaques and neurofibrillary tangles in brain. This study was ...

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Vitamin D Deficiency: Consequence of Obesity? -Cole Cincinelli

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Vitamin D Deficiency: Consequence of Obesity? -Cole Cincinelli

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“Vitamin D (VD) is essential for the maintenance of bone tissue, as well as for homeostasis of the minerals calcium and phosphorus. Its receptors have been found all over the human body indicating multiple functions.”[1]  Serum vitamin D is lower in obese people as it is also distributed throughout fat, muscle and liver. With the increased fat mass in obese individuals, increased levels of vitamin D are store there leaving less in the serum. Although its crucial role in bone tissue maintenance, obese adults do not have lower bone mineral density.[2] Further research is in the process of looking into the full extent of lower serum vitamin D in obese patients and its affect. Bone loss has been an observed effect in patients undergoing bariatric surgery. This could possibly be a result of the low levels of serum vitamin D combined with the malabsorption of nutrients from bariatric surgery. Important clinical implications would be to ensure vitamin D sufficiency in obese patients and especially bariatric patients to help attenuate bone loss.

[1] Vranić, L., Mikolašević, I., & Milić, S. (2019). Vitamin D Deficiency: Consequence or Cause of Obesity?. Medicina (Kaunas, Lithuania), 55(9), 541. https://doi.org/10.3390/medicina55090541

[2] Walsh, J. S., Bowles, S., & Evans, A. L. (2017). Vitamin D in obesity. Current opinion in endocrinology, diabetes, and obesity, 24(6), 389–394. https://doi.org/10.1097/MED.0000000000000371

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Microbial Medicine: Prebiotic and Probiotic Functional Foods to Target Obesity and Metabolic Syndrome - Esraa Zamzami

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Microbial Medicine: Prebiotic and Probiotic Functional Foods to Target Obesity and Metabolic Syndrome - Esraa Zamzami

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For this week’s weekly wall, the paper I chose talks about the relationship between gut microbiota and obesity. Scientists realized the significant difference in the composition and diversity of obese individuals’ gut microbiome related to other lean individuals. Obese individuals have a condition called dysbiosis, which means that the composition and diversity of the healthy microbiota have been disturbed, leading to an unhealthy environment in the gut.
This study is important because obesity is now considered a global epidemic and targeting the microbiome for a potential therapy is less invasive and demanding. One of the main key findings in this paper is that the short-chain fatty acids produced by the gut microbiota can induce modulation of appetite and metabolism in individuals. This can happen through many pathways, for example, stimulating the secretion of glucagon-like peptide 1 that promotes intestinal gluconeogenesis, enhances insulin sensitivity and reduces appetite. Additionally, SCFAs enhance the production of peptide YY and leptin that reduce energy intake and increase satiety levels. This directly relates to nutritional sciences because it affects the appetite and metabolic pathways that may lead to weight reduction. The clinical implication of this study is that recommendations to increase probiotics and prebiotics consumption to enhance the gut microbiome could be helpful.

Green, M., Arora, K., & Prakash, S. (2020). Microbial Medicine: Prebiotic and Probiotic Functional Foods to Target Obesity and Metabolic Syndrome. International journal of molecular sciences, 21(8), 2890. https://doi.org/10.3390/ijms21082890

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Semaglutide an Alternative Treatment  for Obesity- Ashley Wahlman

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Semaglutide an Alternative Treatment  for Obesity- Ashley Wahlman

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This is an important study because it offers obese patients an alternative route to surgery. I believe many people would prefer a solution that first doesn't require surgery and that is exactly what this new medication known as semaglutide provides. This drug is a glucagon like 1 peptide typically used to treat type ll diabetic patients. In a study completed, patients were given semaglutide weekly for 68 weeks along with lifestyle changes such as a low caloric diet. At the end of the 68 weeks 1/3 of these patients had experienced weight loss 20% from their baseline weight, which is comparable to having a post sleeve gastrectomy which yields 20-30% weight loss after 1-3 years.  This is great because individuals who have reached a specific weight and would qualify for bariatric surgery would have an alternative for going under the knife. However, it would be important to stress to patients still the importance of a healthy diet because no matter if you go under the knife or you take a medication the only way you will achieve the best results is through diet. The clinical implications is that people wouldn't have to take unnecessary risk if they are obese they now have another option to explore before completely having to undergo bariatric surgery which comes along with complications. For example, in some cases with bariatric surgery patients may develop an eating disorder, which may lead to anorexia/bulimia. I think this is a great alternative that minimizes some of those risks and provides the patient with another option before completely going under the knife.

