Obesity is a complex disease that is associated with a number of comorbidities, increased mortality, and reduced quality of life. Abdominal obesity is one of the components of metabolic syndrome (MetS), a cluster of risk factors that increases an individual’s risk for chronic conditions such as cardiovascular disease and diabetes. This chapter details the epidemiology of obesity and MetS by age, race, and geographic location and includes data on the direct costs associated with obesity and MetS.
The following tables summarize the most recently published data on the prevalence of obesity in US adults and children, and metabolic syndrome in US adults. In all cases, data from the National Health and Nutrition Examination Survey (NHANES) was used.
BMI ≥ 25 kg/m2
BMI ≥ 30 kg/m2
BMI ≥ 40 kg/m2
|NHANES 2011-2012||Age 20+ years||33.9%||35.1%||6.4%|
Source: Fryar et al. 20141
|Source||Population||OverweightBMI for age ≥ 85th percentile||ObesityBMI for age ≥ 95th percentile|
|NHANES 2011-2012||Age 2-19 years||31.8%||16.9%|
Source: Ogden et al. 20142
|NHANES 2009-2010||Age 20+ years||22.9%|
Source: Beltran-Sanchez et al. 20133
MetS in adults is defined as abnormal values for three or more of the harmonized criteria, including waist circumference, triglycerides, HDL-cholesterol, blood pressure or glucose, as outlined in the 2009 Joint Interim Statement of the International Diabetes Federation Task force on Epidemiology; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity.4
|NHANES 1999-2004||Age 12-19 years||4.5%|
Source: Ford et al. 20085
MetS in adolescents was defined using the International Diabetes Federation’s 2007 definition for use in children and adolescents.6
The direct costs associated with obesity and MetS are for the most part estimated through services rendered, i.e., the costs of treating the attributable consequences of illnesses and comorbidities, as well as (those) costs recorded by healthcare and insurance providers. Importantly, there are additional costs that are more difficult to estimate, such as absenteeism and lost productivity.
Patients with obesity are more susceptible to chronic illnesses, thus contributing to increases in medical spending. While the available estimates of cost burden vary based on data sources used, assumptions made, and mathematical methods applied7, the vast majority of studies agree that patients with obesity spend more on medical care than their normal weight counterparts.
|Annual Medical Spending Per Capita|
|National Medical Expenditure Survey, 1987||Adults age 19+ years||$2,117||$2,154||$2,438|
|Medical Expenditure Panel Survey-Household Component, 2001||Adults age 19+ years||$2,907||$3,247||$3,976|
Source: Thorpe et al. 20048
Thorpe et al. reported that 27% of the overall growth in per-capita spending between 1987-2001 was attributable to increases in the prevalence of obesity (Table 5).8 Alternative estimates have been posted by others, such as the incremental cost of overweight and obesity based on a systematic review of over thirty studies of US populations (Table 6).7
|Systematic review of 33 based studies published between 1992-2008||Adults age 18+ years||$266-$498||$1,630-1,723|
Source: Tsai et al. 20117
It has been suggested that the Medical Expenditure Panel Surveys (MEPS) underestimate spending due to their reliance on self-reporting by survey respondents9. For comparison, an analysis of actual costs in the Medicare population is provided in Table 7.
|Annual incremental Medicare expenditures compared to normal-weight beneficiaries|
|Medicare Current Beneficiary Survey, 1997-2006||Adults 65 years and older||$108||$149|
Source: Alley et al. 201210
With the rise in obesity among the older adult population, the cost burden is predicted to increase substantially: the combined medical costs associated with treatment of obesity-related diseases, cardiovascular diseases, and diabetes in the US alone have been estimated to increase by $48-66 billion/year from 2010 through 2030.11 Apart from medical costs, the associated losses in productivity need to be accounted for. According to a cross-sectional analysis of the Agency for Healthcare Research and Quality’s 2006 MEPS and the 2008 Health and Wellness Survey that calculated the annual cost attributable to obesity among full-time employees, the costs of obesity in the workplace is estimated to be $73.1 billion.12 That increase in cost is attributed to increased health-related work limitations: the time needed to complete tasks and an inability to perform physical job demands.13 As a result, workers with obesity experienced a 4.2% health-related loss in productivity, 1.2% more than all other employees.
|Data Source||Assumption||Incremental lifetime direct medical cost for a 10-year-old child with obesity|
|Systematic review of 6 papers estimating cost of obesity||Relative to normal weight child who maintains normal weight in adulthood||$16,310-19,350|
|Relative to normal weight child who becomes overweight in adulthood||$12,660-19,630|
Source: Finkelstein et al. 201414
Using these cost estimates multiplied by the number of 10-year-old children with obesity in May 2013, Finkelstein et al. proposed a total direct medical cost of obesity of $14 billion for this age group alone.
|Incremental cost compared to children with UNDERWEIGHT OR normal BMI|
|Data Source||Population||Type of Expenditure||Overweight children||Children with obesity|
|Medical Expenditure Panel Survey, 2002-2005||US children age 6-19 years||Outpatient visits||$79 (+13.1%)||$194 (+32.2%)|
|Prescription drugs||$64 (+19.5%)||$114 (+35.0%)|
|Emergency room visits||$25 (+19.9%)||$12 (+9.6%)|
Source: Trasande and Chatterjee. 200915
Extrapolated to the nation, elevated BMI in childhood was associated with $14.1 billion in additional prescription drug, emergency room, and outpatient visit costs annually.15
Due to the complex nature of MetS, treatment costs attributable to any one of its components are difficult to quantify. Recent data suggest, however, that the presence of MetS adds to the cost of treating specific comorbidities. Table 1 presents comparative data on medical costs among patients over the age of 65 with and without MetS for 10 years starting in 1992.
|No Metabolic Syndrome||Metabolic Syndrome|
|Total costs to Medicare||$33,010||$40,873|
|Medicare Part A Inpatient||$18,806||$24,414|
|Medicare Part B Physician||$10,779||$12,299|
|Medicare Part B Institutional||$3,425||$4,359|
Source: Curtis et al. 200716
Brown fat has been seen as a possible target for obesity therapy since the earliest animal studies in the 1970s.17 The physiological mechanisms that point to possible therapeutic targets have been increasingly explored in the past two years after interest was reawakened by finding brown fat scattered along the vertebrae and in the neck of adult human beings. The effects of exercise on muscle stimulate increased expression of a membrane protein that is cleaved and secreted as a newly discovered hormone, irisin. Irisin in turn stimulates the conversion (or “browning”) of white adipose cells into brown adipose tissue (BAT).18
In laboratory experiments using mouse models, hemodynamic responses in BAT metabolism to pharmacological agents, in vivo, revealed the physiological role of fibroblast growth factor 21 (FGF21): it can induce thermogenic gene expression and augmentation of a brown fat-like phenotype in white adipocytes, so called “beige” cells. This beneficial effect of FGF21 suggests it as a potential treatment for obesity.19 Another study presented MRI imaging related to BAT volume, distribution, and metabolic function in both resting and active states in pharmacologically induced responses to a beta3-andrenergic receptor agonist.20
In vitro MRI imaging of adipose tissue samples from mice and post-mortem humans aged 3 days to 18 years showed close similarity of mouse to human BAT in beige/brite cells.21 Another study using mouse models suggested a possible genetic link to obesity: a mitochondrial dysfunction that can lead to obesity, and a possible way that this phenotype lacking in Atg7 was protective against diet-induced obesity by increasing the browning of white adipose tissue through the promotion of FGF21.22
These and other studies have provided greater understanding of brown/beige fat, its mechanisms in mammals and the human body, and possibilities for new anti-obesity therapies.
A landmark study by Ley et al in 200523, built on work by Bäckhed24, introduced the notion that the bacterial population of the gut, the “microbiome,” might play a significant role in the development of obesity. The study by Ley and colleagues, which used mice as the animal model, showed that the gut and its microbial population constituted a host-microbe relationship that allowed extracted energy to be stored in adipocytes. Moreover, it was shown that this pathway involved microbial regulation of the intestinal epithelial expression of fasting-induced adipocyte protein (Fiaf), a circulating inhibitor of lipoprotein lipase.
A 2006 study comparing gut microbiota between genetically altered obese mice and lean littermates showed that obesity is associated with changes in the relative abundance of the two dominant bacterial divisions, the Bacteroidetes and the Firmicutes.25
In a human study of the host-microbe relationship, sequencing human fecal matter identified levels of differing bacterial populations that are predictive of obesity.26 Comparing individuals with lean (BMI <25 kg/m2) to overweight (BMI 25–30 kg/m2) and obese (BMI >30 kg/m2) BMI levels, the authors postulated that differences in a very few groups of bacterial species predispose progression to comorbidities such as type 2 diabetes mellitus (T2DM).
In a 2013 study, human fecal microbiota from adult female twin pairs, where one had obesity and one was normal weight, were transplanted into germ-free mice. The co-housing of mice harboring an obese twin’s microbiota with mice containing the lean co-twin’s microbiota prevented the development of increased body mass and obesity-associated metabolic phenotypes in obese cage mates.27
In 2014, news broke that using artificial “non-caloric” sweeteners (NAS) to avoid intake of calories might actually be detrimental. Suez and colleagues28 showed that consumption of commonly used NAS formulations drives the development of glucose intolerance through induction of compositional and functional alterations to the intestinal microbiota.
Thus, the understanding of how alterations in the microbiome of the human gut, as well as alterations in human dietary habits, might provide the therapeutic means to reduce or prevent obesity.