Works Cited
Bham, A., & Ditta, M. A. (2021). Semaglutide for the treatment of obesity – a review. Middle East Journal of Family Medicine, 19(9), 61–64. https://doi-org.ezproxy.uky.edu/10.5742/MEWFM.2021.94116

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Key facts

In 2022, 1 in 8 people in the world were living with obesity.

Worldwide adult obesity has more than doubled since 1990, and adolescent obesity has quadrupled.

In 2022, 2.5 billion adults (18 years and older) were overweight. Of these, 890 million were living with obesity.

In 2022, 43% of adults aged 18 years and over were overweight and 16% were living with obesity.

In 2022, 37 million children under the age of 5 were overweight.

Over 390 million children and adolescents aged 5-19 years were overweight in 2022, including 160 million who were living with obesity.

Overview

Overweight is a condition of excessive fat deposits.

Obesity is a chronic complex disease defined by excessive fat deposits that can impair health. Obesity can lead to increased risk of type 2 diabetes and heart disease, it can affect bone health and reproduction, it increases the risk of certain cancers. Obesity influences the quality of living, such as sleeping or moving.

The diagnosis of overweight and obesity is made by measuring people's weight and height and by calculating the body mass index (BMI): weight (kg)/height² (m²). The body mass index is a surrogate marker of fatness and additional measurements, such as the waist circumference, can help the diagnosis of obesity.

The BMI categories for defining obesity vary by age and gender in infants, children and adolescents.

Adults

For adults, WHO defines overweight and obesity as follows:

overweight is a BMI greater than or equal to 25; and

obesity is a BMI greater than or equal to 30.

For children, age needs to be considered when defining overweight and obesity.

Children under 5 years of age

For children under 5 years of age:

overweight is weight-for-height greater than 2 standard deviations above WHO Child Growth Standards median; and

obesity is weight-for-height greater than 3 standard deviations above the WHO Child Growth Standards median.

Charts and tables: WHO child growth standards for children aged under 5 years

Children aged between 5-19 years

Overweight and obesity are defined as follows for children aged between 5-19 years:

overweight is BMI-for-age greater than 1 standard deviation above the WHO Growth Reference median; and

obesity is greater than 2 standard deviations above the WHO Growth Reference median.

Charts and tables: WHO growth reference for children aged between 5-19 years

Facts about overweight and obesity

In 2022, 2.5 billion adults aged 18 years and older were overweight, including over 890 million adults who were living with obesity. This corresponds to 43% of adults aged 18 years and over (43% of men and 44% of women) who were overweight; an increase from 1990, when 25% of adults aged 18 years and over were overweight. Prevalence of overweight varied by region, from 31% in the WHO South-East Asia Region and the African Region to 67% in the Region of the Americas.

About 16% of adults aged 18 years and older worldwide were obese in 2022. The worldwide prevalence of obesity more than doubled between 1990 and 2022.

In 2022, an estimated 37 million children under the age of 5 years were overweight. Once considered a high-income country problem, overweight is on the rise in low- and middle-income countries. In Africa, the number of overweight children under 5 years has increased by nearly 23% since 2000. Almost half of the children under 5 years who were overweight or living with obesity in 2022 lived in Asia.

Over 390 million children and adolescents aged 5-19 years were overweight in 2022. The prevalence of overweight (including obesity) among children and adolescents aged 5-19 has risen dramatically from just 8% in 1990 to 20% in 2022. The rise has occurred similarly among both boys and girls: in 2022 19% of girls and 21% of boys were overweight.

While just 2% of children and adolescents aged 5-19 were obese in 1990 (31 million young people), by 2022, 8% of children and adolescents were living with obesity (160 million young people).

Causes of overweight and obesity

Overweight and obesity result from an imbalance of energy intake (diet) and energy expenditure (physical activity).

In most cases obesity is a multifactorial disease due to obesogenic environments, psycho-social factors and genetic variants. In a subgroup of patients, single major etiological factors can be identified (medications, diseases, immobilization, iatrogenic procedures, monogenic disease/genetic syndrome).

The obesogenic environment exacerbating the likelihood of obesity in individuals, populations and in different settings is related to structural factors limiting the availability of healthy sustainable food at locally affordable prices, lack of safe and easy physical mobility into the daily life of all people, and absence of adequate legal and regulatory environment.

At the same time, the lack of an effective health system response to identify excess weight gain and fat deposition in their early stages is aggravating the progression to obesity.

Common health consequences

The health risks caused by overweight and obesity are increasingly well documented and understood.

In 2019, higher-than-optimal BMI caused an estimated 5 million deaths from noncommunicable diseases (NCDs) such as cardiovascular diseases, diabetes, cancers, neurological disorders, chronic respiratory diseases, and digestive disorders (1).

Being overweight in childhood and adolescence affects children's and adolescents' immediate health and is associated with greater risk and earlier onset of various NCDs, such as type 2 diabetes and cardiovascular disease. Childhood and adolescent obesity have adverse psychosocial consequences; it affects school performance and quality of life, compounded by stigma, discrimination and bullying. Children with obesity are very likely to be adults with obesity and are also at a higher risk of developing NCDs in adulthood.

The economic impacts of the obesity epidemic are also important. If nothing is done, the global costs of overweight and obesity are predicted to reach US$ 3 trillion per year by 2030 and more than US$ 18 trillion by 2060 (2).

Finally, the rise in obesity rates in low-and middle-income countries, including among lower socio-economic groups, is fast globalizing a problem that was once associated only with high-income countries.

Facing a double burden of malnutrition

Many low- and middle-income countries face a so-called double burden of malnutrition.

While these countries continue to deal with the problems of infectious diseases and undernutrition, they are also experiencing a rapid upsurge in noncommunicable disease risk factors such as obesity and overweight.

It is common to find undernutrition and obesity co-existing within the same country, the same community and the same household.

Children in low- and middle-income countries are more vulnerable to inadequate pre-natal, infant, and young child nutrition. At the same time, these children are exposed to high-fat, high-sugar, high-salt, energy-dense, and micronutrient-poor foods, which tend to be lower in cost but also lower in nutrient quality. These dietary patterns, in conjunction with lower levels of physical activity, result in sharp increases in childhood obesity while undernutrition issues remain unsolved.

Prevention and management

Overweight and obesity, as well as their related noncommunicable diseases, are largely preventable and manageable.

At the individual level, people may be able to reduce their risk by adopting preventive interventions at each step of the life cycle, starting from pre-conception and continuing during the early years. These include:

ensure appropriate weight gain during pregnancy;

practice exclusive breastfeeding in the first 6 months after birth and continued breastfeeding until 24 months or beyond;

support behaviours of children around healthy eating, physical activity, sedentary behaviours and sleep, regardless of current weight status;

limit screen time;

limit consumption of sugar sweetened beverages and energy-dense foods and promote other healthy eating behaviours;

enjoy a healthy life (healthy diet, physical activity, sleep duration and quality, avoid tobacco and alcohol, emotional self-regulation);

limit energy intake from total fats and sugars and increase consumption of fruit and vegetables, as well as legumes, whole grains and nuts; and

engage in regular physical activity.

Health practitioners need to

assess the weight and height of people accessing the health facilities;

provide counselling on healthy diet and lifestyles;

when a diagnosis of obesity is established, provide integrated obesity prevention and management health services including on healthy diet, physical activity and medical and surgical measures; and

monitor other NCD risk factors (blood glucose, lipids and blood pressure) and assess the presence of comorbidities and disability, including mental health disorders.

The dietary and physical activity patterns for individual people are largely the result of environmental and societal conditions that greatly constrain personal choice. Obesity is a societal rather than an individual responsibility, with the solutions to be found through the creation of supportive environments and communities that embed healthy diets and regular physical activity as the most accessible, available and affordable behaviours of daily life.

Stopping the rise in obesity demands multisectoral actions such as food manufacturing, marketing and pricing and others that seek to address the wider determinants of health (such as poverty reduction and urban planning).

Such policies and actions include:

structural, fiscal and regulatory actions aimed at creating healthy food environments that make healthier food options available, accessible and desirable; and

health sector responses designed and equipped to identify risk, prevent, treat and manage the disease. These actions need to build upon and be integrated into broader efforts to address NCDs and strengthen health systems through a primary health care approach.

The food industry can play a significant role in promoting healthy diets by:

reducing the fat, sugar and salt content of processed foods;

ensuring that healthy and nutritious choices are available and affordable to all consumers;

restricting marketing of foods high in sugars, salt and fats, especially those foods aimed at children and teenagers; and

ensuring the availability of healthy food choices and supporting regular physical activity practice in the workplace.

WHO response

WHO has recognized the need to tackle the global obesity crisis in an urgent manner for many years.

The World Health Assembly Global Nutrition Targets aiming to ensure no increase in childhood overweight, and the NCD target to halt the rise of diabetes and obesity by 2025, were endorsed by WHO Member States. They recognized that accelerated global action is needed to address pervasive and corrosive problem of the double burden of malnutrition.

At the 75 th World Health Assembly in 2022, Member States demanded and adopted new recommendations for the prevention and management of obesity and endorsed the WHO Acceleration plan to stop obesity. Since its endorsement, the Acceleration plan has shaped the political environment to generate impetus needed for sustainable change, created a platform to shape, streamline and prioritize policy, support implementation in countries and drive impact and strengthen accountability at national and global level.
References
1. GBD 2019 Risk Factor Collaborators. "Global Burden of 87 Risk Factors in 204 Countries and Territories, 1990-2019: a systematic analysis for the global burden of disease study 2019". Lancet. 2020;396:1223-1249.