One of the first studies exploring the relationship between sleep deprivation and obesity was published in 2004.29 That study reported a U-shaped curvilinear association between sleep duration and BMI. In subjects sleeping fewer than 8 hours (74.4% of the 1024 person sample), increased BMI was proportional to decreased sleep. Short sleep (habitual sleep of 5 h vs 8 h) was associated with 15.5% lower leptin and 19.9% higher ghrelin, independent of BMI. The authors noted that these differences are likely to increase appetite; however, the study population was too nonselective to enable a definitive statement about a causative correlation with obesity and MetS.
An analysis of the 2004-2005 US National Health Interview Survey for adults aged 18 to 85 noted that sleep duration was frequently more strongly associated with risk of chronic disease than demographic characteristics, geographic region, and other health behaviors such as smoking. Both short and long sleep duration were significantly associated with obesity, relative to sleeping 7 to 8h.30
A 2010 review found that after correcting for obesity and other covariates, both quantity and quality of sleep predicted the risk of development of T2DM31 In 2012, a more specific investigation at the cellular level revealed a role for the adipocyte clock in the temporal organization of energy regulation. This study highlighted timing as a modulator of the adipocyte-hypothalamic axis and showed the impact of timing of food intake on body weight.32
In a comparison using functional MRI to study volunteer participants’ brain responses to food and non-food stimuli under differing sleep conditions, restricted sleep increased overall neuronal activity, which in turn led to a greater propensity to overeat.33 In another study, a definite role was shown for circadian rhythm in maintaining weight loss: circadian rhythms at the beginning of a weight loss program were predictive of the success of future weight loss.34
Finally, a recent review concluded that optimum sleep duration of 7–8 hours per night avoids disturbance of circadian rhythm and the concomitant risks for MetS.35 However, understanding of the specific pathophysiological pathways remains incomplete.
Factors underlying the difficulty of maintaining weight loss have been examined at the physiological and behavioral levels. A 2011 study enrolled 50 overweight patients or patients with obesity, of whom 36 completed the protocol. Before starting a 10-week very-low-energy diet, baseline levels of leptin, ghrelin, peptide YY, gastric inhibitory polypeptide (GIP), glucagon-like peptide 1 (GLP-1), amylin, pancreatic polypeptide, cholecystokinin, and insulin were measured, and a subjective rating of appetite was obtained from all participants.36
Upon completion of the 10-week regimen, the mean weight loss was 13.5 kg, 14% of the net weight at baseline levels. Significant reductions in levels of leptin, peptide YY, cholecystokinin, insulin, and amylin were found at the 10-week point. However, from baseline to week 62, the net percent weight loss was down to 8.2%. Levels of leptin and other hormonal regulators of appetite remained significantly elevated as compared with the 10-week values. The increase of leptin level and weight regained was linear, indicating that these compensatory responses to caloric restriction could help provide a physiological basis for weight regain after the initial loss from changed dietary intake.
Recent obesity studies have utilized advanced radiological techniques and gene sequencing, resulting in a deeper understanding of physiological mechanisms and cell structures that influence obesity. Prenatal exposures to obesogens37, tobacco use during pregnancy38, and endocrine-disrupting chemicals39 have been cited as contributors to epigenetic changes that promote obesity through changes to the satiety response.
Epigenetics involves heritable alterations in gene expression or cellular phenotypes that are not encoded on the DNA sequence itself. Major epigenetic mechanisms include modifications of histone proteins in chromatin and DNA methylation (which does not alter the DNA sequence). Changes that impact the memory of previous events, which can affect even a fetus in utero, can result in pathologies when exposed to a hostile environment. Association of lower birth weight with increased risk of cardiovascular diseases later in life reflects developmental responses of the fetus and/or infant to environmental conditions.38
Studies of the transgenerational inheritance of metabolic disorders have suggested that, in specific instances, certain obesogens act through the peroxisome proliferator activated receptor (PPAR), the master regulator of adipogenesis, whereas others act through currently unidentified pathways.37,40 Generations of mice exposed to DDT showed that the generation exposed to DDT and its progeny did not suffer any obvious changes; however, the next (F1) generation developed a host of serious diseases, and by F3, over 50% of the animals developed obesity.41
A 38-year prospective longitudinal study of a representative birth cohort in New Zealand used gene sequencing for genome-wide association studies of obesity-related phenotypes.42 After birth, children at higher genetic risk gained weight more rapidly and reached adiposity rebound earlier and at a higher BMI. In turn, these developmental phenotypes predicted adult obesity, mediating about half the genetic effect on adult obesity risk. Plastics-derived compounds, BPA and phthalates have also been shown to promote transgenerational changes.43
There is evidence that skeletal muscle is actually a secretory organ; the muscle secretome has been found to consist of several hundred secreted peptides. This provides a conceptual basis and a new paradigm for understanding how muscles communicate with other organs, including adipose tissue.44 Myokines may exert their influence within the muscle itself, providing a potential mechanism for the association between sedentary behavior and chronic disease, including obesity.