2. Okunogbe et al., "Economic Impacts of Overweight and Obesity." 2nd Edition with Estimates for 161 Countries. World Obesity Federation, 2022.

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Obesity and overweight fact sheet from WHO providing key facts and information on causes, health consequences, double burden of disease, prevention, WHO response.

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Weight Discrimination and Risk of Mortality

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Weight Discrimination and Risk of Mortality

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Weight Discrimination and Risk of Mortality (nih.gov)

This study analyzed the health implications that can result or be exacerbated due to weight associated discrimination in health care settings. It was found that of the participants studied, “weight discrimination was associated with nearly 60% increased mortality risk for patients in HRS (renal disease) and MIDUS, that was not accounted for by a common physical and psychological risk factors.” Participants who had experienced healthcare associated weight discrimination were described as having increased food consumption, potential blood pressure increases, and stress as a result. The clinical implications of this study are awareness for health care providers, and patients. This discrimination affects people’s health, willingness to go receive future healthcare, and is not acceptable way of treating anyone. This is another example of potential discrimination that can occur which needs to be addressed in training to alert for any bias (unconscious or conscious). This relates to nutritional science because it can affect patient participation in nutrition counseling, adherence, resistance, or consideration as a provider.

Sutin, Angelina R et al. “Weight Discrimination and Risk of Mortality.” Psychological science vol. 26,11 (2015): 1803-11. doi:10.1177/0956797615601103

Emalee Haynes

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Ultraprocessed Food: Addictive, Toxic, and Ready for Regulation

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Ultraprocessed Food: Addictive, Toxic, and Ready for Regulation

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The research paper selected for this week is titled: “Ultraprocessed Food: Addictive, Toxic, and Ready for Regulation”

This research paper is a 2020 review study from Department of Pediatrics and Institute for Health Policy Studies, University of California, San Francisco, CA 94143, USA and Department of Research, Touro University-California, Vallejo, CA 94592, USA. This single authored encyclopedic review article is by Robert H. Lustig, M.D., M.S.L., Professor emeritus of Pediatrics, Division of Endocrinology at the University of California, San Francisco (UCSF). (This article belongs to the Special Issue The Clinical Utility of Food Addiction and Eating Addiction published by Nutrients.)

This study is important because the pandemic of non-communicable diseases (NCDs; type 2 diabetes, cardiovascular disease, fatty liver disease, hypertension, heart disease, stroke, cancer, and dementia), has its origins in our Western ultraprocessed food diet. NCDs account for 72% of the deaths and 75% of health care dollars. Obesity is a “marker” for the pathophysiology of NCDs, but not a primary cause—because a percentage of normal weight people get NCDs as well, while a percentage of people with obesity are metabolically healthy.

The key findings from this study are that added sugar, and by proxy the ultraprocessed food category, is both toxic and addictive, sugar reduction promotes health, is achievable, and is necessary, and added sugar ultraprocessed foods meets the four criteria set by the public health community as necessary and sufficient for regulation—abuse, toxicity, ubiquity, and externalities. While public educational efforts are necessary to warn about the hazards of chronic excessive sugar consumption, they will not be sufficient, as has been seen for every other hedonic substance.

Clinical implication from of this study is that the health care community needs to support policies that target availability, affordability, or acceptability of added sugar ultraprocessed foods. For example, ban sales of sugar-sweetened beverages in the workplace, tax the sales of added sugar products, and create a stigma on the consumptions of added sugar utraprocessed “foods.” 1

Lustig RH. Ultraprocessed Food: Addictive, Toxic, and Ready for Regulation. Nutrients. 2020; 12(11):3401. https://doi.org/10.3390/nu12113401

Richard Proudfoot

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Katelyn Spain: Physical and mental health related quality of life changes among insurer subgroups following bariatric surgery

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Katelyn Spain: Physical and mental health related quality of life changes among insurer subgroups following bariatric surgery

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Over the past few decades, obesity has increased in the world and over the past few years bariatric surgery has become more known, especially through the media. This study is important because it shows the more long-term effects bariatric surgery can have on patients that were previously diagnosed as morbidly obese. It was found that patients receiving the surgery, after five years, their physical health-related quality of life had significantly improved, while their mental health-related quality of life did not improve significantly. There were a lot of physical-health related factors such as diabetes, hypertension, dyslipidemia, sleep apnea, ischemic heart disease, congestive heart failure, history of stroke, pulmonary hypertension, asthma, history of deep vein thrombosis or pulmonary embolism, and venous edema with ulcerations that contributed to the overall score of physical health improvement. These symptoms and comorbidities were significantly improved with the surgery according to patients. However, one key finding of the study found that the mental health of these patients was similar to those of the general US population and to those patients who had not had bariatric surgery. This study also separated the patients into two groups: those who had the surgery performed under Medicaid insurance and those with private medical insurance. I thought this was interesting and they found no significant differences between these groups. As for the clinical implications from this study, it was proven that bariatric surgery will have positive effects on an obese patient’s physical health and that patients who are morbidly obese should seek this treatment to improve their physical well-being and lifespan. The article also discussed how bariatric surgeries should be more available to patients.
Takemoto, E., Wolfe, B. M., Nagel, C. L., & Boone-Heinonen, J. (2020). Physical and Mental Health-Related Quality of Life Changes Among Insurer Subgroups Following Bariatric Surgery. Obesity (Silver Spring, Md.), 28(3), 669–675. https://doi.org/10.1002/oby.22718

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Abstract

Objective.

We sought to determine improvements in mental and physical health-related quality of life (HRQOL) following bariatric surgery in Medicaid and commercially insured patients.

Methods.

Using data from the Longitudinal Assessment of Bariatric Surgery, an observational cohort study of adults undergoing bariatric surgery (2006-2009), we examined changes in Short Form (SF)-36 mental (MCS) and physical component summary (PCS) scores in 1,529 patients who underwent Roux-en-Y Gastric Bypass (RYGB), Laparoscopic Adjustable Band (LAGB), or Sleeve Gastrectomy (SG) and were followed for 5 years. Piecewise linear mixed-effects models estimated MCS and PCS scores as a function of insurance group (Medicaid, N=177; commercial, N=1,352) in 0-1 and 1-5 years post-surgery, with interactions between insurance group and surgery type.

Results.

Medicaid patients had lower PCS and MCS scores at baseline. At 1-year post-surgery, commercial and Medicaid patients experienced similar improvement in PCS scores (Commercial-Medicaid difference in PCS change [95% CI]: RYGB: 1.5 [−0.2, 3.3]; LAGB: 1.9 [−2.2, 6.0]; SG: 6.4 [0.0, 12.8]). 1-year MCS score improvement was minimal and similar between insurance groups. In years 1-5, PCS and MCS scores were stable in all groups.

Conclusions.

Both insurance groups experience improvements in physical HRQOL and minimal changes in mental HRQOL.

Keywords: Epidemiology, obesity treatment, bariatric surgery, outcomes, Medicaid

Introduction

Severe obesity is associated with impairment to health-related quality of life (HRQOL), a multi-dimensional concept that includes domains related to physical, mental, emotional and social functioning ( 1- 5). Increasing HRQOL is a Healthy People 2020 central public health goal ( 1) and HRQOL measures are considered important indicators of intervention outcomes and are powerful predictors of mortality and morbidity ( 6, 7). Among the general patient population with severe obesity, bariatric surgery has demonstrated effectiveness in improving long-term HRQOL ( 8- 10). However, while current evidence suggests bariatric surgery is effective in improving HRQOL among the general population, bariatric surgery effectiveness is known to vary by individual-level characteristics and less is known about HRQOL among patient subgroups.

Importantly, the Medicaid patient population has a disproportionately high burden of obesity, a greater number of comorbidities, and lower HRQOL as compared to commercially insured patients ( 11- 13). Despite this poorer overall health profile, recent evidence suggests Medicaid patients have similar post-operative improvements in weight ( 14) and comorbidity remission outcomes ( 15, 16), and that these improvements are sustained over time. Yet, no studies to date have focused on post-surgical changes to HRQOL among patients with Medicaid, a critical but understudied potential health benefit of bariatric surgery.

The goal of this investigation was to quantify the changes in physical and mental HRQOL following bariatric surgery in Medicaid and commercially insured patients. We utilize data from the Longitudinal Assessment of Bariatric Surgery (LABS) ( 17), a multi-site, diverse, observational cohort with high levels of follow-up over 5-years post-operatively. We hypothesized that Medicaid patients have lower baseline levels of physical and mental HRQOL, but experience similar improvements following surgery and over time as compared to commercially insured patients.

Methods

Analytic Sample

Of the 2458 LABS participants, we first excluded participants who were missing baseline health insurance information or reported self-paying for surgery (N=389). Next, participants reporting Medicare only (N=210), Tricare only (N=67) or "Other" insurance (N=86) were excluded. Participants undergoing Biliary Pancreatic Diversion with Duodenal Switch (N=16) and Banded Bypass (N=26) were uncommon in this cohort and thus excluded. Finally, participants who contributed fewer than two HRQOL scores over the six time points were excluded from analyses (N=135), leaving 1,529 participants in the final analytic cohort ().

Data collection

LABS-certified trained personnel collected study data using standard protocols ( 18, 19). Data collection consisted of blood and urine samples, physical measurements, self-assessment forms, surgeon and medical staff forms, and chart review procedures. Baseline data were collected within 30 days before surgery. Annual follow-up assessments were conducted within 6 months of surgery anniversary date for five consecutive years. Data were entered twice using a web-based data entry system developed, distributed, and maintained by the University of Pittsburgh LABS Data Coordinating Center.

Study Variables

Short-Form 36 Physical & Mental Component Summary Scores (Outcome)

The SF-36 is a widely used and validated 36-item questionnaire which measures functional health and well-being from the patient's point of view ( 20). The SF-36 includes two psychometrically-based sub-scores, the physical component summary (PCS) and the mental component summary (MCS). MCS and PCS scores are calibrated such that 50 is the average or U.S. population norm score; scores below 50 suggest lower self-rated health ( 20). In this study, MCS and PCS scores were analyzed as a continuous score at each study time point (range 0-100), where a five point increase in score represents a clinically meaningful improvement ( 21, 22).

Insurance Type

Self-reported insurance type was collected using a self-assessment form at the baseline study visit. Participants with available baseline insurance information were classified into two categories: 1) Medicaid with or without Medicare; and 2) Commercial insurance with or without Medicare. Participants reporting other insurance types were excluded from this analysis as they were heterogeneous with regard to their sociodemographic and clinical profile. Insurance classification at baseline was analyzed as a time constant variable; potential changes to insurance status over time were not incorporated given the desire to understand how the differences in baseline health status between groups influenced long-term outcomes.

Surgery type

Three primary weight-loss procedures were ascertained from surgeon reports at baseline: 1) Roux-en-Y Gastric Bypass (RYGB); 2) Laparoscopic Adjustable Gastric Band (LAGB); and 3) Sleeve Gastrectomy (SG). Participants whose initial bariatric surgery was subsequently revised or reversed remain classified with the baseline surgery type. Additionally, the decision to proceed with a specific type of operation is determined by the patient and surgeon as a component of clinical care, LABS did not participate in surgery type selection.

Covariates

Covariates included self-reported age at surgery, sex (male, female), and baseline BMI (kg/m ²). Comorbidities (diabetes, hypertension, dyslipidemia, sleep apnea, ischemic heart disease, congestive heart failure, history of stroke, pulmonary hypertension, asthma, history of deep vein thrombosis or pulmonary embolism, and venous edema with ulcerations) were determined using a combination of self-report, clinical assessment, and medical chart review and are defined elsewhere ( 17). An index of comorbidities was created as the number of comorbidities at baseline (range: 0-11) to provide a rough estimate of disease burden.

Statistical Analysis

Descriptive statistics summarize baseline characteristics for each insurance category. Pearson's chi-square test for categorical variables and t-tests for continuous variables were used to assess statistical significance of differences in baseline characteristics between the payer groups. Data management was conducted using SAS version 9.4; descriptive analyses and mixed models were conducted using Stata version 15.

Piecewise linear mixed-effects models were used to compare changes in continuous PCS and MCS scores over the 5-year post-operative period. Mixed-effects models account for the correlation among repeated measurements taken on the same individual over time and uses all available data at each time point. The piecewise approach allowed us to model non-linear changes in MCS and PCS scores over time by fitting two linear slopes corresponding to the two distinct post-operative periods of change, 0-1 and 1-5 years post-operatively. Further, interactions between insurance group, surgery type, and the two time terms enable direct comparison of 1) baseline MCS and PCS scores, and 2) the magnitude of change in MCS and PCS scores within and between the groups in the two post-operative periods. We included the spline functions as fixed and random effects, to estimate overall mean trajectories at the population level, and individual trajectories at the subject-specific level. Covariates were included as fixed effects.

Results

Description of the sample

At baseline, Medicaid patients were slightly younger (mean age: 43.6 vs 45.6 years), heavier (mean BMI: 51.7 vs 47.9 kg/m ²), and had a higher comorbidity index (mean comorbidities: 2.9 vs 2.5) as compared to commercially insured patients (). Follow-up over at five years was high, ranging from 71-83% of the original sample, and varied across surgery type and by the Medicaid (Medicaid: RYGB: 71%; LAGB: 74%; SG: 83%) and Commercial patient groups (RYGB: 73%; LAGB: 71%; SG: 76%) (data not shown).

Table 1.

Five-year adjusted physical HRQOL changes in patients undergoing bariatric surgery

Adjusted baseline PCS scores for all three surgery types were low in both insurance groups (below the population norm of 50), more so among Medicaid patients (adjusted mean baseline PCS: 34.2 [RYBG], 36.3 [LAGB], 36.2 [SG]) than commercially insured patients (adjusted mean baseline PCS: 39.0 [RYGB], 41.5 [LAGB], 40.2 [SG]) (; ). At 1-year post-surgery, both patient groups had experienced increases in their PCS scores. The increase in insurer group-specific mean score was similar between the two insurance groups undergoing RYGB [PCS change (95% CI): Commercial: 13.5 (12.8, 14.1) and Medicaid: 11.9 (10.3, 13.6); Commercial-Medicaid difference (95% CI): 1.5 (−0.2, 3.3)] and LAGB [PCS increase (95% CI): Commercial: 8.7 (7.6, 9.7) and Medicaid 6.7 (2.8, 10.7); Commercial-Medicaid difference (95% CI): 1.9 (−2.2, 6.0)]. Whereas among patients who underwent SG, commercial patients experienced a 6.4 points (95% CI: 0.0, 12.8) greater increase in PCS score [13.7 (95% CI: 10.2, 17.2)] compared to Medicaid patients [7.3 (95% CI: 1.9, 12.7)]. In the 1-5 year period, scores among both patients groups and following all surgery types were relatively stable maintaining the initial improvements in mean score, with a slight downward trend over time.

Table 2.

Five-year adjusted mental HRQOL changes in patients undergoing bariatric surgery

Adjusted baseline MCS scores were high at baseline and near the population norm of 50 in both insurance groups; however, Medicaid patients (adjusted mean baseline MCS: 47.1 [RYGB], 48.0 [LAGB], 42.9 [SG]) across all three surgery types had slightly lower scores than Commercial patients (adjusted mean baseline MCS: 49.5 [RYGB], 49.5 [LAGB], 46.4 [SG]) (; ). In the 0-1 year period, Medicaid patients undergoing RYGB experienced a minimal increase in MCS scores [MCS increase (95% CI): 1.4 (−0.4, 3.2)] and those undergoing LAGB and SG experienced slight decreases [MCS decrease (95% CI), LAGB: −1.4 (−5.6, 2.9) and SG: −0.7 (−6.4, 5.0)]. Commercial patients across all three surgery types experienced minimal increases in MCS scores (MCS increase (95% CI), RYGB: 2.2 (1.6, 2.9) and LAGB: 1.5 (0.4, 2.6) and SG: 3.2 (−0.5, 7.0)]. None of the differences between insurer groups in MCS score change were statistically significant. Similar to the PCS score, the scores for both patient groups were stable in the 1-5 year period.

Table 3.

Discussion

In this study, we provide new information on long-term improvements to mental and physical HRQOL among Medicaid and commercially insured patients following bariatric surgery. All patient groups had levels of mental HRQOL similar to that of the U.S. population score at pre-operative time points and experienced only minor changes in the post-operative period. Conversely, both insurer groups report low baseline levels of physical HRQOL as compared to the U.S. population norm, experience clinically meaningful increases following surgery, and sustain these improvements over five years post-operatively. Importantly, we observed that Medicaid patients reported lower levels of physical-HRQOL at baseline across all surgery types compared to commercially insured patients. And despite a similar magnitude of improvement in the first post-operative year between groups, scores among Medicaid patients remained lower over time.

To our knowledge, no other studies have reported insurer group-specific changes to HRQOL following bariatric surgery. Findings among the general patient population align with the current study's findings; a recent meta-analysis reported that bariatric surgery had a greater positive influence on physical HRQOL compared to mental HRQOL across 72 studies ( 10). Similarly, a second meta-analysis found no effect of surgery on mental HRQOL following bariatric among 11 randomized clinical trials, findings which also align with the current study's report ( 9). Our findings support reporting group-specific changes following bariatric surgery, as reporting overall findings may mask important policy- and clinically-relevant differences.

Differences in Mental HRQOL

Notably, prior to undergoing bariatric surgery, the MCS scores among the both Medicaid and commercially insured patient groups were at or near the U.S. population norm. These findings generally align with other research on mental HRQOL among patients undergoing bariatric surgery, that obesity has little impact on mental HRQOL ( 9, 10).

In both post-operative time periods, the MCS scores did not clinically meaningfully change among the insurer- and surgery-type groups. Some evidence suggests there may be an inverse association between bariatric surgery and mental HRQOL, where the psychological impacts of the surgery may actually worsen mental HRQOL over time or the incidence of post-operative mental health conditions increases ( 9). Our findings do not support this hypothesis and suggest bariatric surgery has little impact on mental HRQOL post-operatively regardless of the insurer group or surgery type. Our findings suggest future research should focus on other measures of mental health, such as depression, which might vary by patient population, especially historically underserved patient groups. Also, the SF-36 can be further broken down into more specific mental HRQOL sub-domains (e.g., emotional role functioning), and while the overall score is not changed in the post-operative period, certain domains could be more associated with bariatric surgery than others. By focusing on the overall score, changes among specific sub-domains may be masked; as such, future studies should explore these sub-domains.

Differences in Physical HRQOL

Across all surgery types, Medicaid patients had lower PCS scores as compared to commercial patients and we observed substantial improvements in PCS score in the first year following bariatric surgery among both patient groups. For commercially insured patients, improvements to PCS score aligned this group with the U.S. population normed score of 50. For Medicaid patients undergoing RYGB and LAGB, the magnitude of score improvement in the first post-operative year was similar to commercially insured patients, but Medicaid patients remained below the population average at all follow-up time points. Medicaid patients undergoing SG experienced less improvement in PCS score, but this result should be interpreted with caution given the small number of Medicaid patients undergoing SG. A similar pattern emerged in our recent investigations of weight loss ( 14) and comorbidity remission patterns ( 16), where we observe the disease disparity among Medicaid present at baseline patients persist into the post-operative periods despite a similar response to surgery. Finally, despite any difference in PCS score improvement between the insurer groups, improvements across all insurer- and surgery-type groups were greater than 5-points, indicating clinically meaningful improvements ( 21).

The finding that Medicaid patients experience consistently lower HRQOL aligns with the existing body of evidence that reports Medicaid patients have a greater severity of obesity and a greater number of comorbid conditions, which could directly lead to the lower physical HRQOL ( 11- 13, 23). However, it is unclear if the presence of more severe obesity and comorbidities is the only explanation for the lower physical HRQOL. Prior evidence suggests Medicaid patients have obesity at younger ages and have a longer waiting period prior to surgery ( 12, 24), it's possible a greater the length of time with obesity could explain the differential levels of physical HRQOL observed in this study. Similarly, Medicaid patients also may have limited or delayed access to health care ( 12, 24), making it more difficult to achieve adequate control of obesity-related comorbidities, like diabetes, over time. A greater burden of poorly controlled comorbid disease is another potential explanation for why physical HRQOL is consistently observed to be lower among Medicaid patients ( 25- 27).

Clinical and Public Health Implications

Our findings suggest that despite the effectiveness of bariatric surgery in improving a vast array of post-operative outcomes, lower overall baseline health of Medicaid patients is a key contributor to the persistence of a lower post-operative health profile. Our findings highlight the need to increase the availability of effective obesity treatments and provide access to surgical treatment earlier in the clinical progression of disease which may increase the likelihood of overcoming comorbidity-associated clinical deterioration. Also, implementing upstream interventions to prevent the development of obesity and related conditions among underserved populations remains an ongoing area of critical public health importance.

Strengths and Limitations

Study limitations are noted. The primary outcome of interest is a self-reported measure and may be subject to bias. However, the SF-36 is a validated and widely used instrument for capturing health related quality of life. The number of Medicaid patients captured in LABS is only modest, although larger than most other studies of bariatric surgery. A small number of patients underwent SG, one of the most commonly conducted surgeries today. Inference on the effects of SG on HRQOL are limited in this observational study, although the observed associations were similar to the other patient groups. The primary strength of the study is five years of follow-up with a high rate of retention on a large and highly generalizable sample.

Conclusions

In this study, we found that Medicaid patients experienced important and sustained improvements in physical HRQOL following bariatric surgery. We also observed that, despite a high degree of obesity and heavy burden of comorbid disease, mental HRQOL was similar to that of the general U.S. population and surgery had no impact on changes over time. Importantly, despite similar improvement in physical HRQOL, levels remained below commercially insured patients and below the U.S. average over time. These differences highlight the importance of the overall disease burden among Medicaid patients and the need for improved primary prevention and treatment to lessen the burden of disease on quality of life.

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Acknowledgments

Funding: LABS-2 was funded by a cooperative agreement by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Grant numbers: Data Coordinating Center, U01 DK066557; Columbia-Presbyterian, U01- DK66667 (in collaboration with Cornell University Medical Center CTSC, grant UL1-RR024996); University of Washington, U01-DK66568 (in collaboration with CTRC, grant M01 RR-00037); Neuropsychiatric Research Institute, U01- DK66471; East Carolina University, U01-DK66526; University of Pittsburgh Medical Center, U01-DK66585 (in collaboration with CTRC, grant UL1-RR024153); and Oregon Health & Science University, U01-DK66555.

References

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# data[].attributes.attachment_link.title
Physical and mental health related quality of life changes among insurer subgroups following bariatric surgery

# data[].attributes.attachment_link.description
We sought to determine improvements in mental and physical health-related quality of life (HRQOL) following bariatric surgery in Medicaid and commercially insured patients.